Import

APA PsycTests

records_wide <- readRDS("../sober_rubric/raw_data/preprocessed_records.rds")

records_wide %>% group_by(Name)  %>% filter(n()>1) %>% ungroup() %>% summarise(n_distinct(Name), n())
## # A tibble: 1 × 2
##   `n_distinct(Name)` `n()`
##                <int> <int>
## 1               1237  3043
records_wide$first_construct <- str_trim(str_replace_all(str_to_lower(records_wide$first_construct), "[:space:]+", " "))

First EBSCO scrape of APA PsycInfo

psycinfo <- read_tsv('../sober_rubric/raw_data/merged_table_all.tsv') %>% 
  # this tsv can be found in "Scraping-EBSCO-Host\data\merged tables"
#  mutate(Name = toTitleCase(Name)) %>% 
  rename(usage_count = "Hit Count") %>% 
  group_by(Name, Year) %>% 
  summarise(usage_count = sum(usage_count))
## Rows: 309223 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): Name, Journal
## dbl (3): Hit Count, Year, number of search results
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## `summarise()` has grouped output by 'Name'. You can override using the `.groups` argument.

Second EBSCO scrape of APA PsycInfo

overview <- readr::read_tsv("../sober_rubric/raw_data/20230617_ebsco_scrape_clean_overview_table_1.tsv")
## Rows: 71692 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (1): DOI
## dbl (3): first_pub_year, last_pub_year, Hits
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
byyear <- readr::read_tsv("../sober_rubric/raw_data/20230617_ebsco_scrape_table_years_1.tsv")
## Rows: 218142 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (1): DOI
## dbl (2): Year, Hits
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
n_distinct(byyear$DOI)
## [1] 31145
overview %>% filter(is.na(Hits)) %>% nrow()
## [1] 40574
nrow(overview)
## [1] 71692
overview %>% filter(Hits >= 1) %>% nrow()
## [1] 31118
one_hit_wonders <- overview %>% filter(Hits == 1) %>% 
  mutate(Year = first_pub_year)

nrow(one_hit_wonders)
## [1] 13280
# for some few, the call was repeated by year for some reason
# one_hit_wonders %>% select(DOI, first_pub_year) %>% inner_join(byyear, by = "DOI") %>% arrange(DOI)

byyear <- byyear %>% anti_join(one_hit_wonders, by = "DOI")

all <- one_hit_wonders %>% 
  select(DOI, Year, Hits) %>% 
  bind_rows(byyear) %>% 
  left_join(overview %>% rename(total_hits = Hits), by = "DOI")

n_distinct(all$DOI)
## [1] 31145
all %>% filter(Hits > 0) %>% filter(Year < first_pub_year | Year > last_pub_year) %>% nrow()
## [1] 0
all %>% group_by(total_hits, DOI) %>% summarise(hits_by_year = sum(Hits, na.rm = T)) %>% filter(hits_by_year > total_hits) %>% ungroup() %>% select(DOI, everything()) %>% mutate(diff = hits_by_year - total_hits) %>% nrow()
## `summarise()` has grouped output by 'total_hits'. You can override using the
## `.groups` argument.
## [1] 0
# all %>% group_by(total_hits, DOI) %>% summarise(hits_by_year = sum(usage_count, na.rm = T)) %>% filter(hits_by_year < total_hits) %>% select(DOI, everything()) %>% mutate(diff = hits_by_year - total_hits) %>%  View()
all %>% group_by(total_hits, DOI) %>% summarise(hits_by_year = sum(Hits, na.rm = T)) %>% filter(hits_by_year == total_hits) %>% nrow()
## `summarise()` has grouped output by 'total_hits'. You can override using the
## `.groups` argument.
## [1] 31118
all %>% group_by(DOI) %>% summarise(hits_by_year = sum(Hits, na.rm = T)) %>% filter(hits_by_year == 0)
## # A tibble: 27 × 2
##    DOI                hits_by_year
##    <chr>                     <dbl>
##  1 10.1037/t00747-000            0
##  2 10.1037/t00875-000            0
##  3 10.1037/t00878-000            0
##  4 10.1037/t00879-000            0
##  5 10.1037/t02477-000            0
##  6 10.1037/t02488-000            0
##  7 10.1037/t04670-000            0
##  8 10.1037/t04771-000            0
##  9 10.1037/t04776-000            0
## 10 10.1037/t04779-000            0
## # ℹ 17 more rows
all %>% group_by(total_hits, DOI) %>% summarise(hits_by_year = sum(Hits, na.rm = T)) %>% filter(is.na(total_hits)) %>% pull(hits_by_year) %>% table()
## `summarise()` has grouped output by 'total_hits'. You can override using the
## `.groups` argument.
## .
##  0 
## 27
psyctests_info <- records_wide %>% 
  select(DOI, TestYear, Name, first_construct, 
         original_test_DOI, original_DOI_combined, 
         test_type, ConstructList, subdiscipline_1, subdiscipline_2, 
         classification_1, classification_2, instrument_type_broad, 
         InstrumentType,
         number_of_factors_subscales, Name_base) %>%
  distinct() %>% 
  inner_join(all, by = c("DOI" = "DOI"), multiple = "all") %>% 
  rename(usage_count = "Hits")


write_rds(psyctests_info, "../sober_rubric/raw_data/psyctests_info.rds")

2016 changes in standards

test_data <- records_wide %>%
  filter(TestYear <= 2022) %>%
    rowwise() %>%
    mutate(Methodology = length(MethodologyList) >0) %>%
    mutate(AdministrationMethod = length(AdministrationMethodList) >0) %>%
    mutate(PopulationGroup = length(PopulationGroupList) >0) %>%
    mutate(AgeGroup = length(AgeGroupList) >0) %>%
    group_by(TestYear) %>%
    summarise(Reliability = mean(Reliability!="No reliability indicated."),
              FactorAnalysis = mean(FactorAnalysis!="No factor analysis indicated."),
              # Unidimensional = mean(FactorAnalysis=="This is a unidimensional measure."),
              FactorsAndSubscales = mean(!is.na(FactorsAndSubscales)),
              Validity = mean(Validity!="No validity indicated."),
              Format = mean(!is.na(Format)),
              # Fee = mean(Fee == "Yes"),
              Methodology = mean(Methodology),
              AdministrationMethod = mean(AdministrationMethod),
              # AgeGroup = mean(AgeGroup),
              # PopulationGroup = mean(PopulationGroup),
              TestItems = mean(TestItemsAvailable == "Yes")) %>%
    pivot_longer(-TestYear)
test_data %>%
  ggplot(aes(TestYear, value, color = name)) +
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  geom_line() +
  scale_x_continuous("Publication year in APA PsycTests",
                     limits = c(1993, 2030),
                     breaks = seq(1993, 2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022"),
                     expand = expansion(add = c(0, 1.2))) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  geom_text_repel(
    data = test_data %>% drop_na() %>% group_by(name) %>% filter(TestYear == max(TestYear, na.rm = T)),
    aes(label = name),
    segment.color = 'grey',
    xlim = c(2022, 2033),
    box.padding = 0.1,
    # point.padding = 0.6,
    nudge_x = 1.2,
    # nudge_y = 0,
    force = 0.5,
    hjust = 0,
    direction="y",
    na.rm = TRUE
  ) +
  ylab("PsycTests contains information about...") +
  theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")
## Warning: Removed 696 rows containing missing values or values outside the scale range
## (`geom_line()`).

ggsave("figures/changed_standards_2016.pdf", width = 8, height = 4)
## Warning: Removed 696 rows containing missing values or values outside the scale range
## (`geom_line()`).
ggsave("figures/changed_standards_2016.png", width = 8, height = 4)
## Warning: Removed 696 rows containing missing values or values outside the scale range
## (`geom_line()`).
records_wide %>% 
  group_by(TestYear) %>% 
  summarise(number_of_test_items = mean(number_of_test_items, na.rm = T)) %>% 
  filter(TestYear >= 1993, TestYear <= 2022) %>% 
ggplot(aes(TestYear, number_of_test_items)) + 
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  geom_line() +
  scale_x_continuous("Publication year in APA PsycTests",
                     limits = c(1993, NA), 
                     breaks = seq(1993, 2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022")) +
  theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none") +
  xlab("Publication year in APA PsycTests") +
  ylab("Number of items in measure")

records_wide %>% 
  filter(TestYear >= 1993, TestYear <= 2022) %>% 
ggplot(aes(TestYear, number_of_factors_subscales)) + 
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  geom_pointrange(stat = 'summary', fun.data = 'mean_se') +
  xlim(1993, 2022) +
  xlab("Publication year in APA PsycTests") +
  ylab("Number of factors/subscales")
## Warning: Removed 54780 rows containing non-finite outside the scale range
## (`stat_summary()`).

records_wide %>% 
  group_by(InstrumentType, TestYear) %>% 
  summarise(tests = n()) %>% 
  group_by(TestYear) %>% 
  mutate(tests = tests/sum(tests, na.rm = T)) %>% 
  arrange(TestYear) %>% 
  filter(TestYear >= 1993, TestYear <= 2022) %>% 
  # mutate(tests = cumsum(tests)) %>% 
  ggplot(aes(TestYear, tests, color = InstrumentType)) + 
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  scale_color_viridis_d() +
  geom_line() +
  xlim(1993, 2022) +
  xlab("Publication year in APA PsycTests") +
  ylab("Proportion of instrument type")
## `summarise()` has grouped output by 'InstrumentType'. You can override using
## the `.groups` argument.
## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_line()`).

records_wide %>% 
  # filter(instrument_type_broad !=)
  group_by(instrument_type_broad, TestYear) %>% 
  summarise(tests = n()) %>% 
  group_by(TestYear) %>% 
  mutate(tests = tests/sum(tests, na.rm = T)) %>% 
  arrange(TestYear) %>% 
  filter(TestYear >= 1993, TestYear <= 2022) %>% 
  # mutate(tests = cumsum(tests)) %>% 
  ggplot(aes(TestYear, tests, color = instrument_type_broad)) + 
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  geom_line() +
  xlim(1993, 2022) +
  xlab("Publication year in APA PsycTests") +
  ylab("Proportion of instrument type")
## `summarise()` has grouped output by 'instrument_type_broad'. You can override
## using the `.groups` argument.

records_wide %>% 
  filter(instrument_type_broad != "questionnaire") %>% 
  group_by(InstrumentType, TestYear) %>% 
  summarise(tests = n()) %>% 
  group_by(TestYear) %>% 
  mutate(tests = tests/sum(tests, na.rm = T)) %>% 
  arrange(TestYear) %>% 
  filter(TestYear >= 1993, TestYear <= 2022) %>% 
  # mutate(tests = cumsum(tests)) %>% 
  ggplot(aes(TestYear, tests, color = InstrumentType)) + 
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  geom_line() +
  xlim(1993, 2022) +
  xlab("Publication year in APA PsycTests") +
  ylab("Proportion of instrument type") +
  ggtitle("Without questionnaires")
## `summarise()` has grouped output by 'InstrumentType'. You can override using
## the `.groups` argument.

psyctests_info %>% group_by(DOI, TestYear) %>% 
  summarise(used = sum(usage_count, na.rm = T)) %>% 
  full_join(records_wide %>% select(DOI, TestYear)) %>% 
  mutate(used = coalesce(used, 0)) %>% 
  filter(TestYear >= 1993, TestYear <= 2022) %>% 
  group_by(TestYear) %>% 
  summarise(never_reused = mean(used == 0),
            used_once = mean(used == 1),
            used_twice = mean(used == 2),
            used_thrice = mean(used == 3),
            used_more_10 = mean(used > 9)) %>% 
  pivot_longer(-TestYear) %>% 
  ggplot(aes(TestYear, value, color = name)) + 
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  geom_line() +
  xlim(1993, 2022) +
  xlab("Publication year in APA PsycTests") +
  ylab("Frequency")
## `summarise()` has grouped output by 'DOI'. You can override using the `.groups`
## argument.
## Joining with `by = join_by(DOI, TestYear)`

New measures by publication year

count_all <- records_wide %>% 
  group_by(TestYear) %>% 
  summarise(tests = n()) %>% 
  arrange(TestYear)

count_orig <- records_wide %>% 
  filter(test_type == "Original") %>% 
  group_by(TestYear) %>% 
  summarise(tests = n()) %>% 
  arrange(TestYear)

count_base <- records_wide %>% 
  arrange(TestYear) %>% 
  distinct(Name_base, .keep_all = T) %>% 
  group_by(TestYear) %>% 
  summarise(tests = n_distinct(DOI))

count_construct <- records_wide %>% 
  arrange(TestYear) %>% 
  distinct(first_construct, .keep_all = T) %>% 
  group_by(TestYear) %>% 
  summarise(tests = n_distinct(first_construct)) %>% 
  arrange(TestYear)

counts <- bind_rows(
  "novel constructs" = count_construct,
  "novel measures" = count_orig,
  "with translations\n and revisions" = count_all,
  .id = "origin"
  ) %>% 
  rename(Year = TestYear) %>% 
  filter(Year <= 2022)


ggplot(counts, aes(Year, tests, color = origin)) + 
  geom_line() +
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  scale_y_continuous("Number of measures/constructs published") +
  scale_x_continuous("Publication year in APA PsycTests",
                     limits = c(1993, 2030), 
                     breaks = seq(1993, 2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022")) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   ) +
  geom_text_repel(data = counts %>% drop_na() %>% group_by(origin) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ", origin)),
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  lineheight = .9,
                  # segment.color = 'grey',
                  box.padding = 0.1,
                  point.padding = 0.6,
                  nudge_x = 1.5,
                  nudge_y = -15,
                  force = 1,
                  hjust = 0,
                  direction="y",
                  na.rm = F) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")
## Warning: Removed 250 rows containing missing values or values outside the scale range
## (`geom_line()`).

ggsave("figures/counts.pdf", width = 8, height = 4)
## Warning: Removed 250 rows containing missing values or values outside the scale range
## (`geom_line()`).
ggsave("figures/counts.png", width = 8, height = 4)
## Warning: Removed 250 rows containing missing values or values outside the scale range
## (`geom_line()`).

Cumulative number of measures and constructs

cumsum_all <- records_wide %>% 
  group_by(TestYear) %>% 
  summarise(tests = n()) %>% 
  arrange(TestYear) %>% 
  mutate(tests = cumsum(tests)) 

cumsum_orig <- records_wide %>% 
  filter(test_type == "Original") %>% 
  group_by(TestYear) %>% 
  summarise(tests = n()) %>% 
  arrange(TestYear) %>% 
  mutate(tests = cumsum(tests)) 

cumsum_base <- records_wide %>% 
  arrange(TestYear) %>% 
  distinct(Name_base, .keep_all = T) %>% 
  group_by(TestYear) %>% 
  summarise(tests = n_distinct(DOI)) %>% 
  mutate(tests = cumsum(tests)) 

cumsum_construct <- records_wide %>% 
  arrange(TestYear) %>% 
  distinct(first_construct, .keep_all = T) %>% 
  group_by(TestYear) %>% 
  summarise(tests = n_distinct(DOI)) %>% 
  arrange(TestYear) %>% 
  mutate(tests = cumsum(tests)) 

cumsums <- bind_rows(
  "novel constructs" = cumsum_construct,
  "novel measures" = cumsum_orig,
  "with translations\n and revisions" = cumsum_all,
  .id = "origin"
  ) %>% 
  rename(Year = TestYear) %>% 
  filter(Year <= 2022)


ggplot(cumsums, aes(Year, tests, color = origin)) + 
  geom_line() +
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  scale_y_continuous("Cumulative number of measures") +
  scale_x_continuous("Publication year in APA PsycTests",
                     limits = c(1993, 2030), 
                     breaks = seq(1993,2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022"),
                     expand = expansion(add = c(0, 1))) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   )  +
  geom_text_repel(data = cumsums %>% drop_na() %>% group_by(origin) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ", origin, "\n (n = ", tests, ")")),
                  segment.square = TRUE,
                  lineheight = .9,
                  segment.color = 'grey',
                  nudge_x = 1.2,
                  hjust = 0,
                  na.rm = TRUE) +
   # theme_minimal(base_size = 13) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")  +
    guides(
    x = guide_axis(cap = "both"), # Cap both ends
  )
## Warning: Removed 250 rows containing missing values or values outside the scale range
## (`geom_line()`).

ggsave("figures/cumsums.pdf", width = 8, height = 4)
## Warning: Removed 250 rows containing missing values or values outside the scale range
## (`geom_line()`).
ggsave("figures/cumsums.png", width = 8, height = 4)
## Warning: Removed 250 rows containing missing values or values outside the scale range
## (`geom_line()`).

By subdiscipline

cumsum_all <- records_wide %>% 
  group_by(subdiscipline_1, TestYear) %>% 
  summarise(tests = n()) %>% 
  arrange(TestYear) %>% 
  mutate(tests = cumsum(tests)) 
## `summarise()` has grouped output by 'subdiscipline_1'. You can override using
## the `.groups` argument.
cumsum_orig <- records_wide %>% 
  filter(test_type == "Original") %>% 
  group_by(subdiscipline_1, TestYear) %>% 
  summarise(tests = n()) %>% 
  arrange(TestYear) %>% 
  mutate(tests = cumsum(tests)) 
## `summarise()` has grouped output by 'subdiscipline_1'. You can override using
## the `.groups` argument.
cumsum_base <- records_wide %>% 
  arrange(TestYear) %>% 
  distinct(Name_base, .keep_all = T) %>% 
  group_by(subdiscipline_1, TestYear) %>% 
  summarise(tests = n_distinct(DOI)) %>% 
  mutate(tests = cumsum(tests)) 
## `summarise()` has grouped output by 'subdiscipline_1'. You can override using
## the `.groups` argument.
cumsum_construct <- records_wide %>% 
  arrange(TestYear) %>% 
  distinct(first_construct, .keep_all = T) %>% 
  group_by(subdiscipline_1, TestYear) %>% 
  summarise(tests = n_distinct(DOI)) %>% 
  arrange(TestYear) %>% 
  mutate(tests = cumsum(tests)) 
## `summarise()` has grouped output by 'subdiscipline_1'. You can override using
## the `.groups` argument.
cumsums <- bind_rows(
  "constructs" = cumsum_construct,
  "measures" = cumsum_orig,
  "with translations & revisions" = cumsum_all,
  .id = "origin"
  ) %>% 
  rename(Year = TestYear) %>% 
  filter(Year <= 2022)

cumsums$origin <- factor(cumsums$origin, levels = c("with translations & revisions", "constructs", "measures"))
my_colors <- c("with translations & revisions" = "#7570B3",
               "constructs" = "#1B9E77", 
               "measures" = "#D95F02") 

ggplot(cumsums, aes(Year, tests, color = origin)) + 
  geom_line() +
  facet_wrap(~ subdiscipline_1, scales = "free_y", ncol = 2) + 
  scale_y_continuous("Cumulative number of measures") +
  scale_x_continuous("Publication year in APA PsycTests",
                     limits = c(1993, 2022), 
                     breaks = seq(1993, 2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022")) +
  scale_color_manual(values = my_colors, guide = guide_legend(title = NULL)) +
  theme_minimal(base_size = 13) +
  theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = c(0.58, 0.08),
        legend.justification = c(0, 0),
        legend.box.just = "right",
        legend.text = element_text(size = 11))
## Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2
## 3.5.0.
## ℹ Please use the `legend.position.inside` argument of `theme()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Removed 189 rows containing missing values or values outside the scale range
## (`geom_line()`).

ggsave("figures/cumsums_subdiscipline.pdf", width = 8, height = 7)
## Warning: Removed 189 rows containing missing values or values outside the scale range
## (`geom_line()`).
ggsave("figures/cumsums_subdiscipline.png", width = 8, height = 7)
## Warning: Removed 189 rows containing missing values or values outside the scale range
## (`geom_line()`).

Tests by usage frequency

test_frequency <- psyctests_info %>% 
  mutate(Test = if_else(test_type == "Original", DOI, original_test_DOI)) %>% 
  drop_na(Test) %>% 
  # filter(TestYear >= 1990) %>%
  filter(between(Year, 1993, 2022)) %>%
  group_by(Test) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  arrange(n)

test_frequency <- records_wide %>% 
  filter(test_type == "Original", TestYear <= 2022) %>% 
  select(Test = DOI) %>% 
  full_join(test_frequency) %>% 
  mutate(n = coalesce(n, 0.5))
## Joining with `by = join_by(Test)`
test_frequency <- test_frequency %>% 
  group_by(n) %>% 
  summarise(count = n()) %>% 
  ungroup() %>% 
  mutate(percent = count/sum(count))

freq_plot <- ggplot(test_frequency, aes(n, count)) + 
  geom_bar(width = 0.1, fill = colors["novel"], stat = "identity") +
  # facet_wrap(~ subdiscipline_1, scales = "free_y") + 
  # scale_y_sqrt("Number of measures", breaks = c(0, 100, 400, 1000, 2000, 4000, 6000, 10000), limits = c(0, 11500)) +
  scale_y_continuous("Number of measures") +
  scale_x_log10("Usages recorded in APA PsycInfo 1993-2022",
                breaks = c(0.5, 1, 2, 5, 10, 100, 1000, 25000),
               labels = c(0, 1, 2, 5, 10, 100, 1000, 25000)) +
  
  geom_text(aes(label = if_else(n <= 2, sprintf("%.0f%%", percent*100), ""),
                x = n, y = count + 700)) +

  # scale_x_sqrt(breaks = c(0, 1, 2, 3, 4, 5, 10, 20, 40, 50), labels = c(0, 1, 2, 3, 4, 5, 10, 20, 40, "50+")) +
  # geom_text_repel(aes(x = n, label = first_acronym, y = y), 
  #                 data =
  #                   test_frequency %>% group_by(subdiscipline_1) %>% filter(row_number() > (n() - 10) ) %>% left_join(records_wide %>% select(Test = DOI, first_acronym)) %>% 
  #                   mutate(first_acronym = if_else(first_acronym == "HRSD", "HAM-D",
  #                                                  first_acronym)) %>% 
    # mutate(y = 20 + 50*(1+n()-row_number())),
    #               size = 3.3, force = 5, force_pull   = 0, max.time = 1, 
    #               max.overlaps = Inf,
    #               segment.color = "lightgray",
    #               segment.curvature = 1,
    #               hjust = 1,
    #               nudge_y = 10,
    #               direction = "y"
    # ) +
  theme_minimal(base_size = 13) +
  theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")
freq_plot
## Warning: `position_stack()` requires non-overlapping x intervals.

ggsave("figures/frequency_across.pdf", width = 8, height = 4)
## Warning: `position_stack()` requires non-overlapping x intervals.
ggsave("figures/frequency_across.png", width = 8, height = 4)
## Warning: `position_stack()` requires non-overlapping x intervals.
test_frequency <- test_frequency %>% 
  filter(n >= 1) %>% 
  mutate(percent = count/sum(count))

freq_plot <- ggplot(test_frequency, aes(n, count)) + 
  geom_bar(width = 0.1, fill = colors["novel"], stat = "identity") +
  # scale_y_sqrt("Number of measures", breaks = c(0, 100, 400, 1000, 2000, 4000, 6000, 10000), limits = c(0, 11500)) +
  scale_y_continuous("Number of measures") +
  scale_x_log10("Usages recorded in APA PsycInfo 1993-2022",
                breaks = c(1, 2, 5, 10, 100, 1000, 25000),
               labels = c(1, 2, 5, 10, 100, 1000, 25000)) +
  
  geom_text(aes(label = if_else(n <= 2, sprintf("%.0f%%", percent*100), ""),
                x = n, y = count + 700)) +

  # scale_x_sqrt(breaks = c(0, 1, 2, 3, 4, 5, 10, 20, 40, 50), labels = c(0, 1, 2, 3, 4, 5, 10, 20, 40, "50+")) +
  # geom_text_repel(aes(x = n, label = first_acronym, y = y), 
  #                 data =
  #                   test_frequency %>% group_by(subdiscipline_1) %>% filter(row_number() > (n() - 10) ) %>% left_join(records_wide %>% select(Test = DOI, first_acronym)) %>% 
  #                   mutate(first_acronym = if_else(first_acronym == "HRSD", "HAM-D",
  #                                                  first_acronym)) %>% 
    # mutate(y = 20 + 50*(1+n()-row_number())),
    #               size = 3.3, force = 5, force_pull   = 0, max.time = 1, 
    #               max.overlaps = Inf,
    #               segment.color = "lightgray",
    #               segment.curvature = 1,
    #               hjust = 1,
    #               nudge_y = 10,
    #               direction = "y"
    # ) +
  theme_minimal(base_size = 13) +
  theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")
freq_plot
## Warning: `position_stack()` requires non-overlapping x intervals.

ggsave("figures/frequency_across_no0.pdf", width = 8, height = 4)
## Warning: `position_stack()` requires non-overlapping x intervals.
ggsave("figures/frequency_across_no0.png", width = 8, height = 4)
## Warning: `position_stack()` requires non-overlapping x intervals.
test_frequency <- psyctests_info %>% 
  mutate(Test = if_else(test_type == "Original", DOI, original_test_DOI)) %>% 
  drop_na(Test) %>% 
  # filter(TestYear >= 1990) %>%
  filter(between(Year, 1993, 2022)) %>%
  group_by(subdiscipline_1, Test) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  arrange(n)
## `summarise()` has grouped output by 'subdiscipline_1'. You can override using
## the `.groups` argument.
test_frequency <- records_wide %>% 
  filter(test_type == "Original", TestYear <= 2022) %>% 
  select(subdiscipline_1, Test = DOI) %>% 
  full_join(test_frequency) %>% 
  mutate(n = coalesce(n, 0.5))
## Joining with `by = join_by(subdiscipline_1, Test)`
test_frequency <- test_frequency %>% 
  # mutate(n = if_else(n >= 1000, 1000, n)) %>% 
  mutate(subdiscipline_1 = str_replace(subdiscipline_1, " Psychology", "")) %>% 
  group_by(subdiscipline_1, n) %>% 
  summarise(count = n()) %>% 
  group_by(subdiscipline_1) %>% 
  mutate(percent = count/sum(count))
## `summarise()` has grouped output by 'subdiscipline_1'. You can override using
## the `.groups` argument.
freq_plot <- ggplot(test_frequency, aes(n, count)) + 
  geom_bar(width = 0.1, fill = colors["novel"], stat = "identity") +
  facet_wrap(~ subdiscipline_1, scales = "free_y") + 
  # scale_y_sqrt("Number of measures", breaks = c(0, 100, 400, 1000, 2000, 4000, 6000, 10000), limits = c(0, 11500)) +
  scale_y_continuous("Number of measures", expand = expansion(c(0, 0.1))) +
  scale_x_log10("Usages recorded in APA PsycInfo 1993-2022",
                breaks = c(0.5, 1, 2, 10, 100, 1000, 25000),
               labels = c(0, 1, 2, 10, 100, 1000, 25000)) +
  
  geom_text(aes(label = if_else(n <= 2, sprintf("%.0f%%", percent*100), ""),
                x = n, y = count ), size = 3, vjust = -0.4, hjust = 0.4) +

  # scale_x_sqrt(breaks = c(0, 1, 2, 3, 4, 5, 10, 20, 40, 50), labels = c(0, 1, 2, 3, 4, 5, 10, 20, 40, "50+")) +
  # geom_text_repel(aes(x = n, label = first_acronym, y = y), 
  #                 data =
  #                   test_frequency %>% group_by(subdiscipline_1) %>% filter(row_number() > (n() - 10) ) %>% left_join(records_wide %>% select(Test = DOI, first_acronym)) %>% 
  #                   mutate(first_acronym = if_else(first_acronym == "HRSD", "HAM-D",
  #                                                  first_acronym)) %>% 
    # mutate(y = 20 + 50*(1+n()-row_number())),
    #               size = 3.3, force = 5, force_pull   = 0, max.time = 1, 
    #               max.overlaps = Inf,
    #               segment.color = "lightgray",
    #               segment.curvature = 1,
    #               hjust = 1,
    #               nudge_y = 10,
    #               direction = "y"
    # ) +
  theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")

freq_plot
## Warning: `position_stack()` requires non-overlapping x intervals.
## `position_stack()` requires non-overlapping x intervals.
## `position_stack()` requires non-overlapping x intervals.
## `position_stack()` requires non-overlapping x intervals.
## `position_stack()` requires non-overlapping x intervals.

ggsave("figures/frequency.pdf", width = 8, height = 4)
## Warning: `position_stack()` requires non-overlapping x intervals.
## `position_stack()` requires non-overlapping x intervals.
## `position_stack()` requires non-overlapping x intervals.
## `position_stack()` requires non-overlapping x intervals.
## `position_stack()` requires non-overlapping x intervals.
ggsave("figures/frequency.png", width = 8, height = 4)
## Warning: `position_stack()` requires non-overlapping x intervals.
## `position_stack()` requires non-overlapping x intervals.
## `position_stack()` requires non-overlapping x intervals.
## `position_stack()` requires non-overlapping x intervals.
## `position_stack()` requires non-overlapping x intervals.
test_frequency <- test_frequency %>% 
  filter(n >= 1) %>% 
  group_by(subdiscipline_1) %>% 
  mutate(percent = count/sum(count))

freq_plot <- ggplot(test_frequency, aes(n, count)) + 
  geom_bar(width = 0.1, fill = colors["novel"], stat = "identity") +
  facet_wrap(~ subdiscipline_1, scales = "free_y") + 
  # scale_y_sqrt("Number of measures", breaks = c(0, 100, 400, 1000, 2000, 4000, 6000, 10000), limits = c(0, 11500)) +
    scale_y_continuous("Number of measures", expand = expansion(c(0, 0.1))) +
  scale_x_log10("Usages recorded in APA PsycInfo 1993-2022",
                breaks = c(1, 2, 5, 10, 100, 1000, 25000),
               labels = c(1, 2, 5, 10, 100, 1000, 25000)) +
  
  geom_text(aes(label = if_else(n <= 2, sprintf("%.0f%%", percent*100), ""),
                x = n, y = count ), size = 3, vjust = -0.11, hjust = 0.4) +

  # scale_x_sqrt(breaks = c(0, 1, 2, 3, 4, 5, 10, 20, 40, 50), labels = c(0, 1, 2, 3, 4, 5, 10, 20, 40, "50+")) +
  # geom_text_repel(aes(x = n, label = first_acronym, y = y), 
  #                 data =
  #                   test_frequency %>% group_by(subdiscipline_1) %>% filter(row_number() > (n() - 10) ) %>% left_join(records_wide %>% select(Test = DOI, first_acronym)) %>% 
  #                   mutate(first_acronym = if_else(first_acronym == "HRSD", "HAM-D",
  #                                                  first_acronym)) %>% 
    # mutate(y = 20 + 50*(1+n()-row_number())),
    #               size = 3.3, force = 5, force_pull   = 0, max.time = 1, 
    #               max.overlaps = Inf,
    #               segment.color = "lightgray",
    #               segment.curvature = 1,
    #               hjust = 1,
    #               nudge_y = 10,
    #               direction = "y"
    # ) +
  theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")
freq_plot
## Warning: `position_stack()` requires non-overlapping x intervals.
## `position_stack()` requires non-overlapping x intervals.
## `position_stack()` requires non-overlapping x intervals.
## `position_stack()` requires non-overlapping x intervals.
## `position_stack()` requires non-overlapping x intervals.

ggsave("figures/frequency_no0.pdf", width = 8, height = 4)
## Warning: `position_stack()` requires non-overlapping x intervals.
## `position_stack()` requires non-overlapping x intervals.
## `position_stack()` requires non-overlapping x intervals.
## `position_stack()` requires non-overlapping x intervals.
## `position_stack()` requires non-overlapping x intervals.

By instrument type

usage_by_year_instrument_type <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  group_by(instrument_type_broad, Year, Test = DOI) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  group_by(instrument_type_broad) %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(instrument_type_broad, n_tests, Year) %>% 
  summarise(n = sum(n),
            diff_tests = n()) %>% 
  ungroup()
## `summarise()` has grouped output by 'instrument_type_broad', 'Year'. You can
## override using the `.groups` argument.
## `summarise()` has grouped output by 'instrument_type_broad', 'n_tests'. You can
## override using the `.groups` argument.
usage_by_year_instrument_type %>% 
  ggplot(., aes(Year, n, color = instrument_type_broad)) +
  geom_line(size = 0.7) +
  scale_y_continuous("Times tests were coded in PsycInfo") +
  scale_x_continuous(limits = c(1993, 2030), breaks = c(1993, 1998, 2003, 2008, 2013, 2018, 2022)) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   ) + 
  geom_text_repel(aes(label = gsub("^.*$", " ", instrument_type_broad)), # This will force the correct position of the link's right end.
                  data = usage_by_year_instrument_type %>% filter(Year == max(Year, na.rm = T)),
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  segment.color = 'grey',
                  box.padding = 0.1,
                  point.padding = 0.6,
                  nudge_x = 0.15,
                  nudge_y = 0.05,
                  force = 0.5,
                  hjust = 0,
                  direction="y",
                  na.rm = TRUE
  ) +
  geom_text_repel(data = usage_by_year_instrument_type %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0("  ",str_replace(instrument_type_broad, " Psychology", ""), " (n=", n_tests, ")")),
                  segment.alpha = 0, ## This will 'hide' the link
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  # segment.color = 'grey',
                  box.padding = 0.1,
                  point.padding = 0.6,
                  nudge_x = 0.15,
                  nudge_y = 0.05,
                  force = 0.5,
                  hjust = 0,
                  direction="y",
                  na.rm = TRUE)+
  theme_minimal(base_size = 13) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Removed 30 rows containing missing values or values outside the scale range
## (`geom_line()`).

ggsave("figures/usage_by_year_instrument_type.pdf", width = 10, height = 4)
## Warning: Removed 30 rows containing missing values or values outside the scale range
## (`geom_line()`).
ggsave("figures/usage_by_year_instrument_type.png", width = 10, height = 4)
## Warning: Removed 30 rows containing missing values or values outside the scale range
## (`geom_line()`).
usage_by_year_instrument_type <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  group_by(InstrumentType, Year, Test = DOI) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  group_by(InstrumentType) %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(InstrumentType, n_tests, Year) %>% 
  summarise(n = sum(n),
            diff_tests = n()) %>% 
  ungroup()
## `summarise()` has grouped output by 'InstrumentType', 'Year'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'InstrumentType', 'n_tests'. You can
## override using the `.groups` argument.
usage_by_year_instrument_type %>% 
  ggplot(., aes(Year, n, color = InstrumentType)) +
  geom_line(size = 0.7) +
  scale_y_continuous("Times tests were coded in PsycInfo") +
  scale_x_continuous(limits = c(1993, 2035), breaks = c(1993, 1998, 2003, 2008, 2013, 2018, 2022)) +
  scale_color_discrete() +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   ) + 
  geom_text_repel(aes(label = gsub("^.*$", " ", InstrumentType)), # This will force the correct position of the link's right end.
                  data = usage_by_year_instrument_type %>% filter(Year == max(Year, na.rm = T)),
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  segment.color = 'grey',
                  box.padding = 0.1,
                  point.padding = 0.6,
                  nudge_x = 0.15,
                  nudge_y = 0.05,
                  force = 0.5,
                  hjust = 0,
                  direction="y",
                  na.rm = TRUE
  ) +
  geom_text_repel(data = usage_by_year_instrument_type %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0("  ",str_replace(InstrumentType, " Psychology", ""), " (n=", n_tests, ")")),
                  segment.alpha = 0, ## This will 'hide' the link
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  # segment.color = 'grey',
                  box.padding = 0.1,
                  point.padding = 0.6,
                  nudge_x = 0.15,
                  nudge_y = 0.05,
                  force = 0.5,
                  hjust = 0,
                  direction="y",
                  na.rm = TRUE)+
  theme_minimal(base_size = 13) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")
## Warning: ggrepel: 11 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 12 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

ggsave("figures/usage_by_year_instrument_type_narrow.pdf", width = 14, height = 10)
## Warning: ggrepel: 7 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 9 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ggsave("figures/usage_by_year_instrument_type_narrow.png", width = 14, height = 10)
## Warning: ggrepel: 7 unlabeled data points (too many overlaps). Consider increasing max.overlaps
## ggrepel: 9 unlabeled data points (too many overlaps). Consider increasing max.overlaps

Entropy

Entropy by year

byorig_entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  mutate(Test = if_else(test_type == "Original", DOI, original_test_DOI)) %>% 
  drop_na(Test) %>% 
  group_by(Year, Test) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  ungroup() %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(entropy = entropy(n),
            norm_entropy = calc_norm_entropy(n),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'n_tests'. You can override using the
## `.groups` argument.
# what's the point of mutate(n_tests = n_distinct(Test))? just to check?


byconstruct_entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  drop_na(first_construct) %>% 
  group_by(Year, Test = first_construct) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  ungroup() %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(entropy = entropy(n),,
            norm_entropy = calc_norm_entropy(n),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'n_tests'. You can override using the
## `.groups` argument.
all_entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  drop_na(DOI) %>% 
  group_by(Year, Test = DOI) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  ungroup() %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(entropy = entropy(n),,
            norm_entropy = calc_norm_entropy(n),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'n_tests'. You can override using the
## `.groups` argument.
original_entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  filter(test_type == "Original") %>% 
  group_by(Year, Test = DOI) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  ungroup() %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(entropy = entropy(n),,
            norm_entropy = calc_norm_entropy(n),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'n_tests'. You can override using the
## `.groups` argument.
entropy_by_year <- bind_rows(# "all tests" = all_entropy_by_year,
                             "measures" = original_entropy_by_year,
                             "with translations\n and revisions" = byorig_entropy_by_year,
                             # "by name base" = bybase_entropy_by_year,
                             "constructs" = byconstruct_entropy_by_year,
                             .id = "version")


entropy_by_year %>% 
  ggplot(., aes(Year, norm_entropy, color = version)) +
  geom_line(size = 0.7) +
  scale_y_continuous("Normalized Shannon Entropy", limits = c(0, 1), labels = scales::percent) +
  # geom_line(aes(y = log(n)), color = 'red') +
  scale_x_continuous("Usage year as coded in APA PsycInfo", limits = c(1993, 2030), 
                     breaks = seq(1993, 2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022")) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   ) + 
  geom_text_repel(data = entropy_by_year %>% drop_na() %>% group_by(version) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ",version)), # "\n (n = ", n_tests, ")"
                  segment.curvature = -0.5,
                  segment.square = TRUE,
                  segment.color = 'grey', 
                  xlim = c(2023, 2030),
                  nudge_x = 1.14,
                  lineheight = .9,
                  hjust = 0,
                  direction="y",
                  na.rm = TRUE) +
  theme_minimal(base_size = 13) +
   theme(
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")

ggsave("figures/entropy.pdf", width = 8, height = 4)
ggsave("figures/entropy.png", width = 8, height = 4)

by subdiscipline

all measures

entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  group_by(subdiscipline_1, Year, Test = DOI) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  group_by(subdiscipline_1) %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(subdiscipline_1, n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(entropy = entropy(n),,
            norm_entropy = calc_norm_entropy(n),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()
## `summarise()` has grouped output by 'subdiscipline_1', 'Year'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'subdiscipline_1', 'n_tests'. You can
## override using the `.groups` argument.
entropy_by_year %>% 
  ggplot(., aes(Year, norm_entropy, color = subdiscipline_1)) +
  geom_line(size = 0.7) +
  scale_y_continuous("Normalized Shannon Entropy\n(with revisions and translations)", limits = c(0, 1), labels = scales::percent) +
  # geom_line(aes(y = log(n)), color = 'red') +
  scale_x_continuous("Usage year as coded in APA PsycInfo", limits = c(1993, 2033), 
                     breaks = seq(1993,2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022"),
                     expand = expansion(add = c(0, 14))) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggtitle(str_c(n_distinct(tests_by_year$Test), " measures tracked in PsycInfo")) +
 # geom_text_repel(aes(label = gsub("^.*$", " ", subdiscipline_1)), # This will force the correct position of the link's right end.
 #                  data = entropy_by_year %>% drop_na() %>% group_by(subdiscipline_1) %>% filter(Year == max(Year, na.rm = T)),
 #                  segment.curvature = -0.1,
 #                  segment.square = TRUE,
 #                  segment.color = 'grey',
 #                  box.padding = 0.1,
 #                  point.padding = 0.6,
 #                  max.overlaps = Inf,
 #                  nudge_x = 1.3,
 #                  # nudge_y = 0,
 #                  force = 20,
 #                  hjust = 0,
 #                  direction="y",
 #                  na.rm = TRUE
 #  ) +  
  geom_text_repel(data = entropy_by_year %>% drop_na() %>% group_by(subdiscipline_1) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0("  ",str_replace(subdiscipline_1, " Psychology", ""), " (n=", n_tests, ")")),
                  # segment.alpha = 0, ## This will 'hide' the link
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  segment.color = 'grey',
                  box.padding = 0.1,
                  max.overlaps = Inf,
                  point.padding = 0.6,
                  xlim = c(2022, NA),
                  nudge_x = 2,
                  # nudge_y = 0.0,
                  force = 5,
                  hjust = 0,
                  direction="y",
                  na.rm = F) +
  theme_minimal(base_size = 13) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none") +
    coord_cartesian(clip = "off")

ggsave("figures/entropy_subdiscipline_all.pdf", width = 8, height = 4)
ggsave("figures/entropy_subdiscipline_all.png", width = 8, height = 4)

original measures

entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  mutate(Test = if_else(test_type == "Original", DOI, original_test_DOI)) %>% 
  drop_na(Test) %>% 
  group_by(subdiscipline_1, Year, Test) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  group_by(subdiscipline_1) %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(subdiscipline_1, n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(entropy = entropy(n),,
            norm_entropy = calc_norm_entropy(n),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()
## `summarise()` has grouped output by 'subdiscipline_1', 'Year'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'subdiscipline_1', 'n_tests'. You can
## override using the `.groups` argument.
entropy_by_year %>% 
  ggplot(., aes(Year, norm_entropy, color = subdiscipline_1)) +
  geom_line(size = 0.7) +
  scale_y_continuous("Normalized Shannon Entropy\n(novel measures)", limits = c(0, 1), labels = scales::percent) +
  # geom_line(aes(y = log(n)), color = 'red') +
  scale_x_continuous("Usage year as coded in APA PsycInfo", limits = c(1993, 2030), 
                     breaks = seq(1993, 2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022"),
                     expand = expansion(add = c(0, 17))) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggtitle(str_c(n_distinct(tests_by_year$Test), " measures tracked in PsycInfo")) +
 # geom_text_repel(aes(label = gsub("^.*$", " ", subdiscipline_1)), # This will force the correct position of the link's right end.
 #                  data = entropy_by_year %>% drop_na() %>% group_by(subdiscipline_1) %>% filter(Year == max(Year, na.rm = T)),
 #                  segment.curvature = -0.1,
 #                  segment.square = TRUE,
 #                  segment.color = 'grey',
 #                  box.padding = 0.1,
 #                  point.padding = 0.6,
 #                  max.overlaps = Inf,
 #                  nudge_x = 1.3,
 #                  # nudge_y = 0,
 #                  force = 20,
 #                  hjust = 0,
 #                  direction="y",
 #                  na.rm = TRUE
 #  ) +  
  geom_text_repel(data = entropy_by_year %>% drop_na() %>% group_by(subdiscipline_1) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0("  ",str_replace(subdiscipline_1, " Psychology", ""), " (n=", n_tests, ")")),
                  # segment.alpha = 0, ## This will 'hide' the link
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  segment.color = 'grey',
                  box.padding = 0.1,
                  max.overlaps = Inf,
                  point.padding = 0.6,
                  xlim = c(2022, NA),
                  nudge_x = 2,
                  # nudge_y = 0.0,
                  force = 5,
                  hjust = 0,
                  direction="y",
                  na.rm = F)  +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none") +
    coord_cartesian(clip = "off")

ggsave("figures/entropy_subdiscipline_orig.pdf", width = 8, height = 4)
ggsave("figures/entropy_subdiscipline_orig.png", width = 8, height = 4)

constructs

entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  group_by(subdiscipline_1, Year, Test = first_construct) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  group_by(subdiscipline_1) %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(subdiscipline_1, n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(entropy = entropy(n),,
            norm_entropy = calc_norm_entropy(n),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()
## `summarise()` has grouped output by 'subdiscipline_1', 'Year'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'subdiscipline_1', 'n_tests'. You can
## override using the `.groups` argument.
entropy_by_year %>% 
  ggplot(., aes(Year, norm_entropy, color = subdiscipline_1)) +
  geom_line(size = 0.7) +
  scale_y_continuous("Normalized Shannon Entropy (constructs)", limits = c(0, 1), labels = scales::percent) +
  # geom_line(aes(y = log(n)), color = 'red') +
  scale_x_continuous("Usage year as coded in APA PsycInfo", limits = c(1993, 2038), 
                     breaks = seq(1993, 2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022"),
                     expand = expansion(add = c(0, 10))) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggtitle(str_c(n_distinct(tests_by_year$Test), " measures tracked in PsycInfo")) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   ) + 
  geom_text_repel(data = entropy_by_year %>% drop_na() %>% group_by(subdiscipline_1) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0("  ",str_replace(subdiscipline_1, " Psychology", ""), " (n=", n_tests, ")")),
                  # segment.alpha = 0, ## This will 'hide' the link
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  segment.color = 'grey',
                  box.padding = 0.1,
                  max.overlaps = Inf,
                  point.padding = 0.6,
                  xlim = c(2022, NA),
                  nudge_x = 2,
                  # nudge_y = 0.0,
                  force = 5,
                  hjust = 0,
                  direction="y",
                  na.rm = F) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")

ggsave("figures/entropy_subdiscipline_constructs.pdf", width = 8, height = 4)
ggsave("figures/entropy_subdiscipline_constructs.png", width = 8, height = 4)

By instrument type

entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  group_by(instrument_type_broad, Year, Test = DOI) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  group_by(instrument_type_broad) %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(instrument_type_broad, n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(entropy = entropy(n),
            norm_entropy = calc_norm_entropy(n),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()
## `summarise()` has grouped output by 'instrument_type_broad', 'Year'. You can
## override using the `.groups` argument.
## `summarise()` has grouped output by 'instrument_type_broad', 'n_tests'. You can
## override using the `.groups` argument.
entropy_by_year %>% 
  ggplot(., aes(Year, norm_entropy, color = instrument_type_broad)) +
  geom_line(size = 0.7) +
  scale_y_continuous("Normalized Shannon Entropy", limits = c(0, 1), labels = scales::percent) +
  # geom_line(aes(y = log(n)), color = 'red') +
  scale_x_continuous(limits = c(1993, 2027), breaks = c(1993, 1998, 2003, 2008, 2013, 2018, 2022)) +
  scale_color_brewer(type = "qual", guide = "none", palette = 3) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   ) + 
  geom_text_repel(aes(label = gsub("^.*$", " ", instrument_type_broad)), # This will force the correct position of the link's right end.
                  data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  segment.color = 'grey',
                  box.padding = 0.1,
                  point.padding = 0.6,
                  nudge_x = 0.15,
                  nudge_y = 0.05,
                  force = 0.5,
                  hjust = 0,
                  direction="y",
                  na.rm = TRUE
  ) +
  geom_text_repel(data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0("  ",str_replace(instrument_type_broad, " Psychology", ""), " (n=", n_tests, ")")),
                  segment.alpha = 0, ## This will 'hide' the link
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  # segment.color = 'grey',
                  box.padding = 0.1,
                  point.padding = 0.6,
                  nudge_x = 0.15,
                  nudge_y = 0.05,
                  force = 0.5,
                  hjust = 0,
                  direction="y",
                  na.rm = TRUE)+
  theme_minimal(base_size = 13) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")
## Warning: Removed 31 rows containing missing values or values outside the scale range
## (`geom_line()`).

Lorenz curves

test_frequency <- psyctests_info %>% 
  mutate(Test = if_else(test_type == "Original", DOI, original_test_DOI)) %>% 
  drop_na(Test) %>% 
  # filter(TestYear >= 1990) %>%
  filter(between(Year, 1993, 2022)) %>%
  group_by(subdiscipline_1, Test) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  arrange(n) %>% 
  mutate(decile = Hmisc::cut2(n, g = 10)) %>% 
  mutate(cumsum = cumsum(n),
         sum = sum(n))
## `summarise()` has grouped output by 'subdiscipline_1'. You can override using
## the `.groups` argument.
# 
# test_frequency %>% 
#     group_by(subdiscipline_1, decile) %>% 
#     summarise(share = sum(n)/first(sum),
#               median_n = median(n),
#               n_measures = n()) %>% 
#   View()


ggplot(test_frequency, aes(n)) +
  stat_lorenz(desc = F) +
  coord_fixed() +
  geom_abline(linetype = "dashed") +
      theme_minimal() +
    hrbrthemes::scale_x_percent("Cumulative percentage of measures") +
    hrbrthemes::scale_y_percent("Cumulative percentage of measure market share") #+

#    hrbrthemes::theme_ipsum_rc()

ggplot(test_frequency, aes(n, color = subdiscipline_1)) +
  stat_lorenz(desc = F) +
  coord_fixed() +
  geom_abline(linetype = "dashed") +
      theme_minimal() +
    hrbrthemes::scale_x_percent("Cumulative percentage of measures") +
    hrbrthemes::scale_y_percent("Cumulative percentage of measure market share")

Survival

aggregate stats

constructs <- psyctests_info %>% 
     # filter(between(first_pub_year, 1950, 2015)) %>%
     unnest(ConstructList) %>% 
     rowwise() %>% 
     mutate(construct = unlist(ConstructList)) %>% 
     select(-ConstructList) %>% 
     filter(between(Year, 1993, 2022)) %>% 
     drop_na(construct) %>% 
     mutate(survival = last_pub_year - first_pub_year,
            survived_five = if_else(survival >= 5, T, F),
            survived_ten = if_else(survival >= 10, T, F)) %>%
     distinct(construct, .keep_all = TRUE)

mean(constructs$survival, na.rm = T)
## [1] 6.120457
sd(constructs$survival, na.rm = T)
## [1] 8.788988
median(constructs$survival, na.rm = T)
## [1] 2
max(constructs$survival, na.rm = T)
## [1] 122
min(constructs$survival, na.rm = T)
## [1] 0
measures <- psyctests_info %>% 
     filter(between(Year, 1993, 2022))  %>% 
     mutate(survival = last_pub_year - first_pub_year,
            survived_five = if_else(survival >= 5, T, F),
            survived_ten = if_else(survival >= 10, T, F)) %>%
     distinct(DOI, .keep_all = TRUE)

mean(measures$survival, na.rm = T)
## [1] 6.046785
sd(measures$survival, na.rm = T)
## [1] 8.743927
median(measures$survival, na.rm = T)
## [1] 2
max(measures$survival, na.rm = T)
## [1] 122
min(measures$survival, na.rm = T)
## [1] 0

cumulative sum

all constructs

cumsum_construct <- constructs %>% 
  arrange(TestYear) %>% 
  group_by(TestYear) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(TestYear) %>% 
  mutate(constructs = cumsum(constructs)) 

cumsum_construct_survived_5 <- constructs %>% 
  filter(survived_five == T) %>% 
  arrange(TestYear) %>% 
  group_by(TestYear) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(TestYear) %>% 
  mutate(constructs = cumsum(constructs)) 
  
cumsum_construct_survived_10 <- constructs %>% 
  filter(survived_ten == T) %>% 
  arrange(TestYear) %>% 
  group_by(TestYear) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(TestYear) %>% 
  mutate(constructs = cumsum(constructs)) 



cumsums <- bind_rows(
  "all constructs" = cumsum_construct,
  "constructs in use\n for => 5 years" = cumsum_construct_survived_5,
  "constructs in use\n for => 10 years" = cumsum_construct_survived_10,
  .id = "origin"
  ) %>% 
  rename(Year = TestYear) %>% 
  filter(Year <= 2022)


ggplot(cumsums, aes(Year, constructs, color = origin)) + 
  geom_line() +
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  scale_y_continuous("Cumulative number of constructs") +
  scale_x_continuous("Publication year in APA PsycTests",
                     limits = c(1993, 2030), 
                     breaks = seq(1993,2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022"),
                     expand = expansion(add = c(0, 1))) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   )  +
  geom_text_repel(data = cumsums %>% drop_na() %>% group_by(origin) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ", origin, "\n (n = ", constructs, ")")),
                  segment.square = TRUE,
                  lineheight = .9,
                  segment.color = 'grey',
                  nudge_x = 1.2,
                  hjust = 0,
                  na.rm = TRUE)+
   # theme_minimal(base_size = 13) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")  +
    guides(
    x = guide_axis(cap = "both"), # Cap both ends
  )
## Warning: Removed 187 rows containing missing values or values outside the scale range
## (`geom_line()`).

ggsave("figures/cumsums_survival_all.pdf", width = 8, height = 4)
## Warning: Removed 187 rows containing missing values or values outside the scale range
## (`geom_line()`).
ggsave("figures/cumsums_survival_all.png", width = 8, height = 4)
## Warning: Removed 187 rows containing missing values or values outside the scale range
## (`geom_line()`).

first constructs

first_constructs <- constructs %>%
  distinct(first_construct, .keep_all = TRUE)


cumsum_construct <- first_constructs %>% 
  arrange(TestYear) %>% 
  group_by(TestYear) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(TestYear) %>% 
  mutate(constructs = cumsum(constructs)) 

cumsum_construct_survived_5 <- first_constructs %>% 
  filter(survived_five == T) %>% 
  arrange(TestYear) %>% 
  group_by(TestYear) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(TestYear) %>% 
  mutate(constructs = cumsum(constructs)) 
  
cumsum_construct_survived_10 <- first_constructs %>% 
  filter(survived_ten == T) %>% 
  arrange(TestYear) %>% 
  group_by(TestYear) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(TestYear) %>% 
  mutate(constructs = cumsum(constructs)) 



cumsums <- bind_rows(
  "all constructs" = cumsum_construct,
  "constructs in use\n for => 5 years" = cumsum_construct_survived_5,
  "constructs in use\n for => 10 years" = cumsum_construct_survived_10,
  .id = "origin"
  ) %>% 
  rename(Year = TestYear) %>% 
  filter(Year <= 2022)


ggplot(cumsums, aes(Year, constructs, color = origin)) + 
  geom_line() +
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  scale_y_continuous("Cumulative number of constructs") +
  scale_x_continuous("Publication year in APA PsycTests",
                     limits = c(1993, 2030), 
                     breaks = seq(1993,2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022"),
                     expand = expansion(add = c(0, 1))) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   )  +
  geom_text_repel(data = cumsums %>% drop_na() %>% group_by(origin) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ", origin, "\n (n = ", constructs, ")")),
                  segment.square = TRUE,
                  lineheight = .9,
                  segment.color = 'grey',
                  nudge_x = 1.2,
                  hjust = 0,
                  na.rm = TRUE)+
   # theme_minimal(base_size = 13) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")  +
    guides(
    x = guide_axis(cap = "both"), # Cap both ends
  )
## Warning: Removed 187 rows containing missing values or values outside the scale range
## (`geom_line()`).

ggsave("figures/cumsums_survival_first.pdf", width = 8, height = 4)
## Warning: Removed 187 rows containing missing values or values outside the scale range
## (`geom_line()`).
ggsave("figures/cumsums_survival_first.png", width = 8, height = 4)
## Warning: Removed 187 rows containing missing values or values outside the scale range
## (`geom_line()`).

measures

cumsum_all <- measures %>% 
  group_by(TestYear) %>% 
  summarise(tests = n()) %>% 
  arrange(TestYear) %>% 
  mutate(tests = cumsum(tests)) 

cumsum_5 <- measures %>% 
  filter(survived_five == T) %>% 
  group_by(TestYear) %>% 
  summarise(tests = n()) %>% 
  arrange(TestYear) %>% 
  mutate(tests = cumsum(tests)) 

cumsum_10 <- measures %>% 
  filter(survived_ten == T) %>% 
  arrange(TestYear) %>% 
  distinct(Name_base, .keep_all = T) %>% 
  group_by(TestYear) %>% 
  summarise(tests = n_distinct(DOI)) %>% 
  mutate(tests = cumsum(tests)) 

cumsums <- bind_rows(
  "all measures" = cumsum_all,
  "measures in use\n for => 5 years" = cumsum_5,
  "measures in use\n for => 10 years" = cumsum_10,
  .id = "origin"
  ) %>% 
  rename(Year = TestYear) %>% 
  filter(Year <= 2022)


ggplot(cumsums, aes(Year, tests, color = origin)) + 
  geom_line() +
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  scale_y_continuous("Cumulative number of measures") +
  scale_x_continuous("Publication year in APA PsycTests",
                     limits = c(1993, 2030), 
                     breaks = seq(1993,2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022"),
                     expand = expansion(add = c(0, 1))) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   )  +
  geom_text_repel(data = cumsums %>% drop_na() %>% group_by(origin) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ", origin, "\n (n = ", tests, ")")),
                  segment.square = TRUE,
                  lineheight = .9,
                  segment.color = 'grey',
                  nudge_x = 1.2,
                  hjust = 0,
                  na.rm = TRUE) +
   # theme_minimal(base_size = 13) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")  +
    guides(
    x = guide_axis(cap = "both"), # Cap both ends
  )
## Warning: Removed 220 rows containing missing values or values outside the scale range
## (`geom_line()`).

ggsave("figures/cumsums_survival_measures.pdf", width = 8, height = 4)
## Warning: Removed 220 rows containing missing values or values outside the scale range
## (`geom_line()`).
ggsave("figures/cumsums_survival_measures.png", width = 8, height = 4)
## Warning: Removed 220 rows containing missing values or values outside the scale range
## (`geom_line()`).

by first use in PsycInfo instead of publication year in PsycTests

cumsum_construct <- constructs %>% 
  arrange(first_pub_year) %>% 
  group_by(first_pub_year) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(first_pub_year) %>% 
  mutate(constructs = cumsum(constructs)) 

cumsum_construct_survived_5 <- constructs %>% 
  filter(survived_five == T) %>% 
  arrange(first_pub_year) %>% 
  group_by(first_pub_year) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(first_pub_year) %>% 
  mutate(constructs = cumsum(constructs)) 
  
cumsum_construct_survived_10 <- constructs %>% 
  filter(survived_ten == T) %>% 
  arrange(first_pub_year) %>% 
  group_by(first_pub_year) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(first_pub_year) %>% 
  mutate(constructs = cumsum(constructs)) 



cumsums <- bind_rows(
  "all constructs" = cumsum_construct,
  "constructs in use\n for => 5 years" = cumsum_construct_survived_5,
  "constructs in use\n for => 10 years" = cumsum_construct_survived_10,
  .id = "origin"
  ) %>% 
  rename(Year = first_pub_year) %>% 
  filter(Year <= 2022)


ggplot(cumsums, aes(Year, constructs, color = origin)) + 
  geom_line() +
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  scale_y_continuous("Cumulative number of constructs") +
  scale_x_continuous("First usage logged in PsycInfo",
                     limits = c(1993, 2030), 
                     breaks = seq(1993,2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022"),
                     expand = expansion(add = c(0, 1))) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   )  +
  geom_text_repel(data = cumsums %>% drop_na() %>% group_by(origin) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ", origin, "\n (n = ", constructs, ")")),
                  segment.square = TRUE,
                  lineheight = .9,
                  segment.color = 'grey',
                  nudge_x = 1.2,
                  hjust = 0,
                  na.rm = TRUE)+
   # theme_minimal(base_size = 13) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")  +
    guides(
    x = guide_axis(cap = "both"), # Cap both ends
  )
## Warning: Removed 186 rows containing missing values or values outside the scale range
## (`geom_line()`).

by last use in PsycInfo instead of publication year in PsycTests

cumsum_construct <- constructs %>% 
  arrange(last_pub_year) %>% 
  group_by(last_pub_year) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(last_pub_year) %>% 
  mutate(constructs = cumsum(constructs)) 

cumsum_construct_survived_5 <- constructs %>% 
  filter(survived_five == T) %>% 
  arrange(last_pub_year) %>% 
  group_by(last_pub_year) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(last_pub_year) %>% 
  mutate(constructs = cumsum(constructs)) 
  
cumsum_construct_survived_10 <- constructs %>% 
  filter(survived_ten == T) %>% 
  arrange(last_pub_year) %>% 
  group_by(last_pub_year) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(last_pub_year) %>% 
  mutate(constructs = cumsum(constructs)) 



cumsums <- bind_rows(
  "all constructs" = cumsum_construct,
  "constructs in use\n for => 5 years" = cumsum_construct_survived_5,
  "constructs in use\n for => 10 years" = cumsum_construct_survived_10,
  .id = "origin"
  ) %>% 
  rename(Year = last_pub_year) %>% 
  filter(Year <= 2022)


ggplot(cumsums, aes(Year, constructs, color = origin)) + 
  geom_line() +
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  scale_y_continuous("Cumulative number of constructs") +
  scale_x_continuous("Last usage logged in PsycInfo",
                     limits = c(1993, 2030), 
                     breaks = seq(1993,2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022"),
                     expand = expansion(add = c(0, 1))) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   )  +
  geom_text_repel(data = cumsums %>% drop_na() %>% group_by(origin) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ", origin, "\n (n = ", constructs, ")")),
                  segment.square = TRUE,
                  lineheight = .9,
                  segment.color = 'grey',
                  nudge_x = 1.2,
                  hjust = 0,
                  na.rm = TRUE)+
   # theme_minimal(base_size = 13) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")  +
    guides(
    x = guide_axis(cap = "both"), # Cap both ends
  )

counts

count_construct <- constructs %>% 
  arrange(TestYear) %>%
  group_by(TestYear) %>% 
  summarise(tests = n_distinct(construct)) %>% 
  arrange(TestYear)

count_construct_5 <- constructs %>% 
  filter(survived_five == T) %>% 
  arrange(TestYear) %>%
  group_by(TestYear) %>% 
  summarise(tests = n_distinct(construct)) %>% 
  arrange(TestYear)

count_construct_10 <- constructs %>% 
  filter(survived_ten == T) %>% 
  arrange(TestYear) %>%
  group_by(TestYear) %>% 
  summarise(tests = n_distinct(construct)) %>% 
  arrange(TestYear)

counts <- bind_rows(
  "all constructs" = count_construct,
  "constructs in use\n for => 5 years" = count_construct_5,
  "constructs in use\n for => 10 years" = count_construct_10,
  .id = "origin"
  ) %>% 
  rename(Year = TestYear) %>% 
  filter(Year <= 2022)



ggplot(counts, aes(Year, tests, color = origin)) + 
  geom_line() +
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  scale_y_continuous("Number of constructs") +
  scale_x_continuous("Publication year in APA PsycTests",
                     limits = c(1993, 2030), 
                     breaks = seq(1993, 2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022")) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   ) +
  geom_text_repel(data = counts %>% drop_na() %>% group_by(origin) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ", origin)),
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  lineheight = .9,
                  # segment.color = 'grey',
                  box.padding = 0.1,
                  point.padding = 0.6,
                  nudge_x = 1.5,
                  nudge_y = -15,
                  force = 1,
                  hjust = 0,
                  direction="y",
                  na.rm = F) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")
## Warning: Removed 187 rows containing missing values or values outside the scale range
## (`geom_line()`).

ggsave("figures/counts_survival.pdf", width = 8, height = 4)
## Warning: Removed 187 rows containing missing values or values outside the scale range
## (`geom_line()`).
ggsave("figures/counts_survival.png", width = 8, height = 4)
## Warning: Removed 187 rows containing missing values or values outside the scale range
## (`geom_line()`).

Robustness checks

all_constructs_over_time <- records_wide %>% select(subdiscipline_1, DOI, TestYear, ConstructList) %>% 
    unnest(ConstructList) %>% 
    rowwise() %>% 
    mutate(construct = unlist(ConstructList)) %>% 
  select(-ConstructList)

cumsum_all_constructs <- all_constructs_over_time %>% 
  arrange(TestYear) %>% 
  distinct(construct, .keep_all = T) %>% 
  group_by(TestYear) %>% 
  summarise(constructs = n_distinct(construct)) %>% 
  arrange(TestYear) %>% 
  mutate(constructs = cumsum(constructs)) 


cumsum_construct <- records_wide %>% 
  arrange(TestYear) %>% 
  distinct(first_construct, .keep_all = T) %>% 
  group_by(TestYear) %>% 
  summarise(constructs = n_distinct(first_construct)) %>% 
  arrange(TestYear) %>% 
  mutate(constructs = cumsum(constructs))

cumsums <- bind_rows(
  "first constructs" = cumsum_construct,
  "all constructs" = cumsum_all_constructs,
  .id = "origin"
  ) %>% 
  rename(Year = TestYear) %>% 
  filter(Year <= 2022)


ggplot(cumsums, aes(Year, constructs, color = origin)) + 
  geom_line() +
  scale_y_continuous("Cumulative number of constructs") +
  scale_x_continuous("Publication year in APA PsycTests",
                     limits = c(1993, 2027), 
                     breaks = seq(1993, 2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022")) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   ) + 
  geom_text_repel(data = cumsums %>% drop_na() %>% group_by(origin) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ", origin, "\n (n = ", constructs, ")")),
                  segment.square = TRUE,
                  lineheight = .9,
                  segment.color = 'grey',
                  nudge_x = 1.2,
                  hjust = 0,
                  na.rm = TRUE)
## Warning: Removed 170 rows containing missing values or values outside the scale range
## (`geom_line()`).

ggsave("figures/cumsum_all_vs_first.pdf", width = 8, height = 4)
## Warning: Removed 170 rows containing missing values or values outside the scale range
## (`geom_line()`).
ggsave("figures/cumsum_all_vs_first.png", width = 8, height = 4)
## Warning: Removed 170 rows containing missing values or values outside the scale range
## (`geom_line()`).

All or first constructs

For some 20% of tests, two or more constructs were coded. In most plots, we simply use the first construct for each test.

records_wide <- records_wide %>% 
  rowwise() %>% 
  mutate(constructs_n = length(ConstructList)) %>% 
  ungroup()

table(records_wide$constructs_n)
## 
##     1     2     3     4     5     6     7     8     9    10    12    15    16 
## 56645 12421  2134   326    85    37    13    14     3     3     3     1     1 
##    17    21    22    23    25 
##     1     1     1     1     2
round(prop.table(table(records_wide$constructs_n)),2)
## 
##    1    2    3    4    5    6    7    8    9   10   12   15   16   17   21   22 
## 0.79 0.17 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 
##   23   25 
## 0.00 0.00
ggplot(records_wide, aes(constructs_n)) + 
  geom_bar()

Expanding the entropy calculation to all coded constructs makes little difference.

all_constructs_over_time <- psyctests_info %>% select(subdiscipline_1, DOI, TestYear, Year, ConstructList, usage_count) %>% 
    unnest(ConstructList) %>% 
    rowwise() %>% 
    mutate(construct = unlist(ConstructList)) %>% 
  select(-ConstructList)

entropy_all_constructs <- all_constructs_over_time %>% 
  filter(between(Year, 1993, 2022)) %>% 
  drop_na(construct) %>% 
  group_by(Year, construct) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  ungroup() %>% 
  mutate(n_tests = n_distinct(construct)) %>% 
  group_by(n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(entropy = entropy(n),,
            norm_entropy = calc_norm_entropy(n),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'n_tests'. You can override using the
## `.groups` argument.
byconstruct_entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  drop_na(first_construct) %>% 
  group_by(Year, Test = first_construct) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  ungroup() %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(entropy = entropy(n),,
            norm_entropy = calc_norm_entropy(n),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'n_tests'. You can override using the
## `.groups` argument.
entropy_by_year <- bind_rows(# "all tests" = all_entropy_by_year,
                             "all constructs" = entropy_all_constructs,
                             # "by name base" = bybase_entropy_by_year,
                             "first constructs" = byconstruct_entropy_by_year,
                             .id = "version")

entropy_by_year %>% 
  ggplot(., aes(Year, norm_entropy, color = version)) +
  geom_line(size = 0.7) +
  scale_y_continuous("Normalized Shannon Entropy", limits = c(0, 1), labels = scales::percent) +
  # geom_line(aes(y = log(n)), color = 'red') +
  scale_x_continuous("Usage year as coded in APA PsycInfo", limits = c(1993, 2027), 
                     breaks = seq(1993, 2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022")) +

  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   ) + 
  geom_text_repel(data = entropy_by_year %>% drop_na() %>% group_by(version) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ",version)), # "\n (n = ", n_tests, ")"
                  segment.curvature = -0.5,
                  segment.square = TRUE,
                  segment.color = 'grey', 
                  xlim = c(2023, 2030),
                  nudge_x = 1.14,
                  lineheight = .9,
                  hjust = 0,
                  direction="y",
                  na.rm = TRUE) +
  theme_minimal(base_size = 13) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")

ggsave("figures/entropy_all_vs_first.pdf", width = 8, height = 4)
ggsave("figures/entropy_all_vs_first.png", width = 8, height = 4)

Unbiased estimators of Shannon entropy

Does not make much of a difference.

byorig_entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  mutate(Test = if_else(test_type == "Original", DOI, original_test_DOI)) %>% 
  drop_na(Test) %>% 
  group_by(Year, Test) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  ungroup() %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(norm_entropy = calc_norm_entropy(n),
            norm_entropy_MM = entropy(n, method = "MM") / log(n()),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'n_tests'. You can override using the
## `.groups` argument.
1 - (byorig_entropy_by_year$n_tests[1]/ records_wide %>% filter(between(TestYear, 1993, 2022)) %>% 
       mutate(Test = if_else(test_type == "Original", DOI, original_test_DOI)) %>% 
       summarise(n_distinct(Test)))
## # A tibble: 1 × 1
##   `n_distinct(Test)`
##                <dbl>
## 1              0.552
byconstruct_entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  drop_na(first_construct) %>% 
  group_by(Year, Test = first_construct) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  ungroup() %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(norm_entropy = calc_norm_entropy(n),
            norm_entropy_MM = entropy(n, method = "MM") / log(n()),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'n_tests'. You can override using the
## `.groups` argument.
all_entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  drop_na(DOI) %>% 
  group_by(Year, Test = DOI) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  ungroup() %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(norm_entropy = calc_norm_entropy(n),
            norm_entropy_MM = entropy(n, method = "MM") / log(n()),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'n_tests'. You can override using the
## `.groups` argument.
original_entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  filter(test_type == "Original") %>% 
  group_by(Year, Test = DOI) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  ungroup() %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(norm_entropy = calc_norm_entropy(n),
            norm_entropy_MM = entropy(n, method = "MM") / log(n()),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'n_tests'. You can override using the
## `.groups` argument.
entropy_by_year <- bind_rows(# "all tests" = all_entropy_by_year,
  "measures" = original_entropy_by_year,
  "with translations\n and revisions" = byorig_entropy_by_year,
  # "by name base" = bybase_entropy_by_year,
  "constructs" = byconstruct_entropy_by_year,
  .id = "version")


plot_entropy <- entropy_by_year %>% 
  ggplot(., aes(Year, norm_entropy, color = version)) +
  geom_line(size = 0.7, linetype = "dashed") +
  geom_line(aes(y = norm_entropy_MM), size = 0.7) +
  scale_y_continuous("Normalized Shannon Entropy", limits = c(0, 1), labels = scales::percent) +
  # geom_line(aes(y = log(n)), color = 'red') +
  scale_x_continuous("Usage year as coded in APA PsycInfo", limits = c(1993, 2027), 
                     breaks = seq(1993, 2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022")) +
  # ggtitle(str_c(n_distinct(tests_by_year$Test), " measures tracked in PsycInfo")) +
  # annotate("text", x = 1993, y = 1, label = "- each used once", 
  #       size = 3.3, vjust = 0.3, hjust = 0.05) +
  # annotate("text", x = 1993, y = 0, label = "- all used one", 
  #       size = 3.3,  vjust = 0.3, hjust = 0.05) +
  
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   ) + 
  geom_text_repel(aes(label = str_replace_all(version, "[a-z=0-9/() ]+", " ")), # This will force the correct position of the link's right end.
                  data = entropy_by_year %>% drop_na() %>% group_by(version) %>% filter(Year == max(Year, na.rm = T)),
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  lineheight = .9,
                  segment.color = 'grey',
                  box.padding = 0.1,
                  point.padding = 0.6,
                  nudge_x = 1.15,
                  nudge_y = 0.03,
                  force = 0.9,
                  hjust = 0,
                  direction="y",
                  size = 3.3,
                  na.rm = TRUE) +
  geom_text_repel(data = entropy_by_year %>% drop_na() %>% group_by(version) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ",version)), # "\n (n = ", n_tests, ")"
                  segment.alpha = 0, ## This will 'hide' the link
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  # segment.color = 'grey',
                  box.padding = 0.1,
                  point.padding = 0.6,
                  nudge_x = 1.15,
                  nudge_y = 0.0,
                  lineheight = .9,
                  force = 0.9,
                  size = 3.3,
                  hjust = 0,
                  direction="y",
                  na.rm = TRUE) +
  theme_minimal(base_size = 13) +
  theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")
plot_entropy

Entropy by classificaiton

entropy_by_class <- psyctests_info %>% 
  group_by(classification_1, DOI) %>% 
  summarise(n = sum(usage_count, na.rm = T),
            parent = case_when(
                    n > 50 ~ "",
                    n > 20 ~ "used 21-50 times",
                    n > 5 ~ "used 6-20 times",
                    TRUE ~ "used 1-5 times")) %>% 
  group_by(classification_1) %>% 
  # filter(n > 0) %>% 
  summarise(
    entropy = entropy(n),
    norm_entropy = calc_norm_entropy(n)) %>% 
  arrange(norm_entropy)
## `summarise()` has grouped output by 'classification_1'. You can override using
## the `.groups` argument.
kable(entropy_by_class)
classification_1 entropy norm_entropy
Military Personnel, Adjustment, and Training 2.383022 0.4815393
Anxiety and Depression 3.193979 0.4847495
Aptitude and Achievement 2.316471 0.5135321
Neuropsychological Assessment 3.279933 0.5288051
Intelligence 2.257675 0.5350566
General Assessment Tools 2.473627 0.5371412
Functional Status and Adaptive Behavior 3.530552 0.5868450
Trauma, Stress, and Coping 4.183437 0.5982308
Emotional States, Emotional Responses, and Motivation 4.248094 0.6077965
Mental Health/Illness Related Assessment 4.840355 0.6092446
Addiction, Gambling, and Substance Abuse/Use 4.368824 0.6220855
Cognitive Processes, Memory, and Decision Making 4.430633 0.6357767
Sports, Recreation, and Leisure 3.819379 0.6452859
Treatment, Rehabilitation, and Therapeutic Processes 5.157328 0.6514703
Legal and Forensic Evaluation 3.839622 0.6557771
Personality 5.270944 0.6880603
Development and Aging 5.170306 0.7010938
Physical Health/Illness Related Assessment 5.519613 0.7074794
Perceptual, Motor, and Sensory Processing 4.027230 0.7094247
Attitudes, Interests, Values, and Expectancies 4.964110 0.7109935
Communication, Language, and Verbal Processing 4.017755 0.7210416
Family Relationships and Parenting 5.284498 0.7324078
Sex, Gender Roles, and Sexual Behavior 4.922535 0.7417984
Social, Group, and Interpersonal Relationships 5.558756 0.7492815
Religious and Political Beliefs 4.667158 0.7656319
Consumer Behavior, Marketing, and Advertising 4.587740 0.7679755
Culture, Racial, and Ethnic Identity 4.787330 0.7693485
Human Factors and Environmental Engineering 4.212094 0.7776987
Human-Computer Interaction 4.726558 0.7843782
Organizational, Occupational, and Career Development 6.103051 0.7895549
Education, Teaching, and Student Characteristics 6.281884 0.7986947

Entropy by instrument type

psyctests_info %>% 
  group_by(instrument_type_broad, DOI) %>% 
  summarise(n = sum(usage_count, na.rm = T),
            parent = case_when(
                    n > 50 ~ "",
                    n > 20 ~ "used 21-50 times",
                    n > 5 ~ "used 6-20 times",
                    TRUE ~ "used 1-5 times")) %>% 
  group_by(instrument_type_broad) %>% 
  filter(n > 0) %>% 
  summarise(
    entropy = entropy(n),
    norm_entropy = calc_norm_entropy(n)) %>% 
  arrange(norm_entropy) %>% 
  kable()
## `summarise()` has grouped output by 'instrument_type_broad'. You can override
## using the `.groups` argument.
instrument_type_broad entropy norm_entropy
other-rating 4.454314 0.6201748
task 4.041814 0.6228520
NA 3.717207 0.6283096
test 4.704202 0.6359652
questionnaire 7.207948 0.7060498

Tests by exact same name

same_name_tests <- records_wide %>% group_by(name_psycinfo) %>% filter(n_distinct(DOI) > 1) %>% summarise(n = n())
nrow(same_name_tests)
## [1] 1230
mean(same_name_tests$n)
## [1] 2.44878
same_name_tests %>% arrange(desc(n)) %>% head(20)
## # A tibble: 20 × 2
##    name_psycinfo                                n
##    <chr>                                    <int>
##  1 theory of planned behavior questionnaire    18
##  2 job satisfaction scale                      15
##  3 self-efficacy scale                         11
##  4 self-control scale                          10
##  5 behavioral intentions measure                9
##  6 parental involvement scale                   9
##  7 procedural justice scale                     9
##  8 social distance scale                        9
##  9 marital satisfaction measure                 8
## 10 perceived behavioral control measure         8
## 11 religiosity measure                          8
## 12 religiosity scale                            8
## 13 social capital measure                       8
## 14 social support scale                         8
## 15 victimization measure                        8
## 16 attribution questionnaire                    7
## 17 delinquency measure                          7
## 18 fear of crime scale                          7
## 19 food frequency questionnaire                 7
## 20 life satisfaction scale                      7
---
title: "Construct proliferation"
author: "Ruben Arslan"
date: "`r Sys.Date()`"
output: 
  html_document:
    toc: true
    toc_float: true
    code_folding: "hide"
editor_options: 
  chunk_output_type: inline
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, error = T, warning = T, message = T, fig.width = 8, fig.height = 4)

library(groundhog)
groundhog.library(c("tidyverse", "entropy", "ggrepel", "cowplot", "knitr", "readr",
                    "RColorBrewer", "plotly", "gglorenz", "rio", "hrbrthemes"), 
                  date = "2024-02-24")
theme_set(theme_minimal(base_size = 13))

colors <- RColorBrewer::brewer.pal(3, name = "Dark2")
names(colors) <- c("all", "novel", "constructs")

calc_norm_entropy <- function(n) {
  n <- n[n>0]
  entropy::entropy(n)/log(length(n))
}
```


<details><summary>Import</summary>
APA PsycTests

```{r}
records_wide <- readRDS("../sober_rubric/raw_data/preprocessed_records.rds")

records_wide %>% group_by(Name)  %>% filter(n()>1) %>% ungroup() %>% summarise(n_distinct(Name), n())

records_wide$first_construct <- str_trim(str_replace_all(str_to_lower(records_wide$first_construct), "[:space:]+", " "))
```

First EBSCO scrape of APA PsycInfo
```{r}
psycinfo <- read_tsv('../sober_rubric/raw_data/merged_table_all.tsv') %>% 
  # this tsv can be found in "Scraping-EBSCO-Host\data\merged tables"
#  mutate(Name = toTitleCase(Name)) %>% 
  rename(usage_count = "Hit Count") %>% 
  group_by(Name, Year) %>% 
  summarise(usage_count = sum(usage_count))
```


Second EBSCO scrape of APA PsycInfo
```{r}
overview <- readr::read_tsv("../sober_rubric/raw_data/20230617_ebsco_scrape_clean_overview_table_1.tsv")
byyear <- readr::read_tsv("../sober_rubric/raw_data/20230617_ebsco_scrape_table_years_1.tsv")
n_distinct(byyear$DOI)

overview %>% filter(is.na(Hits)) %>% nrow()
nrow(overview)
overview %>% filter(Hits >= 1) %>% nrow()

one_hit_wonders <- overview %>% filter(Hits == 1) %>% 
  mutate(Year = first_pub_year)

nrow(one_hit_wonders)
# for some few, the call was repeated by year for some reason
# one_hit_wonders %>% select(DOI, first_pub_year) %>% inner_join(byyear, by = "DOI") %>% arrange(DOI)

byyear <- byyear %>% anti_join(one_hit_wonders, by = "DOI")

all <- one_hit_wonders %>% 
  select(DOI, Year, Hits) %>% 
  bind_rows(byyear) %>% 
  left_join(overview %>% rename(total_hits = Hits), by = "DOI")

n_distinct(all$DOI)
all %>% filter(Hits > 0) %>% filter(Year < first_pub_year | Year > last_pub_year) %>% nrow()
all %>% group_by(total_hits, DOI) %>% summarise(hits_by_year = sum(Hits, na.rm = T)) %>% filter(hits_by_year > total_hits) %>% ungroup() %>% select(DOI, everything()) %>% mutate(diff = hits_by_year - total_hits) %>% nrow()
# all %>% group_by(total_hits, DOI) %>% summarise(hits_by_year = sum(usage_count, na.rm = T)) %>% filter(hits_by_year < total_hits) %>% select(DOI, everything()) %>% mutate(diff = hits_by_year - total_hits) %>%  View()
all %>% group_by(total_hits, DOI) %>% summarise(hits_by_year = sum(Hits, na.rm = T)) %>% filter(hits_by_year == total_hits) %>% nrow()
all %>% group_by(DOI) %>% summarise(hits_by_year = sum(Hits, na.rm = T)) %>% filter(hits_by_year == 0)
all %>% group_by(total_hits, DOI) %>% summarise(hits_by_year = sum(Hits, na.rm = T)) %>% filter(is.na(total_hits)) %>% pull(hits_by_year) %>% table()

psyctests_info <- records_wide %>% 
  select(DOI, TestYear, Name, first_construct, 
         original_test_DOI, original_DOI_combined, 
         test_type, ConstructList, subdiscipline_1, subdiscipline_2, 
         classification_1, classification_2, instrument_type_broad, 
         InstrumentType,
         number_of_factors_subscales, Name_base) %>%
  distinct() %>% 
  inner_join(all, by = c("DOI" = "DOI"), multiple = "all") %>% 
  rename(usage_count = "Hits")


write_rds(psyctests_info, "../sober_rubric/raw_data/psyctests_info.rds")
```

</details>

# 2016 changes in standards
```{r}
test_data <- records_wide %>%
  filter(TestYear <= 2022) %>%
    rowwise() %>%
    mutate(Methodology = length(MethodologyList) >0) %>%
    mutate(AdministrationMethod = length(AdministrationMethodList) >0) %>%
    mutate(PopulationGroup = length(PopulationGroupList) >0) %>%
    mutate(AgeGroup = length(AgeGroupList) >0) %>%
    group_by(TestYear) %>%
    summarise(Reliability = mean(Reliability!="No reliability indicated."),
              FactorAnalysis = mean(FactorAnalysis!="No factor analysis indicated."),
              # Unidimensional = mean(FactorAnalysis=="This is a unidimensional measure."),
              FactorsAndSubscales = mean(!is.na(FactorsAndSubscales)),
              Validity = mean(Validity!="No validity indicated."),
              Format = mean(!is.na(Format)),
              # Fee = mean(Fee == "Yes"),
              Methodology = mean(Methodology),
              AdministrationMethod = mean(AdministrationMethod),
              # AgeGroup = mean(AgeGroup),
              # PopulationGroup = mean(PopulationGroup),
              TestItems = mean(TestItemsAvailable == "Yes")) %>%
    pivot_longer(-TestYear)
test_data %>%
  ggplot(aes(TestYear, value, color = name)) +
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  geom_line() +
  scale_x_continuous("Publication year in APA PsycTests",
                     limits = c(1993, 2030),
                     breaks = seq(1993, 2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022"),
                     expand = expansion(add = c(0, 1.2))) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  geom_text_repel(
    data = test_data %>% drop_na() %>% group_by(name) %>% filter(TestYear == max(TestYear, na.rm = T)),
    aes(label = name),
    segment.color = 'grey',
    xlim = c(2022, 2033),
    box.padding = 0.1,
    # point.padding = 0.6,
    nudge_x = 1.2,
    # nudge_y = 0,
    force = 0.5,
    hjust = 0,
    direction="y",
    na.rm = TRUE
  ) +
  ylab("PsycTests contains information about...") +
  theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")
ggsave("figures/changed_standards_2016.pdf", width = 8, height = 4)
ggsave("figures/changed_standards_2016.png", width = 8, height = 4)
```


```{r}
records_wide %>% 
  group_by(TestYear) %>% 
  summarise(number_of_test_items = mean(number_of_test_items, na.rm = T)) %>% 
  filter(TestYear >= 1993, TestYear <= 2022) %>% 
ggplot(aes(TestYear, number_of_test_items)) + 
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  geom_line() +
  scale_x_continuous("Publication year in APA PsycTests",
                     limits = c(1993, NA), 
                     breaks = seq(1993, 2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022")) +
  theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none") +
  xlab("Publication year in APA PsycTests") +
  ylab("Number of items in measure")
```


```{r}
records_wide %>% 
  filter(TestYear >= 1993, TestYear <= 2022) %>% 
ggplot(aes(TestYear, number_of_factors_subscales)) + 
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  geom_pointrange(stat = 'summary', fun.data = 'mean_se') +
  xlim(1993, 2022) +
  xlab("Publication year in APA PsycTests") +
  ylab("Number of factors/subscales")
```


```{r}
records_wide %>% 
  group_by(InstrumentType, TestYear) %>% 
  summarise(tests = n()) %>% 
  group_by(TestYear) %>% 
  mutate(tests = tests/sum(tests, na.rm = T)) %>% 
  arrange(TestYear) %>% 
  filter(TestYear >= 1993, TestYear <= 2022) %>% 
  # mutate(tests = cumsum(tests)) %>% 
  ggplot(aes(TestYear, tests, color = InstrumentType)) + 
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  scale_color_viridis_d() +
  geom_line() +
  xlim(1993, 2022) +
  xlab("Publication year in APA PsycTests") +
  ylab("Proportion of instrument type")
```


```{r}
records_wide %>% 
  # filter(instrument_type_broad !=)
  group_by(instrument_type_broad, TestYear) %>% 
  summarise(tests = n()) %>% 
  group_by(TestYear) %>% 
  mutate(tests = tests/sum(tests, na.rm = T)) %>% 
  arrange(TestYear) %>% 
  filter(TestYear >= 1993, TestYear <= 2022) %>% 
  # mutate(tests = cumsum(tests)) %>% 
  ggplot(aes(TestYear, tests, color = instrument_type_broad)) + 
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  geom_line() +
  xlim(1993, 2022) +
  xlab("Publication year in APA PsycTests") +
  ylab("Proportion of instrument type")
```


```{r}
records_wide %>% 
  filter(instrument_type_broad != "questionnaire") %>% 
  group_by(InstrumentType, TestYear) %>% 
  summarise(tests = n()) %>% 
  group_by(TestYear) %>% 
  mutate(tests = tests/sum(tests, na.rm = T)) %>% 
  arrange(TestYear) %>% 
  filter(TestYear >= 1993, TestYear <= 2022) %>% 
  # mutate(tests = cumsum(tests)) %>% 
  ggplot(aes(TestYear, tests, color = InstrumentType)) + 
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  geom_line() +
  xlim(1993, 2022) +
  xlab("Publication year in APA PsycTests") +
  ylab("Proportion of instrument type") +
  ggtitle("Without questionnaires")
```


```{r}
psyctests_info %>% group_by(DOI, TestYear) %>% 
  summarise(used = sum(usage_count, na.rm = T)) %>% 
  full_join(records_wide %>% select(DOI, TestYear)) %>% 
  mutate(used = coalesce(used, 0)) %>% 
  filter(TestYear >= 1993, TestYear <= 2022) %>% 
  group_by(TestYear) %>% 
  summarise(never_reused = mean(used == 0),
            used_once = mean(used == 1),
            used_twice = mean(used == 2),
            used_thrice = mean(used == 3),
            used_more_10 = mean(used > 9)) %>% 
  pivot_longer(-TestYear) %>% 
  ggplot(aes(TestYear, value, color = name)) + 
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  geom_line() +
  xlim(1993, 2022) +
  xlab("Publication year in APA PsycTests") +
  ylab("Frequency")
```

# New measures by publication year
```{r}
count_all <- records_wide %>% 
  group_by(TestYear) %>% 
  summarise(tests = n()) %>% 
  arrange(TestYear)

count_orig <- records_wide %>% 
  filter(test_type == "Original") %>% 
  group_by(TestYear) %>% 
  summarise(tests = n()) %>% 
  arrange(TestYear)

count_base <- records_wide %>% 
  arrange(TestYear) %>% 
  distinct(Name_base, .keep_all = T) %>% 
  group_by(TestYear) %>% 
  summarise(tests = n_distinct(DOI))

count_construct <- records_wide %>% 
  arrange(TestYear) %>% 
  distinct(first_construct, .keep_all = T) %>% 
  group_by(TestYear) %>% 
  summarise(tests = n_distinct(first_construct)) %>% 
  arrange(TestYear)

counts <- bind_rows(
  "novel constructs" = count_construct,
  "novel measures" = count_orig,
  "with translations\n and revisions" = count_all,
  .id = "origin"
  ) %>% 
  rename(Year = TestYear) %>% 
  filter(Year <= 2022)


ggplot(counts, aes(Year, tests, color = origin)) + 
  geom_line() +
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  scale_y_continuous("Number of measures/constructs published") +
  scale_x_continuous("Publication year in APA PsycTests",
                     limits = c(1993, 2030), 
                     breaks = seq(1993, 2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022")) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   ) +
  geom_text_repel(data = counts %>% drop_na() %>% group_by(origin) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ", origin)),
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  lineheight = .9,
                  # segment.color = 'grey',
                  box.padding = 0.1,
                  point.padding = 0.6,
                  nudge_x = 1.5,
                  nudge_y = -15,
                  force = 1,
                  hjust = 0,
                  direction="y",
                  na.rm = F) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")

ggsave("figures/counts.pdf", width = 8, height = 4)
ggsave("figures/counts.png", width = 8, height = 4)
```



# Cumulative number of measures and constructs
```{r}
cumsum_all <- records_wide %>% 
  group_by(TestYear) %>% 
  summarise(tests = n()) %>% 
  arrange(TestYear) %>% 
  mutate(tests = cumsum(tests)) 

cumsum_orig <- records_wide %>% 
  filter(test_type == "Original") %>% 
  group_by(TestYear) %>% 
  summarise(tests = n()) %>% 
  arrange(TestYear) %>% 
  mutate(tests = cumsum(tests)) 

cumsum_base <- records_wide %>% 
  arrange(TestYear) %>% 
  distinct(Name_base, .keep_all = T) %>% 
  group_by(TestYear) %>% 
  summarise(tests = n_distinct(DOI)) %>% 
  mutate(tests = cumsum(tests)) 

cumsum_construct <- records_wide %>% 
  arrange(TestYear) %>% 
  distinct(first_construct, .keep_all = T) %>% 
  group_by(TestYear) %>% 
  summarise(tests = n_distinct(DOI)) %>% 
  arrange(TestYear) %>% 
  mutate(tests = cumsum(tests)) 

cumsums <- bind_rows(
  "novel constructs" = cumsum_construct,
  "novel measures" = cumsum_orig,
  "with translations\n and revisions" = cumsum_all,
  .id = "origin"
  ) %>% 
  rename(Year = TestYear) %>% 
  filter(Year <= 2022)


ggplot(cumsums, aes(Year, tests, color = origin)) + 
  geom_line() +
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  scale_y_continuous("Cumulative number of measures") +
  scale_x_continuous("Publication year in APA PsycTests",
                     limits = c(1993, 2030), 
                     breaks = seq(1993,2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022"),
                     expand = expansion(add = c(0, 1))) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   )  +
  geom_text_repel(data = cumsums %>% drop_na() %>% group_by(origin) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ", origin, "\n (n = ", tests, ")")),
                  segment.square = TRUE,
                  lineheight = .9,
                  segment.color = 'grey',
                  nudge_x = 1.2,
                  hjust = 0,
                  na.rm = TRUE) +
   # theme_minimal(base_size = 13) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")  +
    guides(
    x = guide_axis(cap = "both"), # Cap both ends
  )
ggsave("figures/cumsums.pdf", width = 8, height = 4)
ggsave("figures/cumsums.png", width = 8, height = 4)
```

## By subdiscipline
```{r fig.width=8,fig.height=7}
cumsum_all <- records_wide %>% 
  group_by(subdiscipline_1, TestYear) %>% 
  summarise(tests = n()) %>% 
  arrange(TestYear) %>% 
  mutate(tests = cumsum(tests)) 


cumsum_orig <- records_wide %>% 
  filter(test_type == "Original") %>% 
  group_by(subdiscipline_1, TestYear) %>% 
  summarise(tests = n()) %>% 
  arrange(TestYear) %>% 
  mutate(tests = cumsum(tests)) 

cumsum_base <- records_wide %>% 
  arrange(TestYear) %>% 
  distinct(Name_base, .keep_all = T) %>% 
  group_by(subdiscipline_1, TestYear) %>% 
  summarise(tests = n_distinct(DOI)) %>% 
  mutate(tests = cumsum(tests)) 

cumsum_construct <- records_wide %>% 
  arrange(TestYear) %>% 
  distinct(first_construct, .keep_all = T) %>% 
  group_by(subdiscipline_1, TestYear) %>% 
  summarise(tests = n_distinct(DOI)) %>% 
  arrange(TestYear) %>% 
  mutate(tests = cumsum(tests)) 

cumsums <- bind_rows(
  "constructs" = cumsum_construct,
  "measures" = cumsum_orig,
  "with translations & revisions" = cumsum_all,
  .id = "origin"
  ) %>% 
  rename(Year = TestYear) %>% 
  filter(Year <= 2022)

cumsums$origin <- factor(cumsums$origin, levels = c("with translations & revisions", "constructs", "measures"))
my_colors <- c("with translations & revisions" = "#7570B3",
               "constructs" = "#1B9E77", 
               "measures" = "#D95F02") 

ggplot(cumsums, aes(Year, tests, color = origin)) + 
  geom_line() +
  facet_wrap(~ subdiscipline_1, scales = "free_y", ncol = 2) + 
  scale_y_continuous("Cumulative number of measures") +
  scale_x_continuous("Publication year in APA PsycTests",
                     limits = c(1993, 2022), 
                     breaks = seq(1993, 2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022")) +
  scale_color_manual(values = my_colors, guide = guide_legend(title = NULL)) +
  theme_minimal(base_size = 13) +
  theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = c(0.58, 0.08),
        legend.justification = c(0, 0),
        legend.box.just = "right",
        legend.text = element_text(size = 11))



ggsave("figures/cumsums_subdiscipline.pdf", width = 8, height = 7)
ggsave("figures/cumsums_subdiscipline.png", width = 8, height = 7)
```


# Tests by usage frequency
```{r}
test_frequency <- psyctests_info %>% 
  mutate(Test = if_else(test_type == "Original", DOI, original_test_DOI)) %>% 
  drop_na(Test) %>% 
  # filter(TestYear >= 1990) %>%
  filter(between(Year, 1993, 2022)) %>%
  group_by(Test) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  arrange(n)

test_frequency <- records_wide %>% 
  filter(test_type == "Original", TestYear <= 2022) %>% 
  select(Test = DOI) %>% 
  full_join(test_frequency) %>% 
  mutate(n = coalesce(n, 0.5))

test_frequency <- test_frequency %>% 
  group_by(n) %>% 
  summarise(count = n()) %>% 
  ungroup() %>% 
  mutate(percent = count/sum(count))

freq_plot <- ggplot(test_frequency, aes(n, count)) + 
  geom_bar(width = 0.1, fill = colors["novel"], stat = "identity") +
  # facet_wrap(~ subdiscipline_1, scales = "free_y") + 
  # scale_y_sqrt("Number of measures", breaks = c(0, 100, 400, 1000, 2000, 4000, 6000, 10000), limits = c(0, 11500)) +
  scale_y_continuous("Number of measures") +
  scale_x_log10("Usages recorded in APA PsycInfo 1993-2022",
                breaks = c(0.5, 1, 2, 5, 10, 100, 1000, 25000),
               labels = c(0, 1, 2, 5, 10, 100, 1000, 25000)) +
  
  geom_text(aes(label = if_else(n <= 2, sprintf("%.0f%%", percent*100), ""),
                x = n, y = count + 700)) +

  # scale_x_sqrt(breaks = c(0, 1, 2, 3, 4, 5, 10, 20, 40, 50), labels = c(0, 1, 2, 3, 4, 5, 10, 20, 40, "50+")) +
  # geom_text_repel(aes(x = n, label = first_acronym, y = y), 
  #                 data =
  #                   test_frequency %>% group_by(subdiscipline_1) %>% filter(row_number() > (n() - 10) ) %>% left_join(records_wide %>% select(Test = DOI, first_acronym)) %>% 
  #                   mutate(first_acronym = if_else(first_acronym == "HRSD", "HAM-D",
  #                                                  first_acronym)) %>% 
    # mutate(y = 20 + 50*(1+n()-row_number())),
    #               size = 3.3, force = 5, force_pull	= 0, max.time = 1, 
    #               max.overlaps = Inf,
    #               segment.color = "lightgray",
    #               segment.curvature = 1,
    #               hjust = 1,
    #               nudge_y = 10,
    #               direction = "y"
    # ) +
  theme_minimal(base_size = 13) +
  theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")
freq_plot
ggsave("figures/frequency_across.pdf", width = 8, height = 4)
ggsave("figures/frequency_across.png", width = 8, height = 4)

test_frequency <- test_frequency %>% 
  filter(n >= 1) %>% 
  mutate(percent = count/sum(count))

freq_plot <- ggplot(test_frequency, aes(n, count)) + 
  geom_bar(width = 0.1, fill = colors["novel"], stat = "identity") +
  # scale_y_sqrt("Number of measures", breaks = c(0, 100, 400, 1000, 2000, 4000, 6000, 10000), limits = c(0, 11500)) +
  scale_y_continuous("Number of measures") +
  scale_x_log10("Usages recorded in APA PsycInfo 1993-2022",
                breaks = c(1, 2, 5, 10, 100, 1000, 25000),
               labels = c(1, 2, 5, 10, 100, 1000, 25000)) +
  
  geom_text(aes(label = if_else(n <= 2, sprintf("%.0f%%", percent*100), ""),
                x = n, y = count + 700)) +

  # scale_x_sqrt(breaks = c(0, 1, 2, 3, 4, 5, 10, 20, 40, 50), labels = c(0, 1, 2, 3, 4, 5, 10, 20, 40, "50+")) +
  # geom_text_repel(aes(x = n, label = first_acronym, y = y), 
  #                 data =
  #                   test_frequency %>% group_by(subdiscipline_1) %>% filter(row_number() > (n() - 10) ) %>% left_join(records_wide %>% select(Test = DOI, first_acronym)) %>% 
  #                   mutate(first_acronym = if_else(first_acronym == "HRSD", "HAM-D",
  #                                                  first_acronym)) %>% 
    # mutate(y = 20 + 50*(1+n()-row_number())),
    #               size = 3.3, force = 5, force_pull	= 0, max.time = 1, 
    #               max.overlaps = Inf,
    #               segment.color = "lightgray",
    #               segment.curvature = 1,
    #               hjust = 1,
    #               nudge_y = 10,
    #               direction = "y"
    # ) +
  theme_minimal(base_size = 13) +
  theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")
freq_plot
ggsave("figures/frequency_across_no0.pdf", width = 8, height = 4)
ggsave("figures/frequency_across_no0.png", width = 8, height = 4)
```

```{r}
test_frequency <- psyctests_info %>% 
  mutate(Test = if_else(test_type == "Original", DOI, original_test_DOI)) %>% 
  drop_na(Test) %>% 
  # filter(TestYear >= 1990) %>%
  filter(between(Year, 1993, 2022)) %>%
  group_by(subdiscipline_1, Test) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  arrange(n)

test_frequency <- records_wide %>% 
  filter(test_type == "Original", TestYear <= 2022) %>% 
  select(subdiscipline_1, Test = DOI) %>% 
  full_join(test_frequency) %>% 
  mutate(n = coalesce(n, 0.5))

test_frequency <- test_frequency %>% 
  # mutate(n = if_else(n >= 1000, 1000, n)) %>% 
  mutate(subdiscipline_1 = str_replace(subdiscipline_1, " Psychology", "")) %>% 
  group_by(subdiscipline_1, n) %>% 
  summarise(count = n()) %>% 
  group_by(subdiscipline_1) %>% 
  mutate(percent = count/sum(count))

freq_plot <- ggplot(test_frequency, aes(n, count)) + 
  geom_bar(width = 0.1, fill = colors["novel"], stat = "identity") +
  facet_wrap(~ subdiscipline_1, scales = "free_y") + 
  # scale_y_sqrt("Number of measures", breaks = c(0, 100, 400, 1000, 2000, 4000, 6000, 10000), limits = c(0, 11500)) +
  scale_y_continuous("Number of measures", expand = expansion(c(0, 0.1))) +
  scale_x_log10("Usages recorded in APA PsycInfo 1993-2022",
                breaks = c(0.5, 1, 2, 10, 100, 1000, 25000),
               labels = c(0, 1, 2, 10, 100, 1000, 25000)) +
  
  geom_text(aes(label = if_else(n <= 2, sprintf("%.0f%%", percent*100), ""),
                x = n, y = count ), size = 3, vjust = -0.4, hjust = 0.4) +

  # scale_x_sqrt(breaks = c(0, 1, 2, 3, 4, 5, 10, 20, 40, 50), labels = c(0, 1, 2, 3, 4, 5, 10, 20, 40, "50+")) +
  # geom_text_repel(aes(x = n, label = first_acronym, y = y), 
  #                 data =
  #                   test_frequency %>% group_by(subdiscipline_1) %>% filter(row_number() > (n() - 10) ) %>% left_join(records_wide %>% select(Test = DOI, first_acronym)) %>% 
  #                   mutate(first_acronym = if_else(first_acronym == "HRSD", "HAM-D",
  #                                                  first_acronym)) %>% 
    # mutate(y = 20 + 50*(1+n()-row_number())),
    #               size = 3.3, force = 5, force_pull	= 0, max.time = 1, 
    #               max.overlaps = Inf,
    #               segment.color = "lightgray",
    #               segment.curvature = 1,
    #               hjust = 1,
    #               nudge_y = 10,
    #               direction = "y"
    # ) +
  theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")

freq_plot
ggsave("figures/frequency.pdf", width = 8, height = 4)
ggsave("figures/frequency.png", width = 8, height = 4)

test_frequency <- test_frequency %>% 
  filter(n >= 1) %>% 
  group_by(subdiscipline_1) %>% 
  mutate(percent = count/sum(count))

freq_plot <- ggplot(test_frequency, aes(n, count)) + 
  geom_bar(width = 0.1, fill = colors["novel"], stat = "identity") +
  facet_wrap(~ subdiscipline_1, scales = "free_y") + 
  # scale_y_sqrt("Number of measures", breaks = c(0, 100, 400, 1000, 2000, 4000, 6000, 10000), limits = c(0, 11500)) +
    scale_y_continuous("Number of measures", expand = expansion(c(0, 0.1))) +
  scale_x_log10("Usages recorded in APA PsycInfo 1993-2022",
                breaks = c(1, 2, 5, 10, 100, 1000, 25000),
               labels = c(1, 2, 5, 10, 100, 1000, 25000)) +
  
  geom_text(aes(label = if_else(n <= 2, sprintf("%.0f%%", percent*100), ""),
                x = n, y = count ), size = 3, vjust = -0.11, hjust = 0.4) +

  # scale_x_sqrt(breaks = c(0, 1, 2, 3, 4, 5, 10, 20, 40, 50), labels = c(0, 1, 2, 3, 4, 5, 10, 20, 40, "50+")) +
  # geom_text_repel(aes(x = n, label = first_acronym, y = y), 
  #                 data =
  #                   test_frequency %>% group_by(subdiscipline_1) %>% filter(row_number() > (n() - 10) ) %>% left_join(records_wide %>% select(Test = DOI, first_acronym)) %>% 
  #                   mutate(first_acronym = if_else(first_acronym == "HRSD", "HAM-D",
  #                                                  first_acronym)) %>% 
    # mutate(y = 20 + 50*(1+n()-row_number())),
    #               size = 3.3, force = 5, force_pull	= 0, max.time = 1, 
    #               max.overlaps = Inf,
    #               segment.color = "lightgray",
    #               segment.curvature = 1,
    #               hjust = 1,
    #               nudge_y = 10,
    #               direction = "y"
    # ) +
  theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")
freq_plot
ggsave("figures/frequency_no0.pdf", width = 8, height = 4)
```

## By instrument type
```{r}
usage_by_year_instrument_type <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  group_by(instrument_type_broad, Year, Test = DOI) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  group_by(instrument_type_broad) %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(instrument_type_broad, n_tests, Year) %>% 
  summarise(n = sum(n),
            diff_tests = n()) %>% 
  ungroup()



usage_by_year_instrument_type %>% 
  ggplot(., aes(Year, n, color = instrument_type_broad)) +
  geom_line(size = 0.7) +
  scale_y_continuous("Times tests were coded in PsycInfo") +
  scale_x_continuous(limits = c(1993, 2030), breaks = c(1993, 1998, 2003, 2008, 2013, 2018, 2022)) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   ) + 
  geom_text_repel(aes(label = gsub("^.*$", " ", instrument_type_broad)), # This will force the correct position of the link's right end.
                  data = usage_by_year_instrument_type %>% filter(Year == max(Year, na.rm = T)),
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  segment.color = 'grey',
                  box.padding = 0.1,
                  point.padding = 0.6,
                  nudge_x = 0.15,
                  nudge_y = 0.05,
                  force = 0.5,
                  hjust = 0,
                  direction="y",
                  na.rm = TRUE
  ) +
  geom_text_repel(data = usage_by_year_instrument_type %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0("  ",str_replace(instrument_type_broad, " Psychology", ""), " (n=", n_tests, ")")),
                  segment.alpha = 0, ## This will 'hide' the link
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  # segment.color = 'grey',
                  box.padding = 0.1,
                  point.padding = 0.6,
                  nudge_x = 0.15,
                  nudge_y = 0.05,
                  force = 0.5,
                  hjust = 0,
                  direction="y",
                  na.rm = TRUE)+
  theme_minimal(base_size = 13) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")
ggsave("figures/usage_by_year_instrument_type.pdf", width = 10, height = 4)
ggsave("figures/usage_by_year_instrument_type.png", width = 10, height = 4)
```

```{r}
usage_by_year_instrument_type <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  group_by(InstrumentType, Year, Test = DOI) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  group_by(InstrumentType) %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(InstrumentType, n_tests, Year) %>% 
  summarise(n = sum(n),
            diff_tests = n()) %>% 
  ungroup()



usage_by_year_instrument_type %>% 
  ggplot(., aes(Year, n, color = InstrumentType)) +
  geom_line(size = 0.7) +
  scale_y_continuous("Times tests were coded in PsycInfo") +
  scale_x_continuous(limits = c(1993, 2035), breaks = c(1993, 1998, 2003, 2008, 2013, 2018, 2022)) +
  scale_color_discrete() +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   ) + 
  geom_text_repel(aes(label = gsub("^.*$", " ", InstrumentType)), # This will force the correct position of the link's right end.
                  data = usage_by_year_instrument_type %>% filter(Year == max(Year, na.rm = T)),
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  segment.color = 'grey',
                  box.padding = 0.1,
                  point.padding = 0.6,
                  nudge_x = 0.15,
                  nudge_y = 0.05,
                  force = 0.5,
                  hjust = 0,
                  direction="y",
                  na.rm = TRUE
  ) +
  geom_text_repel(data = usage_by_year_instrument_type %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0("  ",str_replace(InstrumentType, " Psychology", ""), " (n=", n_tests, ")")),
                  segment.alpha = 0, ## This will 'hide' the link
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  # segment.color = 'grey',
                  box.padding = 0.1,
                  point.padding = 0.6,
                  nudge_x = 0.15,
                  nudge_y = 0.05,
                  force = 0.5,
                  hjust = 0,
                  direction="y",
                  na.rm = TRUE)+
  theme_minimal(base_size = 13) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")
ggsave("figures/usage_by_year_instrument_type_narrow.pdf", width = 14, height = 10)
ggsave("figures/usage_by_year_instrument_type_narrow.png", width = 14, height = 10)
```

# Entropy

## Entropy by year
```{r}
byorig_entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  mutate(Test = if_else(test_type == "Original", DOI, original_test_DOI)) %>% 
  drop_na(Test) %>% 
  group_by(Year, Test) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  ungroup() %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(entropy = entropy(n),
            norm_entropy = calc_norm_entropy(n),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()
# what's the point of mutate(n_tests = n_distinct(Test))? just to check?


byconstruct_entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  drop_na(first_construct) %>% 
  group_by(Year, Test = first_construct) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  ungroup() %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(entropy = entropy(n),,
            norm_entropy = calc_norm_entropy(n),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()

all_entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  drop_na(DOI) %>% 
  group_by(Year, Test = DOI) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  ungroup() %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(entropy = entropy(n),,
            norm_entropy = calc_norm_entropy(n),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()

original_entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  filter(test_type == "Original") %>% 
  group_by(Year, Test = DOI) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  ungroup() %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(entropy = entropy(n),,
            norm_entropy = calc_norm_entropy(n),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()

entropy_by_year <- bind_rows(# "all tests" = all_entropy_by_year,
                             "measures" = original_entropy_by_year,
                             "with translations\n and revisions" = byorig_entropy_by_year,
                             # "by name base" = bybase_entropy_by_year,
                             "constructs" = byconstruct_entropy_by_year,
                             .id = "version")


entropy_by_year %>% 
  ggplot(., aes(Year, norm_entropy, color = version)) +
  geom_line(size = 0.7) +
  scale_y_continuous("Normalized Shannon Entropy", limits = c(0, 1), labels = scales::percent) +
  # geom_line(aes(y = log(n)), color = 'red') +
  scale_x_continuous("Usage year as coded in APA PsycInfo", limits = c(1993, 2030), 
                     breaks = seq(1993, 2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022")) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   ) + 
  geom_text_repel(data = entropy_by_year %>% drop_na() %>% group_by(version) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ",version)), # "\n (n = ", n_tests, ")"
                  segment.curvature = -0.5,
                  segment.square = TRUE,
                  segment.color = 'grey', 
                  xlim = c(2023, 2030),
                  nudge_x = 1.14,
                  lineheight = .9,
                  hjust = 0,
                  direction="y",
                  na.rm = TRUE) +
  theme_minimal(base_size = 13) +
   theme(
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")

ggsave("figures/entropy.pdf", width = 8, height = 4)
ggsave("figures/entropy.png", width = 8, height = 4)
```


## by subdiscipline

### all measures
```{r fig.width=8,fig.height=4}
entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  group_by(subdiscipline_1, Year, Test = DOI) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  group_by(subdiscipline_1) %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(subdiscipline_1, n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(entropy = entropy(n),,
            norm_entropy = calc_norm_entropy(n),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()


entropy_by_year %>% 
  ggplot(., aes(Year, norm_entropy, color = subdiscipline_1)) +
  geom_line(size = 0.7) +
  scale_y_continuous("Normalized Shannon Entropy\n(with revisions and translations)", limits = c(0, 1), labels = scales::percent) +
  # geom_line(aes(y = log(n)), color = 'red') +
  scale_x_continuous("Usage year as coded in APA PsycInfo", limits = c(1993, 2033), 
                     breaks = seq(1993,2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022"),
                     expand = expansion(add = c(0, 14))) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggtitle(str_c(n_distinct(tests_by_year$Test), " measures tracked in PsycInfo")) +
 # geom_text_repel(aes(label = gsub("^.*$", " ", subdiscipline_1)), # This will force the correct position of the link's right end.
 #                  data = entropy_by_year %>% drop_na() %>% group_by(subdiscipline_1) %>% filter(Year == max(Year, na.rm = T)),
 #                  segment.curvature = -0.1,
 #                  segment.square = TRUE,
 #                  segment.color = 'grey',
 #                  box.padding = 0.1,
 #                  point.padding = 0.6,
 #                  max.overlaps = Inf,
 #                  nudge_x = 1.3,
 #                  # nudge_y = 0,
 #                  force = 20,
 #                  hjust = 0,
 #                  direction="y",
 #                  na.rm = TRUE
 #  ) +  
  geom_text_repel(data = entropy_by_year %>% drop_na() %>% group_by(subdiscipline_1) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0("  ",str_replace(subdiscipline_1, " Psychology", ""), " (n=", n_tests, ")")),
                  # segment.alpha = 0, ## This will 'hide' the link
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  segment.color = 'grey',
                  box.padding = 0.1,
                  max.overlaps = Inf,
                  point.padding = 0.6,
                  xlim = c(2022, NA),
                  nudge_x = 2,
                  # nudge_y = 0.0,
                  force = 5,
                  hjust = 0,
                  direction="y",
                  na.rm = F) +
  theme_minimal(base_size = 13) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none") +
    coord_cartesian(clip = "off")
ggsave("figures/entropy_subdiscipline_all.pdf", width = 8, height = 4)
ggsave("figures/entropy_subdiscipline_all.png", width = 8, height = 4)
```

### original measures
```{r fig.width=8,fig.height=4}
entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  mutate(Test = if_else(test_type == "Original", DOI, original_test_DOI)) %>% 
  drop_na(Test) %>% 
  group_by(subdiscipline_1, Year, Test) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  group_by(subdiscipline_1) %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(subdiscipline_1, n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(entropy = entropy(n),,
            norm_entropy = calc_norm_entropy(n),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()


entropy_by_year %>% 
  ggplot(., aes(Year, norm_entropy, color = subdiscipline_1)) +
  geom_line(size = 0.7) +
  scale_y_continuous("Normalized Shannon Entropy\n(novel measures)", limits = c(0, 1), labels = scales::percent) +
  # geom_line(aes(y = log(n)), color = 'red') +
  scale_x_continuous("Usage year as coded in APA PsycInfo", limits = c(1993, 2030), 
                     breaks = seq(1993, 2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022"),
                     expand = expansion(add = c(0, 17))) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggtitle(str_c(n_distinct(tests_by_year$Test), " measures tracked in PsycInfo")) +
 # geom_text_repel(aes(label = gsub("^.*$", " ", subdiscipline_1)), # This will force the correct position of the link's right end.
 #                  data = entropy_by_year %>% drop_na() %>% group_by(subdiscipline_1) %>% filter(Year == max(Year, na.rm = T)),
 #                  segment.curvature = -0.1,
 #                  segment.square = TRUE,
 #                  segment.color = 'grey',
 #                  box.padding = 0.1,
 #                  point.padding = 0.6,
 #                  max.overlaps = Inf,
 #                  nudge_x = 1.3,
 #                  # nudge_y = 0,
 #                  force = 20,
 #                  hjust = 0,
 #                  direction="y",
 #                  na.rm = TRUE
 #  ) +  
  geom_text_repel(data = entropy_by_year %>% drop_na() %>% group_by(subdiscipline_1) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0("  ",str_replace(subdiscipline_1, " Psychology", ""), " (n=", n_tests, ")")),
                  # segment.alpha = 0, ## This will 'hide' the link
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  segment.color = 'grey',
                  box.padding = 0.1,
                  max.overlaps = Inf,
                  point.padding = 0.6,
                  xlim = c(2022, NA),
                  nudge_x = 2,
                  # nudge_y = 0.0,
                  force = 5,
                  hjust = 0,
                  direction="y",
                  na.rm = F)  +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none") +
    coord_cartesian(clip = "off")
ggsave("figures/entropy_subdiscipline_orig.pdf", width = 8, height = 4)
ggsave("figures/entropy_subdiscipline_orig.png", width = 8, height = 4)
```
### constructs
```{r}
entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  group_by(subdiscipline_1, Year, Test = first_construct) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  group_by(subdiscipline_1) %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(subdiscipline_1, n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(entropy = entropy(n),,
            norm_entropy = calc_norm_entropy(n),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()


entropy_by_year %>% 
  ggplot(., aes(Year, norm_entropy, color = subdiscipline_1)) +
  geom_line(size = 0.7) +
  scale_y_continuous("Normalized Shannon Entropy (constructs)", limits = c(0, 1), labels = scales::percent) +
  # geom_line(aes(y = log(n)), color = 'red') +
  scale_x_continuous("Usage year as coded in APA PsycInfo", limits = c(1993, 2038), 
                     breaks = seq(1993, 2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022"),
                     expand = expansion(add = c(0, 10))) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggtitle(str_c(n_distinct(tests_by_year$Test), " measures tracked in PsycInfo")) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   ) + 
  geom_text_repel(data = entropy_by_year %>% drop_na() %>% group_by(subdiscipline_1) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0("  ",str_replace(subdiscipline_1, " Psychology", ""), " (n=", n_tests, ")")),
                  # segment.alpha = 0, ## This will 'hide' the link
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  segment.color = 'grey',
                  box.padding = 0.1,
                  max.overlaps = Inf,
                  point.padding = 0.6,
                  xlim = c(2022, NA),
                  nudge_x = 2,
                  # nudge_y = 0.0,
                  force = 5,
                  hjust = 0,
                  direction="y",
                  na.rm = F) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")
ggsave("figures/entropy_subdiscipline_constructs.pdf", width = 8, height = 4)
ggsave("figures/entropy_subdiscipline_constructs.png", width = 8, height = 4)
```

### By instrument type
```{r}
entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  group_by(instrument_type_broad, Year, Test = DOI) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  group_by(instrument_type_broad) %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(instrument_type_broad, n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(entropy = entropy(n),
            norm_entropy = calc_norm_entropy(n),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()



entropy_by_year %>% 
  ggplot(., aes(Year, norm_entropy, color = instrument_type_broad)) +
  geom_line(size = 0.7) +
  scale_y_continuous("Normalized Shannon Entropy", limits = c(0, 1), labels = scales::percent) +
  # geom_line(aes(y = log(n)), color = 'red') +
  scale_x_continuous(limits = c(1993, 2027), breaks = c(1993, 1998, 2003, 2008, 2013, 2018, 2022)) +
  scale_color_brewer(type = "qual", guide = "none", palette = 3) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   ) + 
  geom_text_repel(aes(label = gsub("^.*$", " ", instrument_type_broad)), # This will force the correct position of the link's right end.
                  data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  segment.color = 'grey',
                  box.padding = 0.1,
                  point.padding = 0.6,
                  nudge_x = 0.15,
                  nudge_y = 0.05,
                  force = 0.5,
                  hjust = 0,
                  direction="y",
                  na.rm = TRUE
  ) +
  geom_text_repel(data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0("  ",str_replace(instrument_type_broad, " Psychology", ""), " (n=", n_tests, ")")),
                  segment.alpha = 0, ## This will 'hide' the link
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  # segment.color = 'grey',
                  box.padding = 0.1,
                  point.padding = 0.6,
                  nudge_x = 0.15,
                  nudge_y = 0.05,
                  force = 0.5,
                  hjust = 0,
                  direction="y",
                  na.rm = TRUE)+
  theme_minimal(base_size = 13) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")
```


## Lorenz curves
```{r}
test_frequency <- psyctests_info %>% 
  mutate(Test = if_else(test_type == "Original", DOI, original_test_DOI)) %>% 
  drop_na(Test) %>% 
  # filter(TestYear >= 1990) %>%
  filter(between(Year, 1993, 2022)) %>%
  group_by(subdiscipline_1, Test) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  arrange(n) %>% 
  mutate(decile = Hmisc::cut2(n, g = 10)) %>% 
  mutate(cumsum = cumsum(n),
         sum = sum(n))
# 
# test_frequency %>% 
#     group_by(subdiscipline_1, decile) %>% 
#     summarise(share = sum(n)/first(sum),
#               median_n = median(n),
#               n_measures = n()) %>% 
#   View()


ggplot(test_frequency, aes(n)) +
  stat_lorenz(desc = F) +
  coord_fixed() +
  geom_abline(linetype = "dashed") +
      theme_minimal() +
    hrbrthemes::scale_x_percent("Cumulative percentage of measures") +
    hrbrthemes::scale_y_percent("Cumulative percentage of measure market share") #+
#    hrbrthemes::theme_ipsum_rc()

ggplot(test_frequency, aes(n, color = subdiscipline_1)) +
  stat_lorenz(desc = F) +
  coord_fixed() +
  geom_abline(linetype = "dashed") +
      theme_minimal() +
    hrbrthemes::scale_x_percent("Cumulative percentage of measures") +
    hrbrthemes::scale_y_percent("Cumulative percentage of measure market share")
```

# Survival

## aggregate stats
```{r}

constructs <- psyctests_info %>% 
     # filter(between(first_pub_year, 1950, 2015)) %>%
     unnest(ConstructList) %>% 
     rowwise() %>% 
     mutate(construct = unlist(ConstructList)) %>% 
     select(-ConstructList) %>% 
     filter(between(Year, 1993, 2022)) %>% 
     drop_na(construct) %>% 
     mutate(survival = last_pub_year - first_pub_year,
            survived_five = if_else(survival >= 5, T, F),
            survived_ten = if_else(survival >= 10, T, F)) %>%
     distinct(construct, .keep_all = TRUE)

mean(constructs$survival, na.rm = T)
sd(constructs$survival, na.rm = T)
median(constructs$survival, na.rm = T)
max(constructs$survival, na.rm = T)
min(constructs$survival, na.rm = T)
```

```{r}

measures <- psyctests_info %>% 
     filter(between(Year, 1993, 2022))  %>% 
     mutate(survival = last_pub_year - first_pub_year,
            survived_five = if_else(survival >= 5, T, F),
            survived_ten = if_else(survival >= 10, T, F)) %>%
     distinct(DOI, .keep_all = TRUE)

mean(measures$survival, na.rm = T)
sd(measures$survival, na.rm = T)
median(measures$survival, na.rm = T)
max(measures$survival, na.rm = T)
min(measures$survival, na.rm = T)
```


## cumulative sum 
### all constructs
```{r}
cumsum_construct <- constructs %>% 
  arrange(TestYear) %>% 
  group_by(TestYear) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(TestYear) %>% 
  mutate(constructs = cumsum(constructs)) 

cumsum_construct_survived_5 <- constructs %>% 
  filter(survived_five == T) %>% 
  arrange(TestYear) %>% 
  group_by(TestYear) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(TestYear) %>% 
  mutate(constructs = cumsum(constructs)) 
  
cumsum_construct_survived_10 <- constructs %>% 
  filter(survived_ten == T) %>% 
  arrange(TestYear) %>% 
  group_by(TestYear) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(TestYear) %>% 
  mutate(constructs = cumsum(constructs)) 



cumsums <- bind_rows(
  "all constructs" = cumsum_construct,
  "constructs in use\n for => 5 years" = cumsum_construct_survived_5,
  "constructs in use\n for => 10 years" = cumsum_construct_survived_10,
  .id = "origin"
  ) %>% 
  rename(Year = TestYear) %>% 
  filter(Year <= 2022)


ggplot(cumsums, aes(Year, constructs, color = origin)) + 
  geom_line() +
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  scale_y_continuous("Cumulative number of constructs") +
  scale_x_continuous("Publication year in APA PsycTests",
                     limits = c(1993, 2030), 
                     breaks = seq(1993,2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022"),
                     expand = expansion(add = c(0, 1))) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   )  +
  geom_text_repel(data = cumsums %>% drop_na() %>% group_by(origin) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ", origin, "\n (n = ", constructs, ")")),
                  segment.square = TRUE,
                  lineheight = .9,
                  segment.color = 'grey',
                  nudge_x = 1.2,
                  hjust = 0,
                  na.rm = TRUE)+
   # theme_minimal(base_size = 13) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")  +
    guides(
    x = guide_axis(cap = "both"), # Cap both ends
  )

ggsave("figures/cumsums_survival_all.pdf", width = 8, height = 4)
ggsave("figures/cumsums_survival_all.png", width = 8, height = 4)
```

### first constructs
```{r}
first_constructs <- constructs %>%
  distinct(first_construct, .keep_all = TRUE)


cumsum_construct <- first_constructs %>% 
  arrange(TestYear) %>% 
  group_by(TestYear) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(TestYear) %>% 
  mutate(constructs = cumsum(constructs)) 

cumsum_construct_survived_5 <- first_constructs %>% 
  filter(survived_five == T) %>% 
  arrange(TestYear) %>% 
  group_by(TestYear) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(TestYear) %>% 
  mutate(constructs = cumsum(constructs)) 
  
cumsum_construct_survived_10 <- first_constructs %>% 
  filter(survived_ten == T) %>% 
  arrange(TestYear) %>% 
  group_by(TestYear) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(TestYear) %>% 
  mutate(constructs = cumsum(constructs)) 



cumsums <- bind_rows(
  "all constructs" = cumsum_construct,
  "constructs in use\n for => 5 years" = cumsum_construct_survived_5,
  "constructs in use\n for => 10 years" = cumsum_construct_survived_10,
  .id = "origin"
  ) %>% 
  rename(Year = TestYear) %>% 
  filter(Year <= 2022)


ggplot(cumsums, aes(Year, constructs, color = origin)) + 
  geom_line() +
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  scale_y_continuous("Cumulative number of constructs") +
  scale_x_continuous("Publication year in APA PsycTests",
                     limits = c(1993, 2030), 
                     breaks = seq(1993,2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022"),
                     expand = expansion(add = c(0, 1))) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   )  +
  geom_text_repel(data = cumsums %>% drop_na() %>% group_by(origin) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ", origin, "\n (n = ", constructs, ")")),
                  segment.square = TRUE,
                  lineheight = .9,
                  segment.color = 'grey',
                  nudge_x = 1.2,
                  hjust = 0,
                  na.rm = TRUE)+
   # theme_minimal(base_size = 13) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")  +
    guides(
    x = guide_axis(cap = "both"), # Cap both ends
  )

ggsave("figures/cumsums_survival_first.pdf", width = 8, height = 4)
ggsave("figures/cumsums_survival_first.png", width = 8, height = 4)
```

### measures
```{r}
cumsum_all <- measures %>% 
  group_by(TestYear) %>% 
  summarise(tests = n()) %>% 
  arrange(TestYear) %>% 
  mutate(tests = cumsum(tests)) 

cumsum_5 <- measures %>% 
  filter(survived_five == T) %>% 
  group_by(TestYear) %>% 
  summarise(tests = n()) %>% 
  arrange(TestYear) %>% 
  mutate(tests = cumsum(tests)) 

cumsum_10 <- measures %>% 
  filter(survived_ten == T) %>% 
  arrange(TestYear) %>% 
  distinct(Name_base, .keep_all = T) %>% 
  group_by(TestYear) %>% 
  summarise(tests = n_distinct(DOI)) %>% 
  mutate(tests = cumsum(tests)) 

cumsums <- bind_rows(
  "all measures" = cumsum_all,
  "measures in use\n for => 5 years" = cumsum_5,
  "measures in use\n for => 10 years" = cumsum_10,
  .id = "origin"
  ) %>% 
  rename(Year = TestYear) %>% 
  filter(Year <= 2022)


ggplot(cumsums, aes(Year, tests, color = origin)) + 
  geom_line() +
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  scale_y_continuous("Cumulative number of measures") +
  scale_x_continuous("Publication year in APA PsycTests",
                     limits = c(1993, 2030), 
                     breaks = seq(1993,2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022"),
                     expand = expansion(add = c(0, 1))) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   )  +
  geom_text_repel(data = cumsums %>% drop_na() %>% group_by(origin) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ", origin, "\n (n = ", tests, ")")),
                  segment.square = TRUE,
                  lineheight = .9,
                  segment.color = 'grey',
                  nudge_x = 1.2,
                  hjust = 0,
                  na.rm = TRUE) +
   # theme_minimal(base_size = 13) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")  +
    guides(
    x = guide_axis(cap = "both"), # Cap both ends
  )
ggsave("figures/cumsums_survival_measures.pdf", width = 8, height = 4)
ggsave("figures/cumsums_survival_measures.png", width = 8, height = 4)
```


### by first use in PsycInfo instead of publication year in PsycTests
```{r}
cumsum_construct <- constructs %>% 
  arrange(first_pub_year) %>% 
  group_by(first_pub_year) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(first_pub_year) %>% 
  mutate(constructs = cumsum(constructs)) 

cumsum_construct_survived_5 <- constructs %>% 
  filter(survived_five == T) %>% 
  arrange(first_pub_year) %>% 
  group_by(first_pub_year) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(first_pub_year) %>% 
  mutate(constructs = cumsum(constructs)) 
  
cumsum_construct_survived_10 <- constructs %>% 
  filter(survived_ten == T) %>% 
  arrange(first_pub_year) %>% 
  group_by(first_pub_year) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(first_pub_year) %>% 
  mutate(constructs = cumsum(constructs)) 



cumsums <- bind_rows(
  "all constructs" = cumsum_construct,
  "constructs in use\n for => 5 years" = cumsum_construct_survived_5,
  "constructs in use\n for => 10 years" = cumsum_construct_survived_10,
  .id = "origin"
  ) %>% 
  rename(Year = first_pub_year) %>% 
  filter(Year <= 2022)


ggplot(cumsums, aes(Year, constructs, color = origin)) + 
  geom_line() +
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  scale_y_continuous("Cumulative number of constructs") +
  scale_x_continuous("First usage logged in PsycInfo",
                     limits = c(1993, 2030), 
                     breaks = seq(1993,2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022"),
                     expand = expansion(add = c(0, 1))) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   )  +
  geom_text_repel(data = cumsums %>% drop_na() %>% group_by(origin) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ", origin, "\n (n = ", constructs, ")")),
                  segment.square = TRUE,
                  lineheight = .9,
                  segment.color = 'grey',
                  nudge_x = 1.2,
                  hjust = 0,
                  na.rm = TRUE)+
   # theme_minimal(base_size = 13) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")  +
    guides(
    x = guide_axis(cap = "both"), # Cap both ends
  )
```

### by last use in PsycInfo instead of publication year in PsycTests

```{r}
cumsum_construct <- constructs %>% 
  arrange(last_pub_year) %>% 
  group_by(last_pub_year) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(last_pub_year) %>% 
  mutate(constructs = cumsum(constructs)) 

cumsum_construct_survived_5 <- constructs %>% 
  filter(survived_five == T) %>% 
  arrange(last_pub_year) %>% 
  group_by(last_pub_year) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(last_pub_year) %>% 
  mutate(constructs = cumsum(constructs)) 
  
cumsum_construct_survived_10 <- constructs %>% 
  filter(survived_ten == T) %>% 
  arrange(last_pub_year) %>% 
  group_by(last_pub_year) %>% 
  summarise(constructs = n_distinct(DOI)) %>% 
  arrange(last_pub_year) %>% 
  mutate(constructs = cumsum(constructs)) 



cumsums <- bind_rows(
  "all constructs" = cumsum_construct,
  "constructs in use\n for => 5 years" = cumsum_construct_survived_5,
  "constructs in use\n for => 10 years" = cumsum_construct_survived_10,
  .id = "origin"
  ) %>% 
  rename(Year = last_pub_year) %>% 
  filter(Year <= 2022)


ggplot(cumsums, aes(Year, constructs, color = origin)) + 
  geom_line() +
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  scale_y_continuous("Cumulative number of constructs") +
  scale_x_continuous("Last usage logged in PsycInfo",
                     limits = c(1993, 2030), 
                     breaks = seq(1993,2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022"),
                     expand = expansion(add = c(0, 1))) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   )  +
  geom_text_repel(data = cumsums %>% drop_na() %>% group_by(origin) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ", origin, "\n (n = ", constructs, ")")),
                  segment.square = TRUE,
                  lineheight = .9,
                  segment.color = 'grey',
                  nudge_x = 1.2,
                  hjust = 0,
                  na.rm = TRUE)+
   # theme_minimal(base_size = 13) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")  +
    guides(
    x = guide_axis(cap = "both"), # Cap both ends
  )
```


## counts
```{r}

count_construct <- constructs %>% 
  arrange(TestYear) %>%
  group_by(TestYear) %>% 
  summarise(tests = n_distinct(construct)) %>% 
  arrange(TestYear)

count_construct_5 <- constructs %>% 
  filter(survived_five == T) %>% 
  arrange(TestYear) %>%
  group_by(TestYear) %>% 
  summarise(tests = n_distinct(construct)) %>% 
  arrange(TestYear)

count_construct_10 <- constructs %>% 
  filter(survived_ten == T) %>% 
  arrange(TestYear) %>%
  group_by(TestYear) %>% 
  summarise(tests = n_distinct(construct)) %>% 
  arrange(TestYear)

counts <- bind_rows(
  "all constructs" = count_construct,
  "constructs in use\n for => 5 years" = count_construct_5,
  "constructs in use\n for => 10 years" = count_construct_10,
  .id = "origin"
  ) %>% 
  rename(Year = TestYear) %>% 
  filter(Year <= 2022)



ggplot(counts, aes(Year, tests, color = origin)) + 
  geom_line() +
  geom_vline(xintercept = 2016, linetype = 'dashed') +
  scale_y_continuous("Number of constructs") +
  scale_x_continuous("Publication year in APA PsycTests",
                     limits = c(1993, 2030), 
                     breaks = seq(1993, 2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022")) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   ) +
  geom_text_repel(data = counts %>% drop_na() %>% group_by(origin) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ", origin)),
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  lineheight = .9,
                  # segment.color = 'grey',
                  box.padding = 0.1,
                  point.padding = 0.6,
                  nudge_x = 1.5,
                  nudge_y = -15,
                  force = 1,
                  hjust = 0,
                  direction="y",
                  na.rm = F) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")

ggsave("figures/counts_survival.pdf", width = 8, height = 4)
ggsave("figures/counts_survival.png", width = 8, height = 4)
```







# Robustness checks
```{r}
all_constructs_over_time <- records_wide %>% select(subdiscipline_1, DOI, TestYear, ConstructList) %>% 
    unnest(ConstructList) %>% 
    rowwise() %>% 
    mutate(construct = unlist(ConstructList)) %>% 
  select(-ConstructList)

cumsum_all_constructs <- all_constructs_over_time %>% 
  arrange(TestYear) %>% 
  distinct(construct, .keep_all = T) %>% 
  group_by(TestYear) %>% 
  summarise(constructs = n_distinct(construct)) %>% 
  arrange(TestYear) %>% 
  mutate(constructs = cumsum(constructs)) 


cumsum_construct <- records_wide %>% 
  arrange(TestYear) %>% 
  distinct(first_construct, .keep_all = T) %>% 
  group_by(TestYear) %>% 
  summarise(constructs = n_distinct(first_construct)) %>% 
  arrange(TestYear) %>% 
  mutate(constructs = cumsum(constructs))

cumsums <- bind_rows(
  "first constructs" = cumsum_construct,
  "all constructs" = cumsum_all_constructs,
  .id = "origin"
  ) %>% 
  rename(Year = TestYear) %>% 
  filter(Year <= 2022)


ggplot(cumsums, aes(Year, constructs, color = origin)) + 
  geom_line() +
  scale_y_continuous("Cumulative number of constructs") +
  scale_x_continuous("Publication year in APA PsycTests",
                     limits = c(1993, 2027), 
                     breaks = seq(1993, 2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022")) +
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   ) + 
  geom_text_repel(data = cumsums %>% drop_na() %>% group_by(origin) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ", origin, "\n (n = ", constructs, ")")),
                  segment.square = TRUE,
                  lineheight = .9,
                  segment.color = 'grey',
                  nudge_x = 1.2,
                  hjust = 0,
                  na.rm = TRUE)

ggsave("figures/cumsum_all_vs_first.pdf", width = 8, height = 4)
ggsave("figures/cumsum_all_vs_first.png", width = 8, height = 4)
```

## All or first constructs

For some 20% of tests, two or more constructs were coded. In most plots, we simply use
the first construct for each test.
```{r}
records_wide <- records_wide %>% 
  rowwise() %>% 
  mutate(constructs_n = length(ConstructList)) %>% 
  ungroup()

table(records_wide$constructs_n)
round(prop.table(table(records_wide$constructs_n)),2)

ggplot(records_wide, aes(constructs_n)) + 
  geom_bar()
```


Expanding the entropy calculation to all coded constructs makes little difference.

```{r}
all_constructs_over_time <- psyctests_info %>% select(subdiscipline_1, DOI, TestYear, Year, ConstructList, usage_count) %>% 
    unnest(ConstructList) %>% 
    rowwise() %>% 
    mutate(construct = unlist(ConstructList)) %>% 
  select(-ConstructList)

entropy_all_constructs <- all_constructs_over_time %>% 
  filter(between(Year, 1993, 2022)) %>% 
  drop_na(construct) %>% 
  group_by(Year, construct) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  ungroup() %>% 
  mutate(n_tests = n_distinct(construct)) %>% 
  group_by(n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(entropy = entropy(n),,
            norm_entropy = calc_norm_entropy(n),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()

byconstruct_entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  drop_na(first_construct) %>% 
  group_by(Year, Test = first_construct) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  ungroup() %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(entropy = entropy(n),,
            norm_entropy = calc_norm_entropy(n),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()

entropy_by_year <- bind_rows(# "all tests" = all_entropy_by_year,
                             "all constructs" = entropy_all_constructs,
                             # "by name base" = bybase_entropy_by_year,
                             "first constructs" = byconstruct_entropy_by_year,
                             .id = "version")

entropy_by_year %>% 
  ggplot(., aes(Year, norm_entropy, color = version)) +
  geom_line(size = 0.7) +
  scale_y_continuous("Normalized Shannon Entropy", limits = c(0, 1), labels = scales::percent) +
  # geom_line(aes(y = log(n)), color = 'red') +
  scale_x_continuous("Usage year as coded in APA PsycInfo", limits = c(1993, 2027), 
                     breaks = seq(1993, 2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022")) +

  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   ) + 
  geom_text_repel(data = entropy_by_year %>% drop_na() %>% group_by(version) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ",version)), # "\n (n = ", n_tests, ")"
                  segment.curvature = -0.5,
                  segment.square = TRUE,
                  segment.color = 'grey', 
                  xlim = c(2023, 2030),
                  nudge_x = 1.14,
                  lineheight = .9,
                  hjust = 0,
                  direction="y",
                  na.rm = TRUE) +
  theme_minimal(base_size = 13) +
   theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")


ggsave("figures/entropy_all_vs_first.pdf", width = 8, height = 4)
ggsave("figures/entropy_all_vs_first.png", width = 8, height = 4)
```

## Unbiased estimators of Shannon entropy
Does not make much of a difference.
```{r}
byorig_entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  mutate(Test = if_else(test_type == "Original", DOI, original_test_DOI)) %>% 
  drop_na(Test) %>% 
  group_by(Year, Test) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  ungroup() %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(norm_entropy = calc_norm_entropy(n),
            norm_entropy_MM = entropy(n, method = "MM") / log(n()),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()

1 - (byorig_entropy_by_year$n_tests[1]/ records_wide %>% filter(between(TestYear, 1993, 2022)) %>% 
       mutate(Test = if_else(test_type == "Original", DOI, original_test_DOI)) %>% 
       summarise(n_distinct(Test)))


byconstruct_entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  drop_na(first_construct) %>% 
  group_by(Year, Test = first_construct) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  ungroup() %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(norm_entropy = calc_norm_entropy(n),
            norm_entropy_MM = entropy(n, method = "MM") / log(n()),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()

all_entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  drop_na(DOI) %>% 
  group_by(Year, Test = DOI) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  ungroup() %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(norm_entropy = calc_norm_entropy(n),
            norm_entropy_MM = entropy(n, method = "MM") / log(n()),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()

original_entropy_by_year <- psyctests_info %>% 
  filter(between(Year, 1993, 2022)) %>% 
  filter(test_type == "Original") %>% 
  group_by(Year, Test = DOI) %>% 
  summarise(n = sum(usage_count, na.rm = T)) %>% 
  ungroup() %>% 
  mutate(n_tests = n_distinct(Test)) %>% 
  group_by(n_tests, Year) %>% 
  filter(n > 0) %>% 
  summarise(norm_entropy = calc_norm_entropy(n),
            norm_entropy_MM = entropy(n, method = "MM") / log(n()),
            n = sum(n),
            diff_tests = n()) %>% 
  ungroup()

entropy_by_year <- bind_rows(# "all tests" = all_entropy_by_year,
  "measures" = original_entropy_by_year,
  "with translations\n and revisions" = byorig_entropy_by_year,
  # "by name base" = bybase_entropy_by_year,
  "constructs" = byconstruct_entropy_by_year,
  .id = "version")


plot_entropy <- entropy_by_year %>% 
  ggplot(., aes(Year, norm_entropy, color = version)) +
  geom_line(size = 0.7, linetype = "dashed") +
  geom_line(aes(y = norm_entropy_MM), size = 0.7) +
  scale_y_continuous("Normalized Shannon Entropy", limits = c(0, 1), labels = scales::percent) +
  # geom_line(aes(y = log(n)), color = 'red') +
  scale_x_continuous("Usage year as coded in APA PsycInfo", limits = c(1993, 2027), 
                     breaks = seq(1993, 2022, by = 1),
                     labels = c(1993, "", "", "", "", 1998, "", "", "", "", 2003, "", "", "", "", 2008, "", "", "", "", 2013, "", "", "", "", 2018, "", "", "", "2022")) +
  # ggtitle(str_c(n_distinct(tests_by_year$Test), " measures tracked in PsycInfo")) +
  # annotate("text", x = 1993, y = 1, label = "- each used once", 
  #       size = 3.3, vjust = 0.3, hjust = 0.05) +
  # annotate("text", x = 1993, y = 0, label = "- all used one", 
  #       size = 3.3,  vjust = 0.3, hjust = 0.05) +
  
  scale_color_brewer(type = "qual", guide = "none", palette = 2) +
  # ggrepel::geom_text_repel(
  #   aes(label = str_replace(subdiscipline, " Psychology", "")), data = entropy_by_year %>% filter(Year == max(Year, na.rm = T)),
  #   size = 4, hjust = 1,
  #   ) + 
  geom_text_repel(aes(label = str_replace_all(version, "[a-z=0-9/() ]+", " ")), # This will force the correct position of the link's right end.
                  data = entropy_by_year %>% drop_na() %>% group_by(version) %>% filter(Year == max(Year, na.rm = T)),
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  lineheight = .9,
                  segment.color = 'grey',
                  box.padding = 0.1,
                  point.padding = 0.6,
                  nudge_x = 1.15,
                  nudge_y = 0.03,
                  force = 0.9,
                  hjust = 0,
                  direction="y",
                  size = 3.3,
                  na.rm = TRUE) +
  geom_text_repel(data = entropy_by_year %>% drop_na() %>% group_by(version) %>% filter(Year == max(Year, na.rm = T)),
                  aes(label = paste0(" ",version)), # "\n (n = ", n_tests, ")"
                  segment.alpha = 0, ## This will 'hide' the link
                  segment.curvature = -0.1,
                  segment.square = TRUE,
                  # segment.color = 'grey',
                  box.padding = 0.1,
                  point.padding = 0.6,
                  nudge_x = 1.15,
                  nudge_y = 0.0,
                  lineheight = .9,
                  force = 0.9,
                  size = 3.3,
                  hjust = 0,
                  direction="y",
                  na.rm = TRUE) +
  theme_minimal(base_size = 13) +
  theme(panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(face="bold"),
        legend.position = "none")
plot_entropy
```

## Entropy by classificaiton
```{r}
entropy_by_class <- psyctests_info %>% 
  group_by(classification_1, DOI) %>% 
  summarise(n = sum(usage_count, na.rm = T),
            parent = case_when(
                    n > 50 ~ "",
                    n > 20 ~ "used 21-50 times",
                    n > 5 ~ "used 6-20 times",
                    TRUE ~ "used 1-5 times")) %>% 
  group_by(classification_1) %>% 
  # filter(n > 0) %>% 
  summarise(
    entropy = entropy(n),
    norm_entropy = calc_norm_entropy(n)) %>% 
  arrange(norm_entropy)

kable(entropy_by_class)
```

## Entropy by instrument type
```{r}
psyctests_info %>% 
  group_by(instrument_type_broad, DOI) %>% 
  summarise(n = sum(usage_count, na.rm = T),
            parent = case_when(
                    n > 50 ~ "",
                    n > 20 ~ "used 21-50 times",
                    n > 5 ~ "used 6-20 times",
                    TRUE ~ "used 1-5 times")) %>% 
  group_by(instrument_type_broad) %>% 
  filter(n > 0) %>% 
  summarise(
    entropy = entropy(n),
    norm_entropy = calc_norm_entropy(n)) %>% 
  arrange(norm_entropy) %>% 
  kable()
```

# Tests by exact same name
```{r}
same_name_tests <- records_wide %>% group_by(name_psycinfo) %>% filter(n_distinct(DOI) > 1) %>% summarise(n = n())
nrow(same_name_tests)
mean(same_name_tests$n)
same_name_tests %>% arrange(desc(n)) %>% head(20)
```

