Import data

source("0_helpers.R")
knitr::opts_chunk$set(error = TRUE)

Load data

Demographics

formr_connect(keyring::key_list(service = 'gocd2')[, "username"], keyring::key_get("gocd2"))

s1_demo = formr_results("s1_demo", plot_likert = F, compute_alphas = F, quiet = T)
s1_filter = formr_results("s1_filter", plot_likert = F, compute_alphas = F, quiet = T)

Personality

s2_initial = formr_recognise("s2_initial")
s2_initial = s2_initial %>% rename(
  communal_strength_2R = communal_strength_2,
  ecr_avo_1R = ecr_avo_33R,
  ecr_avo_2 = ecr_avo_11,
  ecr_avo_3R = ecr_avo_35R,
  ecr_avo_4 = ecr_avo_17,
  ecr_avo_5R = ecr_avo_27R,
  ecr_avo_6 = ecr_avo_13,
  ecr_anx_1 = ecr_anx_18,
  ecr_anx_2 = ecr_anx_16,
  ecr_anx_3 = ecr_anx_26,
  ecr_anx_4R = ecr_anx_22R,
  ecr_anx_5 = ecr_anx_32,
  ecr_anx_6 = ecr_anx_6
  )

s2_initial_id = formr_item_displays("s2_initial")
s2_initial = formr_post_process_results(item_list = items(s2_initial), results = s2_initial, plot_likert = F, compute_alphas = F, quiet = T, tag_missings = TRUE, item_displays = s2_initial_id)

Diary

s3_daily = formr_recognise(results = jsonlite::fromJSON("data/s3_daily.json"), item_list = formr_items("s3_daily"))

s3_daily = s3_daily %>% rename(extra_pair_desire_5R = extra_pair_desire_15,
                               in_pair_desire_5R = in_pair_desire_15,
                               extra_pair_went_out = extra_pair_desire_5,
                               in_pair_went_out = in_pair_desire_5)
s3_daily = formr_post_process_results(item_list = items(s3_daily), results = s3_daily, plot_likert = F, compute_alphas = F, quiet = T, tag_missings = FALSE, item_displays = NULL) #item_displays = jsonlite::fromJSON("data/s3_daily_itemdisplay.json"))

Follow-up

s4_timespent = formr_results("s4_timespent", plot_likert = F, compute_alphas = F, quiet = T)
s4_followup = formr_results("s4_followup", plot_likert = F, compute_alphas = F, quiet = T)
s4_followup = s4_followup %>% mutate(medication_name = stringr::str_trim(medication_name))
s5_hadmenstruation = formr_results("s5_hadmenstruation", plot_likert = F, compute_alphas = F, quiet = T)

Post-process

Test sessions (animal names) contain XXX, we filter them and two erroneous dupes.

s1_demo = s1_demo %>% 
    filter(!session %contains% "XXX")
s3_daily = s3_daily %>% 
    filter(!session %contains% "XXX") %>% 
    arrange(session, created, modified) %>% 
    filter(!duplicated(cbind(session, created))) # one dupe mishap

twice_a_days <- s3_daily %>% 
  mutate(created_date = as.Date(created - hours(6))) %>% 
  group_by(session, created_date) %>% 
  filter(n() > 1) %>% 
  arrange(session, is.na(ended), created_date, ended) %>% 
  select(session, created_date, created, ended) %>% 
  filter(row_number() > 1) %>% 
  ungroup() %>% 
  select(session, created)

s3_daily = s3_daily %>% 
  anti_join(twice_a_days, by = c("session", "created"))

s1_filter = s1_filter %>% 
    filter(!session %contains% "XXX")
s2_initial = s2_initial %>% 
    filter(!session %contains% "XXX") %>% 
    filter(!duplicated(session)) # one dupe mishap with empty data
s4_followup = s4_followup %>% 
  filter(!session %contains% 'XXX')


# short names reichen auch aus um nutzer zu unterscheiden
s2_initial$short = stringr::str_sub(s2_initial$session, 1, 7)
s1_filter$short = stringr::str_sub(s1_filter$session, 1, 7)
s1_demo$short = stringr::str_sub(s1_demo$session, 1, 7)
s3_daily$short = stringr::str_sub(s3_daily$session, 1, 7)
s4_followup$short = stringr::str_sub(s4_followup$session, 1, 7)
s4_timespent$short = stringr::str_sub(s4_timespent$session, 1, 7)
s5_hadmenstruation$short = stringr::str_sub(s5_hadmenstruation$session, 1, 7)

Save

save(s1_demo, s1_filter, s2_initial, s3_daily, s4_followup, s4_timespent, s5_hadmenstruation, file = "data/pretty_raw.rdata")