Codebook

library(labelled)
library(codebook)
library(tidyverse)
knitr::opts_chunk$set(
  warning = FALSE, # show warnings during codebook generation
  message = FALSE, # show messages during codebook generation
  error = TRUE, # do not interrupt codebook generation in case of errors,
                # usually makes debugging easier, and sometimes half a codebook
                # is better than none
  echo = FALSE  # don't show the R code
)
ggplot2::theme_set(ggplot2::theme_bw())

Load data

Codebook

Metadata

Description

Dataset name: vcs

The dataset has N=2241 rows and 21 columns. 969 rows have no missing values on any column.

Metadata for search engines

• Date published: 2021-04-10

x voice_id dataset sex age f0 f1 f2 f3 f4 pf neuro extra openn agree consc dominance behavior attitude desire soir_full sex_c

#Variables

voice_id

Distribution

Distribution of values for voice_id

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
voice_id	numeric	0	1	1	1121	2241	1121	647.0653	▇▇▇▇▇	NA

dataset

Distribution

Distribution of values for dataset

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
dataset	numeric	0	1	1	4	11	4.531905	2.864653	▇▃▃▂▁	NA

sex

Sex

Distribution

Distribution of values for sex

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	n_value_labels	hist
sex	Sex	haven_labelled	0	1	-1	-1	1	-0.1726908	0.9851959	2	▇▁▁▁▁▁▁▆

Value labels

Response choices

name	value
male	1
female	-1

age

Age

Distribution

Distribution of values for age

137 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
age	Age	numeric	137	0.9388666	18	23	56	24.452	6.138718	▇▂▁▁▁

f0

Voice pitch

Distribution

Distribution of values for f0

7 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
f0	Voice pitch	numeric	7	0.9968764	-1.8	0.39	2.6	0	1	▇▂▇▆▁

f1

Distribution

Distribution of values for f1

4 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
f1	numeric	4	0.9982151	-2.3	-0.03	6.7	0	1	▅▇▁▁▁	NA

f2

Distribution

Distribution of values for f2

4 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
f2	numeric	4	0.9982151	-3	0.023	5.2	0	1	▁▇▆▁▁	NA

f3

Distribution

Distribution of values for f3

4 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
f3	numeric	4	0.9982151	-2.3	0.12	3.2	0	1	▃▆▇▃▁	NA

f4

Distribution

Distribution of values for f4

4 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
f4	numeric	4	0.9982151	-2.8	0.2	2.7	0	1	▁▇▅▇▁	NA

pf

Formants

Distribution

Distribution of values for pf

4 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
pf	Formants	numeric	4	0.9982151	-2.1	0.053	4.4	0	1	▆▇▇▁▁

neuro

Neuroticism

Distribution

Distribution of values for neuro

802 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
neuro	Neuroticism	numeric	802	0.6421241	-2.4	0.00029	2.5	0	1	▂▆▇▅▂

extra

Extraversion

Distribution

Distribution of values for extra

803 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
extra	Extraversion	numeric	803	0.6416778	-3.2	0.036	2.2	0	1	▁▃▇▇▃

openn

Openness

Distribution

Distribution of values for openn

802 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
openn	Openness	numeric	802	0.6421241	-4.1	0.097	2.1	0	1	▁▁▅▇▃

agree

Agreeableness

Distribution

Distribution of values for agree

802 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
agree	Agreeableness	numeric	802	0.6421241	-3.4	0.037	2.2	0	1	▁▂▇▇▃

consc

Conscientiousness

Distribution

Distribution of values for consc

802 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
consc	Conscientiousness	numeric	802	0.6421241	-3.4	0.053	2.2	0	1	▁▂▇▇▃

dominance

Dominance

Distribution

Distribution of values for dominance

1256 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
dominance	Dominance	numeric	1256	0.4395359	-3.7	0.053	2.6	0	1	▁▂▇▆▂

behavior

Distribution

Distribution of values for behavior

242 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
behavior	numeric	242	0.8920125	-1.1	-0.27	3	0	1	▇▃▃▁▁	NA

attitude

Distribution

Distribution of values for attitude

239 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
attitude	numeric	239	0.8933512	-1.9	0.058	1.5	0	1	▅▅▆▆▇	NA

desire

Distribution

Distribution of values for desire

238 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
desire	numeric	238	0.8937974	-1.8	-0.095	2.2	0	1	▆▇▇▅▂	NA

soir_full

Unrestricted sociosexuality

Distribution

Distribution of values for soir_full

243 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
soir_full	Unrestricted sociosexuality	numeric	243	0.8915663	-2.1	0.017	2.8	0	1	▅▇▇▅▁

sex_c

Distribution

Distribution of values for sex_c

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
sex_c	numeric	0	1	-1	-1	1	-0.1726908	0.9851959	▇▁▁▁▆	NA

Missingness report

Codebook table

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "vcs",
  "datePublished": "2021-04-10",
  "description": "The dataset has N=2241 rows and 21 columns.\n969 rows have no missing values on any column.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name      |label                       | n_missing|\n|:---------|:---------------------------|---------:|\n|voice_id  |NA                          |         0|\n|dataset   |NA                          |         0|\n|sex       |Sex                         |         0|\n|age       |Age                         |       137|\n|f0        |Voice pitch                 |         7|\n|f1        |NA                          |         4|\n|f2        |NA                          |         4|\n|f3        |NA                          |         4|\n|f4        |NA                          |         4|\n|pf        |Formants                    |         4|\n|neuro     |Neuroticism                 |       802|\n|extra     |Extraversion                |       803|\n|openn     |Openness                    |       802|\n|agree     |Agreeableness               |       802|\n|consc     |Conscientiousness           |       802|\n|dominance |Dominance                   |      1256|\n|behavior  |NA                          |       242|\n|attitude  |NA                          |       239|\n|desire    |NA                          |       238|\n|soir_full |Unrestricted sociosexuality |       243|\n|sex_c     |NA                          |         0|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.3).",
  "keywords": ["voice_id", "dataset", "sex", "age", "f0", "f1", "f2", "f3", "f4", "pf", "neuro", "extra", "openn", "agree", "consc", "dominance", "behavior", "attitude", "desire", "soir_full", "sex_c"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "voice_id",
      "@type": "propertyValue"
    },
    {
      "name": "dataset",
      "@type": "propertyValue"
    },
    {
      "name": "sex",
      "description": "Sex",
      "value": "1. male,\n-1. female",
      "maxValue": 1,
      "minValue": -1,
      "@type": "propertyValue"
    },
    {
      "name": "age",
      "description": "Age",
      "@type": "propertyValue"
    },
    {
      "name": "f0",
      "description": "Voice pitch",
      "@type": "propertyValue"
    },
    {
      "name": "f1",
      "@type": "propertyValue"
    },
    {
      "name": "f2",
      "@type": "propertyValue"
    },
    {
      "name": "f3",
      "@type": "propertyValue"
    },
    {
      "name": "f4",
      "@type": "propertyValue"
    },
    {
      "name": "pf",
      "description": "Formants",
      "@type": "propertyValue"
    },
    {
      "name": "neuro",
      "description": "Neuroticism",
      "@type": "propertyValue"
    },
    {
      "name": "extra",
      "description": "Extraversion",
      "@type": "propertyValue"
    },
    {
      "name": "openn",
      "description": "Openness",
      "@type": "propertyValue"
    },
    {
      "name": "agree",
      "description": "Agreeableness",
      "@type": "propertyValue"
    },
    {
      "name": "consc",
      "description": "Conscientiousness",
      "@type": "propertyValue"
    },
    {
      "name": "dominance",
      "description": "Dominance",
      "@type": "propertyValue"
    },
    {
      "name": "behavior",
      "@type": "propertyValue"
    },
    {
      "name": "attitude",
      "@type": "propertyValue"
    },
    {
      "name": "desire",
      "@type": "propertyValue"
    },
    {
      "name": "soir_full",
      "description": "Unrestricted sociosexuality",
      "@type": "propertyValue"
    },
    {
      "name": "sex_c",
      "@type": "propertyValue"
    }
  ]
}`

Gender differences

library(ggstatsplot)

## In case you would like cite this package, cite it as:
##      Patil, I. (2018). ggstatsplot: "ggplot2" Based Plots with Statistical Details. CRAN.
##      Retrieved from https://cran.r-project.org/web/packages/ggstatsplot/index.html

vcs <- vcs %>% filter(!is.na(f0), !is.na(pf), !is.na(age))

vcs$sex <- factor(if_else(vcs$sex_c == 1, "male", "female"))
contrasts(vcs$sex) <- contr.helmert(2)
var_label(vcs$sex) <- "Sex"
ggbetweenstats(vcs, sex, age) +
  scale_color_viridis_d("Sex", breaks = c(-1,1), labels = c("female", "male")) 

## Warning: Ignoring unknown parameters: segment.linetype

## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.

ggbetweenstats(vcs, sex, f0) +
  scale_color_viridis_d("Sex", breaks = c(-1,1), labels = c("female", "male")) 

## Warning: Ignoring unknown parameters: segment.linetype

## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.

ggbetweenstats(vcs, sex, pf) +
  scale_color_viridis_d("Sex", breaks = c(-1,1), labels = c("female", "male")) 

## Warning: Ignoring unknown parameters: segment.linetype

## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.

ggbetweenstats(vcs, sex, dominance) +
  scale_color_viridis_d("Sex", breaks = c(-1,1), labels = c("female", "male")) 

## Warning: Ignoring unknown parameters: segment.linetype

## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.

ggbetweenstats(vcs, sex, extra) +
  scale_color_viridis_d("Sex", breaks = c(-1,1), labels = c("female", "male")) 

## Warning: Ignoring unknown parameters: segment.linetype

## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.

ggbetweenstats(vcs, sex, neuro) +
  scale_color_viridis_d("Sex", breaks = c(-1,1), labels = c("female", "male")) 

## Warning: Ignoring unknown parameters: segment.linetype

## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.

ggbetweenstats(vcs, sex, consc) +
  scale_color_viridis_d("Sex", breaks = c(-1,1), labels = c("female", "male")) 

## Warning: Ignoring unknown parameters: segment.linetype

## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.

ggbetweenstats(vcs, sex, agree) +
  scale_color_viridis_d("Sex", breaks = c(-1,1), labels = c("female", "male")) 

## Warning: Ignoring unknown parameters: segment.linetype

## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.

ggbetweenstats(vcs, sex, openn) +
  scale_color_viridis_d("Sex", breaks = c(-1,1), labels = c("female", "male")) 

## Warning: Ignoring unknown parameters: segment.linetype

## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.

ggbetweenstats(vcs, sex, soir_full) +
  scale_color_viridis_d("Sex", breaks = c(-1,1), labels = c("female", "male")) 

## Warning: Ignoring unknown parameters: segment.linetype

## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.

ggbetweenstats(vcs, sex, behavior) +
  scale_color_viridis_d("Sex", breaks = c(-1,1), labels = c("female", "male")) 

## Warning: Ignoring unknown parameters: segment.linetype

## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.

ggbetweenstats(vcs, sex, attitude) +
  scale_color_viridis_d("Sex", breaks = c(-1,1), labels = c("female", "male")) 

## Warning: Ignoring unknown parameters: segment.linetype

## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.

ggbetweenstats(vcs, sex, desire) +
  scale_color_viridis_d("Sex", breaks = c(-1,1), labels = c("female", "male")) 

## Warning: Ignoring unknown parameters: segment.linetype

## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.

ggplot(vcs, aes(f0, fill = sex)) + 
  geom_histogram(position = "identity", alpha = 0.4)+
  scale_fill_viridis_d("Sex", breaks = c(-1,1), labels = c("female", "male")) 

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(vcs, aes(pf, fill = sex)) + 
  geom_histogram(position = "identity", alpha = 0.4)+
  scale_fill_viridis_d("Sex", breaks = c(-1,1), labels = c("female", "male")) 

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Distributions by dataset

ggplot(vcs, aes(f0, fill = factor(dataset))) + 
  geom_histogram(position = "stack", alpha = 0.4)+
  facet_wrap(~ sex) +
  scale_fill_viridis_d()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(vcs, aes(pf, fill = factor(dataset))) + 
  geom_histogram(position = "stack", alpha = 0.4)+
  facet_wrap(~ sex) +
  scale_fill_viridis_d()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(vcs, aes(pf, fill = factor(dataset))) + 
  geom_histogram(position = "identity", alpha = 0.4)+
  facet_wrap(~ sex) +
  scale_fill_viridis_d()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(vcs, aes(f1, fill = factor(dataset))) + 
  geom_histogram(position = "identity", alpha = 0.4)+
  facet_wrap(~ sex) +
  scale_fill_viridis_d()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(vcs, aes(f2, fill = factor(dataset))) + 
  geom_histogram(position = "identity", alpha = 0.4)+
  facet_wrap(~ sex) +
  scale_fill_viridis_d()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(vcs, aes(f3, fill = factor(dataset))) + 
  geom_histogram(position = "identity", alpha = 0.4)+
  facet_wrap(~ sex) +
  scale_fill_viridis_d()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(vcs, aes(f4, fill = factor(dataset))) + 
  geom_histogram(position = "identity", alpha = 0.4)+
  facet_wrap(~ sex) +
  scale_fill_viridis_d()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(vcs, aes(pf, fill = factor(dataset))) + 
  geom_histogram(position = "stack", alpha = 0.4)+
  facet_wrap(~ sex) +
  scale_fill_viridis_d()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Age trends

ggscatterstats(vcs, age, f0)

ggscatterstats(vcs, age, pf)

ggscatterstats(vcs, age, dominance)

ggscatterstats(vcs, age, extra)

ggscatterstats(vcs, age, neuro)

ggscatterstats(vcs, age, consc)

ggscatterstats(vcs, age, agree)

ggscatterstats(vcs, age, openn)

ggscatterstats(vcs, age, soir_full)

ggscatterstats(vcs, age, behavior)

Correlations

library(kableExtra)

## 
## Attaching package: 'kableExtra'

## The following object is masked from 'package:dplyr':
## 
##     group_rows

vcs %>% select(sex_c, age, f0, pf, f1:f4) %>%  cor(use = 'pairwise') %>% round(2) %>% kable(caption = "Correlations between sex, age, and voice parameters.")

Correlations between sex, age, and voice parameters.

	sex_c	age	f0	pf	f1	f2	f3	f4
sex_c	1.00	0.20	-0.93	-0.80	-0.26	-0.60	-0.83	-0.86
age	0.20	1.00	-0.23	-0.03	0.06	-0.01	-0.08	-0.08
f0	-0.93	-0.23	1.00	0.75	0.24	0.56	0.79	0.82
pf	-0.80	-0.03	0.75	1.00	0.59	0.81	0.91	0.88
f1	-0.26	0.06	0.24	0.59	1.00	0.22	0.34	0.30
f2	-0.60	-0.01	0.56	0.81	0.22	1.00	0.71	0.66
f3	-0.83	-0.08	0.79	0.91	0.34	0.71	1.00	0.87
f4	-0.86	-0.08	0.82	0.88	0.30	0.66	0.87	1.00

vcs %>% select(sex_c, age, neuro:soir_full) %>%  cor(use = 'pairwise') %>% round(2) %>% kable(caption = "Correlations between sex, age, and personality variables")

Correlations between sex, age, and personality variables

	sex_c	age	neuro	extra	openn	agree	consc	dominance	behavior	attitude	desire	soir_full
sex_c	1.00	0.20	-0.26	-0.09	-0.01	-0.13	-0.12	-0.03	0.03	0.24	0.36	0.27
age	0.20	1.00	-0.12	-0.04	0.05	0.09	0.15	-0.12	0.30	0.04	0.10	0.18
neuro	-0.26	-0.12	1.00	-0.23	-0.03	-0.20	-0.26	-0.33	-0.10	-0.08	-0.08	-0.11
extra	-0.09	-0.04	-0.23	1.00	0.26	0.12	0.22	0.51	0.20	0.06	0.03	0.12
openn	-0.01	0.05	-0.03	0.26	1.00	0.02	0.08	0.10	0.05	0.03	0.07	0.06
agree	-0.13	0.09	-0.20	0.12	0.02	1.00	0.23	-0.22	0.01	-0.11	-0.06	-0.07
consc	-0.12	0.15	-0.26	0.22	0.08	0.23	1.00	0.20	0.05	-0.12	-0.07	-0.06
dominance	-0.03	-0.12	-0.33	0.51	0.10	-0.22	0.20	1.00	0.09	0.07	-0.02	0.06
behavior	0.03	0.30	-0.10	0.20	0.05	0.01	0.05	0.09	1.00	0.48	0.33	0.76
attitude	0.24	0.04	-0.08	0.06	0.03	-0.11	-0.12	0.07	0.48	1.00	0.45	0.85
desire	0.36	0.10	-0.08	0.03	0.07	-0.06	-0.07	-0.02	0.33	0.45	1.00	0.75
soir_full	0.27	0.18	-0.11	0.12	0.06	-0.07	-0.06	0.06	0.76	0.85	0.75	1.00

vcs %>% select( sex_c, everything(), -voice_id, -dataset, -sex) %>%  cor(use = 'pairwise') %>% round(2) %>% kable(caption = "All correlations")

All correlations

	sex_c	age	f0	f1	f2	f3	f4	pf	neuro	extra	openn	agree	consc	dominance	behavior	attitude	desire	soir_full
sex_c	1.00	0.20	-0.93	-0.26	-0.60	-0.83	-0.86	-0.80	-0.26	-0.09	-0.01	-0.13	-0.12	-0.03	0.03	0.24	0.36	0.27
age	0.20	1.00	-0.23	0.06	-0.01	-0.08	-0.08	-0.03	-0.12	-0.04	0.05	0.09	0.15	-0.12	0.30	0.04	0.10	0.18
f0	-0.93	-0.23	1.00	0.24	0.56	0.79	0.82	0.75	0.27	0.06	-0.01	0.11	0.10	0.01	-0.07	-0.24	-0.36	-0.29
f1	-0.26	0.06	0.24	1.00	0.22	0.34	0.30	0.59	-0.02	0.09	0.07	-0.03	0.07	-0.02	-0.01	-0.09	-0.09	-0.08
f2	-0.60	-0.01	0.56	0.22	1.00	0.71	0.66	0.81	0.10	0.09	0.02	0.14	0.15	-0.04	0.02	-0.22	-0.26	-0.20
f3	-0.83	-0.08	0.79	0.34	0.71	1.00	0.87	0.91	0.18	0.09	0.04	0.16	0.16	-0.03	0.01	-0.21	-0.27	-0.20
f4	-0.86	-0.08	0.82	0.30	0.66	0.87	1.00	0.88	0.22	0.07	0.01	0.13	0.14	-0.01	0.01	-0.20	-0.29	-0.21
pf	-0.80	-0.03	0.75	0.59	0.81	0.91	0.88	1.00	0.15	0.11	0.04	0.13	0.17	-0.03	0.01	-0.22	-0.28	-0.22
neuro	-0.26	-0.12	0.27	-0.02	0.10	0.18	0.22	0.15	1.00	-0.23	-0.03	-0.20	-0.26	-0.33	-0.10	-0.08	-0.08	-0.11
extra	-0.09	-0.04	0.06	0.09	0.09	0.09	0.07	0.11	-0.23	1.00	0.26	0.12	0.22	0.51	0.20	0.06	0.03	0.12
openn	-0.01	0.05	-0.01	0.07	0.02	0.04	0.01	0.04	-0.03	0.26	1.00	0.02	0.08	0.10	0.05	0.03	0.07	0.06
agree	-0.13	0.09	0.11	-0.03	0.14	0.16	0.13	0.13	-0.20	0.12	0.02	1.00	0.23	-0.22	0.01	-0.11	-0.06	-0.07
consc	-0.12	0.15	0.10	0.07	0.15	0.16	0.14	0.17	-0.26	0.22	0.08	0.23	1.00	0.20	0.05	-0.12	-0.07	-0.06
dominance	-0.03	-0.12	0.01	-0.02	-0.04	-0.03	-0.01	-0.03	-0.33	0.51	0.10	-0.22	0.20	1.00	0.09	0.07	-0.02	0.06
behavior	0.03	0.30	-0.07	-0.01	0.02	0.01	0.01	0.01	-0.10	0.20	0.05	0.01	0.05	0.09	1.00	0.48	0.33	0.76
attitude	0.24	0.04	-0.24	-0.09	-0.22	-0.21	-0.20	-0.22	-0.08	0.06	0.03	-0.11	-0.12	0.07	0.48	1.00	0.45	0.85
desire	0.36	0.10	-0.36	-0.09	-0.26	-0.27	-0.29	-0.28	-0.08	0.03	0.07	-0.06	-0.07	-0.02	0.33	0.45	1.00	0.75
soir_full	0.27	0.18	-0.29	-0.08	-0.20	-0.20	-0.21	-0.22	-0.11	0.12	0.06	-0.07	-0.06	0.06	0.76	0.85	0.75	1.00

cors <- vcs %>% select(sex_c, age, f0, dominance, extra, agree, behavior) %>%  cor(use = 'pairwise') %>% round(2)
cors_sex_partialled_out <- vcs %>% select(sex_c, age, f0, dominance, extra, agree, behavior) %>% mutate_at(vars(age, f0, dominance, extra, agree, behavior), ~resid(lm(. ~ sex_c, na.action = na.exclude))) %>%  cor(use = 'pairwise') %>% round(2)
cors[upper.tri(cors)] <- cors_sex_partialled_out[upper.tri(cors_sex_partialled_out)]

cors %>% kable(caption = "Correlations between sex, age, f0, and preregistered outcomes. Correlations above the diagonal are after partialling out effects of gender.")

Correlations between sex, age, f0, and preregistered outcomes. Correlations above the diagonal are after partialling out effects of gender.

	sex_c	age	f0	dominance	extra	agree	behavior
sex_c	1.00	0.00	0.00	0.00	0.00	0.00	0.00
age	0.20	1.00	-0.12	-0.12	-0.03	0.11	0.30
f0	-0.93	-0.23	1.00	-0.05	-0.06	-0.02	-0.11
dominance	-0.03	-0.12	0.01	1.00	0.51	-0.22	0.10
extra	-0.09	-0.04	0.06	0.51	1.00	0.11	0.20
agree	-0.13	0.09	0.11	-0.22	0.12	1.00	0.01
behavior	0.03	0.30	-0.07	0.09	0.20	0.01	1.00