Table_2

Load relevant packages and data for calculations

library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.1.2     v dplyr   1.0.6
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(dplyr)

cleandata <- read_csv("cleandata.csv")

## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   ParticipantID = col_character(),
##   General_1_MedList = col_character(),
##   General_1_University = col_character()
## )
## i Use `spec()` for the full column specifications.

Calculating data

implicitbiaslevels <- cleandata %>% 
  select(base_IAT_race, base_IAT_gen, pre_IAT_race, pre_IAT_gen)

#now I'm going to calculate the mean and sd of all of these variables:


implicitbiaslevels <- implicitbiaslevels %>% 
  summarise(across(contains("IAT"), list(mean = mean, sd = sd)))

write_csv(implicitbiaslevels, path = "implicitbiaslevels.csv")

## Warning: The `path` argument of `write_csv()` is deprecated as of readr 1.4.0.
## Please use the `file` argument instead.

library(readr)
implicitbiaslevels <- read_csv("~/Coding-R/replication project/implicitbiaslevels.csv")

## 
## -- Column specification --------------------------------------------------------
## cols(
##   base_IAT_race_mean = col_double(),
##   base_IAT_race_sd = col_double(),
##   base_IAT_gen_mean = col_double(),
##   base_IAT_gen_sd = col_double(),
##   pre_IAT_race_mean = col_double(),
##   pre_IAT_race_sd = col_double(),
##   pre_IAT_gen_mean = col_double(),
##   pre_IAT_gen_sd = col_double()
## )

View(implicitbiaslevels)

Load relevant packages

library(knitr)
library(kableExtra)

## 
## Attaching package: 'kableExtra'

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

library(gt)
library(magick)

## Linking to ImageMagick 6.9.12.3
## Enabled features: cairo, freetype, fftw, ghostscript, heic, lcms, pango, raw, rsvg, webp
## Disabled features: fontconfig, x11

library(glue)

## 
## Attaching package: 'glue'

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

Creating tibble

table2 <- tibble(
  label = c("race", "gender"),
  mean1 = c(0.6186929, 0.4943818),
  mean2 = c(0.2023364, 0.3109984),
  SD1 = c(0.4423884, 0.36228),
  SD2 = c(0.5633004, 0.3748071)
)

print(table2)

## # A tibble: 2 x 5
##   label  mean1 mean2   SD1   SD2
##   <chr>  <dbl> <dbl> <dbl> <dbl>
## 1 race   0.619 0.202 0.442 0.563
## 2 gender 0.494 0.311 0.362 0.375

Formatting and labelling table

table2 %>% 
  gt() %>% 
  tab_header(
    title = "Table 2: Race and Gender Implicit Bias Levels") %>% 
  tab_source_note("Implicit bias values are the average D600 score for each timepoint") %>% 
  fmt_number(columns = vars(mean1, mean2,  SD1, SD2), decimals = 2) %>% 
  tab_spanner(
    label = "Baseline",
    columns = c(mean1, SD1)
  ) %>% 
  tab_spanner(
    label = "Prenap",
    columns = c(mean2, SD2)
  ) %>% 
  

  cols_label(mean1 = "mean", mean2 = "mean", SD1 = "SD", SD2 = "SD", label = " ")

## Warning: `columns = vars(...)` has been deprecated in gt 0.3.0:
## * please use `columns = c(...)` instead

## Warning: `columns = vars(...)` has been deprecated in gt 0.3.0:
## * please use `columns = c(...)` instead

Table 2: Race and Gender Implicit Bias Levels
	Baseline		Prenap
	mean	SD	mean	SD
race	0.62	0.44	0.20	0.56
gender	0.49	0.36	0.31	0.37
Implicit bias values are the average D600 score for each timepoint

print(table2)

## # A tibble: 2 x 5
##   label  mean1 mean2   SD1   SD2
##   <chr>  <dbl> <dbl> <dbl> <dbl>
## 1 race   0.619 0.202 0.442 0.563
## 2 gender 0.494 0.311 0.362 0.375