Table3

Table 3. Implicit bias levels by condition

Attempting to recreate Table 3:

Load packages

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ ggplot2 3.3.4     ✓ purrr   0.3.4
## ✓ tibble  3.1.2     ✓ dplyr   1.0.7
## ✓ tidyr   1.1.3     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.1

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

library(dplyr)
library(gt)
library(glue)

## 
## Attaching package: 'glue'

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

library(haven)

load data

replicationdata <- read.sav("Humiston & Wamsley 2019 data.sav")

## Error in read.sav("Humiston & Wamsley 2019 data.sav"): could not find function "read.sav"

cleandata <- replicationdata %>%    
  filter(exclude == "no")

## Error in filter(., exclude == "no"): object 'replicationdata' not found

Table 3 data: simplified code to calculate all the means and SDS for cued and uncued IATs

cued <- cleandata %>% #for cued condition only 
  select(baseIATcued, preIATcued, postIATcued, weekIATcued)

## Error in select(., baseIATcued, preIATcued, postIATcued, weekIATcued): object 'cleandata' not found

 #Selecting these variables from the "cleandata" dataset to create new data "cued"

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

cued <- cued %>% 
  summarise(across(contains("IAT"), list(mean = mean, sd = sd))) #captures and calculates the means and SDs of each variable that contains "IAT" within its variable name. Lists the calculated means and sds for each variable under the label "mean" and "sd". 

## Error in summarise(., across(contains("IAT"), list(mean = mean, sd = sd))): object 'cued' not found

#now the data "cued" has the means and SDs for each of the variables previously selected, instead of having to separately code for each mean and SD needed and then having to create a new dataframe. 

print(cued)

## Error in print(cued): object 'cued' not found

I wanted to see if I could calculate cued and uncued conditions together:

cued_uncued <- cleandata %>% #for cued and uncued conditios  
  select(baseIATcued, preIATcued, postIATcued, weekIATcued,
         baseIATuncued, preIATuncued, postIATuncued, weekIATuncued)

## Error in select(., baseIATcued, preIATcued, postIATcued, weekIATcued, : object 'cleandata' not found

 #Selecting these variables from the "cleandata" dataset to create new data "cued_uncued"

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

cued_uncued <- cued_uncued %>% 
  summarise(across(contains("IAT"), list(mean = mean, sd = sd))) #captures and calculates the means and SDs of each variable that contains "IAT" within its variable name. Lists the calculated means and sds for each variable under the label "mean" and "sd". 

## Error in summarise(., across(contains("IAT"), list(mean = mean, sd = sd))): object 'cued_uncued' not found

#now the data "cued" has the means and SDs for each of the variables previously selected, instead of having to separately code for each mean and SD needed and then having to create a new dataframe. 

print(cued_uncued)

## Error in print(cued_uncued): object 'cued_uncued' not found

Create new dataframe with calculated values from "cued_uncued"

table3 <- tibble(
  label = c("Baseline", "Prenap", "Postnap", "1-week delay"),
  mean1 = c(0.518, 0.211, 0.307, 0.4),
  mean2 = c(0.595, 0.302, 0.249, 0.399),
  SD1 = c(0.363, 0.514, 0.445, 0.387),
  SD2 = c(0.447, 0.442, 0.478, 0.467)
)

print(table3)

## # A tibble: 4 x 5
##   label        mean1 mean2   SD1   SD2
##   <chr>        <dbl> <dbl> <dbl> <dbl>
## 1 Baseline     0.518 0.595 0.363 0.447
## 2 Prenap       0.211 0.302 0.514 0.442
## 3 Postnap      0.307 0.249 0.445 0.478
## 4 1-week delay 0.4   0.399 0.387 0.467

Recreate Table 3

table3 %>% 
  gt() %>% 
  tab_header(
    title = "Table 3. Implicit bias levels by condition") %>% 
  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 = "Cued", #Spanner column label 
    columns = c(mean1, SD1)
    ) %>% 
  tab_spanner(
    label = "Uncued", #Spanner column label 
    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 3. Implicit bias levels by condition
	Cued		Uncued
	Mean	SD	Mean	SD
Baseline	0.52	0.36	0.59	0.45
Prenap	0.21	0.51	0.30	0.44
Postnap	0.31	0.45	0.25	0.48
1-week delay	0.40	0.39	0.40	0.47
Implicit bias values are the average D600 score for each timepoint