Learn R: ANOVAs

library(haven)
library(dplyr)

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library(afex)

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## Loading required package: lme4

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## ************
## Welcome to afex. For support visit: http://afex.singmann.science/

## - Functions for ANOVAs: aov_car(), aov_ez(), and aov_4()
## - Methods for calculating p-values with mixed(): 'S', 'KR', 'LRT', and 'PB'
## - 'afex_aov' and 'mixed' objects can be passed to emmeans() for follow-up tests
## - Get and set global package options with: afex_options()
## - Set sum-to-zero contrasts globally: set_sum_contrasts()
## - For example analyses see: browseVignettes("afex")
## ************

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library(tidyverse)

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ANOVA_Example_Data <- read_sav("ANOVA Example Data.sav")
View(ANOVA_Example_Data)
ANOVA_Example_Data %>% #start with dataset
distinct(Subject, .keep_all = TRUE) -> dataset.unique
(dataset.unique.aov <- aov_ez(id = "Subject", dv = "RaceDFeb", data = dataset.unique, between=c("TrainTyp", "EC")))

## Converting to factor: TrainTyp, EC

## Contrasts set to contr.sum for the following variables: TrainTyp, EC

## Anova Table (Type 3 tests)
## 
## Response: RaceDFeb
##        Effect     df  MSE         F   ges p.value
## 1    TrainTyp 1, 670 0.17      0.51 <.001    .477
## 2          EC 1, 670 0.17 12.40 ***  .018   <.001
## 3 TrainTyp:EC 1, 670 0.17      0.00 <.001    .961
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

(dataset.unique %>% # start with the dataset 
group_by(EC) %>% # group by the training goal variable
summarise(mean = mean(RaceDFeb),  # get the mean of the DV in each group
          SD = sd(RaceDFeb)) -> trainingGoalDescriptives) # get the SD of the DV in each group

## # A tibble: 2 × 3
##   EC                            mean    SD
##   <dbl+lbl>                    <dbl> <dbl>
## 1 0 [Self-Pos Black Training]  0.153 0.386
## 2 1 [Other-Neg Black Training] 0.264 0.435