Learn R: ANOVAs

Load Packages

haven

# Check if haven is already installed and if it is, load it.
if (!require(haven)){
  # If it's not intalled, then tell R to install it.
  install.packages("haven", dependencies = TRUE)
  # Once it's installed, tell R to load it.
  library(haven)
}

tidyverse

if (!require(tidyverse)){
  install.packages("tidyverse", dependencies = TRUE)
  library(tidyverse)
}

Afex

# Check if haven is already installed and if it is, load it.
if (!require(afex)){
  # If it's not intalled, then tell R to install it.
  install.packages("afex", dependencies = TRUE)
  # Once it's installed, tell R to load it.
  library(afex)
}

Import Data

dataset <- read_sav("https://osf.io/p5rqv/download")

dataset %>% #start with dataset
  distinct(Subject, .keep_all = TRUE) -> dataset.unique

Run ANOVA Test

(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

Calculate Means and Standard Deviations

(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

NA