Learn R - Import Data

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()){
  install.packages("", dependencies = TRUE)
  library()
}

afex

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

dataset

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

anova

dataset %>% #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")))

output

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, 710 0.16    0.00 <.0001    >.99
2          EC 1, 710 0.16 7.10 **   .010    .008
3 TrainTyp:EC 1, 710 0.16    0.64  .0009     .43
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1

calculate

(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