Using R

Start up:

rm(list=ls()) #clear environment

cat("\014") #clear console

options(scipen=999) #suppress scientific notation

Opening and Manipulating Data:

Opening a specific sheet of an excel file:

library(openxlsx)

excel_file <- "C:\myexcelfile.xlsx"

data <- xlsx.read(excel_file,"Sheet1")

Opening all sheets of an excel file and combine them row-wise:

library(openxlsx)

excel_file <- "C:\myexcelfile.xlsx"

sheet_names <- getSheetNames(excel_file)

data <- lapply(sheet_names, xlsx.read, xlsxFile = excel_file)

data <- do.call(rbind,data)

How to rename variables/columns in your dataframe:

names(data)[names(data)=="old_variable_name"] <- "new_variable_name"

How to search for and replace certain values in your data:

data$RT[data$RT==9999] <- NA

How to create counter for sequence of control/standard trials:

How to collapse/average data across a certain variable:

use aggregate() function

aggregated_data <- aggregate(cbind(),

How to select a specific subset of dataset:

use subset() function

data2use <- subset(data, RT >= 0 & RT <= 250 & condition %in% c("Go", "Ignore Go"))

How to subset data to match trial numbers across conditions:

Linear Mixed Models:

library(lmerTest)

RT_model <- lmer(RT ~ condition + ERP_amplitude + (1|subject_id), data = data2use)

anova(RT_model)

How to get effect size:

library(r2glmm)

r2beta()

How to get estimated beta-weight:

library(jtools)

summ()

When you get a singularity error:

Follow-Up Analyses:

How to do follow-up pairwise comparisons across factors:

library(emmeans)

How to do multiple specific pairwise comparisons:

• run emmeans

• construct grid and contrasts

• run emmeans again

Plotting:

library(ggplot2)

How to get grand-means with within-subject standard error:

library(Rmisc)

use summarySEwithin()

Export to excel:

library(openxlsx)

wb <- createWorkbook()

addWorksheet(wb, "sheetname")

writeData(wb, "sheetname", mydataframe, startRow = 1, startCol = 1)

saveWorkbook(wb, "filepath", overwrite = TRUE)

Get folder path of R-script:

data_folder <- dirname(rstudioapi::getSourceEditorContext()$path)

Random notes:

Print variable names in console without the cell numbers

cat(paste0('"',names(MyDataFrame),'", '))

Convert all columns in dataframe to character

MyDataFrame <- as.data.frame(lapply(MyDataFrame, as.character))

Replace NA with "" in entire dataframe

MyDataFrame[is.na(MyDataFrame)] <- ""

Comparing column names

setdiff(colnames(df1), colnames(df2))

Connecting to MS Access in R

connection_string <- paste0("Driver={Microsoft Access Driver (*.mdb, *.accdb)};DBQ=",WorkingAccessFilepath,";")

conn <- dbConnect(odbc(), .connection_string = connection_string) # connect to access file

Check if row is NA

complete_na_rows <- apply(df, 1, function(x) all(is.na(x))) \

Remove duplicated rows (get unique)

duplicated_rows <- duplicated(data2use[,c("ColName1", "ColName2")]) # specify columns to look at

unique_rows <- data2use[!duplicated_rows,c("ColName1", "ColName2")] # delete duplicate rows

Concatenate strings while ignoring NA

paste(na.omit(c("string1", "string2')), collapse = ", ")

Connecting with local Microsoft Access file:

library(RODBC)

conn <- odbcConnectAccess2008(AccessFilePath)