Importing data from EPrime to R.

This script puts all eprime data files into R and selects columns of interest for a new data frame. Before you run it, please ensure all of the .txt files are in the current project folder.

Prep

# Let's use 3 packages.
library(tidyverse)
library(rprime)
library(wrapr)

Define a function

# A function to prepare all eprime data simultaneously
reduce_wm <- function(wm_path) {
  # Read in a text file generated by Eprime
  wm_lines <- read_eprime(wm_path)
  # Convert lines from an Eprime file into EprimeFrame objects
  wm_frames <- FrameList(wm_lines)
  # Make it a data frame.
  experiment_df <- to_data_frame(wm_frames)
  # Select the columns we want for a tibble called "dat".
  dat <- as.tibble(experiment_df) %>%
    select(qc(
      Sample, TrialType, Prime, Proc1, Proc2, Proc3, Proc4, Proc5, Target,
      Proc1.RT, Proc2.RT, Proc3.RT, Proc4.RT,
      Proc1VALIDCR, Proc2VALIDCR, Proc3VALIDCR, Proc3VALIDCR,
      MemTarget.RT, MemTarget.ACC
    ))
  # Only put the actual trials in "dat".
  dat <- dat[14:93, ]
}

Try it one time

# Does it work on one eprime text file?
test_dat <- reduce_wm("ProjectWM- Final test-12-1.txt")

There should be 80 rows.

# How many rows in this data frame with 1 participant?
nrow(test_dat)

[1] 80

Try it for all files in the folder simulaneously.

Gather a pointer to all of the .txt data files from EPrime.

# Find all file names ending in .txt located in the current project folder.
files <- dir(pattern = "*.txt")
files

 [1] "ProjectWM- Final test-11-1.txt" "ProjectWM- Final test-12-1.txt"
 [3] "ProjectWM- Final test-13-1.txt" "ProjectWM- Final test-14-1.txt"
 [5] "ProjectWM- Final test-15-1.txt" "ProjectWM- Final test-16-1.txt"
 [7] "ProjectWM- Final test-17-1.txt" "ProjectWM- Final test-18-1.txt"
 [9] "ProjectWM- Final test-19-1.txt" "ProjectWM- Final test-20-1.txt"
[11] "ProjectWM- Final test-21-1.txt" "ProjectWM- Final test-22-1.txt"
[13] "ProjectWM- Final-001-1.txt"     "ProjectWM- Final-002-1.txt"    
[15] "ProjectWM- Final-003-1.txt"     "ProjectWM- Final-004-1.txt"    

# How many individual data files from eprime do we have?
length(files)

[1] 16

Use reduce_wm() as the function repeated for the files we found.

# Apply the map_df function to use reduce_wm() on each participant's text file from eprime.
test_dats <- map_df(files, reduce_wm)

Result

Does our expectation of observations in the data at hand match with the number of rows?

# How many rows in this data frame with 2 participants?
nrow(test_dats)

[1] 1280

Enjoy

# Let's review the variable/column names in the data.
names(test_dats)

 [1] "Sample"        "TrialType"     "Prime"         "Proc1"        
 [5] "Proc2"         "Proc3"         "Proc4"         "Proc5"        
 [9] "Target"        "Proc1.RT"      "Proc2.RT"      "Proc3.RT"     
[13] "Proc4.RT"      "Proc1VALIDCR"  "Proc2VALIDCR"  "Proc3VALIDCR" 
[17] "MemTarget.RT"  "MemTarget.ACC"

Now, using these variables we can begin exploration, visualization, and analysis.