Quick start demonstration"

In rprime: Functions for Working with 'Eprime' Text Files

library("rprime")
library("knitr")
opts_chunk$set(
  comment = "#>",
  error = FALSE,
  tidy = FALSE,
  collapse = TRUE)
options(str = strOptions(vec.len = 2))

rprime is an R package for parsing .txt generated by E-Prime, a program for running psychological experiments.

Overview

The main workflow for rprime involves:

1. read_eprime: reliably read in data from an Eprime log (.txt) file.
2. FrameList: extract the text in each "LogFrame" in the file, storing each log-frame as an R list.
3. preview_levels, preview_eprime: explore the structure of the parsed data.
4. keep_levels, drop_levels, filter_in, filter_out: select and filter particular levels from the txt-file.
5. to_data_frame: make a data-frame from the parsed data.

Installation

To get the current, released version from CRAN:

install.packages("rprime")

Examples

Getting data into R

Load the file with read_eprime and parse its contents with FrameList.

library("rprime")
# Read in an Eprime text file
experiment_lines <- read_eprime("data/SAILS/SAILS_001X00XS1.txt")

# Extract and parse the log-frames from the file
experiment_data <- FrameList(experiment_lines)

Exploring

In the text file, frames were distinguished by the procedure they are running as well as the their level of nesting. Get an overview of the different types of frames with preview_levels.

# There are six different kinds of frames in this file
preview_levels(experiment_data)

Get a preview of the data in each kind of frame with preview_frames.

preview_frames(experiment_data)

preview_eprime (not demonstrated here) does both kinds of previews (levels and frames).

Filtering

Use keep_levels and drop_levels to filter frames according to nesting level.

# Filter (out) by depth of nesting
not_level_1 <- drop_levels(experiment_data, 1)
preview_levels(not_level_1)

# Filter (in) by depth of nesting
just_level_3 <- keep_levels(experiment_data, 3)
preview_levels(just_level_3)

Use filter_in and filter_out to filter frames using attribute values. Use repeated filtering statements to drill down into the list of frames.

# Filter (out) by attribute values
no_header <- filter_out(experiment_data, "Running", values = "Header")
preview_levels(no_header)

# Filter (in) by attribute values
not_practice <- filter_in(experiment_data, "Running", "TrialLists")
preview_levels(not_practice)

# Drill down further into the trials by filtering again
sue_trials <- filter_in(not_practice, "Module", "SUE")
preview_eprime(sue_trials)

Exporting

Convert to a dataframe with to_dataframe. Attribute names in the log-frames become column names in the dataframe.

# Export to dataframe
sue_trials_df <- to_data_frame(sue_trials)
str(sue_trials_df)

# Don't need every column
columns_to_keep <- c("Eprime.Basename", "Module", "Sample", 
                     "Correct", "Response")
sue_trials_df <- sue_trials_df[columns_to_keep]
head(sue_trials_df)

Note: rprime thinks that all the values in the final dataframe are character values. You can use type_convert in the readr package to correct the column types:

# Right now the sample numbers are stored as character values
str(sue_trials_df)

library("readr")
sue_trials_df <- type_convert(sue_trials_df)
# Now, they are stored as integers...
str(sue_trials_df)

rprime documentation built on Oct. 23, 2020, 6:55 p.m.