Preprocess report"

In esmtools: Preprocessing Experience Sampling Method (ESM) Data

# Setup global settings
knitr::opts_chunk$set(message=FALSE, warning=FALSE)

This template follows the preprocessing framework described in Revol and al. (in preparation) and is stored in the esmtools R package. Additional instructions on creating reports and also on preprocessing ESM data can be found on the ESM Preprocessing Gallery website.

Study and data collection procedure

Load packages

Import the packages:

library(esmtools) # For button(), txt() functions
library(ggplot2)

Step 1: Import data and preliminary preprocessing

This section is dedicated to the first look at the data, the merging of data sources the first basic preprocessing methods (e.g., duplicates, branching items check), and checking the variable consistency when the data has just been imported.

Import the data:

file_path = "path/to/file.csv"
data = read.csv(file_path)

Raw dataset info:

read = read.csv
esmtools::dataInfo(file_path = file_path, 
                   read_fun = read,
                   idvar = "id", timevar = "sent")

r txt(id='esm-issue',title='Issue',text="The issue is that ...",count=TRUE)

r txt('esm-inspect','Inspection',"Here we can see that ...")

r txt('esm-mod','Modification',"I changed ...",TRUE)

r button(text = "Description")

# For instance :
# describe(data)

r endbutton()

r button(text = "Supplementary")

r endbutton()

Step 2: Design and sample scheme

This section is dedicated to checking and solving issues due to inconsistencies between the planned and the actual design of the study.

Step 3: Participants response behaviors

This section is dedicated to investigating how well participants engaged with the ESM study looking particularly for problematic patterns of behaviors (e.g., invalid observations, response time, careless responding).

Step 4: Compute and transform variables

This section is dedicated to computing and modifying variables of interest that will later be used in visualization and statistical analysis.

Step 5: Descriptive statistics and visualization

This section is dedicated to examining various aspects of variables (such as distribution) and the differences both within and between participants.

Export preprocessed data

Export the preprocessed data:

file_path_preproc = "path/preprocessed_data.csv"
write.csv(data, file_path_preproc, row.names=FALSE)

Run the data quality report:

# Path to the data quality report (.Rmd format) 
rmark_file = "path/data_quality_report.Rmd"

# Name of the output data quality report. Date is included to keep track of changes
filename_out = paste0(as.Date(Sys.time()), "_Data_Quality_Report.html")

# Knit the data quality report
rmarkdown::render(rmark_file, output_file=filename_out, params=list(file_path_preproc=file_path_preproc))

Session and preprocessed data info

For reproducibility purposes, this section informs about the R session and packages used as well as their versions.

sessionInfo()

Additionally, we display the meta-information of the preprocessed dataset.

esmtools::dataInfo(file_path=file_path_preproc, 
                   read_fun = read.csv,
                   idvar="id", timevar="sent")

esmtools documentation built on May 29, 2024, 6:45 a.m.