In bramzandbelt/irmass: Independent Race Model Analysis of Selective Stopping

# Set root dir to project directory to ensure that code is always run relative to the project directory, no matter if it is run using `knitr` or interactively.
knitr::opts_knit$set(root.dir = rprojroot::find_root(rprojroot::has_file("DESCRIPTION")))

# Attach tideverse package to enable access to pipe (%>%)
require(tidyverse)

Overview

Preliminaries

Identify the project directory

All paths in the code are relative to the project directory.

project_dir <- rprojroot::find_root(rprojroot::has_file("DESCRIPTION"))

Define and verify existence of output directories

derivatives_dir <- file.path(project_dir,"data","derivatives")
figures_dir <- file.path(project_dir, "figures")
notebook_name <- params$title

irmass::verify_output_dirs(base_dirs = list(derivatives_dir, figures_dir),
                           notebook_name = notebook_name)

Read data

Select log files

prac_trial_files <- irmass::select_log_files(stage = "practice", 
                                             filetype = "triallog")
prac_block_files <- irmass::select_log_files(stage = "practice", 
                                             filetype = "blocklog")
expt_trial_files <- irmass::select_log_files(stage = "experiment", 
                                             filetype = "triallog")
expt_block_files <- irmass::select_log_files(stage = "experiment", 
                                             filetype = "blocklog")

Read log files

We read the four types of log files (practice blocklog, practice triallog, experiment blocklog, experiment triallog) of all participants into a tibble using readr::read_csv.

This results in some warnings, because the blocklog files happened to have a column with zeros and no column name. This warning can be ignored and therefore they are not explicitly shown.

prac_sess_data <- irmass::read_log_files(prac_trial_files, data_type = "sess_data")
prac_block_data <- irmass::read_log_files(prac_block_files, data_type = "block_data")
prac_trial_data <- irmass::read_log_files(prac_trial_files, data_type = "trial_data")

expt_sess_data <- irmass::read_log_files(expt_block_files, data_type = "sess_data")
expt_block_data <- irmass::read_log_files(expt_block_files, data_type = "block_data")
expt_trial_data_others <- irmass::read_log_files(expt_trial_files$others, data_type = "trial_data")
expt_trial_data_sub00 <- irmass::read_log_files(expt_trial_files$subj00, data_type = "trial_data_sub00")

Data cleaning and processing

Data clearning involves the following (high-level) steps: - correction of erroneous columns names in the experimental triallog file from participant subj00, and merge data with data from other participants - remove practice trials from experimental session

• do some renaming, mutating, etc.

• tidy the data (see Wickham, J Stat Software, 2014)

Correct erroneous column names in experimental triallog file of sub-00

During the experimental session, participant subj00 practiced the task outside the MRI scanner, using a keyboard rather than the MR-compatible response pad. As a result, this subject's experiment triallog variable names related to key presses reflect the keyboard mapping (LM = f, RM = h, LI = v, RI = b) rather than the response pad mapping (LM = f, RM = b, LI = e, RI = a). The procedure below for subj00 corrects this issue.

expt_trial_data_sub00 <- 
expt_trial_data_sub00 %>%
  dplyr::rename(keyCount_a = keyCount_b,
                rt1_a = rt1_b,
                keyCount_e = keyCount_v,
                rt1_e = rt1_v,
                keyCount_b = keyCount_h,
                rt1_b = rt1_h,
                `rtDiff1_f-b` = `rtDiff1_f-h`,
                `rtDiff1_e-a` = `rtDiff1_v-b`,
         )

Now, merge sub-00's experimental trial data with that from the others

expt_trial_data <- rbind(expt_trial_data_sub00,expt_trial_data_others)

Remove practice blocks/trials data from experimental session data

expt_block_data <- 
  expt_block_data %>%
  dplyr::filter(stringr::str_detect(blockId, '^e.*'))

expt_trial_data <- 
  expt_trial_data %>%
  dplyr::filter(stringr::str_detect(blockId, '^e.*'))

Verify index columns

Columns with column names ending in 'Ix' are index columns. The values in these columns should be of integer type, but are not when NaNs are present. The following code identifies index columns, replaces NaNs with NAs and ensures that the values are coded as integers.

# Index columns (*Ix) should be coded as integers
list2env(purrr::map(list(prac_sess_data = prac_sess_data, 
                         prac_block_data = prac_block_data, 
                         prac_trial_data = prac_trial_data, 
                         expt_sess_data = expt_sess_data,
                         expt_block_data = expt_block_data,
                         expt_trial_data = expt_trial_data), 
                    irmass::verify_ix_cols),
         envir = parent.frame()
         )

Reorganize and tidy data data

The block data is used to determine whether or not performance was in line with preset criteria.

Tidy data has the following characteristics:

• each variable forms one column
• each observation forms one row
• each type of observational unit forms a table

Tidy data aids data analysis and data visualization.

Reorganize and tidy data block data

The block data is used to assess task performance criteria.

prac_block_data_tidied <- irmass::tidy_block_data(prac_block_data, stage = 'prac')
expt_block_data_tidied <- irmass::tidy_block_data(expt_block_data, stage = 'expt')

Print tidied block data for a quick peek

Tidy practice block data

prac_block_data_tidied

Tidy experimental block data

expt_block_data_tidied

Reorganize and tidy data trial data

prac_trial_data_tidied <- irmass::tidy_trial_data(prac_trial_data)
expt_trial_data_tidied <- irmass::tidy_trial_data(expt_trial_data)

expt_trial_data_exploratory_tidied <- irmass::tidy_trial_data(expt_trial_data, analysis_type = 'exploratory')

Tidy practice trial data - responses

prac_trial_data_tidied$resp_df

Tidy practice trial data - response times

prac_trial_data_tidied$RT_df

Tidy experimental trial data - responses

expt_trial_data_tidied$resp_df

Tidy experimental trial data - response times

expt_trial_data_tidied$RT_df

Descriptive statistics

Trial numbers in practice session

irmass::show_trial_numbers(prac_trial_data_tidied$RT_df)

Trial numbers in experimental session

irmass::show_trial_numbers(expt_trial_data_tidied$RT_df)

Inferential statistics

None.

Data visualization

None.

Write data

readr::write_csv(prac_block_data_tidied,
                 path = file.path(derivatives_dir, 
                                  notebook_name, 
                                  'tidy_prac_block_data.csv'),
                 col_names = TRUE)

readr::write_csv(expt_block_data_tidied,
                 path = file.path(derivatives_dir, 
                                  notebook_name, 
                                  'tidy_expt_block_data.csv'),
                 col_names = TRUE)

readr::write_csv(prac_trial_data_tidied$resp_df,
                 path = file.path(derivatives_dir, 
                                  notebook_name, 
                                  'tidy_prac_trial_resp_data.csv'),
                 col_names = TRUE)

readr::write_csv(prac_trial_data_tidied$RT_df,
                 path = file.path(derivatives_dir, 
                                  notebook_name, 
                                  'tidy_prac_trial_rt_data.csv'),
                 col_names = TRUE)

readr::write_csv(expt_trial_data_tidied$resp_df,
                 path = file.path(derivatives_dir, 
                                  notebook_name, 
                                  'tidy_expt_trial_resp_data.csv'),
                 col_names = TRUE)

readr::write_csv(expt_trial_data_tidied$RT_df,
                 path = file.path(derivatives_dir, 
                                  notebook_name, 
                                  'tidy_expt_trial_rt_data.csv'),
                 col_names = TRUE)

readr::write_csv(expt_trial_data_exploratory_tidied$resp_df,
                 path = file.path(derivatives_dir, 
                                  notebook_name, 
                                  'tidy_expt_trial_exploratory_resp_data.csv'),
                 col_names = TRUE)

readr::write_csv(expt_trial_data_exploratory_tidied$RT_df,
                 path = file.path(derivatives_dir, 
                                  notebook_name, 
                                  'tidy_expt_trial_exploratory_rt_data.csv'),
                 col_names = TRUE)

bramzandbelt/irmass documentation built on June 28, 2019, 9:07 p.m.