abcdutils

A collection of utility functions for working with the Adolescent Brain and Cognitive Development dataset.

Installation

You can install abcdutils from GitHub with:

# install.packages("devtools")
devtools::install_github("psvelayudhan/abcdutils") # latest development version

devtools::install_github("psvelayudhan/abcdutils@stable") # latest stable version

devtools::install_github("psvelayudhan/[email protected]") # specific version

See https://github.com/psvelayudhan/abcdutils/tags for available specific versions.

Usage

Working with the data dictionary

library(abcdutils)
library(readr)

# Search the NDA's data dictionary from R
search_dd("traumatic brain injury")

# Go to the data dictionary page of a dataframe based on its short name
abcd_dd("abcd_otbi01")

# Remove the data dictionary (yes, this is just df[-1, ])
abcd_otbi01 <- read_csv("abcd_otbi01.txt")
remove_dd(abcd_otbi01)

Extract cleaned dataframes

Subcortical volumes (structural MRI)

subc_v <- get_subc_v(smrip10201)

Subcortical volumes for a predefined set of subjects and a specific collection event

# Dataframe containing "subjectkey" column
subject_df <- read_csv("subjectlist.csv")

subc_v <- get_subc_v(smrip10201, subjects = subject_df, t = 0)

(where t = 0 refers to baseline data)

Extraction available for a wide range of variables related to neuroimaging, demographics, psychosocial resilience, and medical history:

• get_cbcl_aggressive_r
• get_cbcl_anxiety_r
• get_cbcl_attention_r
• get_cbcl_depress_r
• get_cbcl_dizzy
• get_cbcl_headaches
• get_cbcl_overtired
• get_cbcl_sleeping_less
• get_cbcl_sleeping_more
• get_cbcl_vomiting
• get_cort_sa
• get_cort_t
• get_exercise
• get_family_function
• get_full_sleep_df
• get_gord_cor
• get_gord_var
• get_headaches
• get_income
• get_loneliness
• get_mtbi_age
• get_mtbi_count
• get_mtbi_loc
• get_mtbi_mechanism
• get_mtbi_mem_daze
• get_nihtbx_cardsort_fc
• get_nihtbx_list_fc
• get_nihtbx_pattern_fc
• get_parent_psychopathology
• get_prosocial_behaviour
• get_pubertal_status
• get_race
• get_screen_time
• get_sex
• get_sports_and_activities
• get_subc_cor
• get_subc_v
• get_subc_var
• get_wmnd

Helpful subsetting

abcd_otbi01 <- read_csv("abcd_otbi01.txt")

# Subset to just baseline data
filter_timepoint(abcd_otbi01, 0)

# Or just year 1 follow-up data
filter_timepoint(abcd_otbi01, 1)

# Or just subjects of interest
filter_subjects(abcd_otbi01, subject_list)

Concussion data prep

abcd_otbi01 <- read_csv("abcd_otbi01.txt")

# Renaming columns to be easily interpretable:
rename_tbi(abcd_otbi01)

# Identify which subjects had an mTBI and which had a moderate+ head injury:
identify_all_tbi(abcd_otbi01)

# Identify which head injuries were mTBIs
identify_mtbi(abcd_otbi01)

# When did the mTBIs occur
identify_mtbi_times(abcd_otbi01)

# What mechanism caused their latest mTBI
identify_latest_mtbi_mechanism(abcd_otbi01)

# How many mTBIs did the subject experience
identify_num_mtbi(abcd_otbi01)

# How much LOC occurred for the subject's most recent injury
identify_latest_mtbi_loc(abcd_otbi01)

# Did memory loss / feeling dazed or confused occur on the most recent injury
identify_latest_mtbi_mem_daze(abcd_otbi01)

# Combine all the functions above
detail_mtbi(abcd_otbi01)

Plotting

# Visualize missing data across several dataframes

df_list <- list(
    "loss of consciousness" = as_mtbi_loc,
    "mechanism of injury" = as_mtbi_mechanism,
    "memory loss / dazed" = as_mtbi_mem_daze,
    "income" = d_income)

vis_missing_by_df(df_list)