-
Notifications
You must be signed in to change notification settings - Fork 0
/
README.Rmd
181 lines (132 loc) · 4.01 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# abcdutils
<!-- badges: start -->
<!-- badges: end -->
A collection of utility functions for working with the Adolescent Brain and Cognitive Development dataset.
## Installation
You can install abcdutils from [GitHub](https://github.com/) with:
``` r
# 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
```{r eval = FALSE}
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)
```{r eval = FALSE}
subc_v <- get_subc_v(smrip10201)
```
Subcortical volumes for a predefined set of subjects and a specific collection event
```{r eval = FALSE}
# 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
```{r eval = FALSE}
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
```{r eval = FALSE}
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
```{r eval = FALSE}
# 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)
```