datadict: Data dictionary tools for the OCA data sharing platform

Installation

Install from GitHub with:

# install.packages("remotes")
remotes::install_github("epicentre-msf/datadict")

Example usage

Generate data dictionary from ODK template

The dict_from_odk() function can be used to generate an OCA-style data dictionary from an ODK template (both the ‘survey’ and ‘options’ sheets of the ODK template are required as inputs).

library(datadict)
library(readxl)

# path to example ODK template (a WHO mortality survey)
path_data <- system.file("extdata", package = "datadict")
path_odk_template <- file.path(path_data, "WHOVA2016_v1_5_3_ODK.xlsx")

# read 'survey' sheet and 'choices' sheet
odk_survey <- readxl::read_xlsx(path_odk_template, sheet = "survey")
odk_choices <- readxl::read_xlsx(path_odk_template, sheet = "choices")

# derive OCA-style data dictionary
dict <- dict_from_odk(odk_survey, odk_choices)

# examine first few rows/cols
dict[1:5,1:4]
#> # A tibble: 5 × 4
#>   variable_name short_label                                  type       choices                                         
#>   <chr>         <chr>                                        <chr>      <chr>                                           
#> 1 Id10002       Is this a region of high HIV/AIDS mortality? Coded list high, High | low, Low | veryl, Very low         
#> 2 Id10003       Is this a region of high malaria mortality?  Coded list high, High | low, Low | veryl, Very low         
#> 3 Id10004       During which season did (s)he die?           Coded list wet, Wet | dry, Dry | DK, Doesn't know          
#> 4 Id10007       What is the name of VA respondent?           Free text  <NA>                                            
#> 5 Id10007a      What is the sex of VA respondent?            Coded list female, Female | male, Male | undetermined, Amb…

Generate data dictionary from REDCap template

The dict_from_redcap() function can be used to generate an OCA-style data dictionary from a REDCap data dictionary. The input dictionary can be exported directly from a REDCap project website or fetched via the API using e.g. the R package redcap.

# path to example REDCap template
path_data <- system.file("extdata", package = "datadict")
path_redcap_dict <- file.path(path_data, "REDCapDataDictionaryDemo.csv")

# read dictionary
redcap_dict <- read.csv(path_redcap_dict)

# derive OCA-style data dictionary
dict <- dict_from_redcap(redcap_dict)

# examine first few rows/cols
dict[1:5,1:5]
#> # A tibble: 5 × 5
#>   variable_name short_label                 type      choices origin  
#>   <chr>         <chr>                       <chr>     <chr>   <chr>   
#> 1 study_id      Study ID                    Free text <NA>    original
#> 2 date_enrolled Date subject signed consent Date      <NA>    original
#> 3 first_name    First Name                  Free text <NA>    original
#> 4 last_name     Last Name                   Free text <NA>    original
#> 5 address       Street, City, State, ZIP    Free text <NA>    original

Generate data dictionary template from a dataset

The dict_from_data() function can be used to generate a template OCA-style data dictionary (which may require further processing) from a dataset. Data types are based on the class of each column within in the input dataset, e.g.:

Column class in R	Dictionary data type
Date	Date
POSIX	Datetime
logical	Logical
integer	Numeric
numeric	Numeric
factor	Coded list
character	Coded list or Free text (see argument factor_threshold)

# path to example dataset
path_data <- system.file("extdata", package = "datadict")
path_linelist <- file.path(path_data, "linelist_cleaned.xlsx")

# read data
dat <- readxl::read_xlsx(path_linelist)

# derive OCA-style data dictionary template
dict <- dict_from_data(dat)

# examine first few rows/cols
dict[1:7,1:5]
#> # A tibble: 7 × 5
#>   variable_name        short_label type      choices origin  
#>   <chr>                <chr>       <chr>     <chr>   <chr>   
#> 1 case_id              <NA>        Free text <NA>    original
#> 2 generation           <NA>        Numeric   <NA>    original
#> 3 cohort_fu            <NA>        Logical   <NA>    original
#> 4 date_infection       <NA>        Datetime  <NA>    original
#> 5 date_onset           <NA>        Datetime  <NA>    original
#> 6 date_hospitalisation <NA>        Datetime  <NA>    original
#> 7 date_outcome         <NA>        Datetime  <NA>    original