Learn R Programming

metatools

The goal of metatools is to enable the use of metacore objects. Metatools can be used to build datasets or enhance columns in existing datasets as well as checking datasets against the metadata in metacore.

Installation

You can install the latest released version of metatools from CRAN with:

install.packages("metatools")

And the current development version from GitHub with:

devtools::install_github("pharmaverse/metatools")

Example

Here is a basic example of some functions to build datasets and create new columns.

library(metatools)
library(metacore)
library(haven)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
load(metacore_example("pilot_ADaM.rda"))
metacore <- metacore %>%
  select_dataset("ADSL")
ds_list <- list(DM = read_xpt(metatools_example("dm.xpt")))
# Pull in columns from DM to be in ADSL
adsl <- build_from_derived(metacore, ds_list, predecessor_only = FALSE) 
adsl 
#> # A tibble: 306 × 13
#>    STUDYID     USUBJID SUBJID SITEID TRT01P   AGE AGEU  RACE  SEX   ETHNIC DTHFL
#>    <chr>       <chr>   <chr>  <chr>  <chr>  <dbl> <chr> <chr> <chr> <chr>  <chr>
#>  1 CDISCPILOT… 01-701… 1015   701    Place…    63 YEARS WHITE F     HISPA… ""   
#>  2 CDISCPILOT… 01-701… 1023   701    Place…    64 YEARS WHITE M     HISPA… ""   
#>  3 CDISCPILOT… 01-701… 1028   701    Xanom…    71 YEARS WHITE M     NOT H… ""   
#>  4 CDISCPILOT… 01-701… 1033   701    Xanom…    74 YEARS WHITE M     NOT H… ""   
#>  5 CDISCPILOT… 01-701… 1034   701    Xanom…    77 YEARS WHITE F     NOT H… ""   
#>  6 CDISCPILOT… 01-701… 1047   701    Place…    85 YEARS WHITE F     NOT H… ""   
#>  7 CDISCPILOT… 01-701… 1057   701    Scree…    59 YEARS WHITE F     HISPA… ""   
#>  8 CDISCPILOT… 01-701… 1097   701    Xanom…    68 YEARS WHITE M     NOT H… ""   
#>  9 CDISCPILOT… 01-701… 1111   701    Xanom…    81 YEARS WHITE F     NOT H… ""   
#> 10 CDISCPILOT… 01-701… 1115   701    Xanom…    84 YEARS WHITE M     NOT H… ""   
#> # … with 296 more rows, and 2 more variables: RFSTDTC <chr>, RFENDTC <chr>

# Now we can make some new columns and converting columns into factors
adsl %>%
   select(USUBJID, AGE, ETHNIC) %>% 
   create_cat_var(metacore, AGE, AGEGR1, AGEGR1N) %>% #Add an AGEGR1 and AGEGR1N column
   convert_var_to_fct(metacore, ETHNIC) # Change ETHNIC to as factor 
#> # A tibble: 306 × 5
#>    USUBJID       AGE ETHNIC                 AGEGR1 AGEGR1N
#>    <chr>       <dbl> <fct>                  <chr>    <dbl>
#>  1 01-701-1015    63 HISPANIC OR LATINO     <65          1
#>  2 01-701-1023    64 HISPANIC OR LATINO     <65          1
#>  3 01-701-1028    71 NOT HISPANIC OR LATINO 65-80        2
#>  4 01-701-1033    74 NOT HISPANIC OR LATINO 65-80        2
#>  5 01-701-1034    77 NOT HISPANIC OR LATINO 65-80        2
#>  6 01-701-1047    85 NOT HISPANIC OR LATINO >80          3
#>  7 01-701-1057    59 HISPANIC OR LATINO     <65          1
#>  8 01-701-1097    68 NOT HISPANIC OR LATINO 65-80        2
#>  9 01-701-1111    81 NOT HISPANIC OR LATINO >80          3
#> 10 01-701-1115    84 NOT HISPANIC OR LATINO >80          3
#> # … with 296 more rows

Metatools can also be used to run checks

data <- read_xpt(metatools_example("adsl.xpt"))

# Checks can be run on a single column
check_ct_col(data, metacore, TRT01PN) # Checks column only contains control terminology
#> # A tibble: 254 × 49
#>    STUDYID     USUBJID SUBJID SITEID SITEGR1 ARM   TRT01P TRT01PN TRT01A TRT01AN
#>    <chr>       <chr>   <chr>  <chr>  <chr>   <chr> <chr>    <dbl> <chr>    <dbl>
#>  1 CDISCPILOT… 01-701… 1015   701    701     Plac… Place…       0 Place…       0
#>  2 CDISCPILOT… 01-701… 1023   701    701     Plac… Place…       0 Place…       0
#>  3 CDISCPILOT… 01-701… 1028   701    701     Xano… Xanom…      81 Xanom…      81
#>  4 CDISCPILOT… 01-701… 1033   701    701     Xano… Xanom…      54 Xanom…      54
#>  5 CDISCPILOT… 01-701… 1034   701    701     Xano… Xanom…      81 Xanom…      81
#>  6 CDISCPILOT… 01-701… 1047   701    701     Plac… Place…       0 Place…       0
#>  7 CDISCPILOT… 01-701… 1097   701    701     Xano… Xanom…      54 Xanom…      54
#>  8 CDISCPILOT… 01-701… 1111   701    701     Xano… Xanom…      54 Xanom…      54
#>  9 CDISCPILOT… 01-701… 1115   701    701     Xano… Xanom…      54 Xanom…      54
#> 10 CDISCPILOT… 01-701… 1118   701    701     Plac… Place…       0 Place…       0
#> # … with 244 more rows, and 39 more variables: TRTSDT <date>, TRTEDT <date>,
#> #   TRTDURD <dbl>, AVGDD <dbl>, CUMDOSE <dbl>, AGE <dbl>, AGEGR1 <chr>,
#> #   AGEGR1N <dbl>, AGEU <chr>, RACE <chr>, RACEN <dbl>, SEX <chr>,
#> #   ETHNIC <chr>, SAFFL <chr>, ITTFL <chr>, EFFFL <chr>, COMP8FL <chr>,
#> #   COMP16FL <chr>, COMP24FL <chr>, DISCONFL <chr>, DSRAEFL <chr>, DTHFL <chr>,
#> #   BMIBL <dbl>, BMIBLGR1 <chr>, HEIGHTBL <dbl>, WEIGHTBL <dbl>, EDUCLVL <dbl>,
#> #   DISONSDT <date>, DURDIS <dbl>, DURDSGR1 <chr>, VISIT1DT <date>, …
# Or across all the columns 
check_ct_data(data, metacore) %>%  # Checks control terminology for all columns 
check_variables(metacore) # Check all variables in the metadata are in the dataset and there aren't any extra columns 
#> No missing or extra variables
#> # A tibble: 254 × 49
#>    STUDYID     USUBJID SUBJID SITEID SITEGR1 ARM   TRT01P TRT01PN TRT01A TRT01AN
#>    <chr>       <chr>   <chr>  <chr>  <chr>   <chr> <chr>    <dbl> <chr>    <dbl>
#>  1 CDISCPILOT… 01-701… 1015   701    701     Plac… Place…       0 Place…       0
#>  2 CDISCPILOT… 01-701… 1023   701    701     Plac… Place…       0 Place…       0
#>  3 CDISCPILOT… 01-701… 1028   701    701     Xano… Xanom…      81 Xanom…      81
#>  4 CDISCPILOT… 01-701… 1033   701    701     Xano… Xanom…      54 Xanom…      54
#>  5 CDISCPILOT… 01-701… 1034   701    701     Xano… Xanom…      81 Xanom…      81
#>  6 CDISCPILOT… 01-701… 1047   701    701     Plac… Place…       0 Place…       0
#>  7 CDISCPILOT… 01-701… 1097   701    701     Xano… Xanom…      54 Xanom…      54
#>  8 CDISCPILOT… 01-701… 1111   701    701     Xano… Xanom…      54 Xanom…      54
#>  9 CDISCPILOT… 01-701… 1115   701    701     Xano… Xanom…      54 Xanom…      54
#> 10 CDISCPILOT… 01-701… 1118   701    701     Plac… Place…       0 Place…       0
#> # … with 244 more rows, and 39 more variables: TRTSDT <date>, TRTEDT <date>,
#> #   TRTDURD <dbl>, AVGDD <dbl>, CUMDOSE <dbl>, AGE <dbl>, AGEGR1 <chr>,
#> #   AGEGR1N <dbl>, AGEU <chr>, RACE <chr>, RACEN <dbl>, SEX <chr>,
#> #   ETHNIC <chr>, SAFFL <chr>, ITTFL <chr>, EFFFL <chr>, COMP8FL <chr>,
#> #   COMP16FL <chr>, COMP24FL <chr>, DISCONFL <chr>, DSRAEFL <chr>, DTHFL <chr>,
#> #   BMIBL <dbl>, BMIBLGR1 <chr>, HEIGHTBL <dbl>, WEIGHTBL <dbl>, EDUCLVL <dbl>,
#> #   DISONSDT <date>, DURDIS <dbl>, DURDSGR1 <chr>, VISIT1DT <date>, …

Copy Link

Version

Install

install.packages('metatools')

Version

0.1.6

License

MIT + file LICENSE

Issues

11

Pull Requests

0

Stars

8

Forks

3

Maintainer

Christina Fillmore

Last Published

July 23rd, 2024

Functions in metatools (0.1.6)

drop_unspec_vars

Drop Unspecified Variables
sort_by_key

Sort Rows by Key Sequence
set_variable_labels

Apply labels to a data frame using a metacore object
remove_labels

Remove labels to multiple variables on a data frame
add_labels

Apply labels to multiple variables on a data frame
combine_supp

Combine the Domain and Supplemental Qualifier
check_variables

Check Variable Names
add_variables

Add Missing Variables
check_unique_keys

Check Uniqueness of Records by Key
build_from_derived

Build a dataset from derived
build_qnam

Build the observations for a single QNAM
convert_var_to_fct

Convert Variable to Factor with Levels Set by Control Terms
check_ct_col

Check Control Terminology for a Single Column
check_ct_data

Check Control Terminology for a Dataset
create_cat_var

Create Categorical Variable from Codelist
get_bad_ct

Gets vector of control terminology which should be there
make_supp_qual

Make Supplemental Qualifier
create_subgrps

Create Subgroups
%>%

Pipe operator
metatools_example

Get path to pkg example
order_cols

Sort Columns by Order
create_var_from_codelist

Create Variable from Codelist