Package 'dmtools'

Title: Tools for Clinical Data Management
Description: For checking the dataset from EDC(Electronic Data Capture) in clinical trials. 'dmtools' reshape your dataset in a tidy view and check events. You can reshape the dataset and choose your target to check, for example, the laboratory reference range.
Authors: Konstantin Ryabov [aut, cre]
Maintainer: Konstantin Ryabov <[email protected]>
License: MIT + file LICENSE
Version: 0.2.6.9000
Built: 2024-10-27 03:03:48 UTC
Source: https://github.com/konstantinryabov/dmtools

Help Index

Add columns if columns don't exist

Description

Add columns if columns don't exist

Usage

add_cols(dset, ds_part, target_cols)

Arguments

dset

A data frame. The dataset.
ds_part

A character scalar. Prefix or postfix.
target_cols

A character vector with necessary columns.

Value

A data frame. The dataset.

Function for calculating the difference between two dates

Description

Function for calculating the difference between two dates

Usage

calc_diff(st_inter, dt_item)

Arguments

st_inter

An interval. An object of interval.
dt_item

A date item. An object of date.

Value

An integer scalar. Differences between the two dates.

Check the dataset

Description

Check the dataset

Usage

check(obj, dataset)

Arguments

obj

An object for check.
dataset

A dataset, a type is a data frame.

Value

An object with a check result.

Examples

id <- c("01", "02", "03")
screen_date_E1 <- c("1991-03-13", "1991-03-07", "1991-03-08")
rand_date_E2 <- c("1991-03-15", "1991-03-11", "1991-03-10")
ph_date_E3 <- c("1991-03-21", "1991-03-16", "1991-03-16")
bio_date_E3 <- c("1991-03-23", "1991-03-16", "1991-03-16")

df <- data.frame(id, screen_date_E1, rand_date_E2, ph_date_E3, bio_date_E3,
  stringsAsFactors = FALSE
)

timeline <- system.file("dates.xlsx", package = "dmtools")
obj_date <- date(timeline, id, dplyr::contains)

obj_date <- check(obj_date, df)

Check the dataset

Description

Check the dataset

Usage

## Default S3 method:
check(obj, dataset)

Arguments

obj

An object for check.
dataset

A dataset, a type is a data frame.

Value

An object with a check result.

Examples

id <- c("01", "02", "03")
screen_date_E1 <- c("1991-03-13", "1991-03-07", "1991-03-08")
rand_date_E2 <- c("1991-03-15", "1991-03-11", "1991-03-10")
ph_date_E3 <- c("1991-03-21", "1991-03-16", "1991-03-16")
bio_date_E3 <- c("1991-03-23", "1991-03-16", "1991-03-16")

df <- data.frame(id, screen_date_E1, rand_date_E2, ph_date_E3, bio_date_E3,
  stringsAsFactors = FALSE
)

timeline <- system.file("dates.xlsx", package = "dmtools")
obj_date <- date(timeline, id, dplyr::contains)

obj_date <- check(obj_date, df)

Filter the final result

Description

Filter the final result

Usage

choose_test(obj, test, group_id)

Arguments

obj

An object for check.
test

Parameters, which use to filter the final dataset.
group_id

A logical scalar, default is TRUE.True is grouped by id, otherwise, it isn't grouped.

Value

The filtered dataset.

Examples

id <- c("01", "02", "03")
screen_date_E1 <- c("1991-03-13", "1991-03-07", "1991-03-08")
rand_date_E2 <- c("1991-03-15", "1991-03-11", "1991-03-10")
ph_date_E3 <- c("1991-03-21", "1991-03-16", "1991-03-16")
bio_date_E3 <- c("1991-03-23", "1991-03-16", "1991-03-16")

df <- data.frame(id, screen_date_E1, rand_date_E2, ph_date_E3, bio_date_E3,
  stringsAsFactors = FALSE
)

timeline <- system.file("dates.xlsx", package = "dmtools")
obj_date <- date(timeline, id, dplyr::contains)

obj_date <- check(obj_date, df)
choose_test(obj_date, "out")

Filter the final result of the object date

Description

Filter the final result of the object date

Usage

## S3 method for class 'date'
choose_test(obj, test = "out", group_id = T)

Arguments

obj

An object for calculation. Class date.
test

A character scalar. Parameters, which use to filter the final dataset, default: "out": "out" - dates, which are out of the protocol's timeline, "uneq" - dates, which are unequal, "ok" - correct dates, "skip" - empty dates.
group_id

A logical scalar, default is TRUE.True is grouped by id, otherwise, it isn't grouped.

Value

The dataset by a value of test.

Examples

id <- c("01", "02", "03")
screen_date_E1 <- c("1991-03-13", "1991-03-07", "1991-03-08")
rand_date_E2 <- c("1991-03-15", "1991-03-11", "1991-03-10")
ph_date_E3 <- c("1991-03-21", "1991-03-16", "1991-03-16")
bio_date_E3 <- c("1991-03-23", "1991-03-16", "1991-03-16")

df <- data.frame(id, screen_date_E1, rand_date_E2, ph_date_E3, bio_date_E3,
  stringsAsFactors = FALSE
)

timeline <- system.file("dates.xlsx", package = "dmtools")
obj_date <- date(timeline, id, dplyr::contains)

obj_date <- check(obj_date, df)
choose_test(obj_date, "out")

Filter the final result of the object lab

Description

Filter the final result of the object lab

Usage

## S3 method for class 'lab'
choose_test(obj, test = "mis", group_id = T)

Arguments

obj

An object. Class lab.
test

A character scalar. Parameters, which use to filter the final dataset, default: "mis": "ok" - analysis, which has a correct estimate of the result, "mis" - analysis, which has an incorrect estimate of the result, "skip" - analysis, which has an empty value of the estimate, "null" - analysis, which has an empty result and value of the estimate.
group_id

A logical scalar, default is TRUE.True is grouped by id, otherwise, it isn't grouped.

Value

The filtered dataset by a value of test.

Examples

ID <- c("01", "02", "03")
SITE <- c("site 01", "site 02", "site 03")
AGE <- c("19", "20", "22")
SEX <- c("f", "m", "f")
GLUC_V1 <- c(5.5, 4.1, 9.7)
GLUC_IND_V1 <- c("norm", "no", "cl")
AST_V2 <- c("30", "48", "31")
AST_IND_V2 <- c(NA, "norm", "norm")

df <- data.frame(
  ID, SITE, AGE, SEX,
  GLUC_V1, GLUC_IND_V1,
  AST_V2, AST_IND_V2,
  stringsAsFactors = FALSE
)

refs <- system.file("labs_refer.xlsx", package = "dmtools")
obj_lab <- lab(refs, ID, AGE, SEX, "norm", "no")

obj_lab <- check(obj_lab, df)
choose_test(obj_lab, "mis")

For creating part of the specification

Description

For creating part of the specification

Usage

create_spec(df_spec, all_colname, part_spec, is_pst)

Arguments

df_spec

A dataset, a type is a data frame.
all_colname

A character vector with all names in the dataset.
part_spec

A character scalar. Prefixes or postfixes.
is_pst

A logical scalar, default is TRUE. True is postfix, otherwise, prefix.

Value

A data frame. Part of the specification.

Create object date

Description

Create object date

Usage

date(file, id, get_visit, get_date = dplyr::contains, str_date = "DAT")

Arguments

file

A character scalar. Path to the date's parameters in the excel table.
id

A column name of the subject id in the dataset, without quotes.
get_visit

A function, which select necessary visit or event e.g. dplyr::start_with, dplyr::contains.
get_date

A function, which select dates from necessary visit e.g. dplyr::matches, dplyr::contains, default: dplyr::contains.
str_date

A date's pattern in column names, default: "DAT".

Value

The object date.

Examples

obj_date <- date("dates.xlsx", id, dplyr::contains)
obj_date <- date("dates.xlsx", id, dplyr::contains, "uneq")

dmtools: package to validate data

Description

for checking the dataset from EDC in clinical trials

Find column names

Description

Find column names

Usage

find_colnames(obj, dataset, row_file)

Arguments

obj

An object for check.
dataset

A dataset, a type is a data frame.
row_file

A row of the file.

Value

A data frame. Result of run_tests.

Find column names with dates

Description

Find column names with dates

Usage

## S3 method for class 'date'
find_colnames(obj, dataset, row_file)

Arguments

obj

An object for validation.
dataset

A data frame. Class date.
row_file

A data frame. A data frame with analysis parameters.

Value

A data frame. Visit's dates.

Find column names

Description

Find column names

Usage

## Default S3 method:
find_colnames(obj, dataset, row_file)

Arguments

obj

An object for validation.
dataset

A dataset, a type is a data frame.
row_file

A row of the file.

Value

A data frame. Result of run_tests.

Get the final result of the check

Description

Get the final result of the check

Usage

get_result(obj, group_id = T)

Arguments

obj

An object. Can be all classes: short, lab, date.
group_id

A logical scalar, default is TRUE.True is grouped by id, otherwise, it isn't grouped.

Value

A data frame. The final result.

Examples

id <- c("01", "02", "03")
site <- c("site 01", "site 02", "site 03")
sex <- c("f", "m", "f")
preg_yn_e2 <- c("y", "y", "y")
preg_res_e2 <- c("neg", "neg", "neg")
preg_yn_e3 <- c("y", "y", "n")
preg_res_e3 <- c("neg", "pos", "unnes")

df <- data.frame(
  id, site, sex,
  preg_yn_e2, preg_res_e2,
  preg_yn_e3, preg_res_e3,
  stringsAsFactors = FALSE
)

preg <- system.file("preg.xlsx", package = "dmtools")
obj_short <- short(preg, id, "LBORRES", c("site", "sex"))

obj_short <- check(obj_short, df)
get_result(obj_short)

Create object lab

Description

Create object lab

Usage

lab(
  file,
  id,
  age,
  sex,
  normal,
  abnormal,
  is_post = T,
  name_to_find = "LBNRIND"
)

Arguments

file

A character scalar. Path to the laboratory's reference in the excel table.
id

A column name of the subject id in the dataset, without quotes.
age

A column name of the subject age in the dataset, without quotes.
sex

A column name of the subject sex in the dataset, without quotes.
normal

A normal estimate, for example, "NORMAL".
abnormal

An abnormal estimate, for example, "ABNORMAL".
is_post

A logical scalar, default is TRUE. True is postfix, otherwise, prefix.
name_to_find

A character scalar. For search prefixes or postfixes, default is "LBNRIND".

Value

The object lab.

Examples

obj_lab <- lab("lab_refer.xlsx", ID, AGE, SEX, 1, 2)
obj_lab <- lab("lab_refer.xlsx", ID, AGE, SEX, "NORMAL", "ABNORMAL")
obj_lab <- lab("lab_refer.xlsx", ID, AGE, SEX, "norm", "no", FALSE)

A list to a tibble.

Description

A list to a tibble.

Usage

list_parse(to_tibble)

Arguments

to_tibble

A list with nested lists.

Value

A tibble.

Examples

temp_list <- list(list(a = 1, b = 3), list(a = 4, b = 5))
list_parse(temp_list)

Get the token

Description

Get the token

Usage

meddra_auth(target_url, meddra_id, api_key)

Arguments

target_url

The url for authenticate.
meddra_id

The user's meddra id.
api_key

The user's api key.

Value

A string scalar. The user's token.

Examples

## Not run: 
meddra_auth(url, id, key)

## End(Not run)

Create the post query

Description

Create the post query

Usage

meddra_post(target_url, json, token)

Arguments

target_url

The url for a post query.
json

A string scalar or a list. The json query.
token

The user's token.

Value

A list. The result of query.

Examples

## Not run: 
meddra_post(url, json_body, token)

## End(Not run)

For rename dataset

Description

For rename dataset

Usage

rename_dataset(
  dataset,
  path_crfs,
  no_readable_name,
  readable_name,
  num_sheet = 1,
  extension = "*.xlsx",
  is_post = T
)

Arguments

dataset

A dataset, a type is a data frame.
path_crfs

A character scalar. Path to the specification files the in excel table.
no_readable_name

A character scalar. A column name of no_readable values.
readable_name

A character scalar. A column name of readable values.
num_sheet

An integer scalar, default is the first sheet. A position of a sheet in the excel document.
extension

A character scalar. A extension of files, default is *.xlsx.
is_post

A logical scalar, default is TRUE. True is postfix, otherwise, prefix.

Value

The list with two values: data - renamed dataset, spec - common specification. The common specification is data frame of two values: no_readable_var, readable_var.

Examples

id <- c("01", "02", "03")
age <- c("19", "20", "22")
sex <- c("f", "m", "f")
bio_date_post <- c("1991-03-23", "1991-03-16", "1991-03-16")
gluc_post <- c("5.5", "4.1", "9.7")
gluc_res_post <- c("norm", "no", "norm")


df <- data.frame(
  id, age, sex,
  bio_date_post,
  gluc_post, gluc_res_post,
  stringsAsFactors = FALSE
)

crfs <- system.file("forms", package = "dmtools")

result <- rename_dataset(df, crfs, "old_name", "new_name")
result[["data"]]

Create object short

Description

Create object short

Usage

short(
  file,
  id,
  name_to_find,
  common_cols = NULL,
  extra = NULL,
  is_post = T,
  is_add_cols = F
)

Arguments

file

A character scalar. Path to the excel table.
id

A column name of the subject id in the dataset, without quotes.
name_to_find

A character scalar. For search prefixes or postfixes.
common_cols

A character vector. A column names in the dataset, which common for all events.
extra

A character scalar. For additional information.
is_post

A logical scalar, default is TRUE. True is postfix, otherwise, prefix.
is_add_cols

A logical scalar, default is FALSE. If necessary add columns.

Value

The object short.

Examples

obj_short <- short("preg.xlsx", id, "res", c("site", "sex"))
obj_short <- short("labs.xlsx", id, "name_labs", c("site"), "human_name")

Cast to double type

Description

Cast to double type

Usage

to_dbl(vals)

Arguments

vals

A character or double vector.

Value

A double vector.

Reshape the dataset to a long view

Description

Reshape the dataset to a long view

Usage

to_long(obj, dataset, row_file, part)

Arguments

obj

An object for check.
dataset

A data frame.
row_file

A data frame. A data frame with parameters.
part

A character scalar. Prefixes or postfixes.

Value

A data frame. The part of final result.

Reshape the dataset to a long view

Description

Reshape the dataset to a long view

Usage

## S3 method for class 'date'
to_long(obj, dataset, row_file, date)

Arguments

obj

An object for validation.
dataset

A data frame. Class date.
row_file

A data frame. A data frame with analysis parameters.
date

A column name with dates.

Value

A data frame. Result of the date's validation.

Reshape the dataset to a long view

Description

Reshape the dataset to a long view

Usage

## S3 method for class 'lab'
to_long(obj, dataset, row_file, part)

Arguments

obj

An object. Class lab.
dataset

A data frame.
row_file

A data frame. A data frame with parameters.
part

A character scalar. Prefixes or postfixes.

Value

A data frame. The part of the final result.

Reshape the dataset to a long view

Description

Reshape the dataset to a long view

Usage

## S3 method for class 'short'
to_long(obj, dataset, row_file, part)

Arguments

obj

An object. Class short.
dataset

A data frame.
row_file

A data frame. A data frame with parameters.
part

A character scalar. Prefixes or postfixes.

Value

A data frame. The part of the final result.