Package 'recodeflow'

Returns the name of the table for a table start variable

Description

Returns the name of the table for a table start variable

Usage

get_table_name(table_feeder_var)

Arguments

table_feeder_var

string The table variable start

Value

string The extracted table name

Examples

# Extract table names from table feeder variables
get_table_name("$table:lookup_codes") # Returns "lookup_codes"
get_table_name("$table:reference") # Returns "reference"
get_table_name("$table:values") # Returns "values"

---

Checks whether two values are equal including NA

Description

Compared to the base "==" operator in R, this function returns true if the two values are NA whereas the base "==" operator returns NA

Usage

is_equal(v1, v2)

Arguments

v1	variable 1
v2	variable 2

Value

boolean value of whether or not v1 and v2 are equal

Examples

is_equal(1,2)
# FALSE

is_equal(1,1)
# TRUE

1==NA
# NA

is_equal(1,NA)
# FALSE

NA==NA
# NA

is_equal(NA,NA)
# TRUE

---

label_data

Description

Attaches labels to the data_to_label to preserve metadata

Usage

label_data(label_list, data_to_label)

Arguments

label_list	the label list object that contains extracted labels from variable details
data_to_label	The data that is to be labeled

Value

Returns labeled data

---

The pbc dataset

Description

The pbc dataset

Usage

pbc

Format

A data frame with 418 observations and 20 variables.

id

case number

time

number of days between registration and the earlier of death, transplantation, or study analysis time

status

status at endpoint, 0/1/2 for censored, transplant, dead

trt

1/2/NA for D-penicillamine, placebo, or not randomized

age

age in years

sex

m/f

ascites

presence of ascites

hepato

presence of hepatomegaly or enlarged liver

spiders

blood vessel malformations in the skin

edema

0 no edema, 0.5 untreated or successfully treated, 1 edema despite diuretic therapy

bili

serum bilirubin (mg/dl)

chol

serum cholesterol (mg/dl)

albumin

serum albumin (g/dl)

copper

urine copper (ug/day)

alk.phos

alkaline phosphotase (U/liter)

ast

aspartate aminotransferase (U/ml)

trig

triglycerides (mg/dl)

platelet

platelet count

protime

standardised blood clotting time

stage

histologic stage of disease (1, 2, 3, or 4)

Source

https://cran.r-project.org/web/packages/survival/survival.pdf

---

Metadata for the pbc dataset using the DCIM standard

Description

Metadata for the pbc dataset using the DCIM standard

Usage

pbc_metadata

Format

A list containing DCMI metadata:

title

title

creator

creator

subject

subject

description

description

publisher

publisher

date

date

type

type

format

format

identifier

identifier

source

source

language

language

rights

rights

references

references

---

Variable details sheet for the pbc dataset

Description

Variable details sheet for the pbc dataset

Usage

pbc_variable_details

Format

A data frame with 69 rows and 16 columns:

variable

variable name

dummyVariable

dummy variable name

typeEnd

end type

databaseStart

database start

variableStart

variable start

typeStart

start type

recEnd

record end

recStart

record start

catLabel

category label

catLabelLong

category long label

numValidCat

number of valid categories (numeric)

units

logical indicating presence of units

notes

logical indicating presence of notes

catStartLabel

category start label

variableStartShortLabel

variable start short label

variableStartLabel

variable start label

---

Variables sheet for the pbc dataset

Description

Variables sheet for the pbc dataset

Usage

pbc_variables

Format

A data frame with 24 rows and 11 columns:

variable

variable name

label

variable label

labelLong

variable label long

subject

subject

section

section

variableType

variable type

databaseStart

database start

units

units

variableStart

variable start

notes

logical indicating presence of notes

description

logical indicating presence of description

---

Recode with Table

Description

Creates new variables by recoding variables in a dataset using the rules specified in a variables details sheet

Usage

rec_with_table(
  data,
  variables = NULL,
  database_name = NULL,
  variable_details = NULL,
  else_value = NA,
  append_to_data = FALSE,
  log = FALSE,
  notes = TRUE,
  var_labels = NULL,
  custom_function_path = NULL,
  attach_data_name = FALSE,
  id_role_name = NULL,
  name_of_environment_to_load = NULL,
  append_non_db_columns = FALSE,
  tables = list()
)

Arguments

data	A dataframe containing the variables to be recoded. Can also be a named list of dataframes.
variables	Character vector containing the names of the new variables to recode to or a dataframe containing a variables sheet.
database_name	A String containing the name of the database containing the original variables which should match up with a database from the databaseStart column in the variables details sheet. Should be a character vector if data is a named list where each vector item matches a name in the data list and also matches with a value in the databaseStart column of a variable details sheet.
variable_details	A dataframe containing the specifications for recoding.
else_value	Value (string, number, integer, logical or NA) that is used to replace any values that are outside the specified ranges (no rules for recoding).
append_to_data	Logical, if TRUE (default), the newly created variables will be appended to the original dataset.
log	Logical, if FALSE (default), a log containing information about the recoding will not be printed.
notes	Logical, if FALSE (default), will not print the content inside the 'Note“ column of the variable being recoded.
var_labels	labels vector to attach to variables in variables
custom_function_path	string containing the path to the file containing functions to run for derived variables. This file will be sourced and its functions loaded into the R environment.
attach_data_name	logical to attach name of database to end table
id_role_name	name for the role to be used to generate id column
name_of_environment_to_load	Name of package to load variables and variable_details from
append_non_db_columns	boolean determening if data not present in this cycle should be appended as NA
tables	named list of data.frame A list of reference tables that can be passed as parameters into the function for a derived variable

Details

The variable_details dataframe needs the following columns:

variable

Name of the new variable created. The name of the new variable can be the same as the original variable if it does not change the original variable definition

toType

type the new variable cat = categorical, cont = continuous

databaseStart

Names of the databases that the original variable can come from. Each database name should be seperated by a comma. For eg., "cchs2001_p, cchs2003_p,cchs2005_p,cchs2007_p"

variableStart

Names of the original variables within each database specified in the databaseStart column. For eg. , "cchs2001_p::RACA_6A,cchs2003_p::RACC_6A,ADL_01". The final variable specified is the name of the variable for all other databases specified in databaseStart but not in this column. For eg., ADL_01 would be the original variable name in the cchs2005_p and cchs2007_p databases.

fromType

variable type of start variable. cat = categorical or factor variable cont = continuous variable (real number or integer)

recTo

Value to recode to

recFrom

Value/range being recoded from

Each row in the variables details sheet encodes the rule for recoding value(s) of the original variable to a category in the new variable. The categories of the new variable are encoded in the recTo column and the value(s) of the original variable that recode to this new value are encoded in the recFrom column. These recode columns follow a syntax similar to the sjmisc::rec() function. Whereas in the sjmisc::rec() function the recoding rules are in one string, in the variables details sheet they are encoded over multiple rows and columns (recFrom an recTo). For eg., a recoding rule in the sjmisc function would like like "1=2;2=3" whereas in the variables details sheet this would be encoded over two rows with recFrom and recTo values of the first row being 1 and 2 and similarly for the second row it would be 2 and 3. The rules for describing recoding pairs are shown below:

recode pairs

Each recode pair is a row

multiple values

Multiple values from the old variable that should be recoded into a new category of the new variable should be separated with a comma. e.g., recFrom = "1,2"; recTo = 1

will recode values of 1 and 2 in the original variable to 1 in the new variable

value range

A value range is indicated by a colon, e.g. recFrom= "1:4"; recTo = 1 will recode all values from 1 to 4 into 1

min and max

minimum and maximum values are indicated by min (or lo) and max (or hi), e.g. recFrom = "min:4"; recTo = 1 will recode all values from the minimum value of the original variable to 4 into 1

"else"

All other values, which have not been specified yet, are indicated by else, e.g. recFrom = "else"; recTo = NA will recode all other values (not specified in other rows) of the original variable to "NA")

"copy"

the else token can be combined with copy, indicating that all remaining, not yet recoded values should stay the same (are copied from the original value), e.g. recFrom = "else"; recTo = "copy"

NA's

NA values are allowed both for the original and the new variable, e.g. recFrom "NA"; recTo = 1. or "recFrom = "3:5"; recTo = "NA" (recodes all NA into 1, and all values from 3 to 5 into NA in the new variable)

Value

a dataframe that is recoded according to rules in variable_details.

Examples

var_details <-
  data.frame(
    "variable" = c("time", rep("status", times = 3), rep("trt", times = 2), "age", rep("sex", times = 2), rep("ascites", times = 2), rep("hepato", times = 2), rep("spiders", times = 2), rep("edema", times = 3), "bili", "chol", "albumin", "copper", "alk.phos", "ast", "trig", "platelet", "protime", rep("stage", times = 4)),
    "dummyVariable" = c("NA", "status0", "status1","status2", "trt1","trt2","NA","sexM","sexF", "ascites0", "ascites1","hepato0","hepato1","spiders0","spiders1","edema0.0","edema0.5","edema1.0",rep("NA",times = 9), "stage1", "stage2","stage3","stage4"),
    "typeEnd" = c("cont", rep("cat", times = 3), rep("cat", times = 2), "cont", rep("cat", times = 2), rep("cat", times = 2), rep("cat", times = 2),rep("cat", times = 2), rep("cat", times = 3), rep("cont", times = 9), rep("cat", times = 4)),
    "databaseStart" = rep("tester1, tester2", times = 31),
    "variableStart" = c("[time]", rep("[status]", times = 3), rep("[trt]", times = 2), "[age]", rep("[sex]", times = 2), rep("[ascites]", times = 2), rep("[hepato]", times = 2), rep("[spiders]", times = 2), rep("[edema]", times = 3), "[bili]", "[chol]", "[albumin]", "[copper]", "[alk.phos]", "[ast]", "[trig]", "[platelet]", "[protime]", rep("[stage]", times = 4)),
    "typeStart" = c("cont", rep("cat", times = 3), rep("cat", times = 2), "cont", rep("cat", times = 2), rep("cat", times = 2), rep("cat", times = 2),rep("cat", times = 2), rep("cat", times = 3), rep("cont", times = 9), rep("cat", times = 4)),
    "recEnd" = c("copy", "0", "1","2", "1","2","copy","m","f", "0", "1","0","1","0","1","0.0","0.5","1.0",rep("copy",times = 9), "1", "2","3","4"),
    "catLabel" = c("", "status 0", "status 1","status 2", "trt 1","trt 2","","sex m","sex f", "ascites 0", "ascites 1","hepato 0","hepato 1","spiders 0","spiders 1","edema 0.0","edema 0.5","edema 1.0",rep("",times = 9), "stage 1", "stage 2","stage 3","stage 4"),
    "catLabelLong" = c("", "status 0", "status 1","status 2", "trt 1","trt 2","","sex m","sex f", "ascites 0", "ascites 1","hepato 0","hepato 1","spiders 0","spiders 1","edema 0.0","edema 0.5","edema 1.0",rep("",times = 9), "stage 1", "stage 2","stage 3","stage 4"),
    "recStart" = c("else", "0", "1","2", "1","2","else","m","f", "0", "1","0","1","0","1","0.0","0.5","1.0",rep("else",times = 9), "1", "2","3","4"),
    "catStartLabel" = c("", "status 0", "status 1","status 2", "trt 1","trt 2","","sex m","sex f", "ascites 0", "ascites 1","hepato 0","hepato 1","spiders 0","spiders 1","edema 0.0","edema 0.5","edema 1.0",rep("",times = 9), "stage 1", "stage 2","stage 3","stage 4"),
    "variableStartShortLabel" = c("time", rep("status", times = 3), rep("trt", times = 2), "age", rep("sex", times = 2), rep("ascites", times = 2), rep("hepato", times = 2), rep("spiders", times = 2), rep("edema", times = 3), "bili", "chol", "albumin", "copper", "alk.phos", "ast", "trig", "platelet", "protime", rep("stage", times = 4)),
    "variableStartLabel" = c("time", rep("status", times = 3), rep("trt", times = 2), "age", rep("sex", times = 2), rep("ascites", times = 2), rep("hepato", times = 2), rep("spiders", times = 2), rep("edema", times = 3), "bili", "chol", "albumin", "copper", "alk.phos", "ast", "trig", "platelet", "protime", rep("stage", times = 4)),
    "units" = rep("NA", times = 31),
    "notes" = rep("This is sample survival pbc data", times = 31)
  )
var_sheet <-
  data.frame(
    "variable" = c("time","status","trt", "age","sex","ascites","hepato", "spiders", "edema", "bili", "chol", "albumin", "copper", "alk.phos", "ast", "trig", "platelet", "protime", "stage"),
    "label" = c("time","status","trt", "age","sex","ascites","hepato", "spiders", "edema", "bili", "chol", "albumin", "copper", "alk.phos", "ast", "trig", "platelet", "protime", "stage"),
    "labelLong" = c("time","status","trt", "age","sex","ascites","hepato", "spiders", "edema", "bili", "chol", "albumin", "copper", "alk.phos", "ast", "trig", "platelet", "protime", "stage"),
    "section" = rep("tester", times=19),
    "subject" = rep("tester",times = 19),
    "variableType" = c("cont", "cat", "cat", "cont","cat", "cat", "cat","cat", "cat", rep("cont", times = 9), "cat"),
    "databaseStart" = rep("tester1, tester2", times = 19),
    "units" = rep("NA", times = 19),
    "variableStart" = c("[time]","[status]", "[trt]", "[age]", "[sex]", "[ascites]","[hepato]","[spiders]","[edema]", "[bili]", "[chol]", "[albumin]", "[copper]", "[alk.phos]", "[ast]", "[trig]", "[platelet]", "[protime]","[stage]")
  )
library(survival)
tester1 <- survival::pbc[1:209,]
tester2 <- survival::pbc[210:418,]
db_name1 <- "tester1"
db_name2 <- "tester2"

rec_sample1 <- rec_with_table(data = tester1,
variables = var_sheet,
variable_details = var_details,
database_name = db_name1)

rec_sample2 <- rec_with_table(data = tester2,
variables = var_sheet,
variable_details = var_details,
database_name = db_name2)

---

Vars selected by role

Description

Selects variables from variables sheet based on passed roles

Usage

select_vars_by_role(roles, variables)

Arguments

roles	a vector containing a single or multiple roles to match by
variables	the variables sheet containing variable info

Value

a vector containing the variable names that match the passed roles

---

Set Data Labels

Description

sets labels for passed database, Uses the names of final variables in variable_details/variables_sheet as well as the labels contained in the passed dataframes

Usage

set_data_labels(data_to_label, variable_details, variables_sheet = NULL)

Arguments

data_to_label	newly transformed dataset
variable_details	variable_details.csv
variables_sheet	variables.csv

Value

labeled data_to_label

---

Example variable details sheet for vignettes

Description

Example variable details sheet for vignettes

Usage

tester_variable_details

Format

A data frame with 69 rows and 16 columns:

variable

variable name

dummyVariable

dummy variable name

typeEnd

end type

databaseStart

database start

variableStart

variable start

typeStart

start type

recEnd

record end

recStart

record start

catLabel

category label

catLabelLong

category long label

numValidCat

number of valid categories (numeric)

units

logical indicating presence of units

notes

logical indicating presence of notes

catStartLabel

category start label

variableStartShortLabel

variable start short label

variableStartLabel

variable start label

---

Example variables sheet for vignettes

Description

Example variables sheet for vignettes

Usage

tester_variables

Format

A data frame with 24 rows and 11 columns:

variable

variable name

label

variable label

labelLong

variable label long

subject

subject

section

section

variableType

variable type

databaseStart

database start

units

units

variableStart

variable start

notes

logical indicating presence of notes

description

logical indicating presence of description

---