R/checkDataFormat.R
In MathiasHarrer/metapsyTools: Several R Helper Functions For The "Metapsy" Database
Defines functions
checkDataFormat
Documented in
checkDataFormat
#' Check data format
#'
#'
#' This is a function to check the format of meta-analysis data with class \code{data.frame} for its further applicability to the \code{expandMultiarmTrials} function.
#'
#' @param data Meta-analysis data stored as a data.frame.
#' @param must.contain \code{character} vector, containing all the variable names the data set should contain.
#' @param variable.contains \code{list}, defining which values should only be contained in a variable.
#' @param variable.class \code{list}, defining the required data class for some or all variables. If the class differs, the function will try to convert the variable to the desired class.
#' @param groups.column.indicator \code{character}. If the dataset is in wide format: a character vector with two elements, representing the suffix used to
#' differentiate between the first and second treatment in a comparison.
#' @param data.format \code{character}. Either \code{"long"} or \code{"wide"}, depending on the format of the dataset in
#' \code{data}. \code{NULL} by default, which lets the user define the format after the function has been called.
#'
#' @usage checkDataFormat(data,
#' must.contain = c("study", "condition",
#' "Cond_spec", "is.multiarm",
#' "no.arms", "multiple_arms",
#' "Outc_measure", "Time", "primary",
#' "Time_weeks", "sr_clinician"),
#' variable.contains = list("condition" = c("ig", "cg"),
#' "is.multiarm" = c(0,1)),
#' variable.class = list("study" = "character",
#' "condition" = "character",
#' "Cond_spec" = "character",
#' "is.multiarm" = "numeric",
#' "no.arms" = "numeric",
#' "multiple_arms" = "character",
#' "primary" = "numeric",
#' "Time" = "character",
#' "Time_weeks" = "character",
#' "sr_clinician" = "character",
#' "Post_M" = "numeric",
#' "Post_SD" = "numeric",
#' "Post_N" = "numeric",
#' "Rand_N" = "numeric",
#' "Improved_N" = "numeric",
#' "Change_m" = "numeric",
#' "Change_SD" = "numeric",
#' "Change_N" = "numeric"),
#' groups.column.indicator = c("_trt1", "_trt2"),
#' data.format = NULL)
#'
#' @return \code{checkDataFormat} returns messages that specify if input variables, values and classes of the variables are as defined.
#' @details The function ckecks if:
#' \itemize{
#' \item{the data set contains all relevant variables}
#' \item{variables contain desired values only and}
#' \item{variables are of desired type (if not, it tries to convert them).}
#' }
#' If the dataset format is wide, please specify all variable/column names
#' \emph{without} the group indicator suffix (e.g. for \code{"Cond_spec_trt1"} and
#' \code{"Cond_spec_trt2"}, simply use \code{"Cond_spec"}).
#'
#' @examples
#' \dontrun{
#' data("inpatients")
#'
#' # Example 1: Check data with default arguments
#' checkDataFormat(inpatients)
#'
#' #Example 2: Check for specified variables and corresponding values and classes
#' checkDataFormat(inpatients,
#' must.contain = c("study", "condition",
#' "primary", "year"),
#' variable.contains = list("condition" = c("ig","cg")),
#' variable.class = list(study = "character",
#' no.arms = "numeric"))
#'
#' #Example 3: Convert variable class as predefined
#' checkDataFormat(inpatients,
#' must.contain = c("primary", "study"),
#' variable.class = list(primary = "integer"))
#'
#' #Example 4: Wide format
#' data("psyCtrSubsetWide")
#' checkDataFormat(psyCtrSubsetWide)
#' }
#'
#'
#' @author Mathias Harrer \email{mathias.h.harrer@@gmail.com}, Paula Kuper \email{paula.r.kuper@@gmail.com}, Pim Cuijpers \email{p.cuijpers@@vu.nl}
#'
#' @seealso \code{\link{expandMultiarmTrials}}
#' @export checkDataFormat
#' @importFrom methods as
#' @importFrom stats dffits model.matrix rnorm rstudent
#' @importFrom utils combn
#' @importFrom stringr str_remove_all
checkDataFormat = function(data,
must.contain = c("study", "condition",
"Cond_spec", "is.multiarm",
"no.arms", "multiple_arms",
"Outc_measure", "Time", "primary",
"Time_weeks", "sr_clinician"),
variable.contains = list("condition" = c("ig", "cg"),
"is.multiarm" = c(0,1)),
variable.class = list("study" = "character",
"condition" = "character",
"Cond_spec" = "character",
"is.multiarm" = "numeric",
"no.arms" = "numeric",
"multiple_arms" = "character",
"primary" = "numeric",
"Time" = "character",
"Time_weeks" = "character",
"sr_clinician" = "character",
"Post_M" = "numeric",
"Post_SD" = "numeric",
"Post_N" = "numeric",
"Rand_N" = "numeric",
"Improved_N" = "numeric",
"Change_m" = "numeric",
"Change_SD" = "numeric",
"Change_N" = "numeric"),
groups.column.indicator = c("_trt1", "_trt2"),
data.format = NULL){
if (is.null(data.format)){
# Format switch
input = readline("Enter: Is the data set in long [l] or wide [w] format? ")
if (input[1] %in% c("l", "L", "long", "Long")){
format = "long"
data.format = "long"
} else if (input[1] %in% c("w", "W", "wide", "Wide")){
format = "wide"
data.format = "wide"
} else {
stop("Data format must either be long [l] or wide [w].")
}
}
if (!(data.format[1] %in% c("long", "wide"))){
# Format switch
input = readline("Enter: Is the data set in long [l] or wide [w] format? ")
if (input[1] %in% c("l", "L", "long", "Long")){
format = "long"
} else if (input[1] %in% c("w", "W", "wide", "Wide")){
format = "wide"
} else {
stop("Data format must either be long [l] or wide [w].")
}
} else {format = data.format}
if (format[1] == "wide"){
# 0. Get unique colnames without group indicators
stringr::str_remove_all(
colnames(data),
paste0(groups.column.indicator[1],
"|", groups.column.indicator[2])) %>%
unique() -> colNames
# 1. Check if data set contains all relevant variables.
if (length(must.contain) > 0){
if (sum(colNames %in% must.contain) < length(must.contain)){
must.contain[which(!must.contain %in% colNames)] -> miss
message(paste0("! data set does not contain variable(s) ",
paste(miss, collapse = ", "), "."))
} else {
message("- [OK] data set contains all variables in 'must.contain'.")
}
}
# 2. Check if variables only contain desired values.
if (length(variable.contains) > 0){
issue = FALSE
issueList = list()
for (i in 1:length(variable.contains)){
x = unique(data[colnames(data) %in%
c(names(variable.contains)[i],
paste0(names(variable.contains)[i],
groups.column.indicator))]) %>%
as.list() %>% do.call(c, .)
if (length(x) == length(variable.contains[[i]]) &
sum(x %in% variable.contains[[i]]) == length(x)){
issueList[[i]] = 0
} else {
issue = TRUE
issueList[[i]] = 1
}
}
if (issue == TRUE){
message(paste0("[!] ",
paste(names(variable.contains)[issueList == 1],
collapse = ", "),
" not (only) contains the values specified in 'variable.contains'."))
} else {
message("- [OK] variables contain only the values specified in 'variable.contains'.")
}
}
# 3. Check if variables are of desired type; if not, try to convert
variable.class %>%
append(variable.class %>%
{names(.) = paste0(names(.),
groups.column.indicator[1]);.}) %>%
append(variable.class %>%
{names(.) = paste0(names(.),
groups.column.indicator[2]);.}) %>%
{.[names(.) %in% colnames(data)]} -> variable.class
if (length(variable.class) > 0){
for (i in 1:length(variable.class)){
if (class(data[[names(variable.class)[i]]]) == variable.class[[i]]){
message(paste0("- [OK] '", names(variable.class)[i], "' has desired class ",
variable.class[[i]], "."))
} else {
try({
data[[names(variable.class)[i]]] = as(data[[names(variable.class)[i]]],
variable.class[[i]])},
silent = TRUE) -> try.convert
message(paste0("- [OK] '", names(variable.class)[i], "' has been converted to class ",
variable.class[[i]], "."))
}
}
}
class(data) = c("wide", "data.frame")
}
if (format[1] == "long") {
# 1. Check if data set contains all relevant variables.
if (length(must.contain) > 0){
if (sum(colnames(data) %in% must.contain) < length(must.contain)){
must.contain[which(!must.contain %in% colnames(data))] -> miss
message(paste0("! data set does not contain variable(s) ",
paste(miss, collapse = ", "), "."))
} else {
message("- [OK] data set contains all variables in 'must.contain'.")
}
}
# 2. Check if variables only contain desired values.
if (length(variable.contains) > 0){
issue = FALSE
issueList = list()
for (i in 1:length(variable.contains)){
x = unique(data[[names(variable.contains)[i]]])
if (length(x) == length(variable.contains[[i]]) &
sum(x %in% variable.contains[[i]]) == length(x)){
issueList[[i]] = 0
} else {
issue = TRUE
issueList[[i]] = 1
}
}
if (issue == TRUE){
message(paste0("[!] ",
paste(names(variable.contains)[issueList == 1],
collapse = ", "),
" not (only) contains the values specified in 'variable.contains'."))
} else {
message("- [OK] variables contain only the values specified in 'variable.contains'.")
}
}
# 3. Check if variables are of desired type; if not, try to convert
if (length(variable.class) > 0){
for (i in 1:length(variable.class)){
if (class(data[[names(variable.class)[i]]]) == variable.class[[i]]){
message(paste0("- [OK] '", names(variable.class)[i], "' has desired class ",
variable.class[[i]], "."))
} else {
try({
data[[names(variable.class)[i]]] = as(data[[names(variable.class)[i]]],
variable.class[[i]])},
silent = TRUE) -> try.convert
message(paste0("- [OK] '", names(variable.class)[i], "' has been converted to class ",
variable.class[[i]], "."))
}
}
}
class(data) = c("long", "data.frame")
}
return(data)
}
MathiasHarrer/metapsyTools documentation built on May 1, 2022, 5:14 p.m.
R Package Documentation
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Defines functions checkDataFormat
Documented in checkDataFormat
#' Check data format
#'
#'
#' This is a function to check the format of meta-analysis data with class \code{data.frame} for its further applicability to the \code{expandMultiarmTrials} function.
#'
#' @param data Meta-analysis data stored as a data.frame.
#' @param must.contain \code{character} vector, containing all the variable names the data set should contain.
#' @param variable.contains \code{list}, defining which values should only be contained in a variable.
#' @param variable.class \code{list}, defining the required data class for some or all variables. If the class differs, the function will try to convert the variable to the desired class.
#' @param groups.column.indicator \code{character}. If the dataset is in wide format: a character vector with two elements, representing the suffix used to
#' differentiate between the first and second treatment in a comparison.
#' @param data.format \code{character}. Either \code{"long"} or \code{"wide"}, depending on the format of the dataset in
#' \code{data}. \code{NULL} by default, which lets the user define the format after the function has been called.
#'
#' @usage checkDataFormat(data,
#' must.contain = c("study", "condition",
#' "Cond_spec", "is.multiarm",
#' "no.arms", "multiple_arms",
#' "Outc_measure", "Time", "primary",
#' "Time_weeks", "sr_clinician"),
#' variable.contains = list("condition" = c("ig", "cg"),
#' "is.multiarm" = c(0,1)),
#' variable.class = list("study" = "character",
#' "condition" = "character",
#' "Cond_spec" = "character",
#' "is.multiarm" = "numeric",
#' "no.arms" = "numeric",
#' "multiple_arms" = "character",
#' "primary" = "numeric",
#' "Time" = "character",
#' "Time_weeks" = "character",
#' "sr_clinician" = "character",
#' "Post_M" = "numeric",
#' "Post_SD" = "numeric",
#' "Post_N" = "numeric",
#' "Rand_N" = "numeric",
#' "Improved_N" = "numeric",
#' "Change_m" = "numeric",
#' "Change_SD" = "numeric",
#' "Change_N" = "numeric"),
#' groups.column.indicator = c("_trt1", "_trt2"),
#' data.format = NULL)
#'
#' @return \code{checkDataFormat} returns messages that specify if input variables, values and classes of the variables are as defined.
#' @details The function ckecks if:
#' \itemize{
#' \item{the data set contains all relevant variables}
#' \item{variables contain desired values only and}
#' \item{variables are of desired type (if not, it tries to convert them).}
#' }
#' If the dataset format is wide, please specify all variable/column names
#' \emph{without} the group indicator suffix (e.g. for \code{"Cond_spec_trt1"} and
#' \code{"Cond_spec_trt2"}, simply use \code{"Cond_spec"}).
#'
#' @examples
#' \dontrun{
#' data("inpatients")
#'
#' # Example 1: Check data with default arguments
#' checkDataFormat(inpatients)
#'
#' #Example 2: Check for specified variables and corresponding values and classes
#' checkDataFormat(inpatients,
#' must.contain = c("study", "condition",
#' "primary", "year"),
#' variable.contains = list("condition" = c("ig","cg")),
#' variable.class = list(study = "character",
#' no.arms = "numeric"))
#'
#' #Example 3: Convert variable class as predefined
#' checkDataFormat(inpatients,
#' must.contain = c("primary", "study"),
#' variable.class = list(primary = "integer"))
#'
#' #Example 4: Wide format
#' data("psyCtrSubsetWide")
#' checkDataFormat(psyCtrSubsetWide)
#' }
#'
#'
#' @author Mathias Harrer \email{mathias.h.harrer@@gmail.com}, Paula Kuper \email{paula.r.kuper@@gmail.com}, Pim Cuijpers \email{p.cuijpers@@vu.nl}
#'
#' @seealso \code{\link{expandMultiarmTrials}}
#' @export checkDataFormat
#' @importFrom methods as
#' @importFrom stats dffits model.matrix rnorm rstudent
#' @importFrom utils combn
#' @importFrom stringr str_remove_all
checkDataFormat = function(data,
must.contain = c("study", "condition",
"Cond_spec", "is.multiarm",
"no.arms", "multiple_arms",
"Outc_measure", "Time", "primary",
"Time_weeks", "sr_clinician"),
variable.contains = list("condition" = c("ig", "cg"),
"is.multiarm" = c(0,1)),
variable.class = list("study" = "character",
"condition" = "character",
"Cond_spec" = "character",
"is.multiarm" = "numeric",
"no.arms" = "numeric",
"multiple_arms" = "character",
"primary" = "numeric",
"Time" = "character",
"Time_weeks" = "character",
"sr_clinician" = "character",
"Post_M" = "numeric",
"Post_SD" = "numeric",
"Post_N" = "numeric",
"Rand_N" = "numeric",
"Improved_N" = "numeric",
"Change_m" = "numeric",
"Change_SD" = "numeric",
"Change_N" = "numeric"),
groups.column.indicator = c("_trt1", "_trt2"),
data.format = NULL){
if (is.null(data.format)){
# Format switch
input = readline("Enter: Is the data set in long [l] or wide [w] format? ")
if (input[1] %in% c("l", "L", "long", "Long")){
format = "long"
data.format = "long"
} else if (input[1] %in% c("w", "W", "wide", "Wide")){
format = "wide"
data.format = "wide"
} else {
stop("Data format must either be long [l] or wide [w].")
}
}
if (!(data.format[1] %in% c("long", "wide"))){
# Format switch
input = readline("Enter: Is the data set in long [l] or wide [w] format? ")
if (input[1] %in% c("l", "L", "long", "Long")){
format = "long"
} else if (input[1] %in% c("w", "W", "wide", "Wide")){
format = "wide"
} else {
stop("Data format must either be long [l] or wide [w].")
}
} else {format = data.format}
if (format[1] == "wide"){
# 0. Get unique colnames without group indicators
stringr::str_remove_all(
colnames(data),
paste0(groups.column.indicator[1],
"|", groups.column.indicator[2])) %>%
unique() -> colNames
# 1. Check if data set contains all relevant variables.
if (length(must.contain) > 0){
if (sum(colNames %in% must.contain) < length(must.contain)){
must.contain[which(!must.contain %in% colNames)] -> miss
message(paste0("! data set does not contain variable(s) ",
paste(miss, collapse = ", "), "."))
} else {
message("- [OK] data set contains all variables in 'must.contain'.")
}
}
# 2. Check if variables only contain desired values.
if (length(variable.contains) > 0){
issue = FALSE
issueList = list()
for (i in 1:length(variable.contains)){
x = unique(data[colnames(data) %in%
c(names(variable.contains)[i],
paste0(names(variable.contains)[i],
groups.column.indicator))]) %>%
as.list() %>% do.call(c, .)
if (length(x) == length(variable.contains[[i]]) &
sum(x %in% variable.contains[[i]]) == length(x)){
issueList[[i]] = 0
} else {
issue = TRUE
issueList[[i]] = 1
}
}
if (issue == TRUE){
message(paste0("[!] ",
paste(names(variable.contains)[issueList == 1],
collapse = ", "),
" not (only) contains the values specified in 'variable.contains'."))
} else {
message("- [OK] variables contain only the values specified in 'variable.contains'.")
}
}
# 3. Check if variables are of desired type; if not, try to convert
variable.class %>%
append(variable.class %>%
{names(.) = paste0(names(.),
groups.column.indicator[1]);.}) %>%
append(variable.class %>%
{names(.) = paste0(names(.),
groups.column.indicator[2]);.}) %>%
{.[names(.) %in% colnames(data)]} -> variable.class
if (length(variable.class) > 0){
for (i in 1:length(variable.class)){
if (class(data[[names(variable.class)[i]]]) == variable.class[[i]]){
message(paste0("- [OK] '", names(variable.class)[i], "' has desired class ",
variable.class[[i]], "."))
} else {
try({
data[[names(variable.class)[i]]] = as(data[[names(variable.class)[i]]],
variable.class[[i]])},
silent = TRUE) -> try.convert
message(paste0("- [OK] '", names(variable.class)[i], "' has been converted to class ",
variable.class[[i]], "."))
}
}
}
class(data) = c("wide", "data.frame")
}
if (format[1] == "long") {
# 1. Check if data set contains all relevant variables.
if (length(must.contain) > 0){
if (sum(colnames(data) %in% must.contain) < length(must.contain)){
must.contain[which(!must.contain %in% colnames(data))] -> miss
message(paste0("! data set does not contain variable(s) ",
paste(miss, collapse = ", "), "."))
} else {
message("- [OK] data set contains all variables in 'must.contain'.")
}
}
# 2. Check if variables only contain desired values.
if (length(variable.contains) > 0){
issue = FALSE
issueList = list()
for (i in 1:length(variable.contains)){
x = unique(data[[names(variable.contains)[i]]])
if (length(x) == length(variable.contains[[i]]) &
sum(x %in% variable.contains[[i]]) == length(x)){
issueList[[i]] = 0
} else {
issue = TRUE
issueList[[i]] = 1
}
}
if (issue == TRUE){
message(paste0("[!] ",
paste(names(variable.contains)[issueList == 1],
collapse = ", "),
" not (only) contains the values specified in 'variable.contains'."))
} else {
message("- [OK] variables contain only the values specified in 'variable.contains'.")
}
}
# 3. Check if variables are of desired type; if not, try to convert
if (length(variable.class) > 0){
for (i in 1:length(variable.class)){
if (class(data[[names(variable.class)[i]]]) == variable.class[[i]]){
message(paste0("- [OK] '", names(variable.class)[i], "' has desired class ",
variable.class[[i]], "."))
} else {
try({
data[[names(variable.class)[i]]] = as(data[[names(variable.class)[i]]],
variable.class[[i]])},
silent = TRUE) -> try.convert
message(paste0("- [OK] '", names(variable.class)[i], "' has been converted to class ",
variable.class[[i]], "."))
}
}
}
class(data) = c("long", "data.frame")
}
return(data)
}
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