#' Reshape dataset to wide format - tidytable version
#'
#' @param df dataframe
#' @param case_id_var String with name of ID variable indicating same patient.
#' E.g. \code{idvar="PUBCSNUM"} for SEER data.
#' @param time_id_var String with name of variable that indicates diagnosis per patient.
#' E.g. \code{timevar="SEQ_NUM"} for SEER data.
#' @param timevar_max Numeric; default 6. Maximum number of cases per id.
#' All tumors > timevar_max will be deleted before reshaping.
#' @param datsize Number of rows to be taken from df. This parameter is mainly for testing. Default is Inf so that df is fully processed.
#' @return wide_df
#' @export
#' @examples
#'
#' data(us_second_cancer)
#'
#' msSPChelpR::reshape_wide_tt(us_second_cancer,
#' case_id_var = "fake_id",
#' time_id_var = "SEQ_NUM",
#' timevar_max = 2,
#' datsize = 10000)
#'
reshape_wide_tt <- function(df, case_id_var, time_id_var, timevar_max = 6, datsize = Inf){
case_id_var <- rlang::ensym(case_id_var)
time_id_var <- rlang::ensym(time_id_var)
### restrict size of data.frame to datsize number of rows
if(nrow(df) > datsize){
df <- df[c(1:datsize), ]
}
### get names from df to provide to pivot function
trans_vars <- names(df)[!names(df) %in% c(rlang::as_name(case_id_var), rlang::as_name(time_id_var))]
### determine maximum number of cases per patient and deleting all cases > timevar_max
max_time <- max(as.numeric(df[[rlang::as_name(time_id_var)]]), na.rm = TRUE)
if(max_time > timevar_max){
rlang::inform(paste("Long dataset had too many cases per patient. Wide dataset is limited to ", timevar_max," cases per id as defined in timevar_max option."))
df <- df %>%
#sort by case_id and time_id_var
tidytable::arrange(!!case_id_var, !!time_id_var) %>%
#calculate new renumbered variable group by case_id_var
tidytable::mutate(counter = as.integer(tidytable::row_number()), .by = !!case_id_var) %>%
#filter based on new renumbered variable
tidytable::filter(counter <= timevar_max) %>%
tidytable::select(-counter)
max_time <- timevar_max
}
### prepare name order of cols with all vars per time_id sorted together
col_order <-
#create outer product of trans_vars and time_ids
outer(trans_vars, 1:max_time, paste, sep = ".") %>%
#make vector binding rows, one col after the other
c(.)
### perform pivot_wider
df %>% tidytable::pivot_wider(
names_from = {{time_id_var}},
values_from = tidyselect::all_of(trans_vars),
names_sep = "."
) %>%
#sort by case_id_var
tidytable::arrange(as.numeric(rlang::eval_tidy(!!case_id_var))) %>%
#order columns by old col order
tidytable::relocate(tidyselect::all_of(c(rlang::as_name(case_id_var),
col_order)))
}
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