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Extract the statistical summary generated by calcu for continuous data

Source: R/calcu_med.R
bind_cut_cont.Rd

Parse the statistical summary generated by calcu and generate the statistical results in standard format

Usage

bind_cut_cont(
  gt_table_body,
  aim_name,
  ifmiss = FALSE,
  ppop = NULL,
  iftest = TRUE
)

Arguments

gt_table_body

result of calcu calculation.

aim_name

extract the specified column name.

ifmiss

whether to retain the missing value in the output result,default is FALSE.

ppop

default is NULL. gt_table_body come from a subset of the total population? If you need to calculate the statistics of the subset in the total population, please provide a total population.

iftest

default is TRUE.Provide the proportion of the subset of patients to the total number of people, similar to the statistics of the number of patients in biochemical tests.

Value

A structured data set.A structured dataset. Contains the average and
proportion of continuous variables, as well as the number and proportion
of subsets after the classification of continuous variables.

Examples

if (FALSE) {
data(data_med)

lab_wider = data_med$lab  %>%
  tr(.,c("test_date"),"dat") %>%
  group_by(patient_id,lab_name) %>%
  arrange(test_date) %>%  slice_tail(n =1) %>%  ungroup() %>%
  select(patient_id,lab_name ,lab_va) %>%
  tr(.,c("lab_va"),"num") %>%
  spread(lab_name,lab_va) %>%  distinct()

HbA1c <- c(0,6.5,7.0,8.0,9.0,Inf)
TC <- c(0,5.2,6.2,Inf)
LDL <- c(0,3.4,4.1,Inf)
HDL <- c(0,1.0,Inf)
TG <- c(0,1.7,2.3,Inf)
WBC <- c(0,4,10,Inf)

## keep only the columns related to the analysis
lib_name_list <- names(lab_wider)[-1]
## this step need to adjust the order of list subsets in name_list order
list_cut <- list(HbA1c,HDL,LDL,TC,TG,WBC)
## mutate multiple split variable columns
lab_wider_cut = mmc(lab_wider,lib_name_list,list_cut,digits=2)
## Add a missing data to fully demonstrate the function of the function
lab_wider_cut = lab_wider_cut %>%
  rbind(.,
        matrix(NA,nrow = 1,ncol =dim(lab_wider_cut)[2]) %>% data.frame() %>%
          rename_at(vars(names(.)) ,~ names(lab_wider_cut)) ) %>%
  mutate(patient_id = replace_na(patient_id, "test_id"))

## faster descriptive statistics.
data_lab_calcu <- calcu(lab_wider_cut[,-1],names(lab_wider)[-1])

## Statistical results of a single indicator
bind_cut_cont_test = bind_cut_cont(data_lab_calcu,"HbA1c")

## Statistical results of multiple indicators
purrr::map_df(lib_name_list,bind_cut_cont,
              gt_table_body = data_lab_calcu,ppop=100)
}