3  Understanding data types

library(FreesearchR)

Attaching package: 'FreesearchR'

The following object is masked from 'package:base':

    sort_by

i18n_path <- system.file("translations", package = "FreesearchR")
i18n <- shiny.i18n::Translator$new(translation_csvs_path = i18n_path)

Warning in load_local_config(translation_csv_config): You didn't specify config
translation yaml file. Default settings are used.

i18n$set_translation_language("en")

3.1 A clinical data class

Traditionally in R, data is identified by classes, like numeric, integer, double, logical, factor etc. These classes can be a little confusing from a clinical or operational standpoint. In the FreesearchR app, these classes has been simplified and modified to the following data types, that are assigned on a prioritised order like the following:

data_types() |> purrr::imap(\(.x,.i){
  dplyr::bind_cols("type"=.i,.x,.name_repair = "unique_quiet")
  }) |> dplyr::bind_rows() |> 
  setNames(c("Data type","Description","Data classes included")) |> 
  knitr::kable()

Data type	Description	Data classes included
empty	Variable of all NAs	Any class
monotone	Variable with only one unique value	Any class
dichotomous	Variable with only two unique values	Any class
categorical	Factor variable	factor (ordered or unordered)
text	Character variable	character
datetime	Variable of time, date or datetime values	hms, Date, POSIXct and POSIXt
continuous	Numeric variable	numeric, integer or double
unknown	Anything not falling within the previous	Any other class

Categorising data in this way makes sense when making choices on how to evaluate and analyse data. This is used throughout the FreesearchR app to simplify data handling.