S7 objects introduction

NAMESPACE

@@ -8,17 +8,22 @@ export(clean_file_names)
 export(convert_dots_to_string)
 export(convert_input_to_string)
 export(count_plus)
+export(cp)
 export(create_contonso_duckdb)
 export(cte)
+export(d)
 export(divide)
+export(dod)
 export(dod_dbi)
+export(factor)
 export(is_yyyy_mm_dd)
 export(make_aggregation_dbi)
 export(make_aggregation_tbl)
 export(make_cohort_tbl)
 export(make_segmentation)
 export(mom)
 export(mom_dbi)
+export(pp)
 export(seq_date_sql)
 export(show_in_excel)
 export(sql_query_select)

R/clean_file_names.R

@@ -12,6 +12,21 @@
 #' clean_file_names()
 clean_file_names <- function(file_path,...){
 
+
+  # Assert that the directory exists
+  assertthat::assert_that(
+    fs::dir_exists(dir_path),
+    msg = cli::format_error("Directory {dir_path} does not exist.")
+  )
+
+  # Assert that the directory contains files
+
+  assertthat::assert_that(
+    length(files) > 0,
+    msg = cli::format_error("No files found in directory {dir_path}.")
+  )
+
+
 fp <- file_path
 
   old_names <- list.files(fp,full.names = TRUE)

R/divide.R

@@ -14,14 +14,22 @@
 #' @export
 #'
 #' @examples
-#' divide(mtcars,new_col_name=div_col,numerator=mpg,denominator=0,alternative_result=10)
-divide <- function(.data,new_col_name,numerator,denominator,alternative_result=NA_integer_){
+#' data(mtcars)
+#' mtcars |> divide(new_col_name=div_col,numerator=mpg,denominator=0,alternative_result=10)
+divide <- function(.data,new_col_name,numerator_col,denominator_col,alternative_result=NA_integer_){
 
 
+  assertthat::assert_that(
+    is.numeric(alternative_result),
+    msg = cli::format_error(c(
+      "x" = "Alternative result must be numeric.",
+      "!" = "You provided a value of class {.cls {class(alternative_result)}}."
+    ))
+  )
 
   out <- .data |>
     dplyr::mutate(
-      "{{new_col_name}}":=dplyr::if_else(base::is.na({{numerator}}/{{denominator}})|base::is.infinite({{numerator}}/{{denominator}}),alternative_result,{{numerator}}/{{denominator}})
+      "{{new_col_name}}":=dplyr::if_else(base::is.na({{numerator_col}}/{{denominator_col}})|base::is.infinite({{numerator_col}}/{{denominator_col}}),alternative_result,{{numerator_col}}/{{denominator_col}})
     )
 
   base::return(out)

R/time_intelligence.R

@@ -306,7 +306,7 @@ make_aggregation_dbi <- function(.data, ..., date_var, value_var, time_unit){
 
 
 
-#
+
 # calculate <- function(.data,...,date_var,value_var,time_unit){
 #
 #   # Validate inputs

R/utils-misc.R

@@ -8,7 +8,12 @@
 #' @examples
 #' is_yyyy_mm_dd("2024-01-01")
 is_yyyy_mm_dd <- function(x) {
-  grepl("^\\d{4}-\\d{2}-\\d{2}$", x)
+
+out <-   suppressWarnings(!is.na(lubridate::ymd(x)))
+
+return(out)
+
+
 }
 
 
@@ -64,3 +69,40 @@ convert_dots_to_string <- function(...){
   return(group_var)
 
 }
+
+
+
+#' Converts tibble to csv file and opens up in excel
+#'
+#' @param .data tibble of data
+#'
+#' @return a temp csv file
+#' @export
+#'
+#' @examples
+#' mtcars |>  show_in_excel()
+show_in_excel <- function(.data){
+
+  # supporting arguments
+  rows      <- base::dim(.data)[1]
+  cols      <- base::dim(.data)[2]
+
+  # send message based on table row size
+
+  cli::cli_alert("data contains {rows} row(s) and {cols} column(s)")
+
+  # create temp file path to be used later
+  temp <- base::paste0(base::tempfile(),".csv")
+
+  # save the table as a csv file
+  utils::write.csv(.data,file = temp)
+
+  # open up the csv file
+  fs::file_show(path=temp)
+
+  # alert that successful
+  cli::cli_alert_success("success: temp file {temp}")
+
+}
+
+

README.qmd

@@ -3,6 +3,11 @@ title: "README"
 format: gfm
 ---
 
+ti.tbl_class # method
+make_aggregation_tbl # method
+
+
+
 # Developer Notes
 
 ## 2024-08-10
@@ -93,14 +98,30 @@ format: gfm
 
     -   eg. (lag_quanitity*price)+(price*quantity_prop)
 -   then have collector function eg. pvm() or something else take the columns names that are produced
--   Create the required columns based on the column logic eg lag_quanitty means mutate(lag_quantity=lag(quantity,1))
+-   Create the required columns based on the column logic eg lag_quantity means mutate(lag_quantity=lag(quantity,1))
 -   easier to capture controls and create the problems all at once
 -   now need to figure out to pass lists onto each other pp() to cp() should augment column names with each and then same from data object
 
-- so either we transfrom the dataframe as we go along eg each factor step mutates as we go along creating the columns
+- so either we transform the data frame as we go along eg each factor step mutates as we go along creating the columns
 - or factor step just passes along a list of formula and formula names to transform
 - I went down route B but realizing its easier with route A
 
+2024-09-30
+
+-   learnt about structure and how to pass instructions to data model for execution later on
+-   then factor will read the instructions apply to the formula, create the required columns and then apply them to the model   
+-   structure wont work because it goes away when you apply downstream functions
+-   but if I keep everything 
+
+2024-11-22
+
+-   need re do with S7-- plan out flow (4 hours)
+-   time intelligence (8 hours)
+-   factor ( 16 hours)
+-   mayble sql lm (8 hours)
+-   
+
+
 ## to do list
 
 -   finalize time intelligence functions
@@ -254,8 +275,3 @@ tibble::tribble(
 
 
 
-
-
-
-
-