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Merge pull request #13 from thomaszwagerman/naming_consistency
Moving common functionality to utility function
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,6 +1,7 @@ | ||
| # Generated by roxygen2: do not edit by hand | ||
|
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| export(catch) | ||
| export(create_object_list) | ||
| export(loupe) | ||
| export(release) | ||
| importFrom(lifecycle,deprecated) |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,139 @@ | ||
| #' create_object_list: creates a list of objects used in all butterfly functions | ||
| #' | ||
| #' This function creates a list of objects which is used by all of `loupe()`, | ||
| #' `catch()` and `release()`. | ||
| #' | ||
| #' This function matches two dataframe objects by their unique identifier | ||
| #' (usually "time" or "datetime in a timeseries). | ||
| #' | ||
| #' It informs the user of new (unmatched) rows which have appeared, and then | ||
| #' returns a `waldo::compare()` call to give a detailed breakdown of changes. | ||
| #' | ||
| #' The main assumption is that `df_current` and `df_previous` are a newer and | ||
| #' older versions of the same data, and that the `datetime_variable` variable name always | ||
| #' remains the same. Elsewhere new columns can of appear, and these will be | ||
| #' returned in the report. | ||
| #' | ||
| #' @param df_current data.frame, the newest/current version of dataset x. | ||
| #' @param df_previous data.frame, the old version of dataset, for example x - t1. | ||
| #' @param datetime_variable string, which variable to use as unique ID to join | ||
| #' `df_current` and `df_previous`. Usually a "datetime" variable. | ||
| #' | ||
| #' @returns A list containing boolean where TRUE indicates no changes to | ||
| #' previous data and FALSE indicates unexpected changes, a dataframe of | ||
| #' the current data without new rows and a dataframe of new rows only | ||
| #' | ||
| #' @examples | ||
| #' butterfly_object_list <- butterfly::create_object_list( | ||
| #' butterflycount$february, | ||
| #' butterflycount$january, | ||
| #' datetime_variable = "time" | ||
| #' ) | ||
| #' | ||
| #' butterfly_object_list | ||
| #' | ||
| #' @export | ||
| create_object_list <- function(df_current, df_previous, datetime_variable) { | ||
| # Check input is as expected | ||
| stopifnot("`df_current` must be a data.frame" = is.data.frame(df_current)) | ||
| stopifnot("`df_previous` must be a data.frame" = is.data.frame(df_previous)) | ||
|
|
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| # Check if `datetime_variable` is in both `df_current` and `df_previous` | ||
| if (!datetime_variable %in% names(df_current) || !datetime_variable %in% names(df_previous)) { | ||
| stop( | ||
| "`datetime_variable` must be present in both `df_current` and `df_previous`" | ||
| ) | ||
| } | ||
|
|
||
| # Initialise list to store objects used by `loupe()`, `catch()` and `release()` | ||
| list_butterfly <- list( | ||
| "waldo_object" = character(), | ||
| "df_current_without_new_row" = data.frame(), | ||
| "df_current_new_rows" = data.frame() | ||
| ) | ||
|
|
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| # Using semi_join to extract rows with matching datetime_variables | ||
| # (ie previously generated data) | ||
| df_current_without_new_row <- dplyr::semi_join( | ||
| df_current, | ||
| df_previous, | ||
| by = datetime_variable | ||
| ) | ||
|
|
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| # Obtaining the new rows to provide in feedback | ||
| df_current_new_rows <- dplyr::anti_join( | ||
| df_current, | ||
| df_previous, | ||
| by = datetime_variable | ||
| ) | ||
|
|
||
| # Compare the current data with the previous data, without "new" values | ||
| waldo_object <- waldo::compare( | ||
| df_current_without_new_row, | ||
| df_previous | ||
| ) | ||
|
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| # Creating a feedback message depending on the waldo object's output | ||
| # First checking if there are new rows at all: | ||
| if (nrow(df_current_new_rows) == 0) { | ||
| stop( | ||
| "There are no new rows. Check '", | ||
| deparse(substitute(df_current)), | ||
| "' is your most recent data, and '", | ||
| deparse(substitute(df_previous)), | ||
| "' is your previous data. If comparing like for like, try waldo::compare()." | ||
| ) | ||
| } else { | ||
| # Tell the user which rows are new, regardless of previous data changing | ||
| cli::cat_line( | ||
| "The following rows are new in '", | ||
| deparse(substitute(df_current)), | ||
| "': ", | ||
| col = "green" | ||
| ) | ||
| cli::cat_print( | ||
| df_current_new_rows | ||
| ) | ||
| } | ||
|
|
||
| # Return a simple message if there are no changes in previous data | ||
| if (length(waldo_object) == 0) { | ||
| cli::cat_bullet( | ||
| "And there are no differences with previous data.", | ||
| bullet = "tick", | ||
| col = "green", | ||
| bullet_col = "green" | ||
| ) | ||
|
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| butterfly_status <- TRUE | ||
|
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| } else { | ||
| # Return detailed breakdown and warning if previous data have changed. | ||
| if (length(waldo_object) > 0) { | ||
| cli::cat_line() | ||
|
|
||
| cli::cat_bullet( | ||
| "The following values have changes from the previous data.", | ||
| bullet = "info", | ||
| col = "orange", | ||
| bullet_col = "orange" | ||
| ) | ||
|
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| cli::cat_print( | ||
| waldo_object | ||
| ) | ||
|
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| butterfly_status <- FALSE | ||
|
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| } | ||
| } | ||
|
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| # Populate list with objects | ||
| list_butterfly <- list( | ||
| butterfly_status = butterfly_status, | ||
| df_current_without_new_row = df_current_without_new_row, | ||
| df_current_new_rows = df_current_new_rows | ||
| ) | ||
|
|
||
| return(list_butterfly) | ||
| } |
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