R/ExtractNewValues.R

In l-gorman/rhomis-R-package: Essential Processes for RHoMIS Data

#' Finding Unique Values
#'
#' A function to find the unique values for all of a given tibble.
#'
#' Rpackage file: ExtractNewValues.R
#'
#' @param data A tibble for which we hope to find all of the unique values
#'
#' @return A list of the unique values, NAs excluded
#' @export
#'
#' @examples
#' data <- tibble::tibble(
#'   crop_name_1 = c("maize", "cassava", "millet"),
#'   crop_name_2 = c("potato", NA, "cucumber")
#' )
#' # Expect the result c("maize", "cassava", "millet","potato", "cucumber")
find_unique_values <- function(data) {


    # Finding all of the unique values
    all_values <- as.character(unlist(lapply(data, function(x) unique(x))))
    # Removing NA column
    all_values <- all_values[!is.na(all_values)]
    all_values <- all_values[all_values != "other"]
    all_values <- all_values[all_values != "other1"]
    all_values <- all_values[all_values != "other2"]
    all_values <- all_values[all_values != "other3"]
    # Finding unique values from all the columns
    unique_values <- unique(all_values)
    return(unique_values)
}


#' Extract New Units
#'
#' A function for extracting any new values from a survey
#'
#' Rpackage file: ExtractNewValues.R
#'
#' @param data The whole data-set from which the units are to
#' be extracted from
#' @param loop_or_individual_column Options are: "loop", "column".
#' Specifying whether you want to extract new
#' values from a loop or a column.
#' @param column_pattern The pattern which is used to
#' identify the columns. For example if trying to
#' extract new crop names, these come in the
#' form "crop_name_1", "crop_name_2", "crop_name_3".
#' The pattern here would be "crop_name"
#' @param number_of_loops If loop_or_individual_column=="loop",
#' then here we specify how many loops to search for.
#' @param column_name If loop_or_individual_column=="column",
#' then here we specify which column to search for
#'
#' @return A list of the individual values for that value (excluding NA values)
#' @export
#'
#' @examples
#'
#' # Example code for a crop loop
#' column_pattern <- "crop_name"
#' number_of_loops <- 2
#' data <- tibble::tibble(
#'   crop_name_1 = c("maize", "cassava", "millet"),
#'   random_columns = c("maize", "cassava", "millet"),
#'   crop_name_2 = c("potato", NA, "cucumber")
#' )
#' result <- extract_new_values(data,
#'   loop_or_individual_column = "loop",
#'   column_pattern = "crop_name",
#'   number_of_loops = 2
#' )
#' # expected_result is: c("maize","cassava","millet","potato","cucumber")
#'
#' # Example code for an individual column
extract_new_values <- function(data, loop_or_individual_column = "loop", column_name = NA, column_pattern = NA, number_of_loops = NA) {
    # Extracting new values for a RHoMIS tibble
    if (loop_or_individual_column == "loop") {
        if (is.na(column_pattern)) {
            stop("You have selected to extract new values from loops. Please specify a 'column_pattern' for those loop.")
        }
        if (is.na(number_of_loops)) {
            stop("You have selected to extract new values from loops. Please specify a 'number_of_loops' for those loops.")
        }
        relevant_data <- data[, paste0(column_pattern, "_", 1:number_of_loops)]
        unique_values <- find_unique_values(relevant_data)
        return(unique_values)
    }

    if (loop_or_individual_column == "column") {
        if (is.na(column_name)) {
            stop("You have selected to extract new values from a specific column. Please specify a 'column_name'.")
        }

        if (column_name %in% colnames(data)) {
            relevant_data <- data[, column_name]
            unique_values <- find_unique_values(relevant_data)
        }
        if (column_name %in% colnames(data) == F) {
            return(c(NA))
        }
        return(unique_values)
    }
}

#' Find the Number of Loops and Extract new values
#'
#' Find the number of loops for a looped RHoMIS data
#' set.
#'
#' Rpackage file: ExtractNewValues.R
#'
#' @param data The dataset to extract the new values from
#' @param column_pattern The repeat column pattern e.g. "crop_repeat"
#'
#' @return
#' @export
#'
#' @examples
find_loop_number_and_extract_values <- function(data, column_pattern) {


    # Creating a pattern to search through the column names of the data
    regex_pattern <- paste0("^", column_pattern, "_[[:digit:]]") # Finding columns which start with "column_pattern_Integer"

    # Finding the relevant column names
    relevant_columns <- grep(regex_pattern, colnames(data), value = T)
    # The number of loops for this column
    number_of_loops <- length(relevant_columns)


    if (number_of_loops == 0) {
        return(c(NA))
    }

    if (number_of_loops > 0) {
        new_values <- extract_new_values(data, loop_or_individual_column = "loop", column_pattern = column_pattern, number_of_loops = number_of_loops)
    }
    return(new_values)
}



#' Extract New Core Units
#'
#' A function to extract the new values from a core
#' RHoMIS survey. Note that this function will not
#' work on any dataset which has modified the core
#' questions.
#'
#' Rpackage file: ExtractNewValues.R
#'
#' @param data The RHoMIS core dataset for
#' which we are hoping to extract the new values
#' @return A nested list of all of the unique
#' values for core RHoMIS values
#' @export
#'
#' @examples
#'
#'
#' data <- tibble::tibble(
#'   crop_name_1 = c("maize", "cassava"),
#'   crop_name_2 = c(NA, "cucumber"),
#'   random_crop_name_2 = c("blue", "green"),
#'   livestock_name_1 = c("cow", "chicken"),
#'   livestock_name_2 = c("pig", NA),
#'   crop_yield_units_1 = c("kg", "sacks"),
#'   crop_yield_units_2 = c("wheelbarrow", NA),
#'   crop_yield_units_other_1 = c("other1", NA),
#'   crop_yield_units_other_2 = c("other2", "other3"),
#'   crop_sold_price_quantityunits_1 = c(NA, "price1"),
#'   crop_sold_price_quantityunits_2 = c("price2", "price3"),
#'   crop_price_quantityunits_other_1 = c(NA, "crop_price_1"),
#'   crop_price_quantityunits_other_2 = c(NA, "crop_price_2"),
#'   crop_price_quantityunits_other_3 = c(NA, "crop_price_3"),
#'   crop_price_quantityunits_other_4 = c(NA, NA),
#'   unitland = c("acre", NA),
#'   areaunits_other = c("area1", "area2"),
#'   unitland_owned = c("unitland1", "unitland2"),
#'   unitland_rentin = c("renty1", "renty2"),
#'   unitland_rentout = c("rent1", "rent5"),
#'   milk_units_1 = c("milk1", NA),
#'   milk_units_2 = c("milk4", "milk5"),
#'   milk_amount_units_other_1 = c("milkoth1", "milkoth2"),
#'   milk_amount_units_other_2 = c(NA, "milkoth3"),
#'   milk_sold_price_timeunits_1 = c("mspt1", "mspt3"),
#'   milk_sold_price_timeunits_2 = c(NA, "mspt5"),
#'   milk_amount_time_units_other_1 = c("mspto1", NA),
#'   milk_amount_time_units_other_2 = c("mspto2", "mspto3"),
#'   bees_honey_production_units_1 = c("hnyprod1", "hnyprod2"),
#'   bees_honey_production_units_2 = c(NA, "hnyprod3"),
#'   bees_honey_production_units_other_1 = c("hnyprodoth1", NA),
#'   bees_honey_production_units_other_2 = c("hnyprodoth2", "hnyprodoth3"),
#'   eggs_units_1 = c("egg1", NA),
#'   eggs_units_2 = c("egg2", "egg3"),
#'   eggs_amount_units_other_1 = c("eggoth1", NA),
#'   eggs_amount_units_other_2 = c("eggoth2", "eggoth3"),
#'   eggs_sold_price_timeunits_1 = c("eggtime1", "eggtime2"),
#'   eggs_sold_price_timeunits_2 = c("eggtime3", NA),
#'   eggs_sold_price_timeunits_3 = c(NA, NA),
#'   eggs_sold_price_timeunits_other_1 = c("eggtimeoth1", NA),
#'   eggs_sold_price_timeunits_other_2 = c("eggtimeoth2", "eggtimeoth3"),
#'   fertiliser_units = c("fert1", "fert2"),
#'   fertiliser_units_other = c("fertoth1", "fertoth2")
#' )
#'
#'
#' result <- extract_new_core_units(data)
#'
#' expected_result <- c(list(
#'   "crop_name" = c(
#'     "maize",
#'     "cassava",
#'     "cucumber"
#'   ),
#'   "livestock_name" = c(
#'     "cow",
#'     "chicken",
#'     "pig"
#'   ),
#'   "crop_yield_units" = c(
#'     "kg",
#'     "sacks",
#'     "wheelbarrow",
#'     "other1",
#'     "other2",
#'     "other3"
#'   ),
#'   "crop_sold_price_quantityunits" = c(
#'     "price1",
#'     "price2",
#'     "price3",
#'     "crop_price_1",
#'     "crop_price_2",
#'     "crop_price_3"
#'   ),
#'   "unitland" = c(
#'     "acre",
#'     "hectare",
#'     "m2",
#'     "area1",
#'     "area2",
#'     "area3",
#'     "unitland1",
#'     "unitland2",
#'     "unitland3",
#'     "renty1",
#'     "renty2",
#'     "renty3",
#'     "rent1",
#'     "rent2",
#'     "rent5"
#'   ),
#'   "milk_units" = c(
#'     "milk1",
#'     "milk4",
#'     "milk5",
#'     "milkoth1",
#'     "milkoth2",
#'     "milkoth3"
#'   ),
#'   "milk_sold_price_timeunits" = c(
#'     "mspt1",
#'     "mspt3",
#'     "mspt5",
#'     "mspto1",
#'     "mspto2",
#'     "mspto3"
#'   ),
#'   "bees_honey_production_units" = c(
#'     "hnyprod1",
#'     "hnyprod2",
#'     "hnyprod3",
#'     "hnyprodoth1",
#'     "hnyprodoth2",
#'     "hnyprodoth3"
#'   ),
#'   "eggs_units" = c(
#'     "egg1",
#'     "egg2",
#'     "egg3",
#'     "eggoth1",
#'     "eggoth2",
#'     "eggoth3"
#'   ),
#'   "eggs_sold_price_timeunits" = c(
#'     "eggtime1",
#'     "eggtime2",
#'     "eggtime3",
#'     "eggtimeoth1",
#'     "eggtimeoth2",
#'     "eggtimeoth3"
#'   ),
#'   "fertiliser_units" = c(
#'     "fert1",
#'     "fert2",
#'     "fertoth1",
#'     "fertoth2"
#'   )
#' ))
extract_new_core_units <- function(data) {




    # Return a named list when applying the function
    loop_results <- sapply(pkg.env$loop_values_to_extract, function(x) find_loop_number_and_extract_values(data, x), simplify = FALSE)


    column_results <- sapply(pkg.env$individual_columns_to_extract, function(x) extract_new_values(data, loop_or_individual_column = "column", column_name = x), simplify = FALSE)

    # Extracting livestock_heads values
    livestock_heads_new_values <- grep("livestock_heads_", colnames(data), value = T)
    livestock_heads_new_values <- livestock_heads_new_values[!grepl("other", livestock_heads_new_values)]
    if (length(livestock_heads_new_values) > 0) {
        livestock_heads_new_values <- gsub("livestock_heads_", "", livestock_heads_new_values)
        column_results$livestock_heads <- livestock_heads_new_values
    }




    final_result <- c(loop_results, column_results)



    final_result <- sapply(names(pkg.env$categories_to_merge), function(x) merge_and_simplify_core_values(final_result, main_item = x, pkg.env$categories_to_merge), simplify = FALSE)

    results_to_remove <- is.na(final_result) & names(final_result) %in% pkg.env$optional_units
    final_result <- final_result[!results_to_remove]

    name_conversions <- pkg.env$unit_file_names

    lapply(names(final_result), function(unit_name){
        if (unit_name %in% names(name_conversions)){
            return(as.character(name_conversions[names(name_conversions)==unit_name]))

        }
    }) %>% unlist()

    names(final_result) <- lapply(names(final_result), function(unit_name){
        if (unit_name %in% names(name_conversions)){
                return(as.character(name_conversions[names(name_conversions)==unit_name]))

        }
    }) %>% unlist()


    return(final_result)
}

#' Extract names of Values to Receive Calorie Conversions
#'
#' Rpackage file: ExtractNewValues.R
#'
#' @param data The RHoMIS data set
#'
#' @return
#' @export
#'
#' @examples
extract_calorie_values <- function(data) {
    loop_values_to_extract <- c(
        "crop_name",
        "livestock_name"
    )

    # Return a named list when applying the function
    final_result <- sapply(loop_values_to_extract, function(x) find_loop_number_and_extract_values(data, x), simplify = FALSE)

    if ("crop_name" %in% names(final_result)) {
        final_result$crop_calories <- final_result[["crop_name"]]
    }

    if ("livestock_name" %in% names(final_result)) {
        final_result$milk_calories <- final_result[["livestock_name"]]

        final_result$eggs_calories <- final_result[["livestock_name"]]
        final_result$honey_calories <- final_result[["livestock_name"]]

        final_result$meat_calories <- final_result[["livestock_name"]]
    }

    final_result <- final_result[names(final_result) %in% c("livestock_name", "crop_name") == F]


    return(final_result)
}


#' Extract Values by Project
#'
#' A function to extract all of the units alongside their
#' individual form and project IDs.
#'
#' Rpackage file: ExtractNewValues.R
#'
#' @param data A rhomis data frame
#'
#' @return
#' @export
#'
#' @examples
extract_values_by_project <- function(data) {
    if ("id_rhomis_dataset" %in% colnames(data) == F) {
        stop("Could not find the individual project ID generated for this project")
    }

    # isolate list of unique project ids
    projects <- unique(data$id_rhomis_dataset)

    # create a list of units dataframes, one per project id
    units_by_project_by_unit_type <- sapply(projects, function(x) extract_units_data_frames(data[data$id_rhomis_dataset == x, ]), simplify = F)

    units_by_project_merged <- lapply(names(units_by_project_by_unit_type), function(project) {
        lapply(names(units_by_project_by_unit_type[[project]]), function(conversion_type) {
            conversion_table <- units_by_project_by_unit_type[[project]][[conversion_type]]
            conversion_table$unit_type <- conversion_type
            conversion_table$id_rhomis_dataset <- project
            return(conversion_table)
        })
    })

    units_by_project_merged_df <- dplyr::bind_rows(units_by_project_merged)
    final_units_list <- sapply(unique(units_by_project_merged_df$unit_type), function(unit_type) {
        return(units_by_project_merged_df[units_by_project_merged_df$unit_type == unit_type, ])
    }, simplify = F)

    return(final_units_list)
}


#' Extract Calorie Values by Project
#'
#' A function to identify all of the crops and livestock
#' for which calorie conversion values need to be included for
#'
#' Rpackage file: ExtractNewValues.R
#'
#' @param data RHoMIS Dataset
#'
#' @return
#' @export
#'
#' @examples
extract_calorie_values_by_project <- function(data) {
    if ("id_rhomis_dataset" %in% colnames(data) == F) {
        stop("Could not find the individual project ID generated for this project")
    }
    projects <- unique(data$id_rhomis_dataset)
    units_by_project_by_unit_type <- sapply(projects, function(x) extract_calorie_conversion_data_frames(data[data$id_rhomis_dataset == x, ]), simplify = F)

    units_by_project_merged <- lapply(names(units_by_project_by_unit_type), function(project) {
        lapply(names(units_by_project_by_unit_type[[project]]), function(conversion_type) {
            conversion_table <- units_by_project_by_unit_type[[project]][[conversion_type]]
            conversion_table$unit_type <- conversion_type
            conversion_table$id_rhomis_dataset <- project
            return(conversion_table)
        })
    })

    units_by_project_merged_df <- dplyr::bind_rows(units_by_project_merged)
    final_units_list <- sapply(unique(units_by_project_merged_df$unit_type), function(unit_type) {
        units_by_project_merged_df[units_by_project_merged_df$unit_type == unit_type, ]
    }, simplify = F)

    return(final_units_list)
}



#' Merge and Simplify Core Values
#'
#' When extracting new values from the
#' RHoMIS dataset, some variables appear
#' in multiple columns. For example with
#' units, we can have "crop_yield_units"
#' and "crop_yield_units_other". These are
#' all crop_yield units and we would like
#' to ensure they are aggregated appropriately.
#'
#' This is heavily linked to the "extract_new_core_units"
#' function.
#'
#' Rpackage file: ExtractNewValues.R
#'
#' @param list_of_unique_core_values A nested list of new values
#' from a RHoMIS survey
#' @param main_item The primary item that you want to merge.
#' In "crop_yield_units" and "crop_yield_units_other",
#' the main item would be "crop_yield_units"
#' @param categories_to_merge A named list of the categories
#' which go together, please see example
#'
#' @return
#' @export
#'
#' @examples
#'
#' categories_to_merge <- list(
#'   crop_name = c("crop_name"),
#'   livestock_name = c("livestock_name"),
#'   crop_yield_units = c("crop_yield_units_other"),
#'   crop_sold_price_quantityunits = c("crop_price_quantityunits_other"),
#'   unitland = c(
#'     "areaunits_other",
#'     "unitland_owned",
#'     "unitland_rentin",
#'     "unitland_rentout"
#'   ),
#'   milk_units = c("milk_amount_units_other"),
#'   milk_sold_price_timeunits = c("milk_amount_time_units_other"),
#'   bees_honey_production_units = c("bees_honey_production_units_other"),
#'   eggs_units = c("eggs_amount_units_other"),
#'   eggs_sold_price_timeunits = c("eggs_sold_price_timeunits_other"),
#'   fertiliser_units = c("fertiliser_units_other")
#' )
#'
#' list_of_unique_core_values <- c(list(
#'   crop_sold_price_quantityunits = c(
#'     "cropPriceA",
#'     "cropPriceB",
#'     "cropPriceC"
#'   ),
#'   crop_price_quantityunits_other = c(
#'     "cropPriceD",
#'     "cropPriceE",
#'     "cropPriceF"
#'   ),
#'   random_unimportant_column = c("bla", "bla", "bla")
#' ))
#'
#' main_item <- "crop_sold_price_quantityunits"
#' categories_to_merge <- list(
#'   crop_name = c("crop_name"),
#'   livestock_name = c("livestock_name"),
#'   crop_yield_units = c("crop_yield_units_other"),
#'   crop_sold_price_quantityunits = c("crop_price_quantityunits_other"),
#'   unitland = c(
#'     "areaunits_other",
#'     "unitland_owned",
#'     "unitland_rentin",
#'     "unitland_rentout"
#'   ),
#'   milk_units = c("milk_amount_units_other"),
#'   milk_sold_price_timeunits = c("milk_amount_time_units_other"),
#'   bees_honey_production_units = c("bees_honey_production_units_other"),
#'   eggs_units = c("eggs_amount_units_other"),
#'   eggs_sold_price_timeunits = c("eggs_sold_price_timeunits_other"),
#'   fertiliser_units = c("fertiliser_units_other")
#' )
#'
#' expected_result <- c(
#'   "cropPriceA",
#'   "cropPriceB",
#'   "cropPriceC",
#'   "cropPriceD",
#'   "cropPriceE",
#'   "cropPriceF"
#' )
#' merge_and_simplify_core_values(
#'   list_of_unique_core_values,
#'   main_item,
#'   categories_to_merge
#' )
merge_and_simplify_core_values <- function(list_of_unique_core_values, main_item, categories_to_merge) {
    extras_categories_merge <- categories_to_merge[[main_item]]
    extra_values_to_merge <- list_of_unique_core_values[extras_categories_merge]
    extra_values_to_merge <- unlist(extra_values_to_merge)
    extra_values_to_merge <- unique(extra_values_to_merge)

    list_of_unique_core_values[[main_item]] <- c(list_of_unique_core_values[[main_item]], extra_values_to_merge)
    list_of_unique_core_values[[main_item]] <- unique(list_of_unique_core_values[[main_item]])

    return(list_of_unique_core_values[[main_item]])
}

#' Write Core Values to File
#'
#' This function extracts all of the new units and
#' names in a RHoMIS survey and writes them to a folder
#' You can select the folder that you would like
#' to write all of the files to.
#'
#' Rpackage file: ExtractNewValues.R
#'
#' @param data The data for which you want to
#' extract all of the new files.
#' @param folder The folder where you want to
#' store all of the values you need to convert.
#' Make sure the folder is correct and does not end in "/"
#'
#' @return
#' @export
#'
#' @examples
#' data <- tibble::tibble(
#'   crop_name_1 = c("maize", "cassava"),
#'   crop_name_2 = c(NA, "cucumber"),
#'   random_crop_name_2 = c("blue", "green"),
#'   livestock_name_1 = c("cow", "chicken"),
#'   livestock_name_2 = c("pig", NA),
#'   crop_yield_units_1 = c("kg", "sacks"),
#'   crop_yield_units_2 = c("wheelbarrow", NA),
#'   crop_yield_units_other_1 = c("other1", NA),
#'   crop_yield_units_other_2 = c("other2", "other3"),
#'   crop_sold_price_quantityunits_1 = c(NA, "price1"),
#'   crop_sold_price_quantityunits_2 = c("price2", "price3"),
#'   crop_price_quantityunits_other_1 = c(NA, "crop_price_1"),
#'   crop_price_quantityunits_other_2 = c(NA, "crop_price_2"),
#'   crop_price_quantityunits_other_3 = c(NA, "crop_price_3"),
#'   crop_price_quantityunits_other_4 = c(NA, NA),
#'   unitland = c("acre", NA),
#'   areaunits_other = c("area1", "area2"),
#'   unitland_owned = c("unitland1", "unitland2"),
#'   unitland_rentin = c("renty1", "renty2"),
#'   unitland_rentout = c("rent1", "rent5"),
#'   milk_units_1 = c("milk1", NA),
#'   milk_units_2 = c("milk4", "milk5"),
#'   milk_amount_units_other_1 = c("milkoth1", "milkoth2"),
#'   milk_amount_units_other_2 = c(NA, "milkoth3"),
#'   milk_sold_price_timeunits_1 = c("mspt1", "mspt3"),
#'   milk_sold_price_timeunits_2 = c(NA, "mspt5"),
#'   milk_amount_time_units_other_1 = c("mspto1", NA),
#'   milk_amount_time_units_other_2 = c("mspto2", "mspto3"),
#'   bees_honey_production_units_1 = c("hnyprod1", "hnyprod2"),
#'   bees_honey_production_units_2 = c(NA, "hnyprod3"),
#'   bees_honey_production_units_other_1 = c("hnyprodoth1", NA),
#'   bees_honey_production_units_other_2 = c("hnyprodoth2", "hnyprodoth3"),
#'   eggs_units_1 = c("egg1", NA),
#'   eggs_units_2 = c("egg2", "egg3"),
#'   eggs_amount_units_other_1 = c("eggoth1", NA),
#'   eggs_amount_units_other_2 = c("eggoth2", "eggoth3"),
#'   eggs_sold_price_timeunits_1 = c("eggtime1", "eggtime2"),
#'   eggs_sold_price_timeunits_2 = c("eggtime3", NA),
#'   eggs_sold_price_timeunits_3 = c(NA, NA),
#'   eggs_sold_price_timeunits_other_1 = c("eggtimeoth1", NA),
#'   eggs_sold_price_timeunits_other_2 = c("eggtimeoth2", "eggtimeoth3"),
#'   fertiliser_units = c("fert1", "fert2"),
#'   fertiliser_units_other = c("fertoth1", "fertoth2")
#' )
#'
#' # write_core_values_to_convert_to_file(data, "My/Folder)
write_core_values_to_convert_to_file <- function(data, folder) {
    new_units <- extract_units_data_frames(data)
    sapply(names(new_units), function(x) write_new_values_to_file(specific_value = x, list_of_tibbles = new_units, folder = folder), simplify = FALSE)
}


#' Extract Units to DataFrame
#'
#' Rpackage file: ExtractNewValues.R
#'
#' @param data The core RHoMIS dataset which we are extracting units from
#'
#' @return A list of data frames
#' @export
#'
#' @examples
extract_units_data_frames <- function(data) {
    new_units <- extract_new_core_units(data)
    new_units <- sapply(new_units, function(x) convert_new_values_to_tibble(x), simplify = FALSE)

    return(new_units)
}


#' Extract Units to DataFrame
#'
#' Rpackage file: ExtractNewValues.R
#'
#' @param data The core RHoMIS dataset which we are extracting units from
#'
#' @return A list of data frames
#' @export
#'
#' @examples
extract_calorie_conversion_data_frames <- function(data) {
    new_units <- extract_calorie_values(data)
    new_units <- sapply(new_units, function(x) convert_new_values_to_tibble(x), simplify = FALSE)

    return(new_units)
}


#' Convert New Values to Tibble
#'
#' The output of the "extract_new_core_units" function
#' is a list of lists. With the names of the lists
#' are the variables to be converted and the lists
#' contain all of the new values.
#' This function converts all of these lists into tibbles.
#' With a new column which has to be filled
#' in by the user
#'
#' Rpackage file: ExtractNewValues.R
#'
#' @param new_values A list of lists containing the values to convert
#'
#' @return
#' @export
#'
#' @examples
#'
#' new_values <- c("egg1", "egg2", "egg3")
#' expected_result <- tibble::as_tibble(list(
#'   "survey_value" = c("egg1", "egg2", "egg3"),
#'   "conversion" = c(NA, NA, NA)
#' ))
#'
#' actual_result <- convert_new_values_to_tibble(new_values)
convert_new_values_to_tibble <- function(new_values) {


    # new_values <- c("x","y","z")
    data_to_return <- tibble::as_tibble(list("survey_value" = new_values, "conversion" = rep(NA, length(new_values))))

    return(data_to_return)
}



#' Write units tibble to csv
#'
#' Write the specific tibble to a csv.
#'
#' Rpackage file: ExtractNewValues.R
#'
#' @param specific_value Which attribute are you
#' hoping to write to csv (e.g. "crop_yield_units")
#' @param list_of_tibbles What are all of the units
#' which you have extracted from the RHoMIS project.
#' See function "extract_new_core_units"
#' @param folder What is the folder you hope to write this to.
#' Do not include "/" at the end of the file path
#'
#' @return No return
#' @export
#'
#' @examples
#'
#' # Please make sure the correct folder exists for you
#' specific_value <- "egg_units"
#' new_values <- list(
#'   egg_units = c("egg1", "egg2", "egg3"),
#'   other_units = c("other1", "other2", "other3")
#' )
#' list_of_tibbles <- sapply(new_values, function(x) convert_new_values_to_tibble(x), simplify = FALSE)
#' folder <- "data/test_outputs/write_new_values_to_file"
#'
#'
#' # write_new_values_to_file(specific_value,list_of_tibbles,folder)
write_new_values_to_file <- function(specific_value, list_of_tibbles, folder) {
    data <- list_of_tibbles[[specific_value]]
    readr::write_csv(data, paste0(folder, "/", specific_value, ".csv"))
}