R/pam.R

In datazoom.amazonia: Simplify Access to Data from the Amazon Region

#' @title PAM - Municipal Agricultural Production
#'
#' @description Loads information on the quantity, value and area of temporary and permanent crops cultivated.
#'
#' @param dataset A dataset name ("all_crops", "permanent_crops", "temporary_crops" or many individual crop possibilities (see \code{vignette(load_pam)})). You can also use SIDRA codes (see \url{https://sidra.ibge.gov.br/pesquisa/pam/tabelas})
#' @inheritParams load_baci
#' @param geo_level A \code{string} that defines the geographic level of the data. Can be one of "country", "state" or "municipality".
#'
#' @return A \code{tibble} consisting of geographic units that present positive values for any of the variables in the dataset.
#'
#' @examples \dontrun{
#' # download treated data at the state level from 2010 to 2011 for all crops
#' data <- load_pam(
#'   dataset = "all_crops",
#'   raw_data = FALSE,
#'   geo_level = "state",
#'   time_period = 2010:2011,
#'   language = "eng"
#' )
#' }
#'
#' @export

load_pam <- function(dataset, raw_data = FALSE,
                     geo_level, time_period,
                     language = "eng") {
  ##############################
  ## Binding Global Variables ##
  ##############################

  sidra_code <- nivel_territorial_codigo <- nivel_territorial <- nivel_territorial_codigo <- NULL
  unidade_de_medida_codigo <- variavel_codigo <- ano_codigo <- valor <- NULL
  produto_das_lavouras_temporarias_e_permanentes_codigo <- NULL
  produto_das_lavouras_temporarias_e_permanentes <- NULL
  produto_das_lavouras_permanentes_codigo <- NULL
  produto_das_lavouras_permanentes <- NULL
  produto_das_lavouras_temporarias_codigo <- NULL
  produto_das_lavouras_temporarias <- NULL
  geo_id <- ano <- variavel <- produto_das_lavouras <- NULL
  unidade_de_medida <- available_time <- vars <- NULL
  produto_das_lavouras_codigo <- NULL

  #############################
  ## Define Basic Parameters ##
  #############################

  param <- list()
  param$source <- "pam"
  param$dataset <- dataset
  param$geo_level <- geo_level
  param$time_period <- time_period
  param$language <- language
  param$raw_data <- raw_data

  # check if dataset, geo_level, and time_period are supported

  check_params(param)

  # Extracting sidra info in the form code/classific/category

  if (!is.numeric(param$dataset)) {
    sidra_info <- datasets_link() %>%
      dplyr::filter(dataset == param$dataset) %>%
      dplyr::select(sidra_code) %>%
      unlist() %>%
      stringr::str_split("/") %>%
      unlist()
  } else {
    param$code <- param$dataset
  }

  param$code <- sidra_info[1]
  param$classific <- sidra_info[2]
  param$category <- sidra_info[3]

  ## Dataset

  if (param$code == 1612) { ## This is a subset of all crops
    param$data_name <- "Temporary Crops (Lavouras Temporarias)"
    param$dataset <- "temporary_crops"
  }

  if (param$code == 1613) { ## This is a subset of all crops
    param$data_name <- "Permanent Crops (Lavouras Permanente)"
    param$dataset <- "permanent_crops"
  }

  if (param$code == 5457) {
    param$data_name <- "All Crops (Lavouras)"
  }


  if (param$code %in% c(839, 1001, 1000, 1002)) {
    param$data_name <- "Crop with more than one harvest (Corn, Potato, Peanut or Beans)"
  }

  ##############
  ## Download ##
  ##############

  # We need to show year that is being downloaded as well
  # Heavy Datasets may take several minutes

  dat <- as.list(as.character(param$time_period)) %>%
    purrr::map(function(year_num) {
      # suppressMessages(
      sidra_download(
        sidra_code = param$code,
        classific = param$classific,
        category = list(param$category),
        year = year_num,
        geo_level = param$geo_level
      )
      # )
    }) %>%
    dplyr::bind_rows() %>%
    tibble::as_tibble()

  ## Return Raw Data

  if (param$raw_data) {
    return(dat)
  }

  ######################
  ## Data Enginnering ##
  ######################

  dat <- dat %>%
    janitor::clean_names() %>%
    dplyr::mutate_all(function(var) {
      stringi::stri_trans_general(str = var, id = "Latin-ASCII")
    }) # %>%
  # dplyr::mutate_all(clean_custom)

  dat <- dat %>%
    dplyr::select(-c(nivel_territorial_codigo, nivel_territorial, ano_codigo)) %>%
    dplyr::mutate(valor = as.numeric(valor))

  ## Only Keep Valid Observations

  dat <- dat %>%
    dplyr::filter(!is.na(valor))

  #########################################
  ## Create Geographical Unit Identifier ##
  #########################################

  if (geo_level == "country") {
    dat$geo_id <- dat$brasil
    dat <- dplyr::select(dat, -"brasil_codigo", -"brasil")
  }
  if (geo_level == "region") {
    dat$geo_id <- dat$grande_regiao
    dat <- dplyr::select(dat, -"grande_regiao_codigo", -"grande_regiao")
  }
  if (geo_level == "state") {
    dat$geo_id <- dat$unidade_da_federacao_codigo
    dat <- dplyr::select(dat, -"unidade_da_federacao_codigo", -"unidade_da_federacao")
  }
  if (geo_level == "municipality") {
    dat$geo_id <- dat$municipio_codigo
    dat <- dplyr::select(dat, -"municipio", -"municipio_codigo")
  }

  ################################
  ## Harmonizing Variable Names ##
  ################################

  dat <- dat %>%
    dplyr::select(-unidade_de_medida, -unidade_de_medida_codigo)

  ## Change Variable Names for Common Across Datasets

  if (param$code == 5457) {
    dat <- dat %>%
      dplyr::rename(
        produto_das_lavouras_codigo = produto_das_lavouras_temporarias_e_permanentes_codigo,
        produto_das_lavouras = produto_das_lavouras_temporarias_e_permanentes
      )
  }

  if (param$code == 1613) {
    dat <- dat %>%
      dplyr::rename(
        produto_das_lavouras_codigo = produto_das_lavouras_permanentes_codigo,
        produto_das_lavouras = produto_das_lavouras_permanentes
      )
  }


  if (param$code %in% c(839, 1000, 1001, 1002, 1612)) {
    dat <- dat %>%
      dplyr::rename(
        produto_das_lavouras_codigo = produto_das_lavouras_temporarias_codigo,
        produto_das_lavouras = produto_das_lavouras_temporarias
      )
  }

  ## Translation

  if (language == "pt") {
    dat <- dat %>%
      dplyr::mutate(variavel = dplyr::case_when(
        (variavel_codigo == "112") ~ "rend_medio", # Rendimento medio da producao
        (variavel_codigo == "214") ~ "quant", # Quantidade produzida
        (variavel_codigo == "215") ~ "valor", # Valor da producao
        (variavel_codigo == "216") ~ "area_colhida", # Area colhida
        (variavel_codigo == "8331") ~ "area_plantada_dest_colheita", # Area plantada ou destinada a colheita,
        (variavel_codigo == "109") ~ "area_plantada" # Area plantada,
      ))
  }

  if (language == "eng") {
    dat <- dat %>%
      dplyr::mutate(variavel = dplyr::case_when(
        (variavel_codigo == "112") ~ "avg_yield", # Rendimento medio da producao
        (variavel_codigo == "214") ~ "quant", # Quantidade produzida
        (variavel_codigo == "215") ~ "value", # Valor da producao
        (variavel_codigo == "216") ~ "harvested_area", # Area colhida
        (variavel_codigo == "8331") ~ "planted_to_harvest_area", # Area plantada ou destinada a colheita
        (variavel_codigo == "109") ~ "planted_area" # Area plantada,
      ))
  }


  #############################
  ## Create Long Format Data ##
  #############################

  ## The Output is a tibble with unit and year identifiers + production and/or value of each item

  dat <- dat %>%
    dplyr::select(-"produto_das_lavouras") %>%
    dplyr::arrange(produto_das_lavouras_codigo, variavel) %>%
    tidyr::pivot_wider(
      id_cols = c(geo_id, ano),
      names_from = variavel:produto_das_lavouras_codigo,
      values_from = valor,
      names_sep = "_V",
      values_fn = sum,
      values_fill = NA
    ) %>%
    janitor::clean_names()

  ########################
  ## Changing Year Name ##
  ########################


  if (language == "eng") {
    dat <- dat %>%
      dplyr::rename(year = ano)
  }

  ###############
  ## Labelling ##
  ###############

  labelled <- function(x, label) {
    Hmisc::label(x) <- label
    x
  }

  label_data_eng <- function(df, cols, dic) {
    label_value <- as.character(dic[dic$var_code == cols, "var_eng"])

    df <- df %>%
      dplyr::mutate_at(
        dplyr::vars(tidyr::matches(cols)),
        ~ labelled(., as.character(dic[dic$var_code == cols, "var_eng"]))
      )

    return(df)
  }

  label_data_pt <- function(df, cols, dic) {
    label_value <- as.character(dic[dic$var_code == cols, "var_pt"])

    df <- df %>%
      dplyr::mutate_at(
        dplyr::vars(tidyr::matches(cols)),
        ~ labelled(., as.character(dic[dic$var_code == cols, "var_pt"]))
      )

    return(df)
  }

  ## Load Dictionary

  dic <- load_dictionary(param$dataset)

  types <- as.character(dic$var_code)
  types <- types[types != "0"] ## Remove 0


  if (language == "eng") {
    # f = dat %>%
    #   dplyr::mutate_at(vars(tidyr::matches(as.character(types[1]))),
    #                    ~ labelled::set_variable_labels(. = as.character(dic[dic$var_code == types[1],'var_eng']))
    #   )

    for (i in seq_along(types)) {
      dat <- label_data_eng(dat, cols = types[i], dic = dic)
    }
  }

  if (language == "pt") {
    for (i in seq_along(types)) {
      dat <- label_data_pt(dat, cols = types[i], dic = dic)
    }
  }

  # Labelling the code 0 (total)

  dat <- dat %>%
    dplyr::mutate(
      dplyr::across(
        dplyr::ends_with("_v0"),
        ~ labelled(., "Total")
      )
    )

  ##########################
  ## Returning Data Frame ##
  ##########################

  return(dat)
}