R/seeg.R
In datazoompuc/datazoom.amazonia: Simplify Access to Data from the Amazon Region
Defines functions
load_seeg
Documented in
load_seeg
#' @title Greenhouse gas emission estimates (SEEG)
#'
#' @description Loads data of estimates of emission of greenhouse gases
#'
#' @param dataset A dataset name ("seeg", seeg_farming", "seeg_industry", "seeg_energy", "seeg_land", "seeg_residuals"). On which "seeg" contains all five sectors (only works with raw_data = TRUE) and the others are filtered specifically by a main source of emission.
#' @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}.
#'
#' @export
#'
#' @examples \dontrun{
#' # Download raw data (raw_data = TRUE) of greenhouse gases (dataset = "seeg")
#' # by state (geo_level = "state")
#' data <- load_seeg(
#' dataset = "seeg",
#' raw_data = TRUE,
#' geo_level = "state"
#' )
#'
#' # Download treated data (raw_data = FALSE) of industry greenhouse gases (dataset = "seeg_industry")
#' data <- load_seeg(
#' dataset = "seeg_industry",
#' raw_data = FALSE,
#' geo_level = "state"
#' )
#' }
load_seeg <- function(dataset, raw_data = FALSE,
geo_level, language = "eng") {
# Checking for googledrive package (in Suggests)
if (!requireNamespace("googledrive", quietly = TRUE)) {
stop(
"Package \"googledrive\" must be installed to use this function.",
call. = FALSE
)
}
##############################
## Binding Global Variables ##
##############################
survey <- link <- ibge <- x2000 <- x2018 <- id_code <- tipo_de_emissao <- NULL
city <- state <- nivel_1 <- municipio <- territorio <- x1970 <- x2021 <- NULL
nivel_1_setor <- nivel_2 <- nivel_3 <- nivel_4 <- nivel_5 <- nivel_6 <- NULL
produto <- atividade_economica <- Valor <- Ano <- estado <- setor <- NULL
processos_geradores_emissoes <- fonte_de_emissoes <- emissores <- gas <- NULL
emissao_remocao_bunker <- producao_emissores <- categorias_emissao <- NULL
atividade_geradora <- categorias_processos_geradores <- year <- state <- NULL
sector <- emitters_production <- emitters <- economic_activity <- product <- NULL
value <- emissions_category <- activity <- generating_processes_categories <- NULL
biome <- biome_area <- transition_type <- emission_removal_bunker <- NULL
emissions_sources <- emissions_type <- emissions_generating_processes <- NULL
#############################
## Define Basic Parameters ##
#############################
param <- list()
param$source <- "seeg"
param$dataset <- dataset
param$geo_level <- geo_level
param$language <- language
param$raw_data <- raw_data
# check if dataset and geo_level are supported
check_params(param)
if (!is.numeric(param$dataset)) {
param$code <- datasets_link() %>%
dplyr::filter(dataset == param$dataset) %>%
dplyr::select(link) %>%
unlist()
} else {
param$code <- param$dataset
}
## Dataset
if (param$dataset == "seeg" & param$raw_data == FALSE) {
stop("This dataset only works with raw_data = TRUE")
}
if (param$dataset == "seeg_farming" & param$raw_data == TRUE) {
stop("This dataset only works with raw_data = FALSE")
}
if (param$dataset == "seeg_energy" & param$raw_data == TRUE) {
stop("This dataset only works with raw_data = FALSE")
}
if (param$dataset == "seeg_industry" & param$raw_data == TRUE) {
stop("This dataset only works with raw_data = FALSE")
}
if (param$dataset == "seeg_land" & param$raw_data == TRUE) {
stop("This dataset only works with raw_data = FALSE")
}
if (param$dataset == "seeg_residuals" & param$raw_data == TRUE) {
stop("This dataset only works with raw_data = FALSE")
}
# Picking which sheet to download
sheet_list <- c(
"country" = "GEE Brasil",
"state" = "GEE Estados",
"municipality" = "BD GEE Municipios GWP-AR5"
)
sheet <- param$geo_level %>%
dplyr::recode(!!!sheet_list)
##############
## Download ##
##############
dat <- external_download(
dataset = param$dataset,
source = param$source,
geo_level = param$geo_level,
sheet = sheet
)
dat <- dat %>%
janitor::clean_names() %>%
tibble::as_tibble() %>%
dplyr::mutate_if(is.character, function(var) {
stringi::stri_trans_general(str = var, id = "Latin-ASCII")
})
## Return Raw Data
if (param$dataset == "seeg" & param$raw_data) {
return(dat)
}
## Raw Data = FALSE
if (param$dataset == "seeg_farming" & param$geo_level == "municipality" & param$language == "pt") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x2000:x2018,
names_to = "Ano",
names_prefix = "x",
values_to = "Valor"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1 == "Agropecuaria") %>%
dplyr::rename(
setor = nivel_1, processos_geradores_emissoes = nivel_2,
tipo_emissao = nivel_4,
emissores_diretos_e_indiretos = nivel_3,
fonte_de_emissoes = nivel_5,
emissores = nivel_6,
emissao_remocao = tipo_de_emissao
) %>%
dplyr::mutate(atividade_economica = dplyr::case_when(
atividade_economica == "PEC" ~ "Pecuaria",
atividade_economica == "AGR" ~ "Agricultura"
)) %>%
dplyr::mutate(produto = dplyr::case_when(
produto == "ALIM_BEBIDAS" ~ "Alimentos/Bebidas",
produto == "CAR" ~ "Carne",
produto == "CAR/LEI" ~ "Carne/Leite",
produto == "CAR/LEI/ALIM_BEBIDAS" ~ "Carne/Leite/Alimentos/Bebidas",
produto == "LEI" ~ "Leite"
))
dat <- dat %>%
dplyr::relocate(
Ano, municipio, territorio, ibge, setor, processos_geradores_emissoes, fonte_de_emissoes, emissores, gas, atividade_economica, produto,
Valor
)
dat <- dat %>%
dplyr::mutate(municipio = dplyr::case_when(
municipio == "NA" ~ NA_character_,
TRUE ~ municipio
)) %>%
dplyr::mutate(territorio = dplyr::case_when(
territorio == "NA" ~ NA_character_,
TRUE ~ territorio
)) %>%
dplyr::mutate(ibge = dplyr::case_when(
ibge == "NA" ~ NA_character_,
TRUE ~ ibge
))
}
if (param$dataset == "seeg_farming" & param$geo_level %in% c("country", "state") & param$language == "pt") {
## Create Longer Data - Years as a Variable
dat <- dat %>%
tidyr::pivot_longer(
cols = x1970:x2021,
names_to = "Ano",
names_prefix = "x",
values_to = "Valor"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1_setor == "Agropecuaria") %>%
dplyr::rename(
setor = nivel_1_setor, processos_geradores_emissoes = nivel_2,
tipo_emissao = nivel_4,
emissores_diretos_e_indiretos = nivel_3,
fonte_de_emissoes = nivel_5,
emissores = nivel_6
) %>%
dplyr::mutate(atividade_economica = dplyr::case_when(
atividade_economica == "PEC" ~ "Pecuaria",
atividade_economica == "AGR" ~ "Agricultura"
)) %>%
dplyr::mutate(produto = dplyr::case_when(
produto == "ALIM_BEBIDAS" ~ "Alimentos/Bebidas",
produto == "CAR" ~ "Carne",
produto == "CAR/LEI" ~ "Carne/Leite",
produto == "CAR/LEI/ALIM_BEBIDAS" ~ "Carne/Leite/Alimentos/Bebidas",
produto == "LEI" ~ "Leite"
))
dat <- dat %>%
dplyr::relocate(
Ano, estado, setor, processos_geradores_emissoes, fonte_de_emissoes, emissores, gas, atividade_economica, produto,
Valor
)
dat <- dat %>%
dplyr::mutate(estado = dplyr::case_when(
estado == "NA" ~ NA_character_,
TRUE ~ estado
))
}
if (param$dataset == "seeg_industry" & param$geo_level == "municipality" & param$language == "pt") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x2000:x2018,
names_to = "Ano",
names_prefix = "x",
values_to = "Valor"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1 == "Processos Industriais") %>%
dplyr::rename(
setor = nivel_1, processos_geradores_emissoes = nivel_2,
producao_emissores = nivel_3,
emissores = nivel_4,
emissao = tipo_de_emissao
) %>%
dplyr::select(-c(nivel_6, nivel_5)) %>%
dplyr::mutate(atividade_economica = dplyr::case_when(
atividade_economica == "CIM" ~ "Industria Cimenteira",
atividade_economica == "ENE_ELET" ~ "Industria Energia Eletrica",
atividade_economica == "MET" ~ "Industria Metaleira",
atividade_economica == "Outra_IND" ~ "Outra Industria",
atividade_economica == "HFC" ~ "HFC"
))
dat <- dat %>%
dplyr::relocate(Ano, municipio, territorio, setor, processos_geradores_emissoes, producao_emissores, emissores, gas, atividade_economica, produto, Valor)
dat <- dat %>%
dplyr::mutate(municipio = dplyr::case_when(
municipio == "NA" ~ NA_character_,
TRUE ~ municipio
)) %>%
dplyr::mutate(territorio = dplyr::case_when(
territorio == "NA" ~ NA_character_,
TRUE ~ territorio
)) %>%
dplyr::mutate(ibge = dplyr::case_when(
ibge == "NA" ~ NA_character_,
TRUE ~ ibge
))
}
if (param$dataset == "seeg_industry" & param$geo_level %in% c("country", "state") & param$language == "pt") {
## Create Longer Data - Years as a Variable
dat <- dat %>%
tidyr::pivot_longer(
cols = x1970:x2021,
names_to = "Ano",
names_prefix = "x",
values_to = "Valor"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1_setor == "Processos Industriais") %>%
dplyr::rename(
setor = nivel_1_setor, processos_geradores_emissoes = nivel_2,
producao_emissores = nivel_3,
emissores = nivel_4,
emissao_bunker = emissao_remocao_bunker
) %>%
dplyr::select(-c(nivel_6, nivel_5)) %>%
dplyr::mutate(atividade_economica = dplyr::case_when(
atividade_economica == "CIM" ~ "Industria Cimenteira",
atividade_economica == "ENE_ELET" ~ "Industria Energia Eletrica",
atividade_economica == "MET" ~ "Industria Metaleira",
atividade_economica == "Outra_IND" ~ "Outra Industria",
atividade_economica == "HFC" ~ "HFC"
)) %>%
dplyr::mutate(produto = dplyr::case_when(
produto == "ALU" ~ "Aluminio",
produto == "ACO" ~ "Aco"
))
dat <- dat %>%
dplyr::relocate(Ano, estado, setor, processos_geradores_emissoes, producao_emissores, emissores, gas, atividade_economica, produto, Valor)
dat <- dat %>%
dplyr::mutate(estado = dplyr::case_when(
estado == "NA" ~ NA_character_,
TRUE ~ estado
))
}
if (param$dataset == "seeg_energy" & param$geo_level == "municipality" & param$language == "pt") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x2000:x2018,
names_to = "Ano",
names_prefix = "x",
values_to = "Valor"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1 == "Energia") %>%
dplyr::rename(
setor = nivel_1, tipo_emissao = nivel_2,
processos_geradores_emissoes = nivel_3,
atividade_geradora = nivel_4,
fonte_energetica = nivel_5,
emissores = nivel_6,
emission_bunker = tipo_de_emissao
) %>%
dplyr::mutate(produto = dplyr::case_when(
produto == "ALIM_BEBIDAS" ~ "Alimentos/Bebidas",
produto == "ENE_ELET" ~ "Energia Eletrica",
produto == "ALU" ~ "Aluminio"
)) %>%
dplyr::select(-atividade_economica)
dat <- dat %>%
dplyr::relocate(Ano, municipio, territorio, setor)
dat <- dat %>%
dplyr::mutate(municipio = dplyr::case_when(
municipio == "NA" ~ NA_character_,
TRUE ~ municipio
)) %>%
dplyr::mutate(territorio = dplyr::case_when(
territorio == "NA" ~ NA_character_,
TRUE ~ territorio
)) %>%
dplyr::mutate(ibge = dplyr::case_when(
ibge == "NA" ~ NA_character_,
TRUE ~ ibge
))
}
if (param$dataset == "seeg_energy" & param$geo_level %in% c("country", "state") & param$language == "pt") {
## Create Longer Data - Years as a Variable
dat <- dat %>%
tidyr::pivot_longer(
cols = x1970:x2021,
names_to = "Ano",
names_prefix = "x",
values_to = "Valor"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1_setor == "Energia") %>%
dplyr::rename(
setor = nivel_1_setor, tipo_emissao = nivel_2,
processos_geradores_emissoes = nivel_3,
atividade_geradora = nivel_4,
fonte_energetica = nivel_5,
emissores = nivel_6
) %>%
dplyr::mutate(produto = dplyr::case_when(
produto == "ALIM_BEBIDAS" ~ "Alimentos/Bebidas",
produto == "ENE_ELET" ~ "Energia Eletrica",
produto == "ALU" ~ "Aluminio"
)) %>%
dplyr::select(-atividade_economica)
dat <- dat %>%
dplyr::relocate(Ano, estado, setor)
dat <- dat %>%
dplyr::mutate(estado = dplyr::case_when(
estado == "NA" ~ NA_character_,
TRUE ~ estado
))
}
if (param$dataset == "seeg_land" & param$geo_level == "municipality" & param$language == "pt") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x2000:x2018,
names_to = "Ano",
names_prefix = "x",
values_to = "Valor"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1 == "Mudanca de Uso da Terra e Floresta") %>%
dplyr::filter(!is.na(Valor)) %>%
dplyr::rename(
setor = nivel_1, processos_geradores_emissoes = nivel_2,
bioma = nivel_3,
area_bioma = nivel_4,
local_atividade_geradora = nivel_5,
atividade_geradora = nivel_6,
emissao_remocao = tipo_de_emissao
) %>%
dplyr::mutate(atividade_economica = dplyr::case_when(
atividade_economica == "AGROPEC" ~ "Agropecuaria",
atividade_economica == "Conservacao" ~ "Conservacao"
)) %>%
dplyr::select(-produto)
dat <- dat %>%
dplyr::relocate(Ano, municipio, territorio, setor, processos_geradores_emissoes, atividade_economica)
dat <- dat %>%
dplyr::mutate(municipio = dplyr::case_when(
municipio == "NA" ~ NA_character_,
TRUE ~ municipio
)) %>%
dplyr::mutate(territorio = dplyr::case_when(
territorio == "NA" ~ NA_character_,
TRUE ~ territorio
)) %>%
dplyr::mutate(ibge = dplyr::case_when(
ibge == "NA" ~ NA_character_,
TRUE ~ ibge
))
}
if (param$dataset == "seeg_land" & param$geo_level %in% c("country", "state") & param$language == "pt") {
## Create Longer Data - Years as a Variable
dat <- dat %>%
tidyr::pivot_longer(
cols = x1970:x2021,
names_to = "Ano",
names_prefix = "x",
values_to = "Valor"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1_setor == "Mudanca de Uso da Terra e Floresta") %>%
dplyr::filter(!is.na(Valor)) %>%
dplyr::rename(
setor = nivel_1_setor, processos_geradores_emissoes = nivel_2,
bioma = nivel_3,
area_bioma = nivel_4,
local_atividade_geradora = nivel_5,
atividade_geradora = nivel_6
) %>%
dplyr::mutate(atividade_economica = dplyr::case_when(
atividade_economica == "AGROPEC" ~ "Agropecuaria",
atividade_economica == "Conservacao" ~ "Conservacao"
)) %>%
dplyr::select(-produto)
dat <- dat %>%
dplyr::relocate(Ano, estado, setor, processos_geradores_emissoes, atividade_economica)
dat <- dat %>%
dplyr::mutate(estado = dplyr::case_when(
estado == "NA" ~ NA_character_,
TRUE ~ estado
))
}
if (param$dataset == "seeg_residuals" & param$geo_level == "municipality" & param$language == "pt") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x2000:x2018,
names_to = "Ano",
names_prefix = "x",
values_to = "Valor"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1 == "Residuos") %>%
dplyr::rename(
setor = nivel_1, categorias_emissao = nivel_2,
processos_geradores_emissoes = nivel_3,
atividade_geradora = nivel_4,
categorias_processos_geradores = nivel_5,
emissao_bunker = tipo_de_emissao
) %>%
dplyr::select(-nivel_6) %>%
dplyr::mutate(atividade_economica = dplyr::case_when(
atividade_economica == "PEC" ~ "Pecuaria",
atividade_economica == "Outra_IND" ~ "Outra Industria",
atividade_economica == "SANEAMENTO" ~ "Saneamento"
))
dat <- dat %>%
dplyr::mutate(municipio = dplyr::case_when(
municipio == "NA" ~ NA_character_,
TRUE ~ municipio
)) %>%
dplyr::mutate(territorio = dplyr::case_when(
territorio == "NA" ~ NA_character_,
TRUE ~ territorio
)) %>%
dplyr::mutate(ibge = dplyr::case_when(
ibge == "NA" ~ NA_character_,
TRUE ~ ibge
))
dat <- dat %>%
dplyr::relocate(Ano, municipio, territorio, ibge, setor, categorias_emissao, processos_geradores_emissoes, atividade_geradora, categorias_processos_geradores, atividade_economica, produto, Valor)
}
if (param$dataset == "seeg_residuals" & param$geo_level %in% c("country", "state") & param$language == "pt") {
## Create Longer Data - Years as a Variable
dat <- dat %>%
tidyr::pivot_longer(
cols = x1970:x2021,
names_to = "Ano",
names_prefix = "x",
values_to = "Valor"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1_setor == "Residuos") %>%
dplyr::rename(
setor = nivel_1_setor, categorias_emissao = nivel_2,
processos_geradores_emissoes = nivel_3,
atividade_geradora = nivel_4,
categorias_processos_geradores = nivel_5,
emissao_bunker = emissao_remocao_bunker
) %>%
dplyr::select(-nivel_6) %>%
dplyr::mutate(atividade_economica = dplyr::case_when(
atividade_economica == "PEC" ~ "Pecuaria",
atividade_economica == "Outra_IND" ~ "Outra Industria",
atividade_economica == "SANEAMENTO" ~ "Saneamento"
)) %>%
dplyr::mutate(produto = dplyr::case_when(
produto == "ALIM_BEBIDAS" ~ "Alimentos/Bebidas",
produto == "CAR" ~ "Carne"
)) %>%
dplyr::mutate(estado = dplyr::case_when(
estado == "NA" ~ NA_character_,
TRUE ~ estado
))
dat <- dat %>%
dplyr::relocate(Ano, estado, setor, categorias_emissao, processos_geradores_emissoes, atividade_geradora, categorias_processos_geradores, atividade_economica, produto, Valor)
}
if (param$dataset == "seeg_energy" & param$geo_level == "municipality" & param$language == "eng") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x2000:x2018,
names_to = "year",
names_prefix = "x",
values_to = "value"
)
dat <- dat %>%
dplyr::filter(nivel_1 == "Energia") %>%
dplyr::rename(
sector = nivel_1,
emission_type = nivel_2,
emissions_generating_processes = nivel_3,
activity = nivel_4,
energetic_source = nivel_5,
emitters = nivel_6,
emission_bunker = tipo_de_emissao,
product = produto,
city = municipio,
state = territorio
) %>%
dplyr::mutate(product = dplyr::case_when(
product == "ALIM_BEBIDAS" ~ "Food/Beverages",
product == "ENE_ELET" ~ "Eletric energy",
product == "ALU" ~ "Aluminum"
)) %>%
dplyr::mutate(sector = dplyr::case_when(sector == "Energia" ~ "Energy")) %>%
dplyr::mutate(emission_type = dplyr::case_when(
emission_type == "Emissoes Fugitivas" ~ "Fugitive emissions",
emission_type == "Emissoes pela Queima de Combustiveis" ~ "Emissions from fuel burning"
)) %>%
dplyr::mutate(emissions_generating_processes = dplyr::case_when(
emissions_generating_processes == "Agropecuario" ~ "Farming",
emissions_generating_processes == "Geracao de Eletricidade (Servico Publico)" ~ "Eletricity generation (public service)",
emissions_generating_processes == "Nao Identificado" ~ "Not Identified",
emissions_generating_processes == "Publico" ~ "Public",
emissions_generating_processes == "Transportes" ~ "Transports",
emissions_generating_processes == "Comercial" ~ "Business",
emissions_generating_processes == "Industrial" ~ "Industrial",
emissions_generating_processes == "Producao de Combustiveis" ~ "Fuel production",
emissions_generating_processes == "Residencial" ~ "Residential"
)) %>%
dplyr::mutate(activity = dplyr::case_when(
activity == "Aereo" ~ "Air",
activity == "Cimento" ~ "Cement",
activity == "Ferro Ligas" ~ "Iron garters",
activity == "Mineracao e pelotizacao" ~ "Mining and pelletizing",
activity == "Outras industrias" ~ "Other industries",
activity == "Producao de carvao mineral e outros" ~ " Mineral coal production and others",
activity == "Refino de petroleo" ~ "Oil refining",
activity == "Transporte de gas natural" ~ "Natural gas transport",
activity == "Alimentos e bebidas" ~ "Food and beverages",
activity == "Exploracao de petroleo e gas natural" ~ "Oil and natural gas exploration",
activity == "Ferroviario" ~ "Railroad",
activity == "Nao ferrosos e outros da metalurgia" ~ "Non-ferrous and other metallurgy",
activity == "Papel e celulose" ~ "Paper and Cellulose",
activity == "Producao de carvao vegetal" ~ "Charcoal production",
activity == "Rodoviario" ~ "Road",
activity == "Ceramica" ~ "Ceramics",
activity == "Ferro gusa e aco" ~ "Pig iron and steel",
activity == "Hidroviario" ~ "Waterway",
activity == "NAO SE APLICA" ~ "Not Applicable",
activity == "Producao de Alcool" ~ "Alcohol production",
activity == "Quimica" ~ "Chemical",
activity == "Textil" ~ "Textile"
)) %>%
dplyr::mutate(energetic_source = dplyr::case_when(
energetic_source == "Alcatrao" ~ "Tar",
energetic_source == "Biogas" ~ "Biogas",
energetic_source == "Carvao vapor 3300" ~ "Steam coal 3300",
energetic_source == "Carvao vapor 4500" ~ "Steam coal 4500",
energetic_source == "Carvao vapor 5900" ~ "Steam coal 5900",
energetic_source == "Carvao vegetal" ~ "Charcoal",
energetic_source == "Diesel de petroleo" ~ "Petroleum diesel",
energetic_source == "Gas de coqueria" ~ "Coke oven gas",
energetic_source == "Gas natural seco" ~ "Dry natural gas",
energetic_source == "Gasolina C" ~ "Gasoline C",
energetic_source == "Lenha" ~ "Firewood",
energetic_source == "Nafta" ~ "Naphta",
energetic_source == "Outras biomassas" ~ "Other biomasses",
energetic_source == "Petroleo" ~ "Oil",
energetic_source == "Querosene iluminante" ~ "Illuminating kerosene",
energetic_source == "Petroleo e gas natural" ~ "Oil and natural gas",
energetic_source == "Outras nao renovaveis" ~ "Other non-renewable",
energetic_source == "Oleo combustivel" ~ "Fuel oil",
energetic_source == "Lenha carvoejamento" ~ "Charcoal firewood",
energetic_source == "Gasolina de aviacao" ~ "Aviation gasoline",
energetic_source == "Gas natural umido" ~ "Umid natural gas",
energetic_source == "Gas de refinaria" ~ "Refinery gas",
energetic_source == "Gas canalizado RJ" ~ "Piped gas RJ",
energetic_source == "Coque de carvao mineral" ~ "Coal coke",
energetic_source == "Carvao vapor 6000" ~ "Steam coal 6000",
energetic_source == "Carvao vapor 4700" ~ "Steam coal 4700",
energetic_source == "Carvao vapor 3700" ~ "Steam coal 3700",
energetic_source == "Carvao mineral" ~ "Mineral coal",
energetic_source == "Alcool hidratado" ~ "Hydrated alcohol",
energetic_source == "Bagaco de cana" ~ "Sugarcane bagasse",
energetic_source == "Carvao vapor 3100" ~ "Steam coal 3100",
energetic_source == "Carvao vapor 4200" ~ "Steam coal 4200",
energetic_source == "Carvao vapor 5200" ~ "Steam coal 5200",
energetic_source == "Carvao vapor sem especificacao" ~ "Steam coal without specification",
energetic_source == "Coque de petroleo" ~ "Oil coke",
energetic_source == "Gas canalizadp SP" ~ "Piped gas SP",
energetic_source == "Gas natural" ~ "Natural gas",
energetic_source == "Gasolina autmotiva" ~ "Automotive gasoline",
energetic_source == "GLP" ~ "Liquefied oil gas",
energetic_source == "Lixivia" ~ "Bleach",
energetic_source == "Oleo diesel" ~ "Diesel oil",
energetic_source == "Outros energeticos de petroleo" ~ "Other petroleum energy",
energetic_source == "Querosene de aviacao" ~ "Aviation kerosene"
)) %>%
dplyr::mutate(emitters = dplyr::case_when(
emitters == "Aeronaves" ~ "Airplanes",
emitters == "Carvoarias" ~ "Charcoals",
emitters == "Comerciais Leves" ~ "Light commercials",
emitters == "Locomotivas" ~ "Locomotives",
emitters == "Onibus" ~ "Bus",
emitters == "Automoveis" ~ "Automobiles",
emitters == "Centrais Eletricas Autoprodutoras" ~ "Self-producing power plants",
emitters == "Consumo Final Energetico" ~ "Final energy consumption",
emitters == "Motocicletas" ~ "Motorbikes",
emitters == "Caminhoes" ~ "Trucks",
emitters == "Centrais Eletricas de Servico Publico" ~ "Public service power plants",
emitters == "Embarcacoes" ~ "Vessels",
emitters == "NAO SE APLICA" ~ "Not Applicable"
)) %>%
dplyr::mutate(emission_bunker = dplyr::case_when(
emission_bunker == "Emissao" ~ "Emission",
emission_bunker == "Bunker" ~ "Bunker"
)) %>%
dplyr::select(-atividade_economica)
dat <- dat %>%
dplyr::relocate(year, city, state, sector)
dat <- dat %>%
dplyr::mutate(city = dplyr::case_when(
city == "NA" ~ NA_character_,
TRUE ~ city
)) %>%
dplyr::mutate(state = dplyr::case_when(
state == "NA" ~ NA_character_,
TRUE ~ state
)) %>%
dplyr::mutate(ibge = dplyr::case_when(
ibge == "NA" ~ NA_character_,
TRUE ~ ibge
))
}
if (param$dataset == "seeg_energy" & param$geo_level %in% c("country", "state") & param$language == "eng") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x1970:x2021,
names_to = "year",
names_prefix = "x",
values_to = "value"
)
dat <- dat %>%
dplyr::filter(nivel_1_setor == "Energia") %>%
dplyr::rename(
sector = nivel_1_setor, state = estado,
emission_type = nivel_2,
emissions_generating_processes = nivel_3,
activity = nivel_4,
energetic_source = nivel_5,
emitters = nivel_6,
emission_bunker = emissao_remocao_bunker,
product = produto
) %>%
dplyr::mutate(product = dplyr::case_when(
product == "ALIM_BEBIDAS" ~ "Food/Beverages",
product == "ENE_ELET" ~ "Eletric energy",
product == "ALU" ~ "Aluminum"
)) %>%
dplyr::mutate(sector = dplyr::case_when(sector == "Energia" ~ "Energy")) %>%
dplyr::mutate(emission_type = dplyr::case_when(
emission_type == "Emissoes Fugitivas" ~ "Fugitive emissions",
emission_type == "Emissoes pela Queima de Combustiveis" ~ "Emissions from fuel burning"
)) %>%
dplyr::mutate(emissions_generating_processes = dplyr::case_when(
emissions_generating_processes == "Agropecuario" ~ "Farming",
emissions_generating_processes == "Geracao de Eletricidade (Servico Publico)" ~ "Eletricity generation (public service)",
emissions_generating_processes == "Nao Identificado" ~ "Not Identified",
emissions_generating_processes == "Publico" ~ "Public",
emissions_generating_processes == "Transportes" ~ "Transports",
emissions_generating_processes == "Comercial" ~ "Business",
emissions_generating_processes == "Industrial" ~ "Industrial",
emissions_generating_processes == "Producao de Combustiveis" ~ "Fuel production",
emissions_generating_processes == "Residencial" ~ "Residential"
)) %>%
dplyr::mutate(activity = dplyr::case_when(
activity == "Aereo" ~ "Air",
activity == "Cimento" ~ "Cement",
activity == "Ferro Ligas" ~ "Iron garters",
activity == "Mineracao e pelotizacao" ~ "Mining and pelletizing",
activity == "Outras industrias" ~ "Other industries",
activity == "Producao de carvao mineral e outros" ~ " Mineral coal production and others",
activity == "Refino de petroleo" ~ "Oil refining",
activity == "Transporte de gas natural" ~ "Natural gas transport",
activity == "Alimentos e bebidas" ~ "Food and beverages",
activity == "Exploracao de petroleo e gas natural" ~ "Oil and natural gas exploration",
activity == "Ferroviario" ~ "Railroad",
activity == "Nao ferrosos e outros da metalurgia" ~ "Non-ferrous and other metallurgy",
activity == "Papel e celulose" ~ "Paper and Cellulose",
activity == "Producao de carvao vegetal" ~ "Charcoal production",
activity == "Rodoviario" ~ "Road",
activity == "Ceramica" ~ "Ceramics",
activity == "Ferro gusa e aco" ~ "Pig iron and steel",
activity == "Hidroviario" ~ "Waterway",
activity == "NAO SE APLICA" ~ "Not Applicable",
activity == "Producao de Alcool" ~ "Alcohol production",
activity == "Quimica" ~ "Chemical",
activity == "Textil" ~ "Textile"
)) %>%
dplyr::mutate(energetic_source = dplyr::case_when(
energetic_source == "Alcatrao" ~ "Tar",
energetic_source == "Biogas" ~ "Biogas",
energetic_source == "Carvao vapor 3300" ~ "Steam coal 3300",
energetic_source == "Carvao vapor 4500" ~ "Steam coal 4500",
energetic_source == "Carvao vapor 5900" ~ "Steam coal 5900",
energetic_source == "Carvao vegetal" ~ "Charcoal",
energetic_source == "Diesel de petroleo" ~ "Petroleum diesel",
energetic_source == "Gas de coqueria" ~ "Coke oven gas",
energetic_source == "Gas natural seco" ~ "Dry natural gas",
energetic_source == "Gasolina C" ~ "Gasoline C",
energetic_source == "Lenha" ~ "Firewood",
energetic_source == "Nafta" ~ "Naphta",
energetic_source == "Outras biomassas" ~ "Other biomasses",
energetic_source == "Petroleo" ~ "Oil",
energetic_source == "Querosene iluminante" ~ "Illuminating kerosene",
energetic_source == "Petroleo e gas natural" ~ "Oil and natural gas",
energetic_source == "Outras nao renovaveis" ~ "Other non-renewable",
energetic_source == "Oleo combustivel" ~ "Fuel oil",
energetic_source == "Lenha carvoejamento" ~ "Charcoal firewood",
energetic_source == "Gasolina de aviacao" ~ "Aviation gasoline",
energetic_source == "Gas natural umido" ~ "Umid natural gas",
energetic_source == "Gas de refinaria" ~ "Refinery gas",
energetic_source == "Gas canalizado RJ" ~ "Piped gas RJ",
energetic_source == "Coque de carvao mineral" ~ "Coal coke",
energetic_source == "Carvao vapor 6000" ~ "Steam coal 6000",
energetic_source == "Carvao vapor 4700" ~ "Steam coal 4700",
energetic_source == "Carvao vapor 3700" ~ "Steam coal 3700",
energetic_source == "Carvao mineral" ~ "Mineral coal",
energetic_source == "Alcool hidratado" ~ "Hydrated alcohol",
energetic_source == "Bagaco de cana" ~ "Sugarcane bagasse",
energetic_source == "Carvao vapor 3100" ~ "Steam coal 3100",
energetic_source == "Carvao vapor 4200" ~ "Steam coal 4200",
energetic_source == "Carvao vapor 5200" ~ "Steam coal 5200",
energetic_source == "Carvao vapor sem especificacao" ~ "Steam coal without specification",
energetic_source == "Coque de petroleo" ~ "Oil coke",
energetic_source == "Gas canalizadp SP" ~ "Piped gas SP",
energetic_source == "Gas natural" ~ "Natural gas",
energetic_source == "Gasolina autmotiva" ~ "Automotive gasoline",
energetic_source == "GLP" ~ "Liquefied oil gas",
energetic_source == "Lixivia" ~ "Bleach",
energetic_source == "Oleo diesel" ~ "Diesel oil",
energetic_source == "Outros energeticos de petroleo" ~ "Other petroleum energy",
energetic_source == "Querosene de aviacao" ~ "Aviation kerosene"
)) %>%
dplyr::mutate(emitters = dplyr::case_when(
emitters == "Aeronaves" ~ "Airplanes",
emitters == "Carvoarias" ~ "Charcoals",
emitters == "Comerciais Leves" ~ "Light commercials",
emitters == "Locomotivas" ~ "Locomotives",
emitters == "Onibus" ~ "Bus",
emitters == "Automoveis" ~ "Automobiles",
emitters == "Centrais Eletricas Autoprodutoras" ~ "Self-producing power plants",
emitters == "Consumo Final Energetico" ~ "Final energy consumption",
emitters == "Motocicletas" ~ "Motorbikes",
emitters == "Caminhoes" ~ "Trucks",
emitters == "Centrais Eletricas de Servico Publico" ~ "Public service power plants",
emitters == "Embarcacoes" ~ "Vessels",
emitters == "NAO SE APLICA" ~ "Not Applicable"
)) %>%
dplyr::mutate(emission_bunker = dplyr::case_when(
emission_bunker == "Emissao" ~ "Emission",
emission_bunker == "Bunker" ~ "Bunker"
)) %>%
dplyr::select(-atividade_economica)
dat <- dat %>%
dplyr::mutate(state = dplyr::case_when(
state == "NA" ~ NA_character_,
TRUE ~ state
))
dat <- dat %>%
dplyr::relocate(year, state, sector)
}
if (param$dataset == "seeg_industry" & param$geo_level == "municipality" & param$language == "eng") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x2000:x2018,
names_to = "year",
names_prefix = "x",
values_to = "value"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1 == "Processos Industriais") %>%
dplyr::rename(
sector = nivel_1, emissions_generating_processes = nivel_2,
emitters_production = nivel_3,
emitters = nivel_4,
emission_bunker = tipo_de_emissao,
city = municipio,
state = territorio,
economic_activity = atividade_economica,
product = produto
) %>%
dplyr::select(-c(nivel_6, nivel_5)) %>%
dplyr::mutate(sector = dplyr::case_when(sector == "Processos Industriais" ~ "Industrial Processes")) %>%
dplyr::mutate(emissions_generating_processes = dplyr::case_when(
emissions_generating_processes == "Emissoes de HFCs" ~ "HFC Emissions",
emissions_generating_processes == "Producao de Metais" ~ "Metal Production",
emissions_generating_processes == "Uso de SF6" ~ "SF6 Use",
emissions_generating_processes == "Industria Quimica" ~ "Chemical Industry",
emissions_generating_processes == "Produtos Minerais" ~ "Mineral Products",
emissions_generating_processes == "Uso Nao-Energetico de Combustiveis e Uso de Solventes" ~ "Non-Energy Use of Fuels and Use of Solvents"
)) %>%
dplyr::mutate(emitters_production = dplyr::case_when(
emitters_production == "Consumo de Barrilha" ~ "Barrel Consumption",
emitters_production == "Equipamentos Eletricos" ~ "Eletric Equipment",
emitters_production == "Producao de ABS" ~ "ABS Plastic Production",
emitters_production == "Producao de Acido Fosforico" ~ "Production of Phosphoric Acid",
emitters_production == "Producao de Acrilonitrila" ~ "Acrylonitrile Production",
emitters_production == "Producao de Amonia" ~ "Ammonia Production",
emitters_production == "Producao de Borracha de butadieno estireno (SBR)" ~ "Production of Styrene Butadiene Rubber",
emitters_production == "Producao de Caprolactama" ~ "Production of Caprolactam",
emitters_production == "Producao de Cimento" ~ "Cement Production",
emitters_production == "Producao de Coque de Petroleo Calcinado" ~ "Production of Calcined Petroleum Coke",
emitters_production == "Producao de Estireno" ~ "Styrene production",
emitters_production == "Producao de Etilbenzeno" ~ "Ethylbenzene production",
emitters_production == "Producao de Ferroligas" ~ "Ferroalloy Production",
emitters_production == "Producao de Magnesio" ~ "Magnesium Production",
emitters_production == "Producao de Negro-de-fumo" ~ "Carbon Black Production",
emitters_production == "Producao de Oxido de Eteno" ~ "Ethylene Oxide Production",
emitters_production == "Producao de Polietileno PEAD" ~ "HDPE Polyethylene Production",
emitters_production == "Producao de Polietileno PELBD" ~ "LLDPE Polyethylene Production",
emitters_production == "Producao de Propeno" ~ "Propylene Production",
emitters_production == "Producao de Vidro" ~ "Glass Production",
emitters_production == "Producao de PVC" ~ "PVC Production",
emitters_production == "Producao de Polipropileno" ~ "Polypropylene Production",
emitters_production == "Producao de Polietileno PEBD" ~ "LDPE Polyethylene Production",
emitters_production == "Producao de Poliestireno" ~ "Polystyrene Production",
emitters_production == "Producao de Outros Nao-Ferrosos" ~ "Production of Other Non-Ferrous",
emitters_production == "Producao de Metanol" ~ "Methanol Production",
emitters_production == "Producao de Formaldeido" ~ "Formaldehyde Production",
emitters_production == "Producao de Ferro Gusa e Aco" ~ "Pig Iron and Steel Production",
emitters_production == "Producao de Eteno" ~ "Ethene Production",
emitters_production == "Producao de Dicloroetano" ~ "Dichloroethane Production",
emitters_production == "Producao de Cloreto de Vinila" ~ "Vinyl Chloride Production",
emitters_production == "Producao de Carbureto de Calcio" ~ "Calcium Carbide Production",
emitters_production == "Producao de Cal" ~ "Lime Production",
emitters_production == "Producao de Anidrido Ftalico" ~ "Production of Phthalic Anhydride",
emitters_production == "Producao de Aluminio" ~ "Aluminum Production",
emitters_production == "Producao de Acido Nitrico" ~ "Nitric Acid Production",
emitters_production == "Producao de Acido Adipico" ~ "Adipic Acid Production",
emitters_production == "NAO SE APLICA" ~ "Not Applicable",
emitters_production == "Consumo Final Nao Energetico" ~ "Non-Energy Final Consumption"
)) %>%
dplyr::mutate(emitters = dplyr::case_when(
emitters == "Cal Calcitica" ~ "Calcitic lime",
emitters == "Consumo de Calcario" ~ "Limestone Consumption",
emitters == "Consumo em Outros Setores" ~ "Consumption in Other Sectors",
emitters == "Tecnologia Soderberg" ~ "Soderberg Technology",
emitters == "Cal Dolomitica" ~ "Dolomitic Lime",
emitters == "Consumo de Combustiveis Redutores" ~ "Consumption of Reducing Fuels",
emitters == "NAO SE APLICA" ~ "Not Applicable",
emitters == "Uso de SF6" ~ "SF6 Use",
emitters == "Cal Magnesiana" ~ "Magnesian Lime",
emitters == "Consumo de Dolomita" ~ "Dolomite Consumption",
emitters == "Tecnologia Prebaked Anode" ~ "Prebaked Anode Technology"
)) %>%
dplyr::mutate(emission_bunker = dplyr::case_when(emission_bunker == "Emissao" ~ "Emission")) %>%
dplyr::mutate(economic_activity = dplyr::case_when(
economic_activity == "CIM" ~ "Cement Industry",
economic_activity == "ENE_ELET" ~ "Eletric Power Industry",
economic_activity == "MET" ~ "Metal Industry",
economic_activity == "Outra_IND" ~ "Other Industry",
economic_activity == "HFC" ~ "HFC"
))
dat <- dat %>%
dplyr::mutate(city = dplyr::case_when(
city == "NA" ~ NA_character_,
TRUE ~ city
)) %>%
dplyr::mutate(state = dplyr::case_when(
state == "NA" ~ NA_character_,
TRUE ~ state
)) %>%
dplyr::mutate(ibge = dplyr::case_when(
ibge == "NA" ~ NA_character_,
TRUE ~ ibge
)) %>%
dplyr::mutate(product = dplyr::case_when(
product == "ALU" ~ "Aluminum",
product == "ACO" ~ "Steel"
))
dat <- dat %>%
dplyr::relocate(year, city, state, sector, emissions_generating_processes, emitters_production, emitters, economic_activity, gas, product, value)
}
if (param$dataset == "seeg_industry" & param$geo_level %in% c("country", "state") & param$language == "eng") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x1970:x2021,
names_to = "year",
names_prefix = "x",
values_to = "value"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1_setor == "Processos Industriais") %>%
dplyr::rename(
sector = nivel_1_setor, emissions_generating_processes = nivel_2,
emitters_production = nivel_3,
emitters = nivel_4,
emission_bunker = emissao_remocao_bunker,
economic_activity = atividade_economica,
product = produto,
state = estado
) %>%
dplyr::select(-c(nivel_6, nivel_5)) %>%
dplyr::mutate(sector = dplyr::case_when(sector == "Processos Industriais" ~ "Industrial Processes")) %>%
dplyr::mutate(emissions_generating_processes = dplyr::case_when(
emissions_generating_processes == "Emissoes de HFCs" ~ "HFC Emissions",
emissions_generating_processes == "Producao de Metais" ~ "Metal Production",
emissions_generating_processes == "Uso de SF6" ~ "SF6 Use",
emissions_generating_processes == "Industria Quimica" ~ "Chemical Industry",
emissions_generating_processes == "Produtos Minerais" ~ "Mineral Products",
emissions_generating_processes == "Uso Nao-Energetico de Combustiveis e Uso de Solventes" ~ "Non-Energy Use of Fuels and Use of Solvents"
)) %>%
dplyr::mutate(emitters_production = dplyr::case_when(
emitters_production == "Consumo de Barrilha" ~ "Barrel Consumption",
emitters_production == "Equipamentos Eletricos" ~ "Eletric Equipment",
emitters_production == "Producao de ABS" ~ "ABS Plastic Production",
emitters_production == "Producao de Acido Fosforico" ~ "Production of Phosphoric Acid",
emitters_production == "Producao de Acrilonitrila" ~ "Acrylonitrile Production",
emitters_production == "Producao de Amonia" ~ "Ammonia Production",
emitters_production == "Producao de Borracha de butadieno estireno (SBR)" ~ "Production of Styrene Butadiene Rubber",
emitters_production == "Producao de Caprolactama" ~ "Production of Caprolactam",
emitters_production == "Producao de Cimento" ~ "Cement Production",
emitters_production == "Producao de Coque de Petroleo Calcinado" ~ "Production of Calcined Petroleum Coke",
emitters_production == "Producao de Estireno" ~ "Styrene production",
emitters_production == "Producao de Etilbenzeno" ~ "Ethylbenzene production",
emitters_production == "Producao de Ferroligas" ~ "Ferroalloy Production",
emitters_production == "Producao de Magnesio" ~ "Magnesium Production",
emitters_production == "Producao de Negro-de-fumo" ~ "Carbon Black Production",
emitters_production == "Producao de Oxido de Eteno" ~ "Ethylene Oxide Production",
emitters_production == "Producao de Polietileno PEAD" ~ "HDPE Polyethylene Production",
emitters_production == "Producao de Polietileno PELBD" ~ "LLDPE Polyethylene Production",
emitters_production == "Producao de Propeno" ~ "Propylene Production",
emitters_production == "Producao de Vidro" ~ "Glass Production",
emitters_production == "Producao de PVC" ~ "PVC Production",
emitters_production == "Producao de Polipropileno" ~ "Polypropylene Production",
emitters_production == "Producao de Polietileno PEBD" ~ "LDPE Polyethylene Production",
emitters_production == "Producao de Poliestireno" ~ "Polystyrene Production",
emitters_production == "Producao de Outros Nao-Ferrosos" ~ "Production of Other Non-Ferrous",
emitters_production == "Producao de Metanol" ~ "Methanol Production",
emitters_production == "Producao de Formaldeido" ~ "Formaldehyde Production",
emitters_production == "Producao de Ferro Gusa e Aco" ~ "Pig Iron and Steel Production",
emitters_production == "Producao de Eteno" ~ "Ethene Production",
emitters_production == "Producao de Dicloroetano" ~ "Dichloroethane Production",
emitters_production == "Producao de Cloreto de Vinila" ~ "Vinyl Chloride Production",
emitters_production == "Producao de Carbureto de Calcio" ~ "Calcium Carbide Production",
emitters_production == "Producao de Cal" ~ "Lime Production",
emitters_production == "Producao de Anidrido Ftalico" ~ "Production of Phthalic Anhydride",
emitters_production == "Producao de Aluminio" ~ "Aluminum Production",
emitters_production == "Producao de Acido Nitrico" ~ "Nitric Acid Production",
emitters_production == "Producao de Acido Adipico" ~ "Adipic Acid Production",
emitters_production == "NAO SE APLICA" ~ "Not Applicable",
emitters_production == "Consumo Final Nao Energetico" ~ "Non-Energy Final Consumption"
)) %>%
dplyr::mutate(emitters = dplyr::case_when(
emitters == "Cal Calcitica" ~ "Calcitic lime",
emitters == "Consumo de Calcario" ~ "Limestone Consumption",
emitters == "Consumo em Outros Setores" ~ "Consumption in Other Sectors",
emitters == "Tecnologia Soderberg" ~ "Soderberg Technology",
emitters == "Cal Dolomitica" ~ "Dolomitic Lime",
emitters == "Consumo de Combustiveis Redutores" ~ "Consumption of Reducing Fuels",
emitters == "NAO SE APLICA" ~ "Not Applicable",
emitters == "Uso de SF6" ~ "SF6 Use",
emitters == "Cal Magnesiana" ~ "Magnesian Lime",
emitters == "Consumo de Dolomita" ~ "Dolomite Consumption",
emitters == "Tecnologia Prebaked Anode" ~ "Prebaked Anode Technology"
)) %>%
dplyr::mutate(emission_bunker = dplyr::case_when(emission_bunker == "Emissao" ~ "Emission")) %>%
dplyr::mutate(economic_activity = dplyr::case_when(
economic_activity == "CIM" ~ "Cement Industry",
economic_activity == "ENE_ELET" ~ "Eletric Power Industry",
economic_activity == "MET" ~ "Metal Industry",
economic_activity == "Outra_IND" ~ "Other Industry",
economic_activity == "HFC" ~ "HFC"
)) %>%
dplyr::mutate(product = dplyr::case_when(
product == "ALU" ~ "Aluminum",
product == "ACO" ~ "Steel"
))
dat <- dat %>%
dplyr::mutate(state = dplyr::case_when(
state == "NA" ~ NA_character_,
TRUE ~ state
))
dat <- dat %>%
dplyr::relocate(year, state, sector, emissions_generating_processes, emitters_production, emitters, economic_activity, gas, product, value)
}
if (param$dataset == "seeg_residuals" & param$geo_level == "municipality" & param$language == "eng") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x2000:x2018,
names_to = "year",
names_prefix = "x",
values_to = "value"
)
dat <- dat %>%
dplyr::filter(nivel_1 == "Residuos") %>%
dplyr::rename(
sector = nivel_1, emissions_category = nivel_2,
emissions_generating_processes = nivel_3,
activity = nivel_4,
generating_processes_categories = nivel_5,
emission_bunker = tipo_de_emissao,
product = produto,
economic_activity = atividade_economica,
city = municipio,
state = territorio
) %>%
dplyr::select(-nivel_6) %>%
dplyr::mutate(economic_activity = dplyr::case_when(
economic_activity == "PEC" ~ "Livestock",
economic_activity == "Outra_IND" ~ "Other industry",
economic_activity == "SANEAMENTO" ~ "Sanitation"
)) %>%
dplyr::mutate(product = dplyr::case_when(
product == "ALIM_BEBIDAS" ~ "Food/Beverages",
product == "CAR" ~ "Meat"
)) %>%
dplyr::mutate(sector = dplyr::case_when(sector == "Residuos" ~ "Waste")) %>%
dplyr::mutate(emissions_category = dplyr::case_when(
emissions_category == "Efluentes Liquidos" ~ "Liquid Effluents",
emissions_category == "Residuos Solidos" ~ "Solid Waste"
)) %>%
dplyr::mutate(emissions_generating_processes = dplyr::case_when(
emissions_generating_processes == "Disposicao Final de Residuos Solidos" ~ "Final Disposal of Solid Waste",
emissions_generating_processes == "Efluentes Liquidos Industriais" ~ "Industrial Liquid Effluents",
emissions_generating_processes == "Tratamento Biologico de Residuos Solidos" ~ "Biological Treatment of Solid Waste",
emissions_generating_processes == "Efluentes Liquidos Domesticos" ~ "Domestic Liquid Effluents",
emissions_generating_processes == "Incineracao ou queima a ceu aberto" ~ "Incineration or open burning"
)) %>%
dplyr::mutate(activity = dplyr::case_when(
activity == "Producao de Carne Bovina" ~ "Beef Production",
activity == "NAO SE APLICA" ~ "Not Applicable",
activity == "Producao de Cerveja" ~ "Beer Production",
activity == "Producao de Leite Cru" ~ "Raw Dairy Production",
activity == "Producao de Carne Suina" ~ "Prok Production",
activity == "Producao de Carne Avicola" ~ "Poultry Meat Production",
activity == "Producao de Celulose" ~ "Cellulose Production",
activity == "Producao de Leite Pasteurizado" ~ "Pasteurized Dairy Production",
activity == "Residuos Solidos Urbanos" ~ "Urban Solid Waste",
activity == "Residuos de Servicos de Saude" ~ "Health Services Waste",
activity == "Queima de Residuos a Ceu Aberto" ~ "Open-air Waste Burning",
activity == "Tratamento de Residuos por Incineracao" ~ "Waste Treatment by Incineration",
activity == "Lodo de ETE" ~ "Sewage Treatment Plant Sludge"
)) %>%
dplyr::mutate(generating_processes_categories = dplyr::case_when(
generating_processes_categories == "Compostagem" ~ "Compost",
generating_processes_categories == "Residuos de Servicos de Saude" ~ "Health Services Waste",
generating_processes_categories == "Disposicao em Aterro Sanitario" ~ "Landfill Disposal",
generating_processes_categories == "Disposicao em Aterro Controlado ou Lixao" ~ "Disposal in Controlled Landfill or Landfill",
generating_processes_categories == "NAO SE APLICA" ~ "Not Applicable",
generating_processes_categories == "Residuos Solidos Urbanos" ~ "Urban Solid Waste"
)) %>%
dplyr::mutate(emission_bunker = dplyr::case_when(emission_bunker == "Emissao" ~ "Emission")) %>%
dplyr::mutate(city = dplyr::case_when(
city == "NA" ~ NA_character_,
TRUE ~ city
)) %>%
dplyr::mutate(state = dplyr::case_when(
state == "NA" ~ NA_character_,
TRUE ~ state
)) %>%
dplyr::mutate(ibge = dplyr::case_when(
ibge == "NA" ~ NA_character_,
TRUE ~ ibge
))
dat <- dat %>%
dplyr::relocate(year, city, state, sector, emissions_category, emissions_generating_processes, activity, generating_processes_categories, economic_activity, product, value)
}
if (param$dataset == "seeg_residuals" & param$geo_level %in% c("country", "state") & param$language == "eng") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x1970:x2021,
names_to = "year",
names_prefix = "x",
values_to = "value"
)
dat <- dat %>%
dplyr::filter(nivel_1_setor == "Residuos") %>%
dplyr::rename(
sector = nivel_1_setor, emissions_category = nivel_2,
emissions_generating_processes = nivel_3,
activity = nivel_4,
generating_processes_categories = nivel_5,
emission_bunker = emissao_remocao_bunker,
product = produto,
economic_activity = atividade_economica,
state = estado
) %>%
dplyr::select(-nivel_6) %>%
dplyr::mutate(economic_activity = dplyr::case_when(
economic_activity == "PEC" ~ "Livestock",
economic_activity == "Outra_IND" ~ "Other industry",
economic_activity == "SANEAMENTO" ~ "Sanitation"
)) %>%
dplyr::mutate(product = dplyr::case_when(
product == "ALIM_BEBIDAS" ~ "Food/Beverages",
product == "CAR" ~ "Meat"
)) %>%
dplyr::mutate(state = dplyr::case_when(
state == "BA" ~ "BA",
state == "ES" ~ "ES",
state == "MA" ~ "MA",
state == "NA" ~ as.character(NA),
state == "PA" ~ "PA",
state == "RJ" ~ "RJ",
state == "RS" ~ "RS",
state == "SP" ~ "SP",
state == "AC" ~ "AC",
state == "AL" ~ "AL",
state == "AP" ~ "AP",
state == "CE" ~ "CE",
state == "DF" ~ "DF",
state == "ES" ~ "ES",
state == "GO" ~ "GO",
state == "MG" ~ "MG",
state == "MS" ~ "MS",
state == "MT" ~ "MT",
state == "PB" ~ "PB",
state == "PE" ~ "PE",
state == "PI" ~ "PI",
state == "PR" ~ "PR",
state == "RN" ~ "RN",
state == "RO" ~ "RO",
state == "RR" ~ "RR",
state == "SC" ~ "SC",
state == "SE" ~ "SE",
state == "TO" ~ "TO",
state == "AM" ~ "AM"
)) %>%
dplyr::mutate(sector = dplyr::case_when(sector == "Residuos" ~ "Waste")) %>%
dplyr::mutate(emissions_category = dplyr::case_when(
emissions_category == "Efluentes Liquidos" ~ "Liquid Effluents",
emissions_category == "Residuos Solidos" ~ "Solid Waste"
)) %>%
dplyr::mutate(emissions_generating_processes = dplyr::case_when(
emissions_generating_processes == "Disposicao Final de Residuos Solidos" ~ "Final Disposal of Solid Waste",
emissions_generating_processes == "Efluentes Liquidos Industriais" ~ "Industrial Liquid Effluents",
emissions_generating_processes == "Tratamento Biologico de Residuos Solidos" ~ "Biological Treatment of Solid Waste",
emissions_generating_processes == "Efluentes Liquidos Domesticos" ~ "Domestic Liquid Effluents",
emissions_generating_processes == "Incineracao ou queima a ceu aberto" ~ "Incineration or open burning"
)) %>%
dplyr::mutate(activity = dplyr::case_when(
activity == "Producao de Carne Bovina" ~ "Beef Production",
activity == "NAO SE APLICA" ~ "Not Applicable",
activity == "Producao de Cerveja" ~ "Beer Production",
activity == "Producao de Leite Cru" ~ "Raw Dairy Production",
activity == "Producao de Carne Suina" ~ "Prok Production",
activity == "Producao de Carne Avicola" ~ "Poultry Meat Production",
activity == "Producao de Celulose" ~ "Cellulose Production",
activity == "Producao de Leite Pasteurizado" ~ "Pasteurized Dairy Production",
activity == "Residuos Solidos Urbanos" ~ "Urban Solid Waste",
activity == "Residuos de Servicos de Saude" ~ "Health Services Waste",
activity == "Queima de Residuos a Ceu Aberto" ~ "Open-air Waste Burning",
activity == "Tratamento de Residuos por Incineracao" ~ "Waste Treatment by Incineration",
activity == "Lodo de ETE" ~ "Sewage Treatment Plant Sludge"
)) %>%
dplyr::mutate(generating_processes_categories = dplyr::case_when(
generating_processes_categories == "Compostagem" ~ "Compost",
generating_processes_categories == "Residuos de Servicos de Saude" ~ "Health Services Waste",
generating_processes_categories == "Disposicao em Aterro Sanitario" ~ "Landfill Disposal",
generating_processes_categories == "Disposicao em Aterro Controlado ou Lixao" ~ "Disposal in Controlled Landfill or Landfill",
generating_processes_categories == "NAO SE APLICA" ~ "Not Applicable",
generating_processes_categories == "Residuos Solidos Urbanos" ~ "Urban Solid Waste"
)) %>%
dplyr::mutate(emission_bunker = dplyr::case_when(emission_bunker == "Emissao" ~ "Emission"))
dat <- dat %>%
dplyr::mutate(state = dplyr::case_when(
state == "NA" ~ NA_character_,
TRUE ~ state
))
dat <- dat %>%
dplyr::relocate(year, state, sector, emissions_category, emissions_generating_processes, activity, generating_processes_categories, economic_activity, product, value)
}
if (param$dataset == "seeg_land" & param$geo_level == "municipality" & param$language == "eng") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x2000:x2018,
names_to = "year",
names_prefix = "x",
values_to = "value"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1 == "Mudanca de Uso da Terra e Floresta") %>%
dplyr::filter(!is.na(value)) %>%
dplyr::rename(
sector = nivel_1, emissions_generating_processes = nivel_2,
biome = nivel_3,
biome_area = nivel_4,
transition_type = nivel_5,
category = nivel_6,
emission_removal_bunker = tipo_de_emissao,
economic_activity = atividade_economica,
city = municipio,
state = territorio
) %>%
dplyr::mutate(sector = dplyr::case_when(sector == "Mudanca de Uso da Terra e Floresta" ~ "Land and Forest Use Change")) %>%
dplyr::mutate(emissions_generating_processes = dplyr::case_when(
emissions_generating_processes == "Alteracoes de Uso do Solo" ~ "Changes in Soil Use",
emissions_generating_processes == "Remocao por Vegetacao Secundaria" ~ "Removal by Secondary Vegetation",
emissions_generating_processes == "Remocao em Areas Protegidas" ~ "Removal in Protected Areas",
emissions_generating_processes == "Residuos Florestais" ~ "Forest Waste",
emissions_generating_processes == "Remocao por Mudanca de Uso da Terra" ~ "Removal for Land Use Change"
)) %>%
dplyr::mutate(biome = dplyr::case_when(
biome == "Amazonia" ~ "Amazon",
biome == "Caatinga" ~ "Caatinga",
biome == "Cerrado" ~ "Cerrado",
biome == "Mata Atlantica" ~ "Mata Atlantica",
biome == "Pampa" ~ "Pampa",
biome == "Pantanal" ~ "Pantanal"
)) %>%
dplyr::mutate(biome_area = dplyr::case_when(
biome_area == "em Area Protegida" ~ "In Protected Area",
biome_area == "fora de Area Protegida" ~ "Outside Protected Area",
biome_area == "NAO SE APLICA" ~ "Not Applicable"
)) %>%
dplyr::mutate(transition_type = dplyr::case_when(
transition_type == "Desmatamento" ~ "Deforestation",
transition_type == "Regeneracao" ~ "Regeneration",
transition_type == "NAO SE APLICA" ~ "Not Applicable",
transition_type == "Vegetacao nativa estavel" ~ "Stable Native Vegetation",
transition_type == "Outras Mudancas de uso da terra" ~ "Other Land Use Changes"
)) %>%
dplyr::mutate(activity = dplyr::case_when(
category == "Area sem vegetacao -- Floresta secundaria" ~ "Area without Vegetation -- Secondary Forest",
category == "Area sem vegetacao -- Silvicultura" ~ "Area without Vegetation -- Forestry",
category == "Area sem vegetacao -- Uso agropecuario" ~ "Area without Vegetation -- Farming Use",
category == "Area sem vegetacao -- Vegetacao nao florestal secundaria" ~ "Area without Vegetation -- Secondary Non-Forest Vegetation",
category == "Floresta primaria -- Area sem vegetacao" ~ "Primary Forest -- Area without Vegetation",
category == "Floresta primaria -- Floresta primaria" ~ "Primary Forest -- Primary Forest",
category == "Floresta primaria -- Silvicultura" ~ "Primary Forest -- Forestry",
category == "Floresta primaria -- Uso agropecuario" ~ "Primary Forest -- Farming Use",
category == "Floresta secundaria -- Area sem vegetacao" ~ "Secondary Forest -- Area without Vegetation",
category == "Floresta secundaria -- Floresta secundaria" ~ "Secondary Forest -- Secondary Forest",
category == "Floresta secundaria -- Silvicultura" ~ "Secondary Forest -- Forestry",
category == "Floresta secundaria -- Uso agropecuario" ~ "Secondary Forest -- Farming Use",
category == "NAO SE APLICA" ~ "Not Applicable",
category == "Silvicultura -- Area sem vegetacao" ~ "Forestry -- Area without Vegetation",
category == "Silvicultura -- Floresta Secundaria" ~ "Forestry -- Secondary Forest",
category == "Silvicultura -- Uso agropecuario" ~ "Forestry -- Farming Use",
category == "Silvicultura -- Vegetacao nao florestal secundaria" ~ "Forestry -- Secondary non-Forest Vegetation",
category == "Uso agropecuario -- Area sem vegetacao" ~ "Farming Use -- Area without vegetation",
category == "Uso agropecuario -- Floresta secundaria" ~ "Farming Use -- Secondary Forest",
category == "Uso agropecuario -- Silvicultura" ~ "Farming Use -- Forestry",
category == "Uso agropecuario -- Uso agropecuario" ~ "Farming Use -- Farming Use",
category == "Uso agropecuario -- Vegetacao nao florestal secundaria" ~ "Farming Use -- Secondary non-Forest Vegetation",
category == "Vegetacao nao florestal primaria -- Area sem vegetacao" ~ "Primary non-Forest Vegetation -- Area without Vegetation",
category == "Vegetacao nao florestal primaria -- Silvicultura" ~ "Primary Non-Forest Vegetation -- Forestry",
category == "Vegetacao nao florestal primaria -- Uso agropecuario" ~ "Primary non-Forest Vegetation -- Farming use",
category == "Vegetacao nao florestal primaria -- Vegetacao nao florestal primaria" ~ "Primary non-Forest Vegetation -- Primary non-Forest Vegetation",
category == "Vegetacao nao florestal secundaria -- Area sem vegetacao" ~ "Secondary non-Forest Vegetation -- Area without Vegetation",
category == "Vegetacao nao florestal secundaria -- Silvicultura" ~ "Secondary non-Forest Vegetation -- Forestry",
category == "Vegetacao nao florestal secundaria -- Uso agropecuario" ~ "Secondary non-Forest Vegetation -- Farming use",
category == "Vegetacao nao florestal secundaria -- Vegetacao nao florestal secundaria" ~ "Secondary non-Forest Vegetation"
)) %>%
dplyr::mutate(emission_removal_bunker = dplyr::case_when(
emission_removal_bunker == "Emissao" ~ "Emission",
emission_removal_bunker == "Remocao" ~ "Removal",
emission_removal_bunker == "Remocao proxy" ~ "Proxy Removal"
)) %>%
dplyr::mutate(economic_activity = dplyr::case_when(
economic_activity == "AGROPEC" ~ "Farming",
economic_activity == "Conservacao" ~ "Conservation"
)) %>%
dplyr::select(-produto)
dat <- dat %>%
dplyr::mutate(city = dplyr::case_when(
city == "NA" ~ NA_character_,
TRUE ~ city
)) %>%
dplyr::mutate(state = dplyr::case_when(
state == "NA" ~ NA_character_,
TRUE ~ state
)) %>%
dplyr::mutate(ibge = dplyr::case_when(
ibge == "NA" ~ NA_character_,
TRUE ~ ibge
))
dat <- dat %>%
dplyr::relocate(year, city, state, sector, emissions_generating_processes, economic_activity, biome, biome_area, transition_type, emission_removal_bunker)
}
if (param$dataset == "seeg_land" & param$geo_level %in% c("country", "state") & param$language == "eng") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x1970:x2021,
names_to = "year",
names_prefix = "x",
values_to = "value"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1_setor == "Mudanca de Uso da Terra e Floresta") %>%
dplyr::filter(!is.na(value)) %>%
dplyr::rename(
sector = nivel_1_setor, emissions_generating_processes = nivel_2,
biome = nivel_3,
biome_area = nivel_4,
transition_type = nivel_5,
category = nivel_6,
emission_removal_bunker = emissao_remocao_bunker,
economic_activity = atividade_economica,
state = estado
) %>%
dplyr::mutate(sector = dplyr::case_when(sector == "Mudanca de Uso da Terra e Floresta" ~ "Land and Forest Use Change")) %>%
dplyr::mutate(emissions_generating_processes = dplyr::case_when(
emissions_generating_processes == "Alteracoes de Uso do Solo" ~ "Changes in Soil Use",
emissions_generating_processes == "Remocao por Vegetacao Secundaria" ~ "Removal by Secondary Vegetation",
emissions_generating_processes == "Remocao em Areas Protegidas" ~ "Removal in Protected Areas",
emissions_generating_processes == "Residuos Florestais" ~ "Forest Waste",
emissions_generating_processes == "Remocao por Mudanca de Uso da Terra" ~ "Removal for Land Use Change"
)) %>%
dplyr::mutate(biome = dplyr::case_when(
biome == "Amazonia" ~ "Amazon",
biome == "Caatinga" ~ "Caatinga",
biome == "Cerrado" ~ "Cerrado",
biome == "Mata Atlantica" ~ "Mata Atlantica",
biome == "Pampa" ~ "Pampa",
biome == "Pantanal" ~ "Pantanal"
)) %>%
dplyr::mutate(biome_area = dplyr::case_when(
biome_area == "em Area Protegida" ~ "In Protected Area",
biome_area == "fora de Area Protegida" ~ "Outside Protected Area",
biome_area == "NAO SE APLICA" ~ "Not Applicable"
)) %>%
dplyr::mutate(transition_type = dplyr::case_when(
transition_type == "Desmatamento" ~ "Deforestation",
transition_type == "Regeneracao" ~ "Regeneration",
transition_type == "NAO SE APLICA" ~ "Not Applicable",
transition_type == "Vegetacao nativa estavel" ~ "Stable Native Vegetation",
transition_type == "Outras Mudancas de uso da terra" ~ "Other Land Use Changes"
)) %>%
dplyr::mutate(activity = dplyr::case_when(
category == "Area sem vegetacao -- Floresta secundaria" ~ "Area without Vegetation -- Secondary Forest",
category == "Area sem vegetacao -- Silvicultura" ~ "Area without Vegetation -- Forestry",
category == "Area sem vegetacao -- Uso agropecuario" ~ "Area without Vegetation -- Farming Use",
category == "Area sem vegetacao -- Vegetacao nao florestal secundaria" ~ "Area without Vegetation -- Secondary Non-Forest Vegetation",
category == "Floresta primaria -- Area sem vegetacao" ~ "Primary Forest -- Area without Vegetation",
category == "Floresta primaria -- Floresta primaria" ~ "Primary Forest -- Primary Forest",
category == "Floresta primaria -- Silvicultura" ~ "Primary Forest -- Forestry",
category == "Floresta primaria -- Uso agropecuario" ~ "Primary Forest -- Farming Use",
category == "Floresta secundaria -- Area sem vegetacao" ~ "Secondary Forest -- Area without Vegetation",
category == "Floresta secundaria -- Floresta secundaria" ~ "Secondary Forest -- Secondary Forest",
category == "Floresta secundaria -- Silvicultura" ~ "Secondary Forest -- Forestry",
category == "Floresta secundaria -- Uso agropecuario" ~ "Secondary Forest -- Farming Use",
category == "NAO SE APLICA" ~ "Not Applicable",
category == "Silvicultura -- Area sem vegetacao" ~ "Forestry -- Area without Vegetation",
category == "Silvicultura -- Floresta Secundaria" ~ "Forestry -- Secondary Forest",
category == "Silvicultura -- Uso agropecuario" ~ "Forestry -- Farming Use",
category == "Silvicultura -- Vegetacao nao florestal secundaria" ~ "Forestry -- Secondary non-Forest Vegetation",
category == "Uso agropecuario -- Area sem vegetacao" ~ "Farming Use -- Area without vegetation",
category == "Uso agropecuario -- Floresta secundaria" ~ "Farming Use -- Secondary Forest",
category == "Uso agropecuario -- Silvicultura" ~ "Farming Use -- Forestry",
category == "Uso agropecuario -- Uso agropecuario" ~ "Farming Use -- Farming Use",
category == "Uso agropecuario -- Vegetacao nao florestal secundaria" ~ "Farming Use -- Secondary non-Forest Vegetation",
category == "Vegetacao nao florestal primaria -- Area sem vegetacao" ~ "Primary non-Forest Vegetation -- Area without Vegetation",
category == "Vegetacao nao florestal primaria -- Silvicultura" ~ "Primary Non-Forest Vegetation -- Forestry",
category == "Vegetacao nao florestal primaria -- Uso agropecuario" ~ "Primary non-Forest Vegetation -- Farming use",
category == "Vegetacao nao florestal primaria -- Vegetacao nao florestal primaria" ~ "Primary non-Forest Vegetation -- Primary non-Forest Vegetation",
category == "Vegetacao nao florestal secundaria -- Area sem vegetacao" ~ "Secondary non-Forest Vegetation -- Area without Vegetation",
category == "Vegetacao nao florestal secundaria -- Silvicultura" ~ "Secondary non-Forest Vegetation -- Forestry",
category == "Vegetacao nao florestal secundaria -- Uso agropecuario" ~ "Secondary non-Forest Vegetation -- Farming use",
category == "Vegetacao nao florestal secundaria -- Vegetacao nao florestal secundaria" ~ "Secondary non-Forest Vegetation"
)) %>%
dplyr::mutate(emission_removal_bunker = dplyr::case_when(
emission_removal_bunker == "Emissao" ~ "Emission",
emission_removal_bunker == "Remocao" ~ "Removal",
emission_removal_bunker == "Remocao proxy" ~ "Proxy Removal"
)) %>%
dplyr::mutate(economic_activity = dplyr::case_when(
economic_activity == "AGROPEC" ~ "Farming",
economic_activity == "Conservacao" ~ "Conservation"
)) %>%
dplyr::select(-produto)
dat <- dat %>%
dplyr::mutate(state = dplyr::case_when(
state == "NA" ~ NA_character_,
TRUE ~ state
))
dat <- dat %>%
dplyr::relocate(year, state, sector, emissions_generating_processes, economic_activity, biome, biome_area, transition_type, emission_removal_bunker)
}
if (param$dataset == "seeg_farming" & param$geo_level == "municipality" & param$language == "eng") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x2000:x2018,
names_to = "year",
names_prefix = "x",
values_to = "value"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1 == "Agropecuaria") %>%
dplyr::rename(
sector = nivel_1, emissions_generating_processes = nivel_2,
emissions_type = nivel_4,
direct_indirect_emitters = nivel_3,
emissions_sources = nivel_5,
emitters = nivel_6,
economic_activity = atividade_economica,
product = produto,
emission_removal_bunker = tipo_de_emissao,
city = municipio,
state = territorio
) %>%
dplyr::mutate(economic_activity = dplyr::case_when(
economic_activity == "PEC" ~ "Farming",
economic_activity == "AGR" ~ "Agriculture"
)) %>%
dplyr::mutate(sector = dplyr::case_when(sector == "Agropecuaria" ~ "Farming")) %>%
dplyr::mutate(emissions_generating_processes = dplyr::case_when(
emissions_generating_processes == "Cultivo do Arroz" ~ "Rice Cultivation",
emissions_generating_processes == "Fermentacao Enterica" ~ "Enteric Fermentation",
emissions_generating_processes == "Manejo de Dejetos Animais" ~ "Animal Waste Management",
emissions_generating_processes == "Queima de Residuos Agricolas" ~ "Agricultural Waste Burning",
emissions_generating_processes == "Solos Manejados" ~ "Managed Soils"
)) %>%
dplyr::mutate(emissions_sources = dplyr::case_when(
emissions_sources == "Animal" ~ "Animal",
emissions_sources == "Vegetal" ~ "Plant",
emissions_sources == "Outros" ~ "Others"
)) %>%
dplyr::mutate(emitters = dplyr::case_when(
emitters == "Algodao" ~ "Cottage",
emitters == "Arroz" ~ "Rice",
emitters == "Aves" ~ "Birds",
emitters == "Cana de Acucar" ~ "Sugar Cane",
emitters == "Equino" ~ "Equine",
emitters == "Fertilizantes Sinteticos" ~ "Synthetic Fertilizers",
emitters == "Gado de Corte" ~ "Beef Cattle",
emitters == "Lavouras Cultivadas sob Sistema Convencional" ~ "Crops Cultivated under Conventional System",
emitters == "Mandioca" ~ "Cassava",
emitters == "Muar" ~ "Mule",
emitters == "Outros" ~ "Others",
emitters == "Pastagem" ~ "Pasture",
emitters == "Pastagem Degradada" ~ "Degraded Pasture",
emitters == "Soja" ~ "Soil",
emitters == "Suinos" ~ "Swine",
emitters == "Trigo" ~ "Wheat",
emitters == "Vinhaca" ~ "Vinasse",
emitters == "Uso de Calcario" ~ "Limestone Usage",
emitters == "Torta de Filtro" ~ "Filter Cake",
emitters == "Solos organicos" ~ "Organic Soils",
emitters == "Sistemas Integrados Lavoura-Pecuaria-Floresta" ~ "Integrated Crop-Livestock-Forestry Systems",
emitters == "Pastagem Bem Manejada" ~ "Well Managed Pasture",
emitters == "Ovino" ~ "Sheep",
emitters == "Outras Culturas" ~ "Other Cultures",
emitters == "Milho" ~ "Corn",
emitters == "Lavouras Cultivadas sob Sistema Plantio Direto" ~ "Crops Cultivated under no-till System",
emitters == "Gado de Leite" ~ "Dairy Cattle",
emitters == "Florestas Plantadas" ~ "Planted Forests",
emitters == "Feijao" ~ "Beans",
emitters == "Caprino" ~ "Goat",
emitters == "Bubalino" ~ "Buffalo",
emitters == "Asinino" ~ "Asinine",
emitters == "Aplicacao de Ureia" ~ "Urea Application"
)) %>%
dplyr::mutate(direct_indirect_emitters = dplyr::case_when(
direct_indirect_emitters == "Diretas" ~ "Direct",
direct_indirect_emitters == "Indiretas" ~ "Indirect"
)) %>%
dplyr::mutate(emissions_type = dplyr::case_when(
emissions_type == "Aplicacao de residuos organicos" ~ "Application of Organic Waste",
emissions_type == "Deposicao de dejetos em pastagem" ~ "Pasture Waste Disposal",
emissions_type == "Lixiviacao" ~ "Leaching",
emissions_type == "Outros" ~ "Others",
emissions_type == "Solos organicos" ~ "Organic Soils",
emissions_type == "Deposicao Atmosferica" ~ "Atmospheric Deposition",
emissions_type == "Fertilizantes Sinteticos" ~ "Synthetic Fertilizers",
emissions_type == "Mineralizacao de N associado a perda de C no solo" ~ "Mineralization of N Associated with Loss of C in the Soil",
emissions_type == "Residuos Agricolas" ~ "Agricultural Waste",
emissions_type == "Variacao dos Estoques de Carbono no Solo" ~ "Changes in Carbon Soil Stocks"
)) %>%
dplyr::mutate(emission_removal_bunker = dplyr::case_when(
emission_removal_bunker == "Emissao" ~ "Emission",
emission_removal_bunker == "Emissao NCI" ~ "NCI Emission",
emission_removal_bunker == "Remocao NCI" ~ "NCI Removal"
)) %>%
dplyr::mutate(product = dplyr::case_when(
product == "ALIM_BEBIDAS" ~ "Food/Beverages",
product == "CAR" ~ "Meat",
product == "CAR/LEI" ~ "Meat/Dairy",
product == "CAR/LEI/ALIM_BEBIDAS" ~ "Meat/Dairy/Food/Beverages",
product == "LEI" ~ "Dairy"
))
dat <- dat %>%
dplyr::relocate(
year, city, state, sector, emissions_generating_processes, emissions_sources, emitters, emissions_type, gas, economic_activity, product,
value
)
dat <- dat %>%
dplyr::mutate(city = dplyr::case_when(
city == "NA" ~ NA_character_,
TRUE ~ city
)) %>%
dplyr::mutate(state = dplyr::case_when(
state == "NA" ~ NA_character_,
TRUE ~ state
)) %>%
dplyr::mutate(ibge = dplyr::case_when(
ibge == "NA" ~ NA_character_,
TRUE ~ ibge
))
}
if (param$dataset == "seeg_farming" & param$geo_level %in% c("country", "state") & param$language == "eng") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x1970:x2021,
names_to = "year",
names_prefix = "x",
values_to = "value"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1_setor == "Agropecuaria") %>%
dplyr::rename(
sector = nivel_1_setor, emissions_generating_processes = nivel_2,
emissions_type = nivel_4,
direct_indirect_emitters = nivel_3,
emissions_sources = nivel_5,
emitters = nivel_6,
economic_activity = atividade_economica,
product = produto,
emission_removal_bunker = emissao_remocao_bunker,
state = estado
) %>%
dplyr::mutate(economic_activity = dplyr::case_when(
economic_activity == "PEC" ~ "Farming",
economic_activity == "AGR" ~ "Agriculture"
)) %>%
dplyr::mutate(sector = dplyr::case_when(sector == "Agropecuaria" ~ "Farming")) %>%
dplyr::mutate(emissions_generating_processes = dplyr::case_when(
emissions_generating_processes == "Cultivo do Arroz" ~ "Rice Cultivation",
emissions_generating_processes == "Fermentacao Enterica" ~ "Enteric Fermentation",
emissions_generating_processes == "Manejo de Dejetos Animais" ~ "Animal Waste Management",
emissions_generating_processes == "Queima de Residuos Agricolas" ~ "Agricultural Waste Burning",
emissions_generating_processes == "Solos Manejados" ~ "Managed Soils"
)) %>%
dplyr::mutate(emissions_sources = dplyr::case_when(
emissions_sources == "Animal" ~ "Animal",
emissions_sources == "Vegetal" ~ "Plant",
emissions_sources == "Outros" ~ "Others"
)) %>%
dplyr::mutate(emitters = dplyr::case_when(
emitters == "Algodao" ~ "Cottage",
emitters == "Arroz" ~ "Rice",
emitters == "Aves" ~ "Birds",
emitters == "Cana de Acucar" ~ "Sugar Cane",
emitters == "Equino" ~ "Equine",
emitters == "Fertilizantes Sinteticos" ~ "Synthetic Fertilizers",
emitters == "Gado de Corte" ~ "Beef Cattle",
emitters == "Lavouras Cultivadas sob Sistema Convencional" ~ "Crops Cultivated under Conventional System",
emitters == "Mandioca" ~ "Cassava",
emitters == "Muar" ~ "Mule",
emitters == "Outros" ~ "Others",
emitters == "Pastagem" ~ "Pasture",
emitters == "Pastagem Degradada" ~ "Degraded Pasture",
emitters == "Soja" ~ "Soil",
emitters == "Suinos" ~ "Swine",
emitters == "Trigo" ~ "Wheat",
emitters == "Vinhaca" ~ "Vinasse",
emitters == "Uso de Calcario" ~ "Limestone Usage",
emitters == "Torta de Filtro" ~ "Filter Cake",
emitters == "Solos organicos" ~ "Organic Soils",
emitters == "Sistemas Integrados Lavoura-Pecuaria-Floresta" ~ "Integrated Crop-Livestock-Forestry Systems",
emitters == "Pastagem Bem Manejada" ~ "Well Managed Pasture",
emitters == "Ovino" ~ "Sheep",
emitters == "Outras Culturas" ~ "Other Cultures",
emitters == "Milho" ~ "Corn",
emitters == "Lavouras Cultivadas sob Sistema Plantio Direto" ~ "Crops Cultivated under no-till System",
emitters == "Gado de Leite" ~ "Dairy Cattle",
emitters == "Florestas Plantadas" ~ "Planted Forests",
emitters == "Feijao" ~ "Beans",
emitters == "Caprino" ~ "Goat",
emitters == "Bubalino" ~ "Buffalo",
emitters == "Asinino" ~ "Asinine",
emitters == "Aplicacao de Ureia" ~ "Urea Application"
)) %>%
dplyr::mutate(direct_indirect_emitters = dplyr::case_when(
direct_indirect_emitters == "Diretas" ~ "Direct",
direct_indirect_emitters == "Indiretas" ~ "Indirect"
)) %>%
dplyr::mutate(emissions_type = dplyr::case_when(
emissions_type == "Aplicacao de residuos organicos" ~ "Application of Organic Waste",
emissions_type == "Deposicao de dejetos em pastagem" ~ "Pasture Waste Disposal",
emissions_type == "Lixiviacao" ~ "Leaching",
emissions_type == "Outros" ~ "Others",
emissions_type == "Solos organicos" ~ "Organic Soils",
emissions_type == "Deposicao Atmosferica" ~ "Atmospheric Deposition",
emissions_type == "Fertilizantes Sinteticos" ~ "Synthetic Fertilizers",
emissions_type == "Mineralizacao de N associado a perda de C no solo" ~ "Mineralization of N Associated with Loss of C in the Soil",
emissions_type == "Residuos Agricolas" ~ "Agricultural Waste",
emissions_type == "Variacao dos Estoques de Carbono no Solo" ~ "Changes in Carbon Soil Stocks"
)) %>%
dplyr::mutate(emission_removal_bunker = dplyr::case_when(
emission_removal_bunker == "Emissao" ~ "Emission",
emission_removal_bunker == "Emissao NCI" ~ "NCI Emission",
emission_removal_bunker == "Remocao NCI" ~ "NCI Removal"
)) %>%
dplyr::mutate(product = dplyr::case_when(
product == "ALIM_BEBIDAS" ~ "Food/Beverages",
product == "CAR" ~ "Meat",
product == "CAR/LEI" ~ "Meat/Dairy",
product == "CAR/LEI/ALIM_BEBIDAS" ~ "Meat/Dairy/Food/Beverages",
product == "LEI" ~ "Dairy"
))
dat <- dat %>%
dplyr::relocate(
year, state, sector, emissions_generating_processes, emissions_sources, emitters, emissions_type, gas, economic_activity, product,
value
)
dat <- dat %>%
dplyr::mutate(state = dplyr::case_when(
state == "NA" ~ NA_character_,
TRUE ~ state
))
}
return(dat)
}
datazoompuc/datazoom.amazonia documentation built on April 20, 2024, 8:50 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions load_seeg
Documented in load_seeg
#' @title Greenhouse gas emission estimates (SEEG)
#'
#' @description Loads data of estimates of emission of greenhouse gases
#'
#' @param dataset A dataset name ("seeg", seeg_farming", "seeg_industry", "seeg_energy", "seeg_land", "seeg_residuals"). On which "seeg" contains all five sectors (only works with raw_data = TRUE) and the others are filtered specifically by a main source of emission.
#' @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}.
#'
#' @export
#'
#' @examples \dontrun{
#' # Download raw data (raw_data = TRUE) of greenhouse gases (dataset = "seeg")
#' # by state (geo_level = "state")
#' data <- load_seeg(
#' dataset = "seeg",
#' raw_data = TRUE,
#' geo_level = "state"
#' )
#'
#' # Download treated data (raw_data = FALSE) of industry greenhouse gases (dataset = "seeg_industry")
#' data <- load_seeg(
#' dataset = "seeg_industry",
#' raw_data = FALSE,
#' geo_level = "state"
#' )
#' }
load_seeg <- function(dataset, raw_data = FALSE,
geo_level, language = "eng") {
# Checking for googledrive package (in Suggests)
if (!requireNamespace("googledrive", quietly = TRUE)) {
stop(
"Package \"googledrive\" must be installed to use this function.",
call. = FALSE
)
}
##############################
## Binding Global Variables ##
##############################
survey <- link <- ibge <- x2000 <- x2018 <- id_code <- tipo_de_emissao <- NULL
city <- state <- nivel_1 <- municipio <- territorio <- x1970 <- x2021 <- NULL
nivel_1_setor <- nivel_2 <- nivel_3 <- nivel_4 <- nivel_5 <- nivel_6 <- NULL
produto <- atividade_economica <- Valor <- Ano <- estado <- setor <- NULL
processos_geradores_emissoes <- fonte_de_emissoes <- emissores <- gas <- NULL
emissao_remocao_bunker <- producao_emissores <- categorias_emissao <- NULL
atividade_geradora <- categorias_processos_geradores <- year <- state <- NULL
sector <- emitters_production <- emitters <- economic_activity <- product <- NULL
value <- emissions_category <- activity <- generating_processes_categories <- NULL
biome <- biome_area <- transition_type <- emission_removal_bunker <- NULL
emissions_sources <- emissions_type <- emissions_generating_processes <- NULL
#############################
## Define Basic Parameters ##
#############################
param <- list()
param$source <- "seeg"
param$dataset <- dataset
param$geo_level <- geo_level
param$language <- language
param$raw_data <- raw_data
# check if dataset and geo_level are supported
check_params(param)
if (!is.numeric(param$dataset)) {
param$code <- datasets_link() %>%
dplyr::filter(dataset == param$dataset) %>%
dplyr::select(link) %>%
unlist()
} else {
param$code <- param$dataset
}
## Dataset
if (param$dataset == "seeg" & param$raw_data == FALSE) {
stop("This dataset only works with raw_data = TRUE")
}
if (param$dataset == "seeg_farming" & param$raw_data == TRUE) {
stop("This dataset only works with raw_data = FALSE")
}
if (param$dataset == "seeg_energy" & param$raw_data == TRUE) {
stop("This dataset only works with raw_data = FALSE")
}
if (param$dataset == "seeg_industry" & param$raw_data == TRUE) {
stop("This dataset only works with raw_data = FALSE")
}
if (param$dataset == "seeg_land" & param$raw_data == TRUE) {
stop("This dataset only works with raw_data = FALSE")
}
if (param$dataset == "seeg_residuals" & param$raw_data == TRUE) {
stop("This dataset only works with raw_data = FALSE")
}
# Picking which sheet to download
sheet_list <- c(
"country" = "GEE Brasil",
"state" = "GEE Estados",
"municipality" = "BD GEE Municipios GWP-AR5"
)
sheet <- param$geo_level %>%
dplyr::recode(!!!sheet_list)
##############
## Download ##
##############
dat <- external_download(
dataset = param$dataset,
source = param$source,
geo_level = param$geo_level,
sheet = sheet
)
dat <- dat %>%
janitor::clean_names() %>%
tibble::as_tibble() %>%
dplyr::mutate_if(is.character, function(var) {
stringi::stri_trans_general(str = var, id = "Latin-ASCII")
})
## Return Raw Data
if (param$dataset == "seeg" & param$raw_data) {
return(dat)
}
## Raw Data = FALSE
if (param$dataset == "seeg_farming" & param$geo_level == "municipality" & param$language == "pt") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x2000:x2018,
names_to = "Ano",
names_prefix = "x",
values_to = "Valor"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1 == "Agropecuaria") %>%
dplyr::rename(
setor = nivel_1, processos_geradores_emissoes = nivel_2,
tipo_emissao = nivel_4,
emissores_diretos_e_indiretos = nivel_3,
fonte_de_emissoes = nivel_5,
emissores = nivel_6,
emissao_remocao = tipo_de_emissao
) %>%
dplyr::mutate(atividade_economica = dplyr::case_when(
atividade_economica == "PEC" ~ "Pecuaria",
atividade_economica == "AGR" ~ "Agricultura"
)) %>%
dplyr::mutate(produto = dplyr::case_when(
produto == "ALIM_BEBIDAS" ~ "Alimentos/Bebidas",
produto == "CAR" ~ "Carne",
produto == "CAR/LEI" ~ "Carne/Leite",
produto == "CAR/LEI/ALIM_BEBIDAS" ~ "Carne/Leite/Alimentos/Bebidas",
produto == "LEI" ~ "Leite"
))
dat <- dat %>%
dplyr::relocate(
Ano, municipio, territorio, ibge, setor, processos_geradores_emissoes, fonte_de_emissoes, emissores, gas, atividade_economica, produto,
Valor
)
dat <- dat %>%
dplyr::mutate(municipio = dplyr::case_when(
municipio == "NA" ~ NA_character_,
TRUE ~ municipio
)) %>%
dplyr::mutate(territorio = dplyr::case_when(
territorio == "NA" ~ NA_character_,
TRUE ~ territorio
)) %>%
dplyr::mutate(ibge = dplyr::case_when(
ibge == "NA" ~ NA_character_,
TRUE ~ ibge
))
}
if (param$dataset == "seeg_farming" & param$geo_level %in% c("country", "state") & param$language == "pt") {
## Create Longer Data - Years as a Variable
dat <- dat %>%
tidyr::pivot_longer(
cols = x1970:x2021,
names_to = "Ano",
names_prefix = "x",
values_to = "Valor"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1_setor == "Agropecuaria") %>%
dplyr::rename(
setor = nivel_1_setor, processos_geradores_emissoes = nivel_2,
tipo_emissao = nivel_4,
emissores_diretos_e_indiretos = nivel_3,
fonte_de_emissoes = nivel_5,
emissores = nivel_6
) %>%
dplyr::mutate(atividade_economica = dplyr::case_when(
atividade_economica == "PEC" ~ "Pecuaria",
atividade_economica == "AGR" ~ "Agricultura"
)) %>%
dplyr::mutate(produto = dplyr::case_when(
produto == "ALIM_BEBIDAS" ~ "Alimentos/Bebidas",
produto == "CAR" ~ "Carne",
produto == "CAR/LEI" ~ "Carne/Leite",
produto == "CAR/LEI/ALIM_BEBIDAS" ~ "Carne/Leite/Alimentos/Bebidas",
produto == "LEI" ~ "Leite"
))
dat <- dat %>%
dplyr::relocate(
Ano, estado, setor, processos_geradores_emissoes, fonte_de_emissoes, emissores, gas, atividade_economica, produto,
Valor
)
dat <- dat %>%
dplyr::mutate(estado = dplyr::case_when(
estado == "NA" ~ NA_character_,
TRUE ~ estado
))
}
if (param$dataset == "seeg_industry" & param$geo_level == "municipality" & param$language == "pt") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x2000:x2018,
names_to = "Ano",
names_prefix = "x",
values_to = "Valor"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1 == "Processos Industriais") %>%
dplyr::rename(
setor = nivel_1, processos_geradores_emissoes = nivel_2,
producao_emissores = nivel_3,
emissores = nivel_4,
emissao = tipo_de_emissao
) %>%
dplyr::select(-c(nivel_6, nivel_5)) %>%
dplyr::mutate(atividade_economica = dplyr::case_when(
atividade_economica == "CIM" ~ "Industria Cimenteira",
atividade_economica == "ENE_ELET" ~ "Industria Energia Eletrica",
atividade_economica == "MET" ~ "Industria Metaleira",
atividade_economica == "Outra_IND" ~ "Outra Industria",
atividade_economica == "HFC" ~ "HFC"
))
dat <- dat %>%
dplyr::relocate(Ano, municipio, territorio, setor, processos_geradores_emissoes, producao_emissores, emissores, gas, atividade_economica, produto, Valor)
dat <- dat %>%
dplyr::mutate(municipio = dplyr::case_when(
municipio == "NA" ~ NA_character_,
TRUE ~ municipio
)) %>%
dplyr::mutate(territorio = dplyr::case_when(
territorio == "NA" ~ NA_character_,
TRUE ~ territorio
)) %>%
dplyr::mutate(ibge = dplyr::case_when(
ibge == "NA" ~ NA_character_,
TRUE ~ ibge
))
}
if (param$dataset == "seeg_industry" & param$geo_level %in% c("country", "state") & param$language == "pt") {
## Create Longer Data - Years as a Variable
dat <- dat %>%
tidyr::pivot_longer(
cols = x1970:x2021,
names_to = "Ano",
names_prefix = "x",
values_to = "Valor"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1_setor == "Processos Industriais") %>%
dplyr::rename(
setor = nivel_1_setor, processos_geradores_emissoes = nivel_2,
producao_emissores = nivel_3,
emissores = nivel_4,
emissao_bunker = emissao_remocao_bunker
) %>%
dplyr::select(-c(nivel_6, nivel_5)) %>%
dplyr::mutate(atividade_economica = dplyr::case_when(
atividade_economica == "CIM" ~ "Industria Cimenteira",
atividade_economica == "ENE_ELET" ~ "Industria Energia Eletrica",
atividade_economica == "MET" ~ "Industria Metaleira",
atividade_economica == "Outra_IND" ~ "Outra Industria",
atividade_economica == "HFC" ~ "HFC"
)) %>%
dplyr::mutate(produto = dplyr::case_when(
produto == "ALU" ~ "Aluminio",
produto == "ACO" ~ "Aco"
))
dat <- dat %>%
dplyr::relocate(Ano, estado, setor, processos_geradores_emissoes, producao_emissores, emissores, gas, atividade_economica, produto, Valor)
dat <- dat %>%
dplyr::mutate(estado = dplyr::case_when(
estado == "NA" ~ NA_character_,
TRUE ~ estado
))
}
if (param$dataset == "seeg_energy" & param$geo_level == "municipality" & param$language == "pt") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x2000:x2018,
names_to = "Ano",
names_prefix = "x",
values_to = "Valor"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1 == "Energia") %>%
dplyr::rename(
setor = nivel_1, tipo_emissao = nivel_2,
processos_geradores_emissoes = nivel_3,
atividade_geradora = nivel_4,
fonte_energetica = nivel_5,
emissores = nivel_6,
emission_bunker = tipo_de_emissao
) %>%
dplyr::mutate(produto = dplyr::case_when(
produto == "ALIM_BEBIDAS" ~ "Alimentos/Bebidas",
produto == "ENE_ELET" ~ "Energia Eletrica",
produto == "ALU" ~ "Aluminio"
)) %>%
dplyr::select(-atividade_economica)
dat <- dat %>%
dplyr::relocate(Ano, municipio, territorio, setor)
dat <- dat %>%
dplyr::mutate(municipio = dplyr::case_when(
municipio == "NA" ~ NA_character_,
TRUE ~ municipio
)) %>%
dplyr::mutate(territorio = dplyr::case_when(
territorio == "NA" ~ NA_character_,
TRUE ~ territorio
)) %>%
dplyr::mutate(ibge = dplyr::case_when(
ibge == "NA" ~ NA_character_,
TRUE ~ ibge
))
}
if (param$dataset == "seeg_energy" & param$geo_level %in% c("country", "state") & param$language == "pt") {
## Create Longer Data - Years as a Variable
dat <- dat %>%
tidyr::pivot_longer(
cols = x1970:x2021,
names_to = "Ano",
names_prefix = "x",
values_to = "Valor"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1_setor == "Energia") %>%
dplyr::rename(
setor = nivel_1_setor, tipo_emissao = nivel_2,
processos_geradores_emissoes = nivel_3,
atividade_geradora = nivel_4,
fonte_energetica = nivel_5,
emissores = nivel_6
) %>%
dplyr::mutate(produto = dplyr::case_when(
produto == "ALIM_BEBIDAS" ~ "Alimentos/Bebidas",
produto == "ENE_ELET" ~ "Energia Eletrica",
produto == "ALU" ~ "Aluminio"
)) %>%
dplyr::select(-atividade_economica)
dat <- dat %>%
dplyr::relocate(Ano, estado, setor)
dat <- dat %>%
dplyr::mutate(estado = dplyr::case_when(
estado == "NA" ~ NA_character_,
TRUE ~ estado
))
}
if (param$dataset == "seeg_land" & param$geo_level == "municipality" & param$language == "pt") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x2000:x2018,
names_to = "Ano",
names_prefix = "x",
values_to = "Valor"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1 == "Mudanca de Uso da Terra e Floresta") %>%
dplyr::filter(!is.na(Valor)) %>%
dplyr::rename(
setor = nivel_1, processos_geradores_emissoes = nivel_2,
bioma = nivel_3,
area_bioma = nivel_4,
local_atividade_geradora = nivel_5,
atividade_geradora = nivel_6,
emissao_remocao = tipo_de_emissao
) %>%
dplyr::mutate(atividade_economica = dplyr::case_when(
atividade_economica == "AGROPEC" ~ "Agropecuaria",
atividade_economica == "Conservacao" ~ "Conservacao"
)) %>%
dplyr::select(-produto)
dat <- dat %>%
dplyr::relocate(Ano, municipio, territorio, setor, processos_geradores_emissoes, atividade_economica)
dat <- dat %>%
dplyr::mutate(municipio = dplyr::case_when(
municipio == "NA" ~ NA_character_,
TRUE ~ municipio
)) %>%
dplyr::mutate(territorio = dplyr::case_when(
territorio == "NA" ~ NA_character_,
TRUE ~ territorio
)) %>%
dplyr::mutate(ibge = dplyr::case_when(
ibge == "NA" ~ NA_character_,
TRUE ~ ibge
))
}
if (param$dataset == "seeg_land" & param$geo_level %in% c("country", "state") & param$language == "pt") {
## Create Longer Data - Years as a Variable
dat <- dat %>%
tidyr::pivot_longer(
cols = x1970:x2021,
names_to = "Ano",
names_prefix = "x",
values_to = "Valor"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1_setor == "Mudanca de Uso da Terra e Floresta") %>%
dplyr::filter(!is.na(Valor)) %>%
dplyr::rename(
setor = nivel_1_setor, processos_geradores_emissoes = nivel_2,
bioma = nivel_3,
area_bioma = nivel_4,
local_atividade_geradora = nivel_5,
atividade_geradora = nivel_6
) %>%
dplyr::mutate(atividade_economica = dplyr::case_when(
atividade_economica == "AGROPEC" ~ "Agropecuaria",
atividade_economica == "Conservacao" ~ "Conservacao"
)) %>%
dplyr::select(-produto)
dat <- dat %>%
dplyr::relocate(Ano, estado, setor, processos_geradores_emissoes, atividade_economica)
dat <- dat %>%
dplyr::mutate(estado = dplyr::case_when(
estado == "NA" ~ NA_character_,
TRUE ~ estado
))
}
if (param$dataset == "seeg_residuals" & param$geo_level == "municipality" & param$language == "pt") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x2000:x2018,
names_to = "Ano",
names_prefix = "x",
values_to = "Valor"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1 == "Residuos") %>%
dplyr::rename(
setor = nivel_1, categorias_emissao = nivel_2,
processos_geradores_emissoes = nivel_3,
atividade_geradora = nivel_4,
categorias_processos_geradores = nivel_5,
emissao_bunker = tipo_de_emissao
) %>%
dplyr::select(-nivel_6) %>%
dplyr::mutate(atividade_economica = dplyr::case_when(
atividade_economica == "PEC" ~ "Pecuaria",
atividade_economica == "Outra_IND" ~ "Outra Industria",
atividade_economica == "SANEAMENTO" ~ "Saneamento"
))
dat <- dat %>%
dplyr::mutate(municipio = dplyr::case_when(
municipio == "NA" ~ NA_character_,
TRUE ~ municipio
)) %>%
dplyr::mutate(territorio = dplyr::case_when(
territorio == "NA" ~ NA_character_,
TRUE ~ territorio
)) %>%
dplyr::mutate(ibge = dplyr::case_when(
ibge == "NA" ~ NA_character_,
TRUE ~ ibge
))
dat <- dat %>%
dplyr::relocate(Ano, municipio, territorio, ibge, setor, categorias_emissao, processos_geradores_emissoes, atividade_geradora, categorias_processos_geradores, atividade_economica, produto, Valor)
}
if (param$dataset == "seeg_residuals" & param$geo_level %in% c("country", "state") & param$language == "pt") {
## Create Longer Data - Years as a Variable
dat <- dat %>%
tidyr::pivot_longer(
cols = x1970:x2021,
names_to = "Ano",
names_prefix = "x",
values_to = "Valor"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1_setor == "Residuos") %>%
dplyr::rename(
setor = nivel_1_setor, categorias_emissao = nivel_2,
processos_geradores_emissoes = nivel_3,
atividade_geradora = nivel_4,
categorias_processos_geradores = nivel_5,
emissao_bunker = emissao_remocao_bunker
) %>%
dplyr::select(-nivel_6) %>%
dplyr::mutate(atividade_economica = dplyr::case_when(
atividade_economica == "PEC" ~ "Pecuaria",
atividade_economica == "Outra_IND" ~ "Outra Industria",
atividade_economica == "SANEAMENTO" ~ "Saneamento"
)) %>%
dplyr::mutate(produto = dplyr::case_when(
produto == "ALIM_BEBIDAS" ~ "Alimentos/Bebidas",
produto == "CAR" ~ "Carne"
)) %>%
dplyr::mutate(estado = dplyr::case_when(
estado == "NA" ~ NA_character_,
TRUE ~ estado
))
dat <- dat %>%
dplyr::relocate(Ano, estado, setor, categorias_emissao, processos_geradores_emissoes, atividade_geradora, categorias_processos_geradores, atividade_economica, produto, Valor)
}
if (param$dataset == "seeg_energy" & param$geo_level == "municipality" & param$language == "eng") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x2000:x2018,
names_to = "year",
names_prefix = "x",
values_to = "value"
)
dat <- dat %>%
dplyr::filter(nivel_1 == "Energia") %>%
dplyr::rename(
sector = nivel_1,
emission_type = nivel_2,
emissions_generating_processes = nivel_3,
activity = nivel_4,
energetic_source = nivel_5,
emitters = nivel_6,
emission_bunker = tipo_de_emissao,
product = produto,
city = municipio,
state = territorio
) %>%
dplyr::mutate(product = dplyr::case_when(
product == "ALIM_BEBIDAS" ~ "Food/Beverages",
product == "ENE_ELET" ~ "Eletric energy",
product == "ALU" ~ "Aluminum"
)) %>%
dplyr::mutate(sector = dplyr::case_when(sector == "Energia" ~ "Energy")) %>%
dplyr::mutate(emission_type = dplyr::case_when(
emission_type == "Emissoes Fugitivas" ~ "Fugitive emissions",
emission_type == "Emissoes pela Queima de Combustiveis" ~ "Emissions from fuel burning"
)) %>%
dplyr::mutate(emissions_generating_processes = dplyr::case_when(
emissions_generating_processes == "Agropecuario" ~ "Farming",
emissions_generating_processes == "Geracao de Eletricidade (Servico Publico)" ~ "Eletricity generation (public service)",
emissions_generating_processes == "Nao Identificado" ~ "Not Identified",
emissions_generating_processes == "Publico" ~ "Public",
emissions_generating_processes == "Transportes" ~ "Transports",
emissions_generating_processes == "Comercial" ~ "Business",
emissions_generating_processes == "Industrial" ~ "Industrial",
emissions_generating_processes == "Producao de Combustiveis" ~ "Fuel production",
emissions_generating_processes == "Residencial" ~ "Residential"
)) %>%
dplyr::mutate(activity = dplyr::case_when(
activity == "Aereo" ~ "Air",
activity == "Cimento" ~ "Cement",
activity == "Ferro Ligas" ~ "Iron garters",
activity == "Mineracao e pelotizacao" ~ "Mining and pelletizing",
activity == "Outras industrias" ~ "Other industries",
activity == "Producao de carvao mineral e outros" ~ " Mineral coal production and others",
activity == "Refino de petroleo" ~ "Oil refining",
activity == "Transporte de gas natural" ~ "Natural gas transport",
activity == "Alimentos e bebidas" ~ "Food and beverages",
activity == "Exploracao de petroleo e gas natural" ~ "Oil and natural gas exploration",
activity == "Ferroviario" ~ "Railroad",
activity == "Nao ferrosos e outros da metalurgia" ~ "Non-ferrous and other metallurgy",
activity == "Papel e celulose" ~ "Paper and Cellulose",
activity == "Producao de carvao vegetal" ~ "Charcoal production",
activity == "Rodoviario" ~ "Road",
activity == "Ceramica" ~ "Ceramics",
activity == "Ferro gusa e aco" ~ "Pig iron and steel",
activity == "Hidroviario" ~ "Waterway",
activity == "NAO SE APLICA" ~ "Not Applicable",
activity == "Producao de Alcool" ~ "Alcohol production",
activity == "Quimica" ~ "Chemical",
activity == "Textil" ~ "Textile"
)) %>%
dplyr::mutate(energetic_source = dplyr::case_when(
energetic_source == "Alcatrao" ~ "Tar",
energetic_source == "Biogas" ~ "Biogas",
energetic_source == "Carvao vapor 3300" ~ "Steam coal 3300",
energetic_source == "Carvao vapor 4500" ~ "Steam coal 4500",
energetic_source == "Carvao vapor 5900" ~ "Steam coal 5900",
energetic_source == "Carvao vegetal" ~ "Charcoal",
energetic_source == "Diesel de petroleo" ~ "Petroleum diesel",
energetic_source == "Gas de coqueria" ~ "Coke oven gas",
energetic_source == "Gas natural seco" ~ "Dry natural gas",
energetic_source == "Gasolina C" ~ "Gasoline C",
energetic_source == "Lenha" ~ "Firewood",
energetic_source == "Nafta" ~ "Naphta",
energetic_source == "Outras biomassas" ~ "Other biomasses",
energetic_source == "Petroleo" ~ "Oil",
energetic_source == "Querosene iluminante" ~ "Illuminating kerosene",
energetic_source == "Petroleo e gas natural" ~ "Oil and natural gas",
energetic_source == "Outras nao renovaveis" ~ "Other non-renewable",
energetic_source == "Oleo combustivel" ~ "Fuel oil",
energetic_source == "Lenha carvoejamento" ~ "Charcoal firewood",
energetic_source == "Gasolina de aviacao" ~ "Aviation gasoline",
energetic_source == "Gas natural umido" ~ "Umid natural gas",
energetic_source == "Gas de refinaria" ~ "Refinery gas",
energetic_source == "Gas canalizado RJ" ~ "Piped gas RJ",
energetic_source == "Coque de carvao mineral" ~ "Coal coke",
energetic_source == "Carvao vapor 6000" ~ "Steam coal 6000",
energetic_source == "Carvao vapor 4700" ~ "Steam coal 4700",
energetic_source == "Carvao vapor 3700" ~ "Steam coal 3700",
energetic_source == "Carvao mineral" ~ "Mineral coal",
energetic_source == "Alcool hidratado" ~ "Hydrated alcohol",
energetic_source == "Bagaco de cana" ~ "Sugarcane bagasse",
energetic_source == "Carvao vapor 3100" ~ "Steam coal 3100",
energetic_source == "Carvao vapor 4200" ~ "Steam coal 4200",
energetic_source == "Carvao vapor 5200" ~ "Steam coal 5200",
energetic_source == "Carvao vapor sem especificacao" ~ "Steam coal without specification",
energetic_source == "Coque de petroleo" ~ "Oil coke",
energetic_source == "Gas canalizadp SP" ~ "Piped gas SP",
energetic_source == "Gas natural" ~ "Natural gas",
energetic_source == "Gasolina autmotiva" ~ "Automotive gasoline",
energetic_source == "GLP" ~ "Liquefied oil gas",
energetic_source == "Lixivia" ~ "Bleach",
energetic_source == "Oleo diesel" ~ "Diesel oil",
energetic_source == "Outros energeticos de petroleo" ~ "Other petroleum energy",
energetic_source == "Querosene de aviacao" ~ "Aviation kerosene"
)) %>%
dplyr::mutate(emitters = dplyr::case_when(
emitters == "Aeronaves" ~ "Airplanes",
emitters == "Carvoarias" ~ "Charcoals",
emitters == "Comerciais Leves" ~ "Light commercials",
emitters == "Locomotivas" ~ "Locomotives",
emitters == "Onibus" ~ "Bus",
emitters == "Automoveis" ~ "Automobiles",
emitters == "Centrais Eletricas Autoprodutoras" ~ "Self-producing power plants",
emitters == "Consumo Final Energetico" ~ "Final energy consumption",
emitters == "Motocicletas" ~ "Motorbikes",
emitters == "Caminhoes" ~ "Trucks",
emitters == "Centrais Eletricas de Servico Publico" ~ "Public service power plants",
emitters == "Embarcacoes" ~ "Vessels",
emitters == "NAO SE APLICA" ~ "Not Applicable"
)) %>%
dplyr::mutate(emission_bunker = dplyr::case_when(
emission_bunker == "Emissao" ~ "Emission",
emission_bunker == "Bunker" ~ "Bunker"
)) %>%
dplyr::select(-atividade_economica)
dat <- dat %>%
dplyr::relocate(year, city, state, sector)
dat <- dat %>%
dplyr::mutate(city = dplyr::case_when(
city == "NA" ~ NA_character_,
TRUE ~ city
)) %>%
dplyr::mutate(state = dplyr::case_when(
state == "NA" ~ NA_character_,
TRUE ~ state
)) %>%
dplyr::mutate(ibge = dplyr::case_when(
ibge == "NA" ~ NA_character_,
TRUE ~ ibge
))
}
if (param$dataset == "seeg_energy" & param$geo_level %in% c("country", "state") & param$language == "eng") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x1970:x2021,
names_to = "year",
names_prefix = "x",
values_to = "value"
)
dat <- dat %>%
dplyr::filter(nivel_1_setor == "Energia") %>%
dplyr::rename(
sector = nivel_1_setor, state = estado,
emission_type = nivel_2,
emissions_generating_processes = nivel_3,
activity = nivel_4,
energetic_source = nivel_5,
emitters = nivel_6,
emission_bunker = emissao_remocao_bunker,
product = produto
) %>%
dplyr::mutate(product = dplyr::case_when(
product == "ALIM_BEBIDAS" ~ "Food/Beverages",
product == "ENE_ELET" ~ "Eletric energy",
product == "ALU" ~ "Aluminum"
)) %>%
dplyr::mutate(sector = dplyr::case_when(sector == "Energia" ~ "Energy")) %>%
dplyr::mutate(emission_type = dplyr::case_when(
emission_type == "Emissoes Fugitivas" ~ "Fugitive emissions",
emission_type == "Emissoes pela Queima de Combustiveis" ~ "Emissions from fuel burning"
)) %>%
dplyr::mutate(emissions_generating_processes = dplyr::case_when(
emissions_generating_processes == "Agropecuario" ~ "Farming",
emissions_generating_processes == "Geracao de Eletricidade (Servico Publico)" ~ "Eletricity generation (public service)",
emissions_generating_processes == "Nao Identificado" ~ "Not Identified",
emissions_generating_processes == "Publico" ~ "Public",
emissions_generating_processes == "Transportes" ~ "Transports",
emissions_generating_processes == "Comercial" ~ "Business",
emissions_generating_processes == "Industrial" ~ "Industrial",
emissions_generating_processes == "Producao de Combustiveis" ~ "Fuel production",
emissions_generating_processes == "Residencial" ~ "Residential"
)) %>%
dplyr::mutate(activity = dplyr::case_when(
activity == "Aereo" ~ "Air",
activity == "Cimento" ~ "Cement",
activity == "Ferro Ligas" ~ "Iron garters",
activity == "Mineracao e pelotizacao" ~ "Mining and pelletizing",
activity == "Outras industrias" ~ "Other industries",
activity == "Producao de carvao mineral e outros" ~ " Mineral coal production and others",
activity == "Refino de petroleo" ~ "Oil refining",
activity == "Transporte de gas natural" ~ "Natural gas transport",
activity == "Alimentos e bebidas" ~ "Food and beverages",
activity == "Exploracao de petroleo e gas natural" ~ "Oil and natural gas exploration",
activity == "Ferroviario" ~ "Railroad",
activity == "Nao ferrosos e outros da metalurgia" ~ "Non-ferrous and other metallurgy",
activity == "Papel e celulose" ~ "Paper and Cellulose",
activity == "Producao de carvao vegetal" ~ "Charcoal production",
activity == "Rodoviario" ~ "Road",
activity == "Ceramica" ~ "Ceramics",
activity == "Ferro gusa e aco" ~ "Pig iron and steel",
activity == "Hidroviario" ~ "Waterway",
activity == "NAO SE APLICA" ~ "Not Applicable",
activity == "Producao de Alcool" ~ "Alcohol production",
activity == "Quimica" ~ "Chemical",
activity == "Textil" ~ "Textile"
)) %>%
dplyr::mutate(energetic_source = dplyr::case_when(
energetic_source == "Alcatrao" ~ "Tar",
energetic_source == "Biogas" ~ "Biogas",
energetic_source == "Carvao vapor 3300" ~ "Steam coal 3300",
energetic_source == "Carvao vapor 4500" ~ "Steam coal 4500",
energetic_source == "Carvao vapor 5900" ~ "Steam coal 5900",
energetic_source == "Carvao vegetal" ~ "Charcoal",
energetic_source == "Diesel de petroleo" ~ "Petroleum diesel",
energetic_source == "Gas de coqueria" ~ "Coke oven gas",
energetic_source == "Gas natural seco" ~ "Dry natural gas",
energetic_source == "Gasolina C" ~ "Gasoline C",
energetic_source == "Lenha" ~ "Firewood",
energetic_source == "Nafta" ~ "Naphta",
energetic_source == "Outras biomassas" ~ "Other biomasses",
energetic_source == "Petroleo" ~ "Oil",
energetic_source == "Querosene iluminante" ~ "Illuminating kerosene",
energetic_source == "Petroleo e gas natural" ~ "Oil and natural gas",
energetic_source == "Outras nao renovaveis" ~ "Other non-renewable",
energetic_source == "Oleo combustivel" ~ "Fuel oil",
energetic_source == "Lenha carvoejamento" ~ "Charcoal firewood",
energetic_source == "Gasolina de aviacao" ~ "Aviation gasoline",
energetic_source == "Gas natural umido" ~ "Umid natural gas",
energetic_source == "Gas de refinaria" ~ "Refinery gas",
energetic_source == "Gas canalizado RJ" ~ "Piped gas RJ",
energetic_source == "Coque de carvao mineral" ~ "Coal coke",
energetic_source == "Carvao vapor 6000" ~ "Steam coal 6000",
energetic_source == "Carvao vapor 4700" ~ "Steam coal 4700",
energetic_source == "Carvao vapor 3700" ~ "Steam coal 3700",
energetic_source == "Carvao mineral" ~ "Mineral coal",
energetic_source == "Alcool hidratado" ~ "Hydrated alcohol",
energetic_source == "Bagaco de cana" ~ "Sugarcane bagasse",
energetic_source == "Carvao vapor 3100" ~ "Steam coal 3100",
energetic_source == "Carvao vapor 4200" ~ "Steam coal 4200",
energetic_source == "Carvao vapor 5200" ~ "Steam coal 5200",
energetic_source == "Carvao vapor sem especificacao" ~ "Steam coal without specification",
energetic_source == "Coque de petroleo" ~ "Oil coke",
energetic_source == "Gas canalizadp SP" ~ "Piped gas SP",
energetic_source == "Gas natural" ~ "Natural gas",
energetic_source == "Gasolina autmotiva" ~ "Automotive gasoline",
energetic_source == "GLP" ~ "Liquefied oil gas",
energetic_source == "Lixivia" ~ "Bleach",
energetic_source == "Oleo diesel" ~ "Diesel oil",
energetic_source == "Outros energeticos de petroleo" ~ "Other petroleum energy",
energetic_source == "Querosene de aviacao" ~ "Aviation kerosene"
)) %>%
dplyr::mutate(emitters = dplyr::case_when(
emitters == "Aeronaves" ~ "Airplanes",
emitters == "Carvoarias" ~ "Charcoals",
emitters == "Comerciais Leves" ~ "Light commercials",
emitters == "Locomotivas" ~ "Locomotives",
emitters == "Onibus" ~ "Bus",
emitters == "Automoveis" ~ "Automobiles",
emitters == "Centrais Eletricas Autoprodutoras" ~ "Self-producing power plants",
emitters == "Consumo Final Energetico" ~ "Final energy consumption",
emitters == "Motocicletas" ~ "Motorbikes",
emitters == "Caminhoes" ~ "Trucks",
emitters == "Centrais Eletricas de Servico Publico" ~ "Public service power plants",
emitters == "Embarcacoes" ~ "Vessels",
emitters == "NAO SE APLICA" ~ "Not Applicable"
)) %>%
dplyr::mutate(emission_bunker = dplyr::case_when(
emission_bunker == "Emissao" ~ "Emission",
emission_bunker == "Bunker" ~ "Bunker"
)) %>%
dplyr::select(-atividade_economica)
dat <- dat %>%
dplyr::mutate(state = dplyr::case_when(
state == "NA" ~ NA_character_,
TRUE ~ state
))
dat <- dat %>%
dplyr::relocate(year, state, sector)
}
if (param$dataset == "seeg_industry" & param$geo_level == "municipality" & param$language == "eng") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x2000:x2018,
names_to = "year",
names_prefix = "x",
values_to = "value"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1 == "Processos Industriais") %>%
dplyr::rename(
sector = nivel_1, emissions_generating_processes = nivel_2,
emitters_production = nivel_3,
emitters = nivel_4,
emission_bunker = tipo_de_emissao,
city = municipio,
state = territorio,
economic_activity = atividade_economica,
product = produto
) %>%
dplyr::select(-c(nivel_6, nivel_5)) %>%
dplyr::mutate(sector = dplyr::case_when(sector == "Processos Industriais" ~ "Industrial Processes")) %>%
dplyr::mutate(emissions_generating_processes = dplyr::case_when(
emissions_generating_processes == "Emissoes de HFCs" ~ "HFC Emissions",
emissions_generating_processes == "Producao de Metais" ~ "Metal Production",
emissions_generating_processes == "Uso de SF6" ~ "SF6 Use",
emissions_generating_processes == "Industria Quimica" ~ "Chemical Industry",
emissions_generating_processes == "Produtos Minerais" ~ "Mineral Products",
emissions_generating_processes == "Uso Nao-Energetico de Combustiveis e Uso de Solventes" ~ "Non-Energy Use of Fuels and Use of Solvents"
)) %>%
dplyr::mutate(emitters_production = dplyr::case_when(
emitters_production == "Consumo de Barrilha" ~ "Barrel Consumption",
emitters_production == "Equipamentos Eletricos" ~ "Eletric Equipment",
emitters_production == "Producao de ABS" ~ "ABS Plastic Production",
emitters_production == "Producao de Acido Fosforico" ~ "Production of Phosphoric Acid",
emitters_production == "Producao de Acrilonitrila" ~ "Acrylonitrile Production",
emitters_production == "Producao de Amonia" ~ "Ammonia Production",
emitters_production == "Producao de Borracha de butadieno estireno (SBR)" ~ "Production of Styrene Butadiene Rubber",
emitters_production == "Producao de Caprolactama" ~ "Production of Caprolactam",
emitters_production == "Producao de Cimento" ~ "Cement Production",
emitters_production == "Producao de Coque de Petroleo Calcinado" ~ "Production of Calcined Petroleum Coke",
emitters_production == "Producao de Estireno" ~ "Styrene production",
emitters_production == "Producao de Etilbenzeno" ~ "Ethylbenzene production",
emitters_production == "Producao de Ferroligas" ~ "Ferroalloy Production",
emitters_production == "Producao de Magnesio" ~ "Magnesium Production",
emitters_production == "Producao de Negro-de-fumo" ~ "Carbon Black Production",
emitters_production == "Producao de Oxido de Eteno" ~ "Ethylene Oxide Production",
emitters_production == "Producao de Polietileno PEAD" ~ "HDPE Polyethylene Production",
emitters_production == "Producao de Polietileno PELBD" ~ "LLDPE Polyethylene Production",
emitters_production == "Producao de Propeno" ~ "Propylene Production",
emitters_production == "Producao de Vidro" ~ "Glass Production",
emitters_production == "Producao de PVC" ~ "PVC Production",
emitters_production == "Producao de Polipropileno" ~ "Polypropylene Production",
emitters_production == "Producao de Polietileno PEBD" ~ "LDPE Polyethylene Production",
emitters_production == "Producao de Poliestireno" ~ "Polystyrene Production",
emitters_production == "Producao de Outros Nao-Ferrosos" ~ "Production of Other Non-Ferrous",
emitters_production == "Producao de Metanol" ~ "Methanol Production",
emitters_production == "Producao de Formaldeido" ~ "Formaldehyde Production",
emitters_production == "Producao de Ferro Gusa e Aco" ~ "Pig Iron and Steel Production",
emitters_production == "Producao de Eteno" ~ "Ethene Production",
emitters_production == "Producao de Dicloroetano" ~ "Dichloroethane Production",
emitters_production == "Producao de Cloreto de Vinila" ~ "Vinyl Chloride Production",
emitters_production == "Producao de Carbureto de Calcio" ~ "Calcium Carbide Production",
emitters_production == "Producao de Cal" ~ "Lime Production",
emitters_production == "Producao de Anidrido Ftalico" ~ "Production of Phthalic Anhydride",
emitters_production == "Producao de Aluminio" ~ "Aluminum Production",
emitters_production == "Producao de Acido Nitrico" ~ "Nitric Acid Production",
emitters_production == "Producao de Acido Adipico" ~ "Adipic Acid Production",
emitters_production == "NAO SE APLICA" ~ "Not Applicable",
emitters_production == "Consumo Final Nao Energetico" ~ "Non-Energy Final Consumption"
)) %>%
dplyr::mutate(emitters = dplyr::case_when(
emitters == "Cal Calcitica" ~ "Calcitic lime",
emitters == "Consumo de Calcario" ~ "Limestone Consumption",
emitters == "Consumo em Outros Setores" ~ "Consumption in Other Sectors",
emitters == "Tecnologia Soderberg" ~ "Soderberg Technology",
emitters == "Cal Dolomitica" ~ "Dolomitic Lime",
emitters == "Consumo de Combustiveis Redutores" ~ "Consumption of Reducing Fuels",
emitters == "NAO SE APLICA" ~ "Not Applicable",
emitters == "Uso de SF6" ~ "SF6 Use",
emitters == "Cal Magnesiana" ~ "Magnesian Lime",
emitters == "Consumo de Dolomita" ~ "Dolomite Consumption",
emitters == "Tecnologia Prebaked Anode" ~ "Prebaked Anode Technology"
)) %>%
dplyr::mutate(emission_bunker = dplyr::case_when(emission_bunker == "Emissao" ~ "Emission")) %>%
dplyr::mutate(economic_activity = dplyr::case_when(
economic_activity == "CIM" ~ "Cement Industry",
economic_activity == "ENE_ELET" ~ "Eletric Power Industry",
economic_activity == "MET" ~ "Metal Industry",
economic_activity == "Outra_IND" ~ "Other Industry",
economic_activity == "HFC" ~ "HFC"
))
dat <- dat %>%
dplyr::mutate(city = dplyr::case_when(
city == "NA" ~ NA_character_,
TRUE ~ city
)) %>%
dplyr::mutate(state = dplyr::case_when(
state == "NA" ~ NA_character_,
TRUE ~ state
)) %>%
dplyr::mutate(ibge = dplyr::case_when(
ibge == "NA" ~ NA_character_,
TRUE ~ ibge
)) %>%
dplyr::mutate(product = dplyr::case_when(
product == "ALU" ~ "Aluminum",
product == "ACO" ~ "Steel"
))
dat <- dat %>%
dplyr::relocate(year, city, state, sector, emissions_generating_processes, emitters_production, emitters, economic_activity, gas, product, value)
}
if (param$dataset == "seeg_industry" & param$geo_level %in% c("country", "state") & param$language == "eng") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x1970:x2021,
names_to = "year",
names_prefix = "x",
values_to = "value"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1_setor == "Processos Industriais") %>%
dplyr::rename(
sector = nivel_1_setor, emissions_generating_processes = nivel_2,
emitters_production = nivel_3,
emitters = nivel_4,
emission_bunker = emissao_remocao_bunker,
economic_activity = atividade_economica,
product = produto,
state = estado
) %>%
dplyr::select(-c(nivel_6, nivel_5)) %>%
dplyr::mutate(sector = dplyr::case_when(sector == "Processos Industriais" ~ "Industrial Processes")) %>%
dplyr::mutate(emissions_generating_processes = dplyr::case_when(
emissions_generating_processes == "Emissoes de HFCs" ~ "HFC Emissions",
emissions_generating_processes == "Producao de Metais" ~ "Metal Production",
emissions_generating_processes == "Uso de SF6" ~ "SF6 Use",
emissions_generating_processes == "Industria Quimica" ~ "Chemical Industry",
emissions_generating_processes == "Produtos Minerais" ~ "Mineral Products",
emissions_generating_processes == "Uso Nao-Energetico de Combustiveis e Uso de Solventes" ~ "Non-Energy Use of Fuels and Use of Solvents"
)) %>%
dplyr::mutate(emitters_production = dplyr::case_when(
emitters_production == "Consumo de Barrilha" ~ "Barrel Consumption",
emitters_production == "Equipamentos Eletricos" ~ "Eletric Equipment",
emitters_production == "Producao de ABS" ~ "ABS Plastic Production",
emitters_production == "Producao de Acido Fosforico" ~ "Production of Phosphoric Acid",
emitters_production == "Producao de Acrilonitrila" ~ "Acrylonitrile Production",
emitters_production == "Producao de Amonia" ~ "Ammonia Production",
emitters_production == "Producao de Borracha de butadieno estireno (SBR)" ~ "Production of Styrene Butadiene Rubber",
emitters_production == "Producao de Caprolactama" ~ "Production of Caprolactam",
emitters_production == "Producao de Cimento" ~ "Cement Production",
emitters_production == "Producao de Coque de Petroleo Calcinado" ~ "Production of Calcined Petroleum Coke",
emitters_production == "Producao de Estireno" ~ "Styrene production",
emitters_production == "Producao de Etilbenzeno" ~ "Ethylbenzene production",
emitters_production == "Producao de Ferroligas" ~ "Ferroalloy Production",
emitters_production == "Producao de Magnesio" ~ "Magnesium Production",
emitters_production == "Producao de Negro-de-fumo" ~ "Carbon Black Production",
emitters_production == "Producao de Oxido de Eteno" ~ "Ethylene Oxide Production",
emitters_production == "Producao de Polietileno PEAD" ~ "HDPE Polyethylene Production",
emitters_production == "Producao de Polietileno PELBD" ~ "LLDPE Polyethylene Production",
emitters_production == "Producao de Propeno" ~ "Propylene Production",
emitters_production == "Producao de Vidro" ~ "Glass Production",
emitters_production == "Producao de PVC" ~ "PVC Production",
emitters_production == "Producao de Polipropileno" ~ "Polypropylene Production",
emitters_production == "Producao de Polietileno PEBD" ~ "LDPE Polyethylene Production",
emitters_production == "Producao de Poliestireno" ~ "Polystyrene Production",
emitters_production == "Producao de Outros Nao-Ferrosos" ~ "Production of Other Non-Ferrous",
emitters_production == "Producao de Metanol" ~ "Methanol Production",
emitters_production == "Producao de Formaldeido" ~ "Formaldehyde Production",
emitters_production == "Producao de Ferro Gusa e Aco" ~ "Pig Iron and Steel Production",
emitters_production == "Producao de Eteno" ~ "Ethene Production",
emitters_production == "Producao de Dicloroetano" ~ "Dichloroethane Production",
emitters_production == "Producao de Cloreto de Vinila" ~ "Vinyl Chloride Production",
emitters_production == "Producao de Carbureto de Calcio" ~ "Calcium Carbide Production",
emitters_production == "Producao de Cal" ~ "Lime Production",
emitters_production == "Producao de Anidrido Ftalico" ~ "Production of Phthalic Anhydride",
emitters_production == "Producao de Aluminio" ~ "Aluminum Production",
emitters_production == "Producao de Acido Nitrico" ~ "Nitric Acid Production",
emitters_production == "Producao de Acido Adipico" ~ "Adipic Acid Production",
emitters_production == "NAO SE APLICA" ~ "Not Applicable",
emitters_production == "Consumo Final Nao Energetico" ~ "Non-Energy Final Consumption"
)) %>%
dplyr::mutate(emitters = dplyr::case_when(
emitters == "Cal Calcitica" ~ "Calcitic lime",
emitters == "Consumo de Calcario" ~ "Limestone Consumption",
emitters == "Consumo em Outros Setores" ~ "Consumption in Other Sectors",
emitters == "Tecnologia Soderberg" ~ "Soderberg Technology",
emitters == "Cal Dolomitica" ~ "Dolomitic Lime",
emitters == "Consumo de Combustiveis Redutores" ~ "Consumption of Reducing Fuels",
emitters == "NAO SE APLICA" ~ "Not Applicable",
emitters == "Uso de SF6" ~ "SF6 Use",
emitters == "Cal Magnesiana" ~ "Magnesian Lime",
emitters == "Consumo de Dolomita" ~ "Dolomite Consumption",
emitters == "Tecnologia Prebaked Anode" ~ "Prebaked Anode Technology"
)) %>%
dplyr::mutate(emission_bunker = dplyr::case_when(emission_bunker == "Emissao" ~ "Emission")) %>%
dplyr::mutate(economic_activity = dplyr::case_when(
economic_activity == "CIM" ~ "Cement Industry",
economic_activity == "ENE_ELET" ~ "Eletric Power Industry",
economic_activity == "MET" ~ "Metal Industry",
economic_activity == "Outra_IND" ~ "Other Industry",
economic_activity == "HFC" ~ "HFC"
)) %>%
dplyr::mutate(product = dplyr::case_when(
product == "ALU" ~ "Aluminum",
product == "ACO" ~ "Steel"
))
dat <- dat %>%
dplyr::mutate(state = dplyr::case_when(
state == "NA" ~ NA_character_,
TRUE ~ state
))
dat <- dat %>%
dplyr::relocate(year, state, sector, emissions_generating_processes, emitters_production, emitters, economic_activity, gas, product, value)
}
if (param$dataset == "seeg_residuals" & param$geo_level == "municipality" & param$language == "eng") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x2000:x2018,
names_to = "year",
names_prefix = "x",
values_to = "value"
)
dat <- dat %>%
dplyr::filter(nivel_1 == "Residuos") %>%
dplyr::rename(
sector = nivel_1, emissions_category = nivel_2,
emissions_generating_processes = nivel_3,
activity = nivel_4,
generating_processes_categories = nivel_5,
emission_bunker = tipo_de_emissao,
product = produto,
economic_activity = atividade_economica,
city = municipio,
state = territorio
) %>%
dplyr::select(-nivel_6) %>%
dplyr::mutate(economic_activity = dplyr::case_when(
economic_activity == "PEC" ~ "Livestock",
economic_activity == "Outra_IND" ~ "Other industry",
economic_activity == "SANEAMENTO" ~ "Sanitation"
)) %>%
dplyr::mutate(product = dplyr::case_when(
product == "ALIM_BEBIDAS" ~ "Food/Beverages",
product == "CAR" ~ "Meat"
)) %>%
dplyr::mutate(sector = dplyr::case_when(sector == "Residuos" ~ "Waste")) %>%
dplyr::mutate(emissions_category = dplyr::case_when(
emissions_category == "Efluentes Liquidos" ~ "Liquid Effluents",
emissions_category == "Residuos Solidos" ~ "Solid Waste"
)) %>%
dplyr::mutate(emissions_generating_processes = dplyr::case_when(
emissions_generating_processes == "Disposicao Final de Residuos Solidos" ~ "Final Disposal of Solid Waste",
emissions_generating_processes == "Efluentes Liquidos Industriais" ~ "Industrial Liquid Effluents",
emissions_generating_processes == "Tratamento Biologico de Residuos Solidos" ~ "Biological Treatment of Solid Waste",
emissions_generating_processes == "Efluentes Liquidos Domesticos" ~ "Domestic Liquid Effluents",
emissions_generating_processes == "Incineracao ou queima a ceu aberto" ~ "Incineration or open burning"
)) %>%
dplyr::mutate(activity = dplyr::case_when(
activity == "Producao de Carne Bovina" ~ "Beef Production",
activity == "NAO SE APLICA" ~ "Not Applicable",
activity == "Producao de Cerveja" ~ "Beer Production",
activity == "Producao de Leite Cru" ~ "Raw Dairy Production",
activity == "Producao de Carne Suina" ~ "Prok Production",
activity == "Producao de Carne Avicola" ~ "Poultry Meat Production",
activity == "Producao de Celulose" ~ "Cellulose Production",
activity == "Producao de Leite Pasteurizado" ~ "Pasteurized Dairy Production",
activity == "Residuos Solidos Urbanos" ~ "Urban Solid Waste",
activity == "Residuos de Servicos de Saude" ~ "Health Services Waste",
activity == "Queima de Residuos a Ceu Aberto" ~ "Open-air Waste Burning",
activity == "Tratamento de Residuos por Incineracao" ~ "Waste Treatment by Incineration",
activity == "Lodo de ETE" ~ "Sewage Treatment Plant Sludge"
)) %>%
dplyr::mutate(generating_processes_categories = dplyr::case_when(
generating_processes_categories == "Compostagem" ~ "Compost",
generating_processes_categories == "Residuos de Servicos de Saude" ~ "Health Services Waste",
generating_processes_categories == "Disposicao em Aterro Sanitario" ~ "Landfill Disposal",
generating_processes_categories == "Disposicao em Aterro Controlado ou Lixao" ~ "Disposal in Controlled Landfill or Landfill",
generating_processes_categories == "NAO SE APLICA" ~ "Not Applicable",
generating_processes_categories == "Residuos Solidos Urbanos" ~ "Urban Solid Waste"
)) %>%
dplyr::mutate(emission_bunker = dplyr::case_when(emission_bunker == "Emissao" ~ "Emission")) %>%
dplyr::mutate(city = dplyr::case_when(
city == "NA" ~ NA_character_,
TRUE ~ city
)) %>%
dplyr::mutate(state = dplyr::case_when(
state == "NA" ~ NA_character_,
TRUE ~ state
)) %>%
dplyr::mutate(ibge = dplyr::case_when(
ibge == "NA" ~ NA_character_,
TRUE ~ ibge
))
dat <- dat %>%
dplyr::relocate(year, city, state, sector, emissions_category, emissions_generating_processes, activity, generating_processes_categories, economic_activity, product, value)
}
if (param$dataset == "seeg_residuals" & param$geo_level %in% c("country", "state") & param$language == "eng") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x1970:x2021,
names_to = "year",
names_prefix = "x",
values_to = "value"
)
dat <- dat %>%
dplyr::filter(nivel_1_setor == "Residuos") %>%
dplyr::rename(
sector = nivel_1_setor, emissions_category = nivel_2,
emissions_generating_processes = nivel_3,
activity = nivel_4,
generating_processes_categories = nivel_5,
emission_bunker = emissao_remocao_bunker,
product = produto,
economic_activity = atividade_economica,
state = estado
) %>%
dplyr::select(-nivel_6) %>%
dplyr::mutate(economic_activity = dplyr::case_when(
economic_activity == "PEC" ~ "Livestock",
economic_activity == "Outra_IND" ~ "Other industry",
economic_activity == "SANEAMENTO" ~ "Sanitation"
)) %>%
dplyr::mutate(product = dplyr::case_when(
product == "ALIM_BEBIDAS" ~ "Food/Beverages",
product == "CAR" ~ "Meat"
)) %>%
dplyr::mutate(state = dplyr::case_when(
state == "BA" ~ "BA",
state == "ES" ~ "ES",
state == "MA" ~ "MA",
state == "NA" ~ as.character(NA),
state == "PA" ~ "PA",
state == "RJ" ~ "RJ",
state == "RS" ~ "RS",
state == "SP" ~ "SP",
state == "AC" ~ "AC",
state == "AL" ~ "AL",
state == "AP" ~ "AP",
state == "CE" ~ "CE",
state == "DF" ~ "DF",
state == "ES" ~ "ES",
state == "GO" ~ "GO",
state == "MG" ~ "MG",
state == "MS" ~ "MS",
state == "MT" ~ "MT",
state == "PB" ~ "PB",
state == "PE" ~ "PE",
state == "PI" ~ "PI",
state == "PR" ~ "PR",
state == "RN" ~ "RN",
state == "RO" ~ "RO",
state == "RR" ~ "RR",
state == "SC" ~ "SC",
state == "SE" ~ "SE",
state == "TO" ~ "TO",
state == "AM" ~ "AM"
)) %>%
dplyr::mutate(sector = dplyr::case_when(sector == "Residuos" ~ "Waste")) %>%
dplyr::mutate(emissions_category = dplyr::case_when(
emissions_category == "Efluentes Liquidos" ~ "Liquid Effluents",
emissions_category == "Residuos Solidos" ~ "Solid Waste"
)) %>%
dplyr::mutate(emissions_generating_processes = dplyr::case_when(
emissions_generating_processes == "Disposicao Final de Residuos Solidos" ~ "Final Disposal of Solid Waste",
emissions_generating_processes == "Efluentes Liquidos Industriais" ~ "Industrial Liquid Effluents",
emissions_generating_processes == "Tratamento Biologico de Residuos Solidos" ~ "Biological Treatment of Solid Waste",
emissions_generating_processes == "Efluentes Liquidos Domesticos" ~ "Domestic Liquid Effluents",
emissions_generating_processes == "Incineracao ou queima a ceu aberto" ~ "Incineration or open burning"
)) %>%
dplyr::mutate(activity = dplyr::case_when(
activity == "Producao de Carne Bovina" ~ "Beef Production",
activity == "NAO SE APLICA" ~ "Not Applicable",
activity == "Producao de Cerveja" ~ "Beer Production",
activity == "Producao de Leite Cru" ~ "Raw Dairy Production",
activity == "Producao de Carne Suina" ~ "Prok Production",
activity == "Producao de Carne Avicola" ~ "Poultry Meat Production",
activity == "Producao de Celulose" ~ "Cellulose Production",
activity == "Producao de Leite Pasteurizado" ~ "Pasteurized Dairy Production",
activity == "Residuos Solidos Urbanos" ~ "Urban Solid Waste",
activity == "Residuos de Servicos de Saude" ~ "Health Services Waste",
activity == "Queima de Residuos a Ceu Aberto" ~ "Open-air Waste Burning",
activity == "Tratamento de Residuos por Incineracao" ~ "Waste Treatment by Incineration",
activity == "Lodo de ETE" ~ "Sewage Treatment Plant Sludge"
)) %>%
dplyr::mutate(generating_processes_categories = dplyr::case_when(
generating_processes_categories == "Compostagem" ~ "Compost",
generating_processes_categories == "Residuos de Servicos de Saude" ~ "Health Services Waste",
generating_processes_categories == "Disposicao em Aterro Sanitario" ~ "Landfill Disposal",
generating_processes_categories == "Disposicao em Aterro Controlado ou Lixao" ~ "Disposal in Controlled Landfill or Landfill",
generating_processes_categories == "NAO SE APLICA" ~ "Not Applicable",
generating_processes_categories == "Residuos Solidos Urbanos" ~ "Urban Solid Waste"
)) %>%
dplyr::mutate(emission_bunker = dplyr::case_when(emission_bunker == "Emissao" ~ "Emission"))
dat <- dat %>%
dplyr::mutate(state = dplyr::case_when(
state == "NA" ~ NA_character_,
TRUE ~ state
))
dat <- dat %>%
dplyr::relocate(year, state, sector, emissions_category, emissions_generating_processes, activity, generating_processes_categories, economic_activity, product, value)
}
if (param$dataset == "seeg_land" & param$geo_level == "municipality" & param$language == "eng") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x2000:x2018,
names_to = "year",
names_prefix = "x",
values_to = "value"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1 == "Mudanca de Uso da Terra e Floresta") %>%
dplyr::filter(!is.na(value)) %>%
dplyr::rename(
sector = nivel_1, emissions_generating_processes = nivel_2,
biome = nivel_3,
biome_area = nivel_4,
transition_type = nivel_5,
category = nivel_6,
emission_removal_bunker = tipo_de_emissao,
economic_activity = atividade_economica,
city = municipio,
state = territorio
) %>%
dplyr::mutate(sector = dplyr::case_when(sector == "Mudanca de Uso da Terra e Floresta" ~ "Land and Forest Use Change")) %>%
dplyr::mutate(emissions_generating_processes = dplyr::case_when(
emissions_generating_processes == "Alteracoes de Uso do Solo" ~ "Changes in Soil Use",
emissions_generating_processes == "Remocao por Vegetacao Secundaria" ~ "Removal by Secondary Vegetation",
emissions_generating_processes == "Remocao em Areas Protegidas" ~ "Removal in Protected Areas",
emissions_generating_processes == "Residuos Florestais" ~ "Forest Waste",
emissions_generating_processes == "Remocao por Mudanca de Uso da Terra" ~ "Removal for Land Use Change"
)) %>%
dplyr::mutate(biome = dplyr::case_when(
biome == "Amazonia" ~ "Amazon",
biome == "Caatinga" ~ "Caatinga",
biome == "Cerrado" ~ "Cerrado",
biome == "Mata Atlantica" ~ "Mata Atlantica",
biome == "Pampa" ~ "Pampa",
biome == "Pantanal" ~ "Pantanal"
)) %>%
dplyr::mutate(biome_area = dplyr::case_when(
biome_area == "em Area Protegida" ~ "In Protected Area",
biome_area == "fora de Area Protegida" ~ "Outside Protected Area",
biome_area == "NAO SE APLICA" ~ "Not Applicable"
)) %>%
dplyr::mutate(transition_type = dplyr::case_when(
transition_type == "Desmatamento" ~ "Deforestation",
transition_type == "Regeneracao" ~ "Regeneration",
transition_type == "NAO SE APLICA" ~ "Not Applicable",
transition_type == "Vegetacao nativa estavel" ~ "Stable Native Vegetation",
transition_type == "Outras Mudancas de uso da terra" ~ "Other Land Use Changes"
)) %>%
dplyr::mutate(activity = dplyr::case_when(
category == "Area sem vegetacao -- Floresta secundaria" ~ "Area without Vegetation -- Secondary Forest",
category == "Area sem vegetacao -- Silvicultura" ~ "Area without Vegetation -- Forestry",
category == "Area sem vegetacao -- Uso agropecuario" ~ "Area without Vegetation -- Farming Use",
category == "Area sem vegetacao -- Vegetacao nao florestal secundaria" ~ "Area without Vegetation -- Secondary Non-Forest Vegetation",
category == "Floresta primaria -- Area sem vegetacao" ~ "Primary Forest -- Area without Vegetation",
category == "Floresta primaria -- Floresta primaria" ~ "Primary Forest -- Primary Forest",
category == "Floresta primaria -- Silvicultura" ~ "Primary Forest -- Forestry",
category == "Floresta primaria -- Uso agropecuario" ~ "Primary Forest -- Farming Use",
category == "Floresta secundaria -- Area sem vegetacao" ~ "Secondary Forest -- Area without Vegetation",
category == "Floresta secundaria -- Floresta secundaria" ~ "Secondary Forest -- Secondary Forest",
category == "Floresta secundaria -- Silvicultura" ~ "Secondary Forest -- Forestry",
category == "Floresta secundaria -- Uso agropecuario" ~ "Secondary Forest -- Farming Use",
category == "NAO SE APLICA" ~ "Not Applicable",
category == "Silvicultura -- Area sem vegetacao" ~ "Forestry -- Area without Vegetation",
category == "Silvicultura -- Floresta Secundaria" ~ "Forestry -- Secondary Forest",
category == "Silvicultura -- Uso agropecuario" ~ "Forestry -- Farming Use",
category == "Silvicultura -- Vegetacao nao florestal secundaria" ~ "Forestry -- Secondary non-Forest Vegetation",
category == "Uso agropecuario -- Area sem vegetacao" ~ "Farming Use -- Area without vegetation",
category == "Uso agropecuario -- Floresta secundaria" ~ "Farming Use -- Secondary Forest",
category == "Uso agropecuario -- Silvicultura" ~ "Farming Use -- Forestry",
category == "Uso agropecuario -- Uso agropecuario" ~ "Farming Use -- Farming Use",
category == "Uso agropecuario -- Vegetacao nao florestal secundaria" ~ "Farming Use -- Secondary non-Forest Vegetation",
category == "Vegetacao nao florestal primaria -- Area sem vegetacao" ~ "Primary non-Forest Vegetation -- Area without Vegetation",
category == "Vegetacao nao florestal primaria -- Silvicultura" ~ "Primary Non-Forest Vegetation -- Forestry",
category == "Vegetacao nao florestal primaria -- Uso agropecuario" ~ "Primary non-Forest Vegetation -- Farming use",
category == "Vegetacao nao florestal primaria -- Vegetacao nao florestal primaria" ~ "Primary non-Forest Vegetation -- Primary non-Forest Vegetation",
category == "Vegetacao nao florestal secundaria -- Area sem vegetacao" ~ "Secondary non-Forest Vegetation -- Area without Vegetation",
category == "Vegetacao nao florestal secundaria -- Silvicultura" ~ "Secondary non-Forest Vegetation -- Forestry",
category == "Vegetacao nao florestal secundaria -- Uso agropecuario" ~ "Secondary non-Forest Vegetation -- Farming use",
category == "Vegetacao nao florestal secundaria -- Vegetacao nao florestal secundaria" ~ "Secondary non-Forest Vegetation"
)) %>%
dplyr::mutate(emission_removal_bunker = dplyr::case_when(
emission_removal_bunker == "Emissao" ~ "Emission",
emission_removal_bunker == "Remocao" ~ "Removal",
emission_removal_bunker == "Remocao proxy" ~ "Proxy Removal"
)) %>%
dplyr::mutate(economic_activity = dplyr::case_when(
economic_activity == "AGROPEC" ~ "Farming",
economic_activity == "Conservacao" ~ "Conservation"
)) %>%
dplyr::select(-produto)
dat <- dat %>%
dplyr::mutate(city = dplyr::case_when(
city == "NA" ~ NA_character_,
TRUE ~ city
)) %>%
dplyr::mutate(state = dplyr::case_when(
state == "NA" ~ NA_character_,
TRUE ~ state
)) %>%
dplyr::mutate(ibge = dplyr::case_when(
ibge == "NA" ~ NA_character_,
TRUE ~ ibge
))
dat <- dat %>%
dplyr::relocate(year, city, state, sector, emissions_generating_processes, economic_activity, biome, biome_area, transition_type, emission_removal_bunker)
}
if (param$dataset == "seeg_land" & param$geo_level %in% c("country", "state") & param$language == "eng") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x1970:x2021,
names_to = "year",
names_prefix = "x",
values_to = "value"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1_setor == "Mudanca de Uso da Terra e Floresta") %>%
dplyr::filter(!is.na(value)) %>%
dplyr::rename(
sector = nivel_1_setor, emissions_generating_processes = nivel_2,
biome = nivel_3,
biome_area = nivel_4,
transition_type = nivel_5,
category = nivel_6,
emission_removal_bunker = emissao_remocao_bunker,
economic_activity = atividade_economica,
state = estado
) %>%
dplyr::mutate(sector = dplyr::case_when(sector == "Mudanca de Uso da Terra e Floresta" ~ "Land and Forest Use Change")) %>%
dplyr::mutate(emissions_generating_processes = dplyr::case_when(
emissions_generating_processes == "Alteracoes de Uso do Solo" ~ "Changes in Soil Use",
emissions_generating_processes == "Remocao por Vegetacao Secundaria" ~ "Removal by Secondary Vegetation",
emissions_generating_processes == "Remocao em Areas Protegidas" ~ "Removal in Protected Areas",
emissions_generating_processes == "Residuos Florestais" ~ "Forest Waste",
emissions_generating_processes == "Remocao por Mudanca de Uso da Terra" ~ "Removal for Land Use Change"
)) %>%
dplyr::mutate(biome = dplyr::case_when(
biome == "Amazonia" ~ "Amazon",
biome == "Caatinga" ~ "Caatinga",
biome == "Cerrado" ~ "Cerrado",
biome == "Mata Atlantica" ~ "Mata Atlantica",
biome == "Pampa" ~ "Pampa",
biome == "Pantanal" ~ "Pantanal"
)) %>%
dplyr::mutate(biome_area = dplyr::case_when(
biome_area == "em Area Protegida" ~ "In Protected Area",
biome_area == "fora de Area Protegida" ~ "Outside Protected Area",
biome_area == "NAO SE APLICA" ~ "Not Applicable"
)) %>%
dplyr::mutate(transition_type = dplyr::case_when(
transition_type == "Desmatamento" ~ "Deforestation",
transition_type == "Regeneracao" ~ "Regeneration",
transition_type == "NAO SE APLICA" ~ "Not Applicable",
transition_type == "Vegetacao nativa estavel" ~ "Stable Native Vegetation",
transition_type == "Outras Mudancas de uso da terra" ~ "Other Land Use Changes"
)) %>%
dplyr::mutate(activity = dplyr::case_when(
category == "Area sem vegetacao -- Floresta secundaria" ~ "Area without Vegetation -- Secondary Forest",
category == "Area sem vegetacao -- Silvicultura" ~ "Area without Vegetation -- Forestry",
category == "Area sem vegetacao -- Uso agropecuario" ~ "Area without Vegetation -- Farming Use",
category == "Area sem vegetacao -- Vegetacao nao florestal secundaria" ~ "Area without Vegetation -- Secondary Non-Forest Vegetation",
category == "Floresta primaria -- Area sem vegetacao" ~ "Primary Forest -- Area without Vegetation",
category == "Floresta primaria -- Floresta primaria" ~ "Primary Forest -- Primary Forest",
category == "Floresta primaria -- Silvicultura" ~ "Primary Forest -- Forestry",
category == "Floresta primaria -- Uso agropecuario" ~ "Primary Forest -- Farming Use",
category == "Floresta secundaria -- Area sem vegetacao" ~ "Secondary Forest -- Area without Vegetation",
category == "Floresta secundaria -- Floresta secundaria" ~ "Secondary Forest -- Secondary Forest",
category == "Floresta secundaria -- Silvicultura" ~ "Secondary Forest -- Forestry",
category == "Floresta secundaria -- Uso agropecuario" ~ "Secondary Forest -- Farming Use",
category == "NAO SE APLICA" ~ "Not Applicable",
category == "Silvicultura -- Area sem vegetacao" ~ "Forestry -- Area without Vegetation",
category == "Silvicultura -- Floresta Secundaria" ~ "Forestry -- Secondary Forest",
category == "Silvicultura -- Uso agropecuario" ~ "Forestry -- Farming Use",
category == "Silvicultura -- Vegetacao nao florestal secundaria" ~ "Forestry -- Secondary non-Forest Vegetation",
category == "Uso agropecuario -- Area sem vegetacao" ~ "Farming Use -- Area without vegetation",
category == "Uso agropecuario -- Floresta secundaria" ~ "Farming Use -- Secondary Forest",
category == "Uso agropecuario -- Silvicultura" ~ "Farming Use -- Forestry",
category == "Uso agropecuario -- Uso agropecuario" ~ "Farming Use -- Farming Use",
category == "Uso agropecuario -- Vegetacao nao florestal secundaria" ~ "Farming Use -- Secondary non-Forest Vegetation",
category == "Vegetacao nao florestal primaria -- Area sem vegetacao" ~ "Primary non-Forest Vegetation -- Area without Vegetation",
category == "Vegetacao nao florestal primaria -- Silvicultura" ~ "Primary Non-Forest Vegetation -- Forestry",
category == "Vegetacao nao florestal primaria -- Uso agropecuario" ~ "Primary non-Forest Vegetation -- Farming use",
category == "Vegetacao nao florestal primaria -- Vegetacao nao florestal primaria" ~ "Primary non-Forest Vegetation -- Primary non-Forest Vegetation",
category == "Vegetacao nao florestal secundaria -- Area sem vegetacao" ~ "Secondary non-Forest Vegetation -- Area without Vegetation",
category == "Vegetacao nao florestal secundaria -- Silvicultura" ~ "Secondary non-Forest Vegetation -- Forestry",
category == "Vegetacao nao florestal secundaria -- Uso agropecuario" ~ "Secondary non-Forest Vegetation -- Farming use",
category == "Vegetacao nao florestal secundaria -- Vegetacao nao florestal secundaria" ~ "Secondary non-Forest Vegetation"
)) %>%
dplyr::mutate(emission_removal_bunker = dplyr::case_when(
emission_removal_bunker == "Emissao" ~ "Emission",
emission_removal_bunker == "Remocao" ~ "Removal",
emission_removal_bunker == "Remocao proxy" ~ "Proxy Removal"
)) %>%
dplyr::mutate(economic_activity = dplyr::case_when(
economic_activity == "AGROPEC" ~ "Farming",
economic_activity == "Conservacao" ~ "Conservation"
)) %>%
dplyr::select(-produto)
dat <- dat %>%
dplyr::mutate(state = dplyr::case_when(
state == "NA" ~ NA_character_,
TRUE ~ state
))
dat <- dat %>%
dplyr::relocate(year, state, sector, emissions_generating_processes, economic_activity, biome, biome_area, transition_type, emission_removal_bunker)
}
if (param$dataset == "seeg_farming" & param$geo_level == "municipality" & param$language == "eng") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x2000:x2018,
names_to = "year",
names_prefix = "x",
values_to = "value"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1 == "Agropecuaria") %>%
dplyr::rename(
sector = nivel_1, emissions_generating_processes = nivel_2,
emissions_type = nivel_4,
direct_indirect_emitters = nivel_3,
emissions_sources = nivel_5,
emitters = nivel_6,
economic_activity = atividade_economica,
product = produto,
emission_removal_bunker = tipo_de_emissao,
city = municipio,
state = territorio
) %>%
dplyr::mutate(economic_activity = dplyr::case_when(
economic_activity == "PEC" ~ "Farming",
economic_activity == "AGR" ~ "Agriculture"
)) %>%
dplyr::mutate(sector = dplyr::case_when(sector == "Agropecuaria" ~ "Farming")) %>%
dplyr::mutate(emissions_generating_processes = dplyr::case_when(
emissions_generating_processes == "Cultivo do Arroz" ~ "Rice Cultivation",
emissions_generating_processes == "Fermentacao Enterica" ~ "Enteric Fermentation",
emissions_generating_processes == "Manejo de Dejetos Animais" ~ "Animal Waste Management",
emissions_generating_processes == "Queima de Residuos Agricolas" ~ "Agricultural Waste Burning",
emissions_generating_processes == "Solos Manejados" ~ "Managed Soils"
)) %>%
dplyr::mutate(emissions_sources = dplyr::case_when(
emissions_sources == "Animal" ~ "Animal",
emissions_sources == "Vegetal" ~ "Plant",
emissions_sources == "Outros" ~ "Others"
)) %>%
dplyr::mutate(emitters = dplyr::case_when(
emitters == "Algodao" ~ "Cottage",
emitters == "Arroz" ~ "Rice",
emitters == "Aves" ~ "Birds",
emitters == "Cana de Acucar" ~ "Sugar Cane",
emitters == "Equino" ~ "Equine",
emitters == "Fertilizantes Sinteticos" ~ "Synthetic Fertilizers",
emitters == "Gado de Corte" ~ "Beef Cattle",
emitters == "Lavouras Cultivadas sob Sistema Convencional" ~ "Crops Cultivated under Conventional System",
emitters == "Mandioca" ~ "Cassava",
emitters == "Muar" ~ "Mule",
emitters == "Outros" ~ "Others",
emitters == "Pastagem" ~ "Pasture",
emitters == "Pastagem Degradada" ~ "Degraded Pasture",
emitters == "Soja" ~ "Soil",
emitters == "Suinos" ~ "Swine",
emitters == "Trigo" ~ "Wheat",
emitters == "Vinhaca" ~ "Vinasse",
emitters == "Uso de Calcario" ~ "Limestone Usage",
emitters == "Torta de Filtro" ~ "Filter Cake",
emitters == "Solos organicos" ~ "Organic Soils",
emitters == "Sistemas Integrados Lavoura-Pecuaria-Floresta" ~ "Integrated Crop-Livestock-Forestry Systems",
emitters == "Pastagem Bem Manejada" ~ "Well Managed Pasture",
emitters == "Ovino" ~ "Sheep",
emitters == "Outras Culturas" ~ "Other Cultures",
emitters == "Milho" ~ "Corn",
emitters == "Lavouras Cultivadas sob Sistema Plantio Direto" ~ "Crops Cultivated under no-till System",
emitters == "Gado de Leite" ~ "Dairy Cattle",
emitters == "Florestas Plantadas" ~ "Planted Forests",
emitters == "Feijao" ~ "Beans",
emitters == "Caprino" ~ "Goat",
emitters == "Bubalino" ~ "Buffalo",
emitters == "Asinino" ~ "Asinine",
emitters == "Aplicacao de Ureia" ~ "Urea Application"
)) %>%
dplyr::mutate(direct_indirect_emitters = dplyr::case_when(
direct_indirect_emitters == "Diretas" ~ "Direct",
direct_indirect_emitters == "Indiretas" ~ "Indirect"
)) %>%
dplyr::mutate(emissions_type = dplyr::case_when(
emissions_type == "Aplicacao de residuos organicos" ~ "Application of Organic Waste",
emissions_type == "Deposicao de dejetos em pastagem" ~ "Pasture Waste Disposal",
emissions_type == "Lixiviacao" ~ "Leaching",
emissions_type == "Outros" ~ "Others",
emissions_type == "Solos organicos" ~ "Organic Soils",
emissions_type == "Deposicao Atmosferica" ~ "Atmospheric Deposition",
emissions_type == "Fertilizantes Sinteticos" ~ "Synthetic Fertilizers",
emissions_type == "Mineralizacao de N associado a perda de C no solo" ~ "Mineralization of N Associated with Loss of C in the Soil",
emissions_type == "Residuos Agricolas" ~ "Agricultural Waste",
emissions_type == "Variacao dos Estoques de Carbono no Solo" ~ "Changes in Carbon Soil Stocks"
)) %>%
dplyr::mutate(emission_removal_bunker = dplyr::case_when(
emission_removal_bunker == "Emissao" ~ "Emission",
emission_removal_bunker == "Emissao NCI" ~ "NCI Emission",
emission_removal_bunker == "Remocao NCI" ~ "NCI Removal"
)) %>%
dplyr::mutate(product = dplyr::case_when(
product == "ALIM_BEBIDAS" ~ "Food/Beverages",
product == "CAR" ~ "Meat",
product == "CAR/LEI" ~ "Meat/Dairy",
product == "CAR/LEI/ALIM_BEBIDAS" ~ "Meat/Dairy/Food/Beverages",
product == "LEI" ~ "Dairy"
))
dat <- dat %>%
dplyr::relocate(
year, city, state, sector, emissions_generating_processes, emissions_sources, emitters, emissions_type, gas, economic_activity, product,
value
)
dat <- dat %>%
dplyr::mutate(city = dplyr::case_when(
city == "NA" ~ NA_character_,
TRUE ~ city
)) %>%
dplyr::mutate(state = dplyr::case_when(
state == "NA" ~ NA_character_,
TRUE ~ state
)) %>%
dplyr::mutate(ibge = dplyr::case_when(
ibge == "NA" ~ NA_character_,
TRUE ~ ibge
))
}
if (param$dataset == "seeg_farming" & param$geo_level %in% c("country", "state") & param$language == "eng") {
dat <- dat %>%
tidyr::pivot_longer(
cols = x1970:x2021,
names_to = "year",
names_prefix = "x",
values_to = "value"
)
## Changing column name, filtering by the specific sector and harmonizing variables
dat <- dat %>%
dplyr::filter(nivel_1_setor == "Agropecuaria") %>%
dplyr::rename(
sector = nivel_1_setor, emissions_generating_processes = nivel_2,
emissions_type = nivel_4,
direct_indirect_emitters = nivel_3,
emissions_sources = nivel_5,
emitters = nivel_6,
economic_activity = atividade_economica,
product = produto,
emission_removal_bunker = emissao_remocao_bunker,
state = estado
) %>%
dplyr::mutate(economic_activity = dplyr::case_when(
economic_activity == "PEC" ~ "Farming",
economic_activity == "AGR" ~ "Agriculture"
)) %>%
dplyr::mutate(sector = dplyr::case_when(sector == "Agropecuaria" ~ "Farming")) %>%
dplyr::mutate(emissions_generating_processes = dplyr::case_when(
emissions_generating_processes == "Cultivo do Arroz" ~ "Rice Cultivation",
emissions_generating_processes == "Fermentacao Enterica" ~ "Enteric Fermentation",
emissions_generating_processes == "Manejo de Dejetos Animais" ~ "Animal Waste Management",
emissions_generating_processes == "Queima de Residuos Agricolas" ~ "Agricultural Waste Burning",
emissions_generating_processes == "Solos Manejados" ~ "Managed Soils"
)) %>%
dplyr::mutate(emissions_sources = dplyr::case_when(
emissions_sources == "Animal" ~ "Animal",
emissions_sources == "Vegetal" ~ "Plant",
emissions_sources == "Outros" ~ "Others"
)) %>%
dplyr::mutate(emitters = dplyr::case_when(
emitters == "Algodao" ~ "Cottage",
emitters == "Arroz" ~ "Rice",
emitters == "Aves" ~ "Birds",
emitters == "Cana de Acucar" ~ "Sugar Cane",
emitters == "Equino" ~ "Equine",
emitters == "Fertilizantes Sinteticos" ~ "Synthetic Fertilizers",
emitters == "Gado de Corte" ~ "Beef Cattle",
emitters == "Lavouras Cultivadas sob Sistema Convencional" ~ "Crops Cultivated under Conventional System",
emitters == "Mandioca" ~ "Cassava",
emitters == "Muar" ~ "Mule",
emitters == "Outros" ~ "Others",
emitters == "Pastagem" ~ "Pasture",
emitters == "Pastagem Degradada" ~ "Degraded Pasture",
emitters == "Soja" ~ "Soil",
emitters == "Suinos" ~ "Swine",
emitters == "Trigo" ~ "Wheat",
emitters == "Vinhaca" ~ "Vinasse",
emitters == "Uso de Calcario" ~ "Limestone Usage",
emitters == "Torta de Filtro" ~ "Filter Cake",
emitters == "Solos organicos" ~ "Organic Soils",
emitters == "Sistemas Integrados Lavoura-Pecuaria-Floresta" ~ "Integrated Crop-Livestock-Forestry Systems",
emitters == "Pastagem Bem Manejada" ~ "Well Managed Pasture",
emitters == "Ovino" ~ "Sheep",
emitters == "Outras Culturas" ~ "Other Cultures",
emitters == "Milho" ~ "Corn",
emitters == "Lavouras Cultivadas sob Sistema Plantio Direto" ~ "Crops Cultivated under no-till System",
emitters == "Gado de Leite" ~ "Dairy Cattle",
emitters == "Florestas Plantadas" ~ "Planted Forests",
emitters == "Feijao" ~ "Beans",
emitters == "Caprino" ~ "Goat",
emitters == "Bubalino" ~ "Buffalo",
emitters == "Asinino" ~ "Asinine",
emitters == "Aplicacao de Ureia" ~ "Urea Application"
)) %>%
dplyr::mutate(direct_indirect_emitters = dplyr::case_when(
direct_indirect_emitters == "Diretas" ~ "Direct",
direct_indirect_emitters == "Indiretas" ~ "Indirect"
)) %>%
dplyr::mutate(emissions_type = dplyr::case_when(
emissions_type == "Aplicacao de residuos organicos" ~ "Application of Organic Waste",
emissions_type == "Deposicao de dejetos em pastagem" ~ "Pasture Waste Disposal",
emissions_type == "Lixiviacao" ~ "Leaching",
emissions_type == "Outros" ~ "Others",
emissions_type == "Solos organicos" ~ "Organic Soils",
emissions_type == "Deposicao Atmosferica" ~ "Atmospheric Deposition",
emissions_type == "Fertilizantes Sinteticos" ~ "Synthetic Fertilizers",
emissions_type == "Mineralizacao de N associado a perda de C no solo" ~ "Mineralization of N Associated with Loss of C in the Soil",
emissions_type == "Residuos Agricolas" ~ "Agricultural Waste",
emissions_type == "Variacao dos Estoques de Carbono no Solo" ~ "Changes in Carbon Soil Stocks"
)) %>%
dplyr::mutate(emission_removal_bunker = dplyr::case_when(
emission_removal_bunker == "Emissao" ~ "Emission",
emission_removal_bunker == "Emissao NCI" ~ "NCI Emission",
emission_removal_bunker == "Remocao NCI" ~ "NCI Removal"
)) %>%
dplyr::mutate(product = dplyr::case_when(
product == "ALIM_BEBIDAS" ~ "Food/Beverages",
product == "CAR" ~ "Meat",
product == "CAR/LEI" ~ "Meat/Dairy",
product == "CAR/LEI/ALIM_BEBIDAS" ~ "Meat/Dairy/Food/Beverages",
product == "LEI" ~ "Dairy"
))
dat <- dat %>%
dplyr::relocate(
year, state, sector, emissions_generating_processes, emissions_sources, emitters, emissions_type, gas, economic_activity, product,
value
)
dat <- dat %>%
dplyr::mutate(state = dplyr::case_when(
state == "NA" ~ NA_character_,
TRUE ~ state
))
}
return(dat)
}
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