R/readFRA2015Doc.R
In pik-piam/moinput: Model Operations Input Data Library
Defines functions
readFRA2015Doc
#' Read FRA2015Doc
#'
#' Read-in an FRA data from 2015 (forest resource assessment) pdf file.
#'
#'
#' @param subtype data subtype.
#' @return magpie object of the FRA 2015 data
#' @author Abhijeet Mishra
#' @seealso \code{\link{readSource}}
#' @examples
#'
#' \dontrun{ a <- readSource("FRA2015Doc","forest_area")
#' }
#'
#' @importFrom magclass as.magpie
#' @importFrom madrat toolSubtypeSelect
#' @import readxl
#' @import countrycode
readFRA2015Doc <- function(subtype){
## Mapping file
iso_country <- read.csv2(system.file("extdata","FAOiso_faocode.csv",package = "moinput"),row.names=NULL)
## Original data file name
location <- "FRA2015_data.xlsx"
### Aggregated region rows in original data which have to be removed
remove_region_rows <- c("Eastern and Southern Africa","Northern Africa","Western and Central Africa",
"East Asia","South and Southeast Asia","Western and Central Asia",
"Europe","Caribbean","Central America","North America","Oceania","South America")
## Year Names
column_names <- c("Country",paste0("y",c(1990,2000,2005,2010,2015)))
## Define standard function
fra_reader <- function(file_name,sheet_location,skip_rows,skip_cols,rows_delete,col_vector){
dat <- as.data.frame(read_xlsx(path = file_name,sheet = sheet_location,skip = skip_rows,n_max = skip_cols))
dat <- dat[,-c(2:10,16:ncol(dat))]
## Rename columns
colnames(dat) <- col_vector
## Remove rows we don't need
dat <- dat[!(dat[,1] %in% rows_delete),]
return(dat)
}
############ FOREST AREA ############
if (subtype=="forest_area") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "1.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="other_wooded_land") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "1.2",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="primary_forest") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "2.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="secondary_forest") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "2.2",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="plantation_forest") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "2.3",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="mangrove_forest") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "2.4",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="forest_gs") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "3.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## This is already in mio. m3
} else if (subtype=="abvg_bm") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "3.2",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## This is already in mio. tonnes
} else if (subtype=="belg_bm") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "3.3",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## This is already in mio. tonnes
} else if (subtype=="dead_wood") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "3.4",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## This is already in mio. tonnes
} else if (subtype=="c_abvg_bm") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "3.5",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## This is already in mio. tonnes
} else if (subtype=="c_belg_bm") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "3.6",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## This is already in mio. tonnes
} else if (subtype=="c_dead_wood") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "3.7",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## This is already in mio. tonnes
} else if (subtype=="c_litter") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "3.8",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## This is already in mio. tonnes
} else if (subtype=="c_soil") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "3.9",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## This is already in mio. tonnes
} else if (subtype=="production_forest") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "4.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="multiple_use_forest") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "4.2",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="biodiversity_conservation") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "5.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="forest_within_protected") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "5.2",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="public_own") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "6.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Data for 2015 doesn't exist but is read in using the dtandard function we defined earlier
## This results in reading an additional column we don't need so we drop it here.
x <- x[,-ncol(x)]
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="private_own") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "6.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Data for 2015 doesn't exist but is read in using the dtandard function we defined earlier
## This results in reading an additional column we don't need so we drop it here.
x <- x[,-ncol(x)]
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="unknown_own") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "6.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Data for 2015 doesn't exist but is read in using the dtandard function we defined earlier
## This results in reading an additional column we don't need so we drop it here.
x <- x[,-ncol(x)]
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="public_admin") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "6.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Data for 2015 doesn't exist but is read in using the dtandard function we defined earlier
## This results in reading an additional column we don't need so we drop it here.
x <- x[,-ncol(x)]
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="individual_admin") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "6.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Data for 2015 doesn't exist but is read in using the dtandard function we defined earlier
## This results in reading an additional column we don't need so we drop it here.
x <- x[,-ncol(x)]
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="private_admin") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "6.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Data for 2015 doesn't exist but is read in using the dtandard function we defined earlier
## This results in reading an additional column we don't need so we drop it here.
x <- x[,-ncol(x)]
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="community_admin") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "6.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Data for 2015 doesn't exist but is read in using the dtandard function we defined earlier
## This results in reading an additional column we don't need so we drop it here.
x <- x[,-ncol(x)]
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="other_admin") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "6.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Data for 2015 doesn't exist but is read in using the dtandard function we defined earlier
## This results in reading an additional column we don't need so we drop it here.
x <- x[,-ncol(x)]
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else {stop("Invalid subtype ", subtype)}
x$iso3 <- countrycode(sourcevar = x$Country,origin = "country.name",destination = "iso3c",warn = TRUE)
mo_final <- x[,c(7,1:6)]
mo_final <- mo_final[,c(-2)]
mo_final <- mo_final[complete.cases(mo_final$iso3),]
mo_final <- magpiesort(as.magpie(mo_final,spatial=1)) ## Dropping the first column because we now have ISO codes in last column
mo_final <- round(mo_final,2)
getNames(mo_final) <- subtype
mo_final_1995 <- setYears((setYears(mo_final[,"y1990",],NULL) + setYears(mo_final[,"y2000",],NULL))/2,"y1995")
mo_final <- mbind(mo_final,mo_final_1995)
mo_final <- magpiesort(mo_final)
return(mo_final)
}
pik-piam/moinput documentation built on June 9, 2020, 12:23 p.m.
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Defines functions readFRA2015Doc
#' Read FRA2015Doc
#'
#' Read-in an FRA data from 2015 (forest resource assessment) pdf file.
#'
#'
#' @param subtype data subtype.
#' @return magpie object of the FRA 2015 data
#' @author Abhijeet Mishra
#' @seealso \code{\link{readSource}}
#' @examples
#'
#' \dontrun{ a <- readSource("FRA2015Doc","forest_area")
#' }
#'
#' @importFrom magclass as.magpie
#' @importFrom madrat toolSubtypeSelect
#' @import readxl
#' @import countrycode
readFRA2015Doc <- function(subtype){
## Mapping file
iso_country <- read.csv2(system.file("extdata","FAOiso_faocode.csv",package = "moinput"),row.names=NULL)
## Original data file name
location <- "FRA2015_data.xlsx"
### Aggregated region rows in original data which have to be removed
remove_region_rows <- c("Eastern and Southern Africa","Northern Africa","Western and Central Africa",
"East Asia","South and Southeast Asia","Western and Central Asia",
"Europe","Caribbean","Central America","North America","Oceania","South America")
## Year Names
column_names <- c("Country",paste0("y",c(1990,2000,2005,2010,2015)))
## Define standard function
fra_reader <- function(file_name,sheet_location,skip_rows,skip_cols,rows_delete,col_vector){
dat <- as.data.frame(read_xlsx(path = file_name,sheet = sheet_location,skip = skip_rows,n_max = skip_cols))
dat <- dat[,-c(2:10,16:ncol(dat))]
## Rename columns
colnames(dat) <- col_vector
## Remove rows we don't need
dat <- dat[!(dat[,1] %in% rows_delete),]
return(dat)
}
############ FOREST AREA ############
if (subtype=="forest_area") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "1.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="other_wooded_land") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "1.2",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="primary_forest") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "2.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="secondary_forest") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "2.2",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="plantation_forest") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "2.3",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="mangrove_forest") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "2.4",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="forest_gs") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "3.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## This is already in mio. m3
} else if (subtype=="abvg_bm") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "3.2",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## This is already in mio. tonnes
} else if (subtype=="belg_bm") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "3.3",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## This is already in mio. tonnes
} else if (subtype=="dead_wood") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "3.4",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## This is already in mio. tonnes
} else if (subtype=="c_abvg_bm") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "3.5",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## This is already in mio. tonnes
} else if (subtype=="c_belg_bm") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "3.6",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## This is already in mio. tonnes
} else if (subtype=="c_dead_wood") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "3.7",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## This is already in mio. tonnes
} else if (subtype=="c_litter") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "3.8",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## This is already in mio. tonnes
} else if (subtype=="c_soil") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "3.9",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## This is already in mio. tonnes
} else if (subtype=="production_forest") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "4.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="multiple_use_forest") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "4.2",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="biodiversity_conservation") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "5.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="forest_within_protected") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "5.2",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="public_own") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "6.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Data for 2015 doesn't exist but is read in using the dtandard function we defined earlier
## This results in reading an additional column we don't need so we drop it here.
x <- x[,-ncol(x)]
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="private_own") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "6.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Data for 2015 doesn't exist but is read in using the dtandard function we defined earlier
## This results in reading an additional column we don't need so we drop it here.
x <- x[,-ncol(x)]
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="unknown_own") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "6.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Data for 2015 doesn't exist but is read in using the dtandard function we defined earlier
## This results in reading an additional column we don't need so we drop it here.
x <- x[,-ncol(x)]
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="public_admin") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "6.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Data for 2015 doesn't exist but is read in using the dtandard function we defined earlier
## This results in reading an additional column we don't need so we drop it here.
x <- x[,-ncol(x)]
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="individual_admin") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "6.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Data for 2015 doesn't exist but is read in using the dtandard function we defined earlier
## This results in reading an additional column we don't need so we drop it here.
x <- x[,-ncol(x)]
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="private_admin") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "6.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Data for 2015 doesn't exist but is read in using the dtandard function we defined earlier
## This results in reading an additional column we don't need so we drop it here.
x <- x[,-ncol(x)]
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="community_admin") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "6.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Data for 2015 doesn't exist but is read in using the dtandard function we defined earlier
## This results in reading an additional column we don't need so we drop it here.
x <- x[,-ncol(x)]
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else if (subtype=="other_admin") {
## Read using pre-define function
x <- fra_reader(file_name = location,sheet_location = "6.1",
skip_rows = 3,skip_cols = 245,rows_delete = remove_region_rows,col_vector = column_names)
## Data for 2015 doesn't exist but is read in using the dtandard function we defined earlier
## This results in reading an additional column we don't need so we drop it here.
x <- x[,-ncol(x)]
## Convert to mio. ha
x[,2:ncol(x)] <- x[,2:ncol(x)]/1000
} else {stop("Invalid subtype ", subtype)}
x$iso3 <- countrycode(sourcevar = x$Country,origin = "country.name",destination = "iso3c",warn = TRUE)
mo_final <- x[,c(7,1:6)]
mo_final <- mo_final[,c(-2)]
mo_final <- mo_final[complete.cases(mo_final$iso3),]
mo_final <- magpiesort(as.magpie(mo_final,spatial=1)) ## Dropping the first column because we now have ISO codes in last column
mo_final <- round(mo_final,2)
getNames(mo_final) <- subtype
mo_final_1995 <- setYears((setYears(mo_final[,"y1990",],NULL) + setYears(mo_final[,"y2000",],NULL))/2,"y1995")
mo_final <- mbind(mo_final,mo_final_1995)
mo_final <- magpiesort(mo_final)
return(mo_final)
}
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