R/koeppen_geiger.R
In ecor/ClimClass: Climate Classification According To Several Indices
NULL
#' @description General climate classification after Koeppen - Geiger.
#'
#' @param clim_norm average values (climate normals) for the desired period.
#' @param A_B_C_special_sub.classes logical. Sets if calculations have to consider sub-classes based on rain features in climate types A, B, and C (see details). Default is \code{FALSE}.
#' @param clim.resume_verbose logical. If \code{TRUE} (default) a resume of the climatic indices used for the Koeppen - Geiger classification is added to the output.
#' @param class.nr logical. If \code{FALSE} (default) class is expressed by letters, otherwise by numbers (see details).
#'
#'
#' @title Koeppen - Geiger's climate classification
#' @author Emanuele Eccel
#'
#' @return A one-line data frame reporting a resume of climatic features useful for the classification (if \code{clim.resume_verbose} is \code{TRUE}), and one last field (1 type - or "climate" - plus 1 or 2 sub-types) reporting Koeppen - Geiger's climate classification. See details.
#'
#' @details \code{clim_norm} is a monthly data frame of climate normals, with column names: "P", "Tn", "Tx", "Tm" (precipitation, minimum, maximum and mean temperature, respectively). It can be the output of function \code{\link{climate}}.
#'
#' Koeppen - Geiger's classification is based on Trewartha and Lyle, 1980. The function also holds for Southern emisphere, except for the "Gange" sub-type ("Ag" and "Cg"). Type "H" (highland climate) and sub-types "Bn" and "Cn" (where n stands for Nebel) are never attributed, being based on a qualitative description in the quoted reference.
#'
#' Sub-type "w" (wet-and-dry) or "m" (monsoon) in climate "A" is set according to the definition after Encyclopaedia Britannica (\url{http://www.britannica.com/EBchecked/topic/322068/Koppen-climate-classification}): if P in the 4 driest months is less than 1/5 of the wettest months and if both the 4 driest and wettest months are split over non-contiguous seasons (either 2 months per season or 1 and 3 months per season), then sub-type is "".
#'
#' For climate "A", the letter "m" is attributed to the first sub-type.
#'
#' Climates "Cx" have P[May + June] >= 1.3 P [Aug. + Sept.] in N emisphere, and P[Nov. + Dec.] >= 1.3 P[Febr. + March] in S emisphere.
#'
#' \code{A_B_C_special_sub.classes}, if \code{TRUE}, adds a letter to the second sub-type of climates: "i" or "g" (climate A), "w" or "s" (climate B), and "i", "g", or "x" (climate C).
#'
#' The returned data frame contains the following fields:
#'
#' \code{T_w.m} = temperature of the warmest month (degrees C)
#'
#' \code{T_c.m} = temperature of the coldest month (degrees C)
#'
#' \code{T_avg} = average temperature (degrees C)
#'
#' \code{P_tot} = total precipitation depth (mm)
#'
#' \code{P_wint} = precipitation depth in the 6 coldest (winter) months (mm)
#'
#' \code{P_summ} = precipitation depth in the 6 warmest (summer) months (mm)
#'
#' \code{P_d.m} = precipitation depth in the driest month (mm)
#'
#' \code{P_d.m.summ} = precipitation depth in the driest month of "summer" half of the year (mm)
#'
#' \code{P_d.m.wint} = precipitation depth in the driest month of "winter" half of the year (mm)
#'
#' \code{P_w.m} = precipitation depth in the wettest month (mm)
#'
#' \code{P_w.m.summ} = precipitation depth in the driest month of "summer" half of the year (mm)
#'
#' \code{P_w.m.wint} = precipitation depth in the wettest month of "winter" half of the year (mm)
#'
#' \code{T_4th_w.m} = temperature of the 4th warmest month (degrees C)
#'
#' \code{class} = climatic class. If \code{class.nr}=\code{FALSE} (default), it results from the merging of "climate" (A to E) and sub-type(s).
#' If \code{class.nr}=\code{TRUE} the class comes from the sum of the numeric equivalent of "type" (A,..E) + "first sub type" (only first letter),
#' according to the following scheme:
#' A B C D E
#' 10 20 30 40 50
#' f W s s T 1
#' w S w w F 2
#' m f f 3
#'
#' (e.g: Af = 11, Cw = 32, EF = 52)
#'
#' @export
#'
#' @references
#' Trewartha, G.T. and Lyle, H.H., 1980: An Introduction to Climate. MacGraw - Hill, 5th Ed. Appendix: Koeppen's Classification of Climates.
#'
#' @examples
#' data(Trent_climate)
#' # clima_81_10 is a list of data frames having climatic means of temperature and precipitation as
#' # required by Koeppen - Geiger classification, each one referring to one station.
#' # It can be the output of function climate.
#' class_clim_l<-lapply(clima_81_10, FUN=koeppen_geiger, A_B_C_special_sub.classes=TRUE)
#'
#' @seealso \code{\link{climate}}
koeppen_geiger<-function (clim_norm, A_B_C_special_sub.classes = FALSE, clim.resume_verbose=TRUE, class.nr=FALSE)
{
if (sum(is.na(clim_norm$Tm)) > 0 | sum(is.na(clim_norm$P)) >
0)
print("12 monthly values of temp. and prec. required!",
quote = F)
else {
T_warm_month <- max(clim_norm$Tm)
T_cold_month <- min(clim_norm$Tm)
T_avg <- mean(clim_norm$Tm)
P_tot <- sum(clim_norm$P)
wint_months <- order(clim_norm$Tm)[1:6]
summ_months <- order(clim_norm$Tm)[7:12]
if (1 %in% wint_months | 2 %in% wint_months) {
autumn_months <- 9:11
solst_months <- 5:7
late.spring_early.summer_months <- 5:6
late.summer_months <- 8:9
}
else {
autumn_months <- 3:5
solst_months <- c(11:12, 1)
late.spring_early.summer_months <- 11:12
late.summer_months <- 2:3
}
P_wint.half <- sum(clim_norm$P[clim_norm$month %in% wint_months])
P_summ.half <- sum(clim_norm$P[clim_norm$month %in% summ_months])
P_driest_month <- min(clim_norm$P)
P_driest_month_summ.half <- min(clim_norm$P[clim_norm$month %in%
summ_months])
P_driest_month_wint.half <- min(clim_norm$P[clim_norm$month %in%
wint_months])
P_wettest_month <- max(clim_norm$P)
P_wettest_month_summ.half <- max(clim_norm$P[clim_norm$month %in%
summ_months])
P_wettest_month_wint.half <- max(clim_norm$P[clim_norm$month %in%
wint_months])
T_4th_warm_month <- sort(clim_norm$Tm, decreasing = TRUE)[4]
matr_indices <- round(data.frame(T_warm_month, T_cold_month,
T_avg, P_tot, P_wint.half, P_summ.half, P_driest_month,
P_driest_month_summ.half, P_driest_month_wint.half,
P_wettest_month, P_wettest_month_summ.half, P_wettest_month_wint.half,
T_4th_warm_month), 1)
names(matr_indices) <- c("T_w.m", "T_c.m", "T_avg", "P_tot",
"P_wint", "P_summ", "P_d.m", "P_d.m.summ", "P_d.m.wint",
"P_w.m", "P_w.m.summ", "P_w.m.wint", "T_4th_w.m")
type <- NA
thresh <- NULL
sub.type_1 <- NULL
sub.type_2 <- NULL
rain.reg<-NULL
if (T_cold_month >= 18)
type <- "A"
if (T_cold_month < 18 & T_cold_month > -3 & T_warm_month >=
10)
type <- "C"
if (T_cold_month <= -3 & T_warm_month >= 10)
type <- "D"
if (T_warm_month < 10)
type <- "E"
if (P_summ.half >= 0.7 * P_tot & P_tot < 20 * T_avg +
280) {
type <- "B"
rain.reg <- "summ"
thresh <- 20 * T_avg + 280
}
else if (P_wint.half >= 0.7 * P_tot & P_tot < 20 * T_avg) {
type <- "B"
rain.reg <- "wint"
thresh <- 20 * T_avg
}
else if (P_summ.half < 0.7 * P_tot & P_wint.half < 0.7 *
P_tot & P_tot < 20 * T_avg + 140) {
type <- "B"
thresh <- 20 * T_avg + 140
}
if (type == "A") {
if (P_driest_month >= 60)
sub.type_1 <- "f"
if (P_driest_month < 60)
sub.type_1 <- "w"
if (P_driest_month < 60 & P_driest_month >= 100 -
P_tot/25)
sub.type_1 <- "m"
if (order(clim_norm$P)[12] %in% autumn_months)
sub.type_2 <- "w'"
if (order(clim_norm$P)[1] %in% solst_months)
sub.type_2 <- "s"
dry.4 <- order(clim_norm$P)[1:4]
wet.4 <- order(clim_norm$P)[9:12]
seas.1 <- c(12, 1, 2)
seas.2 <- 3:5
seas.3 <- 6:8
seas.4 <- 9:11
if (sum(clim_norm$P[dry.4]) * 5 < sum(clim_norm$P[wet.4]) &
((sum(dry.4 %in% c(seas.1, seas.3)) == 4 & sum(wet.4 %in%
c(seas.2, seas.4)) == 4) | (sum(dry.4 %in%
c(seas.2, seas.4)) == 4 & sum(wet.4 %in% c(seas.1,
seas.3)) == 4)))
sub.type_2 <- noquote("w\"")
if (A_B_C_special_sub.classes == TRUE) {
if (T_warm_month - T_cold_month < 5)
sub.type_2 <- paste(sub.type_2, "i", sep = "")
if (1 %in% wint_months & order(clim_norm$Tm)[12] <=
6 & sum(order(clim_norm$P)[11:12] %in% 7:9) ==
2)
sub.type_2 <- paste(sub.type_2, "g", sep = "")
}
}
if (type == "B") {
if (P_tot < thresh/2)
sub.type_1 <- "W"
else sub.type_1 <- "S"
if (T_avg >= 18)
sub.type_2 <- "h"
else {
if (T_warm_month < 18)
sub.type_2 <- "k'"
else sub.type_2 <- "k"
}
if (A_B_C_special_sub.classes == TRUE & !is.null(rain.reg)) {
if (rain.reg == "summ")
sub.type_2 <- paste(sub.type_2, "w", sep = "")
if (rain.reg == "wint")
sub.type_2 <- paste(sub.type_2, "s", sep = "")
}
}
if (type == "C") {
if (P_driest_month_summ.half < 30 & P_driest_month_summ.half <
P_wettest_month_wint.half/3)
sub.type_1 <- "s"
else if (P_driest_month_wint.half < P_wettest_month_summ.half/10)
sub.type_1 <- "w"
else sub.type_1 <- "f"
if (T_warm_month >= 22)
sub.type_2 <- "a"
else {
if (T_4th_warm_month > 10)
sub.type_2 <- "b"
else sub.type_2 <- "c"
}
if (A_B_C_special_sub.classes == TRUE) {
if (T_warm_month - T_cold_month < 5)
sub.type_2 <- paste(sub.type_2, "i", sep = "")
if (1 %in% wint_months & order(clim_norm$Tm)[12] <=
6 & sum(order(clim_norm$P)[11:12] %in% 7:9) ==
2)
sub.type_2 <- paste(sub.type_2, "g", sep = "")
if (order(clim_norm$P)[12] %in% late.spring_early.summer_months &
sum(clim_norm$P[late.spring_early.summer_months]) >
sum(clim_norm$P[late.summer_months]) * 1.3)
sub.type_2 <- paste(sub.type_2, "x", sep = "")
}
}
if (type == "D") {
if (P_driest_month_summ.half < 30 & P_driest_month_summ.half <
P_wettest_month_wint.half/3)
sub.type_1 <- "s"
else if (P_driest_month_wint.half < P_wettest_month_summ.half/10)
sub.type_1 <- "w"
else sub.type_1 <- "f"
if (T_warm_month >= 22)
sub.type_2 <- "a"
else {
if (T_4th_warm_month > 10)
sub.type_2 <- "b"
else sub.type_2 <- "c"
}
if (T_cold_month < -38)
sub.type_2 <- "d"
}
if (type == "E") {
if (T_warm_month > 0)
sub.type_1 <- "T"
else sub.type_1 <- "F"
sub.type_2 <- NULL
}
if(class.nr == FALSE)
{
matr_climate <- data.frame(paste(type, sub.type_1, sub.type_2,sep = ""))
} else
{
types_numbers<-seq(10,50,by=10)
types_letters<-c("A","B","C","D","E")
type_n <-types_numbers[types_letters==type]
if(type=="A") types.2_letters<-c("f","w","m")
if(type=="B") types.2_letters<-c("W","S")
if(type=="C" | type=="D") types.2_letters<-c("s","w","f")
if(type=="E") types.2_letters<-c("T","F")
types.2_numbers<- 1:length(types.2_letters)
type.2_n<-types.2_numbers[types.2_letters==sub.type_1]
matr_climate <- data.frame(type_n + type.2_n)
}
names(matr_climate) <- "class"
if(clim.resume_verbose == TRUE) matr_climate <- data.frame(matr_indices, matr_climate)
return(matr_climate)
}
}
ecor/ClimClass documentation built on May 15, 2019, 8 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
NULL
#' @description General climate classification after Koeppen - Geiger.
#'
#' @param clim_norm average values (climate normals) for the desired period.
#' @param A_B_C_special_sub.classes logical. Sets if calculations have to consider sub-classes based on rain features in climate types A, B, and C (see details). Default is \code{FALSE}.
#' @param clim.resume_verbose logical. If \code{TRUE} (default) a resume of the climatic indices used for the Koeppen - Geiger classification is added to the output.
#' @param class.nr logical. If \code{FALSE} (default) class is expressed by letters, otherwise by numbers (see details).
#'
#'
#' @title Koeppen - Geiger's climate classification
#' @author Emanuele Eccel
#'
#' @return A one-line data frame reporting a resume of climatic features useful for the classification (if \code{clim.resume_verbose} is \code{TRUE}), and one last field (1 type - or "climate" - plus 1 or 2 sub-types) reporting Koeppen - Geiger's climate classification. See details.
#'
#' @details \code{clim_norm} is a monthly data frame of climate normals, with column names: "P", "Tn", "Tx", "Tm" (precipitation, minimum, maximum and mean temperature, respectively). It can be the output of function \code{\link{climate}}.
#'
#' Koeppen - Geiger's classification is based on Trewartha and Lyle, 1980. The function also holds for Southern emisphere, except for the "Gange" sub-type ("Ag" and "Cg"). Type "H" (highland climate) and sub-types "Bn" and "Cn" (where n stands for Nebel) are never attributed, being based on a qualitative description in the quoted reference.
#'
#' Sub-type "w" (wet-and-dry) or "m" (monsoon) in climate "A" is set according to the definition after Encyclopaedia Britannica (\url{http://www.britannica.com/EBchecked/topic/322068/Koppen-climate-classification}): if P in the 4 driest months is less than 1/5 of the wettest months and if both the 4 driest and wettest months are split over non-contiguous seasons (either 2 months per season or 1 and 3 months per season), then sub-type is "".
#'
#' For climate "A", the letter "m" is attributed to the first sub-type.
#'
#' Climates "Cx" have P[May + June] >= 1.3 P [Aug. + Sept.] in N emisphere, and P[Nov. + Dec.] >= 1.3 P[Febr. + March] in S emisphere.
#'
#' \code{A_B_C_special_sub.classes}, if \code{TRUE}, adds a letter to the second sub-type of climates: "i" or "g" (climate A), "w" or "s" (climate B), and "i", "g", or "x" (climate C).
#'
#' The returned data frame contains the following fields:
#'
#' \code{T_w.m} = temperature of the warmest month (degrees C)
#'
#' \code{T_c.m} = temperature of the coldest month (degrees C)
#'
#' \code{T_avg} = average temperature (degrees C)
#'
#' \code{P_tot} = total precipitation depth (mm)
#'
#' \code{P_wint} = precipitation depth in the 6 coldest (winter) months (mm)
#'
#' \code{P_summ} = precipitation depth in the 6 warmest (summer) months (mm)
#'
#' \code{P_d.m} = precipitation depth in the driest month (mm)
#'
#' \code{P_d.m.summ} = precipitation depth in the driest month of "summer" half of the year (mm)
#'
#' \code{P_d.m.wint} = precipitation depth in the driest month of "winter" half of the year (mm)
#'
#' \code{P_w.m} = precipitation depth in the wettest month (mm)
#'
#' \code{P_w.m.summ} = precipitation depth in the driest month of "summer" half of the year (mm)
#'
#' \code{P_w.m.wint} = precipitation depth in the wettest month of "winter" half of the year (mm)
#'
#' \code{T_4th_w.m} = temperature of the 4th warmest month (degrees C)
#'
#' \code{class} = climatic class. If \code{class.nr}=\code{FALSE} (default), it results from the merging of "climate" (A to E) and sub-type(s).
#' If \code{class.nr}=\code{TRUE} the class comes from the sum of the numeric equivalent of "type" (A,..E) + "first sub type" (only first letter),
#' according to the following scheme:
#' A B C D E
#' 10 20 30 40 50
#' f W s s T 1
#' w S w w F 2
#' m f f 3
#'
#' (e.g: Af = 11, Cw = 32, EF = 52)
#'
#' @export
#'
#' @references
#' Trewartha, G.T. and Lyle, H.H., 1980: An Introduction to Climate. MacGraw - Hill, 5th Ed. Appendix: Koeppen's Classification of Climates.
#'
#' @examples
#' data(Trent_climate)
#' # clima_81_10 is a list of data frames having climatic means of temperature and precipitation as
#' # required by Koeppen - Geiger classification, each one referring to one station.
#' # It can be the output of function climate.
#' class_clim_l<-lapply(clima_81_10, FUN=koeppen_geiger, A_B_C_special_sub.classes=TRUE)
#'
#' @seealso \code{\link{climate}}
koeppen_geiger<-function (clim_norm, A_B_C_special_sub.classes = FALSE, clim.resume_verbose=TRUE, class.nr=FALSE)
{
if (sum(is.na(clim_norm$Tm)) > 0 | sum(is.na(clim_norm$P)) >
0)
print("12 monthly values of temp. and prec. required!",
quote = F)
else {
T_warm_month <- max(clim_norm$Tm)
T_cold_month <- min(clim_norm$Tm)
T_avg <- mean(clim_norm$Tm)
P_tot <- sum(clim_norm$P)
wint_months <- order(clim_norm$Tm)[1:6]
summ_months <- order(clim_norm$Tm)[7:12]
if (1 %in% wint_months | 2 %in% wint_months) {
autumn_months <- 9:11
solst_months <- 5:7
late.spring_early.summer_months <- 5:6
late.summer_months <- 8:9
}
else {
autumn_months <- 3:5
solst_months <- c(11:12, 1)
late.spring_early.summer_months <- 11:12
late.summer_months <- 2:3
}
P_wint.half <- sum(clim_norm$P[clim_norm$month %in% wint_months])
P_summ.half <- sum(clim_norm$P[clim_norm$month %in% summ_months])
P_driest_month <- min(clim_norm$P)
P_driest_month_summ.half <- min(clim_norm$P[clim_norm$month %in%
summ_months])
P_driest_month_wint.half <- min(clim_norm$P[clim_norm$month %in%
wint_months])
P_wettest_month <- max(clim_norm$P)
P_wettest_month_summ.half <- max(clim_norm$P[clim_norm$month %in%
summ_months])
P_wettest_month_wint.half <- max(clim_norm$P[clim_norm$month %in%
wint_months])
T_4th_warm_month <- sort(clim_norm$Tm, decreasing = TRUE)[4]
matr_indices <- round(data.frame(T_warm_month, T_cold_month,
T_avg, P_tot, P_wint.half, P_summ.half, P_driest_month,
P_driest_month_summ.half, P_driest_month_wint.half,
P_wettest_month, P_wettest_month_summ.half, P_wettest_month_wint.half,
T_4th_warm_month), 1)
names(matr_indices) <- c("T_w.m", "T_c.m", "T_avg", "P_tot",
"P_wint", "P_summ", "P_d.m", "P_d.m.summ", "P_d.m.wint",
"P_w.m", "P_w.m.summ", "P_w.m.wint", "T_4th_w.m")
type <- NA
thresh <- NULL
sub.type_1 <- NULL
sub.type_2 <- NULL
rain.reg<-NULL
if (T_cold_month >= 18)
type <- "A"
if (T_cold_month < 18 & T_cold_month > -3 & T_warm_month >=
10)
type <- "C"
if (T_cold_month <= -3 & T_warm_month >= 10)
type <- "D"
if (T_warm_month < 10)
type <- "E"
if (P_summ.half >= 0.7 * P_tot & P_tot < 20 * T_avg +
280) {
type <- "B"
rain.reg <- "summ"
thresh <- 20 * T_avg + 280
}
else if (P_wint.half >= 0.7 * P_tot & P_tot < 20 * T_avg) {
type <- "B"
rain.reg <- "wint"
thresh <- 20 * T_avg
}
else if (P_summ.half < 0.7 * P_tot & P_wint.half < 0.7 *
P_tot & P_tot < 20 * T_avg + 140) {
type <- "B"
thresh <- 20 * T_avg + 140
}
if (type == "A") {
if (P_driest_month >= 60)
sub.type_1 <- "f"
if (P_driest_month < 60)
sub.type_1 <- "w"
if (P_driest_month < 60 & P_driest_month >= 100 -
P_tot/25)
sub.type_1 <- "m"
if (order(clim_norm$P)[12] %in% autumn_months)
sub.type_2 <- "w'"
if (order(clim_norm$P)[1] %in% solst_months)
sub.type_2 <- "s"
dry.4 <- order(clim_norm$P)[1:4]
wet.4 <- order(clim_norm$P)[9:12]
seas.1 <- c(12, 1, 2)
seas.2 <- 3:5
seas.3 <- 6:8
seas.4 <- 9:11
if (sum(clim_norm$P[dry.4]) * 5 < sum(clim_norm$P[wet.4]) &
((sum(dry.4 %in% c(seas.1, seas.3)) == 4 & sum(wet.4 %in%
c(seas.2, seas.4)) == 4) | (sum(dry.4 %in%
c(seas.2, seas.4)) == 4 & sum(wet.4 %in% c(seas.1,
seas.3)) == 4)))
sub.type_2 <- noquote("w\"")
if (A_B_C_special_sub.classes == TRUE) {
if (T_warm_month - T_cold_month < 5)
sub.type_2 <- paste(sub.type_2, "i", sep = "")
if (1 %in% wint_months & order(clim_norm$Tm)[12] <=
6 & sum(order(clim_norm$P)[11:12] %in% 7:9) ==
2)
sub.type_2 <- paste(sub.type_2, "g", sep = "")
}
}
if (type == "B") {
if (P_tot < thresh/2)
sub.type_1 <- "W"
else sub.type_1 <- "S"
if (T_avg >= 18)
sub.type_2 <- "h"
else {
if (T_warm_month < 18)
sub.type_2 <- "k'"
else sub.type_2 <- "k"
}
if (A_B_C_special_sub.classes == TRUE & !is.null(rain.reg)) {
if (rain.reg == "summ")
sub.type_2 <- paste(sub.type_2, "w", sep = "")
if (rain.reg == "wint")
sub.type_2 <- paste(sub.type_2, "s", sep = "")
}
}
if (type == "C") {
if (P_driest_month_summ.half < 30 & P_driest_month_summ.half <
P_wettest_month_wint.half/3)
sub.type_1 <- "s"
else if (P_driest_month_wint.half < P_wettest_month_summ.half/10)
sub.type_1 <- "w"
else sub.type_1 <- "f"
if (T_warm_month >= 22)
sub.type_2 <- "a"
else {
if (T_4th_warm_month > 10)
sub.type_2 <- "b"
else sub.type_2 <- "c"
}
if (A_B_C_special_sub.classes == TRUE) {
if (T_warm_month - T_cold_month < 5)
sub.type_2 <- paste(sub.type_2, "i", sep = "")
if (1 %in% wint_months & order(clim_norm$Tm)[12] <=
6 & sum(order(clim_norm$P)[11:12] %in% 7:9) ==
2)
sub.type_2 <- paste(sub.type_2, "g", sep = "")
if (order(clim_norm$P)[12] %in% late.spring_early.summer_months &
sum(clim_norm$P[late.spring_early.summer_months]) >
sum(clim_norm$P[late.summer_months]) * 1.3)
sub.type_2 <- paste(sub.type_2, "x", sep = "")
}
}
if (type == "D") {
if (P_driest_month_summ.half < 30 & P_driest_month_summ.half <
P_wettest_month_wint.half/3)
sub.type_1 <- "s"
else if (P_driest_month_wint.half < P_wettest_month_summ.half/10)
sub.type_1 <- "w"
else sub.type_1 <- "f"
if (T_warm_month >= 22)
sub.type_2 <- "a"
else {
if (T_4th_warm_month > 10)
sub.type_2 <- "b"
else sub.type_2 <- "c"
}
if (T_cold_month < -38)
sub.type_2 <- "d"
}
if (type == "E") {
if (T_warm_month > 0)
sub.type_1 <- "T"
else sub.type_1 <- "F"
sub.type_2 <- NULL
}
if(class.nr == FALSE)
{
matr_climate <- data.frame(paste(type, sub.type_1, sub.type_2,sep = ""))
} else
{
types_numbers<-seq(10,50,by=10)
types_letters<-c("A","B","C","D","E")
type_n <-types_numbers[types_letters==type]
if(type=="A") types.2_letters<-c("f","w","m")
if(type=="B") types.2_letters<-c("W","S")
if(type=="C" | type=="D") types.2_letters<-c("s","w","f")
if(type=="E") types.2_letters<-c("T","F")
types.2_numbers<- 1:length(types.2_letters)
type.2_n<-types.2_numbers[types.2_letters==sub.type_1]
matr_climate <- data.frame(type_n + type.2_n)
}
names(matr_climate) <- "class"
if(clim.resume_verbose == TRUE) matr_climate <- data.frame(matr_indices, matr_climate)
return(matr_climate)
}
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.