Rfmerge Error

[Ahmadffrahmand]

Respected Sir,

I created a zoo object from my precipitation file of 11 station and projected as utm zone 42, but following the instruction when i want to create a spatial object of the precipitation data i got the NA projection.

all other layers have the same utm zone 42 projection. but after running the Rfmerge i am getting this error.
setwd("D:\Time Series\chirps\station data and precipitation data")
ValparaisoPPts<-read.csv(file="data_ALLLL_copy.csv",header=TRUE, stringsAsFactors = FALSE)

dd<-seq(as.Date("2008-05-01"), as.Date("2019-12-31"), by = "day")

####################################################################
ldates <- hydroTSM::dip("2008-05-01", "2019-12-31")
ldates <- seq(from=as.Date("2008-05-01"), to=as.Date("2019-12-31"), by="day")

class(ValparaisoPPts)
names(CHIRPS5km) <- ldates
names(PERSIANNcdr5km) <- ldates
head(ValparaisoPPgis)
main <- paste("Daily precipitation for the station", ValparaisoPPgis$Code[1])
ylab <- "Precipitation [mm]"
x.ts <- ValparaisoPPts[,1]
hydroTSM::hydroplot(x.ts, pfreq="o", main=main, ylab= ylab)
plot(x.ts, main=main, ylab= ylab, col="blue")
grid()
chirps.total <- sum(CHIRPS5km, na.rm= FALSE)
persiann.total <- sum(PERSIANNcdr5km, na.rm= FALSE)
plot(chirps.total, main = "CHIRPSv2 [May - Aug] ", xlab = "Longitude", ylab = "Latitude")
plot(ValparaisoSHP[1], add=TRUE, col="transparent")
plot(persiann.total, main = "PERSIANN-CDR [May - Dec]", xlab = "Longitude", ylab = "Latitude")
plot(ValparaisoSHP[1], add=TRUE, col="transparent")
#################reading as zoo object
K101111<- zoo(x=ValparaisoPPts$K101111, order.by=dd)
K101112<- zoo(x=ValparaisoPPts$K101112, order.by=dd)
K101113<- zoo(x=ValparaisoPPts$K101113, order.by=dd)
K101114<- zoo(x=ValparaisoPPts$K101114, order.by=dd)
K101115<- zoo(x=ValparaisoPPts$K101115, order.by=dd)
K101116<- zoo(x=ValparaisoPPts$K101116, order.by=dd)
K101117<- zoo(x=ValparaisoPPts$K101117, order.by=dd)
K101118<- zoo(x=ValparaisoPPts$K101118, order.by=dd)
K101119<- zoo(x=ValparaisoPPts$K101119, order.by=dd)
K1011110<- zoo(x=ValparaisoPPts$K1011110, order.by=dd)
K1011111<- zoo(x=ValparaisoPPts$K1011111, order.by=dd)
ValparaisoPPts = cbind(K101111, K101112, K101113, K101114, K101115, K101116, K101117, K101118, K101119, K1011110, K1011111)

######################### Station csv which is Station location in studyarea ###
ValparaisoPPgis<-read.csv(file="kunduz_ST4.csv",header=TRUE, stringsAsFactors = FALSE)
names(ValparaisoPPgis)[1]<-c("Code")
str(ValparaisoPPgis)
class(ValparaisoPPgis)
ValparaisoSHP <- sf::st_transform(ValparaisoPPgis, crs=32642)
stations <- ValparaisoPPgis
( stations <- st_as_sf(stations, coords = c('lon', 'lat'), crs = 32642) )
stations.utm <- sf::st_transform(stations, crs=32642)
st.coords <- st_coordinates(stations.utm)
lon <- st.coords[, "X"]
lat <- st.coords[, "Y"]

ValparaisoPPgis.utm <- data.frame(Id=stations.utm[["Code"]], lon=lon, lat=lat)
ValparaisoPPgis<-ValparaisoPPgis.utm
crs(ValparaisoPPgis)
class(ValparaisoPPgis)
ValparaisoPPgis <- st_set_crs(ValparaisoPPgis, crs = +proj=longlat +datum=WGS84 +no_defs )
#################################################################
##################################Study area Border#############
setwd("D:\Time Series\chirps\studyarea")
ValparaisoSHP = st_read(dsn = "kunduz_WGS.shp",layer="kunduz_WGS")
ValparaisoSHP = st_read(dsn = ("kunduz_WGS.shp"))
ValparaisoSHP <- sf::st_transform(ValparaisoSHP, crs=32642)
ValparaisoSHP.utm <- sf::st_transform(ValparaisoSHP, crs=32642)
crs(ValparaisoSHP)
ValparaisoSHP<-ValparaisoSHP.utm
plot(ValparaisoSHP)
#######################################Chirps_v2_monthly data
setwd("D:\Time Series\chirps\chirsps2")
chirps<-list.files(path=getwd(), pattern="tif$") # 140 files
ch<-raster(chirps[1])
#crs(ch)<- +proj=utm +zone=42 +datum=WGS84 +units=m +no_defs
ch <- st_set_crs(ch, "+proj=utm +zone=42 +datum=WGS84 +units=m +no_defs")
crs(ch) <- CRS('+init=EPSG:32642')
crs(ch)
crs(CHIRPS5km)<- CRS('+init=EPSG:32642')
crs(CHIRPS5km)
######################################################
############### ersion Data set #############
setwd("D:\Time Series\chirps\cdr")
prsnncdr.fname<-list.files(path=getwd(), pattern=".tif$") # 140 files
cdr<-raster(prsnncdr.fname[1])
crs(cdr) <- CRS('+init=EPSG:32642')
crs(cdr)<- CRS('+init=EPSG:32642')
crs(cdr)
crs(PERSIANNcdr5km)<- CRS('+init=EPSG:32642')
crs(PERSIANNcdr5km)
#########################################################
############# Dem of study area ####################
setwd("D:\Time Series\chirps\dem")
dem.fname<-raster("Kunduz_Dem.tif")
dem<-raster(dem.fname)
crs(dem) <- CRS('+init=EPSG:32642')
crs(dem)
crs(ValparaisoDEM5km)<- CRS('+init=EPSG:32642')
crs(ValparaisoDEM5km)
plot(ValparaisoDEM5km, main="SRTM-v4", xlab="Longitude", ylab="Latitude", col=terrain.colors(255))
plot(ValparaisoSHP[1], add=TRUE, col="transparent")
plot(stations[1], add=TRUE, pch = 16, col="black")

#########################################################3
CHIRPS5km <- brick(ch)
PERSIANNcdr5km <- brick(cdr)
ValparaisoDEM5km <- raster(dem)
######################### resampling of all same spatial resolution ###
rsch<-resample(PERSIANNcdr5km,CHIRPS5km, method='bilinear')
rsdem<-resample(ValparaisoDEM5km ,CHIRPS5km,method='bilinear')

CHIRPS5km <- brick(CHIRPS5km)
ValparaisoDEM5km <- raster(rsdem)
PERSIANNcdr5km<-brick(rsch)
############################################################3

CHIRPS5km <- brick(CHIRPS5km)
PERSIANNcdr5km <- brick(rsch)
ValparaisoDEM5km <- raster(rsdem)
#####################################################projections####
################################################################
extent(ValparaisoDEM5km)
#crs(cdr)<- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +to wgs84=0,0,0"
extent(rsch)
##########################################################

covariates <- list(chirps=CHIRPS5km, persianncdr=PERSIANNcdr5km,
dem=ValparaisoDEM5km)
################################################################
onWin <- ( (R.version$os=="mingw32") | (R.version$os=="mingw64") )
ifelse(onWin, parallel <- "parallelWin", parallel <- "parallel")
par.nnodes <- min(parallel::detectCores()-1,5)
###################### RfMerge ###############################

rfmep <- RFmerge(x=ValparaisoPPts, metadata=ValparaisoPPgis, cov=covariates,
id="Id", lon="lon", lat = "lat", mask=ValparaisoSHP,
training=0.8, parallel=parallel, par.nnodes=par.nnodes)

Error in sapply(x, fromDisk) & sapply(x, inMemory) :
operations are possible only for numeric, logical or complex types
In addition: Warning message:
In RFmerge.zoo(x = ValparaisoPPts, metadata = ValparaisoPPgis, cov = covariates, :
Invalid argument: 'cov' and 'mask' have different CRS. Please ensure they actually have the same CRS !

Also when i run this part of the code i get this error.

names(CHIRPS5km) <- ldates
Error in names<-(*tmp*, value = c(14000, 14001, 14002, 14003, 14004, :
incorrect number of layer names
i have daily precipitation data and monthly chirps data.
could you please instruct me how to resolve this part.
the code i run is attached .

Regards