How to apply for loop with ddply function? - r

Question

I want to calculate the number of days in each month with rainfall >= 2.5 mm for every column. I was able to calculate it for a single column after taking help from this post like
require(seas)
library (zoo)
data(mscdata)
dat.int <- (mksub(mscdata, id=1108447))
dat.int$yearmon <- as.yearmon(dat.int$date, "%b %y")
require(plyr)
rainydays_by_yearmon <- ddply(dat.int, .(yearmon), summarize, rainy_days=sum(rain >= 1.0) )
print.data.frame(rainydays_by_yearmon)
Now I want to apply it for all the columns. I have tried the following code
for(i in 1:length(dat.int)){
y1 <- dat.int[[i]]
rainydays <- ddply(dat.int, .(yearmon), summarize, rainy_days=sum(y1 >= 2.5))
if(i==1){
m1 <- rainydays
}
else{
m1 <- cbind(rainydays, m1)
}
print(i)
}
m1
But I am unable to get the desired results. Please help me out!!!

Answer 1

I would use dplyr and tidyr from tidyverse instead. pivot_longer puts the data into long form with is easier to manipulate. pivot_wider makes it wide again (probably unnecessary depending on your next step)
library(seas)
library(tidyverse)
library(zoo)
data(mscdata)
dat.int <- (mksub(mscdata, id=1108447))
dat.int %>%
as_tibble() %>% # for easier viewing
mutate(yearmon = as.yearmon(dat.int$date, "%b %y")) %>%
select(-date, -year, -yday) %>%
pivot_longer(cols = -yearmon, names_to = "variable", values_to = "value") %>%
group_by(yearmon, variable) %>%
summarise(rainy_days = sum(value > 2.5)) %>%
pivot_wider(names_from = "variable", values_from = "rainy_days")

Answer 2

if you don't mind using the data.table library, see the solution below.
library('data.table')
library('seas')
setDT(mscdata)
mscdata[id == 1108447 & rain >= 2.5, .(rain_ge_2.5mm = .N),
by = .(year, month = format(date, "%m"))]
Output
# year month rain_ge_2.5mm
# 1: 1975 01 12
# 2: 1975 02 8
# 3: 1975 03 10
# 4: 1975 04 2
# 5: 1975 05 4
# ---
# 350: 2004 07 2
# 351: 2004 08 5
# 352: 2004 10 10
# 353: 2004 11 14
# 354: 2004 12 14
If you want to process all ids, then you can group data by id as below.
For rain only:
mscdata[, .(rain_ge_2.5mm = sum(rain >= 2.5)),
by = .(id, year, month = format(date, "%m"))]
For rain, snow, and precip
mscdata[, .(rain_ge_2.5mm = sum(rain >= 2.5),
snow_ge_2 = sum(snow >= 2.0),
precip_ge_2 = sum(precip >= 2.0)),
by = .(id, year, month = format(date, "%m"))]
# id year month rain_ge_2.5mm snow_ge_2 precip_ge_2
# 1: 1096450 1975 01 1 10 9
# 2: 1096450 1975 02 0 5 3
# 3: 1096450 1975 03 1 9 9
# 4: 1096450 1975 04 1 2 3
# 5: 1096450 1975 05 5 1 6
# ---
# 862: 2100630 2000 07 NA NA 3
# 863: 2100630 2000 08 NA NA 8
# 864: 2100630 2000 09 NA NA 6
# 865: 2100630 2000 11 NA NA NA
# 866: 2100630 2001 01 NA NA NA