r-如何使用ddply函数申请循环

Question

我想计算每个月的天数，每个列的降雨量>=2.5毫米。在得到这样的帖子的帮助后，我能够为一列计算它

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)

现在我想将它应用于所有列。我已经尝试了以下代码

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

但是我无法得到想要的结果。请帮帮我！！！

Answer 1

我会使用tidyverse中的dplyr和tidyr。pivot_longer将数据放入长格式，更易于操作。pivot_wider使其再次变宽(可能不必要，取决于您的下一步(

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

如果您不介意使用data.table库，请参阅下面的解决方案。

library('data.table')
library('seas')
setDT(mscdata)
mscdata[id == 1108447 & rain >= 2.5, .(rain_ge_2.5mm = .N), 
by = .(year, month = format(date, "%m"))]

输出

#    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

如果您想处理所有id，那么您可以按id对数据进行分组，如下所示。

仅限下雨：

mscdata[, .(rain_ge_2.5mm = sum(rain >= 2.5)),
by = .(id, year, month = format(date, "%m"))]

对于雨、雪和悬崖

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