如何使用ddply函数申请循环？

Question

我想为每一列计算每个月降雨量 >= 2.5 毫米的天数。在 this post like

的帮助下，我能够为单个列计算它

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。我试过下面的代码

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

我会改用 tidyverse 中的 dplyr 和 tidyr。 pivot_longer 将数据放入更易于操作的长格式。 pivot_wider 再次变宽（可能不需要，具体取决于您的下一步）

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

如果你想处理所有的id，那么你可以按id分组数据，如下所示。

仅下雨：

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

对于雨、雪和降水

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

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