行を列に変換して日付に結合する

Question

Rのデータフレームに毎日の気象情報を追加しようとしているので、df1 + df2 = df3が目立ちます。私はdf1の日付でdf2をサブセット化し、df2を折りたたんだりdf1でrbind.fillを作成するのは方法ですが、問題があります。

date2 <- c('2014-06-03','2017-05-20') 
date <- c('2014-06-01','2017-05-15') 
df1 <- data.frame(date,date2) 
date 

#> df1 
#  date  date2 
#1 2014-06-01 2014-06-03 
#2 2017-05-15 2017-05-20 

date3 <- c('2014-06-01','2014-06-02','2014-06-03','2017-05-15','2017-05-16','2017-05-17','2017-05-18','2017-05-19','2017-05-20') 
rain <- c(3,   4,    3,   5,   5,   6,   7,   6   ,6) 
sun <- c ( 10,10,10, 15,15,15,16,15,15) 
df2 <- data.frame(date3,rain,sun) 

#> df2 
#  date3 rain sun 
#1 2014-06-01 3 10 
#2 2014-06-02 4 10 
#3 2014-06-03 3 10 
#4 2017-05-15 5 15 
#5 2017-05-16 5 15 
#6 2017-05-17 6 15 
#7 2017-05-18 7 16 
#8 2017-05-19 6 15 
#9 2017-05-20 6 15 

rain_day1 <- c(3,5) 
rain_day2 <- c(4,5) 
rain_day3 <- c(3,6) 
rain_day4 <- c(NA,7) 
rain_day5 <- c(NA,6) 
rain_day6 <- c(NA,6) 
sun_day1 <- c(10,15) 
sun_day2 <- c(10,15) 
sun_day3 <- c(10,15) 
sun_day4 <- c(NA,15) 
sun_day5 <- c(NA,16) 
sun_day6 <- c(NA,15) 
date5 <- c('2014-06-03','2017-05-20') 
date4 <- c('2014-06-01','2017-05-15') 
df3 <- data.frame(date4,date5,rain_day1,sun_day1,rain_day2,sun_day2,rain_day3,sun_day3,rain_day4,sun_day4,rain_day5,sun_day5,rain_day6,sun_day6) 

#> df3 
#  date4  date5 rain_day1 sun_day1 rain_day2 sun_day2 rain_day3 sun_day3 rain_day4 sun_day4 
#1 2014-06-01 2014-06-03   3  10   4  10   3  10  NA  NA 
#2 2017-05-15 2017-05-20   5  15   5  15   6  15   7  15 
# rain_day5 sun_day5 rain_day6 sun_day6 
#1  NA  NA  NA  NA 
#2   6  16   6  15 

助けてください。事前に感謝

Answer 1

「私は、DF1上の日付でDF2をsubseting DF2を崩壊し、DF1でそれらをrbind.fillする方法だろうというthougth」......それは望んでいたよりも時間がかかりましたが、私はそれを作りました！

lapply(df1, class) 
df1$date <- sapply(df1$date , function(x) as.character(x)) 
df1$date2 <- sapply(df1$date2 , function(x) as.character(x)) 
lapply(df2, class) 
df2$date3 <- sapply(df2$date3 , function(x) as.character(x)) 
lapply(df3, class) 
df3$date4 <- sapply(df3$date4 , function(x) as.character(x)) 
df3$date5 <- sapply(df3$date5 , function(x) as.character(x)) 


tryCatch(library("plyr") , 
     error = function(e) { 
      install.packages("plyr") 
      library("plyr") 
     } 
) 
df4 <- df1 
for (i in 1:nrow(df1)){ 
    dff <- df2[ which(df2$date3 <= df1$date2[i]) , ] 
    dff <- dff[ which(dff$date3 >= df1$date[i]) , ] 
    dff <- as.data.frame(t(unlist(dff))) 
    colnames(dff)[1] <- "date" 
    df5 <- merge(x = df1, y = dff, by = "date") 
    df5 <- rbind.fill(df4,df5) 
    df5<-df5[-1,] 
    df4 <- df5 
} 

df5 <- df5[, -grep("^date3", colnames(df5))] 

Answer 2

これを試してください。私は自分のパイプ散文を改善することを歓迎します、私はそれに取り組んでいます。

library(magrittr) 
df1$date <- as.Date(df1$date) 
df1$date2 <- as.Date(df1$date2) 
df2$date3 <- as.Date(df2$date3) 
df2 %<>% setNames(c("date3","rain_day","sun_day")) 
row_list <- df1 %>% apply(1,function(x){ df2 %>% 
            subset(date3 >= x["date"] & date3 <= x["date2"]) %>%  # subsetting 
            "["(2:3) %>%            # selecting relevant column 
            unlist}) %>%            # spearding all data into a vector 
    sapply(.,function(x){x[rep(c(0,length(x)/2),length(x)/2)+rep(1:(length(x)/2),each=2)]}) # reordering 

row_names <- row_list %>% sapply(length) %>% which.max %>% "[["(row_list,.) %>% names() # taking the names from longest list 
row_list %>% sapply(function(x){c(x,rep(NA,max(sapply(.,length))-length(x)))}) %>% # complete with NAs 
t %>% as.data.frame %>% setNames(row_names) %>% cbind(df1,.)      # transpose, convert, set names and append to df1 
#   date  date2 rain_day1 sun_day1 rain_day2 sun_day2 rain_day3 sun_day3 rain_day4 sun_day4 rain_day5 sun_day5 rain_day6 sun_day6 
# 1 2014-06-01 2014-06-03   3  10   4  10   3  10  NA  NA  NA  NA  NA  NA 
# 2 2017-05-15 2017-05-20   5  15   5  15   6  15   7  16   6  15   6  15