4

这是我的原始输出。

BizDate     Description                             TotalAmount TotalQty
2013-09-01 NIN JIOM COUGH SYRUP 75ML                11.8        2
2013-09-01 BREACOL COUGH SYRUP ADULTS 120ML         15.8        2
2013-09-02 AFRICAN SEA-COCONUT COUGH SYRUP 177ML    8.5         1
2013-09-03 AFRICAN SEA COCONUT COUGH SYRUP 177ML    8.2         1
2013-09-01 THREE LEGS COOLING WATER 200ML           21.0        14
2013-09-01 SEAHORSE BRAND COOLING WATER 200ML       4.4         4
2013-09-05 AFRICAN SEA-COCONUT COUGH SYRUP 177ML    8.5         1
2013-09-06 AFRICAN SEA COCONUT COUGH SYRUP 177ML    8.2         1

我怎么把它变成这样的东西

 Description                              2013-09-01   2013-09-02 .......
 NIN JIOM COUGH SYRUP 75ML                some number some number .......
 BREACOL COUGH SYRUP ADULTS 120ML         some number some number .......
 AFRICAN SEA-COCONUT COUGH SYRUP 177ML    some number some number .......
 AFRICAN SEA COCONUT COUGH SYRUP 177ML    some number some number .......
 THREE LEGS COOLING WATER 200ML           some number some number .......
 SEAHORSE BRAND COOLING WATER 200ML       some number some number .......
 AFRICAN SEA-COCONUT COUGH SYRUP 177ML    some number some number .......
 AFRICAN SEA COCONUT COUGH SYRUP 177ML    some number some number .......

这是一些假数据。

set.seed(1)
dat <- data.frame(product = rep(letters[1:4], each = 4)
    , date = sample(seq(as.Date("2013-01-01"), as.Date("2013-01-10"), by = 1), 16, TRUE)
    , amount = sample(1:100, 16, TRUE)
    , qty = sample(1:4, 16, TRUE))

dat
   product       date amount qty
1        a 2013-01-03     72   2
2        a 2013-01-04    100   1
3        a 2013-01-06     39   4
4        a 2013-01-10     78   3
5        b 2013-01-03     94   4
6        b 2013-01-09     22   1
7        b 2013-01-10     66   3
8        b 2013-01-07     13   2
9        c 2013-01-07     27   4
10       c 2013-01-01     39   3
11       c 2013-01-03      2   4
12       c 2013-01-02     39   3
13       d 2013-01-07     87   3
14       d 2013-01-04     35   4
15       d 2013-01-08     49   1
16       d 2013-01-05     60   2

我这样做的愚蠢方式。有没有更聪明的方法来做到这一点？我正在考虑使用 plyr。只需将我引导到正确的包裹或方式。

dat1<-subset(dat,date=="2013-01-01")
colnames(dat1)[c(3,4)]<-c("2013-01-01.amount","2013-01-01.qty")
dat1<-dat1[-2]
dat2<-subset(dat,date=="2013-01-02")
colnames(dat2)[c(3,4)]<-c("2013-01-02.amount","2013-01-02.qty")
dat2<-dat2[-2]
dat3<-subset(dat,date=="2013-01-03")
colnames(dat3)[c(3,4)]<-c("2013-01-03.amount","2013-01-03.qty")
dat3<-dat3[-2]

new.dat<-merge(dat1,dat2,by="product",all.x=T,all.y=T)
new.dat<-merge(new.dat,dat3,by="product",all.x=T,all.y=T)
new.dat[is.na(new.dat)]<-0

new.dat
  product 2013-01-01.amount 2013-01-01.qty 2013-01-02.amount 2013-01-02.qty
1       a                 0              0                 0              0
2       b                 0              0                 0              0
3       c                39              3                39              3
  2013-01-03.amount 2013-01-03.qty
1                72              2
2                94              4
3                 2              4
4

2 回答 2

4

考虑到数据的当前形式，只需reshape从基础 R 使用：

> reshape(dat, direction = "wide", idvar="product", timevar="date")
   product amount.2013-01-03 qty.2013-01-03 amount.2013-01-04 qty.2013-01-04 amount.2013-01-06
1        a                72              2               100              1                39
5        b                94              4                NA             NA                NA
9        c                 2              4                NA             NA                NA
13       d                NA             NA                35              4                NA
   qty.2013-01-06 amount.2013-01-10 qty.2013-01-10 amount.2013-01-09 qty.2013-01-09
1               4                78              3                NA             NA
5              NA                66              3                22              1
9              NA                NA             NA                NA             NA
13             NA                NA             NA                NA             NA
   amount.2013-01-07 qty.2013-01-07 amount.2013-01-01 qty.2013-01-01 amount.2013-01-02
1                 NA             NA                NA             NA                NA
5                 13              2                NA             NA                NA
9                 27              4                39              3                39
13                87              3                NA             NA                NA
   qty.2013-01-02 amount.2013-01-08 qty.2013-01-08 amount.2013-01-05 qty.2013-01-05
1              NA                NA             NA                NA             NA
5              NA                NA             NA                NA             NA
9               3                NA             NA                NA             NA
13             NA                49              1                60              2

或者，您可以考虑“reshape2”包：

library(reshape2)
datL <- melt(dat, id.vars=c("product", "date"))
dcast(datL, product ~ date + variable, value.var="value")
于 2013-11-12T09:53:29.893 回答
3

尝试这个：

#Dummy data
df <- read.table(text="
BizDate Description TotalAmount TotalQty
2013-09-01 NIN 11.8 2
2013-09-01 BREACOL 15.8 2
2013-09-02 AFRICAN 8.5 1
2013-09-03 AFRICAN 8.2 1
2013-09-01 THREE 21.0 14
2013-09-01 SEAHORSE 4.4 4
2013-09-05 AFRICAN 8.5 1
2013-09-06 AFRICAN 8.2 1",
                 header=TRUE)

library(reshape2)

dcast(df,Description~BizDate,value="TotalAmount")
#Description 2013-09-01 2013-09-02 2013-09-03 2013-09-05 2013-09-06
#1     AFRICAN         NA        8.5        8.2        8.5        8.2
#2     BREACOL       15.8         NA         NA         NA         NA
#3         NIN       11.8         NA         NA         NA         NA
#4    SEAHORSE        4.4         NA         NA         NA         NA
#5       THREE       21.0         NA         NA         NA         NA
dcast(df,Description~BizDate,value="TotalQty")
#Description 2013-09-01 2013-09-02 2013-09-03 2013-09-05 2013-09-06
#1     AFRICAN         NA          1          1          1          1
#2     BREACOL          2         NA         NA         NA         NA
#3         NIN          2         NA         NA         NA         NA
#4    SEAHORSE          4         NA         NA         NA         NA
#5       THREE         14         NA         NA         NA         NA
于 2013-11-12T09:36:50.320 回答