Question

不可否认，我是R的新手，但我设法获取了一个大型数据集并提取了我想要的数据并使用plyr将其放入数据框中。我一直试图组合（并计算）重复的行和列。

举个例子，我有......

> df
   X x.APPLES x.BANANAS x.PEARS x.ORANGES x.GRAPES x.KIWIS x.APPLES.1 x.ORANGES.1
1  A   APPLES                                                                    
2  B   APPLES                                                                    
3  C   APPLES                                                                    
4  D            BANANAS                                                          
5  E            BANANAS                                                          
6  F            BANANAS                                                          
7  G            BANANAS                                                          
8  H                      PEARS   ORANGES   GRAPES                               
9  I                      PEARS   ORANGES   GRAPES                               
10 C                      PEARS   ORANGES   GRAPES                               
11 C                      PEARS   ORANGES   GRAPES                               
12 R                      PEARS   ORANGES   GRAPES                               
13 A                                                 KIWIS                       
14 B                                                           APPLES            
15 Y                                                           APPLES            
16 A                                                                      ORANGES
17 J                                                                      ORANGES

我希望......

       X   x.APPLES   x.BANANAS   x.PEARS   x.ORANGES   x.GRAPES   x.KIWIS COUNT
1      A APPLES (1)                       ORANGES (1)            KIWIS (1)     3
2      B APPLES (2)                                                            2
3      C APPLES (1)             PEARS (1) ORANGES (2) GRAPES (2)               3
4      D            BANANAS (1)                                                1
5      E            BANANAS (1)                                                1
6      F            BANANAS (1)                                                1
7      G            BANANAS (1)                                                1
8      H                        PEARS (1) ORANGES (1) GRAPES (1)               1
9      I                        PEARS (1) ORANGES (1) GRAPES (1)               1
10     R                        PEARS (1) ORANGES (1) GRAPES (1)               1
11     Y APPLES (1)                                                            1
12     J                                  ORANGES (1)                          1
13 COUNT          5           4        4            7          5         1    NA

这是我的实际代码：

library("jsonlite")
library("plyr")
anom <- fromJSON("https://api.fda.gov/drug/event.json?search=_exists_:seriousnesscongenitalanomali&limit=25")
reactions <- anom$results$patient$reaction
drugs <- llply(anom$results$patient$drug, function(x) x$medicinalproduct)
l <- mapply(c, reactions, drugs, SIMPLIFY=FALSE)
df <- ldply (l, data.frame)

Answer 1

使用OP数据编辑：

我下载了您的实际数据，并将数据转换为两列data.frame，您可以使用下面的示例转换为您想要的输出。

require(jsonlite)
anom <- fromJSON("https://api.fda.gov/drug/event.json?search=_exists_:seriousnesscongenitalanomali&limit=5")

## Extract the reactions and drugs as character vectors
reactions <- lapply(anom$results$patient$reaction, 
                    function(x) as.character(unlist(x)))
drugs <- lapply(anom$results$patient$drug, 
                function(x) as.character(unlist(x$medicinalproduct)))

## Use expand.grid to make subset data.frames with all drug/reaction
## combinations for every patient
l <- mapply(expand.grid, reactions, drugs, SIMPLIFY = FALSE)

## Collapse all the subset data.frames into one
two_col <- do.call(rbind, l)

原始示例：

如果我们假设你有两列data.frame要开始：

require(reshape2)
fruits <- c("Bannana", "Apple", "Orange", "Grape", "Kiwi")
example <- data.frame(ID = sample(LETTERS[1:6], 25, replace = TRUE),
                      Fruit = sample(fruits, 25, replace = TRUE))

# > example
#    ID   Fruit
# 1   F    Kiwi
# 2   A   Apple
# 3   F    Kiwi
# ...

dcast(example, ID~Fruit, length, value.var = "Fruit")

more_complex <- function(x) {
  x_len <- length(x)
  x <- paste0(unique(x), " (", x_len, ")")
  x
}

dcast(example, ID~Fruit, more_complex, value.var = "Fruit")

# > dcast(example, ID~Fruit, more_complex, value.var = "Fruit")
#   ID     Apple     Bannana     Grape     Kiwi     Orange
# 1  A Apple (2) Bannana (2) Grape (2)      (0) Orange (2)
# 2  B Apple (1)         (0)       (0) Kiwi (1) Orange (2)
# 3  C       (0) Bannana (2)       (0) Kiwi (1) Orange (1)
# 4  D       (0) Bannana (1)       (0)      (0) Orange (1)
# 5  E       (0)         (0) Grape (1) Kiwi (1)        (0)
# 6  F       (0) Bannana (1) Grape (1) Kiwi (2) Orange (1)

another_option <- function(x) {
  x_len <- length(x)
  if (x_len == 0) return(NA_character_)
  x <- paste0(unique(x), " (", x_len, ")")
  x
}

dcast(example, ID~Fruit, another_option, value.var = "Fruit")

# > dcast(example, ID~Fruit, another_option, value.var = "Fruit")
#   ID     Apple     Bannana     Grape     Kiwi     Orange
# 1  A Apple (2) Bannana (2) Grape (2)     <NA> Orange (2)
# 2  B Apple (1)        <NA>      <NA> Kiwi (1) Orange (2)
# 3  C      <NA> Bannana (2)      <NA> Kiwi (1) Orange (1)
# 4  D      <NA> Bannana (1)      <NA>     <NA> Orange (1)
# 5  E      <NA>        <NA> Grape (1) Kiwi (1)       <NA>
# 6  F      <NA> Bannana (1) Grape (1) Kiwi (2) Orange (1)

结合行和数据框中的列

1 个答案:

使用OP数据编辑：

原始示例：