将普通的R数据帧转换为粗糙的格式(la ftable) [英] Convert normal R data frame into ragged format (a la ftable)
问题描述
在 R 中，函数 ftable()默认情况下会创建一个具有所谓的衣衫r外观的表:
In R the function ftable() creates by default a table with a so called ragged appearance:
data(UCBAdmissions)
ftable(UCBAdmissions)
...
Dept A B C D E F
Admit Gender
Admitted Male 512 353 120 138 53 22
Female 89 17 202 131 94 24
Rejected Male 313 207 205 279 138 351
Female 19 8 391 244 299 317
行和列参差不齐"，因为标签仅在它们更改时才显示，并且明显的约定是从上至下读取行，从左至右读取列.( https://cran.r-project.org/doc/manuals/r-devel/R-data.html#Flat-contingency-tables )
Rows and columns are "ragged" in the sense that labels are only displayed when they change, with the obvious convention that rows are read from top to bottom and columns are read from left to right. (https://cran.r-project.org/doc/manuals/r-devel/R-data.html#Flat-contingency-tables)
问题:
如何为普通的 data.frame 对象获得相同的参差不齐"外观?
How can I get same "ragged" appearance for a normal data.frame object?
可复制的示例:
before= data.frame(C1= c(rep("A", 5), rep("L", 2)),
C2= c("B", rep("E", 3), rep("K", 2), "L"),
C3= c("C", "F", rep("H", 5)),
C4= c("D", "G", "I", rep("J", 4)),
stringsAsFactors = FALSE)
before
...
C1 C2 C3 C4
1 A B C D
2 A E F G
3 A E H I
4 A E H J
5 A K H J
6 L K H J
7 L L H J
函数的外观如何，它将对象 before 转换为类 data.frame 的新对象 after ，该对象被打印到带有 print(after)的控制台，如下所示...
How does a function look like which converts the object before to a new object after of class data.frame, which is printed to console with print(after) as follows...
C1 C2 C3 C4
1 A B C D
2 E F G
3 H I
4 J
5 K H J
6 L K H J
7 L H J
如有必要，可以接受这种演示格式丢失的遗留数据.
If necessary, it is acceptable that the left out data are lost for this presentation format.
推荐答案
也许不是最优雅的解决方案(a.很多 for 循环，b.将任何类型的列强制转换为字符，c.没有输入断言，例如d.slow等)，但是遵循函数 rag_blank 似乎基本上可以按照示例中的要求进行操作:
Maybe not the most elegant solution (a. lots of for loops, b. coercing any type of column to character, c. no input assertions, d. slow, etc.), but following function rag_blank seems to basically work as requested on the example:
## Task
before= data.frame(C1= c(rep("A", 5), rep("L", 2)),
C2= c("B", rep("E", 3), rep("K", 2), "L"),
C3= c("C", "F", rep("H", 5)),
C4= c("D", "G", "I", rep("J", 4)),
stringsAsFactors = FALSE)
before
## Solution
library(dplyr)
rag_blank= function(x, cols= seq_along(x), blank= ":"){
# Copy input
res= x
# 1st step: blank trailing cells
for(df_col in cols){
res[, df_col]= as.character(unlist(res[, df_col]))
x[, df_col]= as.character(unlist(x[, df_col]))
re= rle(unlist(res[, df_col]))
re_df= data.frame(value= re$values,
length= re$lengths,
stringsAsFactors = F) %>%
mutate(idx_start= cumsum(length) - length + 2,
idx_end= idx_start + length -2)
for(re_row in 1:nrow(re_df)){
if(re_df$idx_start[re_row]<= re_df$idx_end[re_row]){
res[(re_df$idx_start[re_row]:re_df$idx_end[re_row]), df_col]= blank
}
}
}
# 2nd step: restore value if blank, resp. changed from 1st step but left cell it is not blank
for(df_col in cols[-1]){
changed_before= res[, df_col]!= x[, df_col]
left_not_changed= res[, df_col-1]== x[, df_col-1]
to_change= changed_before & left_not_changed
res[to_change, df_col]= x[to_change, df_col]
}
res
}
rag_blank(before)
...
C1 C2 C3 C4
1 A B C D
2 : E F G
3 : : H I
4 : : : J
5 : K H J
6 L K H J
7 : L H J
在某些情况下，不适合使用空格，这可能会有所帮助:
In some cases applying blanks is not appropriate, then this may be helpful:
rag_index= function(x){
rag_blank(x) != x
}
rag_index(before)
...
C1 C2 C3 C4
[1,] FALSE FALSE FALSE FALSE
[2,] TRUE FALSE FALSE FALSE
[3,] TRUE TRUE FALSE FALSE
[4,] TRUE TRUE TRUE FALSE
[5,] TRUE FALSE FALSE FALSE
[6,] FALSE FALSE FALSE FALSE
[7,] TRUE FALSE FALSE FALSE
更忙的示例:
data("diamonds", package = "ggplot2")
print(rag_blank(x= head(diamonds, 30)), n= 100)
...
Source: local data frame [30 x 10]
carat cut color clarity depth table price x y z
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
4 0.29 Premium I VS2 62.4 58 334 4.2 4.23 2.63
5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
7 : : I VVS1 62.3 57 336 3.95 3.98 2.47
8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
10 0.23 Very Good H VS1 59.4 61 338 4 4.05 2.39
11 0.3 Good J SI1 64 55 339 4.25 4.28 2.73
12 0.23 Ideal J VS1 62.8 56 340 3.93 3.9 2.46
13 0.22 Premium F SI1 60.4 61 342 3.88 3.84 2.33
14 0.31 Ideal J SI2 62.2 54 344 4.35 4.37 2.71
15 0.2 Premium E SI2 60.2 62 345 3.79 3.75 2.27
16 0.32 Premium E I1 60.9 58 345 4.38 4.42 2.68
17 0.3 Ideal I SI2 62 54 348 4.31 4.34 2.68
18 : Good J SI1 63.4 54 351 4.23 4.29 2.7
19 : : : : 63.8 56 351 4.23 4.26 2.71
20 : Very Good J SI1 62.7 59 351 4.21 4.27 2.66
21 : Good I SI2 63.3 56 351 4.26 4.3 2.71
22 0.23 Very Good E VS2 63.8 55 352 3.85 3.92 2.48
23 : : H VS1 61 57 353 3.94 3.96 2.41
24 0.31 Very Good J SI1 59.4 62 353 4.39 4.43 2.62
25 : : : : 58.1 62 353 4.44 4.47 2.59
26 0.23 Very Good G VVS2 60.4 58 354 3.97 4.01 2.41
27 0.24 Premium I VS1 62.5 57 355 3.97 3.94 2.47
28 0.3 Very Good J VS2 62.2 57 357 4.28 4.3 2.67
29 0.23 Very Good D VS2 60.5 61 357 3.96 3.97 2.4
30 : : F VS1 60.9 57 357 3.96 3.99 2.42
如果有更好的解决方案，请感谢您的反馈.
In case there are more elegant solutions, appreciate your feedback.
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