我有一個這樣的資料集：
ID color_1 color_2 shape_1 shape_2 size class
55 red blue circle triangle small A
83 blue yellow circle NA large B
78 red yellow square circle large B
43 green NA square circle small C
29 yellow green circle triangle medium A
在我問一個關于如何為我的資料制作頻率表的問題并收到這個答案之前：
pat_words <- c("color", "shape", "size")
out <- df %>%
pivot_longer(
-ID,
names_to = "Question",
values_to = "Response"
) %>% mutate(Question = str_extract(Question, str_c(pat_words, collapse="|"))) %>% group_by(Question, Response) %>%
count(Response, name = "Freq") %>%
na.omit() %>%
group_by(Question) %>%
mutate(Percent = round(Freq/sum(Freq)*100, 2)) %>%
group_split() %>%
adorn_totals() %>%
bind_rows() %>%
mutate(Response = ifelse(Response == last(Response), last(Question), Response)) %>%
mutate(Question = ifelse(duplicated(Question) |
Question == "Total", NA, Question)) %>%
as.data.frame
并且能夠獲得以下輸出：
Variable Level Freq Percent
color blue 2 22.22
red 2 22.22
yellow 3 33.33
green 2 22.22
total 9 100.00
shape circle 5 50.0
triangle 3 30.0
square 2 20.0
total 10 100.0
size small 2 33.3
medium 2 33.3
large 2 33.3
total 6 100.0
但是現在我想按新變數“類”分組：
Class A B C
Variable Level Freq Percent Freq Percent Freq Percent
color blue 1 25 1 25 0 0
red 1 25 1 25 0 0
yellow 1 25 2 50 0 0
green 1 25 0 0 1 100
total 4 100.00 4 100.00 1 100
shape circle 2 50 2 66.7 1 50
triangle 2 50 0 0 0 0
square 0 0 1 33.3 1 50
total 4 100.0 3 100.0 2 100
size small 1 50 0 0 1 100
medium 1 50 0 0 0 0
large 0 0 2 100 0 0
total 2 100.0 2 100 1 100
我試圖將類添加到 group by 陳述句，但出現錯誤。任何幫助是極大的贊賞！
uj5u.com熱心網友回復：
我們可以修改分組中的 OP 代碼，即按“類”、“變數”分組，計算“百分比”，并adorn_totals在重新整形回“寬”之前創建“總計”行pivot_wider
library(dplyr)
library(tidyr)
library(janitor)
library(stringr)
out <- df %>%
pivot_longer(cols = -c(ID, class), names_to = 'Variable',
values_to = 'Level', values_drop_na = TRUE) %>%
mutate(Variable = str_remove(Variable, "_\\d ")) %>%
count(class, Variable, Level, name = "Freq") %>%
group_by(class, Variable) %>%
mutate(Percent = round(100 * Freq/sum(Freq))) %>%
group_modify(~ .x %>%
adorn_totals()) %>%
ungroup %>%
pivot_wider(names_from = class, values_from = c(Freq, Percent),
values_fill = 0) %>%
select(Variable, Level, ends_with('A'), ends_with('B'), ends_with('C')) %>%
arrange(Variable)
-輸出
out
# A tibble: 13 × 8
Variable Level Freq_A Percent_A Freq_B Percent_B Freq_C Percent_C
<chr> <chr> <int> <dbl> <int> <dbl> <int> <dbl>
1 color blue 1 25 1 25 0 0
2 color green 1 25 0 0 1 100
3 color red 1 25 1 25 0 0
4 color yellow 1 25 2 50 0 0
5 color Total 4 100 4 100 1 100
6 shape circle 2 50 2 67 1 50
7 shape triangle 2 50 0 0 0 0
8 shape Total 4 100 3 100 2 100
9 shape square 0 0 1 33 1 50
10 size medium 1 50 0 0 0 0
11 size small 1 50 0 0 1 100
12 size Total 2 100 2 100 1 100
13 size large 0 0 2 100 0 0
上面的輸出可以轉換為 flextable
library(flextable)
out %>%
mutate(Variable = replace(Variable, duplicated(Variable), NA)) %>%
flextable()
-輸出
資料
df <- structure(list(ID = c(55L, 83L, 78L, 43L, 29L), color_1 = c("red",
"blue", "red", "green", "yellow"), color_2 = c("blue", "yellow",
"yellow", NA, "green"), shape_1 = c("circle", "circle", "square",
"square", "circle"), shape_2 = c("triangle", NA, "circle", "circle",
"triangle"), size = c("small", "large", "large", "small", "medium"
), class = c("A", "B", "B", "C", "A")), class = "data.frame", row.names = c(NA,
-5L))
uj5u.com熱心網友回復：
您可以使用
uj5u.com熱心網友回復：
另一種可能的解決方案，基于purrr::reduce和dplyr::full_join：
library(tidyverse)
df %>%
pivot_longer(cols = !contains(c("ID", "class")), names_to = "Variable",
values_to = "Level", values_drop_na = T) %>%
add_count(class, Variable = str_remove(Variable,"_\\d$")) %>%
group_by(class, Variable, Level) %>%
summarise(count = n(), perc = 100*count/n, .groups = "drop") %>%
group_split(class) %>%
reduce(full_join, by=c("Variable", "Level"), suffix=c(".A",".B")) %>%
select(-starts_with("class")) %>% distinct %>%
arrange(Variable) %>%
rename("count.C" = "count", "perc.C" = "perc") %>%
mutate(across(contains("."), ~ replace_na(.x,0)))
#> # A tibble: 10 × 8
#> Variable Level count.A perc.A count.B perc.B count.C perc.C
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 color blue 1 25 1 25 0 0
#> 2 color green 1 25 0 0 1 100
#> 3 color red 1 25 1 25 0 0
#> 4 color yellow 1 25 2 50 0 0
#> 5 shape circle 2 50 2 66.7 1 50
#> 6 shape triangle 2 50 0 0 0 0
#> 7 shape square 0 0 1 33.3 1 50
#> 8 size medium 1 50 0 0 0 0
#> 9 size small 1 50 0 0 1 100
#> 10 size large 0 0 2 100 0 0
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