![](/img/trans.png)
[英]Categorize a continuous predictor variable and calculate proportion of binary outcome
[英]Calculate proportion of several binary variables by another variable
我有幾個二進制變量的數據,我想通過另一個變量計算每個變量的比例。
我調查人們並問他們:
請標出您喜歡以下哪種水果(可多選):
☐ 香蕉 ☐ 蘋果 ☐ 橙子 ☐ 草莓 ☐ 桃子
選中該框的每個人在數據中都得到1
,當留空時,它表示為0
。 數據如下所示:
library(dplyr)
set.seed(2021)
my_df <-
matrix(rbinom(n = 100, size = 1, prob = runif(1)), ncol = 5) %>%
as.data.frame() %>%
cbind(1:20, ., sample(c("male", "female"), size = 20, replace = T)) %>%
setNames(c("person_id", "banana", "apple", "orange", "strawberry", "peach", "gender"))
my_df
#> person_id banana apple orange strawberry peach gender
#> 1 1 1 1 1 0 0 female
#> 2 2 1 0 0 0 1 female
#> 3 3 0 0 1 0 1 female
#> 4 4 1 1 0 1 0 female
#> 5 5 1 1 1 0 0 male
#> 6 6 1 1 1 0 1 female
#> 7 7 0 1 0 1 1 male
#> 8 8 1 1 0 0 0 male
#> 9 9 1 1 1 0 0 female
#> 10 10 0 0 0 0 0 male
#> 11 11 1 1 1 1 1 male
#> 12 12 1 1 0 0 1 male
#> 13 13 1 1 0 1 0 male
#> 14 14 1 1 0 0 0 male
#> 15 15 0 0 0 0 1 male
#> 16 16 0 1 0 0 1 male
#> 17 17 1 0 0 0 1 male
#> 18 18 1 1 1 1 1 male
#> 19 19 0 0 1 1 1 female
#> 20 20 0 0 0 0 0 female
由reprex package (v0.3.0) 於 2021 年 2 月 1 日創建
我想得到每個水果的比例,按gender
划分。 從這個答案中,我學會了如何為一個變量(例如, banana
)做這件事:
my_df %>%
group_by(gender) %>%
summarise(n_of_observations = n(), prop = sum(banana == 1)/n())
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 2 x 3
## gender n_of_observations prop
## <chr> <int> <dbl>
## 1 female 10 0.6
## 2 male 10 0.4
但是我怎樣才能得到一張適合所有水果的桌子呢?
所需的 output:
## fruit gender prop
## <chr> <chr> <dbl>
## 1 banana female 0.6
## 2 banana male 0.4
## 3 apple female 0.4
## 4 apple male 0.3
## 5 orange female 0.3
## 6 orange male 0.1
## 7 strawberry female 0.4
## 8 strawberry male 0.4
## 9 peach female 0.3
## 10 peach male 0.6
如果可能的話,我正在尋找dplyr
解決方案。 非常感謝!
您可以使用 cross 一次匯總across
變量:
my_df %>%
group_by(gender) %>%
summarise(across(banana:peach, list(n = ~length(.x), prop = ~sum(.x == 1) / n())))
# A tibble: 2 x 11
gender banana_n banana_prop apple_n apple_prop orange_n orange_prop strawberry_n strawberry_prop peach_n peach_prop
<chr> <int> <dbl> <int> <dbl> <int> <dbl> <int> <dbl> <int> <dbl>
1 female 8 0.625 8 0.5 8 0.625 8 0.25 8 0.5
2 male 12 0.667 12 0.75 12 0.25 12 0.333 12 0.583
請注意,cross 的第一個參數指定要匯總的變量。 在這里,我寫了banana:peach
表示banana
和peach
之間的所有列。
您可以先使用tidyr
來 pivot 您的數據,然后對其進行匯總:
library(tidyr)
tidyr::pivot_longer(my_df, banana:peach,
names_to = "fruit") %>%
dplyr::group_by(gender, fruit) %>%
dplyr::summarize(prop = sum(value) / n())
gender fruit prop
<chr> <chr> <dbl>
1 female apple 0.5
2 female banana 0.625
3 female orange 0.625
4 female peach 0.5
5 female strawberry 0.25
6 male apple 0.75
7 male banana 0.667
8 male orange 0.25
9 male peach 0.583
10 male strawberry 0.333
如果你想按fruit
排序,你可以 pipe 它來arrange
。 您還可以在summarize
function 中添加觀察數,其中n = n()
。
聲明:本站的技術帖子網頁,遵循CC BY-SA 4.0協議,如果您需要轉載,請注明本站網址或者原文地址。任何問題請咨詢:yoyou2525@163.com.