[英]dplyr summarise : Group by multiple variables in a loop and add results in the same dataframe
我想計算幾個變量的不同模態的指標,然后將這些結果添加到單個數據框中。 我可以毫無問題地使用幾個summarise
加上group_by
,然后執行rbind
來收集結果。 下面,我去做就hdv2003數據(從questionr
包),並且我rbind
上變量“sexe”,“trav.satisf”和“菜”創建的結果。
library(questionr)
library(tidyverse)
data(hdv2003)
tmp_sexe <- hdv2003 %>%
group_by(sexe) %>%
summarise(n = n(),
percent = round((n()/nrow(hdv2003))*100, digits = 1),
femmes = round((sum(sexe == "Femme", na.rm = TRUE)/sum(!is.na(sexe)))*100, digits = 1),
age = round(mean(age, na.rm = TRUE), digits = 1)
)
names(tmp_sexe)[1] <- "group"
tmp_trav.satisf <- hdv2003 %>%
group_by(trav.satisf) %>%
summarise(n = n(),
percent = round((n()/nrow(hdv2003))*100, digits = 1),
femmes = round((sum(sexe == "Femme", na.rm = TRUE)/sum(!is.na(sexe)))*100, digits = 1),
age = round(mean(age, na.rm = TRUE), digits = 1)
)
names(tmp_trav.satisf)[1] <- "group"
tmp_cuisine <- hdv2003 %>%
group_by(cuisine) %>%
summarise(n = n(),
percent = round((n()/nrow(hdv2003))*100, digits = 1),
femmes = round((sum(sexe == "Femme", na.rm = TRUE)/sum(!is.na(sexe)))*100, digits = 1),
age = round(mean(age, na.rm = TRUE), digits = 1)
)
names(tmp_cuisine)[1] <- "group"
synthese <- rbind (tmp_sexe,
tmp_trav.satisf,
tmp_cuisine)
結果如下:
# A tibble: 8 x 5
group n percent femmes age
<fct> <int> <dbl> <dbl> <dbl>
1 Homme 899 45 0 48.2
2 Femme 1101 55 100 48.2
3 Satisfaction 480 24 51.5 41.4
4 Insatisfaction 117 5.9 47.9 40.3
5 Equilibre 451 22.6 49.9 40.9
6 NA 952 47.6 60.2 56
7 Non 1119 56 43.8 50.1
8 Oui 881 44 69.4 45.6
問題是這篇文章太長了,難以管理。 所以我想用 for 循環產生相同的結果。 但是我在 R 中使用循環有很多麻煩,我做不到。 這是我的嘗試:
groups <- c("sexe",
"trav.satisf",
"cuisine")
synthese <- tibble()
for (i in seq_along(groups)) {
tmp <- hdv2003 %>%
group_by(!!groups[i]) %>%
summarise(n = n(),
percent = round((n()/nrow(hdv2003))*100, digits = 1),
femmes = round((sum(sexe == "Femme", na.rm = TRUE)/sum(!is.na(sexe)))*100, digits = 1),
age = round(mean(age, na.rm = TRUE), digits = 1)
)
names(tmp)[1] <- "group"
synthese <- bind_rows(synthese, tmp)
}
它有效,但沒有產生預期的結果,我不明白為什么:
# A tibble: 3 x 5
group n percent femmes age
<chr> <int> <dbl> <dbl> <dbl>
1 sexe 2000 100 55 48.2
2 trav.satisf 2000 100 55 48.2
3 cuisine 2000 100 55 48.2
library(questionr)
library(tidyverse)
data(hdv2003)
list("trav.satisf", "cuisine", "sexe") %>%
map(~ {
hdv2003 %>%
group_by_at(.x) %>%
summarise(
n = n(),
percent = round((n() / nrow(hdv2003)) * 100, digits = 1),
femmes = round((sum(sexe == "Femme", na.rm = TRUE) / sum(!is.na(sexe))) * 100, digits = 1),
age = round(mean(age, na.rm = TRUE), digits = 1)
) %>%
rename_at(1, ~"group") %>%
mutate(grouping = .x)
}) %>%
bind_rows() %>%
select(grouping, group, everything())
#> # A tibble: 8 x 6
#> grouping group n percent femmes age
#> <chr> <fct> <int> <dbl> <dbl> <dbl>
#> 1 trav.satisf Satisfaction 480 24 51.5 41.4
#> 2 trav.satisf Insatisfaction 117 5.9 47.9 40.3
#> 3 trav.satisf Equilibre 451 22.6 49.9 40.9
#> 4 trav.satisf <NA> 952 47.6 60.2 56
#> 5 cuisine Non 1119 56 43.8 50.1
#> 6 cuisine Oui 881 44 69.4 45.6
#> 7 sexe Homme 899 45 0 48.2
#> 8 sexe Femme 1101 55 100 48.2
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