Conditional documentr：：R中数据帧的summarize()

发布于02月10日

在我的DATA以下，我想知道如何summarize()的6个不同的种族(Hispanic,AmIndian,Asian,White,Pacific,AsiaPacific)的数量 Select ("Y")当Ethinc_overall!="B"？

library(tidyverse)

DATA <- read.table(h=TRUE,text=
"EL_Type Language Black Hispanic AmIndian Asian White Pacific AsiaPacific Ethinc_overall
 Current  English Black        Y    N     N     N       N           N              H
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        Y    N     N     N       N           N              H
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     Y     N       N           Y              M
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        Y    Y     N     Y       N           N              H
 Current  English Black        Y    Y     N     N       N           N              H
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        Y    Y     N     Y       N           N              H
 Current  English Black        Y    Y     N     Y       N           N              H
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     N     Y       N           N              M
 Current  English Black        N    Y     N     N       N           N              M
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     Y     N       N           Y              M
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        Y    Y     N     N       N           N              H
 Current  English Black        Y    N     N     N       N           N              H
 Current  English Black        Y    N     N     N       N           N              H
 Current  English Black        Y    N     N     Y       N           N              H
 Current  English Black        Y    Y     N     N       Y           Y              H
 Current  English Black        Y    Y     N     N       Y           Y              H
 Current  English Black        Y    N     N     N       N           N              H
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        Y    Y     Y     Y       Y           Y              H
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        Y    N     N     Y       N           N              H
 Current  English Black        Y    N     N     Y       N           N              H
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        Y    N     N     N       N           N              H
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        Y    N     N     Y       N           N              H
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        Y    N     N     Y       N           N              H
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        Y    N     N     N       N           N              H
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        N    N     N     N       N           N              B
 Current  English Black        Y    N     N     N       N           N              H
 Current  English Black        Y    N     N     N       N           N              H
 Current  English Black        Y    N     N     N       N           N              H
 Current  English Black        N    N     N     Y       N           N              M
 Current  English Black        N    N     N     N       N           N              B")

DATA %>% filter(Ethinc_overall != "B") %>% summarise(across(Hispanic:AsiaPacific, list(Y = ~ sum(. == "Y"))), .by = Ethinc_overall) %>% pivot_longer(-Ethinc_overall, values_to = "count") %>% separate(name, into = c("ethnicity", "Y")) %>% # filter(count > 0) %>% select(-Y) #> # A tibble: 12 x 3 #> Ethinc_overall ethnicity count #> <chr> <chr> <int> #> 1 H Hispanic 23 #> 2 H AmIndian 8 #> 3 H Asian 1 #> 4 H White 9 #> 5 H Pacific 3 #> 6 H AsiaPacific 3 #> 7 M Hispanic 0 #> 8 M AmIndian 1 #> 9 M Asian 2 #> 10 M White 2 #> 11 M Pacific 0 #> 12 M AsiaPacific 2

DATA %>% filter(Ethinc_overall != "B") %>% pivot_longer(Hispanic:AsiaPacific, names_to = "ethnicity") %>% summarise(count = sum(value =="Y"), .by = c(Ethinc_overall, ethnicity)) #> # A tibble: 12 x 3 #> Ethinc_overall ethnicity count #> <chr> <chr> <int> #> 1 H Hispanic 23 #> 2 H AmIndian 8 #> 3 H Asian 1 #> 4 H White 9 #> 5 H Pacific 3 #> 6 H AsiaPacific 3 #> 7 M Hispanic 0 #> 8 M AmIndian 1 #> 9 M Asian 2 #> 10 M White 2 #> 11 M Pacific 0 #> 12 M AsiaPacific 2

Conditional documentr：：R中数据帧的summarize()

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