R 中 data.frame 的条件 dplyr::summarize()

Question

在我下面的

DATA

中，我想知道如何

summarize()

选择6个不同种族（

Hispanic

，

AmIndian

，

Asian

，

White

，

Pacific

，

AsiaPacific

）的数量（

"Y" 

）什么时候

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")

Answer 1

您的

Ethinc_overall == "B"

没有任何“是”值。所以我对每个种族进行了总结。但是您可以取消注释实际数据集的

filter()

行，这应该可以满足您的需求。

DATA %>% 
  # filter(Ethinc_overall == "B") %>% ## you don't have any Y's for B
  summarise(across(Hispanic:AsiaPacific, 
                   list(Yes = ~ sum(. == "Y"),
                        No  = ~ sum(. == "N"))), .by = Ethinc_overall) %>% 
  pivot_longer(-Ethinc_overall, values_to = "count") %>% 
  separate(name, into = c("ethnicity", "Yes/No")) %>% 
  filter(count > 0)

#> # A tibble: 27 x 4
#>    Ethinc_overall ethnicity   `Yes/No` count
#>    <chr>          <chr>       <chr>    <int>
#>  1 H              Hispanic    Yes         23
#>  2 H              AmIndian    Yes          8
#>  3 H              AmIndian    No          15
#>  4 H              Asian       Yes          1
#>  5 H              Asian       No          22
#>  6 H              White       Yes          9
#>  7 H              White       No          14
#>  8 H              Pacific     Yes          3
#>  9 H              Pacific     No          20
#> 10 H              AsiaPacific Yes          3
#> # i 17 more rows

^{创建于 2024-02-09，使用 reprex v2.0.2}