Question

我有一个由三个分类变量组成的数据框，我想找到每个组合的频率，并按频率按降序对结果进行排序，如下所示：

我的数据：

   A LEVEL1 PASS
   A LEVEL1 FAIL
   B LEVEL2 PASS
   A LEVEL1 PASS
   B LEVEL2 PASS
   A LEVEL1 PASS

结果如下：

   A LEVEL1 PASS 3
   B LEVEL2 PASS 2
   A LEVEL1 FAIL 1

我使用plyr库，

  myfreq<-count(myresult,vars = NULL, wt_var = NULL) 
  myfreq<-myfreq[order-myfreq$freq,]

在开始时它可以工作，但它只是给我这个错误：

Error in grouped_df_impl(data, unname(vars), drop) : 
Column `vars` is unknown

我使用的其他图书馆是rJava和dplyr

感谢

Answer 1

我建议使用dplyr包含的tidyverse。

我不知道数据框中列的名称是什么，所以我在下面的示例中将它们命名为col1，col2和col3。

library(tidyverse)

df <- tribble(
  ~ col1, ~col2, ~col3,
  "A", "LEVEL1", "PASS",
  "A", "LEVEL1", "FAIL",
  "A", "LEVEL1", "PASS",
  "B", "LEVEL2", "PASS",
  "A", "LEVEL1", "PASS")

# here is where the magic happens
df %>% count(col1, col2, col3, sort = TRUE)

Answer 2

您可以在dplyr中使用group_by：

library(dplyr)


x <- data.frame(letter = c("A", "A", "B", "A", "B", "A"), level = c("LEVEL 1", "LEVEL 1", "LEVEL 2", "LEVEL 1", "LEVEL 2", "LEVEL 1"), text = c("PASS", "FAIL", "PASS", "PASS", "PASS", "PASS"))

df <- x %>%
     group_by_all() %>%
     count()

或者你可以这样做：

df <- x %>%
     group_by(letter, level, text) %>%
     count()

输出：

> df <- x %>% group_by_all() %>% count()
> df
# A tibble: 3 x 4
# Groups:   x, y, z [3]
       x       y      z     n
  <fctr>  <fctr> <fctr> <int>
1      A LEVEL 1   FAIL     1
2      A LEVEL 1   PASS     3
3      B LEVEL 2   PASS     2

Answer 3

您可以使用table功能。

ex <- data.frame("letter" = c("A", "A", "B", "A", "B", "A"),
                 "level" = c("LEVEL1", "LEVEL1", "LEVEL2", "LEVEL1", "LEVEL2", "LEVEL1"),
                 "test" = c("PASS", "FAIL", rep("PASS", 4)))


ex

res <- data.frame(table(ex$level, ex$test))
colnames(res) <- c("level", "test", "freq")

您可以稍后将结果data.frame与原始数据合并。

Answer 4

这是与n（）

的整齐

df <- tibble(
  id = c("A", "A", "B", "A", "B", "A"),
  level = c("LEVEL1", "LEVEL1", "LEVEL2", "LEVEL1", "LEVEL2", "LEVEL1"),
  type = factor(c("PASS", "FAIL", "PASS", "PASS", "PASS", "PASS"))
)

df %>% 
  group_by(id, level, type) %>%
  summarise(n = n()) %>%
  arrange(desc(n))

# A tibble: 3 x 4
# Groups:   id, level [?]
     id  level   type     n
  <chr>  <chr> <fctr> <int>
1     A LEVEL1   FAIL     1
2     A LEVEL1   PASS     3
3     B LEVEL2   PASS     2

计算R中分类变量的出现次数

4 个答案: