创建列出不同观察结果的列

Creating column that lists distinct observations

我有一个观察数据框架,看起来像这样(显示每个学期开设的大学 类 的课程编号)。列很长,长度不一

  spring   summer   fall
   4a       5b       5c
   4a       9c       11b
   7c       5b       8a 
   ...      ...      ...

我想重新格式化它,使其看起来像这样。首先,我想创建一个列 "Course_Names",显示所有可能的不同课程设置的名称。然后,我想统计每个学期开设的每门课程的节数。

   Course_Names   spring   summer   fall
   4a             2        0        0
   5b             0        2        0
   5c             0        0        1
   7c             1        0        0
   8a             1        0        1
   9c             0        1        0
   11b            0        0        1        

任何建议或相关帖子的链接将不胜感激!谢谢!

base R 中,一个选项是 stack 将 data.frame 放入两列数据集并使用 table

table(stack(df1))
#    ind
#values spring summer fall
#   11b      0      0    1
#   4a       2      0    0
#   5b       0      2    0
#   5c       0      0    1
#   7c       1      0    0
#   8a       0      0    1
#   9c       0      1    0

或者在 tidyverse 中,我们可以使用 pivot_longer 重塑为 'long' 格式,获取 count 并重塑为 'wide

library(dplyr)
library(tidyr)
df1 %>%
    pivot_longer(everything()) %>%
    count(name, Course_Names = value) %>%
    pivot_wider(names_from = name, values_from = n, values_fill = list(n = 0))
# A tibble: 7 x 4
#  Course_Names  fall spring summer
#  <chr>        <int>  <int>  <int>
#1 11b              1      0      0
#2 5c               1      0      0
#3 8a               1      0      0
#4 4a               0      2      0
#5 7c               0      1      0
#6 5b               0      0      2
#7 9c               0      0      1

数据

df1 <- structure(list(spring = c("4a", "4a", "7c"), summer = c("5b", 
"9c", "5b"), fall = c("5c", "11b", "8a")), class = "data.frame", row.names = c(NA, 
-3L))

您可以通过收集数据然后使用 tidyr 包中的这些函数再次传播它来完成此操作,如下所示;

library(dplyr)
library(tidyr)

data <-
  data.frame(
    spring = c("4a", "4a", "7c"),
    summer = c("5b", "9c", "5b"),
    fall = c("5c", "11b", "8a")
  )

result <-
  data %>%
  gather(key = "Course_Names", value = "Course") %>%
  group_by(Course_Names, Course) %>%
  count() %>%
  spread(key = Course_Names, value = n) %>%
  replace(is.na(.), 0)