如何使用 spread() 获得所需的输出

Question

假设，我有一个如下所示的数据框
df1:

+------+--+------+--------+
| ID   |  | Type | Points |
+------+--+------+--------+
| DJ45 |  | A    | 69.2 |
| DJ45 |  | F    | 60.8 |
| DJ45 |  | C    |  2.9 |
| DJ46 |  | B    | 22.7 |
| DJ46 |  | D    | 18.7 |
| DJ46 |  | A    | 16.1 |
| DJ47 |  | E    | 67.2 |
| DJ47 |  | C    | 63.1 |
| DJ47 |  | F    | 16.7 |
| DJ48 |  | D    |  8.4 |
+------+--+------+------+

我想获得一个结果，它将提供以下格式的类型的前 2 个值（逐点）：

输出：

+------+---------+---------+
| ID   | Type1   | Type2   |
+------+---------+---------+
| DJ45 |   A     | F       | 
| DJ46 |   B     | D       | 
| DJ47 |   E     | C       | 
| DJ48 |   D     | NA      |

我用过：

df1 %>%
  group_by(Id) %>%
  top_n(2,wt=Points) %>%
  mutate(val = paste("Type", row_number())) %>% 
  filter(row_number()<=2) %>% 
  select(-Points) %>% 
  spread(val, Type)

但我得到以下答案：

输出：

+------+------+--------+---------+
| ID   |Points|Type1   | Type2   |
+------+------+--------+---------+
| DJ45 | 69.2 |  A     | NA      | 
| DJ45 | 60.8 |  NA    | F       | 
| DJ46 | 22.7 |  B     | NA      | 
| DJ46 | 18.7 |  NA    | D       | 
| DJ47 | 67.2 |  E     | NA      | 
| DJ47 | 63.1 |  NA    | C       |
| DJ48 |  8.4 |  D     | NA      |

Answer 1

df <- read.table(header = T, stringsAsFactors = F, text = "
ID Type Points
DJ45 A 69.2
DJ45 F 60.8
DJ45 C 2.9
DJ46 B 22.7
DJ46 D 18.7
DJ46 A 16.1
DJ47 E 67.2
DJ47 C 63.1
DJ47 F 16.7
DJ48 D 8.4
")

library(dplyr)
library(tidyr)

df %>%
  group_by(ID) %>%
  top_n(2, wt = Points) %>%
  arrange(-Points) %>% 
  mutate(Points = paste0('Type', row_number())) %>% 
  spread(Points, Type)

top_n(2, wt = Points) 根据 Points 在 ID 组
arrange(-Points) 降序排列
mutate(Points = paste0('Type', row_number())) 修改 Points 等于 'Type' + ID 组内的行号（1 到 2）
spread(Points, Type) 为 Points 中的每个唯一值创建列，并将 Type 的适当值放入其中

如何使用 spread() 获得所需的输出

How to use spread() to get desired output

r

spread

dplyr

tidyr

tidyverse