我有一个2018年所有销售额的数据集,并试图进行帕累托分析。数据应该具有产品类别,其中大多数都有,但是1/5没有。现在我想用另一个数据框中的产品类别填充这个NA值,但是我失败了。
简化示例如下:
df1 <- data.frame(ID = c("1000", "1000", "1000", "1000", "1010", "1020", "1030", "1030", "1000"),
name = c("A", "B", "C", "D", "A", "A", "B", "F", "G"),
group_ID = c(NA, NA, NA, NA, NA, NA, NA, NA, NA), stringsAsFactors = FALSE)
df2 <- data.frame(IDx = c("1000", "1000", "1000", "1000", "1000", "1000", "1000", "1000", "1000"),
group_ID = c("blankets", "blankets", "blankets", "blankets", "blankets", "blankets", "blankets", "blankets", "blankets"),
stringsAsFactors = FALSE)
df1[is.na(df1)] <- "None"
df1 %>%
left_join(df2, by = c("ID" = "IDx")) %>%
mutate(group_ID = coalesce(group_ID.y, group_ID.x)) %>%
select(-group_ID.x, -group_ID.y)
此代码的结果是以下数据帧:
ID name group_ID
1 1000 A blankets
2 1000 A blankets
3 1000 A blankets
4 1000 A blankets
5 1000 A blankets
6 1000 A blankets
7 1000 A blankets
8 1000 A blankets
9 1000 A blankets
10 1000 B blankets
11 1000 B blankets
12 1000 B blankets
13 1000 B blankets
14 1000 B blankets
15 1000 B blankets
16 1000 B blankets
17 1000 B blankets
18 1000 B blankets
19 1000 C blankets
20 1000 C blankets
21 1000 C blankets
22 1000 C blankets
23 1000 C blankets
24 1000 C blankets
25 1000 C blankets
26 1000 C blankets
27 1000 C blankets
28 1000 D blankets
29 1000 D blankets
30 1000 D blankets
31 1000 D blankets
32 1000 D blankets
33 1000 D blankets
34 1000 D blankets
35 1000 D blankets
36 1000 D blankets
37 1010 A None
38 1020 A None
39 1030 B None
40 1030 F None
41 1000 G blankets
42 1000 G blankets
43 1000 G blankets
44 1000 G blankets
45 1000 G blankets
46 1000 G blankets
47 1000 G blankets
48 1000 G blankets
49 1000 G blankets
我不想要这个。我想要的东西:
ID name group_ID
1 1000 A blankets
2 1000 B blankets
3 1000 C blankets
4 1000 D blankets
5 1010 A None
6 1020 A None
7 1030 B None
8 1030 F None
9 1000 G blankets
我尝试了多个连接,并在互联网上环顾四周,但我无法解决我的问题。
希望你能帮忙!
我认为unique(df1)
可能有效。
data.table解决方案
样本数据
df1 <- data.frame(ID = c("1000", "1000", "1000", "1000", "1010", "1020", "1030", "1030", "1000"),
name = c("A", "B", "C", "D", "A", "A", "B", "F", "G"), stringsAsFactors = FALSE)
我遗漏了group_id列...你将用连接创建那个。
df2 <- data.frame(IDx = c("1000", "1000", "1000", "1000", "1000", "1000", "1000", "1000", "1000"),
group_ID = c("blankets", "blankets", "blankets", "blankets", "blankets", "blankets", "blankets", "blankets", "blankets"),
stringsAsFactors = FALSE)
码
library(data.table)
setDT(df1)[setDT(df2), group_ID := i.group_ID, on = .(ID = IDx)][]
我使用setDT()
从data.frames df1和df2中创建data.tables。剩下的就是“简单”左边连接参考。
产量
# ID name group_ID
# 1: 1000 A blankets
# 2: 1000 B blankets
# 3: 1000 C blankets
# 4: 1000 D blankets
# 5: 1010 A <NA>
# 6: 1020 A <NA>
# 7: 1030 B <NA>
# 8: 1030 F <NA>
# 9: 1000 G blankets
你可以使用distinct()
。这是完整的代码:
distinct(
df1 %>%
left_join(df2, by = c("ID" = "IDx")) %>%
mutate(group_ID = coalesce(group_ID.y, group_ID.x)) %>%
select(-group_ID.x, -group_ID.y))