我想使用查找表创建一个新变量。数据框如下所示:
id sex age length
1 Female 1 45
2 Female 2 54
3 Female 3 56
4 Female 4 60
5 Female 5 60
6 Female 6 61
7 Female 7 63
8 Male 1 55
9 Male 2 54
10 Male 3 58
11 Male 4 61
12 Male 5 65
13 Male 6 63
14 Male 7 65
15 Male 8 67
16 Male 9 68
17 Male 10 69
并且查找表看起来像这样
sex age length
Female 1 50
Female 2 53
Female 3 56
Female 4 58
Female 5 60
Female 6 61
Female 7 63
Male 1 50
Male 2 54
Male 3 57
Male 4 60
Male 5 62
Male 6 63
Male 7 65
Male 8 66
Male 9 67
Male 10 69
我想创建一个具有两个级别的新变量growth.rate
:“正常”和“低”,所以最终的数据帧看起来像这样,
id sex age length growth.rate
1 Female 1 45 Low
2 Female 2 54 Normal
3 Female 3 56 Low
4 Female 4 60 Normal
5 Female 5 60 Low
6 Female 6 61 Low
7 Female 7 63 Low
8 Male 1 55 Normal
9 Male 2 54 Low
10 Male 3 58 Normal
11 Male 4 61 Normal
12 Male 5 65 Normal
13 Male 6 63 Low
14 Male 7 65 Low
15 Male 8 67 Normal
16 Male 9 68 Normal
17 Male 10 69 Low
在此示例中,id 1的growth.rate为“ Low”,因为其长度小于1岁女性的查找表中的值。
相反,id 2的growth.rate为“ Normal”,因为她的长度大于2岁女性的查找表中的值。
我试图改编此解决方案,但未成功Getting contextstack overflow error - too many nested ifelse statements within for loop?
非常感谢您的帮助
如果我们在第一个和基于'sex','age的查找数据集之间进行left_join
,我们将获得两个'length'列,在这些列之间进行比较,并使用ifelse
或[C0创建一个新列]
case_when
在library(dplyr)
left_join(df1, lookup, by = c('sex', 'age')) %>%
transmute(id, sex, age,
growth.rate = case_when(length.x <= length.y ~ "Low",
TRUE ~ "Normal"), length = length.x)
# id sex age growth.rate length
#1 1 Female 1 Low 45
#2 2 Female 2 Normal 54
#3 3 Female 3 Low 56
#4 4 Female 4 Normal 60
#5 5 Female 5 Low 60
#6 6 Female 6 Low 61
#7 7 Female 7 Low 63
#8 8 Male 1 Normal 55
#9 9 Male 2 Low 54
#10 10 Male 3 Normal 58
#11 11 Male 4 Normal 61
#12 12 Male 5 Normal 65
#13 13 Male 6 Low 63
#14 14 Male 7 Low 65
#15 15 Male 8 Normal 67
#16 16 Male 9 Normal 68
#17 17 Male 10 Low 69
中,可以使其更紧凑
data.table
或带有索引
library(data.table)
setDT(df1)[lookup, growth.rate := fcase(length <= i.length, "Low",
"Normal"), on = .(sex, age)]
setDT(df1)[lookup, growth.rate :=
c("Normal", "Low")[1 + (length <= i.length)], on = .(sex, age)]
在基数R中,我们可以使用df1 <- structure(list(id = 1:17, sex = c("Female", "Female", "Female",
"Female", "Female", "Female", "Female", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male"), age = c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L
), length = c(45L, 54L, 56L, 60L, 60L, 61L, 63L, 55L, 54L, 58L,
61L, 65L, 63L, 65L, 67L, 68L, 69L)), class = "data.frame", row.names = c(NA,
-17L))
lookup <- structure(list(sex = c("Female", "Female", "Female", "Female",
"Female", "Female", "Female", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male"), age = c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L
), length = c(50L, 53L, 56L, 58L, 60L, 61L, 63L, 50L, 54L, 57L,
60L, 62L, 63L, 65L, 66L, 67L, 69L)), class = "data.frame", row.names = c(NA,
-17L))
通过merge
和sex
合并两个数据帧,并通过使用age
检查条件来创建新列。
ifelse
您可以删除不需要的列。