I want to create a new variable using a lookup table. The dataframe looks like this:
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
and the lookup table looks like this
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
I want to create a new variable growth.rate with two levels: "Normal" and "Low", so the final data frame looks like this ,
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
In this example, the growth.rate for id 1 is "Low" because her length is lower than the value in the lookup table for females age 1.
Conversely, the growth.rate for id 2 is "Normal" because her length is higher than the value in the lookup table for females age 2.
I tried to adapt this solution without success Getting contextstack overflow error - too many nested ifelse statements within for loop?
any help is much appreciated
If we do a left_join betweeen the first and lookup dataset based on 'sex', 'age, we get two 'length' column, do the comparison between those columns and create a new column with ifelse or case_when
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
In data.table, this can be made more compact
library(data.table)
setDT(df1)[lookup, growth.rate := fcase(length <= i.length, "Low",
"Normal"), on = .(sex, age)]
Or with an index
setDT(df1)[lookup, growth.rate :=
c("Normal", "Low")[1 + (length <= i.length)], on = .(sex, age)]
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))