Left join with multiple conditions in R - r

Question

I'm trying to replace ids for their respective values. The problem is that each id has a different value according to the previous column type, like this:
>df
type id
1 q1 1
2 q1 2
3 q2 1
4 q2 3
5 q3 1
6 q3 2
Here's the type ids with its value:
>q1
id value
1 1 yes
2 2 no
>q2
id value
1 1 one hour
2 2 two hours
3 3 more than two hours
>q3
id value
1 1 blue
2 2 yellow
I've tried something like this:
df <- left_join(subset(df, type %in% c("q1"), q1, by = "id"))
But it removes the other values.
I' like to know how to do a one liner solution (or kind of) because there are more than 20 vectors with types description.
Any ideias on how to do it?
This is the df i'm expecting:
>df
type id value
1 q1 1 yes
2 q1 2 no
3 q2 1 one hour
4 q2 3 more than two hours
5 q3 1 blue
6 q3 2 yellow

Answer 1

You can join on more than one variable. The example df you give would actually make a suitable lookup table for this:
value_lookup <- data.frame(
type = c('q1', 'q1', 'q2', 'q2', 'q3', 'q3'),
id = c(1, 2, 1, 3, 1, 2),
value = c('yes', 'no', 'one hour', 'more than two hours', 'blue', 'yellow')
)
Then you just merge on both type and id:
df <- left_join(df, value_lookup, by = c('type', 'id'))
Usually when I need a lookup table like that I store it in a CSV rather than write it all out in the code, but do whatever suits you.

Answer 2

tempList = split(df, df$type)
do.call(rbind,
lapply(names(tempList), function(nm)
merge(tempList[[nm]], get(nm))))
# id type value
#1 1 q1 yes
#2 2 q1 no
#3 1 q2 one hour
#4 3 q2 more than two hours
#5 1 q3 blue
#6 2 q3 yellow

Answer 3

Get the values of 'q\d+' data.frame object identifiers in a list, bind them together into a single data.frame with bind_rows while creating the 'type' column as the identifier name and right_join with the dataset object 'df'
library(tidyverse)
mget(paste0("q", 1:3)) %>%
bind_rows(.id = 'type') %>%
right_join(df)
# type id value
#1 q1 1 yes
#2 q1 2 no
#3 q2 1 one hour
#4 q2 3 more than two hours
#5 q3 1 blue
#6 q3 2 yellow

Answer 4

You can do it by a series of left joins:
df1 = left_join(df, q1, by='id') %>% filter(type=="q1")
> df1
type id value
1 q1 1 yes
2 q1 2 no
df2 = left_join(df, q2, by='id') %>% filter(type=="q2")
> df2
type id value
1 q2 1 one hour
2 q2 3 more than two hours
df3 = left_join(df, q3, by='id') %>% filter(type=="q3")
> df3
type id value
1 q3 1 blue
2 q3 2 yellow
> rbind(df1,df2,df3)
type id value
1 q1 1 yes
2 q1 2 no
3 q2 1 one hour
4 q2 3 more than two hours
5 q3 1 blue
6 q3 2 yellow
One liner would be:
rbind(left_join(df, q1, by='id') %>% filter(type=="q1"),
left_join(df, q2, by='id') %>% filter(type=="q2"),
left_join(df, q3, by='id') %>% filter(type=="q3"))
If you have more vectors then probably you should loop through the names of vector types and execute left_join and bind_rows one by one as:
vecQs = c(paste("q", seq(1,3,1),sep="")) #Types of variables q1, q2 ...
result = tibble()
#Execute left_join for the types and store it in result.
for(i in vecQs) {
result = bind_rows(result, left_join(df,eval(as.symbol(i)) , by='id') %>% filter(type==!!i))
}
This will give:
> result
# A tibble: 6 x 3
type id value
<chr> <int> <chr>
1 q1 1 yes
2 q1 2 no
3 q2 1 one hour
4 q2 3 more than two hours
5 q3 1 blue
6 q3 2 yellow