Turning a data frame and a list into long format with dplyr
In base R, you can get your result with stack followed by merge:
res <- merge(stack(dl), dd, by.x="ind", by.y="Name")
head(res)
# ind values ID
#1 A -0.79616693 1
#2 A 0.37720953 1
#3 A 1.30273712 1
#4 A 0.19483859 1
#5 B 0.18770716 2
#6 B -0.02226917 2
NB: I supposed the names for dl were supposed to be in uppercases but if they are indeed lowercase, the following line needs to be pass instead:
res <- merge(stack(setNames(dl, toupper(names(dl)))), dd, by.x="ind", by.y="Name")
Since a dplyr solution has already been provided, another option is to subset dl for each Name value in dd using data.table grouping
library(data.table)
setDT(dd)
dd[, .(values = dl[[tolower(Name)]]), by = .(ID, Name)]
# ID Name values
# 1: 1 A -1.09633600
# 2: 1 A -1.26238190
# 3: 1 A 1.15220845
# 4: 1 A -1.45741071
# 5: 2 B -0.49318131
# 6: 2 B 0.59912670
# 7: 2 B -0.73117632
# 8: 2 B -1.09646143
# 9: 2 B -0.79409753
# 10: 3 C -0.08205888
# 11: 3 C 0.21503398
# 12: 3 C -1.17541571
# 13: 3 C -0.10020616
# 14: 3 C -1.01152362
# 15: 3 C -1.03693337
We can create a list column and unnest
library(tidyverse)
dd %>%
mutate(value = dl) %>%
unnest
# ID Name value
#1 1 A 1.57984385
#2 1 A 0.66831102
#3 1 A -0.45472145
#4 1 A 2.33807619
#5 2 B 1.56716709
#6 2 B 0.74982763
#7 2 B 0.07025534
#8 2 B 1.31174561
#9 2 B 0.57901536
#10 3 C -1.36629653
#11 3 C -0.66437155
#12 3 C 2.12506187
#13 3 C 1.20220402
#14 3 C 0.10687018
#15 3 C 0.15973401
Note that if the criteria is based on the compactness of code, if we remove the %>%
unnest(mutate(dd, value = dl))
Or another option is uncount and mutate
dd %>%
uncount(lengths(dl)) %>%
mutate(value = flatten_dbl(unname(dl)))
If it needs a join based on the names of the 'dl'
enframe(dl, name = 'Name') %>%
mutate(Name = toupper(Name)) %>%
left_join(dd) %>%
unnest
In base R, we can replicate the rows of 'dd' with lengths of 'dl' and transform to create the 'value' as unlisted 'dl'
transform(dd[rep(seq_len(nrow(dd)), lengths(dl)),], value = unlist(dl))