I would like to join two data frames. Some of the column names overlap, and there are NA entries in one of the data frame's overlapping columns. Here is a simplified example:
df1 <- data.frame(fruit = c('apples','oranges','bananas','grapes'), var1 = c(1,2,3,4), var2 = c(3,NA,6,NA), stringsAsFactors = FALSE)
df2 <- data.frame(fruit = c('oranges','grapes'), var2=c(5,6), var3=c(7,8), stringsAsFactors = FALSE)
Can I use dplyr join functions to join these data frames and automatically prioritize the non-NA entry so that I get the "var2" column to have no NA entries in the joined data frame? As it is now, if I call left_join, it keeps the NA entries, and if I call full_join it duplicates the rows.
coalesce might be something you need. It fills the NA from the first vector with values from the second vector at corresponding positions:
library(dplyr)
df1 %>%
left_join(df2, by = "fruit") %>%
mutate(var2 = coalesce(var2.x, var2.y)) %>%
select(-var2.x, -var2.y)
# fruit var1 var3 var2
# 1 apples 1 NA 3
# 2 oranges 2 7 5
# 3 bananas 3 NA 6
# 4 grapes 4 8 6
Or use data.table, which does in-place replacing:
library(data.table)
setDT(df1)[setDT(df2), on = "fruit", `:=` (var2 = i.var2, var3 = i.var3)]
df1
# fruit var1 var2 var3
# 1: apples 1 3 NA
# 2: oranges 2 5 7
# 3: bananas 3 6 NA
# 4: grapes 4 6 8
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