Merge rows of one data frame into multiple columns of another based on an ID

Question

I have a data frame that has an ID number and corresponding data, some of the ID numbers are repeated in multiple rows, and I want to merge this data frame with another that has one ID number per row. So the result would be to add multiple columns to each row/ID to cover the duplicates.

I've been playing around with the merge() and aggregate() functions trying to get this to work, but have not come close to what I want. I've also spent a lot of time searching stack overflow to find a solution and haven't been able to find anything.

This is what the first data frame looks like:

df1 <- data.frame(ID = c(90051, 90051, 90051, 90229, 90229, 91120, 91120, 89649), 
              SPP = c("ABLA", "PICO", "POTR5", "ABLA", "PICO", "ABLA", "POTR5", "ABLA"), 
              COUNT = c(5, 4, 1, 7, 1, 3, 5, 11))

This is what the data frame that I want to modify looks like

df2 <- data.frame(ID = c(85470, 90051, 90229, 91120, 89649, 84364), 
              COUNTY = c(49, 57, 107, 107, 117, 37), STATUS = c(1, 1, 1, 2, 1, 3))

And this is what I want my resulting data frame to look like

df3 <- data.frame(ID = c(85470, 90051, 90229, 91120, 89649, 84364), 
              COUNTY = c(49, 57, 107, 107, 117, 37), STATUS = c(1, 1, 1, 2, 1, 3),
              ABLA = c(NA, 5, 7, 3, 11, NA), PICO = c(NA, 4, 1, NA, NA, NA), POTR5 = c(NA, 7, NA, 5, NA, NA))

Answer 1

I believe this should do it. By using all.x = TRUE you make the merge like a left outer join from SQL.

merge(x = df1, y = df2, by = "ID", all.x = TRUE)

Answer 2

I think you can first use spread and then you can do a right_join .

library(tidyr)
library(dplyr)

result <- spread(df1, key = SPP, value = COUNT) %>%
  right_join(df2, by = "ID")

Giving you the desired result:

> result
     ID ABLA PICO POTR5 COUNTY STATUS
1 85470   NA   NA    NA     49      1
2 90051    5    4     1     57      1
3 90229    7    1    NA    107      1
4 91120    3   NA     5    107      2
5 89649   11   NA    NA    117      1
6 84364   NA   NA    NA     37      3