CODEWITHSUNDEEP
rdplyrdata.tableaggregateplyr
M.Teich
M.Teich

Reputation: 607

Calculate median for multiple columns by group based on subsets defined by other columns

I am trying to calculate the median (but that could be substituted by similar metrics) by group for multiple columns based on subsets defined by other columns. This is direct follow-on question from this previous post of mine. I have attempted to incorporate calculating the median via aggregate into the Map(function(x,y) dosomething, x, y) solution kindly provided by @Frank, but that didn't work. Let me illustrate:

Calculate median for A and B by groups GRP1 and GRP2

df <- data.frame(GRP1 = c("A","A","A","A","A","A","B","B","B","B","B","B"), GRP2 = c("A","A","A","B","B","B","A","A","A","B","B","B"), A = c(0,4,6,7,0,1,9,0,0,8,3,4), B = c(6,0,4,8,6,7,0,9,9,7,3,0))

med <- aggregate(.~GRP1+GRP2,df,FUN=median)

Simple. Now add columns defining which rows to be used for calculating the median, i.e. rows with NAs should be dropped, column a defines which rows to be used for calculating the median in column A, same for columns b and B:

a <- c(1,4,7,3,NA,3,7,NA,NA,4,8,1)
b <- c(5,NA,7,9,5,6,NA,8,1,7,2,9)
df1 <- cbind(df,a,b)

As mentioned above, I have tried combining Map and aggregate, but that didn't work. I assume that Map doesn't know what to do with GRP1 and GRP2.

med1 <- Map(function(x,y) aggregate(.~GRP1+GRP2,df1[!is.na(y)],FUN=median), x=df1[,3:4], y=df1[, 5:6])

This is the result I'm looking for:

  GRP1 GRP2 A B
1    A    A 4 5
2    B    A 9 9
3    A    B 4 7
4    B    B 4 3

Any help will be much appreciated!

Upvotes: 2

Views: 1921

Answers (2)

s_baldur
s_baldur

Reputation: 33603

Using data.table

library(data.table)
setDT(df1)

df1[, .(A = median(A[!is.na(a)]), B = median(B[!is.na(b)])), by = .(GRP1, GRP2)]

   GRP1 GRP2 A B
1:    A    A 4 5
2:    A    B 4 7
3:    B    A 9 9
4:    B    B 4 3

Same logic in dplyr

library(dplyr)
df1 %>%
  group_by(GRP1, GRP2) %>%
  summarise(A = median(A[!is.na(a)]), B = median(B[!is.na(b)]))

The original df1:

df1 <- data.frame(
  GRP1 = c("A", "A", "A", "A", "A", "A", "B", "B", "B", "B", "B", "B"),
  GRP2 = c("A", "A", "A", "B", "B", "B", "A", "A", "A", "B", "B", "B"),
  A = c(0, 4, 6, 7, 0, 1, 9, 0, 0, 8, 3, 4),
  B = c(6, 0, 4, 8, 6, 7, 0, 9, 9, 7, 3, 0),
  a = c(1, 4, 7, 3, NA, 3, 7, NA, NA, 4, 8, 1),
  b = c(5, NA, 7, 9, 5, 6, NA, 8, 1, 7, 2, 9)
)

Upvotes: 2

RLave
RLave

Reputation: 8374

With dplyr:

library(dplyr)
df1 %>% 
  mutate(A = ifelse(is.na(a), NA, A),
         B = ifelse(is.na(b), NA, B)) %>% 
# I use this to put as NA the values we don't want to include
  group_by(GRP1, GRP2) %>% 
  summarise(A = median(A, na.rm = T),
            B = median(B, na.rm = T))

# A tibble: 4 x 4
# Groups:   GRP1 [?]
  GRP1  GRP2      A     B
  <fct> <fct> <dbl> <dbl>
1 A     A         4     5
2 A     B         4     7
3 B     A         9     9
4 B     B         4     3

Upvotes: 1

Related Questions