CODEWITHSUNDEEP
r
moth
moth

Reputation: 2409

subset data.frame for every level of a factor

Given the following vectors to build a dataframe :

set.seed(1)
x <- sample( LETTERS[1:4], 100, replace=TRUE)
y <- runif(100,0,100)
df <- data.frame(x,y)

I would like to have if possible, a clean code with a loop or apply or any other method to subset the data.frame by different conditionals for every level of factor x. For example:

level A  y >30 | y <20
level B  y >21 | y <12
level C  y >42 | y <21
level D  y >58 | y <13

Upvotes: 1

Views: 53

Answers (3)

dww
dww

Reputation: 31454

using library(data.table) we can do

lower = c(20, 12, 21, 13)
upper = c(30, 21, 42, 58)
setDT(df)[!between(y, lower[x], upper[x]), .SD, keyby=x]

#    x         y
# 1: A 63.349326
# 2: A 59.876097
# 3: A 97.617069
# 4: A 73.179251
# 5: A 49.559358
# 6: A 17.344233
# 7: A 51.116978
# ...

Upvotes: 0

markus
markus

Reputation: 26373

A split apply combine approach where we use Map to iterate over the subsets and the conditions in parallel. 

do.call(rbind,
        Map(function(data, left, right) {
          subset(x = data, subset = y > left | y < right) 
          },
          data = split(df, df$x),
          left = c(30, 21, 42, 58),
          right = c(20, 12, 21, 13)
        ))
#      x         y
#A.5   A 63.349326
#A.10  A 59.876097
#A.11  A 97.617069
#A.12  A 73.179251
#A.22  A 49.559358
#A.24  A 17.344233
# ...

We split your data by x, subset each according to your conditions and combine the list to a single dataframe.

Upvotes: 1

niko
niko

Reputation: 5281

What about something like this

df[df$x == 'A' & (df$y > 30 | df$y < 20),]
#    x         y
# 2  A 71.117606
# 3  A 44.438057
# 6  A 63.244699
# 7  A 54.185802
# 11 A 39.577617
# 13 A  8.681545
# 29 A 94.437431
# ...
# or depending on what you mean by '&'
df[df$x == 'A' & df$y > 30,]
#    x        y
# 2  A 71.11761
# 3  A 44.43806
# 6  A 63.24470
# 7  A 54.18580
# 11 A 39.57762
# 29 A 94.43743
# 31 A 54.17604
# ...

# and then accordingly for the other cases

Upvotes: 0

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