group by and filter data management using dplyr

18
votes

Take a simple dataset

a <- c(1,2,3,4,5,6,7,8)
b <- c(1,2,2,1,2,2,2,2)
c <- c(1,1,1,2,2,2,3,3)
d <- data.frame(a,b,c)

now I want to filter my data, so that we group_by(c) and then remove all data where no b=1occurs.

Thus the results (e) should look like d but without the two bottom rows

I have tried using

e <- d %>%
  group_by(c) %>%
  filter(n(b)>1)

The output should contain the data in green below and remove the data in red

enter image description here

2
rdplyr
e <- d %>% group_by(c) %>% filter( !b == 1) - SabDeM
!b == 1 is equal to b != 1 which is perhaps a bit more readable? - Roman Luštrik
not exactly, so for i want to remove each unique ´c´ row where each ´c´ identity does not have a ´b==1´ - lukeg
this doesn't work either e <- d %>% group_by(c) %>% filter( b == 1 & b == 2 ) - lukeg
Not sure why no one said this before, but the function you're looking for is n_distinct, like d %>% group_by(c) %>% filter(n_distinct(b)>1) - Frank

2 Answers

33
votes

Try

d %>% 
  group_by(c) %>% 
  filter(any(b == 1))

Which gives:

#Source: local data frame [6 x 3]
#Groups: c
#
#  a b c
#1 1 1 1
#2 2 2 1
#3 3 2 1
#4 4 1 2
#5 5 2 2
#6 6 2 2
0
votes

You can try

df <- d %>% mutate(test = ifelse((b != 1) == T, 0, 1)) %>% group_by(c) %>% 
            mutate(test = sum(test)) %>% filter(test != 0) %>% select(-test)

which yields

#  a b c
#1 1 1 1
#2 2 2 1
#3 3 2 1
#4 4 1 2
#5 5 2 2
#6 6 2 2