Filtering using dplyr filter() on multiple conditions

Question

I have a dataframe containing unique values of two variables:

df <- data.frame(V1=LETTERS,V2=c(1:26))

I'd like to filter another dataframe for values in df$V1 and corresponding value of df$V2. This is what I have tried which obviously doesn't produce the desired result:

df2 <- data.frame(V1=c('A','A','B','B','A'),
                    V2=c(1,2,2,3,4))
df2 %>% filter(V1 %in% unique(df$V1) & V2 %in% unique(df$V2))

The result I am expecting post filtering is:

  V1 V2
1  A  1
2  B  2

How do I achieve this?

AntoniosK · Accepted Answer · 2018-07-05 09:25:01Z

3

You can also use

df[df$V1 %in% unique(df2$V1),]

#   V1 V2
# 1  A  1
# 2  B  2

or

library(tidyverse)

df %>% filter(V1 %in% unique(df2$V1))

#   V1 V2
# 1  A  1
# 2  B  2

In both cases above, you'll get the rows of df where V1 of df matches the (unique) values of V1 of df2.

answered Jul 5, 2018 at 9:25

AntoniosK

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Onyambu · Accepted Answer · 2018-07-05 08:57:06Z

2

merge(df,df2)
  V1 V2
1  A  1
2  B  2

library(tidyverse)
inner_join(df,df2)
  V1 V2
1  A  1
2  B  2

answered Jul 5, 2018 at 8:57

Onyambu

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Comments

akrun · Accepted Answer · 2018-07-05 16:27:06Z

1

Another option is intersect

library(dplyr)
intersect(df, df2)
#  V1 V2
#1  A  1
#2  B  2

Or using data.table (assuming both are data.table objects and have the same attributes)

library(data.table)
fintersect(df, df2)
#   V1 V2
#1:  A  1
#2:  B  2

answered Jul 5, 2018 at 16:27

akrun

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