Clash Royale CLAN TAG#URR8PPP

filter a data frame and applying a cutoff on multiple columns in r

I have a data frame as follows:

[,1] [,2] [,3] [,4] [,5] [,6] [,7]

[1,] A 4 NA NA 1.55 4 NA

[2,] B NA NA 4 0.56 NA NA

[3,] C 4 4 NA 0.62 4 4

[4,] D NA NA NA 1.61 4 NA

[5,] E 4 NA NA 0.5 4 NA


What I would like to get as the output after filtering is:

[,1] [,2] [,3] [,4] [,5] [,6] [,7]


[3,] C 4 4 NA 0.62 4 4


[5,] E 4 NA NA 0.5 4 NA


I would like to have at least one value equals to 4 in columns 2 to 4 & at least one value equals to 4 in columns 6 to 7.

I was thinking to use the following command But I am not sure how to use it in a proper way that gives me the correct output.

here is the command:

new.df <- df %>%
dplyr::filter_at((vars(c(2:4)), any_vars(. == 4) & vars(c(6:7)), any_vars(. == 4))


Do you have any idea how can I get the desired new.df?
Thanks!

dput

3 Answers
3

In base R you could do something like:

df[rowSums(df[2:4]==4,T)>0 & rowSums(df[6:7]==4,T)>0,]
col1 col2 col3 col4 col5 col6 col7
1 A 4 NA NA 1.55 4 NA
3 C 4 4 NA 0.62 4 4
5 E 4 NA NA 0.50 4 NA


A slightly different use of dplyr:

dplyr

df %>%
filter_at(vars(col2, col3, col4), any_vars(. == 4)) %>%
filter_at(vars(col6, col7), any_vars(. == 4))

col1 col2 col3 col4 col5 col6 col7
1 A 4 NA NA 1.55 4 NA
2 C 4 4 NA 0.62 4 4
3 E 4 NA NA 0.50 4 NA


With column positions:

df %>%
filter_at(c(2,3,4), any_vars(. == 4)) %>%
filter_at(c(6,7), any_vars(. == 4))


I am not certain what is wrong with unless it is too verbose for you and you want a way to not name the columns.

df = data.frame(col1 = c("A", "B", "C", "D", "E"),
col2 = c(4, NA, 4, NA, 4),
col3 = c(NA, NA, 4, NA, NA),
col4 = c(NA, 4, NA, NA, NA),
col5 = c(1.55, 0.56, 0.62, 1.61, 0.5 ),
col6 = c(4, NA, 4, 4, 4),
col7 = c(NA, NA, 4, NA, NA))

df %>% filter((col2 == 4| col3 == 4 | col4 == 4) & (col6 == 4 | col7 == 4))


Which produces:

col1 col2 col3 col4 col5 col6 col7
1 A 4 NA NA 1.55 4 NA
2 C 4 4 NA 0.62 4 4
3 E 4 NA NA 0.50 4 NA


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