I have a very large dataframe(df) with approximately 35-45 columns(variables) and rows greater than 300. Some of the rows contains NA,NaN,Inf,-Inf values in
It took me awhile to work this out for dplyr 1.0.0 so i thought i would put up the new version of @sbha solutions using c_across since filter_all, filter_if are getting deprecated.
library(dplyr)
df <- tibble(a = c(1, 2, 3, NA), b = c(5, Inf, 8, 8), c = c(9, 10, Inf, 11), d = c('a', 'b', 'c', 'd'))
# a b c d
#
# 1 1 5 9 a
# 2 2 Inf 10 b
# 3 3 8 Inf c
# 4 NA 8 11 d
df %>%
rowwise %>%
filter(!all(is.infinite(c_across(where(is.numeric)))))
# # A tibble: 4 x 4
# # Rowwise:
# a b c d
#
# 1 1 5 9 a
# 2 2 Inf 10 b
# 3 3 8 Inf c
# 4 NA 8 11 d
df %>%
rowwise %>%
filter(!any(is.infinite(c_across(where(is.numeric)))))
# # A tibble: 2 x 4
# # Rowwise:
# a b c d
#
# 1 1 5 9 a
# 2 NA 8 11 d
df %>%
rowwise %>%
filter(!any(is.infinite(c_across(a:c))))
# # A tibble: 2 x 4
# # Rowwise:
# a b c d
#
# 1 1 5 9 a
# 2 NA 8 11 d
To be honest I think @sbha answer is simpler!