R/finalfit_internal_functions.R
rm_empty_block.RdIt is common to want to remove cases/rows where all variables in a particular set are missing, e.g. all symptom variables are missing in a health care dataset.
rm_empty_block(.data, ...)Dataframe.
Unquoted variable/column names.
Data frame.
# Pretend that we want to remove rows that are missing in group1, group2, and group3
# but keep rest of dataset.
colon_s %>%
dplyr::mutate(
group1 = rep(c(NA, 1), length.out = 929),
group2 = rep(c(NA, 1), length.out = 929),
group3 = rep(c(NA, 1), length.out = 929)
) %>%
rm_empty_block(group1, group2, group3) %>%
head()
#> id rx sex age obstruct perfor adhere nodes status differ extent surg
#> 2 2 Lev+5FU 1 63 0 0 0 1 0 2 3 0
#> 4 4 Lev+5FU 0 66 1 0 0 6 1 2 3 1
#> 6 6 Lev+5FU 0 57 0 0 0 9 1 2 3 0
#> 8 8 Obs 1 54 0 0 0 1 0 2 3 0
#> 10 10 Lev+5FU 0 68 0 0 0 1 0 2 3 1
#> 12 12 Lev+5FU 1 52 0 0 0 2 0 3 3 1
#> node4 time sex.factor rx.factor obstruct.factor perfor.factor adhere.factor
#> 2 0 3087 Male Lev+5FU No No No
#> 4 1 293 Female Lev+5FU Yes No No
#> 6 1 1767 Female Lev+5FU No No No
#> 8 0 3192 Male Obs No No No
#> 10 0 3308 Female Lev+5FU No No No
#> 12 0 3309 Male Lev+5FU No No No
#> differ.factor extent.factor surg.factor node4.factor status.factor
#> 2 Moderate Serosa Short No Alive
#> 4 Moderate Serosa Long Yes Died
#> 6 Moderate Serosa Short Yes Died
#> 8 Moderate Serosa Short No Alive
#> 10 Moderate Serosa Long No Alive
#> 12 Poor Serosa Long No Alive
#> age.factor loccomp loccomp.factor time.years mort_5yr age.10 mort_5yr.num
#> 2 60+ years 0 No 8.4575342 Alive 6.3 1
#> 4 60+ years 1 Yes 0.8027397 Died 6.6 2
#> 6 40-59 years 0 No 4.8410959 Died 5.7 2
#> 8 40-59 years 0 No 8.7452055 Alive 5.4 1
#> 10 60+ years 0 No 9.0630137 Alive 6.8 1
#> 12 40-59 years 0 No 9.0657534 Alive 5.2 1
#> hospital group1 group2 group3
#> 2 hospital_3 1 1 1
#> 4 hospital_4 1 1 1
#> 6 hospital_4 1 1 1
#> 8 hospital_2 1 1 1
#> 10 hospital_2 1 1 1
#> 12 hospital_1 1 1 1