I have a problem to solve how to remove rows with a Zero value in R. In others hand, I can use na.omit() to delete all the NA values or use complete.cases
In base R, we can select the columns which we want to test using grep, compare the data with 0, use rowSums to select rows which has all non-zero values.
cols <- grep("^Mac", names(df))
df[rowSums(df[cols] != 0) == length(cols), ]
# DateTime Mac1 Mac2 Mac3 Mac4
#1 2011-04-02 06:05 21 21 21 21
#2 2011-04-02 06:10 22 22 22 22
#3 2011-04-02 06:20 24 24 24 24
Doing this with inverted logic but giving the same output
df[rowSums(df[cols] == 0) == 0, ]
In dplyr, we can use filter_at to test for specific columns and use all_vars to select rows where all the values are not equal to 0.
library(dplyr)
df %>% filter_at(vars(starts_with("Mac")), all_vars(. != 0))
data
df <- structure(list(DateTime = structure(1:6, .Label = c("2011-04-02 06:00",
"2011-04-02 06:05", "2011-04-02 06:10", "2011-04-02 06:15", "2011-04-02 06:20",
"2011-04-02 06:25"), class = "factor"), Mac1 = c(20L, 21L, 22L,
23L, 24L, 0L), Mac2 = c(0L, 21L, 22L, 23L, 24L, 25L), Mac3 = c(20L,
21L, 22L, 0L, 24L, 25L), Mac4 = c(20L, 21L, 22L, 23L, 24L, 0L
)), class = "data.frame", row.names = c(NA, -6L))