I would like to know how I can remove from a dataset the records that have more than 5 null values in the columns that define them. The following code allows you to delete records with any NA in any column. However, how can I modify it to do exactly what I ask? Any ideas?
df [ complete.cases (df),]
CodePudding user response:
Here is an example data frame. One of the rows has 6 NA values. We sum the NA values by row in a new column, filter where the number of NA is less than or equal to 5, then remove the new column.
df <- data.frame(a = c(1,NA,1,1),
b = c(1, NA, NA, 1),
c = c(1, NA, NA, NA),
d = c(1, NA, NA ,NA),
e = c(1, NA, NA, NA),
f = c(1, NA, NA, NA))
a b c d e f
1 1 1 1 1 1 1
2 NA NA NA NA NA NA
3 1 NA NA NA NA NA
4 1 1 NA NA NA NA
df %>%
mutate(count = rowSums(is.na(df))) %>%
filter(count <= 5) %>%
select(-count)
a b c d e f
1 1 1 1 1 1 1
2 1 NA NA NA NA NA
3 1 1 NA NA NA NA
CodePudding user response:
I'm assuming you are referring to values of NA in your data indicating a missing value. NULL is returned by expressions and functions whose value is undefined. First create some reproducible data:
set.seed(42)
vals <- sample.int(1000, 250)
idx <- sample.int(250, 100)
vals[idx] <- NA
example <- as.data.frame(matrix(vals, 25))
Now compute the number of missing values by row and exclude the rows with more than 5 missing values:
na.count <- rowSums(is.na(example))
example[na.count<=5, ]