Removing empty rows of a data file in R - r

Question

I have a dataset with empty rows. I would like to remove them:
myData<-myData[-which(apply(myData,1,function(x)all(is.na(x)))),]
It works OK. But now I would like to add a column in my data and initialize the first value:
myData$newCol[1] <- -999
Error in `$<-.data.frame`(`*tmp*`, "newCol", value = -999) :
replacement has 1 rows, data has 0
Unfortunately it doesn't work and I don't really understand why and I can't solve this.
It worked when I removed one line at a time using:
TgData = TgData[2:nrow(TgData),]
Or anything similar.
It also works when I used only the first 13.000 rows.
But it doesn't work with my actual data, with 32.000 rows.
What did I do wrong? It seems to make no sense to me.

Answer 1

I assume you want to remove rows that are all NAs. Then, you can do the following :
data <- rbind(c(1,2,3), c(1, NA, 4), c(4,6,7), c(NA, NA, NA), c(4, 8, NA)) # sample data
data
[,1] [,2] [,3]
[1,] 1 2 3
[2,] 1 NA 4
[3,] 4 6 7
[4,] NA NA NA
[5,] 4 8 NA
data[rowSums(is.na(data)) != ncol(data),]
[,1] [,2] [,3]
[1,] 1 2 3
[2,] 1 NA 4
[3,] 4 6 7
[4,] 4 8 NA
If you want to remove rows that have at least one NA, just change the condition :
data[rowSums(is.na(data)) == 0,]
[,1] [,2] [,3]
[1,] 1 2 3
[2,] 4 6 7

Answer 2

If you have empty rows, not NAs, you can do:
data[!apply(data == "", 1, all),]
To remove both (NAs and empty):
data <- data[!apply(is.na(data) | data == "", 1, all),]

Answer 3

Here are some dplyr options:
# sample data
df <- data.frame(a = c('1', NA, '3', NA), b = c('a', 'b', 'c', NA), c = c('e', 'f', 'g', NA))
library(dplyr)
# remove rows where all values are NA:
df %>% filter_all(any_vars(!is.na(.)))
df %>% filter_all(any_vars(complete.cases(.)))
# remove rows where only some values are NA:
df %>% filter_all(all_vars(!is.na(.)))
df %>% filter_all(all_vars(complete.cases(.)))
# or more succinctly:
df %>% filter(complete.cases(.))
df %>% na.omit
# dplyr and tidyr:
library(tidyr)
df %>% drop_na

Answer 4

Alternative solution for rows of NAs using janitor package
myData %>% remove_empty("rows")

Answer 5

This is similar to some of the above answers, but with this, you can specify if you want to remove rows with a percentage of missing values greater-than or equal-to a given percent (with the argument pct)
drop_rows_all_na <- function(x, pct=1) x[!rowSums(is.na(x)) >= ncol(x)*pct,]
Where x is a dataframe and pct is the threshold of NA-filled data you want to get rid of.
pct = 1 means remove rows that have 100% of its values NA.
pct = .5 means remome rows that have at least half its values NA

Answer 6

Using dplyr's if_all/if_any
Drop rows with any NA OR Select rows with no NA value.
df %>% filter(!if_any(a:c, is.na))
# a b c
#1 1 a e
#2 3 c g
#Also
df %>% filter(if_all(a:c, Negate(is.na)))
Drop rows with all NA values or select rows with at least one non-NA value.
df %>% filter(!if_all(a:c, is.na))
# a b c
#1 1 a e
#2 <NA> b f
#3 3 c g
#Also
df %>% filter(if_any(a:c, Negate(is.na)))
data
Using data from #sbha -
df <- data.frame(a = c('1', NA, '3', NA),
b = c('a', 'b', 'c', NA),
c = c('e', 'f', 'g', NA))

Answer 7

Here's yet another answer if you just want a handy function wrapper. Also, many of the above solutions remove a row with ANY NAs, whereas this one only removes rows that are ALL NAs.
data <- rbind(c(1,2,3), c(1, NA, 4), c(4,6,7), c(NA, NA, NA), c(4, 8, NA)) # sample data
data
rmNArows<-function(d){
goodRows<-apply(d,1,function(x) sum(is.na(x))!=ncol(d))
d[goodRows,]
}
rmNArows(data)