Replace missing values (NA) with blank (empty string)

Question

I have a dataframe with an NA row:

 df = data.frame(c("classA", NA, "classB"), t(data.frame(rep("A", 5), rep(NA, 5), rep("B", 5))))  rownames(df) <- c(1,2,3)  colnames(df) <- c("class", paste("Year", 1:5, sep = ""))   > df    class Year1 Year2 Year3 Year4 Year5 1 classA     A     A     A     A     A 2   <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 3 classB     B     B     B     B     B 

I introduced the empty row (NA row) on purpose because I wanted to have some space between classA row and classB row.

Now, I would like to substitute the <NA> by blank, so that the second row looks like an empty row.

I tried:

 df[is.na(df)] <- "" 

and

 df[df == "NA"] <- "" 

but it didn't work..

Any ideas? Thanks!

Answer 1

Another alternative:

df <- sapply(df, as.character) # since your values are `factor` df[is.na(df)] <- 0 

If you want blanks instead of zeroes

> df <- sapply(df, as.character) > df[is.na(df)] <- " " > df      class    Year1 Year2 Year3 Year4 Year5 [1,] "classA" "A"   "A"   "A"   "A"   "A"   [2,] " "      " "   " "   " "   " "   " "   [3,] "classB" "B"   "B"   "B"   "B"   "B"   

If you want a data.frame, then just use as.data.drame

> as.data.frame(df)    class Year1 Year2 Year3 Year4 Year5 1 classA     A     A     A     A     A 2                                      3 classB     B     B     B     B     B 

Answer 2

This answer is more of an extended comment.

What you're trying to do isn't what I would consider good practice. R is not, say, Excel, so doing something like this just to create visual separation in your data is just going to give you a headache later on down the line.

If you really only cared about the visual output, I can offer two suggestions:

1. Use the na.print argument to print when you want to view the data with that visual separation.

   print(df, na.print = "") #    class Year1 Year2 Year3 Year4 Year5 # 1 classA     A     A     A     A     A # 2                                      # 3 classB     B     B     B     B     B 

2. Realize that even the above is not the best suggestion. Get both visual and content separation by converting your data.frame to a list:

   split(df, df$class) # $classA #    class Year1 Year2 Year3 Year4 Year5 # 1 classA     A     A     A     A     A #  # $classB #    class Year1 Year2 Year3 Year4 Year5 # 3 classB     B     B     B     B     B