CODEWITHSUNDEEP
rdataframetidyr
abraham
abraham

Reputation: 743

fill missing elements in each row in a R data.frame

i have a csv file imported as data.frame, the problem is that each row must have 4 elements (4 columns), and some of them could have different number of elements, i mean something like: 

ID  col1 col2 col3 col4
id1  dA  dB    dC   dD
id2  aA  aB    aC   aD
id3  mA  mB    mC
id4  xA  xB    xC   XD

I'm using tidyr, and when I import the data it fill each missing element with NA, in this case the id3 at the col4.

id3  mA  mB    mC NA

I want to fix all the row that have less than 4 elements in each row (like id3), just to add in the missing element a unclassified (UNC) something like:

ID  col1 col2 col3 col4
id1  dA  dB    dC   dD
id2  aA  aB    aC   aD
id3  mA  mB    mC  UNC
id4  xA  xB    xC   XD

Well this is my code:

df <- read.csv("file.csv", comment.char = "#", header = TRUE, sep = "\t")

#add the id as row name:
rownames(df) <- paste("id", 1:nrow(df), sep = "")

# eliminate some elements of the data frame 
df[, 2:ncol(df)] <- NULL

# add a name of each column and split elements based in ";" character 
#at this point the "df" has a single column named "old_name":

df <- df %>% tidyr::separate(old_name, c("col1", "col2", "col3", "col4"), sep = ";", extra="drop")

any suggestion !!!

thanks so much

Upvotes: 2

Views: 89

Answers (1)

akrun
akrun

Reputation: 886928

We can use 

library(dplyr)
df1 %>%
    mutate_if(is.character, ~ replace(., is.na(.), "UNC"))

Or in base R

i1 <- sapply(df1, is.character)
df1[i1][is.na(df1[i1])] <- "UNC"

Upvotes: 2

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