r - How can I remove all cells with "NA" value by columns -


this question not duplicate because data.frame does not have same amount of na values in columns , therefore solution mentioned in question not work.

i have data.frame lot of na values , delete cells (important: not rows or columns, cells) have na values. original this:

a  b 1  na na 2 2  na na na na na na 4 3  5

the desired result this:

a  b 1  2 2  4  3  5

the number of columns have stay same, not matter if values remain on same rows. can moved up.

i image 1 delete cells condition na (maybe apply) , result. or maybe simple sorting ?

thanks.

update:

a   b   c 1       3     2    4       3      1   2  3       5         4     9    7       1

the op has requested remove nas columnwise has pointed out there might different numbers of na in each column.

this can solved using data.table in 2 steps:

library(data.table) # step 1: coerce data.table in place, move nas bottom of each column,  # maintain original order of non-na values result <- data.table(df)[, lapply(.sd, function(x) x[order(is.na(x))])] 
      b  c  1:  1  2  3  2:  4  1  3  3:  3  9  2  4:  7 na  5  5: na na  4  6: na na  1  7: na na na  8: na na na  9: na na na 10: na na na 
# step 2: trim result # either using reduce result[!result[, reduce(`&`, lapply(.sd, is.na))]]  # or using zoo::na.trim() zoo::na.trim(result, is.na = "all") 
     b c 1:  1  2 3 2:  4  1 3 3:  3  9 2 4:  7 na 5 5: na na 4 6: na na 1

so, there nas @ end of each colummn unavoidably because columns in data.frame have same length.

or, alternatively, complete rows can kept using is.na parameter na.trim():

zoo::na.trim(result, is.na = "any") 
   b c 1: 1 2 3 2: 4 1 3 3: 3 9 2

an alternative solution

as mentioned before, data.frames , cbind() expect column vectors have same length. here alternative solution without data.table uses cbind.fill() function rows package pads vectors fill value until same length:

setnames(do.call(function(...) rowr::cbind.fill(..., fill = na), lapply(df, na.omit)),          colnames(df)) 
    b c 1  1  2 3 2  4  1 3 3  3  9 2 4  7 na 5 5 na na 4 6 na na 1

data

as supplied op in update:

df <- structure(list(a = c(1l, na, 4l, na, na, na, 3l, na, na, 7l),      b = c(na, 2l, na, na, 1l, na, na, na, 9l, na), c = c(3l,      na, 3l, na, 2l, na, 5l, 4l, na, 1l)), .names = c("a", "b",  "c"), row.names = c(na, -10l), class = "data.frame")

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