Replace Missing Values (Na) with Blank (Empty String)

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

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

How to replace empty strings in a dataframe with NA (missing value) not NA string

By specifying just NA, according to ?NA -"NA is a logical constant of length 1 which contains a missing value."

The class can be checked

class(NA)
#[1] "logical"
class(NA_character_) 
#[1] "character"

and both of them is identified by standard functions such as is.na

is.na(NA)
#[1] TRUE
is.na(NA_character_)
#[1] TRUE

The if_else is type sensitive, so instead of specifying as NA which returns a logical output, it can specified as either NA_real_, NA_integer_, NA_character_ depending on the type of the 'boat' column. Assuming that the 'boat' is character class, we may need NA_character_

titanic %>% 
       mutate(boat = if_else(boat=="", NA_character_ ,boat))

How to replace NA's with blank value?

If your column is of type double (numbers), you can't replace NAs (which is the R internal for missings) by a character string. And "" IS a character string even though you think it's empty, but it is not.

So you need to choose: converting you whole column to type character or leave the missings as NA.

EDIT:

• If you really want to covnert your numeric column to character, you can just use as.character(MYCOLUMN). But I think what you really want is:
• Telling your exporting function how to treat NA'S, which is easy, e.g. write.csv(df, na = ""). Also check the help function with ?write.csv.

How to replace empty string with NA in R dataframe?

I'm not sure why df[df==""]<-NA would not have worked for OP. Let's take a sample data.frame and investigate options.

Option#1: Base-R

df[df==""]<-NA

df
#    One  Two Three Four
# 1    A    A  <NA>  AAA
# 2 <NA>    B    BA <NA>
# 3    C <NA>    CC  CCC

Option#2: dplyr::mutate_all and na_if. Or mutate_if if the data frame has multiple types of columns

library(dplyr)

mutate_all(df, list(~na_if(.,"")))

OR

#if data frame other types of character Then
df %>% mutate_if(is.character, list(~na_if(.,""))) 

#    One  Two Three Four
# 1    A    A  <NA>  AAA
# 2 <NA>    B    BA <NA>
# 3    C <NA>    CC  CCC

Toy Data:

df <- data.frame(One=c("A","","C"), 
                 Two=c("A","B",""), 
                 Three=c("","BA","CC"), 
                 Four=c("AAA","","CCC"), 
                 stringsAsFactors = FALSE)

df
#   One Two Three Four
# 1   A   A        AAA
# 2       B    BA     
# 3   C        CC  CCC

Pandas Replace NaN with blank/empty string

import numpy as np
df1 = df.replace(np.nan, '', regex=True)

This might help. It will replace all NaNs with an empty string.

Replacing blank values (white space) with NaN in pandas

I think df.replace() does the job, since pandas 0.13:

df = pd.DataFrame([
    [-0.532681, 'foo', 0],
    [1.490752, 'bar', 1],
    [-1.387326, 'foo', 2],
    [0.814772, 'baz', ' '],     
    [-0.222552, '   ', 4],
    [-1.176781,  'qux', '  '],         
], columns='A B C'.split(), index=pd.date_range('2000-01-01','2000-01-06'))

# replace field that's entirely space (or empty) with NaN
print(df.replace(r'^\s*$', np.nan, regex=True))

Produces:

                   A    B   C
2000-01-01 -0.532681  foo   0
2000-01-02  1.490752  bar   1
2000-01-03 -1.387326  foo   2
2000-01-04  0.814772  baz NaN
2000-01-05 -0.222552  NaN   4
2000-01-06 -1.176781  qux NaN

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As Temak pointed it out, use df.replace(r'^\s+$', np.nan, regex=True) in case your valid data contains white spaces.

Replace NA with blank but keep class as numeric in R

First: vectors in R can't contain mixed classes. If you want numbers to be numeric, then missing values have to be NA. If you want missing values to be empty strings, then other values have to be characters.

However, it appears that you want to process data and output it for use with WOMBAT. In this case, the output is a plain ASCII text file. All that's required is that the text format be correct for WOMBAT - the class of the columns in R is not relevant if you are no longer in R.

So you need to read the WOMBAT manual regarding input format, then use write.table to create the file. Look at write.table for the options. In particular, you will probably need quote = FALSE and row.names = FALSE.

Change the Blank Cells to NA

I'm assuming you are talking about row 5 column "sex." It could be the case that in the data2.csv file, the cell contains a space and hence is not considered empty by R.

Also, I noticed that in row 5 columns "axles" and "door", the original values read from data2.csv are string "NA". You probably want to treat those as na.strings as well. To do this,

dat2 <- read.csv("data2.csv", header=T, na.strings=c("","NA"))

EDIT:

I downloaded your data2.csv. Yes, there is a space in row 5 column "sex". So you want

na.strings=c(""," ","NA")

Replace NA with empty string in a list

You can do this with lapply:

# Setup sample data frame
dat = list(matrix(c(NA, "a", "b", NA), nrow=2),
           matrix(c(rep("r", 8), NA), nrow=3))
dat
# [[1]]
#      [,1] [,2]
# [1,] NA   "b" 
# [2,] "a"  NA  
# 
# [[2]]
#      [,1] [,2] [,3]
# [1,] "r"  "r"  "r" 
# [2,] "r"  "r"  "r" 
# [3,] "r"  "r"  NA  

# Do conversion
dat <- lapply(dat, function(x) { x[is.na(x)] <- "" ; x })
dat
# [[1]]
#      [,1] [,2]
# [1,] ""   "b" 
# [2,] "a"  ""  
# 
# [[2]]
#      [,1] [,2] [,3]
# [1,] "r"  "r"  "r" 
# [2,] "r"  "r"  "r" 
# [3,] "r"  "r"  ""  

Function to change blanks to NA

You can directly index fields that match a logical criterion. So you can just write:

df[is_empty(df)] = NA

Where is_empty is your comparison, e.g. df == "":

df[df == ""] = NA

But note that is.null(df) won’t work, and would be weird anyway¹. I would advise against merging the logic for columns of different types, though! Instead, handle them separately.

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¹ You’ll almost never encounter NULL inside a table since that only works if the underlying vector is a list. You can create matrices and data.frames with this constraint, but then is.null(df) will never be TRUE because the NULL values are wrapped inside the list).

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