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I have tab delimited text file, named 'a.txt'. The D column is empty.
A B C D 10 20 NaN 30 40 40 30 20 20 NA 20 I want to have the dataframe looking and acting exactly as the text file, with a space in the 2nd row and in the 2nd column.
Unfortunately, read.csv is converting all the blanks and NA to "NA". I want to read NA and NaN as characters.
b<- read.csv("a.txt",sep="\t", skip =0, header = TRUE, comment.char = "",check.names = FALSE, quote="", ) To summarize: I want to replicate the same values in output file without modifying them:
967
Empty Strings and NULL Values In CSV files, a NULL value is typically represented by two successive delimiters (e.g. ,, ) to indicate that the field contains no data; however, you can use string values to denote NULL (e.g. null ) or any unique string.
Therefore, it is important to convert blanks to NA's first. In R, the easiest way to replace blanks with NA's is by using the na_if() function from the dplyr package. This function checks if a value meets a specific condition (e.g., a blank) and converts it into a NA.
In most CSV files I've seen, the number of fields is constant per row (although that doesn't have to be so), so unless a CSV file only has one column, I would expect empty lines (outside of quoted fields) to be a mistake. I just checked: Python's CSV parser ignores empty lines.
Late edit: After re-reading this after the edits and extended comments, I'm wondering if what was needed (or asked for, at least) was pretty much the exact opposite of what I advise below. The request for this:
Unfortunately, read.csv is converting all the blanks and NA to "NA". I want to read NA and NaN as characters.
,,, might have been satisfied (somewhat paradoxically) with the arguments: colClasses="character", stringsAsFactors=FALSE, na.strings="."`
Then any character value including an empty string would come in as itself. Arguing against this is the acceptance of the answer that converts empty character values ("") to R _NA_character values.
Here's a test example with various results:
sapply(read.csv(text='A\tB\tC\tD\na\t""\tNA\tNaN', sep='\t', na.strings=""), class ) # A B C D # "factor" "logical" "factor" "numeric" sapply(read.csv(text='A\tB\tC\tD\na\t""\tNA\tNaN', sep='\t', na.strings="x"), class ) # A B C D # "factor" "logical" "factor" "numeric" sapply(read.csv(text='A\tB\tC\tD\na\t""\tNA\tNaN', sep='\t', na.strings="x", stringsAsFactors=FALSE), class ) # A B C D #"character" "logical" "character" "numeric" #Almost the expressed desired result sapply(read.csv(text='A\tB\tC\tD\na\t""\tNA\tNaN', sep='\t', #colClasses="character", stringsAsFactors=FALSE), class ) # A B C D #"character" "character" "character" "character" #But ... still get a real R <NA> read.csv(text='A\tB\tC\tD\na\t""\tNA\tNaN', sep='\t', colClasses="character", stringsAsFactors=FALSE) # A B C D #1 a <NA> NaN #So add all three read.csv(text='A\tB\tC\tD\na\t""\tNA\tNaN', sep='\t', colClasses="character", stringsAsFactors=FALSE,na.strings=".") # A B C D #1 a NA NaN # Finally all columns are character and no "real" R NA's The default for na.strings is just "NA", so you perhaps need to add "NaN". True blanks ("") are set to missing but spaces (" ") are not:
b<- read.csv("a.txt", skip =0, comment.char = "",check.names = FALSE, quote="", na.strings=c("NA","NaN", " ") ) It's not clear that this is the problem since your data example is malformed and does not have commas. That may be the fundamental problem since read.csv does not allow tab-separation. Use read.delim or read.table if your data has tab-separation.
b<- read.table("a.txt", sep="\t" skip =0, header = TRUE, comment.char = "",check.names = FALSE, quote="", na.strings=c("NA","NaN", " ") ) # worked example for csv text file connection bt <- "A,B,C 10,20,NaN 30,,40 40,30,20 ,NA,20" b<- read.csv(text=bt, sep=",", comment.char = "",check.names = FALSE, quote="\"", na.strings=c("NA","NaN", " ") ) b #-------------- A B C 1 10 20 NA 2 30 NA 40 3 40 30 20 4 NA NA 20 Example 2:
bt <- "A,B,C,D 10,20,NaN 30,,40 40,30,20 ,NA,20" b<- read.csv(text=bt, sep=",", comment.char = "",check.names = FALSE, quote="\"", na.strings=c("NA","NaN", " ") , colClasses=c(rep("numeric", 3), "logical")) b #---------------- A B C D 1 10 20 NA NA 2 30 NA 40 NA 3 40 30 20 NA 4 NA NA 20 NA > str(b) 'data.frame': 4 obs. of 4 variables: $ A: num 10 30 40 NA $ B: num 20 NA 30 NA $ C: num NA 40 20 20 $ D: logi NA NA NA NA It's mildly interesting that NA and NaN are not identical for numeric vectors. NaN is returned by operations that have no mathematical meaning (but as noted in the help page you get with ?NaN, the results of operations may depend on the particular OS. Tests of equality are not appropriate for either NaN or NA. There are specific is functions for them:
> Inf*0 [1] NaN > is.nan(c(1,2.2,3,NaN, NA) ) [1] FALSE FALSE FALSE TRUE FALSE > is.na(c(1,2.2,3,NaN, NA) ) [1] FALSE FALSE FALSE TRUE TRUE # note the difference After reading the csv file, try the following. It will replace the NA values with "".
b[is.na(b)]<-"" Fairly certain that won't fix your NaN values. That will need to be resolved in a separate statement
b[is.nan(b)]<-"" If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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