Read.CSV Blank Fields to Na

read.csv blank fields to NA

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)]<-""

Using read.csv, empty fields have NA added to data.frame

After the import, try this:

x[is.na(x)] <- c("") ## Refills NA with blank character.

This converts the column to character but if you already have mixed strings I imagine it already is.

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")

How can I get R to read these cells as blank instead of NA?

From help('read.csv'), section Arguments, my emphasis:

na.strings

a character vector of strings which are to be interpreted
as NA values. Blank fields are also considered to be missing values in
logical, integer, numeric and complex fields. Note that the test
happens after white space is stripped from the input, so na.strings
values may need their own white space stripped in advance.

The above highlighted part does not refer to "character" class. So define the column of interest to be of class "character".

In the question's case, all 3 columns are of the same class and since argument colClasses recycles its value, the following is enough.

read.csv("file.csv", colClasses = "character")
#    column1 column2 column3
#1      data                
#2 more data    data        

R: reading in a .csv turns all (blank spaces) to NA

there is the parameter na you can specify

write.csv(data, "data.csv", row.names=FALSE, na="")

When you read it again you need to convert NA to blank everytime though

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

How to import a CSV with a last empty column into R?

The real problem is that empty column doesn't have a header. If they had only had the extra comma at the end of the header line this probably wouldn't be as messy. But you can also do a bit of column shuffling with fill=TRUE. For example

dd <- read.table("~/../Downloads/jcr ecology 2020.csv", sep=",", 
   skip=2, fill=T, header=T, row.names=NULL)
names(dd)[-ncol(dd)] <- names(dd)[-1]
dd <- dd[,-ncol(dd)] 

This reads in the data but puts the rows names in the data.frame and fills the last column with NA. Then you shift all the column names over to the left and drop the last column.

Get pandas.read_csv to read empty values as empty string instead of nan

I added a ticket to add an option of some sort here:

https://github.com/pydata/pandas/issues/1450

In the meantime, result.fillna('') should do what you want

EDIT: in the development version (to be 0.8.0 final) if you specify an empty list of na_values, empty strings will stay empty strings in the result

reading blanks as NAN - R

When we read the dataset, there is an option to specify the NA elements with na.strings

df1 <- read.csv('file.csv', na.strings = c('NaN', ''))

Now, we can check with is.na

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If we are continuing the original approach, there is an option to check for NaN with is.nan, but it can only check on vectors or columns of dataset

sum(sapply(df, is.nan))

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