I'm trying to read a .csv file into R where all the column are numeric. However, they get converted to factor everytime I import them.
Here's a sample of how my CSV looks like:
This is my code:
options(StringsAsFactors=F)
data<-read.csv("in.csv", dec = ",", sep = ";")
As you can see, I set dec
to ,
and sep
to ;
. Still, all the vectors that should be numerics are factors!
Can someone give me some advice? Thanks!
Export data to a text file by saving itGo to File > Save As. Click Browse. In the Save As dialog box, under Save as type box, choose the text file format for the worksheet; for example, click Text (Tab delimited) or CSV (Comma delimited).
In R, you can convert multiple numeric variables to factor using lapply function. The lapply function is a part of apply family of functions. They perform multiple iterations (loops) in R. In R, categorical variables need to be set as factor variables.
csv file in reading mode using open() function. Then, the csv. reader() is used to read the file, which returns an iterable reader object. The reader object is then iterated using a for loop to print the contents of each row.
If you have stringsAsFactors = TRUE in options or in read.csv (default), the column is further converted to factor. You can use the argument na.strings to tell read.csv which strings should be interpreted as NA.
Your NA strings in the csv file, N/A, are interpreted as character and then the whole column is converted to character. If you have stringsAsFactors = TRUE in options or in read.csv (default), the column is further converted to factor.
Having a trouble while reading a csv file. All variables (Factors, numeric etc) are automatically converted into Character strings. Can someone help me? Thank you!
Defining the data type of each column when reading a CSV file If you want to set the data type for each column when reading a CSV file, you can use the argument dtype when loading data with read_csv (): The dtype argument takes a dictionary with the key representing the column and the value representing the data type.
Your NA
strings in the csv file, N/A
, are interpreted as character
and then the whole column is converted to character
. If you have stringsAsFactors = TRUE
in options
or in read.csv
(default), the column is further converted to factor
. You can use the argument na.strings
to tell read.csv
which strings should be interpreted as NA
.
A small example:
df <- read.csv(text = "x;y
N/A;2,2
3,3;4,4", dec = ",", sep = ";")
str(df)
df <- read.csv(text = "x;y
N/A;2,2
3,3;4,4", dec = ",", sep = ";", na.strings = "N/A")
str(df)
Update following comment
Although not apparent from the sample data provided, there is also a problem with instances of '$' concatenated to the numbers, e.g. '$3,3'. Such values will be interpreted as character
, and then the dec = ","
doesn't help us. We need to replace both the '$' and the ',' before the variable is converted to numeric.
df <- read.csv(text = "x;y;z
N/A;1,1;2,2$
$3,3;5,5;4,4", dec = ",", sep = ";", na.strings = "N/A")
df
str(df)
df[] <- lapply(df, function(x){
x2 <- gsub(pattern = "$", replacement = "", x = x, fixed = TRUE)
x3 <- gsub(pattern = ",", replacement = ".", x = x2, fixed = TRUE)
as.numeric(x3)
}
)
df
str(df)
You could have gotten your original code to work actually - there's a tiny typo ('stringsAsFactors', not 'StringsAsFactors'). The options command wont complain with the wrong text, but it just wont work. When done correctly, it'll read it as char, instead of factors. You can then convert columns to whatever format you want.
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