Reputation: 33
I'd like to open a random csv-file like New_York_City_Leading_Causes_of_Death.csv (just a dummy, from a tutorial but happens with my own data the same way; replaced , with ;) https://www.dropbox.com/s/3usf8y75vz2cpj4/New_York_City_Leading_Causes_of_Death.csv?dl=0
The outline says: Error in read_csv("New_York_City_Leading_Causes_of_Death.csv", sep = ";") : unused argument (sep = ";")
I created several csv-files to debug I tried readr::read_csv as well as utils::read_csv -> both same problem I made sure that csv-file and .r are in the same folder I am using a pc by the university (maybe there's the bottleneck?)
Thank you in advance :)
library(utils)
library(utf8)
df <- read_csv("New_York_City_Leading_Causes_of_Death.csv", sep = ";" )
head(df)
That's the error message I get:
Error in read_csv("New_York_City_Leading_Causes_of_Death.csv", sep = ";") : unused argument (sep = ";")
I expect the output to be a data-frame.
Upvotes: 2
Views: 11886
Reputation: 1
Same happened to me! My solution was to change read_csv for read.csv
My example:
instead of X1_bank_full <- read_csv("C:/Users/User Name/Downloads/1-bank-full.csv", sep=";")
correct one is: X1_bank_full <- read.csv("C:/Users/User Name/Downloads/1-bank-full.csv", sep=";")
Upvotes: 0
Reputation: 518
The utils
package only has the read.csv()
function--notice the .
instead of a _
. As for readr
's read_csv()
function, it does not have a parameter called sep
. This is why an error message about having an unused argument sep
appears every time you run your code.
I tried executing just read_csv('New_York_City_Leading_Causes_of_Death.csv')
without any arguments and was successfully able to read in the following tibble
:
# A tibble: 1,094 x 7
Year `Leading Cause` Sex `Race Ethnicity` Deaths `Death Rate` `Age Adjusted Dea~
<dbl> <chr> <chr> <chr> <chr> <chr> <chr>
1 2010 Assault (Homicide: Y87.1, X85-Y09) M Black Non-Hispanic 299 35.1 35.5
2 2011 Mental and Behavioral Disorders due to Acciden~ M Not Stated/Unknown 5 . .
3 2011 Diseases of Heart (I00-I09, I11, I13, I20-I51) M Black Non-Hispanic 1840 215.7 268.3
4 2008 Certain Conditions originating in the Perinata~ F Other Race/ Ethnic~ . . .
5 2014 Accidents Except Drug Posioning (V01-X39, X43,~ F Hispanic 64 5.1 5.4
6 2007 Intentional Self-Harm (Suicide: X60-X84, Y87.0) M Not Stated/Unknown 5 . .
7 2012 Accidents Except Drug Posioning (V01-X39, X43,~ M Black Non-Hispanic 152 17.8 18.6
8 2009 All Other Causes M Asian and Pacific ~ 220 43.1 56.1
9 2013 Diseases of Heart (I00-I09, I11, I13, I20-I51) F Asian and Pacific ~ 437 72.8 81.8
10 2014 Accidents Except Drug Posioning (V01-X39, X43,~ M Other Race/ Ethnic~ 12 . .
# ... with 1,084 more rows
Alternatively, for faster performance on larger datasets, consider using data.table::fread()
. The fread()
function from the data.table
package allows you to specify the delimiter to be ;
using the argument sep = ';'
,.
Do note that fread()
can only return a data.table
or a data.frame
. By default, fread()
returns a data.table
, but can return a data.frame
if the argument data.table = FALSE
is passed in. However, if you would prefer working with a tibble
, you can simply wrap the fread()
call in as_tibble()
.
Upvotes: 3