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I have a zip archive with several csv files in it. I would like to use fread to import selected csv files into R.
With read.csv I can get the data as follows without extracting the archive.
con <- unz("myarchive.zip", "file2.csv")
file2 <- read.csv(con, sep = ",", stringsAsFactors = FALSE)
on.exit(close(con))
How to use data.table::fread to import the the data in the csv file into R from the archive without extracting it?
942
I understand that fread() should be faster than read. csv() because it tries to first read rows into memory as character and then tries to convert them into integer and factor as data types. On the other hand, fread() simply reads everything as character.
We're able to successfully import the CSV file using the fread() function. Note: We used double backslashes (\\) in the file path to avoid a common import error. Notice that we didn't have to specify the delimiter either since the fread() function automatically detected that it was a comma.
To read a zip file and extract data from it to R environment, we can use the download. file() to download the zip, then unzip() allows to unzip the same and extract files using read. csv().
fread can run shell commands to pre-process the file, so e.g.: on Linux/Windows this works:
write.csv(mtcars, 'mtcars.csv')
zip('mtcars.csv.zip', 'mtcars.csv')
# adding: mtcars.csv (deflated 52%)
fread(cmd = 'unzip -cq mtcars.csv.zip')
# V1 mpg cyl disp hp drat wt qsec vs am gear carb
# 1: Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
# 2: Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
# 3: Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
# 4: Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
# 5: Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
# 6: Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
# 7: Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
# 8: Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
# 9: Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
# 10: Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
# 11: Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
# 12: Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
# 13: Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
# 14: Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
# 15: Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
# 16: Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
# 17: Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
# 18: Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
# 19: Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
# 20: Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
# 21: Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
# 22: Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
# 23: AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
# 24: Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
# 25: Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
# 26: Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
# 27: Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
# 28: Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
# 29: Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
# 30: Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
# 31: Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
# 32: Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
# V1 mpg cyl disp hp drat wt qsec vs am gear carb
unzip flags:
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