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The easiest method of creating a data file to import into R is to enter your data into a spreadsheet using either Microsoft Excel or LibreOffice Calc and save the spreadsheet as a tab delimited file.
Most DAT files contain text, so you can open them with text editors, like Notepad, Notepad++, VS Code, and so on. If you are sure the information contained in the DAT file is a video or audio, then your media player can open it. If it's a PDF, then Adobe Reader can open it, and so on.
In RStudio, click on the Workspace tab, and then on “Import Dataset” -> “From text file”. A file browser will open up, locate the . csv file and click Open. You'll see a dialog that gives you a few options on the import.
The dat file has some lines of extra information before the actual data. Skip them with the skip argument:
read.table("http://www.nilu.no/projects/ccc/onlinedata/ozone/CZ03_2009.dat",
header=TRUE, skip=3)
An easy way to check this if you are unfamiliar with the dataset is to first use readLines to check a few lines, as below:
readLines("http://www.nilu.no/projects/ccc/onlinedata/ozone/CZ03_2009.dat",
n=10)
# [1] "Ozone data from CZ03 2009" "Local time: GMT + 0"
# [3] "" "Date Hour Value"
# [5] "01.01.2009 00:00 34.3" "01.01.2009 01:00 31.9"
# [7] "01.01.2009 02:00 29.9" "01.01.2009 03:00 28.5"
# [9] "01.01.2009 04:00 32.9" "01.01.2009 05:00 20.5"
Here, we can see that the actual data starts at [4], so we know to skip the first three lines.
If you really only wanted the Value column, you could do that by:
as.vector(
read.table("http://www.nilu.no/projects/ccc/onlinedata/ozone/CZ03_2009.dat",
header=TRUE, skip=3)$Value)
Again, readLines is useful for helping us figure out the actual name of the columns we will be importing.
But I don't see much advantage to doing that over reading the whole dataset in and extracting later.
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