You can read it with the following code if you want to also display the header (column names). Sep = "", # Separator of the columns of the fileĭec = ".") # Character used to separate decimals of the numbers in the fileĬonsider, for instance, that yo have a TXT file called my_file.txt and you have put it in your R working directory. Header = FALSE, # Whether to display the header (TRUE) or not (FALSE) read.table(file, # TXT data file indicated as string or full path to the file This basic syntax affects to almost all TXT data files. In the following subsections we will explain two more ( skip and skipNul) if needed, but in case you want to know all the arguments you can find them in the read.table function documentation or calling ?read.table. Importing TXT into R rarely needs more arguments than specified. You can read a TXT file in R with the read.table function. A not-open connection will be opened in mode 'rb' and closed after use. It can read a compressed file (see save ) directly from a file or from a suitable connection (including a call to url ). 1.2 How to identify NULL values in a TXT file? Details load can load R objects saved in the current or any earlier format.In general it seems that converting a column to character data turns all the NAs to the same format. For example data$column.name <- as.numeric(as.character(data$column.name)) A general purpose solution is to conver the whole column to character data, then convert it to what ever its suppossed to be, either factor or numeric. To convert bad factor variables back to character data use data$column.name įor some reaason when you're cleaning data - especially if you clean it within R or reshaping your data in R - you can get a mix of these NA designators within the same column, which can create problems. If you have problems with numeric columns you can set '“colClasses = character” ' and import EVERYTHING as character data. In general, its useful when 1st importing data to set ' “stringsAsFactors = FALSE”' and converting things to factors by hand. So you'll get a different factor variable for each and every number in that column - which is real pain for continous variables! So, if you have “R” and “NR” for “reproductive” and “non-reproductive” and some typos like “N R” and “ NR” then instead of two factor you'll end up with four.Īlso, if there are any typos in numeric data such as extra spaces are stray letters (eg “ 101.00” or “101.00 ”) then R will, first, interprete this as character data and 2nd, convert it to a factor. However, if there are typos, factor levels AND every typo gets converted to a factor. This works fine as long as there are no typos. The default behavior of R is to convert columns that contains characters (eg letters) to factor variables. StringsAsFactors = FALSE #Another control for deciding whether characters should be converted to factor For example, " 0.1" = "0.1"įill = TRUE, #fill in rows that have unequal numbers of columnsĬomment.char = "#", #character used for comments that should not be read in Strip.white = TRUE, #strip out extra white space in strings. # if there are comments contained at the top of the file The basic code for loading data is data.object 0 Below are some hints for getting recalcitrent files into R by modifying the read.csv command. Then I can re-open the file in Excel and look at a particular column to fix. In general I like to load something into R and use R's summary command to identify columns that aren't behaving properly. Set the Find routine to “Find entire cells only” and search for things like spaces, periods, and other punctuation marks.Ĩ) You can highlight individual columns of numeric data and search for alphabetical characters to try to weed them out. See below.ħ) Highlight all of your data and use “control-F” to weed out typos and blank spaces. R's deafult NA value is “NA” but you can tell it to use some other character such as “.” if the data was previously set up for SAS. If you happen to have two spaces you'll get 2 periodsĥ) Make sure all of your column names are unique.Ħ) Fill in blank spaces with NA values. So, change “2001 mass” to “mass 2001.” Better yet, change it to “mass_2001” or “mass.2001”Ĥ) R can handle spaces in column headings but will add a period to them. R doesn't allow numbers to be the first thing in a column heading. Then highlight several dozen clumns to the right and tell Excel to “clear all.” This will get rid any stray key strokes and formatting that you don't intend to load into R, or which R can't interpret.Ģ) Then, go to the last row and do the same.ģ) Make sure all of your column names start with a character, not a number. csv file for the first time, open it up and go to the last column. Here are some hints.ġ) Before you try to load a.
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