In this article I will introduce to you the functions that make your life in R so much easier. For example purposes I will use the “mtcars” data frame.
The ? functions:
This is the most important one, it print the help content related to the functions, for example:
> ?plot
Will provide you with help on the ‘plot’ function, if your are able to circumvent the daunting layout it can provides you inestimable service avoiding you to google or to ask your timeless supervisor.
The str function:
> data(mtcars) > str(mtcars) 'data.frame': 32 obs. of 11 variables: $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... $ cyl : num 6 6 4 6 8 6 8 4 4 6 ... $ disp: num 160 160 108 258 360 ... $ hp : num 110 110 93 110 175 105 245 62 95 123 ... $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ... $ wt : num 2.62 2.88 2.32 3.21 3.44 ... $ qsec: num 16.5 17 18.6 19.4 17 ... $ vs : num 0 0 1 1 0 1 0 1 1 1 ... $ am : num 1 1 1 0 0 0 0 0 0 0 ... $ gear: num 4 4 4 3 3 3 3 4 4 4 ... $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
The str function returns the structure of the object, in our case we have a ‘data.frame’ which consists of 32 observations (rows) of 11 variables (columns) and R gives us the name of each variables (like $mpg) and the class of this variable here ‘num’ for numeric, plus a sample of values in the variables.
This function is extremely useful, it allows you to understand what type of data you are dealing with and when you have data frames of thousands of rows you don’t want to look through them, the output of str is very concise and as every thing you need to know.
The summary function:
> summary(mtcars) mpg cyl disp hp drat wt Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0 Min. :2.760 Min. :1.513 1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5 1st Qu.:3.080 1st Qu.:2.581 Median :19.20 Median :6.000 Median :196.3 Median :123.0 Median :3.695 Median :3.325 Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7 Mean :3.597 Mean :3.217 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0 3rd Qu.:3.920 3rd Qu.:3.610 Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0 Max. :4.930 Max. :5.424 qsec vs am gear carb Min. :14.50 Min. :0.0000 Min. :0.0000 Min. :3.000 Min. :1.000 1st Qu.:16.89 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000 Median :17.71 Median :0.0000 Median :0.0000 Median :4.000 Median :2.000 Mean :17.85 Mean :0.4375 Mean :0.4062 Mean :3.688 Mean :2.812 3rd Qu.:18.90 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000 Max. :22.90 Max. :1.0000 Max. :1.0000 Max. :5.000 Max. :8.000
The summary function returns some basic statistics for every variables (for a data frame if you use this function for other type of objects like matrix or lists you will get different output). You can with one look get a better idea of your values and which should be the next step (test, transformation…).
The head function:
> head(mtcars) mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
Most of the time to comprehend our data we still need to actually see them, usually we don’t need to see every observations (rows) but a subset of them, this is what the head function does, it returns the first rows of a data frame and allows you to see how are your data organised and if certain operations that you made on them resulted in what you expected or not (like sorting, changing names…).
The write.table function:
Now that you worked on your data frame you want to save your results in a spreadsheet to read it in excel for example.
> write.table(mtcars,file="Data.csv",sep=",",col.names=NA)
This will save the object mtcars in the current directory, to access it use: getwd().
The argument ‘col.names=NA’ is used to indicate that the first column is the row name column otherwise every column would be drifted to the left.
The read.table function:
Now the opposite you typed in data in a spreadsheet and want to load them into R:
> mtcars<-read.table("/home/lionel/Data.csv",sep=",",header=TRUE,row.names=1)
This will open the table in the mtcars object. The header argument is to indicate that the first row is the column names and the row.names is to indicate that the first column contain the row names.
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