Introduction to R

Try typing in the console (the lower left corner window in R Studio) some basic instructions.

Commit the instructions by pressing [enter] button.

Arithmetic operations

> 1+1
[1] 2

> 2*4
[1] 8

Using variables

You can assign a value to a variable using <- (more popular) or = operator. You can find some basic examples below.

Numeric variables

> x <- 1+1
> x
[1] 2

Text variables

> z <- "Hello world"
> z
[1] "Hello world"

Vectors

> v <- c(1,2,3,4,5)
> v
[1] 1 2 3 4 5

Data frames

More popular than one dimensional vector is multidimensional data structure called data.frame.

Data returned from Google Analytics API query will also be saved as a data.frame

Creating data frame

Let's create a simple data frame (i.e. number of sessions by city in 2016-01-01)

df <- data.frame(
                date = c("20160101","20160101","20160101",
                "20160101","20160101","20160101","20160101"),
                city = c("London","Warsaw","Krakow",
                "New York","Paris","Zurich","Sydney"),
                sessions =  c(101,80,70,50,30,60,20)
                )

To display data frame type the data frame's name: df

> df

      date     city sessions
1 20160101   London      101
2 20160101   Warsaw       80
3 20160101   Krakow       70
4 20160101 New York       50
5 20160101    Paris       30
6 20160101   Zurich       60
7 20160101   Sydney       20

Basic operations on data frames

To preview a data frame (by default first 6 rows, which IS useful for bigger datasets):

> head(df)

      date     city sessions
1 20160101   London      101
2 20160101   Warsaw       80
3 20160101   Krakow       70
4 20160101 New York       50
5 20160101    Paris       30
6 20160101   Zurich       60

To display column names of a data frame:

> colnames(df)

[1] "date"     "city"     "sessions"

You can refer to column by dataframe$colname operator:

> df$city

[1] London   Warsaw   Krakow   New York Paris    Zurich   Sydney  
Levels: Krakow London New York Paris Sydney Warsaw Zurich

And select only unique values of column (we have sessions for only one date: 2016-01-01):

> unique(df$date)

[1] 20160101
Levels: 20160101

You can alternatively select columns and rows by number: df[rownumber,colnumber]

Select column 2:

> df[,2]

[1] London   Warsaw   Krakow   New York Paris    Zurich   Sydney  
Levels: Krakow London New York Paris Sydney Warsaw Zurich

Select row 1:

> df[1,]

      date   city sessions
1 20160101 London      101

Select only one element:

> df[1,1]

[1] 20160101
Levels: 20160101

These basic operations are enough to start your journey with R language :)