R Studio Basics
Below are examples of how to begin a new code block, inspect environment variables and load libraries. You will be using these every week - bookmark this section.
Note
Right click on a GIF and ‘open image in new tab’ to view full size.
Creating Code Blocks
In RStudio you can create a code block in the Code editor by clicking on the + button:
Insert code you want to execute in these blocks - any text outside of a code block is treated as regular text and is not evaluated.
Inspecting Variables
Rstudio has the benefit of being able to visually inspect variables in the workspace. To do this, click on an object in the environment window pane:
Loading Libraries
Each week we will be using libraries to perform an analysis or data visualisation. It is common practice to load libraries at the top of your Markdown document for your analysis:
Note
When compiling your document, you must load libraries before any line of code that calls a function from that library. This is why we use library() at the very top of documents.
Viewing Documentation
This is in my opinion, the most important aspect of this section. You must become self sufficient at reading documentation.
In R, to view the documentation for a function, place the cursor on the function name and hit the F1 key:
Basic Data Structures
Variables
The first concept we will cover are variables. Variables are placeholders for a quantity that may change, or any mathematical object. In particular, a variable may represent a number, a vector, a matrix, a function, the list goes on.
To assign a variable in R, you can use the <- notation or = notation.
In the code block below, we create the string "Hello World" and assign it to the variable greeting.
greeting <- "Hello World"
To print the contents of the variable greeting, you can use the print function:
print(greeting)
[1] "Hello World"
Notice how the variable has been stored in our environment (the upper right box in RStudio) as a value.
As previously mentioned, variables are placholders and as such, can be overwritten and modified.
Change the greeting variable to hold the string "Hello user" and print the contents:
greeting <- "Hello user"
print(greeting)
Note the environment variable has been updated to reflect this change.
Vectors
Vectors are a collection of the same data type. Be careful not to mix data types in a vector!.
Attention
Data types help R interpret our code inputs. For example, anything surrounded in double quotes is interpreted as a character string. Integers and floats are interpeted as numerics which we can perform mathematical operations on. TRUE / FALSE statements are known as Booleans.
To initialise a vector, we use the c function - which stands for concatenate - with parentheses.
Below we will create two vectors:
racing_number <- c(33, 44, 11, 4 , 3)
driver_names <- c("Verstappen", "Hamilton", "Perez", "Norris", "Riccardo")
Named Vectors
We can use the driver_names vector variable to assign names to the racing_number vector using the names() function. Store the racing_number variable as drivers to avoid confusion!
names(racing_number) <- driver_names
drivers <- racing_number
print(drivers)
Verstappen Hamilton Perez Norris Riccardo
33 44 11 4 3
Manipulating Vectors
Let’s update our previous vectors to include two new drivers and their racing numbers:
racing_number <- c(racing_number, 16, 24)
driver_names <- c(driver_names, "Leclerc", "Zhou")
names(racing_number) <- driver_names
drivers <- racing_number
print(drivers)
Verstappen Hamilton Perez Norris Riccardo Leclerc Zhou
33 44 11 4 3 16 24
Before showing you how to delete items from a vector, we need to cover vector indexing. Indexing allows us to access specific items in a vector.
In the example below, we will access the first, last and 2nd to 4th drivers in our vector:
drivers[1]
drivers[7]
drivers[2:4]
Verstappen
33
Zhou
24
Hamilton Perez Norris
44 11 4
Note
Instead of drivers[7] we could use drivers(length(drivers)) to access the last element in the vector. This saves you having to count the items manually and is programatically robust to future changes to the vector.
To delete an item from the vector, we place a minus infront of the corresponding index we want to drop.
Drop Lewis Hamilton from our drivers vector. Don’t forget to assign the operation to the drivers variable if you want to save the changes.
drivers[-2]
Verstappen Perez Norris Riccardo Leclerc Zhou
33 11 4 3 16 24
Higher Level Data Structures
Lists
Lists can be used to store mutliple vectors in a single data structure. We can name the vectors in the list, adding another element to this data structure.
To amuse myself, we will continue with the Formula 1 and create a named list attributing four driver pairings to their respective teams.
F1_teams <- list(Scuderia_Ferrari=c("Charles Leclerc", "Carlos Sainz"),
Scuderia_Alpha_Tauri_Honda=c("Pierre Gasly", "Yuki Tsunoda"),
Alfa_Romeo_Racing_ORLEN=c("Valterri Bottas", "Guanyu Zhou"),
HASS_F1_Team=c("Mick Schumacher", "Kevin Magnussen"))
Constructing a list is simple - just assign multiple vectors (e.g Scuderia_Ferrari=c("Charles Leclerc", "Carlos Sainz") - each separated by a comma.
The benefit of lists like these are that you can easily access items in the list using human readable names instead of numerical indexes (which still work!).
Below are a few examples of how to access the Ferrari drivers:
F1_teams$Scuderia_Ferrari
F1_teams[1]
F1_teams["Scuderia_Ferrari"]
These all achieve the same result. If you want to find out who the number 2 driver at HAAS is, apply the same logic used in vector indexing:
F1_teams$HASS_F1_Team[2]
"Kevin Magnussen"
Dataframes
Dataframes are a superior method to lists for storing multiple vectors. Typically, each row in a dataframe corresponds to an observation (person, event, sample), whilst columns correspond to the variable being recorded (e.g height, age, eye color).
Go to RStudio Cloud and open your session. Load in the Iris dataset:
iris <- datasets::iris
You can see a newly created ‘Data’ object in your environment called iris with 150 obs of 5 variables. That is to say we have 150 rows and 5 columns.
Colnames & Rownames
A simple rule applies to colnames and rownames: they must be unique. This is because R uses both colnames and rownames to index each column and row respectively, duplicate entries are not allowed.
Inspect the column names and row names of a dataframe:
colnames(iris)
rownames(iris)
[1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
[1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17" "18" "19"
[20] "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36" "37" "38"
[39] "39" "40" "41" "42" "43" "44" "45" "46" "47" "48" "49" "50" "51" "52" "53" "54" "55" "56" "57"
[58] "58" "59" "60" "61" "62" "63" "64" "65" "66" "67" "68" "69" "70" "71" "72" "73" "74" "75" "76"
[77] "77" "78" "79" "80" "81" "82" "83" "84" "85" "86" "87" "88" "89" "90" "91" "92" "93" "94" "95"
[96] "96" "97" "98" "99" "100" "101" "102" "103" "104" "105" "106" "107" "108" "109" "110" "111" "112" "113" "114"
[115] "115" "116" "117" "118" "119" "120" "121" "122" "123" "124" "125" "126" "127" "128" "129" "130" "131" "132" "133"
[134] "134" "135" "136" "137" "138" "139" "140" "141" "142" "143" "144" "145" "146" "147" "148" "149" "150"
Note that the rownames in this dataset are not important, they are just automatically incremented integers.
Manipulating Dataframes
Dataframe Indexes
There are situations where we will need to isolate columns or rows for an analysis. The same numerical indexing logic from vectors applies, but there are two entries to the square brackets - one for rows, and one for columns.
Like lists, we can provide human readable names to access a specific column: iris$Sepal.Width.
Subsetting Dataframes
Now that we know how to isolate specific cells of a dataframe, the next step is to apply these changes by ‘slicing the dataframe’. Slicing a subsetting are interchangeable - I will nearly always call it subsetting.
In our Iris dataset, make a new dataframe that contains only numerical measurements for Petals:
petal_data <- iris[, 3:4]
Now make a dataframe that contains only the numerical observations (i.e drop the column species):
numerical_data <- iris[,-5]
Personally, I prefer the use of the subset() function. The above operations are performed using subset below:
petal_data <- subset(iris, select = c(Petal.Width, Petal.Length))
numerical_data <- subset(iris, select = -c(Species))
Note
It is rare that you would select/drop observations from a dataset in this manner (do not cherry pick your data). This is why the examples are all done on columns.
Filtering Dataframes
Filtering dataframes is an extension of dataframe subsetting, performed using logical operators:
<: less than<=: less than or equal to>: greater than>=: greater than or equal to==: exactly equal to!=: not equal to!x: Not xx | y: x OR yx & y: x AND y
Using the Iris dataset as an example, subset the original dataframe to isolate data that belongs to the species Setosa:
setosa_data <- subset(iris, iris$Species == "Setosa")
Updating Dataframes
To create a new variable in our dataframe, we can use the $ operator.
In the example below, we will subtract Petal.Length from Sepal.Length and store it as a new column.
iris$sepal_less_petal_len <- iris$Sepal.Length - iris$Petal.Length
IfElse
We can use ifelse() to create new variables based on conditional statements.
In the example below we will use a vector, however this applies to dataframe columns too:
Note
The ifelse() function is an example of a ternary operator which reads as follows: `` A ? B : C`` - If A is true choose B, else choose C.
vector <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
test <- ifelse(vector > 5, "greater than 5", "less than 5")
print(test)
[1] "less than 5" "less than 5" "less than 5" "less than 5" "less than 5" "greater than 5" "greater than 5" "greater than 5" "greater than 5" "greater than 5"
But what about the 5th element? 5 is not less than 5.
To add a second layer of conditionals we will re-use the ifelse() function:
test <- ifelse(vector == 5, "five", test)
print(test)
[1] "less than 5" "less than 5" "less than 5" "less than 5" "five" "greater than 5" "greater than 5" "greater than 5" "greater than 5" "greater than 5"