Basic R
R as a huge calculator
#addition, subtraction, multiplications, powers
2 + 3
## [1] 5
## [1] 27
## [1] 0.2857143
## [1] 324
#modulo (remainder)
33%%4
## [1] 1
Using functions
Functions are reusable pieces of code. R has a ton of functions built in, and even more that you can load separately (packages)
#pass values to functions as parameters
log(2, base = 2)
## [1] 1
## [1] 2.718282
## [1] 1
Storing values
We can store numbers or the results of calling functions for later use in variables using the assignment operator <-.
#assignment
x <- 10
y <- cos(2*pi)
We can then use these variables later, for more calculation
#calculation on variable values
exp(x)
## [1] 22026.47
## [1] 10
Why the funny arrow assignment?
Why don’t we just use = (which also works)?
- directional assignment, can also use
-> for assigning left to right
- history/quirks
Variable creation tips
- Variable names can’t start with a number or underscore
- Variables (and everything) is case sensitive in
R
- Avoid names that are already used in
R (c, I, T, F, t, q)
- Some words are reserved (
for, if, in, while, else, repeat, break, next)
- Be consistent with your capitalization and underscores (
camelCase vs. snake_case vs. caterpillar.case)
- I prefer
snake_case because you never have to remember capitals and the . can have consequences later.
Vectors
We can store multiple values in a variable using vectors. To create a vector, we can combine values (c), or use sequencing functions. Each element in a vector must be of the same type.
#combine values
(nightstand_books <- c("Harry Potter and the Goblet of Fire", "A Storm of Swords", "On Writing Well", "Advanced R", "R Packages"))
## [1] "Harry Potter and the Goblet of Fire"
## [2] "A Storm of Swords"
## [3] "On Writing Well"
## [4] "Advanced R"
## [5] "R Packages"
#sequences
(nums <- 1:10)
## [1] 1 2 3 4 5 6 7 8 9 10
(nums_2 <- seq(1, 10, by = 2))
## [1] 1 3 5 7 9
Your turn
helpful functions
-
Use the
rep function to create and store a vector containing 111222333444555
-
Use the
rep function to create and store a vector containing 123451234512345
-
What is the third power of each integer from 1 to 50?
Data frames
- Data frames hold data sets
- Each column can be of different type, but within a column the type is consistent (kind of like a spreadsheet)
- Each column in a data frame is a vector
## 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
Accessing parts of data ($)
Depending on what we have (data frame vs. vector), there are multiple ways to access part of the data.
For data frames, we can access columns using the $ operator
#accessing data using $
head(mtcars$mpg)
## [1] 21.0 21.0 22.8 21.4 18.7 18.1
Accessing parts of data (indices)
We can also use numeric indexing (starts at 1). Or, we can access columns in a data frame using their string names
#accessing data using numeric indexing
mtcars[1:6, 1]
## [1] 21.0 21.0 22.8 21.4 18.7 18.1
## [1] 21
#accessing data using string names of columns
mtcars[c(1, 3, 5), "mpg"]
## [1] 21.0 22.8 18.7
Logicals
R has support for logicals with TRUE and FALSE as built in Boolean values. We can create logicals with <, >, <=, >=, ==, !=, is.na, etc.
## [1] TRUE
x <- c(2, 4, 5, NA, 100)
is.na(x)
## [1] FALSE FALSE FALSE TRUE FALSE
We can combine logical statements using and & and or ||.
Indexing with logicals
We can also use logical statements to access parts of our data (think filtering).
#indexing with logicals
x[!is.na(x)]
## [1] 2 4 5 100
## [1] NA 100
## [1] 100
Your turn
Using the mtcars dataset,
-
Which car has greater than 30 mpg and over 100 horsepower?
-
How many cars have 4 cylinders? Hint: Using the
sum function on logicals returns how many TRUE values there are.
-
Calculate the average horsepower for the cars with 4 cylinders.
Modifying data
We can use indexing to change values of data. Or add columns to a data frame.
#using indexing to change certain values
hp_per_cyl <- mtcars$hp/mtcars$cyl
hp_per_cyl[1] <- NA
hp_per_cyl[2] <- "???"
hp_per_cyl[1:10]
## [1] NA "???" "23.25"
## [4] "18.3333333333333" "21.875" "17.5"
## [7] "30.625" "15.5" "23.75"
## [10] "20.5"
#add column
mtcars$hp_per_cyl <- hp_per_cyl
head(mtcars, 1)
## mpg cyl disp hp drat wt qsec vs am gear carb hp_per_cyl
## Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4 4 <NA>
Data types
- Use
class to find out what data type we’re talking about. For more informative look, use str (structure).
- Numeric, integer, character, Date, and factor most common data types.
- Can convert between types using
as.type.
#structure
str(mtcars[, 1:3])
## 'data.frame': 32 obs. of 3 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 ...
#type conversion
as.character(mtcars$mpg)[1:6]
## [1] "21" "21" "22.8" "21.4" "18.7" "18.1"
Moar functions
There are a lot more functions. Here are some examples.
#moar functions
x <- mtcars$mpg
length(x)
## [1] 32
## [1] 642.9
Stats
#basic statistical functions
mean(x)
## [1] 20.09062
## [1] 6.026948
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.40 15.42 19.20 20.09 22.80 33.90
## 50%
## 19.2