Before you start make sure you have started a new project and know which directory you are working in.
Check R is working by printing Hello World (use brackets)
print("Hello World")To check you’re in the right place use
getwd()If you’re in the wrong place use setwd() to change directory.
In R you can assign values to objects by using = or <-. This will save your object to the global environment.
x <- 2
x = 2
y <- 5
y = 5
# stay consistent either = or <-
2+5
x+yTo remove that object from the global environment you can use rm()
rm(x)
rm(y)Once you have assigned value to objects you can continue to use them in calculations.
# Make a variable
weight_kg = 55
# print that variable
print(weight_kg)
# Change weight from kg to grams
weight_kg * 1000
weight_g = weight_kg * 1000
# assign a new value to weight_kg
weight_kg = 57.5
weight_g = weight_kg * 1000
There is a set of standard operators that we can use to compare two numbers or objects, the results will return the Boolean values TRUE or FALSE * < - Greater Than and <= Greater than or equal to * > - Less Than and <= less than or equal to * == equal (= cannot be used since it assigns values) * != not equal to
2==2 # Equal
2!=2 #Inequal
2 <= 2
Functions execute a defined set of commands and automate a process
You need an input, the function which has a set of commands and you recieve an output
For example the square root function is:
sqrt(25)we will use the round function, search in help for how to use This will tell you the package, the arguments to use and how to use
?round
# if there are several packages with the same package use:
?base::roundLet’s use the round function with pi:
# Define pi
pi = 3.142
# Round to 2 decimal places
round(pi, 2)
# round to 0 decimal places as default (don't define the second argument of digits)
round(pi)
round(pi, 0)When writing the arguments of functions we can use either named or unnamed arguments, generally if it’s your first time using the function its best to
# Example of named argments
round(x = 3.142, digits = 2)
# Example of unnamed argments
round(3.142, 2)
# If you name them you can change the order of arguments
round(digits = 2, x = 3.142) ### this is the good practice
# Yet this won't work with no
round(2, 3.142) #wrong!
# Make a variable called result so it can be saved into memory
result = round(x = 3.142, digits = 2)If you can’t find a function in help section use Google
animal = "cat" # example of character, words or strings
status = TRUE # example of logical
weight = 50 # example of integar e.g. whole numbers
pi = 3.142 #example of double or dblIf you want to know the data type use the class() function
vector = c(4,12,7,9)
class(vector) #numeric
Data in R can be structured in different ways such as vectors, factors and dataframes.
Vector: list of items of the same data type e.g. 4,6,9,12
vector = c(4,12,7,9) # this is one directional list of items, everything in () is a function Factor: categorical data (has to be a character vector), not too popular in tidyverse
# First create a vector of some ordered data using c()
mood = c("unhappy", "awesome", "ok", "awesome", "unhappy")
# Combine the ordered data together using factor()
factor = factor(mood)data.frame: contains tabular data - normally data is loaed into data.frame when reading in a file
# Create three vectors with the necessary information using c()
employee <- c('John Doe','Peter Gynn','Jolie Hope')
salary <- c(21000, 23400, 26800)
startdate <- as.Date(c('2010-11-1','2008-3-25','2007-3-14'))
# Combine the three vectors together as a dataframe using data.frame()
employ_data_frame <- data.frame(employee, salary, startdate)
To create a vector we must always use the c() function, this combines their elements together.
Vectors can be either numbers or characters, but all the items must be the same datatype.
An example of integer vector would be
weight_g = c(50,60,65,82) # num means numeric, both dbl and integar are numeric An example of character vector would be
animals = c("mouse", "rat", "dog") # chr means character An example of logical vector would be
logic = c(TRUE, FALSE, FALSE) We can then investigate the properties of the vector
# Find length of vector with the length() function
length(animals)
length(weight_g)
# str() fucntion checks the structure of yoru data
str(weight_g) # a numeric vector of 4 items
str(animals) # a character vector of 3 items
# Adding extra items to a vector
weight_g = c(weight_g, 90) # add 90 to end of vector
weight_g = c(30, weight_g) # add 30 to start of vector
# To add an item to a chosen point within the vector use the append() function
append(x = vector, values = 10, after = 1)
# x is vector, values are the values to be included, after - after whcih the values will be appended
# Working with heights
height_mm = c(100,150,99,87)
total_height = sum(height_mm) # sum adds up all items in vector
height_mm = c(220, height_mm)
total_height = sum(height_mm)To look for vector indices we can use []
#get the second item from my vector
animals[2]
#get the first and second
animals[c(1,2)]
# get multiple items - even repetitions
animals[c(1,1,1,3,2,1,2,2)]R indices start at 1 unlike other programming languages like Python which counts from 0
Factors are categorical variables, they can de split into different types such as nominal, ordinal and binary.
Nominal = c("turtle", "snail", "butterfly") # unordered descriptions
Ordinal = c("small", "medium", "large") # ordered descriptions
Binary = c("Extinct", "Existing") # only two mutually exclusive outcomesOrdinal variables
# Create a vector containing chr describing mood
mood = c("unhappy", "awesome", "ok", "awesome", "unhappy") Convert mood vector to a factor
factor(mood)
mood = factor(mood)
# factors can only contain pre-defined levels
# R orders factors in alphabetical order
#Re-order levels in a factor
factor(mood, levels = c("unhappy", "okay", "awesome"))
Dataframes are a type of list used to store datasets as tables. The rows are cases and columns are variables, therefore a column can either be a vector or a factor. So we can build a dataframe from the vectors upwards.
Dataframes can be created using the data.frame() function
# Create three vectors with the necessary information using c()
name <- c('John','Peter','Jolie')
gender <- c('M', 'M', 'F')
age <- c(22, 42, 57)
# Combine the three vectors together as a dataframe using data.frame()
people_data_frame <- data.frame(name, gender, age)To access aspects of the dataframe we can use $ to subset a dataframe by name
people_data_frame$name # first column
people_data_frame$gender # second column
people_data_frame$age # third column There is more information on working with dataframes in Importing and Inspecting Data
We can subset a vector by using a logical vector. TRUE will select the element with the same index, while FALSE will not.
# which values in weight_g are less than 60
# R asks each item in the vector - "are you less than 60?"
c(TRUE, TRUE, FALSE, FALSE, FALSE, FALSE)
# Apply this to the weight_g vector
weight_g[c(TRUE, TRUE, FALSE, FALSE, FALSE, FALSE)]
# We computed this ourselves as to whne the condition was and wasn't met
# Now let R compute it automatically
weight_g < 60 Now to get the values as an answer, without the [] I will not get the values Automatic way to get results from queries
weight_g[weight_g < 60] # greater than 60 Other ways to subset conditonally include
weight_g[weight_g <= 60] # greater than or equal to 60
weight_g[weight_g == 60] # equal to 60
weight_g[weight_g > 60] # less than 60
weight_g[weight_g >= 60] # less than or equal to 60As well as TRUE and FALSE we can use other logical expression such as &, | and &in%.
using AND &
Both conditions must be true to allow TRUE Select values less than 80 and greater than 50
weight_g[weight_g < 80 & weight_g > 50]## [1] 60 65using OR |
If one condition is true it will allow TRUE
weight_g[weight_g < 80 | weight_g > 50]## [1] 30 50 60 65 82 90These are useful for filtering data
To check if an item is present in a character list
animals[animals == "rat"]
animals[animals == "cat"] # character(0) means no cat found in vectorusing %in%
The %in% in operator can be used for character data only
This can be used to look for 100 significnat genes in a type II diabetes gene list of 100 genes
animals %in% c("rat", "dog") # see if two items are present
animals[animals %in% c("rat", "dog")]As an alterantive to the %in% operator we can use the intersect() function
intersect(animals, c("rat"))
intersect(animals, c("rat", "dog"))
heights = c(3, 5, 8, 12, 6)
# introducing missing values
heights = c(3, 5, 8, 12, 6, NA)# calculate the mean (average of heights)
mean(heights) #NA is returned
mean(heights, na.rm = TRUE) # to remove missing values use na.rm
min(heights, na.rm = TRUE)We can remove missing values from the vector in several ways.
We can use na.omit
na.omit(heights) #removes all NAs in object
heights_no_na = na.omit(heights)We can use is.na() and the ! operator
is.na(heights) # returns logical
!is.na(heights) # to negate the is.na function use ! - so find all things that aren't missing
heights[] #open up heights
heights[!is.na(heights)] # find all values in heights which are not NAs using the negating ! operation
heights_no_na = heights[!is.na(heights)]We can also use complete.cases
heights[complete.cases(heights)]
heights_no_na = heights[complete.cases(heights)]