Introduction to R

Mathematical operations in R

Many familiar operators work in R, allowing you to work with numbers like you would in a calculator. Operators such as inequalities also work, returning “TRUE” if the proposed logical expression is true and “FALSE” otherwise.

2+4 #addition

[1] 6

2-4 #subtraction

[1] -2

2*4 #multiplication

[1] 8

2/4 #division

[1] 0.5

2^4 #exponentiation

[1] 16

log(2) #the default log base is the natural log

[1] 0.6931472

2 < 4

[1] TRUE

2 > 4

[1] FALSE

2 >= 4 #greater than or equal to 

[1] FALSE

2 == 2 #is equal to (notice that there are two equal signs, as a single equal sign denotes assignment)

[1] TRUE

2 != 4 #is not equal to 

[1] TRUE

2 != 4 | 2 + 2 == 4 #OR

[1] TRUE

2 != 4 & 2 + 2 == 4 #AND

[1] TRUE

"Red" == "Red"

[1] TRUE

Objects

In addition to being able to work with actual numbers, R works in objects, which can represent anything from numbers to strings to vectors to matrices. Everything in R is an object. The best practice for assigning variable names to objects is the “<-” operator. After objects are created, they are stored in in the “Environment” tab in your RStudio console and can be called upon to perform different operations.

R has many data structures, including:

• atomic vector
• list
• matrix
• data frame
• factors

R has 6 atomic vector types, or classes. Atomic means that a vector only contains elements of one class (i.e. the elements inside the vector do not come from mutliple classes).

• character
• numeric (real or decimal)
• integer
• logical (TRUE or FALSE)
• complex (containing i)

a <- 2
b <- 3
a + b

[1] 5

class(a) #the "class" function tells you what class of object a is

[1] "numeric"

d <- c(1,2,3,4,5) #the "c" function concatenates the arguments contained within it into a vector
d

[1] 1 2 3 4 5

d <- c(d, 1) #The "c" function also allows you to append items to an existing vector
d

[1] 1 2 3 4 5 1

class(d)

[1] "numeric"

d[3] #brackets allow you index vectors or matrices. Here, we call the third value from our d vector.

[1] 3

#Matrices are just like vectors, but with two dimensions
my_matrix <- matrix(seq(1:9), ncol = 3)
my_matrix

     [,1] [,2] [,3]
[1,]    1    4    7
[2,]    2    5    8
[3,]    3    6    9

#vectors and matrices can only contain objects of one class. If you include objects of multiple types into the same vector, R will perform coersion to force all the objects contained in the vector into a shared class
x <- c(1.7, "a")
x

[1] "1.7" "a"  

class("a")

[1] "character"

class(1.7)

[1] "numeric"

class(x)

[1] "character"

y <- c(TRUE, 2)
y

[1] 1 2

z <- c("a", TRUE)
z

[1] "a"    "TRUE"

#If you would like to store objects of multiple classes into one object, a list can accomodate such a task.
x_y_z_list <- list(x,y,z)
x_y_z_list

[[1]]
[1] "1.7" "a"  

[[2]]
[1] 1 2

[[3]]
[1] "a"    "TRUE"

#to index an element in a list, use double brackets [[]]. You can further index elements within an element of a list.
x_y_z_list[[1]]

[1] "1.7" "a"  

x_y_z_list[[1]][2]

[1] "a"

#elements in a list can be assigned names
x_y_z_list <- list(a=x, b=y, c=z)
x_y_z_list

$a
[1] "1.7" "a"  

$b
[1] 1 2

$c
[1] "a"    "TRUE"

#Dataframes are a very commonly used type of object in R. You can think of a dataframe as a rectangular combination of lists.
#The below code stores the stated values in a dataframe which contains employee ids, names, salaries, and start dates for 5 employees
emp.data <- data.frame(
   emp_id = c (1:5), 
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25), 
   
   start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11",
      "2015-03-27")),
   stringsAsFactors = FALSE
)

emp.data

  emp_id emp_name salary start_date
1      1     Rick 623.30 2012-01-01
2      2      Dan 515.20 2013-09-23
3      3 Michelle 611.00 2014-11-15
4      4     Ryan 729.00 2014-05-11
5      5     Gary 843.25 2015-03-27

emp.data$emp_id #the $ operator calls on a certain column of a dataframe

[1] 1 2 3 4 5

class(emp.data$emp_id) #As noted earlier, a dataframe can be thought of as a rectangular list, combining different data classes together, each in a different column.

[1] "integer"

class(emp.data$salary)

[1] "numeric"

emp.data$emp_name[emp.data$salary > 620] #You can combine logical operators, brackets, and the $ sign to subset your dataframe in any way you choose! Here, we print out all the employee names for employees who have a salary greater than 620.

[1] "Rick" "Ryan" "Gary"

ls() #ls lists all the variable names that have been assigned to objects in your workspace

[1] "a"          "b"          "d"          "emp.data"   "my_matrix" 
[6] "x"          "x_y_z_list" "y"          "z"         

Using Packages in R

In addition to the basic functions provided in R, oftentimes we will be working with packages that contain functions written by other people to perform common tasks or specific analyses. Packages can also contain datasets. We can load these packages into our R environment after installing them in R.

usePackage <- function(p) 
{
  if (!is.element(p, installed.packages()[,1]))
    install.packages(p, dep = TRUE)
  require(p, character.only = TRUE)
}

usePackage("gapminder")#This code installs the gapminder packages, which contains vital statistics data from multiple countries. install.packages() is the function that will install a package for you if you know it's name.

Loading required package: gapminder

library("gapminder") #After installing the package, we need to tell R to load it into our current environment with this function.
head(gapminder) #The package gapminder contains a dataset called gapminder. We can use the "head" function to print out the first 6 rows of this dataset.

# A tibble: 6 x 6
  country     continent  year lifeExp      pop gdpPercap
  <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
1 Afghanistan Asia       1952    28.8  8425333      779.
2 Afghanistan Asia       1957    30.3  9240934      821.
3 Afghanistan Asia       1962    32.0 10267083      853.
4 Afghanistan Asia       1967    34.0 11537966      836.
5 Afghanistan Asia       1972    36.1 13079460      740.
6 Afghanistan Asia       1977    38.4 14880372      786.

?gapminder #the ? operator lauches a help page to describe a particular function, including the arguments it takes. Whenever using a new function, it is good practice to first explore it through ?.

Loops

Oftentimes, we may want to perform the same operation or function many many times. Rather than having to explicitly write out each individual operation, we can make use of loops. For example, let’s say that we want to raise the number 2 to the power of each integer from 0 to 20. We could either write out 2^0, 2^1, 2^2 …, or make use of a for loop to condense our code while getting the same result.

2^0

[1] 1

2^1

[1] 2

2^2

[1] 4

2^3

[1] 8

# ...

#This is a for loop. in the parentheses after the for function, we specify over what range of values we want to loop over, and assign a dummy variable name to take on each of those values in sequence. Within the curly braces, we state what operation we want to perform over all the values taken on by the dummy variable.
for(i in 0:20){
  print(2^i)
}

[1] 1
[1] 2
[1] 4
[1] 8
[1] 16
[1] 32
[1] 64
[1] 128
[1] 256
[1] 512
[1] 1024
[1] 2048
[1] 4096
[1] 8192
[1] 16384
[1] 32768
[1] 65536
[1] 131072
[1] 262144
[1] 524288
[1] 1048576

User-defined Functions (UDF)

Another way to avoid writing out or copy-pasting the same exact thing over and over again when working with data is to write a function to contain a certain combination of operations you find yourself running mutliple times. For example, you may find yourself needing to calculate the Hardy-Weinberg Equillibrium genotype frequencies of a population given the allele frequencies. We can wrap up all the code that you would need to calculate this in a function that we can call upon again and again.

calc_HWE_geno <- function(p = 0.5){ 
  q <- 1-p
  
  pp <- p^2
  pq <- 2*p*q
  qq <- q^2
  
  return(c(pp, pq, qq))
}

log. <- function(number){ 
  return(log(number))
}

calc_HWE_geno(p = 0.1)

[1] 0.01 0.18 0.81

#note that in our UDF we assigned a default value to p (p = 0.5). This means that if we do not specify a value for our argument of p, it will default to using that value.

calc_HWE_geno()

[1] 0.25 0.50 0.25

Plots

In addition to mathematical operations, R can help with data visualization. Base R has a few useful plotting functions, but popular packages such as ggplot2 give more customization and control to the user.

hist(gapminder$lifeExp)

Past versions of Plotting-1.png

boxplot(lifeExp ~ continent, data = gapminder) #box plot for the life expectancies of all years per continent

Past versions of Plotting-2.png

Setting a random seed

R has many functions that use a random number generator to generate an output. For example, the r____ functions (e.g. rbinom, runif) pull numbers from a probability distribution of your choice. In order to create reproducible analyses, it is often advantageous to be able to reliably obtain the same “random” number after running the same function over again. In order to do so, we can set a seed for the random number generator.

runif(1,0,1) #runif pulls a number from the uniform distribution with a set of given parameters

[1] 0.1944457

runif(1,0,1) #we can see that running runif twice gives you differnt results

[1] 0.205278

set.seed(1234) #setting a seed allows us to obtain reproducible results from functions that use the random number generator
runif(1,0,1)

[1] 0.1137034

set.seed(1234)
runif(1,0,1)

[1] 0.1137034

Reading and writing data in R

Finally, let us address probably one of the most important points when working with statistics in science: how to get the data you have collected into your R environment. For this part of the lesson, we will be working with the bandersnatch.csv file (created by Katie Long) located here: https://raw.githubusercontent.com/anthonyhung/MSTPsummerstatistics/master/data/bandersnatch.csv. If you would like to have your own copy of this dataset, you can open up a terminal window and run the commands.

cd ~/Desktop
mkdir data
cd data
wget https://raw.githubusercontent.com/anthonyhung/MSTPsummerstatistics/master/data/bandersnatch.csv

Now that we have a copy of the data in a data directory on our desktop, we can load it into R using a relative or absolute directory path and the read.csv function.

data <- read.csv("~/Desktop/data/bandersnatch.csv")
#let's take a look at the dataset we've just loaded
head(data)

  Color  Fur Baseline.Frumiosity Post.Frumiosity
1   Red Nude            4.477127        11.46590
2   Red Nude            4.113727        11.09354
3   Red Nude            4.806221        11.81268
4   Red Nude            5.357348        12.36704
5   Red Nude            5.951754        12.96135
6   Red Nude            3.593995        10.62375

summary(data)

  Color           Fur         Baseline.Frumiosity Post.Frumiosity 
 Blue:200000   Furry:200000   Min.   :-1.108      Min.   : 1.887  
 Red :200000   Nude :200000   1st Qu.: 4.001      1st Qu.: 4.044  
                              Median : 6.980      Median : 8.976  
                              Mean   : 6.001      Mean   : 9.001  
                              3rd Qu.: 8.961      3rd Qu.:13.956  
                              Max.   : 9.174      Max.   :16.192  

#what is the difference between these two function calls?
head(read.csv("~/Desktop/data/bandersnatch.csv", header = T))

  Color  Fur Baseline.Frumiosity Post.Frumiosity
1   Red Nude            4.477127        11.46590
2   Red Nude            4.113727        11.09354
3   Red Nude            4.806221        11.81268
4   Red Nude            5.357348        12.36704
5   Red Nude            5.951754        12.96135
6   Red Nude            3.593995        10.62375

head(read.csv("~/Desktop/data/bandersnatch.csv", header = F))

     V1   V2                  V3              V4
1 Color  Fur Baseline Frumiosity Post-Frumiosity
2   Red Nude         4.477127244     11.46590322
3   Red Nude         4.113726932     11.09353649
4   Red Nude         4.806220524     11.81268077
5   Red Nude         5.357347878     12.36703951
6   Red Nude         5.951754455     12.96134984

#let's look at the structure of the data
class(data)

[1] "data.frame"

class(data$Color)

[1] "factor"

class(data$Baseline.Frumiosity)

[1] "numeric"

#let's make some plots with the data
hist(data$Baseline.Frumiosity)

Past versions of “read write-1.png”

hist(data$Post.Frumiosity)

Past versions of “read write-2.png”

plot(data$Baseline.Frumiosity, data$Post.Frumiosity)

Past versions of “read write-3.png”

#we can also write data files and export them using R
data$Size <- rnorm(nrow(data))
write.csv(data, "~/Desktop/data/new_bandersnatch.csv")