Introduction to R

• R and RStudio
• R basics
• Using R packages
• Importing data
• Data exploration

• This section covers the two pieces of software you need to download
• R is the core piece
• RStudio is a nice integrated development environment (IDE) that makes it much easier to use R

• R is a free, open-source language and environment for statistical analysis
• It's been very popular in academia for more than a decade
• R is becoming more popular outside of academia, including industry and government

• To download R for Windows, see this page
• If you open R itself, it will look very plain

• RStudio makes R a little more user friendly
• It's free and can be downloaded at rstudio.com
• It's not necessary to open RStudio to use R, but in these slides we will assume that RStudio is your interface to R

When you first open RStudio, this is what you see

• The left panel is the console for R
• Type 1 + 1 then hit “Enter” and R will return the answer

• It's a good idea to use a script so you can save your code
• Open a new script by selecting “File” -> “New File” -> “R Script” and it will appear in the top left panel of RStudio

• This is basically a text document that can be saved (go to “File” -> “Save As”)
• You can type and run more than one line at a time by highlighting and clicking the “Run” button on the script tool bar

• The bottom right panel can be used to find and open files, view plots, load packages, and look at help pages
• The top right panel gives you information about what variables you're working with during your R session
• We'll explain more about what to look for in those panels later

• Open up a script if you haven't already (“File” -> “New File” -> “R Script”)
• Try some math by either typing the lines below or copying and pasting the lines into your script

10 + 5
10 - 5
10 * 5
10 / 5

• Remember, to run the lines, highlight your code and click the “Run” button on the toolbar of the script panel

In RStudio, you will see the variables we created in the top right panel

• If you've already created a variable, you can replace the value with another value

x

[1] 10

x <- 20
x

[1] 20

In the top right panel you can see that the number stored in the variable x has changed

R has three main variable types

There are several ways to group data to make them easier to work with:

• Vectors - contain multiple values of the same type (e.g., all numbers or all words)
• Lists - contain multiple values of different types (e.g., some numbers and some words)
• Matrix - a table, like a spreadsheet, with only one data type
• Data Frames - Like a matrix, but you can mix data types

• Vectors are variables with an ordered set of values
• They contain only one type of data (numeric, character, or logical)
• We use c( ) as a container for vector elements

x <- c(1, 2, 3, 4, 5)
x

[1] 1 2 3 4 5

• Lists are like vectors but can contain any mix of data types
• We use list() as a container for list items

x <- list("Benzene", 1.3, TRUE)
x

[[1]]
[1] "Benzene"

[[2]]
[1] 1.3

[[3]]
[1] TRUE

• Data frames are spreadsheet-like tables in R
• We use data.frame() as a container for many vectors of the same length

pollutant <- c("Benzene", "Toluene", "Xylenes")
concentration <- c(1.3, 5.5, 6.0)
carcinogen <- c(TRUE, FALSE, FALSE)
my.data <- data.frame(pollutant, concentration, carcinogen)
my.data

  pollutant concentration carcinogen
1   Benzene           1.3       TRUE
2   Toluene           5.5      FALSE
3   Xylenes           6.0      FALSE

• Functions are a way to repeat the same task on different data
• R has many built-in functions that perform common tasks

x <- c(4, 8, 1, 14, 34)
mean(x) # Calculate the mean of the data set

[1] 12.2

y <- c(1, 4, 3, 5, 10)
mean(y) # Mean of a different data set

[1] 4.6

• To write a comment in your script that will not be evaluated, type # in front of your comment
• The text after# will not be evaluated
• Run all of the code below and see what gets returned in the R console (bottom left panel in RStudio)

# Full line comment
x # partial line comment
"new line"

• Back to functions: they all have the form function()
• function is the name, which usually gives you a clue about what it does
• () is where you put your data or indicate options
• To see what goes inside (), type a question mark in front of the function and run it

?mean()

In RStudio, you will see the help page for mean() in the bottom right corner

• On the help page, under Usage, you see mean(x, ...)
• This means that the only necessary thing that has to go into () is x
• On the help page under Arguments you will find a description of what x needs to be
• (For most purposes, you will want the x in the mean function to be a numeric vector)

• Another example of a function is plot()
• At a minimum it takes two arguments, plot(x, y)
• x is a numeric vector that will be the x-axis coordinates of the plot
• y is a numeric vector (of the same length as x) that will be the y-axis coordinates of the plot

benzene <- c(1.3, 4.5, 2.6, 3.4, 6.4)
day <- c(1, 2, 3, 4, 5)
plot(x = day, y = benzene)

• R comes with basic functionality, meaning that some functions will always be available when you start an R session
• However, anyone can write functions for R that are not part of the base functionality and make it available to other R users in a package
• Packages must be installed first then loaded before using it
• This is similar to a mobile app: you must first install the R package (like first downloading an app) then you must load the package before using its functions (like opening an app to use it)

• For example, lets say that R doesn't have a function you need
• The best way to find out if another R package does have that function is to ask Google
• Use a search with key words describing what you want the function to do and just add “R package” to the end

• Let's say what you want to do is find serial correlation in an environmental data set
• Google tells you that the R package EnvStats has a function called serialCorrelationTest()
• First, try to use the function

x <- c(1.3, 3.5, 2.6, 3.4, 6.4)
serialCorrelationTest(x)

• It's not available because we need to install the package first (again, like initially downloading an app)
• In the bottom right panel of RStudio, click on the “Packages” tab then click “Install Packages” in the tool bar

• A window will pop up
• Start typing “EnvStats” into the “Packages” box, select that package, and click “Install”

• Now that we've installed the package, we still can't use the function we want
• We've got to load the package first (opening the app)
• For this, we will use the library() function

library("EnvStats")

Now we can use the function we want

x <- c(1.3, 3.5, 2.6, 3.4, 6.4)
serialCorrelationTest(x)

Results of Hypothesis Test
--------------------------

Null Hypothesis:                 rho = 0

Alternative Hypothesis:          True rho is not equal to 0

Test Name:                       Rank von Neumann Test for
                                 Lag-1 Autocorrelation
                                 (Exact Method)

Estimated Parameter(s):          rho = -0.01876

Estimation Method:               Yule-Walker

Data:                            x

Sample Size:                     5

Test Statistic:                  RVN = 1.8

P-value:                         0.7833

Confidence Interval for:         rho

Confidence Interval Method:      Normal Approximation

Confidence Interval Type:        two-sided

Confidence Level:                95%

Confidence Interval:             LCL = -0.8951
                                 UCL =  0.8576

• Remember, when you close down RStudio, then start it up again, you don't have to download the package again
• But you do have to load the package to use any function that's not in the R core functionality (this is very easy to forget)

• There are many ways to import data into R
• We will be importing from an Excel spreadsheet
• Packages exist to automate this
  • xlsx
  • XLConnect
• But it's often easier to just save the Excel document as a csv file first, and then import that file

• For this example, we will be using a file named “airqality.xlsx”
• This file is located on the IDEM Data Analysis Sharepoint site under “Shared Documents”
• Download the Excel file to whatever location you prefer

• Open the spreadsheet in Excel
• Click the “Office Button” in the upper-left corner
• Choose “Save As”, then “Other Formats”

• Select “CSV (Comma delimited)” from “Save as type:”
• Click “Save”
• Close Excel

• In RStudio, go to “Tools” -> “Import Dataset” -> “From Text File”
• Select your file using the window

Accept the defaults in the popup window and click “Import”

• In the top right panel you will see a variable named airquality that is a data frame of the spreadsheet we imported
• In the top left panel the data frame is displayed

• You can also easily import csv files from the command line
• read.csv() is a function that takes the name of a csv file as its main argument
  • It reads the csv file and converts it to a data frame
  • It assumes that the first row contains column names
• You must assign the output of read.csv() to a variable to be able to work with the data

• Let's say you downloaded your file to a folder named “My Data” in your C: drive
• Use the entire file path as the argument in read.csv()

airquality <- read.csv("C:/My Data/airquality.csv")
airquality

   Ozone Solar.R Wind Temp Month Day
1     41     190  7.4   67     5   1
2     36     118  8.0   72     5   2
3     12     149 12.6   74     5   3
4     18     313 11.5   62     5   4
5     NA      NA 14.3   56     5   5
6     28      NA 14.9   66     5   6
7     23     299  8.6   65     5   7
8     19      99 13.8   59     5   8
9      8      19 20.1   61     5   9
10    NA     194  8.6   69     5  10

• The data we imported in the previous section can actually be obtained in R by using the data() function
• If you were not able to download that Excel file, use the code below to obtain the data

data(airquality)
airquality

  Ozone Solar.R Wind Temp Month Day
1    41     190  7.4   67     5   1
2    36     118  8.0   72     5   2
3    12     149 12.6   74     5   3
4    18     313 11.5   62     5   4
5    NA      NA 14.3   56     5   5
6    28      NA 14.9   66     5   6

• airquality is a data frame with ozone readings from a monitor in New York
• What column names are in the data frame?

colnames(airquality)

[1] "Ozone"   "Solar.R" "Wind"    "Temp"    "Month"   "Day"    

• How many observations does the dataset contain?
• We use the nrow() function to get the number of rows

nrow(airquality)

[1] 153

RStudio has a special function called View() that makes it easier to look at data in a data frame

View(airquality)

• You can refer to specific columns in a data frame with the $ operator
• Use this to feed specific columns into a function

mean(airquality$Temp) # Calculate the mean temperature

[1] 77.88

Take a look at the data using plot(x, y)

plot(airquality$Temp, airquality$Ozone)

Use lm(y ~ x, data) to fit a linear regression to a data set and summary() to see the results

fit <- lm(Ozone ~ Temp, airquality)
summary(fit)

• You can plot the fitted-line using abline()
• R will automatically add it to the last plot drawn

abline(fit)

With a little extra code we can get a better fit with the data by using a quadratic model

temp <- airquality$Temp
temp2 <- temp^2 # ^ = expontentiation
ozone <- airquality$Ozone
fit2 <- lm(ozone ~ temp + temp2)
temp.curve <- seq(min(temp), max(temp), length=50)
lines(temp.curve, predict(fit2, list(temp=temp.curve, temp2 = temp.curve^2)))

Based on this dataset and our quadratic model, what would we expect the ozone concentration to be when the temperature is 75 degrees?

prediction <- predict(fit2, list(temp = 75, temp2 = 75^2))
prediction

    1 
28.32