1. Introduction to R
Carolina Salge
March 29, 2017

2. R
R is a free software environment for statistical computing and graphics
Object-oriented
It runs on a wide variety of platforms
Highly extensible (through packages)

3. Files, plots, packages, & help
R Studio
Datasets
Scripts
Results
Files, plots, packages, & help

4. Agenda for Intro to R
You will learn a few R basics, including how to create vectors, matrices, arrays, data frames, and lists but also how to omit missing values from a dataset
You will also learn about packages (how to install and load tem, for example). Further, you will learn how to read a file into R (e.g., csv), inspect it, reference it, manipulate it, and save it to your machine. Finally, we will finish off with sqldf, notebook compiling, and more resources that you can exploit on your own!

5. Script A script is a set of R commands A program
c is short for combine in c(369.40, …)
# CO2 parts per million for
co2 <- c(369.40,371.07,373.17,375.78,377.52,379.76,381.85,383.71,385.57,384.78)
year <- (2000:2009) # A range of values
# Show values
co2
year
# Compute mean and standard deviation
mean(co2)
sd(co2)
plot(year,co2)

6. Exercise
Plot kWh per square foot by year for the following University of Georgia data
year
sqfeet
kWh
2007
14,214,216
2,141,705
2008
14,359,041
2,108,088
2009
14,752,886
2,150,841
2010
15,341,886
2,211,414
2011
15,573,100
2,187,164
2012
15,740,742
2,057,364

7. Datasets A dataset is a table Same as the relational model
One row for each observation
Columns contain observation values
Same as the relational model
R supports multiple data structures and multiple data types

8. Data structures Vector Matrix
A single row table where data are all of the same type (here it is numeric)
Matrix
A table where all data are of the same type
co2 <- c(369.40,371.07,373.17,375.78,377.52,379.76,381.85,383.71,385.57,384.78)
year <- (2000:2009)
co2[2] # get the second value
m <- matrix(1:12, nrow=4,ncol=3) m
m[4,3] # fourth row in third column

9. Exercise
Create a matrix with 6 rows and 3 columns containing the numbers 1 through 18

10. Data structures Array Data frame
Extends a matrix beyond two dimensions
Data frame
Same as a relational table
Columns can have different data types
Typically, read a file to create a data frame
a <- array(1:24, c(4,3,2)) # 4 rows, 3 columns, 2 dimensions
a[1,1,1] # row 1 of column 1 in dimension 1
gender <- c("m","f","f")
age <- c(5,8,3)
df <- data.frame(gender,age)
df[1,2] # first row of column 2
df[1,] # all columns in row 1
df[,2] # all rows in column 2

11. Data structures List An ordered collection of objects
Can store a variety of objects under one name
l <- list(co2,m,df) # a list with a vector, a matrix, and a data frame
l[[3]] # list 3 or df
l[[2]] # list 2 or m
l[[1]] # list 1 or co2
l[[1]][2] # second element of list 1 (or co2)
l[[2]][2,2] # second row of second column of list 2

12. Are Celsius and Fahrenheit interval or ratio data?
Types of data
Classification
Nominal (high, medium, or low)
Sorting or ranking
Ordinal (ranking of tennis players)
Intervals between ordinal data are not necessarily equal. Murray (ranked 1) maybe a lot better than Djokovic (ranked 2) but Djokovic may be not a lot better than Wawrinka (ranked 3)
Measurement
Interval
Ratio (time, distance)
Ratio data have equal intervals
Are Celsius and Fahrenheit interval or ratio data?
See here

13. Factors Nominal and ordinal data are factors
By default, strings are treated as factors
Determine how data are analyzed and presented
Failure to realize a column contains a factor, can cause confusion
Use str() to find out a frame’s data structure

14. Missing values Missing values are indicated by NA (not available)
Arithmetic expressions and functions containing missing values generate missing values
Use the na.rm=T option to exclude missing values from calculations
sum(c(1,NA,2))
sum(c(1,NA,2),na.rm=T)

15. Missing values You remove rows with missing values by using na.omit()
gender <- c("m","f","f","f")
age <- c(5,8,3,NA)
df <- data.frame(gender,age)
df2 <- na.omit(df)
View(df2)
View(df)

16. Packages
R’s base set of packages can be extended by installing additional packages
Over 4,000 packages
Search the R Project site to identify packages and functions
Install using R studio
Packages must be installed prior to use and their use specified in a script
require(packagename)
library(packagename)

17. Packages # install ONCE on your computer
# can also use Rstudio to install
install.packages("knitr")
# require EVERY TIME before using a package in a session
# loads the package to memory
require(knitr)
library(knitr)