1. Introduction to R
August 2016

2. What is R?
R is:
A programming language used for statistical computing and graphics.
Open source and freely available under the GNU General Public License.
Supported by the R Project for Statistical Computing
Download latest version of R via:
The use of R can be facilitated through the use of Rstudio.
Download Rstudio via:

3. R Packages
The capabilities of R are being continually expanded and improved through the creation of “packages”
A package may be developed to:
Develop capabilities previously unavailable
Improve upon existing capabilities
Focus upon the needs of a particular user group.
Currently R packages
Explore the world of R packages: project.org/web/packages/available_packages_by_name.html
Examples of R packages:
dplyr: manipulates data by taking subsets, summarizing, rearranging and joining data sets.
tidyr: reformats layouts of data sets to make them more compatible with R.
lubridate: simplifies working with dates and times.
survival: used for survival analysis.
babynames: US Baby Names
WDI: used for downloading World Development Indicators data from the World Bank

4. Some Distinguishing Characteristics
R does not use GUIs; it is entirely command-line based.
Coding can be simplified through the use of products such as RStudio.
Commands typically consist of a function with associated arguments that define the manner in which the function should be executed.
For example, a command to load a comma-delimited data file:
Command
Arguments
read.table("2009education.csv",
header=TRUE,
sep=",",
colClasses = c("character", rep("numeric",3)))
Functions are case-sensitive.
For example, typing “xhwe” will not execute the XHWE function

5. Installing Packages Most commonly used functions are part of “Base R”.
If you have installed R, you automatically use these functions.
But what if…
Need to identify the package that contains the “babynames” function
In this case, the package is also named “babynames”
Install package and add to library.

6. Installing Packages To install package: Add package to library:
Use your function(s):

7. Unsure about syntax?
If unsure about the arguments or syntax for a function:
Type a question mark (“?”) followed by the function name (with arguments undefined).
For example, if uncertain how to use the read.table function cited in the previous slide,
?read.table()
This will pull up a reference sheet detailing to proper use of the function.

8. Accessing Data Sets
Data sets may be imported from the Internet or from a computer directory.
R can import data in a wide variety of formats; some of the more common are:
Excel
CSV
TXT
SPSS
Access
SQL Server
Minitab
R also includes a set of standard data sets (for you to practice/play with)
To list available datasets, type the following command in R or Rstudio:
library(help="datasets")
A more complete description of many of these may be found on the following site:

9. Basic R Data Modes Numeric Integer Complex Logical Character Factor
While other modes of data exist, they are far less common than those listed above.

10. Common R Objects Name Dimensions Contents Example Vector 1
Series of values
Single data mode
Matrix 2
Values stored in rows and columns
All values of the same data mode
List
Allows multiple modes
Denver TRUE
Data Frame
Different columns may have different data modes
Topeka FALSE
In all of the above, integers are treated as numeric values.

11. Examples of How to Specify Elements within Objects
For a one-dimensional object named “x”
x[n] indicates the nth element
x[m:n] indicates the mth through the nth elements
x[c(m, n)] indicates the mth and the nth elements
x[“name”] indicates the element named “name”
x[x > k] indicates those elements that are greater than k
For a two-dimensional object named “x”
x[m,n] indicates the element in the mth row and the nth column
x[m,] indicates all of the mth row
x[, n] indicates all of the nth column
x[, j:n] indicates all of the jth through the nth columns
x[, c(j, n)] indicates all of the jth and nth columns
x[“name”, ] indicates all of the row named “name”
X$name indicates all of the column named “name” (for data frames only)

12. Fire it up!!! (in other words, how to start R)
A couple of different options (via the Windows “Start” button):
Start “R” directly or
Start “Rstudio”
provides a more user-friendly “front end” for using R
Windows “Start”

13. Explore the Neighborhood
Where am I?
getwd()
What else is here?
list.files()
list.dirs.()
Get working directory
List files
List directories

14. Move to a New Neighborhood
Want to change your working directory from /udrive/faculty/rsippel/Jaguars to /udrive/faculty/rsippel/Tigers?
setwd("/udrive/faculty/rsippel/Tigers") or
setwd(“../Tigers")
Function
Arguments
Set working directory
“..” is shorthand for the parent directory to the directory you are now in.
Therefore, “../Tigers” means move up one level in the file structure (to the parent directory and then move down to the “Tigers” directory.

15. Let’s get some data! To download data from the Internet:
download.file("en.openei.org/doe- opendata/dataset/3e a146-49b5-978a- e699334d2e1f/resource/3f00482e-8ea0-4b a212b6322e74/download/iouzipcodes2011.csv", "electricity_rates.csv")
…and what if the files are in a zip file?
unzip("electricity.zip")
Function
Arguments
Indicate URL where data is located.
Provide a filename for the downloaded data
Function
Arguments
Provide pathname for the zip file from which the files will be extracted.

16. Importing Data into R
Generic read.table function to read data from a table:
rates <- read.table("electricity_rates.csv",
header = TRUE,
sep = ",",
colClasses = c("character", "character",
"factor", "factor", "factor", "factor", "numeric", "numeric", "numeric"),
na.strings = "0")
Object being created
Function
Arguments
Data source
Data has headers
Values separated by commas
Specify mode corresponding to each column
colClasses argument may also be written
Treat “0” values as being not available
colClasses = c(rep("character", 2), rep("factor", 4), rep("numeric", 3))

17. Variants of read.table
Variants of read.table for handling specific file formats
read.csv()
For reading CSV files; assumes comma-separated fields.
read.csv2()
Used for data formatted in countries that use commas as decimal points.
Assumes use of semi-colon instead of comma to separate fields.
read.delim()/read.delim2()
Assumes delimited values; defaults to tab-delimited values
All of the above variants assume the presence of headers in the data file being read.

18. Quick Peeks Getting a Feel for your Data
Does your data come with a “code book”?
Code books define the contents of a data set, including parameter definitions, units, data precision, locations and dates of collection, etc.
How big is my data set?
object.size(“electricity_rates.csv”)

19. Quick Peeks Getting a Feel for your Data
Summarize the structure of an R object
str(rates)
Or perhaps you want only some more specific information?
class(rates) names(rates) dim(rates)
“dim” = dimensions

20. Quick Peeks Getting a Feel for your Data
Look at the top or bottom lines of your data set
head(rates, 10)
tail(rates)
Lists top lines, optional 2nd argument specifies number of lines
Lists bottom lines; if 2nd argument is not defined, defaults to 6 lines

21. Quick Peeks Getting a Feel for your Data
What if you want to identify the highest (or lowest) value records?
Start by ordering the contents by the parameter of interest.
order_by_hp <- mtcars [order(mtcars$hp, decreasing = TRUE), ]
Display the top lines
head(order_by_hp)
Place sorted contents in a new object
Object being sorted
Function
Arguments
Use this field as the basis for sorting
Sort in descending order

22. Quick Peeks Getting a Feel for your Data
What if you want to know if values in a data set meet a certain criterion?
For example, do any of the residential electricity rates (“res_rate”) in our rates data frame exceed a value of 0.5?
any(mtcars$hp>200)
…but do all of the residential electricity rates exceed 0.5?
all(mtcars$hp>200)
Function
Arguments
…of course, you knew the answer to this from the previous slide.
Function
Arguments

23. Quick Peeks Getting a Feel for your Data
Perhaps you want to know the unique values in a data field.
For example, how many test subjects are in the dataset Theoph (which contains pharmokinetics data for the drug Theophylline)?
unique(Theoph$Subject)
Function
Arguments

24. Uh-Oh That data’s not right!!
What can you do if you discover some data values that you know are incorrect?
One option, if you know what the values should be, is to replace the incorrect values with the corrected ones.
For example, suppose that, in the Theoph dataset, the test subject whose weight was recorded as 86.4 actually weighs 84.6?
Theoph$Wt <- sub(86.4, 84.6, Theoph$Wt)
Field to receive results
Function
Arguments
Existing value
Place the changed names back in the original data field
New value
Impacted field

25. Quick Statistics For a quick “grab bag” of popular statistics…
summary(rates)
Characters Factors Numbers
The statistics generated will depend upon the mode of each field
You may also generate a summary for a specific field (e.g. summary(rates$ind_rate))
Function
Arguments

26. Quick Statistics How about some quantiles?
Basic quantile
quantile(rates$ind_rate, na.rm = TRUE)
specify field remove NA values
…or, you can specify your break points
quantile(rates$ind_rate, probs = seq(0, 1, 0.1), na.rm = TRUE)
Generates a sequence from 0 to 1 at intervals of 0.1
Function
Arguments

27. Quick Statistics Perhaps some boxplots for your amusement?
boxplot(res_rate~state,
data=rates,
main="Residential Electricity Rates",
xlab="State",
ylab="Rate")
Function
Arguments
Plot residential rate as a function of state
Data source
Main title
X-axis label
Y-axis label

28. Quick Statistics
…and this is what the boxplot looks like.

29. Quick Statistics …or you can have a histogram.
hist(rates$ind_rate[which(rates$ind_rate != "NA")])
Use this data where the data is not equal to NA
Function
Arguments

30. Quick Statistics Other popular statistics.
Function
Description
Example
mean(x)
mean of object(x)
sd(x)
standard deviation of object(x)
median(x)
median of object(x)
range(x)
range of object(x)
sum(x)
sum of object(x)
min(x)
minimum
max(x)
maximum
Note: if data set includes NA values, use “na.rm = TRUE” argument to remove NA values from computations.

31. Popular Mathematical Functions
All of the following functions may be applied to either single values or to vectors of values.
For example, abs(-7.74) or abs(rates$comm_rate)
Function
Description
Example
abs(x)
absolute value(s)
sqrt(x)
square root(s)
ceiling(x)
rounds to nearest integer of greater value
floor(x)
rounds to nearest integer of lesser value
trunc(x)
truncates to integer value
round(x, n)
rounds to specified (n) number of places beyond the decimal point
signif(x, n)
rounds to specified (n) number of significant digits

32. Popular Mathematical Functions
Description
Example
sin(x)
sine
cos(x)
cosine
tan(x)
tangent
log(x)
natural logarithm (base = e)
log10(x)
base-10 logarithm
exp(x)
e to the x-power
Note: the trigonometric functions (sine, cosine, tangent) assume units of radians

33. Aggregation (no, not aggravation)
What if you wanted to calculate mean commercial electricity rates by state?
aggregate(rates$comm_rate,
by=list(rates$state),
FUN=mean,
na.rm=TRUE)
Function
Arguments
Aggregate the commercial rate data
Group the data by state
Calculate mean values
Remove NA values when doing computations
Can also use other functions: median, sd, sum, min, max, etc.

34. Aggregation You can also group data using multiple fields.
rates_agg <- aggregate(rates$comm_rate,
by=list(rates$state, rates$utility_name),
FUN=median,
na.rm=TRUE)
rates_agg[order(rates_agg$Group.1,
decreasing = FALSE), ]
New Object
Function
Arguments
Group using state and utility name

35. Taking subsets of a data set
Suppose that you are only interested in the electricity rates for Florida.
rates_FL <- subset(rates, state == "FL")
New Object
Command
Arguments
Data set Criteria

36. Object Conversions
Functions are often specific to only one type of object (vector, list, matrix, or data frame) or mode (e.g. numerical or character).
However, functions are available to convert (coerce) types.
as.data.frame(X, …)
as.list(X, …)
as.character(X, …)
as.numeric(X, …)
Object “X” coerced into a Data Frame object
Object “X” coerced into a List object
Object “X” coerced to Character mode
Object “X” coerced to Numeric mode
In addition to specifying the object name, arguments may specify row names, column names, whether character vectors should be converted to factors, etc.

37. Exporting R Data
Suppose that you had placed the 2014 data from the “babynames” function in a data frame, “names_2014”
How can you export that data from R?
General purpose function: write.table()
write.table(rates_FL,
file = "FL_Rates.csv",
sep = ",",
row.names = FALSE,
col.names = TRUE)
Command
Arguments
Object being exported
Path name for new file
How values will be separated
Do not include row names
Include column names

38. Variants of write.table
Variants of write.table for handling specific file formats
write.csv()
For writing to CSV files; assumes comma-separated fields.
write.csv2()
Used for data formatted in countries that use commas as decimal points.
Assumes use of semi-colon instead of comma to separate fields.
write.xlsx()
For writing to and Excel spreadsheet
write.foreign()
For writing to SPSS, Stata, SAS

39. Appendix Reference Materials for Commands used in this Workshop

40. Function Package Reference Materials
abs
base
aggregate
stats
all
any
as.character
as.data.frame
devel/library/base/html/as.data.frame.html
as.list
as.numeric
babynames
boxplot
graphics c
ceiling
class
cos
dim
download.file
utils
devel/library/utils/html/download.file.html
exp

41. Function Package Reference Materials
floor
base
getwd
head
utils
hist
graphics
install.packages
devel/library/utils/html/install.packages.html
library
list.dirs
list.files
log
log10
max
mean
median
stats
min
names

42. Function Package Reference Materials
object.size
utils
order
base
quantile
stats
range
read.csv
read.csv2
read.delim
read.delim2
read.table
rep
round sd
setwd
signif
sin

43. Function Package Reference Materials
sqrt
base
str
utils
sub
subset
sum
summary
tail
tan
trunc
unique
unzip
write.csv
write.csv2
write.foreign
foreign
devel/library/foreign/html/write.foreign.html
write.table
write.xlsx
xlsx

44. We would appreciate your feedback on this workshop