July 11, 2016

## Getting Started

• Open R Studio
• Files –> New –> R Script
• Save the blank R script as "day1.R" in a directory of your choosing

## Commenting in Scripts

Add a comment header to day1.R :# is the comment symbol

#################
# Title: Demo R Script
# Author: Andrew Jaffe
# Date: 7/11/2016
# Purpose: Demonstrate comments in R
###################

# nothing to its right is evaluated

# this # is still a comment
### you can use many #'s as you want

# sometimes you have a really long comment,
#    like explaining what you are doing
#    for a step in analysis.
# Take it to another line

## Explaining output on slides

In slides, a command (we'll also call them code or a code chunk) will look like this

print("I'm code")
[1] "I'm code"

And then directly after it, will be the output of the code.
So print("I'm code") is the code chunk and [1] "I'm code" is the output.

## Data Input

• 'Reading in' data is the first step of any real project/analysis
• R can read almost any file format, especially via add-on packages
• We are going to focus on simple delimited files first
• tab delimited (e.g. '.txt')
• comma separated (e.g. '.csv')
• Microsoft excel (e.g. '.xlsx')

## Data Input

R Studio features some nice "drop down" support, where you can run some tasks by selecting them from the toolbar.

For example, you can easily import text datasets using the "Tools –> Import Dataset" command. Selecting this will bring up a new screen that lets you specify the formatting of your text file.

After importing a datatset, you get the corresponding R commands that you can enter in the console if you want to re-import data.

## Data Input

So what is going on "behind the scenes"?

read.table(): Reads a file in table format and creates a data frame from it, with cases corresponding to lines and variables to fields in the file.

# the four ones I've put at the top are the important inputs
header = FALSE, # are there column names?
sep = "", # what separates columns?
as.is = !stringsAsFactors, # do you want character strings as factors or characters?
quote = "\"'",  dec = ".", row.names, col.names,
na.strings = "NA", nrows = -1,
skip = 0, check.names = TRUE, fill = !blank.lines.skip,
strip.white = FALSE, blank.lines.skip = TRUE, comment.char = "#",
stringsAsFactors = default.stringsAsFactors())

# for example: read.table("file.txt", header = TRUE, sep="\t", as.is=TRUE)

## Data Input

• The filename is the path to your file, in quotes
• The function will look in your "working directory" if no absolute file path is given
• Note that the filename can also be a path to a file on a website (e.g. 'www.someurl.com/table1.txt')

## Data Input

There is a 'wrapper' function for reading CSV files:

read.csv
function (file, header = TRUE, sep = ",", quote = "\"", dec = ".",
fill = TRUE, comment.char = "", ...)
dec = dec, fill = fill, comment.char = comment.char, ...)
<bytecode: 0x000000000613fd70>
<environment: namespace:utils>

Note: the ... designates extra/optional arguments that can be passed to read.table() if needed

## Before we get started: Working Directories

• R looks for files on your computer relative to the "working" directory
• It's always safer to set the working directory at the beginning of your script. Note that setting the working directory created the necessary code that you can copy into your script.
• Example of help file
## get the working directory
getwd()
# setwd("~/summerR_2016/Lectures")

## Setting a Working Directory

• Setting the directory can sometimes be finicky
• Windows: Default directory structure involves single backslashes (""), but R interprets these as "escape" characters. So you must replace the backslash with forward slashed ("/") or two backslashes ("\")
• Mac/Linux: Default is forward slashes, so you are okay
• Typical linux/DOS directory structure syntax applies
• ".." goes up one level
• "./" is the current directory
• "~" is your home directory

## Data Input

• Here would be reading in the data from the command line, specifying the file path:
mon = read.csv("../data/Monuments.csv",header=TRUE,as.is=TRUE)
head(mon)
                              name zipCode neighborhood councilDistrict
1           James Cardinal Gibbons   21201     Downtown              11
2              The Battle Monument   21202     Downtown              11
3 Negro Heroes of the U.S Monument   21202     Downtown              11
4              Star Bangled Banner   21202     Downtown              11
5  Flame at the Holocaust Monument   21202     Downtown              11
6                   Calvert Statue   21202     Downtown              11
policeDistrict                       Location.1
1        CENTRAL  408 CHARLES ST\nBaltimore, MD\n
2        CENTRAL
3        CENTRAL
4        CENTRAL 100 HOLLIDAY ST\nBaltimore, MD\n
5        CENTRAL    50 MARKET PL\nBaltimore, MD\n
6        CENTRAL  100 CALVERT ST\nBaltimore, MD\n

## What is going on "behind the scenes"

• R is reading in the Monuments CSV with specific arguments
• R assigns the read-in data to a newly-created R object called mon

## R as a calculator

2 + 2
[1] 4
2 * 4
[1] 8
2 ^ 3
[1] 8

Note, when you type your command, R inherently thinks you want to print the result.

## R as a calculator

• The R console is a full calculator
• Try to play around with it:
• +, -, /, * are add, subtract, divide and multiply
• ^ or ** is power
• parentheses – ( and ) – work with order of operations

## R as a calculator

2 + (2 * 3)^2
[1] 38
(1 + 3) / 2 + 45
[1] 47

## R as a calculator

Try evaluating the following:

• 2 + 2 * 3 / 4 -3
• 2 * 3 / 4 * 2
• 2^4 - 1

## Assignment

• You can create variables from within the R environment and from files on your computer
• R uses "=" or "<-" to assign values to a variable name
• Variable names are case-sensitive, i.e. X and x are different
x = 2 # Same as: x <- 2
x
[1] 2
x * 4
[1] 8
x + 2
[1] 4

## R variables

• The most comfortable and familiar class/data type for many of you will be data.frame
• You can think of these as essentially Excel spreadsheets with rows (usually subjects or observations) and columns (usually variables)

## Data Classes:

• One dimensional classes ('vectors'):
• Character: strings or individual characters, quoted
• Numeric: any real number(s)
• Integer: any integer(s)/whole numbers
• Factor: categorical/qualitative variables
• Logical: variables composed of TRUE or FALSE
• Date/POSIXct: represents calendar dates and times

## R variables

• data.frames are somewhat advanced objects in R; we will start with simpler objects;
• Here we introduce "1 dimensional" classes; these are often referred to as 'vectors'
• Vectors can have multiple sets of observations, but each observation has to be the same class.
class(x)
[1] "numeric"
y = "hello world!"
print(y)
[1] "hello world!"
class(y)
[1] "character"

## R variables

Try assigning your full name to an R variable called name

## R variables

Try assigning your full name to an R variable called name

name = "Andrew Jaffe"
name
[1] "Andrew Jaffe"

## The 'combine' function

The function c() collects/combines/joins single R objects into a vector of R objects. It is mostly used for creating vectors of numbers, character strings, and other data types.

x <- c(1, 4, 6, 8)
x
[1] 1 4 6 8
class(x)
[1] "numeric"

## The 'combine' function

Try assigning your first and last name as 2 separate character strings into a single vector called name2

## The 'combine' function

Try assigning your first and last name as 2 separate character strings into a length-2 vector called name2

name2 = c("Andrew","Jaffe")
name2
[1] "Andrew" "Jaffe"

## R variables

length(): Get or set the length of vectors (including lists) and factors, and of any other R object for which a method has been defined.

length(x)
[1] 4
y
[1] "hello world!"
length(y)
[1] 1

## R variables

What do you expect for the length of the name variable? What about the name2 variable?

What are the lengths of each?

## R variables

What do you expect for the length of the name variable? What about the name2 variable?

What are the lengths of each?

length(name)
[1] 1
length(name2)
[1] 2

## R variables

You can perform functions to entire vectors of numbers very easily.

x + 2
[1]  3  6  8 10
x * 3
[1]  3 12 18 24
x + c(1, 2, 3, 4)
[1]  2  6  9 12

## R variables

But things like algebra can only be performed on numbers.

> name2 + 4
[1] Error in name2 * 4 : non-numeric argument
to binary operator

## R variables

And save these modified vectors as a new vector.

y = x + c(1, 2, 3, 4)
y 
[1]  2  6  9 12

Note that the R object y is no longer "Hello World!" - It has effectively been overwritten by assigning new data to the variable

## R variables

• You can get more attributes than just class. The function str gives you the structure of the object.
str(x)
 num [1:4] 1 4 6 8
str(y)
 num [1:4] 2 6 9 12

This tells you that x is a numeric vector and tells you the length.

## Logical

logical is a class that only has two possible elements: TRUE and FALSE

x = c(TRUE, FALSE, TRUE, TRUE, FALSE)
class(x)
[1] "logical"
is.numeric(c("Andrew", "Jaffe"))
[1] FALSE
is.character(c("Andrew", "Jaffe"))
[1] TRUE

## Logical

Note that logical elements are NOT in quotes.

z = c("TRUE", "FALSE", "TRUE", "FALSE")
class(z)
[1] "character"
as.logical(z)
[1]  TRUE FALSE  TRUE FALSE

Bonus: sum() and mean() work on logical vectors - they return the total and proportion of TRUE elements, respectively.

sum(as.logical(z))
[1] 2

## General Class Information

There are two useful functions associated with practically all R classes, which relate to logically checking the underlying class (is.CLASS_()) and coercing between classes (as.CLASS_()).

is.numeric(c("Andrew", "Jaffe"))
[1] FALSE
is.character(c("Andrew", "Jaffe"))
[1] TRUE

## General Class Information

There are two useful functions associated with practically all R classes, which relate to logically checking the underlying class (is.CLASS_()) and coercing between classes (as.CLASS_()).

as.character(c(1, 4, 7))
[1] "1" "4" "7"
as.numeric(c("Andrew", "Jaffe"))
Warning: NAs introduced by coercion
[1] NA NA

## Factors

A factor is a special character vector where the elements have pre-defined groups or 'levels'. You can think of these as qualitative or categorical variables:

x = factor(c("boy", "girl", "girl", "boy", "girl"))
x 
[1] boy  girl girl boy  girl
Levels: boy girl
class(x)
[1] "factor"

Note that levels are, by default, in alphanumerical order.

## Factors

Factors are used to represent categorical data, and can also be used for ordinal data (ie categories have an intrinsic ordering)

Note that R reads in character strings as factors by default in functions like read.table()

'The function factor is used to encode a vector as a factor (the terms 'category' and 'enumerated type' are also used for factors). If argument ordered is TRUE, the factor levels are assumed to be ordered.'

factor(x = character(), levels, labels = levels,
exclude = NA, ordered = is.ordered(x))

## Factors

Suppose we have a vector of case-control status

cc = factor(c("case","case","case",
"control","control","control"))
cc
[1] case    case    case    control control control
Levels: case control
levels(cc) = c("control","case")
cc
[1] control control control case    case    case
Levels: control case

## Factors

Note that the levels are alphabetically ordered by default. We can also specify the levels within the factor call

casecontrol = c("case","case","case","control",
"control","control")
factor(casecontrol, levels = c("control","case") )
[1] case    case    case    control control control
Levels: control case
factor(casecontrol, levels = c("control","case"),
ordered=TRUE)
[1] case    case    case    control control control
Levels: control < case

## Factors

Factors can be converted to numeric or character very easily

x = factor(casecontrol,
levels = c("control","case") )
as.character(x)
[1] "case"    "case"    "case"    "control" "control" "control"
as.numeric(x)
[1] 2 2 2 1 1 1

## Factors

However, you need to be careful modifying the labels of existing factors, as its quite easy to alter the meaning of the underlying data.

xCopy = x
levels(xCopy) = c("case", "control") # wrong way
xCopy        
[1] control control control case    case    case
Levels: case control
as.character(xCopy) # labels switched
[1] "control" "control" "control" "case"    "case"    "case"
as.numeric(xCopy)
[1] 2 2 2 1 1 1

## Date

You can convert date-like strings in the Date class (http://www.statmethods.net/input/dates.html for more info)

theDate = c("01/21/1990", "02/01/1989", "03/23/1988")
sort(theDate)
[1] "01/21/1990" "02/01/1989" "03/23/1988"
newDate <- as.Date(theDate, "%m/%d/%Y")
sort(newDate)
[1] "1988-03-23" "1989-02-01" "1990-01-21"

## Date

However, the lubridate package is much easier for generating explicit dates:

library(lubridate) # great for dates!
newDate2 = mdy(theDate)
newDate2
[1] "1990-01-21" "1989-02-01" "1988-03-23"

## POSIXct

The POSIXct class is like a more general date format (with hours, minutes, seconds).

theTime = Sys.time()
theTime
[1] "2016-07-11 13:12:24 PDT"
class(theTime)
[1] "POSIXct" "POSIXt"
theTime + as.period(20, unit = "minutes") # the future
[1] "2016-07-11 13:32:24 PDT"

## Data Classes:

• Two dimensional classes:
• data.frame: traditional 'Excel' spreadsheets
• Each column can have a different class, from above
• Matrix: two-dimensional data, composed of rows and columns. Unlike data frames, the entire matrix is composed of one R class, e.g. all numeric or all characters.

## Matrices

n = 1:9
n
[1] 1 2 3 4 5 6 7 8 9
mat = matrix(n, nrow = 3)
mat
     [,1] [,2] [,3]
[1,]    1    4    7
[2,]    2    5    8
[3,]    3    6    9

## Matrix (and Data frame) Functions

These are in addition to the previous useful vector functions:

• nrow() displays the number of rows of a matrix or data frame
• ncol() displays the number of columns
• dim() displays a vector of length 2: # rows, # columns
• colnames() displays the column names (if any) and rownames() displays the row names (if any)

## Data Frames

The data.frame is the other two dimensional variable class.

Again, data frames are like matrices, but each column is a vector that can have its own class. So some columns might be character and others might be numeric, while others maybe a factor.

## Lists

• One other data type that is the most generic are lists.
• Can be created using list()
• Can hold vectors, strings, matrices, models, list of other list, lists upon lists!
> mylist <- list(letters=c("A", "b", "c"),
+         numbers=1:3, matrix(1:25, ncol=5))
> mylist
$letters [1] "A" "b" "c"$numbers
[1] 1 2 3

[[3]]
[,1] [,2] [,3] [,4] [,5]
[1,]    1    6   11   16   21
[2,]    2    7   12   17   22
[3,]    3    8   13   18   23
[4,]    4    9   14   19   24
[5,]    5   10   15   20   25

## More on Data Frames

colnames(mon) # column names
[1] "name"            "zipCode"         "neighborhood"    "councilDistrict"
[5] "policeDistrict"  "Location.1"     
head(mon$zipCode) # first few rows [1] 21201 21202 21202 21202 21202 21202 ## Data Input The read.table() function returns a data.frame, which is the primary data format for most data cleaning and analyses str(mon) # structure of an R object 'data.frame': 84 obs. of 6 variables:$ name           : chr  "James Cardinal Gibbons" "The Battle Monument" "Negro Heroes of the U.S Monument" "Star Bangled Banner" ...
$zipCode : int 21201 21202 21202 21202 21202 21202 21202 21211 21213 21211 ...$ neighborhood   : chr  "Downtown" "Downtown" "Downtown" "Downtown" ...
$councilDistrict: int 11 11 11 11 11 11 11 7 14 14 ...$ policeDistrict : chr  "CENTRAL" "CENTRAL" "CENTRAL" "CENTRAL" ...
\$ Location.1     : chr  "408 CHARLES ST\nBaltimore, MD\n" "" "" "100 HOLLIDAY ST\nBaltimore, MD\n" ...

## Data Output

While its nice to be able to read in a variety of data formats, it's equally important to be able to output data somewhere.

write.table(): prints its required argument x (after converting it to a data.frame if it is not one nor a matrix) to a file or connection.

write.table(x,file = "", append = FALSE, quote = TRUE, sep = " ",
eol = "\n", na = "NA", dec = ".", row.names = TRUE,
col.names = TRUE, qmethod = c("escape", "double"),
fileEncoding = "")

## Data Output

x: the R data.frame or matrix you want to write

file: the file name where you want to R object written. It can be an absolute path, or a filename (which writes the file to your working directory)

sep: what character separates the columns?

• "," = .csv - Note there is also a write.csv() function
• "" = tab delimited

row.names: I like setting this to FALSE because I email these to collaborators who open them in Excel

## Data Output

For example, we can write back out the Monuments dataset with the new column name:

write.csv(mon, file="monuments_new.csv", row.names=FALSE)

Note that row.names=TRUE would make the first column contain the row names, here just the numbers 1:nrow(mon), which is not very useful for Excel. Note that row names can be useful/informative in R if they contain information (but then they would just be a separate column).

## Data Input - Excel

Many data analysts collaborate with researchers who use Excel to enter and curate their data. Often times, this is the input data for an analysis. You therefore have two options for getting this data into R:

• Saving the Excel sheet as a .csv file, and using read.csv()
• Using an add-on package, like xlsx, readxl, or openxlsx

For single worksheet .xlsx files, I often just save the spreadsheet as a .csv file (because I often have to strip off additional summary data from the columns)

For an .xlsx file with multiple well-formated worksheets, I use the xlsx, readxl, or openxlsx package for reading in the data.

## Data Input - Other Software

• haven package (https://cran.r-project.org/web/packages/haven/index.html) reads in SAS, SPSS, Stata formats
• readxl package - the read_excel function can read Excel sheets easily