Chapter 2: Basics of the R Language

Website for all data files:
http://r-marketing.r-forge.r-project.org/data.html

\( \rightarrow \) We'll cover some of the basic object types in R

\( \rightarrow \) Objects in R include variables, data sets, and functions

The assignment operator <- assigns a value to a named object.

x <- c(2, 4, 6, 8)
x

[1] 2 4 6 8

Object names are case sensitive. Instead of 'x', 'X' produces an error:

X

Error in eval(expr, envir, enclos): object 'X' not found

We've just seen how to create a vector: the c() function concatenates individual items into a vector.

xNum  <- c(1, 3.14159, 5, 7)
xNum

[1] 1.00000 3.14159 5.00000 7.00000

xLog  <- c(TRUE, FALSE, TRUE, TRUE)
xLog

[1]  TRUE FALSE  TRUE  TRUE

xChar <- c("foo", "bar", "boo", "far")
xChar

[1] "foo" "bar" "boo" "far"

A vector can only hold a single type of value (number, text, etc). Values are coerced to the most general type.

xMix  <- c(1, TRUE, 3, "Hello, world!") 
xMix

[1] "1"             "TRUE"          "3"             "Hello, world!"

c() can be used to add vectors just as it adds single items:

x2 <- c(x, x)
x2
[1] 2 4 6 8 2 4 6 8

Type coercion will be applied as needed:

c(x2, 100)

[1]   2   4   6   8   2   4   6   8 100

c(x2, "Hello")

[1] "2"     "4"     "6"     "8"     "2"     "4"     "6"     "8"     "Hello"

xMix

[1] "1"             "TRUE"          "3"             "Hello, world!"

xMix[1]   # we'll see more on indices later

[1] "1"

as.numeric(xMix[1])   # forces it to "numeric"

[1] 1

as.numeric(xMix[1]) + 1.5

[1] 2.5

There are many ways to get help for R:

The summary() function summarizes an object in a way that is (usually) appropriate for its data type:

summary(xNum)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   2.606   4.071   4.035   5.500   7.000 

summary(xChar)

   Length     Class      Mode 
        4 character character 

x2

[1] 2 4 6 8 2 4 6 8

x2 + 1

[1] 3 5 7 9 3 5 7 9

x2 * pi 

[1]  6.283185 12.566371 18.849556 25.132741  6.283185 12.566371 18.849556
[8] 25.132741

R will generalize operations across multiple vectors (or matrices) as best it can:

x

[1] 2 4 6 8

x2    # longer than x

[1] 2 4 6 8 2 4 6 8

(x+cos(0.5)) * x2     # x is recycled to match x2

[1]  5.755165 19.510330 41.265495 71.020660  5.755165 19.510330 41.265495
[8] 71.020660

So vectors can be recycled. How do you find the length?

length(x)

[1] 4

length(x2)

[1] 8

A more general solution is to investigate the structure:

str(x2)

 num [1:8] 2 4 6 8 2 4 6 8

str(xChar)

 chr [1:4] "foo" "bar" "boo" "far"

Basic 1-by-1 integer sequences are constructed with the “:” operator:

xSeq <- 1:10
xSeq

 [1]  1  2  3  4  5  6  7  8  9 10

Be careful with operator precedence … clarify liberally with parentheses:

1:5*2

[1]  2  4  6  8 10

1:(5*2)

 [1]  1  2  3  4  5  6  7  8  9 10

Basic indexing uses a set of integers to select positions:

xNum

[1] 1.00000 3.14159 5.00000 7.00000

xNum[2:4]

[1] 3.14159 5.00000 7.00000

xNum[c(1,3)]

[1] 1 5

Variables and math operators can be used as well:

myStart <- 2
xNum[myStart:sqrt(myStart+7)]

[1] 3.14159 5.00000

A negative index omits elements (returns everything else):

xSeq

 [1]  1  2  3  4  5  6  7  8  9 10

xSeq[-5:-7]

[1]  1  2  3  4  8  9 10

Boolean values of TRUE and FALSE can be used to select items:

xNum

[1] 1.00000 3.14159 5.00000 7.00000

xNum[c(FALSE, TRUE, TRUE, TRUE)]

[1] 3.14159 5.00000 7.00000

This is most often used with comparative operators to select items:

xNum > 3

[1] FALSE  TRUE  TRUE  TRUE

xNum[xNum > 3]

[1] 3.14159 5.00000 7.00000

my.test.scores <- c(91, 93, NA, NA)

mean(my.test.scores)

[1] NA

max(my.test.scores)

[1] NA

mean(my.test.scores, na.rm=TRUE)

[1] 92

max(my.test.scores, na.rm=TRUE)

[1] 93

na.omit(my.test.scores)

[1] 91 93
attr(,"na.action")
[1] 3 4
attr(,"class")
[1] "omit"

mean(na.omit(my.test.scores))

[1] 92

is.na(my.test.scores)

[1] FALSE FALSE  TRUE  TRUE

my.test.scores[!is.na(my.test.scores)]

[1] 91 93

• Skipping lists for today's tutorial
• They are important but not directly used as often as other data formats
• See the book for detail, section 2.4.7

\( \rightarrow \) Data frames are the most common way to handle data sets in R.

x.df <- data.frame(xNum, xLog, xChar)
x.df

     xNum  xLog xChar
1 1.00000  TRUE   foo
2 3.14159 FALSE   bar
3 5.00000  TRUE   boo
4 7.00000  TRUE   far

Data frames have names and are indexed row, column.

x.df[2,1]

[1] 3.14159

x.df[1,3]

[1] foo
Levels: bar boo far foo

By default, text data is converted to factors. You'll often want to turn that off:

x.df[1,3]

[1] foo
Levels: bar boo far foo

x.df <- data.frame(xNum, xLog, xChar, stringsAsFactors=FALSE)
x.df[1,3]

[1] "foo"

x.df[2, ]  # all of row 2

     xNum  xLog xChar
2 3.14159 FALSE   bar

x.df[ ,3]  # all of column 3

[1] "foo" "bar" "boo" "far"

x.df[2:3, ] 

     xNum  xLog xChar
2 3.14159 FALSE   bar
3 5.00000  TRUE   boo

x.df[ ,1:2] 

     xNum  xLog
1 1.00000  TRUE
2 3.14159 FALSE
3 5.00000  TRUE
4 7.00000  TRUE

x.df[-3, ]  # omit the third observation

     xNum  xLog xChar
1 1.00000  TRUE   foo
2 3.14159 FALSE   bar
4 7.00000  TRUE   far

x.df[, -2]  # omit the second column

     xNum xChar
1 1.00000   foo
2 3.14159   bar
3 5.00000   boo
4 7.00000   far

Warning: we're about to delete everything first

rm(list=ls())    # caution, deletes all objects

store.num <- factor(c(3, 14, 21, 32, 54)) # store id
store.rev <- c(543, 654, 345, 678, 234)   # store revenue, $K
store.visits <- c(45, 78, 32, 56, 34)     # visits, 1000s
store.manager <- c("Annie", "Bert", "Carla", "Dave", "Ella")

(store.df <- data.frame(store.num, store.rev, store.visits,
                        store.manager, stringsAsFactors=F))

  store.num store.rev store.visits store.manager
1         3       543           45         Annie
2        14       654           78          Bert
3        21       345           32         Carla
4        32       678           56          Dave
5        54       234           34          Ella

summary(store.df)   # always recommended!

 store.num   store.rev      store.visits store.manager     
 3 :1      Min.   :234.0   Min.   :32    Length:5          
 14:1      1st Qu.:345.0   1st Qu.:34    Class :character  
 21:1      Median :543.0   Median :45    Mode  :character  
 32:1      Mean   :490.8   Mean   :49                      
 54:1      3rd Qu.:654.0   3rd Qu.:56                      
           Max.   :678.0   Max.   :78                      

store.df$store.manager

[1] "Annie" "Bert"  "Carla" "Dave"  "Ella" 

mean(store.df$store.rev)

[1] 490.8

write.csv(store.df, row.names=FALSE)

"store.num","store.rev","store.visits","store.manager"
"3",543,45,"Annie"
"14",654,78,"Bert"
"21",345,32,"Carla"
"32",678,56,"Dave"
"54",234,34,"Ella"

write.csv(store.df, file="store-df.csv", row.names=FALSE)
read.csv("store-df.csv")  # "file=" is optional

  store.num store.rev store.visits store.manager
1         3       543           45         Annie
2        14       654           78          Bert
3        21       345           32         Carla
4        32       678           56          Dave
5        54       234           34          Ella

Access the Salaries data set:

library(car)    # install.packages("car") if needed
data(Salaries)

1. How many variables and observations are there in the data set?
2. How many professors have more than 40 years of service?
   (\( \rightarrow \) hint: you can sum() a logical vector)
3. How many have salary > $150000?
4. What is the mean salary for professors with >20 years service?
5. How do you find out more about the data set?

1. How many variables and observations are there in the data?
2. How many professors have more than 40 years of service?
3. Which observations have < 1 year of service?
4. What is the mean salary for professors with >20 years service?
5. How do you find out more about the data set?

dim(Salaries)                             # or even better: str(Salaries)

[1] 397   6

sum(Salaries$yrs.service > 40)

[1] 21

Salaries[Salaries$yrs.service > 20, ]      # output not shown

mean(Salaries[Salaries$yrs.service > 20, "salary"])

[1] 122103.9

?Salaries

• Basic Functions
• Sequences, again
• Interesting numbers
• Load and save raw data

se <- function(x) { sd(x) / sqrt(length(x)) }

se(store.df$store.visits)

[1] 8.42615

mean(store.df$store.visits) + 1.96 * se(store.df$store.visits)

[1] 65.51525

A function has:

• an assigned name (created with '<-')
• zero or more arguments that it operates on (in () )
• a body (usually in { }) with lines of code
• a return value (the last computed value, by default)

se <- function(x) {
  # computes standard error of the mean
  tmp.sd <- sd(x)      # standard deviation
  tmp.N  <- length(x)  # sample size
  tmp.se <- tmp.sd / sqrt(tmp.N)   # std error of the mean
  return(tmp.se)       # return() is optional but clear
}

se(store.df$store.visits)

[1] 8.42615

This is much better! You can examine it to see what it does:

se

function(x) {
  # computes standard error of the mean
  tmp.sd <- sd(x)      # standard deviation
  tmp.N  <- length(x)  # sample size
  tmp.se <- tmp.sd / sqrt(tmp.N)   # std error of the mean
  return(tmp.se)       # return() is optional but clear
}

The seq() function constructs sequences in various ways:

seq(from=-5, to=28, by=4)

[1] -5 -1  3  7 11 15 19 23 27

seq(from=-5, to=28, length=6)

[1] -5.0  1.6  8.2 14.8 21.4 28.0

The rep() (repeat) function is also useful. It is especially good for constructing indices into data sets with repeating structure:

rep(c(1,2,3), each=3)

[1] 1 1 1 2 2 2 3 3 3

rep(seq(from=-3, to=13, by=4), c(1, 2, 3, 2, 1))

[1] -3  1  1  5  5  5  9  9 13

1/0

[1] Inf

log(c(-1,0,1))

[1]  NaN -Inf    0

sqrt(-2)

[1] NaN

sqrt(2i)

[1] 1+1i

You can use these values yourself (occasionally it makes sense):

10 < Inf

[1] TRUE

save(store.df, file="store-df-backup.RData")

rm(store.df)        
mean(store.df$store.rev)     # error

Error in mean(store.df$store.rev): object 'store.df' not found

load("store-df-backup.RData")
mean(store.df$store.rev)     # works now

[1] 490.8

store.df <- 5
store.df

[1] 5

load("store-df-backup.RData")
store.df

  store.num store.rev store.visits store.manager
1         3       543           45         Annie
2        14       654           78          Bert
3        21       345           32         Carla
4        32       678           56          Dave
5        54       234           34          Ella

Save to “.Rdata”:

• save.image()

Save to arbitrary filename

• save.image(“mywork.RData”)
  

Load an image

• load(“mywork.RData”)

Break time

This presentation is based on Chapter 6 of Chapman and Feit, R for Marketing Research and Analytics © 2015 Springer. http://r-marketing.r-forge.r-project.org/

All code in the presentation is licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\ Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.