• The RStudio environment
• Basic operators
• Basic data types
• Define variables
  • variable_name <- some_value
  • target <- data

• Advanced data types
  1. Vector
  2. List
  3. Matrix
  4. Data frame

• Numeric
• Character
• Logic
• Factor
• Vector: a set of values, all of the same data type
• List: a set of values, potentially with different data types
• Matrix: special 2D numerical structure
• Data frame: like an Excel sheet or a database table

A vector is a sequence of values, all of the same type

x <- c(1, 3, 7, 15)   # c stands for "combine"
x

## [1]  1  3  7 15

is.vector(x)

## [1] TRUE

length(x)             # find the number of elements in a vector

## [1] 4

seq(from=1, to=10)         # sequence

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

1:10                       # sequence shorthand

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

seq(from=1, to=10, by=2)   # sequence 

## [1] 1 3 5 7 9

rep(7, times=3)            # repeat

## [1] 7 7 7

vec <- c(10, 20, 7, 13) # assigning a vector to a variable
vec

## [1] 10 20  7 13

names(vec) <- c("value1", "value2", "value3", "value4")
vec

## value1 value2 value3 value4 
##     10     20      7     13

vec <- c("value1"=10, "value2"=20, "value3"=7, "value4"=13) #same result
vec <- c(value1=10, value2=20, value3=7, value4=13)        #same result

vec1 <- c(1, 3, 5)
vec2 <- c(11, 13, 15)
c(vec1, vec2, c(21, 23, 25))

## [1]  1  3  5 11 13 15 21 23 25

Try to find the most efficient way (fewer characters) to create the following vectors:

##  [1] 10 11 12 13 14 15 16 17 18 19 20

##  [1] -30 -25 -20 -15 -10  -5   0   5  10  15  20  25  30

##  apple banana cherry 
##      4      5      3

Vector computations are performed element-wise

earnings <- c(10, 20, 30, 40) 
expenses <- c(5, 25, 25, 10) 
5 * earnings 

## [1]  50 100 150 200

earnings - expenses 

## [1]  5 -5  5 30

earnings * c(1, 2, 3, 4) 

## [1]  10  40  90 160

x <- c(10, 20, 30) 

Is there a value of 20 in x?

20 %in% x

## [1] TRUE

Is there a value of 40 in x?

40 %in% x

## [1] FALSE

basket <- c("apple", "banana", "cherry") 

Does basket have apple?

"apple" %in% basket

## [1] TRUE

Does basket have cheese?

"cheese" %in% basket

## [1] FALSE

In real world, your data may contain missing values. In R, we use NA (upper case) to represent a missing value.

vec = c(1, 4, NA, 2)
vec

## [1]  1  4 NA  2

sum(vec)

## [1] NA

max(vec)

## [1] NA

NA creates problems for most numerical functions.

For example, we cannot add NA to other numbers.

To apply these numerical functions on data with NAs, we simply just remove NAs from the calculation. That is,

sum(vec, na.rm = T) # remove NAs before calculating the sum

## [1] 7

max(vec, na.rm = T) # remove NAs before getting the max value

## [1] 4

Recycling repeats elements in the shorter vector until its length matches the longer vector

u <- c(10, 20) 
v <- c(1, 2, 3, 4, 5)
u + v  # the shorter vector will be recycled to match the longer vector

## [1] 11 22 13 24 15

Under the hood:

u + v

= c(10, 20) + c(1, 2, 3, 4, 5)

= c(10, 20, 10, 20, 10) + c(1, 2, 3, 4, 5) # recycling

= c(10+1, 20+2, 10+3, 20+4, 10+5) # element-wise operation

= c(11, 22, 13, 24, 15)

Without typing the following into R, guess what the result of the last line would be:

vec1 <- 1:4
vec2 <- c(1, 0, 0,0)
vec1 - vec2

Another more challenging one:

seq(from=0, to=10, by=2) + 3:1

You can retrieve elements from a vector by specifying the indexes of the elements. This operation is also known as subsetting.

vec <- c("value1"=10, "value2"=20, "value3"=30, "value4"=40)
vec[1] # get the element at index 1

## value1 
##     10

vec["value3"] # get the element whose name matches the string

## value3 
##     30

You can provide more than just one index.

vec[1:3] # specify a vector of indexes

## value1 value2 value3 
##     10     20     30

vec[c(3, 2, 1, 4)] # return with the specified order

## value3 value2 value1 value4 
##     30     20     10     40

vec[c("value4", "value4")]

## value4 value4 
##     40     40

vec <- c("value1"=10, "value2"=20, "value3"=30, "value4"=40)
vec[-1] # all but the first element

## value2 value3 value4 
##     20     30     40

vec[-c(1, 2)] # all but the first and the second elements

## value3 value4 
##     30     40

List is also a container for values, but can accommodate items of different data types.

x <- list("Bob", c(100,80,90))
x

## [[1]]
## [1] "Bob"
## 
## [[2]]
## [1] 100  80  90

Just like vectors, you can give each element a name:

x <- list(name="Bob", grades=c(100,80,90))
x

## $name
## [1] "Bob"
## 
## $grades
## [1] 100  80  90

x[2]           # get the second elment as a list

## $grades
## [1] 100  80  90

x["grades"]    # get the elment named "grades" as a list

## $grades
## [1] 100  80  90

y1 = x[2]
class(y1)

## [1] "list"

x[[2]]         # get the second elment as a vector

## [1] 100  80  90

y2 = x[[2]]
class(y2)

## [1] "numeric"

x[["grades"]]  # get the elment named "grades" as a vector

## [1] 100  80  90

x$grades       # most common/readable way to retrieve an elment by name

## [1] 100  80  90

Create the following list and use the function mean() to get the GPA (grade point average) from her grade points:

## $name
## [1] "Anna"
## 
## $is_female
## [1] TRUE
## 
## $age
## [1] 22
## 
## $enrollment
## [1] "MIS4710" "MIS4720" "MIS4730"
## 
## $grade_point
## [1] 4 3 4

A matrix is a collection of data elements arranged in a two-dimensional rectangular layout.

A <- matrix( 
  1:6,                 # the data elements 
  nrow=2,              # number of rows 
  ncol=3,              # number of columns 
  byrow = TRUE)        # fill matrix by rows 
A

##      [,1] [,2] [,3]
## [1,]    1    2    3
## [2,]    4    5    6

A data frame is a set of vectors of equal length. Consider data frame as an Excel sheet or a database table.

Column names are preserved or guessed if not explicitly set

course <- c("MIS4730", "MIS4710", "MIS4950", "MIS1234") 
num_of_students <- c(20, 10, 40, 30) 
data_analytics_minor <- c(TRUE, TRUE, TRUE, FALSE) 
df <- data.frame(course, n_students=num_of_students, data_analytics_minor,
                stringsAsFactors=F)
df # notice the column names and row names

##    course n_students data_analytics_minor
## 1 MIS4730         20                 TRUE
## 2 MIS4710         10                 TRUE
## 3 MIS4950         40                 TRUE
## 4 MIS1234         30                FALSE

ncol(df) # number of columns

## [1] 3

nrow(df) # number of rows

## [1] 4

colnames(df) # get column names

## [1] "course"               "n_students"           "data_analytics_minor"

rownames(df) # get row names

## [1] "1" "2" "3" "4"

You can change column and row names:

df2 <- df # create a copy of df, and name it as "df2"
colnames(df2) <- c("col1", "col2", "col3") # assign column names
colnames(df2)   # they were "course", "n_students", "ba_minor_course"

## [1] "col1" "col2" "col3"

rownames(df2) <- c("row1", "row2", "row3", "row4") # assign row names
rownames(df2)   # they were "1", "2", "3", "4"

## [1] "row1" "row2" "row3" "row4"

There are many ways you can get values out of a column:

• The most readable way: dataframe_name$column_name

df$course

## [1] "MIS4730" "MIS4710" "MIS4950" "MIS1234"

df$n_students

## [1] 20 10 40 30

df

##    course n_students data_analytics_minor
## 1 MIS4730         20                 TRUE
## 2 MIS4710         10                 TRUE
## 3 MIS4950         40                 TRUE
## 4 MIS1234         30                FALSE

All of a row

df[2,]  # row 2

##    course n_students data_analytics_minor
## 2 MIS4710         10                 TRUE

df

##    course n_students data_analytics_minor
## 1 MIS4730         20                 TRUE
## 2 MIS4710         10                 TRUE
## 3 MIS4950         40                 TRUE
## 4 MIS1234         30                FALSE

Multiple rows

df[c(1,3),]  # rows 1 & 3

##    course n_students data_analytics_minor
## 1 MIS4730         20                 TRUE
## 3 MIS4950         40                 TRUE

df[2,1]              # row 2, column 1

## [1] "MIS4710"

df[c(3,4),c(1,2)]    # rows 3 & 4, columns 1 & 2

##    course n_students
## 3 MIS4950         40
## 4 MIS1234         30

df["2","n_students"] # "2": row name, "n_students": column name

## [1] 10

Rows matching a condition:

df

##    course n_students data_analytics_minor
## 1 MIS4730         20                 TRUE
## 2 MIS4710         10                 TRUE
## 3 MIS4950         40                 TRUE
## 4 MIS1234         30                FALSE

# Be careful that it's df$course in the brackets, not just course
df[df$course == 'MIS4730', ] 

##    course n_students data_analytics_minor
## 1 MIS4730         20                 TRUE

df

##    course n_students data_analytics_minor
## 1 MIS4730         20                 TRUE
## 2 MIS4710         10                 TRUE
## 3 MIS4950         40                 TRUE
## 4 MIS1234         30                FALSE

df$course == 'MIS4730'

## [1]  TRUE FALSE FALSE FALSE

df[df$course == 'MIS4730', ]  # is interpreted by R as the following
df[c(TRUE, FALSE, FALSE, FALSE), ] 

The result is that you are getting the TRUE row(s).

• In R, there are data types for single values.
  • numeric, character, logic, factor
• There are also data types for a collection of values.
  • vector, list, matrix, data frame
• How would you store the following data:
  • Term-document incidence matrix
  • Inverted index
  • JSON data

This lab assignment is the first part of Assignment 1. It involves 2 tasks (see the next 2 slides). Once you finish the following tasks, please put everything in one single R file with the file name assignment1-Part1.R (.R is the file extension) and upload it to Canvas (Lab Assignment 1-Part 1).

You will lose 50% of the points if you use a different file name or put your code in multiple files.

In addition, lab assignments will be graded based on:

• Accuracy: whether the R script achieves the objectives
• Readability: whether the R script is clean, well-formatted, and easily readable
  • You risk loosing 10% points if your code has no proper indentation or has more than 80 characters in a line.