Vector

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

Name a vector

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

Combining vectors

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

Your turn

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 arithmetics

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

Test if a vector has a specific value

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

Test if a vector has a specific value

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

Does basket have apple?

"apple" %in% basket

## [1] TRUE

Does basket have cheese?

"cheese" %in% basket

## [1] FALSE

Missing values: NA

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

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)

Quiz

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

Vector indexing

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

Subsetting all but some

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

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

List indexing

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

Your turn

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] "CIS4710" "CIS4720" "CIS4730"
## 
## $grade_point
## [1] 4 3 4

Matrix

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

Data frame

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("CIS4730", "CIS4710", "CIS4950", "CIS1234") 
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 CIS4730         20                 TRUE
## 2 CIS4710         10                 TRUE
## 3 CIS4950         40                 TRUE
## 4 CIS1234         30                FALSE

Useful functions for data frames

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"

Getting values from a column

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

The most readable way: dataframe_name$column_name

df$course

## [1] "CIS4730" "CIS4710" "CIS4950" "CIS1234"

df$n_students

## [1] 20 10 40 30

Getting values from rows

df

##    course n_students data_analytics_minor
## 1 CIS4730         20                 TRUE
## 2 CIS4710         10                 TRUE
## 3 CIS4950         40                 TRUE
## 4 CIS1234         30                FALSE

All of a row

df[2,]  # row 2

##    course n_students data_analytics_minor
## 2 CIS4710         10                 TRUE

df

##    course n_students data_analytics_minor
## 1 CIS4730         20                 TRUE
## 2 CIS4710         10                 TRUE
## 3 CIS4950         40                 TRUE
## 4 CIS1234         30                FALSE

Multiple rows

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

##    course n_students data_analytics_minor
## 1 CIS4730         20                 TRUE
## 3 CIS4950         40                 TRUE

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

## [1] "CIS4710"

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

##    course n_students
## 3 CIS4950         40
## 4 CIS1234         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 CIS4730         20                 TRUE
## 2 CIS4710         10                 TRUE
## 3 CIS4950         40                 TRUE
## 4 CIS1234         30                FALSE

# Be careful that it's df$course in the brackets, not just course
df[df$course == 'CIS4730', ]

##    course n_students data_analytics_minor
## 1 CIS4730         20                 TRUE

df

##    course n_students data_analytics_minor
## 1 CIS4730         20                 TRUE
## 2 CIS4710         10                 TRUE
## 3 CIS4950         40                 TRUE
## 4 CIS1234         30                FALSE

df$course == 'CIS4730'

## [1]  TRUE FALSE FALSE FALSE

df[df$course == 'CIS4730', ]  # is interpreted by R as the following
df[c(TRUE, FALSE, FALSE, FALSE), ]

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

Summary of data types

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:

Lab assignment

This lab assignment 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.R (.R is the file extension) and upload it to iCollege (Lab Assignment 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.

Lab assignment 1/2

## $name
## [1] "Alex"   "Bob"    "Claire" "Denise"
## 
## $female
## [1] FALSE FALSE  TRUE  TRUE
## 
## $age
## [1] 20 25 30 35

## [1] "Bob"

Create the above list
Get the name “Bob” from the list
- Hint: Get the name vector from the list and then get the second element in the vector
- Note: You will not get any point if you get “Bob” directly from list you created.

Lab assignment 2/2

##         name female age
## row_1   Alex  FALSE  20
## row_2    Bob  FALSE  25
## row_3 Claire   TRUE  30
## row_4 Denise   TRUE  35

## [1] 27.5

## [1] 30

Create the above data frame (don’t forget the column/row names!)
Obtain the mean of the age column from the data frame
- Note: You will not get any point if you do not get the answer through the data.frame.
Retrieve Claire’s age from the data frame
- Note: You will not get any point if you do not get the answer through the data.frame.

CIS 4730
Unstructured Data Management

Lab: Data types

Recap

Agenda

R Data Types

Vector

Name a vector

Combining vectors

Your turn

Vector arithmetics

Test if a vector has a specific value

Test if a vector has a specific value

Missing values: NA

Recycling

Quiz

Vector indexing

Subsetting all but some

List

List indexing

Your turn

Matrix

Data frame

Useful functions for data frames

Getting values from a column

Getting values from rows

Summary of data types

Lab assignment

Lab assignment 1/2

Lab assignment 2/2

CIS 4730Unstructured Data Management

Lab: Data types

Recap

Agenda

R Data Types

Vector

Name a vector

Combining vectors

Your turn

Vector arithmetics

Test if a vector has a specific value

Test if a vector has a specific value

Missing values: NA

Recycling

Quiz

Vector indexing

Subsetting all but some

List

List indexing

Your turn

Matrix

Data frame

Useful functions for data frames

Getting values from a column

Getting values from rows

Summary of data types

Lab assignment

Lab assignment 1/2

Lab assignment 2/2

CIS 4730
Unstructured Data Management