This document discusses data manipulation and visualization in R. It begins by introducing R and some of its basic functions and syntax for working with data, including creating variables, vectors, and data frames. It then covers reading in data, exploring and selecting subsets of data, and performing basic operations on vectors and data frames. The goal is to provide an overview of the essential R skills needed for data manipulation and visualization.

1. Data Manipulation and
Visualization in R
Hyebong Choi
School of ICT and Global Entrepreneurship
Handong Global University

2. Big Data: Why it matters
2
 In the past, data(record) are only for important people
and events
 e.g. royal family, war records, …

3. Big Data: Why it matters
3
 In the past, data(record) are expensive and for very
important event or a few privileged people
 e.g. royal family, war records, …
 Now data is for everyone and every moment

4. Data easily go beyond Human and …
4

5. Data easily go beyond Human and computer
5
Moore’s Law is a computing term which originated around 1970; the simplified
version of this law states that
processor speeds, or overall processing power for computers will double
every two years.

6. 6

7. What we call Big Data
7
3V definition

8. Data Science
 Data Science aims to derive knowledge fr
om big data, efficiently and intelligently
 Data Science encompasses the set of acti
vities, tools, and methods that enable da
ta-driven activities in science, business,
medicine, and government
 Machine Learning(or Data Mining) is agai
n one of the core technologies that enabl
es Data Science
http://www.oreilly.com/data/free/what-is-data-science.csp
8
Data Science is process to get Actionable Insight from Big DATA

9. Night time Bus by Seoul Local Government
 Effective Bus Route Design with Big Data
 5 Million Records of Taxi Take on/off
 3 Billion Night time call records(location) from telco company

10. Singapore Traffic visualization
 Data from LTA
 Subway, bus, taxi traffic info.
 By data analytics team in Institute for Infocomm Research

11. Big data and Data Science

12. What We Cover Here
• Data Manipulation and Visualization in R
12

13. R
• Most common tools for data scientist other than DBMS
• Cover wide range of data scientist – Data engineer, Statistician, Data main
expert, … rather than just computer engineer
• Providing thousands of ready-to-use powerful packages for data scientist
• Well documented
13
http://blog.revolutionanalytics.com/2014/01/in-data-scientist-survey-r-is-the-most-used-tool-other-than-databases.html

14. R How to Start
• Explanation of windows in R studio
14

15. R
• Getting help
▫ help(command) or ?command
▫ example(command) to see examples
15

16. Package Installation and loading
• install.packages(“package name”)
• to load package
▫ library(“package name”)
▫ or require(“package name”)
16

17. Variable
• Variable is a container to hold data (or information) that
we want to work with
• Variable can hold
▫ a single value: 10, 10.5, “abc”, factor, NA, NULL
▫ multiple values: vector, matrix, list
▫ specially formatted data (values): data.frame
17

18. How you assign a value to variable
• var <- value
18
my_first_variable <- 35.121
New variable is now assigned and
available in working environment

19. Operators
19
a <- 10.5
b <- 20
c <- 4
a + b ## addition
## [1] 30.5
a - c ## substraction
## [1] 6.5
a * c ## mulitiplication
## [1] 42
b / c ## division
## [1] 5
a %% c ## remainder
## [1] 2.5
a > b ## inequality
## [1] FALSE
a*2 == b ## equality
## [1] FALSE
!(a > b) ## negation
## [1] TRUE
(b > a) & (b > c) ## logical AND
## [1] TRUE
(a > b) | (a > c) ## logical OR
## [1] TRUE

20. Data Type – Missing Value (NA)
• Sometimes values are missing, and R represent the
missing values as NAs
20

21. Vector
• A vector is a sequence of data elements of the same basic type.
• All members should be of same data type
21
numeric_vector <- c(1, 10, 49)
character_vector <- c("a", "b", "c")
boolean_vector <- c(TRUE, FALSE, TRUE)
typeof(numeric_vector)
## [1] "double"
typeof(character_vector)
## [1] "character"
typeof(boolean_vector)
## [1] "logical"
length(numeric_vector) ## number of members in the vector
## [1] 3
new_vector <- c(numeric_vector, 50)
new_vector
## [1] 1 10 49 50

22. Vector
• R’s vector index starts from 1
▫ 1,2,3,4, …
• Minus Index means “except for”
22

23. Vector with named elements
• We can give name to each element of vector
• and we can use the name instead of index number
23
some_vector <- c("John Doe", "poker player")
names(some_vector) <- c("Name", "Profession")
some_vector
## Name Profession
## "John Doe" "poker player"
some_vector['Name']
## Name
## "John Doe"
some_vector['Profession']
## Profession
## "poker player"
some_vector[1]
## Name
## "John Doe"

24. Vector with named elements
• We can give name to each element of vector
• and we can use the name instead of index number
24
weather_vector <- c("Mon" = "Sunny", "Tues" = "Rainy",
"Wed" = "Cloudy", "Thur" = "Foggy",
"Fri" = "Sunny", "Sat" = "Sunny",
"Sun" = "Cloudy")
weather_vector
## Mon Tues Wed Thur Fri Sat Sun
## "Sunny" "Rainy" "Cloudy" "Foggy" "Sunny" "Sunny" "Cloudy“
names(weather_vector)
## [1] "Mon" "Tues" "Wed" "Thur" "Fri" "Sat" "Sun"

25. Short-cut to make numeric vector
25
a_vector <- 1:10 ## numbers from 1 to 10
b_vector <- seq(1, 10, 2) ## numbers from 1 to 10 increasing by 2
a_vector
## [1] 1 2 3 4 5 6 7 8 9 10
b_vector
## [1] 1 3 5 7 9
c_vector <- rep(1:3, 3)
d_vector <- rep(1:3, each = 3)
c_vector
## [1] 1 2 3 1 2 3 1 2 3
d_vector
## [1] 1 1 1 2 2 2 3 3 3
c(a_vector, b_vector) ## combine vectors to single vector
## [1] 1 2 3 4 5 6 7 8 9 10 1 3 5 7 9

26. Basic Vector operations
26
a_vector <- c(1,5,2,7,8)
b_vector <- seq(1, 10, 2)
sum(a_vector) ## summation
## [1] 23
mean(a_vector) ## average
## [1] 4.6
# operation of Vector and Scala
a_vector + 10
## [1] 11 15 12 17 18
a_vector > 4
## [1] FALSE TRUE FALSE TRUE TRUE
sum(a_vector > 4) ## what does this mean?
## [1] 3
# operation of Vector and Vector
a_vector - b_vector
## [1] 0 2 -3 0 -1
a_vector == b_vector
## [1] TRUE FALSE FALSE TRUE FALSE
sum(a_vector == b_vector) ## what does this mean?
## [1] 2

27. Vector Indexing (Selection)
27
sample_vector <- c(1, 4, NA, 2, 1, NA, 4, NA) ## vector with some missing values
sample_vector[1:5]
## [1] 1 4 NA 2 1
sample_vector[c(1,3,5)]
## [1] 1 NA 1
sample_vector[-1]
## [1] 4 NA 2 1 NA 4 NA
sample_vector[c(-1, -3, -5)]
## [1] 4 2 NA 4 NA
sample_vector[c(T, T, F, T, F, T, F, T)]
## [1] 1 4 2 NA NA
is.na(sample_vector)
## [1] FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE
sum(is.na(sample_vector))
## [1] 3
## can you select non-NA elements from the vector?
Selection by numeric vector
Selection by logical vector

28. Data Frame
• Very commonly datasets contains variables of different kinds
▫ e.g. student dataset may contain name(character), age(integer), major(factor),
gpa(numeric, real number)…
• Vector and metric can have values of same data type
• A data frame has the variables of a data set as columns and the
observations as rows.
28
mtcars
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3

29. Overviewing of Data frame
• head functions shows the first n (6 by default) observation of dataframe
• tail functions shows the last n (6 by default) observation of dataframe
29
head(mtcars)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
head(mtcars, 10) ## try to see what happens
tail(mtcars)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.7 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.5 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.5 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.6 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.6 1 1 4 2
tail(mtcars, 10) ## try to see what happens

30. Overviewing of Data frame
• str function shows the structure of your data set, it tells you
▫ The total number of observations (e.g. 32 car types)
▫ The total number of variables (e.g. 11 car features)
▫ A full list of the variables names (e.g. mpg, cyl ... )
▫ The data type of each variable (e.g. num)
▫ The first few observations
30
str(mtcars)
## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...

31. Creating Data frame
• data.frame function with vectors (of same length and possibly different
type) makes you a data frame
31
# Definition of vectors
name <- c("Mercury", "Venus", "Earth", "Mars", "Jupiter", "Saturn", "Uranus", "Neptune")
type <- c("Terrestrial planet", "Terrestrial planet", "Terrestrial planet",
"Terrestrial planet", "Gas giant", "Gas giant", "Gas giant", "Gas giant")
diameter <- c(0.382, 0.949, 1, 0.532, 11.209, 9.449, 4.007, 3.883)
rotation <- c(58.64, -243.02, 1, 1.03, 0.41, 0.43, -0.72, 0.67)
rings <- c(FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE)
# Create a data frame from the vectors
planets_df <- data.frame(name, type, diameter, rotation, rings)
planets_df
## name type diameter rotation rings
## 1 Mercury Terrestrial planet 0.382 58.64 FALSE
## 2 Venus Terrestrial planet 0.949 -243.02 FALSE
## 3 Earth Terrestrial planet 1.000 1.00 FALSE
## 4 Mars Terrestrial planet 0.532 1.03 FALSE
## 5 Jupiter Gas giant 11.209 0.41 TRUE
## 6 Saturn Gas giant 9.449 0.43 TRUE
## 7 Uranus Gas giant 4.007 -0.72 TRUE
## 8 Neptune Gas giant 3.883 0.67 TRUE

32. Creating Data frame
• you may specify the variables as parameters
32
my.df <- data.frame(name = c('John', 'Kim', 'Kaith'), job =
c('Teacher', 'Policeman', 'Secertary'), age = c(32, 25, 28))
my.df
## name job age
## 1 John Teacher 32
## 2 Kim Policeman 25
## 3 Keith Secretary 28

33. Selection of data frame elements
Similar to vectors and matrices, you select elements from a data frame with
the help of square brackets [ ].
By using a comma, you can indicate what to select from the rows and the
columns respectively.
33
# Print out diameter of Mercury (row 1, column 3)
planets_df[1,3]
## [1] 0.382
# Print out data for Mars (entire fourth row)
planets_df[4, ]
## name type diameter rotation rings
## 4 Mars Terrestrial planet 0.532 1.03 FALSE
# you can use of directly variable name
# Select first 5 values of diameter column
planets_df[1:5, 'diameter']
## [1] 0.382 0.949 1.000 0.532 11.209

34. Selection of data frame elements
You will often want to select an entire column, namely one specific variable from a data
frame. If you want to select all elements of the variable diameter, for example, both of
these will do the trick:
planets_df[,3]
planets_df[,"diameter"]
However, there is a short-cut. If your columns have names, you can use the $ sign:
planets_df$diameter
34

35. Selection of data frame elements
- a tricky part
• You can use a logical vector to select from data frame
35
## find planets with rings
planets_df[planets_df$rings, ]
## name type diameter rotation rings
## 5 Jupiter Gas giant 11.209 0.41 TRUE
## 6 Saturn Gas giant 9.449 0.43 TRUE
## 7 Uranus Gas giant 4.007 -0.72 TRUE
## 8 Neptune Gas giant 3.883 0.67 TRUE
## select names of planets with rings
planets_df[planets_df$rings, 'name']
## [1] Jupiter Saturn Uranus Neptune
## Levels: Earth Jupiter Mars Mercury Neptune Saturn Uranus Venus
## find planets with larger diameter than earth
planets_df$diameter > 1
## [1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
planets_df[planets_df$diameter > 1, ]
## name type diameter rotation rings
## 5 Jupiter Gas giant 11.209 0.41 TRUE
## 6 Saturn Gas giant 9.449 0.43 TRUE
## 7 Uranus Gas giant 4.007 -0.72 TRUE
## 8 Neptune Gas giant 3.883 0.67 TRUE

36. Handling Big Data using R
<2st> Package “dplyr”, essential for data preprocessing in R
36
HyunJae Jo
School of ICT and Global Entrepreneurship
Handong Global University

37. Contents
• Data preprocessing
• dplyr packages
• Lab example
37

38. Why we have to do “Data preprocessing”
• Data preprocessing is the process of making raw data
suitable for data analysis.
38
• Data Cleaning
 Correction of missing value,
outlier and noisy data
• Data Integration
 Combining data from multiple
sources
• Data Reduction
 Reduce only the data needed for
analysis
• Data Transformation
 Data transformation to maximize
efficiency of data analytics

39. Data Example of “Data Integration”
39
Missing
value Noisy data
Outlier
1. Data Integration
 Integrating “Female Population”
Column
“Data preprocessing”

40. Example of “Data Cleaning”
40
Missing
value Noisy data
Outlier
“Data preprocessing”
2. Data Cleaning
 Correction “Male Pop.” column
 Remove missing value(“others” column)
 Correction outliers

41. Example of “Data Reduce”
41
Missing
value Noisy data
Outlier
“Data preprocessing”
3. Data Reduce
 Top 5 Population Regions by Total Population

42. Example of “Data Transformation”
42
Missing
value Noisy data
Outlier
“Data preprocessing”
4. Data Transfromation
 Ratio region population to total population

43. What is “dplyr”
• "dplyr" is an R package that specializes in data processing.
• Functions of “dplyr”
▫ mutate() adds new variables that are functions of existing variables
▫ select() picks variables based on their names.
▫ filter() picks cases based on their values.
▫ summarise() reduces multiple values down to a single summary.
▫ arrange() changes the ordering of the rows.
▫ group_by() allows to perform any operation “by group”
▫ %>%(chain) connect each operation and perform it at once
43

44. How we can use “dplyr”
• Install packages and load packages.
• If you want to see package manual in R, you can use “help” function
44

45. imdb dataset
• IMDb (Internet Movie Database) is an online database of information related to
films, television programs, home videos and video games, and streaming content
online -- including cast, production crew and personnel biographies, plot
summaries, trivia, and fan reviews and ratings. As of October 2018, IMDb has
approximately 5.3 million titles (including episodes) and 9.3 million
personalities in its database, as well as 83 million registered users. (Wikipedia)
• We use reduced imdb dataset and you can load imdb dataset using read.csv()
function with URL link:
• https://raw.githubusercontent.com/myhan0710/prac_dplyr/master/imdb.csv
45

46. Overviewing: imdb dataset
46
(e.g. 15,190 movies)
(e.g. 44 related variable)
(e.g. fn, tid, title,wordsInTitle, …)
(e.g. chr, num, int)

47. Lab Example
• We will now look at the functions of the dplyr library through the imdb
dataset.
• Familiarize yourself with the roles of the given functions, and just enter
the code in your RStudio.
• Through the Lab, we can finally obtain data from the imdb dataset that
meet the following conditions:
47

48. dplyr: filter
48
• Use filter() function to extract specific
row that fits the condition.
• First, we can find recent, high rating and
• many rating counts movie like this:

49. dplyr: mutate
49
• Use mutate() function to make new column.
• Second, we can make column that indicates
whether a movie contains action and adventure
genres:
• If a certain movie’s Action column value is 1 and
Adventure column value is 0, it means that movie
includes action genre, but not Advenutre genre.
• So, ActionAdven column value 1 means a movie
includes both action and adventure genres.

50. dplyr: select
• Use select() function to extract specific column
that fits the condition.
• We only need a few columns:
- wordsInTitle
- imdbRating
- ratingCount
- Year
- ActionAven
50

51. dplyr: arrange
51
• Use the arrange() function to sort data from small to large values based on a
specified column.
• Fourth, we would like to sort the year
in ascending order and then grade in descending order.
• desc() function allows it to be sorted in descending order.

52. dplyr: summarise
• Use the summarise() function to obtain basic statistics by specifying
functions such as mean(), var(), and median()
• Now, we want to know average rating, average year, and number of
Action-Adventure genre.
52

53. dplyr: group_by
• Use the group_by() function, then you can group data by level in the
specified column.
• After group_by() function, You can associate the results with summarise()
to view the results of each level.
53

54. dplyr: %>%
• Use the chain function(%>%) to write a code with all functions at once.
• Then, the results are same. (imdb_chain, imdb7)
54

55. All contents
55

56. Excercise
① Extract movies from the original imdb dataset between 1970
and 2000.
② Using chain function, extract movies that has both drama
and family genre. Then, columns are only year, duration,
nrOfphotos.
• Please replace <Fill In> with your solution.
• After replacing your solution, check your solution is right on the next page.
56

57. Exercise key
① Key
② Key
57

58. 58

59. Data Visualization
• Essential component of skill set as a data scientist
• With ever increasing volume of data, it is impossible to tell
stories without visualizations.
• Data visualization is an art of how to turn “Big Data” into
useful knowledge
59

60. Data Visualization
• Data Visualization
60
Statistics Design
Graphical
Data Analysis
Communication &
Perception

61. Data Visualization
• Exploratory Visualization
▫ Help you see what is in the data
• Explanatory Visualization
▫ Shows others what you’ve found in your data
• R supports both types of visualizations
61

62. Data Visualization
• Exploratory Visualization
▫ Help you see what is in the data
▫ Keep as much as detail as possible
▫ Practical Limit: how much can you see and interpret
• Explanatory Visualization
▫ Help us share our understanding with others
▫ Shows others what you’ve found in your data
▫ Requires editorial decisions:
▫ Highlight the key features you want to emphasize
▫ Eliminate extraneous details
62

63. Exploratory Visualzation
63

64. Explanatory Visualization
64

65. ggplot2
• Author: Hadley Wickham
• Open Source implementation of the layered grammar of
graphics
• High-level R package for creating publication-quality statistical
graphics
▫ Carefully chosen defaults following basic graphical design rules
• Flexible set of components for creating any type of graphics
• Things you cannot do With ggplot2
▫ 3-dimensional graphics
▫ Graph-theory type graphs (nodes/edges layout)
65

66. ggplot2 installation
• In R console:
install.packages("ggplot2")
library(ggplot2)
66

67. Toy examples
67

68. Grammar of Graphics
68

69. The quick brown fox jumps over the lazy dog
69

70. Grammar of Graphics
70
• Plotting Framework
• Leland Wilkinson, Grammar of Graphics, 1999
• 2 principles
▫ Graphics = distinct layers of grammatical elements
▫ Meaningful plots through aesthetic mapping

71. Essential Grammatical Elements
71
Element Description
Data The dataset being plotted.
Aesthetics The scales onto which we map our data.
Geometries The visual elements used for our data.

72. All Grammatical Elements
72
Element Description
Data The dataset being plotted.
Aesthetics The scales onto which we map our data.
Geometries The visual elements used for our data.
Facets Plotting small multiples
Statistics Representations of our data to aid understanding.
Coordinates The space on which the data will be plotted.
Themes All non-data ink.

73. Diagram
73

74. Grammar of Graphics
• Building blocks
• Solid, creative, meaningful visualizations
• Essential Layers: Data, Aesthetics, Geometries
• For Enhancement: Facets, Statistics, Coordinates,
Themes
74

75. Example – First trial of ggplot
• To get a first feel for ggplot2, let's try to run some basic ggplot2 commands.
Together, they build a plot of the mtcars dataset that contains information about
32 cars from a 1973 Motor Trend magazine. This dataset is small, intuitive, and
contains a variety of continuous and categorical variables.
75

76. Example – First trial of ggplot
• The plot from the previous exercise wasn't really satisfying.
Although cyl (the number of cylinders) is categorical, it is classified
as numeric in mtcars. You'll have to explicitly tell ggplot2 that cyl is
a categorical variable.
76

77. more examples
77

78. more examples
78

79. more examples
79

80. more examples
80

81. more examples
81

82. more examples
82

83. more examples
83

84. more examples
84

85. more examples
85

86. Practice
86
https://gist.githubusercontent.com/tiangechen/b68782efa49a1
6edaf07dc2cdaa855ea/raw/0c794a9717f18b094eabab2cd6a6b9a2269
03577/movies.csv

87. Practice
87

88. Practice
88

89. Practice
89

90. Practice
90
https://raw.githubusercontent.com/meKIDO/MyanmarData/master/MichelinNY.csv'

91. Practice
91

92. Practice
92

93. Practice
93

94. Practice
94

95. Practice
95
https://raw.githubusercontent.com/meKIDO/MyanmarData/master/M
yanmarDB.csv

96. Practice
96

97. Thank you
97