A quick look at how/why we process data visualization, a brief history of visualizations, and an intro to R.

1. Hacking Data Visualisations
MELINDA SECKINGTON
!
@MSECKINGTON

2. @mseckington

6. Hacking data visualisations
@mseckington

7. Why?

8. https://www.flickr.com/photos/laurenmanning/6632168961/

9. https://www.flickr.com/photos/jamjar/5491205608

10. “I feel that everyday, all of us now are being blasted by information design. It's
being poured into our eyes through the Web, and we're all visualizers now; we're all
demanding a visual aspect to our information. There's something almost quite
magical about visual information. It's effortless, it literally pours in. And if you're
navigating a dense information jungle, coming across a beautiful graphic or a lovely
data visualization, it's a relief, it's like coming across a clearing in the jungle.”
DAVID MCCANDLESS - THE BEAUTY OF DATA VISUALIZATION
@mseckington

11. THE BANDWIDTH OF OUR SENSES
Tor Norretranders
@mseckington

16. A brief history of data
visualisations

17. Theatrum Orbis Terrarum
May 20, 1570
The first modern atlas, collected by Abraham
Ortelis.
!
This was a first attempt to gather all maps
that were known to man at the time and bind
them together.
A BRIEF HISTORY OF DATA VISUALISATION

18. https://www.flickr.com/photos/smailtronic/2361594300

19. A BRIEF HISTORY OF DATA VISUALISATION
Bills of Mortality
From 1603, London parish clerks collected health-related
population data in order to monitor plague
deaths, publishing the London Bills of Mortality on
a weekly basis.
!
John Graunt amalgamated 50 years of information
from the bills, producing the first known tables of
public health data.
BEAUTIFUL SCIENCE AT THE BRITISH LIBRARY -
THE GUARDIAN

20. A BRIEF HISTORY OF DATA VISUALISATION
1644: First known graph of statistical data
!
MICHAEL VAN LANGREN -
ESTIMATES OF DISTANCE IN LONGITUDE BETWEEN TOLEDO AND ROME

21. A BRIEF HISTORY OF DATA VISUALISATION

22. A BRIEF HISTORY OF DATA VISUALISATION
1786 first bar chart
William Playfair
Exports and imports of Scotland to and from
different parts for one Year from Christmas
1780 to Christmas 1781

23. A BRIEF HISTORY OF DATA VISUALISATION
Street map of cholera deaths in Soho
1853 John Snow
Snow's 'ghost map' shows deaths from cholera
around Broad Street between 19 August and 30
September 1854. Snow simplified the street layout,
highlighting the 13 water pumps serving the area
and representing each death as a black bar. His
map demonstrates how cholera was spreading, not
by a 'miasma' rising from the Thames, but in water
contaminated by human waste
BEAUTIFUL SCIENCE AT THE BRITISH LIBRARY -
THE GUARDIAN

24. A BRIEF HISTORY OF DATA VISUALISATION
Diagram of the Causes of Mortality
in the Army in the East
!
1858 Florence Nightingale
In her seminal ‘rose diagram’, Nightingale
demonstrated that far more soldiers died
from preventable epidemic diseases (blue)
than from wounds inflicted on the
battlefield (red) or other causes (black)
during the Crimean War (1853-56)
BEAUTIFUL SCIENCE AT THE BRITISH LIBRARY -
THE GUARDIAN

25. How?

26. HOW?
https://www.flickr.com/photos/jdhancock/8031897271

27. https://www.flickr.com/photos/laurenmanning/5658951917/

28. HOW?
@mseckington

29. HOW?
@mseckington

30. HOW?
@mseckington

31. HOW?
@mseckington

32. HOW?
@mseckington

33. A quick intro to R

34. A QUICK INTRO TO R
What is R?
!
@mseckington

35. A QUICK INTRO TO R
What is R?
!
R is a free programming language and environment for statistical
computing and graphics.
!
@mseckington

36. A QUICK INTRO TO R
What is R?
!
R is a free programming language and environment for statistical
computing and graphics.
!
Created by statisticians for statisticians.
@mseckington

37. A QUICK INTRO TO R
What is R?
!
R is a free programming language and environment for statistical
computing and graphics.
!
Created by statisticians for statisticians.
!
Comes with a lot of facilities for data manipulation, calculation, data
analysis and graphical display.
@mseckington

38. A QUICK INTRO TO R
What is R?
!
R is a free programming language and environment for statistical
computing and graphics.
!
Created by statisticians for statisticians.
!
Comes with a lot of facilities for data manipulation, calculation, data
analysis and graphical display.
!
Highly and easily extensible.
@mseckington

39. A QUICK INTRO TO R

41. !
> data()!
!
list all datasets available
!
@mseckington

42. !
> data()!
!
list all datasets available
!
> movies = data(movies)!
> movies <- data(movies)!
!
assign movies data to movies variable
!
@mseckington

43. !
> data()!
!
list all datasets available
!
> movies = data(movies)!
> movies <- data(movies)!
!
assign movies data to movies variable
!
> dim(movies)!
[1] 58788! 24!
!
@mseckington

44. !
> data()!
!
list all datasets available
!
> movies = data(movies)!
> movies <- data(movies)!
!
assign movies data to movies variable
!
> dim(movies)!
[1] 58788! 24!
!
> names(movies)!
[1] "title" “year" “length" “budget" "rating" “votes" !
[7] “r1" “r2" “r3" “r4" “r5" “r6"!
[13] “r7" “r8" “r9" “r10" “mpaa" “Action" !
[19] “Animation" "Comedy" “Drama" “Documentary" “Romance”"Short"!
@mseckington

45. !
> movies[7079,]!
!
!! title ! ! ! ! ! year ! length budget rating votes !
7079 Bourne Identity, The 2002 !119!! 75000000 7.3 ! 29871 !
!
r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 mpaa !
4.5 4.5 4.5 4.5 4.5 14.5 24.5 34.5 14.5 4.5 PG-13!
!
Action Animation Comedy Drama Documentary Romance Short!
1 0 0 1 0 0 0!
!
returns 1 row => all the data for 1 movies
!
@mseckington

46. !
> movies[7079,]!
!
!! title ! ! ! ! ! year ! length budget rating votes !
7079 Bourne Identity, The 2002 !119!! 75000000 7.3 ! 29871 !
!
r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 mpaa !
4.5 4.5 4.5 4.5 4.5 14.5 24.5 34.5 14.5 4.5 PG-13!
!
Action Animation Comedy Drama Documentary Romance Short!
1 0 0 1 0 0 0!
!
returns 1 row => all the data for 1 movies
!
> movies[1:10,]!
. . . !
!
returns rows 1 to 10
@mseckington

47. !
> movies[,1]!
. . .!
!
returns 1 column => titles of all movies
@mseckington

48. !
> movies[,1]!
. . .!
!
returns 1 column => titles of all movies
!
> movies$title!
. . .!
!
same as movies[,1]!
returns column with the label ‘title
!
@mseckington

49. !
> movies[,1]!
. . .!
!
returns 1 column => titles of all movies
!
> movies$title!
. . .!
!
same as movies[,1]!
returns column with the label ‘title
!
> movies[,1:10]!
. . .!
!
returns columns 1 to 10
@mseckington

50. !
> hist(movies$year)
@mseckington

51. !
> hist(movies$year)
Histogram of movies$year
movies$year
Frequency
1900 1920 1940 1960 1980 2000
0 2000 4000 6000 8000
@mseckington

52. !
> hist(movies$year)!
!
> hist(movies$rating)
@mseckington

53. !
> hist(movies$year)!
!
> hist(movies$rating)
Histogram of movies$rating
movies$rating
Frequency
2 4 6 8 10
0 2000 4000 6000 8000
@mseckington

54. !
> hist(movies$year)!
!
> hist(movies$rating)!
!
> library(ggplot2)
@mseckington

55. !
> hist(movies$year)!
!
> hist(movies$rating)!
!
> library(ggplot2)!
!
> qplot(rating, !! !
!! data=movies, !
!! geom="histogram")
@mseckington

56. !
> hist(movies$year)!
!
> hist(movies$rating)!
!
> library(ggplot2)!
!
> qplot(rating, !! !
!! data=movies, !
!! geom=“histogram")!
!
> qplot(rating, !!
!! data=movies, !
!! geom="histogram",
!! binwidth=1)
@mseckington

57. !
> m = ggplot(movies, aes(rating))!
!
> m + geom_histogram()
@mseckington

58. !
> m = ggplot(movies, aes(rating))!
!
> m + geom_histogram()!
!
> m + geom_histogram(!
! ! ! aes(fill = ..count..))
@mseckington

59. !
> m = ggplot(movies, aes(rating))!
!
> m + geom_histogram()!
!
> m + geom_histogram(!
! ! ! aes(fill = ..count..))!
!
> m + geom_histogram(!
! ! ! colour = "darkgreen", !
! ! ! fill = "white", !
! ! ! binwidth = 0.5)!
!
@mseckington

60. !
> m = ggplot(movies, aes(rating))!
!
> m + geom_histogram()!
!
> m + geom_histogram(!
! ! ! aes(fill = ..count..))!
!
> m + geom_histogram(!
! ! ! colour = "darkgreen", !
! ! ! fill = "white", !
! ! ! binwidth = 0.5)!
!
> x = m + geom_histogram(!
! ! ! ! binwidth = 0.5)!
> x + facet_grid(Action ~ Comedy)!
@mseckington

61. !
> library(twitteR)!
!
> setup_twitter_oauth(!
! ! "API key”, "API secret", "Access token", "Access secret”)!
!
@mseckington

62. FUTURELEARN STATS

63. !
> fl = read.csv(!
! ! "futurelearn_dataset.csv",
! ! header=TRUE)!
!
@mseckington

64. !
> fl = read.csv(!
! ! "futurelearn_dataset.csv",
! ! header=TRUE)!
!
> source_table = table(fl$age)!
> pie(source_table)
@mseckington

65. !
> fl = read.csv(!
! ! "futurelearn_dataset.csv",
! ! header=TRUE)!
!
> source_table = table(fl$age)!
> pie(source_table)!
!
> pie(source_table, !
! ! radius=0.6, !
! ! col=rainbow(8))
@mseckington

67. !
> library(twitteR)!
!
> setup_twitter_oauth(!
! ! "API key”, "API secret", "Access token", "Access secret”)!
!
> tweets <- searchTwitter('futurelearn', n=100)
@mseckington

69. !
> library(twitteR)!
!
> setup_twitter_oauth(!
! ! "API key”, "API secret", "Access token", "Access secret”)!
!
> tweets <- searchTwitter('futurelearn', n=100)!
!
> library(“tm”)!
!
> tweet_text <- sapply(tweets, function(x) x$getText())!
> tweet_corpus <- Corpus(VectorSource(tweet_text))!
!
@mseckington

70. !
> library(twitteR)!
!
> setup_twitter_oauth(!
! ! "API key”, "API secret", "Access token", "Access secret”)!
!
> tweets <- searchTwitter('futurelearn', n=100)!
!
> library(“tm”)!
!
> tweet_text <- sapply(tweets, function(x) x$getText())!
> tweet_corpus <- Corpus(VectorSource(tweet_text))!
!
> tweet_corpus <- tm_map(tweet_corpus, !!
! ! ! ! ! ! ! ! ! content_transformer(tolower))!
> tweet_corpus <- tm_map(tweet_corpus, removePunctuation)!
> tweet_corpus <- tm_map(tweet_corpus, !! !
! ! ! ! ! ! ! ! function(x)removeWords(x,stopwords()))

72. !
> library(wordcloud)!
!
> wordcloud(tweet_corpus)
@mseckington

73. !
> library(wordcloud)!
!
> wordcloud(tweet_corpus)
@mseckington

74. What next?

75. A QUICK INTRO TO R

76. A QUICK INTRO TO R

78. WHAT NEXT?
@mseckington

79. https://www.flickr.com/photos/jamjar/5491205608

80. @mseckington

81. Recap

82. Data visualisations
are awesome
@mseckington

83. R is awesome
@mseckington

84. Any questions?
!
@mseckington