- Recap traditional visualization methods
- Techniques for advanced visualizing
- Examples
- Summary
- For you...
Data Visualization
An art field in vision
Nan Meng
Ph.D
Overview
Recap visualization methods(traditional)
data1 <- read.csv("1_data_generated.csv",header = T)
head(data1, 10) # The first 10 rows in data1
## language date price
## 1 Spanish Dec 1850 45871359
## 2 Spanish Dec 1851 53328379
## 3 Spanish Dec 1852 47419413
## 4 Spanish Dec 1853 41855112
## 5 Spanish Dec 1854 40154362
## 6 Spanish Dec 1855 43819130
## 7 Spanish Dec 1856 49579310
## 8 Spanish Dec 1857 55593229
## 9 Spanish Dec 1858 51145836
## 10 Spanish Dec 1859 55139219
Recap visualization methods(traditional)
head(data1, 10) # The first 10 rows in data1
- lines
- horizons
- areas
- stacked areas
- streamgraph
- overlapping areas
- grouped bars
- stacked bars
- bars
- donut
What data visualization really is?
Book Data
data2 <- read.csv("2_Export_Num.csv",header = T)
head(data2, 6) # The first 6 rows in data2
## date Philosopy.Social.Science Culture.and.Education Literature.and.Art
## 1 2014 111.65 216.22 143.87
## 2 2013 159.54 386.27 203.52
## 3 2012 173.44 350.60 233.41
## 4 2011 101.52 158.08 144.66
## 5 2010 105.32 124.24 129.70
## 6 2009 84.04 123.97 105.12
## Natural.Science.and.S.T For.Children General.Books
## 1 41.53 807.08 145.40
## 2 62.26 724.39 201.60
## 3 85.49 538.23 295.99
## 4 57.99 205.23 188.27
## 5 59.86 140.47 147.64
## 6 90.11 70.64 150.96
Thinking about it in 10 seconds ......
Motion Chart
data2 <- read.csv("2_Export_Num.csv",header = T)
head(data2, 6) # The first 6 rows in data2
Radio
- Proportional Information
- Tendency
Fruits Data
## Fruit Year Location Sales Expenses Profit Date
## 1 Apples 2008 West 98 78 20 2008-12-31
## 2 Apples 2009 West 111 79 32 2009-12-31
## 3 Apples 2010 West 89 76 13 2010-12-31
## 4 Oranges 2008 East 96 81 15 2008-12-31
## 5 Bananas 2008 East 85 76 9 2008-12-31
## 6 Oranges 2009 East 93 80 13 2009-12-31
## 7 Bananas 2009 East 94 78 16 2009-12-31
## 8 Oranges 2010 East 98 91 7 2010-12-31
## 9 Bananas 2010 East 81 71 10 2010-12-31
Motion Chart
- Checkbox
- ScrollBar
- Menu
More complex data ......
Geography Data
Warming up
data3 <- read.csv("3_sell.csv",header = T)
head(data3, 6) # The first 6 rows in data3
## Province total Y2013 Y2014
## 1 Beijing 371 185 186
## 2 Tianjin 646 324 322
## 3 Hebei 1166 562 604
## 4 Shanxi 117 60 57
## 5 Mongolia 78 40 38
## 6 Liaoning 613 327 286
Choropleth
Time for you
World Heritage Data
data4 <- read.csv("whc-sites-2015.csv",header = T)
head(data4[,c(4,10,13,14,15,27,29)], 5) # The first 5 rows in data4
## name_en
## 1 Cultural Landscape and Archaeological Remains of the Bamiyan Valley
## 2 Minaret and Archaeological Remains of Jam
## 3 Historic Centres of Berat and Gjirokastra
## 4 Butrint
## 5 Al Qal'a of Beni Hammad
## date_inscribed longitude latitude area_hectares category states_name_en
## 1 2003 67.82525 34.84694 158.9265 Cultural Afghanistan
## 2 2002 64.51606 34.39656 70.0000 Cultural Afghanistan
## 3 2005 20.13333 40.06944 58.9000 Cultural Albania
## 4 1992 20.02611 39.75111 NA Cultural Albania
## 5 1980 4.78684 35.81844 150.0000 Cultural Algeria
Thinking about it in 10 seconds ......
Symbol Map & Crossfilter
Hmmmmm how about Network !!!
Can we do that ......
Flights Data
data5 <- read.csv("5_flight.csv",header = T)
head(data5, 3) # The first 3 rows in data5
## residence.area. workplace.area. X.categories WorkAtHome Underground
## 1 E02000001 E02000001 1506 0 73
## 2 E02000001 E02000014 2 0 2
## 3 E02000001 E02000016 3 0 1
## Train Bus Taxi Other.method F10 F11 F12 F13 F14
## 1 41 32 9 1 8 1 33 1304 4
## 2 0 0 0 0 0 0 0 0 0
## 3 0 2 0 0 0 0 0 0 0
Flights Data
library(ggmap)
library(geosphere)
library(rgdal)
library(threejs)
library(plyr)
library(ggplot2)
library(maptools)
#######################################################################################
# https://web.archive.org/web/20150405212054/http://spatial.ly/2015/03/mapping-flows/ #
#######################################################################################
input<-read.table("wu03ew_v1.csv", sep=",", header=T)
input<- input[,1:3]
names(input)<- c("origin", "destination","total")
centroids<- read.csv("msoa_popweightedcentroids.csv")
or.xy<- merge(input, centroids, by.x="origin", by.y="Code")
names(or.xy)<- c("origin", "destination", "trips", "o_name", "oX", "oY")
dest.xy<- merge(or.xy, centroids, by.x="destination", by.y="Code")
names(dest.xy)<- c("origin", "destination", "trips", "o_name", "oX", "oY","d_name", "dX", "dY")
# removes the axes in the resulting plot.
xquiet<- scale_x_continuous("", breaks=NULL)
yquiet<-scale_y_continuous("", breaks=NULL)
quiet<-list(xquiet, yquiet)
dest <- dest.xy
dest.xy <- dest.xy[1:2000,]
# build the plot
# First we specify the dataframe we need, with a filter excluding flows of <10
ggplot(dest.xy[which(dest.xy$trips>10),], aes(oX, oY))+
# tells ggplot that we wish to plot line segments. The "alpha=" is line transparency and used below
geom_segment(aes(x=oX, y=oY,xend=dX, yend=dY, alpha=trips), col="white")+
# Here is the magic bit that sets line transparency - essential to make the plot readable
scale_alpha_continuous(range = c(0.03, 0.3))+
# Set black background, remove axes and fix aspect ratio
theme(panel.background = element_rect(fill='black',colour='black'))+quiet+coord_equal()
Migration Data
data6 <- read.csv("6_migration.csv",header = T, sep=";")
head(data6[,c(-1,-5,-10)], 3) # The first 3 rows in data6
## PrsLabel BYear BLocLabel BLocLat BLocLong DYear DLocLabel DLocLat
## 1 David -1039 Bethlehem 31.70310 35.19560 -969 Jerusalem 31.78333
## 2 Josiah -648 Jerusalem 31.78333 35.21667 -608 Jerusalem 31.78333
## 3 Ezekiel -621 Jerusalem 31.78333 35.21667 -569 Babylon 32.54167
## DLocLong Gender PerformingArts Creative Gov.Law.Mil.Act.Rel
## 1 35.21667 Male 0 0 1
## 2 35.21667 Male 0 0 0
## 3 44.42333 Male 0 0 1
## Academic.Edu.Health Sports Business.Industry.Travel
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
Migration Data
Just do it !!!
Summary
Recap some traditional visualization methods
Advanced Techniques(Radio, Checkbox, ScrollBar, Menu, Choropleth, Symbol Map, Crossfilter)
Networks(Facebook, Flights, Migration)
Learning Object:
Master the visualization techniques for
different kind of data...
For test
Visualize Text Data
## word counts
## 1 Peking opera 13443635
## 2 Confucius 4352466
## 3 Lion dance 426534
## 4 Chinese 2452436
## 5 Pipa 34224555
## 6 Chinese knot 7454635
## 7 Dumplings 3546546
## 8 Couplet 73565436
## 9 Facial Makeup 873536
## 10 Culture 5693465
## 11 Tang poetry 45678
