Data visualization

DDaattaa VViissuuaalliizzaattiioonnclass 5

Vivian Zhang | Scott KostyshakCTO @Supstat Inc | Data Scientist @Supstat Inc

Data Visualization http://nycdatascience.com/part4_en/

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DDaattaa vviissuuaalliizzaattiioonnWe will study the application of primary drawing functions and advanced drawing functions in R andwill focus on understanding the methods of data exploration by visualization.

Case study and excercise: Analyzing the NBA data with graphics

The related functions in R

The properties of a single variable

Displaying compositions

The relationship between variables

Exhibiting change over time

Geographic information

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Why use visualization?


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DDaattaa vviissuuaalliizzaattiioonnA figure is worth a thousand words.

data <- read.table('data/anscombe.txt',T)data <- data[,-1]head(data)

x1 x2 x3 x4 y1 y2 y3 y41 10 10 10 8 8.04 9.14 7.46 6.582 8 8 8 8 6.95 8.14 6.77 5.763 13 13 13 8 7.58 8.74 12.74 7.714 9 9 9 8 8.81 8.77 7.11 8.845 11 11 11 8 8.33 9.26 7.81 8.476 14 14 14 8 9.96 8.10 8.84 7.04


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DDaattaa vviissuuaalliizzaattiioonnTry to calculate some statistical indicators. First calculate the mean of these datasets, and thencalculate the correlation coefficient of the four groups of data

colMeans(data)

x1 x2 x3 x4 y1 y2 y3 y4 9.0 9.0 9.0 9.0 7.5 7.5 7.5 7.5

sapply(1:4,function(x) cor(data[,x],data[,x+4]))

[1] 0.816 0.816 0.816 0.817


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DDaattaa vviissuuaalliizzaattiioonn


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SSoommee bbaassiicc pprriinncciipplleessDetermine the target of visualization from the beginning1.

Understanding the characteristics of the data and the audience2.

Keep concise but give enough information3.

Exploratory visualization

Explanatory visualization

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Which variables are important and interesting

Consider the role and background of the audience

Select a proper mapping

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MMaappppiinngg eelleemmeennttss ooff aa ggrraapphh::Coordinate position1.

Line2.

Size3.

Color4.

Shape5.

Text6.


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Visualization functions in R


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VViissuuaalliizzaattiioonn ffuunnccttiioonnss iinn RRbase graphics

lattice

ggplot2

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EElleemmeennttaarryy ggrraapphhiinngg ffuunnccttiioonnssplot(cars$dist~cars$speed)


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EElleemmeennttaarryy ggrraapphhiinngg ffuunnccttiioonnssplot(cars$dist,type='l')


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EElleemmeennttaarryy ggrraapphhiinngg ffuunnccttiioonnssplot(cars$dist,type='h')


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EElleemmeennttaarryy ggrraapphhiinngg ffuunnccttiioonnsshist(cars$dist)


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llaattttiiccee ppaacckkaaggeelibrary(lattice)num <- sample(1:3,size=50,replace=T)barchart(table(num))


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llaattttiiccee ppaacckkaaggeeqqmath(rnorm(100))


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llaattttiiccee ppaacckkaaggeestripplot(~ Sepal.Length | Species, data = iris,layout=c(1,3))


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llaattttiiccee ppaacckkaaggeedensityplot(~ Sepal.Length, groups=Species, data = iris,plot.points=FALSE)


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llaattttiiccee ppaacckkaaggeebwplot(Species~ Sepal.Length, data = iris)


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llaattttiiccee ppaacckkaaggeexyplot(Sepal.Width~ Sepal.Length, groups=Species, data = iris)


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llaattttiiccee ppaacckkaaggeesplom(iris[1:4])


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llaattttiiccee ppaacckkaaggeehistogram(~ Sepal.Length | Species, data = iris,layout=c(1,3))


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TThhrreeee--ddiimmeennssiioonnaall ggrraapphhss iinn tthhee llaattttiicceeppaacckkaaggeelibrary(plyr)func3d <- function(x,y) { sin(x^2/2 - y^2/4) * cos(2*x - exp(y))}vec1 <- vec2 <- seq(0,2,length=30)para <- expand.grid(x=vec1,y=vec2)result6 <- mdply(.data=para,.fun=func3d)


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TThhrreeee--ddiimmeennssiioonnaall ggrraapphhss iinn tthhee llaattttiicceeppaacckkaaggeelibrary(lattice)wireframe(V1~x*y,data=result6,scales = list(arrows = FALSE), drape = TRUE, colorkey = F)


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ggggpplloott ppaacckkaaggeeData, Mapping and Geom

library(ggplot2)p <- ggplot(data=mpg,mapping=aes(x=cty,y=hwy)) + geom_point()print(p)


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ggggpplloott ppaacckkaaggeeObserve the internal structure

summary(p)

data: manufacturer, model, displ, year, cyl, trans, drv, cty, hwy, fl, class [234x11]mapping: x = cty, y = hwyfaceting: facet_null() -----------------------------------geom_point: na.rm = FALSE stat_identity: position_identity: (width = NULL, height = NULL)


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ggggpplloott ppaacckkaaggeeAdd other data mappings

p <- ggplot(data=mpg,mapping=aes(x=cty,y=hwy,colour=factor(year)))p <- p + geom_point()print(p)


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ggggpplloott ppaacckkaaggeeAdd a statistical transformation such as a smooth

p <- ggplot(data=mpg,mapping=aes(x=cty,y=hwy,colour=factor(year)))p <- p + geom_smooth()print(p)


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ggggpplloott ppaacckkaaggeeAdd points and smooth lines on the plot layer

p <- ggplot(data=mpg,mapping=aes(x=cty,y=hwy)) + geom_point(aes(colour=factor(year))) + geom_smooth()


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ggggpplloott ppaacckkaaggeeScale control

p <- ggplot(data=mpg,mapping=aes(x=cty,y=hwy)) + geom_point(aes(colour=factor(year))) + geom_smooth() + scale_color_manual(values=c('blue2','red4'))


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ggggpplloott ppaacckkaaggeeFacet control

p <- ggplot(data=mpg,mapping=aes(x=cty,y=hwy)) + geom_point(aes(colour=factor(year))) + geom_smooth() + scale_color_manual(values=c('blue2','red4')) + facet_wrap(~ year,ncol=1)


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ggggpplloott ppaacckkaaggeePolishing your plots for publication

p <- ggplot(data=mpg, mapping=aes(x=cty,y=hwy)) + geom_point(aes(colour=class,size=displ), alpha=0.5,position = "jitter") + geom_smooth() + scale_size_continuous(range = c(4, 10)) + facet_wrap(~ year,ncol=1) + opts(title='Vehicle model and fuel consumption') + labs(y='Highway miles per gallon', x='Urban miles per gallon', size='Displacement', colour = 'Model')


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ggggpplloott eexxeerrcciissee IIchange the coordinate system,such as coord_flip() , coord_polar(),coord_cartesian()

p <- ggplot(data=mpg, mapping=aes(x=cty,y=hwy)) + geom_point(aes(colour=factor(year),size=displ), alpha=0.5,position = "jitter")+ stat_smooth()+ scale_color_manual(values =c('steelblue','red4'))+ scale_size_continuous(range = c(4, 10))


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The properties of a single variable


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HHiissttooggrraammlibrary(ggplot2)p <- ggplot(data=iris,aes(x=Sepal.Length))+ geom_histogram()print(p)


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HHiissttooggrraammWe can customize the histogram as follows:

p <- ggplot(iris,aes(x=Sepal.Length))+ geom_histogram(binwidth=0.1, # Set the group gap fill='skyblue', # Set the fill color colour='black') # Set the border color


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HHiissttooggrraammss pplluuss ddeennssiittyy ccuurrvveeThe main role of the histogram of is to show counting by groups and distribution characteristics. Thedistribution of a sample in traditional statistics is of important significance. But there is anothermethod that can also show the distribution of data, namely the kernel density estimation curve. Wecan estimate a density curve that represents the distribution, according to the data. We can displaythe histogram and density curve at the same time.

p <- ggplot(iris,aes(x=Sepal.Length)) + geom_histogram(aes(y=..density..), fill='skyblue', color='black') + geom_density(color='black', linetype=2,adjust=2)


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DDeennssiittyy ccuurrvveeSimilar to the window width parameter, the adjust parameter will control the presentation of thedensity curve. We try different parameters to draw mutiple density curves. The smaller the parameteris, the more volatile and sensitive the curve is.

p <- ggplot(iris,aes(x=Sepal.Length)) + geom_histogram(aes(y=..density..), # Note: set y to relative frequency fill='gray60', color='gray') + geom_density(color='black',linetype=1,adjust=0.5) + geom_density(color='black',linetype=2,adjust=1) + geom_density(color='black',linetype=3,adjust=2)


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DDeennssiittyy ccuurrvveeDensity curve is also convenient for comparison between different data. For example, we want tocompare the Sepal.Length distribution of three different flowers of the iris, like this:

p <- ggplot(iris,aes(x=Sepal.Length,fill=Species)) + geom_density(alpha=0.5,color='gray')print(p)


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BBooxxpplloottIn addition to the histograms and density map, We can also use boxplots to show the distribution ofone-dimensional data. The boxplot is also convenient for comparison of different data.

p <- ggplot(iris,aes(x=Species,y=Sepal.Length,fill=Species)) + geom_boxplot()print(p)


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VViioolliinn pplloottA violin plot contains more information than a boxplot about the (sub-)distributions of the data:

p <- ggplot(iris,aes(x=Species,y=Sepal.Length,fill=Species)) + geom_violin()print(p)


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VViioolliinn pplloott pplluuss ppooiinnttssp <- ggplot(iris,aes(x=Species,y=Sepal.Length, fill=Species)) + geom_violin(fill='gray',alpha=0.5) + geom_dotplot(binaxis = "y", stackdir = "center")print(p)


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Displaying compositions


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BBaarr cchhaarrttThe proportion of each vehicle model in the mpg dataset and these proportions grouped by years

p <- ggplot(mpg,aes(x=class)) + geom_bar()print(p)


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SSttaacckkeedd bbaarr cchhaarrttThe proportion of each vehicle model in the mpg dataset and these proportions grouped by years

mpg$year <- factor(mpg$year)p <- ggplot(mpg,aes(x=class,fill=year)) + geom_bar(color='black')


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SSttaacckkeedd bbaarr cchhaarrttStacked bar chart

p <- ggplot(mpg,aes(x=class,fill=year)) + geom_bar(color='black', position=position_dodge())


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PPiiee cchhaarrttp <- ggplot(mpg, aes(x = factor(1), fill = factor(class))) + geom_bar(width = 1)+ coord_polar(theta = "y")


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RRoossee ddiiaaggrraammWind rose, a commonly used graphics tool by meteorologists, describes the wind speed anddirection distributions in a specific place.

set.seed(1)# Randomly generate 100 wind directions, and divide them into 16 intervals.dir <- cut_interval(runif(100,0,360),n=16)# Randomly generate 100 wind speed, and divide them into 4 intensities.mag <- cut_interval(rgamma(100,15),4) sample <- data.frame(dir=dir,mag=mag)# Map wind direction to X-axie, frequency to Y-axie and speed to fill colors. Transform the coordinates of p <- ggplot(sample,aes(x=dir,fill=mag)) + geom_bar()+ coord_polar()


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MMoossaaiicc PPlloottDivide the data according to different variables, and then use rectangles of different sizes torepresent different groups of data. Let's look at the gender breakdown of survivors:


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TThhee pprrooppoorrttiioonn ssttrruuccttuurree ooff ccoonnttiinnuuoouuss ddaattaadata <- read.csv('data/soft_impact.csv',T)library(reshape2)data.melt <- melt(data,id='Year')p <- ggplot(data.melt,aes(x=Year,y=value, group=variable,fill=variable)) + geom_area(color='black',size=0.3, position=position_fill()) + scale_fill_brewer()


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The relationship between variables


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SSccaatttteerr ddiiaaggrraammShow the relationship between two variables with a scatter diagram.

p <- ggplot(data=mpg,aes(x=cty,y=hwy)) + geom_point()print(p)


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SSccaatttteerr pplloott ooff mmuullttiiddiimmeennssiioonnaall ddaattaampg$year <- factor(mpg$year)p <- ggplot(data=mpg,aes(x=cty,y=hwy)) + geom_point(aes(color=year))print(p)


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SSccaatttteerr pplloott ooff mmuullttiiddiimmeennssiioonnaall ddaattaaRepresent different years with different shapes

mpg$year <- factor(mpg$year)p <- ggplot(data=mpg,aes(x=cty,y=hwy)) + geom_point(aes(color=year,shape=year))print(p)


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SSccaatttteerr pplloott ooff mmuullttiiddiimmeennssiioonnaall ddaattaaWith large data sets, the points in a scatter plot may obscure each other due to overplotting, we canmake some random disturbance to solve this problem.

p <- ggplot(data=mpg,aes(x=cty,y=hwy)) + geom_point(aes(color=year),alpha=0.5,position = print(p)


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SSccaatttteerr pplloott ooff mmuullttiiddiimmeennssiioonnaall ddaattaaFor the trend of the scatterplot, we can draw out the regression line.

p <- ggplot(data=mpg,aes(x=cty,y=hwy)) + geom_point(aes(color=year),alpha=0.5,position = "jitter") + geom_smooth(method='lm')print(p)


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SSccaatttteerr pplloott ooff mmuullttiiddiimmeennssiioonnaall ddaattaaIn addition to color, We can also use the size of the dot to reflect another variable, such as the sizeof the cylinder. Some refer to plots like this as "bubble charts".

p <- ggplot(data=mpg,aes(x=cty,y=hwy)) + geom_point(aes(color=year,size=displ),alpha=0.5,position = "jitter") + geom_smooth(method='lm') + scale_size_continuous(range = c(4, 10))


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SSccaatttteerr pplloott ooff mmuullttiiddiimmeennssiioonnaall ddaattaaAlthough we can show all the variables in a picture, we can also split it into multiple pictures to showthe characteristics of different variables. This method is called grouping, conditioning, or faceting.

p <- ggplot(data=mpg,aes(x=cty,y=hwy)) + geom_point(aes(colour=class,size=displ), alpha=0.5,position = "jitter") + geom_smooth() + scale_size_continuous(range = c(4, 10)) + facet_wrap(~ year,ncol=1)


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ggggpplloott eexxeerrcciissee IIIImake scatter plot for diamond data

use transparency and small size points, look into size and alpha option in geom_point()

use bin chart to observe intensity of points,look into stat_bin2d()

estimate data dentisy,look into stat_density2d() and use+cooord_cartesian(xlim=c(0,1.5), ylim=c(0,6000))

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SSccaatttteerr pplloott ooff mmuullttiiddiimmeennssiioonnaall ddaattaaThe typical scatter plot is to show a relationship between two variables. When you want to look atmany bivariate relationships at once, you can use a scatter plot matrix.


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SSccaatttteerr pplloott ooff mmuullttiiddiimmeennssiioonnaall ddaattaaif given many numerical variables, concentrated display can be done.


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Change over time


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CChhaannggee oovveerr ttiimmeeFor visualization of time series data, the first step is looking at how the variable changes over time.For example, we'll have a look at American employment GDP data visualization.

fillcolor <- ifelse(economics[440:470,'unemploy']<8000,'steelblue','red4')p <- ggplot(economics[440:470,],aes(x=date,y=unemploy)) + geom_bar(stat='identity', fill=fillcolor)


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CChhaannggee oovveerr ttiimmeeFor the time series of small amount of data, we can use the bar graph to display. At the same timedisplay the number of positive and negative values with different colors.For the time series of largescale data, the bar will be crowded, and lines and points can be used to represent the strip.

p <- ggplot(economics[300:470,],aes(x=date,ymax=psavert,ymin=0)) + geom_linerange(color='grey20',size=0.5) + geom_point(aes(y=psavert),color='red4') + theme_bw()


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CChhaannggee oovveerr ttiimmeeWhen the data is more intensive, we can use line graph or area chart to show the change of a trend.Also, some important time points or time interval can be marked in the time series graph, such asmarking 80's as a key time.

fill.color <- ifelse(economics$date > '1980-01-01' & economics$date < '1990-01-01', 'steelblue','red4')p <- ggplot(economics,aes(x=date,ymax=psavert,ymin=0)) + geom_linerange(color=fill.color,size=0.9) + geom_text(aes(x=as.Date("1985-01-01",'%Y-%m-%d'),y=13),label="1980'") + theme_bw()


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Geographic informationvisualization


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MMaappTwo types of drawing map

Download the geographic information data, and then draw the geographical boundaries, andidentify areas and locations according to the need

Download bitmap data of Google map, and then mark the location and path information on thegoogle map

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MMaappworld map

library(ggplot2)world <- map_data("world")worldmap <- ggplot(world, aes(x=long, y=lat, group=group)) + geom_path(color='gray10',size=0.3) + geom_point(x=114,y=30,size=10,shape='*') + scale_y_continuous(breaks=(-2:2) * 30) + scale_x_continuous(breaks=(-4:4) * 45) + coord_map("ortho", orientation=c(30, 120, 0)) + theme(panel.grid.major = element_line(colour = "gray50"), panel.background = element_rect(fill = "white"), axis.text=element_blank(), axis.ticks=element_blank(), axis.title=element_blank())


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mmaapp ooff tthhee UU..SS..map <- map_data('state')arrests <- USArrestsnames(arrests) <- tolower(names(arrests))arrests$region <- tolower(rownames(USArrests))

usmap <- ggplot(data=arrests) + geom_map(map =map,aes(map_id = region,fill = murder),color='gray40' ) + expand_limits(x = map$long, y = map$lat) + scale_fill_continuous(high='red2',low='white') + theme_bw() + theme(panel.grid.major = element_blank(), panel.background = element_blank(), axis.text=element_blank(), axis.ticks=element_blank(), axis.title=element_blank(), legend.position = c(0.95,0.28), legend.background=element_rect(fill="white", colour="white"))+ coord_map('mercator'


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DDrraawwiinngg aa mmaapp ooff CChhiinnaa bbaasseedd oonn aa bbiittmmaappAnother method to drawing China map is to download a document containing bitmap data fromGoogle or openstreetmap, and then to overlap points and lines elements on it with ggplot2. Thisdocument does not include information of latitude and longitude, just a simple bitmap, for fastmapping.

library(ggmap)library(XML)webpage <-'http://data.earthquake.cn/datashare/globeEarthquake_csn.html'tables <- readHTMLTable(webpage,stringsAsFactors = FALSE)raw <- tables[[6]]data <- raw[,c(1,3,4)]names(data) <- c('date','lan','lon')data$lan <- as.numeric(data$lan)data$lon <- as.numeric(data$lon)data$date <- as.Date(data$date, "%Y-%m-%d")#Read the map data from Google by the ggmap package, and mark the previous data on the map.earthquake <- ggmap(get_googlemap(center = 'china', zoom=4,maptype='terrain'),extent='device' geom_point(data=data,aes(x=lon,y=lan),colour = 'red',alpha=0.7)+ theme(legend.position = "none")


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RR aanndd iinntteerraaccttiivvee vviissuuaalliizzaattiioonnGoogleVis is R package providing a interface between R and Google visualization API. It allows theuser to use the Google Visualization API for data visualization without the need to upload data.

We want to compare the development trajectory of 20 country group over the past several years. Inorder to obtain the data, we selected three variables from the world bank database, which reflect thechange of GDP, CO2 emissions and life expectancy between 2001 to 2009.

library(googleVis)library(WDI)DF <- WDI(country=c("CN","RU","BR","ZA","IN",'DE','AU','CA','FR','IT','JP','MX','GB','US'M <- gvisMotionChart(DF, idvar="country", timevar="year", xvar='EN.ATM.CO2E.KT', yvar='NY.GDP.MKTP.CD')plot(M)


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Case study and excercise


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EExxeerrcciissee IIIIII:: AAnnaallyyzziinngg NNBBAA ddaattaaCalculate the seasonal winning rate, and draw a bar chart

Calculating the seasonal winning rate at home and on the road, and draw a bar chart

According to the seasonal scores of home side, draw a set of four histograms

According to the seasonal scores of home side，draw the boxplots of five seasons

Draw the boxplots of scores of all competitions for home side and opposite side

Calculate the average and winning percentage for each opponent, and make a scatterplot to findthe strong and the weak team.

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Education

Data visualization