ggplot2 the easiest path to graphics
qplot
data.table(facebook)
## userid age dob_day dob_year dob_month gender tenure friend_count
## 1: 2094382 14 19 1999 11 male 266 0
## 2: 1192601 14 2 1999 11 female 6 0
## 3: 2083884 14 16 1999 11 male 13 0
## 4: 1203168 14 25 1999 12 female 93 0
## 5: 1733186 14 4 1999 12 male 82 0
## ---
## 98999: 1268299 68 4 1945 4 female 541 2118
## 99000: 1256153 18 12 1995 3 female 21 1968
## 99001: 1195943 15 10 1998 5 female 111 2002
## 99002: 1468023 23 11 1990 4 female 416 2560
## 99003: 1397896 39 15 1974 5 female 397 2049
## friendships_initiated likes likes_received mobile_likes
## 1: 0 0 0 0
## 2: 0 0 0 0
## 3: 0 0 0 0
## 4: 0 0 0 0
## 5: 0 0 0 0
## ---
## 98999: 341 3996 18089 3505
## 99000: 1720 4401 13412 4399
## 99001: 1524 11959 12554 11959
## 99002: 185 4506 6516 4506
## 99003: 768 9410 12443 9410
## mobile_likes_received www_likes www_likes_received
## 1: 0 0 0
## 2: 0 0 0
## 3: 0 0 0
## 4: 0 0 0
## 5: 0 0 0
## ---
## 98999: 11887 491 6202
## 99000: 10592 2 2820
## 99001: 11462 0 1092
## 99002: 5760 0 756
## 99003: 9530 0 2913
qplot(dob_day,data=facebook)
qplot(dob_day,data=facebook,breaks = 1:31)
qplot(as.factor(dob_day),data=facebook)
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
qplot(x=gender,y=friend_count,data=facebook)
qplot(x=gender,y=friend_count,data=facebook,geom="boxplot")
qplot(x=gender,y=friend_count,data=facebook,geom="boxplot",ylim = c(0, 1000))
qplot( friend_count,likes,data=facebook)
qplot( friend_count,likes,data=facebook,alpha=I(0.1) )
qplot( friend_count,likes,data=facebook,alpha=I(0.1), color=gender)
Themes
library(ggthemes)
base <- qplot(dob_day,data=facebook,breaks = 1:31)
base+theme_economist()+scale_colour_economist()
base+theme_tufte()
base<-base+theme_minimal()
base$labels
theme_set(theme_grey())
geom_histogram
base <- ggplot(aes(x = dob_day), data = facebook)
base
## Error: No layers in plot
head(base$data)
## userid age dob_day dob_year dob_month gender tenure friend_count
## 1 2094382 14 19 1999 11 male 266 0
## 2 1192601 14 2 1999 11 female 6 0
## 3 2083884 14 16 1999 11 male 13 0
## 4 1203168 14 25 1999 12 female 93 0
## 5 1733186 14 4 1999 12 male 82 0
## 6 1524765 14 1 1999 12 male 15 0
## friendships_initiated likes likes_received mobile_likes
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## mobile_likes_received www_likes www_likes_received
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
base$labels
## $x
## [1] "dob_day"
base$layers
## list()
base_histograma <- ggplot(aes(x = dob_day), data = facebook) +
geom_histogram()
base_mes_histograma <- base + geom_histogram()
base_histograma_axes <- ggplot(aes(x = dob_day), data = facebook) +
geom_histogram() +
scale_x_discrete(breaks = round(seq(1,31,4)))
aes
| x |
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| y |
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| colour |
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| fill |
x |
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| size |
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| shape |
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| group |
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ggplot(data = facebook) + geom_histogram(aes(x = dob_day, group=as.factor(dob_month)),color="black")
ggplot(data = facebook) + geom_histogram(aes(x = dob_day, fill =as.factor(dob_month)),color="black",linetype="dashed")
ggplot(data = facebook) + geom_histogram(aes(x = dob_day, color=as.factor(dob_month)))
p <- ggplot() +
geom_histogram(data = facebook[facebook$dob_day==1 & facebook$dob_month==1 & !is.na(facebook$dob_year) ,] ,
aes(x=dob_year , fill=as.factor(age)) , color="black" , binwidth=1)
head(p$data)
## list()
p$layers
## [[1]]
## mapping: x = dob_year, fill = as.factor(age)
## geom_histogram: colour = black
## stat_bin: binwidth = 1
## position_stack: (width = NULL, height = NULL)
head(p$layers[[1]]$data)
## userid age dob_day dob_year dob_month gender tenure friend_count
## 9 1365174 13 1 2000 1 male 81 0
## 34 1871735 14 1 1999 1 female 578 0
## 35 1459785 14 1 1999 1 male 478 0
## 36 1215208 14 1 1999 1 male 170 0
## 123 1505398 16 1 1997 1 male 108 0
## 124 2071007 16 1 1997 1 male 29 0
## friendships_initiated likes likes_received mobile_likes
## 9 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 123 0 0 0 0
## 124 0 0 0 0
## mobile_likes_received www_likes www_likes_received
## 9 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 123 0 0 0
## 124 0 0 0
p
Colors
ggplot(data=data.frame(dimensions=letters[1:2]),aes(x=dimensions,fill=dimensions))+geom_histogram()+coord_polar()
ggplot(data=data.frame(dimensions=letters[1:3]),aes(x=dimensions,fill=dimensions))+geom_histogram()+coord_polar()
ggplot(data=data.frame(dimensions=letters[1:5]),aes(x=dimensions,fill=dimensions))+geom_histogram()+coord_polar()
ggplot(data=data.frame(dimensions=letters[1:20]),aes(x=dimensions,fill=dimensions))+geom_histogram()+coord_polar()
position
fc.ten <-ggplot(aes(x = tenure/365, fill=gender), data = facebook)
fc.ten + geom_histogram(binwidth=.25,position="dodge")
fc.ten + geom_histogram(binwidth=.25,position="identity")
fc.ten + geom_histogram(binwidth=.25,position="identity",alpha=0.5)
fc.ten + geom_histogram(binwidth=.25,position="fill",alpha=0.5)
ggplot.obj <- fc.ten + geom_histogram(binwidth=.25,position="fill",alpha=0.5)
ggplot.obj$layers
## [[1]]
## geom_histogram: alpha = 0.5
## stat_bin: binwidth = 0.25
## position_fill: (width = NULL, height = NULL)
ggplot.obj$mapping
## List of 2
## $ x : language tenure/365
## $ fill: symbol gender
ggplot.obj$labels
## $x
## [1] "tenure/365"
##
## $fill
## [1] "gender"
##
## $y
## [1] "count"
geom_density
fc.ten <-ggplot(aes(x = tenure/365, fill=gender), data = facebook)
fc.ten + geom_histogram(binwidth=.25,position="dodge")
fc.ten + geom_histogram(aes(y = ..count..),binwidth=.25,position="dodge")
fc.ten + geom_histogram(aes(y = ..density..),binwidth=.25,position="dodge")
fc.ten + geom_density(alpha=0.5)
fc.ten + geom_density(alpha=0.5,position="stack")
fc.ten + geom_density(alpha=0.5) + geom_histogram(aes(y = ..density..),binwidth=.25,position="dodge")
ggplot(aes(x = age), data = facebook) + geom_density(alpha=0.5)
ggplot(aes(x = age, fill=gender), data = facebook) + geom_density(alpha=0.5)
ggplot(aes(x = age, fill=gender), data = facebook[!is.na(facebook$gender),]) + geom_density(alpha=0.5)
ggplot(aes(x = age, fill=gender), data = facebook[facebook$gender=="female",])+geom_density(alpha=0.5)
Labe and Axes
ggplot(aes(x = friend_count, y = ..count..), data = subset(facebook, !is.na(gender))) +
geom_freqpoly(aes(color = gender),binwidth=25)
ggplot(aes(x = friend_count, y = ..count..), data = subset(facebook, !is.na(gender))) +
geom_freqpoly(aes(color = gender),binwidth=25) +
scale_x_continuous(limits = c(0, 1000), breaks = seq(0, 1000, 100)) +
xlab('Friend Count') +
ylab('users with that friend count')
ggplot(aes(x = friend_count, y = ..count../sum(..count..)), data = subset(facebook, !is.na(gender))) +
geom_freqpoly(aes(color = gender),binwidth=25) +
scale_x_continuous(limits = c(0, 1000), breaks = seq(0, 1000, 100)) +
scale_y_continuous(labels =c("0%","5%","10%"),breaks = c(0,0.05,0.10))+
xlab('Friend Count') +
ylab('Percentage of users with that friend count')
geom_boxplot
fcg <-ggplot(aes(x=gender,y=age), data = subset(facebook, !is.na(gender)))+
geom_boxplot()
fcg
fcg + geom_jitter(,alpha=1/20)
fcg + ylim(0,60)
fcg + scale_y_continuous(limits = c(0, 60))
fcg + coord_cartesian(ylim = c(0, 60))
Multi Layer
head(mpg)
## manufacturer model displ year cyl trans drv cty hwy fl class
## 1 audi a4 1.8 1999 4 auto(l5) f 18 29 p compact
## 2 audi a4 1.8 1999 4 manual(m5) f 21 29 p compact
## 3 audi a4 2.0 2008 4 manual(m6) f 20 31 p compact
## 4 audi a4 2.0 2008 4 auto(av) f 21 30 p compact
## 5 audi a4 2.8 1999 6 auto(l5) f 16 26 p compact
## 6 audi a4 2.8 1999 6 manual(m5) f 18 26 p compact
ggplot(data=mpg,aes(x=as.factor(cyl),y=cty))+geom_boxplot(color="blue")
ggplot(data=mpg,aes(x=as.factor(cyl),y=hwy))+geom_boxplot(color="brown")
ggplot(data=mpg,aes(x=as.factor(cyl)))+geom_boxplot(aes(y=cty),color="blue")+geom_boxplot(aes(y=hwy),color="brown")
ggplot(data=mpg,aes(x=as.factor(cyl)))+
geom_violin(aes(y=cty),color="blue",alpha=0.5)+
geom_violin(aes(y=hwy),color="brown",alpha=0.5)+
geom_point(aes(y=(cty+hwy)/2),color="black",alpha=0.5,position=position_jitter(h=0,w=0.2 ) )
geom_point
fcp <- ggplot(aes(x = age, y = friend_count), data = facebook)
fcp + geom_point()
fcp <-fcp + xlim(min(facebook$age),100)
fcp + geom_point()
fcp + geom_point ( alpha=1/20)
fcp + geom_jitter( alpha=1/20)
fcp + geom_point ( alpha=1/20) + coord_trans(y = 'sqrt')
fcp + geom_point ( alpha=1/20, position=position_jitter() ) + coord_trans(y = 'sqrt')
## Error in if (zero_range(range)) {: valor ausente donde TRUE/FALSE es necesario
fcp + geom_point ( alpha=1/20, position=position_jitter(h=0) ) + coord_trans(y = 'sqrt')
fcp + geom_point ( alpha=1/20, position=position_jitter(h=0) ) + scale_y_log10()
stat=“summary”
facebook <- as.data.table(facebook)
gruped.friend_count <- facebook[,.(friend_count_mean=mean(friend_count)),by=.(age,gender)]
gruped.friend_count
## age gender friend_count_mean
## 1: 14 male 164.1456
## 2: 14 female 362.4286
## 3: 13 male 102.1340
## 4: 13 female 259.1606
## 5: 15 male 200.6658
## ---
## 270: 33 NA 493.0000
## 271: 36 NA 515.0000
## 272: 95 NA 830.0000
## 273: 68 NA 790.0000
## 274: 50 NA 863.0000
gruped.friend_count <- spread(gruped.friend_count,gender,friend_count_mean)
gruped.friend_count
## age female male NA
## 1: 13 259.1606 102.1340 NA
## 2: 14 362.4286 164.1456 NA
## 3: 15 538.6813 200.6658 116.0000
## 4: 16 519.5145 239.6748 NA
## 5: 17 538.9943 236.4924 106.5000
## ---
## 97: 109 207.5000 163.0000 NA
## 98: 110 239.1429 410.5714 503.0000
## 99: 111 244.4286 246.5000 148.0000
## 100: 112 201.2000 594.0769 NA
## 101: 113 278.6571 410.3956 166.3333
gather(gruped.friend_count,gender,friend_count_mean,-age)
## age gender friend_count_mean
## 1: 13 female 259.1606
## 2: 14 female 362.4286
## 3: 15 female 538.6813
## 4: 16 female 519.5145
## 5: 17 female 538.9943
## ---
## 299: 109 NA NA
## 300: 110 NA 503.0000
## 301: 111 NA 148.0000
## 302: 112 NA NA
## 303: 113 NA 166.3333
ggplot(aes(x=age,y=female/male) , data = gruped.friend_count )+
geom_line()+
geom_hline(yintercept=1 , alpha=0.3 , linetype=2)
facebook$Year_joined<-floor(2014-facebook$tenure/365)
facebook$Year_joined_buket<-cut(facebook$Year_joined, c(2004,2009,2011,2012,2014))
facebook.joined <- facebook[!is.na(gender),.(friend_count.mean=mean(friend_count),friend_count.sum=sum(friend_count)),by=.(Year_joined_buket,age)]
facebook.joined
## Year_joined_buket age friend_count.mean friend_count.sum
## 1: (2012,2014] 14 208.7742 302305
## 2: (2012,2014] 13 135.6682 57659
## 3: (2012,2014] 15 212.3026 326946
## 4: (2011,2012] 14 350.8129 157515
## 5: (2012,2014] 16 211.3181 356705
## ---
## 390: (2011,2012] 105 315.4000 1577
## 391: NA 103 137.0000 137
## 392: (2004,2009] 16 963.1429 6742
## 393: (2009,2011] 111 457.3333 1372
## 394: (2004,2009] 15 1871.0000 1871
ggplot(data=facebook.joined)+geom_point(aes(x=age,y=friend_count.sum,color=Year_joined_buket))
ggplot(data=facebook.joined)+geom_line(aes(x=age,y=friend_count.sum,color=Year_joined_buket))
fc.NoNA <- ggplot(aes(x=age,y=friend_count) , data = subset(facebook, !is.na(gender)) )
fc.NoNA + geom_point(aes(color=Year_joined_buket))
fc.NoNA + geom_point(aes(color=Year_joined_buket), stat="summary",fun.y=sum)
fc.NoNA + geom_line (aes(color=Year_joined_buket), stat="summary",fun.y=sum)
geom_line
fc.NoNA + geom_line (aes(color=Year_joined_buket), stat="summary",fun.y=mean)
fc.NoNA + geom_line (aes(color=Year_joined_buket), stat="summary",fun.y=mean)+
geom_line (stat="summary",fun.y=mean,linetype=2)
fcp + geom_point(alpha=1/10,
position=position_jitter(h=0),
color="darkgreen") +
geom_line(stat="summary",fun.y=mean) +
geom_line(stat="summary",fun.y=quantile,prob=.1,linetype=2,color="blue") +
geom_line(stat="summary",fun.y=quantile,prob=.5,linetype=1,color="blue") +
geom_line(stat="summary",fun.y=quantile,prob=.9,linetype=2,color="blue") +
coord_trans(y = "sqrt")
ggplot( aes(x=tenure,y=friendships_initiated), data=subset(facebook,tenure>0) )+
geom_line(aes(color=Year_joined_buket), stat="summary", fun.y=mean)
line.layer <- geom_line(aes(color=Year_joined_buket), stat="summary", fun.y=mean)
ggplot( aes(x=tenure ,y=friendships_initiated/tenure), data=subset(facebook,tenure>0) ) + line.layer
ggplot( aes(x=7*round(tenure/7) ,y=friendships_initiated/tenure), data=subset(facebook,tenure>0) ) + line.layer
ggplot( aes(x=90*round(tenure/90),y=friendships_initiated/tenure), data=subset(facebook,tenure>0) ) + line.layer
geom_smooth
fsmoth <- ggplot(aes(x=tenure,y=friendships_initiated/tenure), data=subset(facebook,tenure>0) )
fsmoth + geom_smooth(aes(color=Year_joined_buket,fill=Year_joined_buket))
fsmoth + geom_smooth(aes(group=Year_joined_buket))
fsmoth + geom_smooth()
geom_rug
rug.plot <- ggplot(aes(x=tenure,y=friendships_initiated/tenure), data=subset(facebook,tenure>0) )+
geom_smooth(aes(color=Year_joined_buket,fill=Year_joined_buket))
rug.plot + geom_rug()
rug.plot + geom_rug(data=facebook[facebook$tenure>0,][sample(nrow(facebook[facebook$tenure>0,]), 1000),] ,alpha=0.1)
rug.plot + geom_rug(data=facebook[facebook$tenure>0,][sample(nrow(facebook[facebook$tenure>0,]), 1000),] ,alpha=0.1)+ coord_cartesian(ylim = c(-0.5, 5))
sample.facebook <- facebook[facebook$tenure>0,][sample(nrow(facebook[facebook$tenure>0,]), 1000),]
fc.ten.sample <-ggplot(aes(x = tenure/365, fill=gender), data = sample.facebook )
fc.ten.sample + geom_density(alpha=0.5)
fc.ten.sample + geom_density(alpha=0.5)+geom_rug(alpha=0.1)
fc.ten.sample + geom_density(alpha=0.5)+geom_rug(aes(color=gender),alpha=0.1)
Faceting
fc.ten.sample + geom_density(alpha=0.5)+geom_rug(aes(color=gender),alpha=0.1)+ facet_wrap(~gender)
f.dob_day <- ggplot(aes(x = dob_day), data = facebook) +
geom_histogram() +
scale_x_discrete(breaks = round(seq(1,31,4)))
f.dob_day + facet_wrap(~dob_month)
f.dob_day + facet_wrap(~dob_month,nrow = 2, ncol = 6)
f.dob_day + facet_grid(gender~dob_month)
f.dob_day + facet_grid(gender~dob_month,scales="free")
f.dob_day + facet_grid(gender~dob_month,scales="free",space="free")
ggpairs
library(GGally)
set.seed(1234)
facebook_subset<-facebook[,.(gender,tenure,friend_count,friendships_initiated, likes, likes_received)]
ggpairs(facebook_subset[sample.int(nrow(facebook_subset),1000),])
geom_tiles
pp <- function (n,r=4) {
x <- seq(-r*pi, r*pi, len=n)
df <- expand.grid(x=x, y=x)
df$r <- sqrt(df$x^2 + df$y^2)
df$z <- cos(df$r^2)*exp(-df$r/6)
df
}
dt <- data.table( pp(200))
dt
## x y r z
## 1: -12.56637 -12.56637 17.77153 -0.005023290
## 2: -12.44008 -12.56637 17.68245 0.004230445
## 3: -12.31378 -12.56637 17.59383 -0.005105300
## 4: -12.18749 -12.56637 17.50567 0.007714216
## 5: -12.06119 -12.56637 17.41798 -0.012093846
## ---
## 39996: 12.06119 12.56637 17.41798 -0.012093846
## 39997: 12.18749 12.56637 17.50567 0.007714216
## 39998: 12.31378 12.56637 17.59383 -0.005105300
## 39999: 12.44008 12.56637 17.68245 0.004230445
## 40000: 12.56637 12.56637 17.77153 -0.005023290
ggplot(data=dt) + geom_tile(aes(x=x, y=y, fill=z))
dt$xbind <- cut(dt$x,breaks=seq(min(dt$x),max(dt$x),length.out = 100))
dt$ybind <- cut(dt$y,breaks=seq(min(dt$y),max(dt$y),length.out = 100))
dt.bind<- dt[,.(mean.x=mean(x),mean.y=mean(y),fill.z=mean(z)),by=.(xbind,ybind)]
ggplot( data=dt[,.(fill.z=mean(z)),by=.(xbind,ybind)] ) + geom_tile(aes(x=xbind, y=ybind, fill=fill.z))
dt <- data.frame(cbind(rnorm(10000),rnorm(10000)) %*% matrix(c(2,1,3,3), nrow=2))
ggplot(data=dt) + geom_bin2d(aes(x=X1, y=X2), binwidth = c(0.2, 0.2))
ggplot(data=dt) + geom_density2d(aes(x=X1, y=X2))
ggplot(data=dt) + stat_density2d(aes(x=X1, y=X2,fill=..level..,alpha=..level..), geom="polygon")
ggplot(data=dt) + geom_bin2d(aes(x=X1, y=X2), binwidth = c(0.2, 0.2)) + geom_density2d(aes(x=X1, y=X2),color="white")
Maps
library(ggmap)
library(sp)
MunicipisCataluna <- fread("MunicipisCataluña.csv",sep=";",stringsAsFactors=T)
MunicipisCataluna[,c("x","y"):=list(as.numeric(gsub(",",".",lon,fixed = T)),as.numeric(gsub(",",".",lat,fixed = T)))]
## municipio codigoine compontnt order lon lat
## 1: Guixers 25111 1 1 1,52059949 42,15180967
## 2: Guixers 25111 1 2 1,52952835 42,15179882
## 3: Guixers 25111 1 3 1,53167629 42,15205594
## 4: Guixers 25111 1 4 1,53842482 42,15184257
## 5: Guixers 25111 1 5 1,545793 42,15217141
## ---
## 450103: Cervià de Ter 17050 1 340 2,8841063 42,0626147
## 450104: Cervià de Ter 17050 1 341 2,88436502 42,06279942
## 450105: Cervià de Ter 17050 1 342 2,88448528 42,06305428
## 450106: Cervià de Ter 17050 1 343 2,88426993 42,0649017
## 450107: Cervià de Ter 17050 1 344 2,88472987 42,06615732
## x y
## 1: 1.520599 42.15181
## 2: 1.529528 42.15180
## 3: 1.531676 42.15206
## 4: 1.538425 42.15184
## 5: 1.545793 42.15217
## ---
## 450103: 2.884106 42.06261
## 450104: 2.884365 42.06280
## 450105: 2.884485 42.06305
## 450106: 2.884270 42.06490
## 450107: 2.884730 42.06616
MunicipisCataluna[,c("lat","lon"):=NULL]
## municipio codigoine compontnt order x y
## 1: Guixers 25111 1 1 1.520599 42.15181
## 2: Guixers 25111 1 2 1.529528 42.15180
## 3: Guixers 25111 1 3 1.531676 42.15206
## 4: Guixers 25111 1 4 1.538425 42.15184
## 5: Guixers 25111 1 5 1.545793 42.15217
## ---
## 450103: Cervià de Ter 17050 1 340 2.884106 42.06261
## 450104: Cervià de Ter 17050 1 341 2.884365 42.06280
## 450105: Cervià de Ter 17050 1 342 2.884485 42.06305
## 450106: Cervià de Ter 17050 1 343 2.884270 42.06490
## 450107: Cervià de Ter 17050 1 344 2.884730 42.06616
MunicipisCataluna<-unite(MunicipisCataluna,region,c(municipio,compontnt),sep=" ",remove=FALSE)
MunicipisCataluna
## region municipio codigoine compontnt order x
## 1: Guixers 1 Guixers 25111 1 1 1.520599
## 2: Guixers 1 Guixers 25111 1 2 1.529528
## 3: Guixers 1 Guixers 25111 1 3 1.531676
## 4: Guixers 1 Guixers 25111 1 4 1.538425
## 5: Guixers 1 Guixers 25111 1 5 1.545793
## ---
## 450103: Cervià de Ter 1 Cervià de Ter 17050 1 340 2.884106
## 450104: Cervià de Ter 1 Cervià de Ter 17050 1 341 2.884365
## 450105: Cervià de Ter 1 Cervià de Ter 17050 1 342 2.884485
## 450106: Cervià de Ter 1 Cervià de Ter 17050 1 343 2.884270
## 450107: Cervià de Ter 1 Cervià de Ter 17050 1 344 2.884730
## y
## 1: 42.15181
## 2: 42.15180
## 3: 42.15206
## 4: 42.15184
## 5: 42.15217
## ---
## 450103: 42.06261
## 450104: 42.06280
## 450105: 42.06305
## 450106: 42.06490
## 450107: 42.06616
ggplot(MunicipisCataluna, aes(map_id = region)) +
geom_map(aes(fill = codigoine),color="black", map = MunicipisCataluna) +
expand_limits(x = MunicipisCataluna$x, y = MunicipisCataluna$y)+
theme(legend.position="none")
background.map <- get_map(location=c(mean(MunicipisCataluna$x),mean(MunicipisCataluna$y)),source="google",maptype="toner",zoom=8)
mon.shapes <- geom_polygon(data=MunicipisCataluna,aes(x = MunicipisCataluna$x, y = MunicipisCataluna$y-0.017,group = region, fill=codigoine),alpha=0.7,color=rgb(0,0,0,.3))
ggmap(background.map) +
mon.shapes +
expand_limits(x = MunicipisCataluna$x, y = MunicipisCataluna$y)+
theme(legend.position="none")
