Visualisasi Aliran & Jaringan
Visualisasi Aliran
Berikut ini adalah ilustrasi yang diperoleh dari
library(tidyverse)
flight<-read_csv("https://raw.githubusercontent.com/raoy/data/master/flight.csv")
flight## # A tibble: 11 x 5
## Route Source.lat Source.lon Dest.lat Dest.lon
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Los Angeles-Seattle 34.5 -118. 47.6 -122.
## 2 Los Angeles-Houston 34.5 -118. 29.8 -95.4
## 3 Los Angeles-Salt Lake 34.5 -118. 40.8 -112.
## 4 Los Angeles-Denver 34.5 -118. 39.7 -105.
## 5 Los Angeles-Chicago 34.5 -118. 41.9 -87.6
## 6 Los Angeles-Phoenix 34.5 -118. 33.4 -112.
## 7 Atlanta-New York 33.8 -84.4 40.7 -74.0
## 8 Atlanta-Pittsburgh 33.8 -84.4 40.4 -80
## 9 Atlanta-Newark 33.8 -84.4 40.7 -74.2
## 10 Atlanta-Boston 33.8 -84.4 42.4 -71.1
## 11 Atlanta-Minneapolis 33.8 -84.4 45.0 -93.3#other map arguments include "world", "usa" and "county"
usMap <- borders("state", colour="grey", fill="white")
ggplot() + usMap
flightplot <- ggplot() + usMap +
geom_curve(data=flight,
aes(x=Source.lon, y=Source.lat, xend=Dest.lon, yend=Dest.lat),
col="#00008b",
size=.5,
curvature=0.2) +
geom_point(data=flight,
aes(x=Source.lon, y=Source.lat),
colour="blue",
size=1.5) +
geom_point(data=flight,
aes(x=Dest.lon, y=Dest.lat),
colour="blue") +
theme(axis.line=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
axis.ticks=element_blank(),
plot.title=element_text(hjust=0.5, size=12)) +
ggtitle("Flight Map")flightplot
Visualisasi Jaringan
library(GGally)
library(network)
library(sna)
# random graph
net = rgraph(10, mode = "graph", tprob = 0.5)
net = network(net, directed = FALSE)
# vertex names
network.vertex.names(net) = letters[1:10]
ggnet2(net)
Untuk mempelajari lebih lanjut mengenai visualisasi ini, dapat dipelajari pada Briatte (2019).
Sebagai ilustrasi lainnya, kita akan membangkitkan data dari sebaran binom seperti di bawah ini.
m <- matrix(rbinom(100,1,.4),10,10)
diag(m) <- 0
g <- network(m, directed=FALSE)
summary(g)## Network attributes:
## vertices = 10
## directed = FALSE
## hyper = FALSE
## loops = FALSE
## multiple = FALSE
## bipartite = FALSE
## total edges = 27
## missing edges = 0
## non-missing edges = 27
## density = 0.6
##
## Vertex attributes:
## vertex.names:
## character valued attribute
## 10 valid vertex names
##
## No edge attributes
##
## Network adjacency matrix:
## 1 2 3 4 5 6 7 8 9 10
## 1 0 0 0 1 1 1 0 1 0 1
## 2 0 0 0 1 1 1 1 1 1 1
## 3 0 0 0 0 1 1 0 0 1 1
## 4 1 1 0 0 0 0 1 1 0 0
## 5 1 1 1 0 0 0 0 1 1 0
## 6 1 1 1 0 0 0 1 1 0 0
## 7 0 1 0 1 0 1 0 0 1 1
## 8 1 1 0 1 1 1 0 0 1 1
## 9 0 1 1 0 1 0 1 1 0 1
## 10 1 1 1 0 0 0 1 1 1 0Selanjutnya matriks diubah menjadi objek network.
ggnet2(g, label = TRUE, label.alpha = 0.75)
Ilustrasi: French MPs on Twitter
Ilustrasi berikut ini diambil dari Briatte (2019), menggunakan data yang terdiri dari 339 anggota parlemen dan informasi tentang akun twitter mereka yang saling follow.
# root URL
r = "https://raw.githubusercontent.com/briatte/ggnet/master/"
# read nodes
v = read.csv(paste0(r, "inst/extdata/nodes.tsv"), sep = "\t")
names(v)## [1] "Sexe" "PrÃ.nom"
## [3] "Nom" "Groupe"
## [5] "DÃ.partement.d.Ã.lection" "Num.circonscription"
## [7] "Commission.permanente" "Twitter"# read edges
e = read.csv(paste0(r, "inst/extdata/network.tsv"), sep = "\t")
names(e)## [1] "Source" "Target"# network object
net = network(e, directed = TRUE)
# party affiliation
x = data.frame(Twitter = network.vertex.names(net))
x = merge(x, v, by = "Twitter", sort = FALSE)$Groupe
net %v% "party" = as.character(x)
# color palette
y = RColorBrewer::brewer.pal(9, "Set1")[ c(3, 1, 9, 6, 8, 5, 2) ]
names(y) = levels(as.factor(x))
# network plot
ggnet2(net, color = "party", palette = y, alpha = 0.75, size = 4, edge.alpha = 0.5)
Visualisasi Geospasial
Ilustrasi di bawah ini dirujuk dari Data Technik (2019). Data tersedia pada package datasets, berisi 50 observasi dan 9 peubah.
states<-as.data.frame(state.x77)
head(states)## Population Income Illiteracy Life Exp Murder HS Grad Frost Area
## Alabama 3615 3624 2.1 69.05 15.1 41.3 20 50708
## Alaska 365 6315 1.5 69.31 11.3 66.7 152 566432
## Arizona 2212 4530 1.8 70.55 7.8 58.1 15 113417
## Arkansas 2110 3378 1.9 70.66 10.1 39.9 65 51945
## California 21198 5114 1.1 71.71 10.3 62.6 20 156361
## Colorado 2541 4884 0.7 72.06 6.8 63.9 166 103766Pertama, kita akan coba menampilkan visualisasi data populasi di U.S.
states$region <- tolower(rownames(states))
states_map <- map_data("state")
fact_join <- left_join(states_map, states, by = "region")
ggplot(fact_join, aes(long, lat, group = group))+
geom_polygon(aes(fill = Population), color = "white")+
scale_fill_viridis_c(option = "C")+
theme_classic()
Kita dapat pula mengganti pewarnaan grafik seperti di bawah ini.
ggplot(fact_join, aes(long, lat, group = group))+
geom_polygon(aes(fill = Income), color = "white")+
scale_fill_viridis_c(option = "C")+
theme_classic()
Berikut apabila ingin menampilkan peubah lain, misalnya usia harapan hidup.
fact_join$`Life Exp` <- as.numeric(fact_join$`Life Exp`)
ggplot(fact_join, aes(long, lat, group = group))+
geom_polygon(aes(fill = `Life Exp`), color = "white")+
scale_fill_viridis_c(option = "C")+
theme_classic()
Visualisasi Multivariat : Plot Korespondensi
library(PogromcyDanych)
tab = table(auta2012$Marka, auta2012$Rodzaj.paliwa)
tab = tab[rowSums(tab) > 300, c(1,2,6)]library(gplots)
balloonplot(t(tab),
main = '',
xlab = '',
ylab = '',
label = FALSE,
show.margins = FALSE)
library(ca)
ca.fit <- ca(tab)
ca.plot <- plot(ca.fit)
Plot korespondensi dapat pula ditampilkan menggunakan ggplot, menggunakan fungsi yang dijelaskan oleh Rcrastinate (2019) berikut ini.
make.ca.plot.df <- function (ca.plot.obj,
row.lab = "Rows",
col.lab = "Columns") {
df <- data.frame(Label = c(rownames(ca.plot.obj$rows),
rownames(ca.plot.obj$cols)),
Dim1 = c(ca.plot.obj$rows[,1], ca.plot.obj$cols[,1]),
Dim2 = c(ca.plot.obj$rows[,2], ca.plot.obj$cols[,2]),
Variable = c(rep(row.lab, nrow(ca.plot.obj$rows)),
rep(col.lab, nrow(ca.plot.obj$cols))))
rownames(df) <- 1:nrow(df)
df
}
ca.plot.df <- make.ca.plot.df(ca.plot,
row.lab = "Manufacturer",
col.lab = "Fuel")
ca.plot.df$Size <- ifelse(ca.plot.df$Variable == "Manufacturer", 2, 1)
ca.plot.df## Label Dim1 Dim2 Variable Size
## 1 AlfaRomeo -0.11276955 -0.101517432 Manufacturer 2
## 2 Audi 0.29120210 0.058559213 Manufacturer 2
## 3 BMW 0.12084709 0.074627184 Manufacturer 2
## 4 Chevrolet -0.79348810 -0.002026356 Manufacturer 2
## 5 Chrysler -0.37626926 0.449452991 Manufacturer 2
## 6 Citroen 0.15776514 -0.072258801 Manufacturer 2
## 7 Dacia -0.49854992 0.064652808 Manufacturer 2
## 8 Daewoo -1.10349094 0.700683682 Manufacturer 2
## 9 Dodge -0.79296694 0.727442283 Manufacturer 2
## 10 Fiat -0.44712268 0.094139809 Manufacturer 2
## 11 Ford 0.14350320 -0.033517359 Manufacturer 2
## 12 Honda -0.80197745 -0.107891614 Manufacturer 2
## 13 Hyundai -0.32481426 -0.141269268 Manufacturer 2
## 14 Jaguar -0.54494081 -0.152736541 Manufacturer 2
## 15 Jeep -0.24231617 0.615451455 Manufacturer 2
## 16 Kia -0.13071588 -0.078069572 Manufacturer 2
## 17 Lancia -0.21659958 -0.148368213 Manufacturer 2
## 18 LandRover 0.42410102 0.090955584 Manufacturer 2
## 19 Lexus -0.82823565 -0.167230915 Manufacturer 2
## 20 Skoda -0.03952143 -0.054355982 Manufacturer 2
## 21 Mazda -0.12660612 -0.084311401 Manufacturer 2
## 22 Mercedes-Benz 0.16042381 0.037113303 Manufacturer 2
## 23 Mini -0.78518561 -0.412141050 Manufacturer 2
## 24 Mitsubishi -0.30755311 -0.105606196 Manufacturer 2
## 25 Nissan -0.13805025 -0.045816302 Manufacturer 2
## 26 Opel 0.01767288 0.064296078 Manufacturer 2
## 27 Peugeot 0.17528767 -0.082442991 Manufacturer 2
## 28 Porsche -0.76141615 -0.382389712 Manufacturer 2
## 29 Renault 0.05528161 -0.021484043 Manufacturer 2
## 30 Rover -0.30657641 -0.044304474 Manufacturer 2
## 31 Saab -0.16995358 -0.032204461 Manufacturer 2
## 32 Seat 0.05743904 -0.019509575 Manufacturer 2
## 33 Smart -0.65591512 -0.417481117 Manufacturer 2
## 34 Subaru -0.78642633 -0.065129766 Manufacturer 2
## 35 Suzuki -0.60191511 -0.227358628 Manufacturer 2
## 36 Toyota -0.03786536 -0.104010734 Manufacturer 2
## 37 Volkswagen 0.24092237 0.020003210 Manufacturer 2
## 38 Volvo 0.21993182 0.078633443 Manufacturer 2
## 39 benzyna -0.31054437 -0.069150075 Fuel 1
## 40 benzyna+LPG -0.32819850 0.511937021 Fuel 1
## 41 olej napedowy (diesel) 0.24706413 0.001483113 Fuel 1Untuk membuat plot korespondensi, kita hanya menggunakan 2 dimensi saja, keragaman yang diperoleh pada kedua dimensi tersebut dapat diketahui menggunakan fungsi berikut ini.
ca.sum <- summary(ca.fit)
dim.var.percs <- ca.sum$scree[,"values2"]
dim.var.percs## [1] 83.50121 16.49879Berdasarkan objek yang telah dibuat sebelumnya, selanjutnya kita dapat membuat plot korespondensi menggunakan fungsi ggplot().
library(ggplot2)
library(ggrepel)
p <- ggplot(ca.plot.df, aes(x = Dim1, y = Dim2,
col = Variable, shape = Variable,
label = Label, size = Size)) +
geom_vline(xintercept = 0, lty = "dashed", alpha = .5) +
geom_hline(yintercept = 0, lty = "dashed", alpha = .5) +
geom_point(aes(size=1)) +
geom_text(aes(label=Label), h=0,v=0,
size=2.5, color="steelblue")
p
References
Aprilliant, A. (2021, June 15). Introduction to correspondence analysis using R and Indonesia real dataset. Medium. https://towardsdatascience.com/correspondence-analysis-using-r-cd57675ffc3a
Briatte, F. (2019). Ggnet2: Network visualization with ggplot2. briatte.github.io. https://briatte.github.io/ggnet/
[Data Technik]. (2019, December 6). Choropleth map in ggplot2. R-bloggers. https://www.r-bloggers.com/2019/12/choropleth-map-in-ggplot2/
Holtz, Y. (n.d.). How to draw connecting routes on map with R. The R Graph Gallery – Help and inspiration for R charts. https://www.r-graph-gallery.com/how-to-draw-connecting-routes-on-map-with-r-and-great-circles.html
Rcrastinate. (2019, August 19). Correspondence analysis visualization using ggplot. R-bloggers. https://www.r-bloggers.com/2019/08/correspondence-analysis-visualization-using-ggplot/