R_ggplot_Choropleth_Treemap
- 作圖的目的有:
- 了解資料的特性
- 尋找資料的模式(patterns)
- 建議資料分析與建模的策略
- 結果呈現與溝通
rm(list = ls()); gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 530772 28.4 1205827 64.4 621328 33.2
## Vcells 996965 7.7 8388608 64.0 1600722 12.3library(ggplot2)
library(dplyr) # %>% 符號##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(SportsAnalytics) # NBA球員資料## Warning: package 'SportsAnalytics' was built under R version 3.6.3NBA1516 <- fetch_NBAPlayerStatistics("15-16")- 簡單來說，做圖的文法包括兩個最主要元素：
- Aesthetic attributes：包括顏色、形狀、點的大小與線的粗細等
- Geometric objects：包括點、線、盒狀圖、直條圖等
- 其他元素包括：
- Facets：提供在同一張圖內做多個子圖的方法，只要使用Faceting功能設定子圖分類的依據參數即可
- Stats：將資料做統計轉換
- Scales：修改點線的顏色、形狀、xy軸的範圍等
e.g. NBA球員不同位置FieldGoalsAttempted與TotalPoints點圖:
# colnames(NBA1516)
qplot(FieldGoalsAttempted, TotalPoints, data = NBA1516,
color = Position)
NBA1516 %>% ggplot(aes(x = FieldGoalsAttempted,
y = TotalPoints,
color = Position)) + geom_point()
- 針對做圖的文法的第二個主要元素Geometric（包括點、線、盒狀圖、直條圖等），也可透過增加指令修改，如使用geom = c(“point”, “smooth”) 在圖上加點與漸進線:
qplot(FieldGoalsAttempted, TotalPoints, data = NBA1516,
color = Position,
geom = c("point", "smooth"))## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
- Facets元素可提供在同一張圖內做多個子圖的方法:
#facets = . ~ Position 用守備位置Position分群畫圖(橫向)
qplot(FieldGoalsAttempted, TotalPoints, data = NBA1516,
facets = . ~ Position)
- 單變量時，預設圖形會從散佈圖變為Histograms直方圖:
qplot(TotalPoints, data = NBA1516, fill = Position,
bins = 80)
colnames(NBA1516)## [1] "League" "Name" "Team"
## [4] "Position" "GamesPlayed" "TotalMinutesPlayed"
## [7] "FieldGoalsMade" "FieldGoalsAttempted" "ThreesMade"
## [10] "ThreesAttempted" "FreeThrowsMade" "FreeThrowsAttempted"
## [13] "OffensiveRebounds" "TotalRebounds" "Assists"
## [16] "Steals" "Turnovers" "Blocks"
## [19] "PersonalFouls" "Disqualifications" "TotalPoints"
## [22] "Technicals" "Ejections" "FlagrantFouls"
## [25] "GamesStarted"- 每支球隊的三分球命中率的boxplot:
NBA1516 %>% mutate(ThreepointAcu = ThreesMade/ThreesAttempted) %>% ggplot() + geom_boxplot(aes(x = Team ,y = ThreepointAcu))## Warning: Removed 42 rows containing non-finite values (stat_boxplot).
- 聯盟各個位置球員數:
NBA1516 %>% group_by(Position) %>%
summarise(count = n()) %>%
ggplot(aes(x = Position, y = count)) +
geom_bar(stat = "identity")## Warning: Factor `Position` contains implicit NA, consider using
## `forcats::fct_explicit_na`
- 各隊在各個位置命中三分球數量:
NBA1516 %>% group_by(Team,Position) %>% summarise(totalThree = sum(ThreesMade)) %>% arrange(desc(totalThree)) %>%
ggplot() + geom_tile(aes(x = Position, y = Team,
fill = totalThree,
color = "white")) +
scale_fill_gradient(low = "white", high = "steelblue")## Warning: Factor `Position` contains implicit NA, consider using
## `forcats::fct_explicit_na`
NBA1516 %>% group_by(Team,Position) %>% summarise(totalThree = sum(ThreesMade)) %>% arrange(desc(totalThree)) ## Warning: Factor `Position` contains implicit NA, consider using
## `forcats::fct_explicit_na`另外參考 D:(swirl)(ggplot2).R
ggplot2+地圖
- Choropleth map面量圖:
Choropleth map面量圖是指把統計資料用顏色畫在對應的地圖上的一種資料視覺化方式，在R中可以使用choroplethr(Lamstein 2018) package來畫面量圖， choroplethr package是一個基於ggplot2 package的面量圖做圖工具，使用前需要先安裝，建議同時安裝choroplethrMaps package
# install.packages(c("choroplethr","choroplethrMaps"))
library(choroplethr)## Warning: package 'choroplethr' was built under R version 3.6.3## Loading required package: acs## Warning: package 'acs' was built under R version 3.6.3## Loading required package: stringr## Loading required package: XML##
## Attaching package: 'acs'## The following object is masked from 'package:dplyr':
##
## combine## The following object is masked from 'package:base':
##
## apply- 內建美國各州地圖與人口學資料，所以可以輕鬆使用state_choropleth()函式畫出美國各州人口分布:
data(df_pop_state)
data(continental_us_states)
state_choropleth(df_pop_state,
title = "US 2012 State Population Estimates",
legend = "Population",
zoom = continental_us_states,
reference_map = FALSE)
- 因google地圖需要使用API key，因此須先使用ggmap套件的register_google(key = “your key”)，帶入google金鑰設定授權金鑰。以下金鑰為示範金鑰，目前已失效，請自行至Google Cloud Platform，點選API及服務申請Maps Static API後，複製API key。
Treemap
- Treemap(矩形式樹狀結構繪圖法)是以二維平面的方式展示包含階層結構（hierarchical）形式的統計資訊，在R中可以使用treemapify(Wilkins 2019) packages:
# install.packages("devtools")
library(devtools)## Warning: package 'devtools' was built under R version 3.6.3## Loading required package: usethis# install_github("wilkox/ggfittext") wrong!
# install_github("wilkox/treemapify") wrong!
# install.packages("treemapify") successful!
library(treemapify)## Warning: package 'treemapify' was built under R version 3.6.3- G20 Dataset包含20個國家的資訊，資料欄位有：
- Region 國家所在區域
- Country 國家名稱
- Trade.mil.USD 總貿易額，以百萬為單位
- Nom.GDP.mil.USD 名義國內生產總值，以百萬為單位
- HDI 人類發展指數（維基百科）
- Population 人口數
- Economic.classification 經濟狀況分類
data(G20)#載入範例資料
ggplot(G20, aes(area = gdp_mil_usd, fill = hdi, label = country)) + geom_treemap() +
scale_fill_gradient(low = "white",high = "steelblue") +
geom_treemap_text(fontface = "italic", colour = "black", place = "centre",grow = TRUE)
- treemap(Tennekes 2017) package也提供相同的功能
# install.packages("treemap")
library(treemap)## Warning: package 'treemap' was built under R version 3.6.3treemap(G20,
index = c("econ_classification","country"), #分組依據
vSize = "gdp_mil_usd", #區塊大小
vColor = "hdi", #顏色深淺
type = "value")
ggvis
- ggvis(Chang and Wickham 2018)是RStudio開發的互動式繪圖套件，繪圖語法與ggplot2(Wickham, Chang, et al. 2019)套件類似，基本概念是使用R做資料處理與分析，然後利用網頁的方式做視覺化呈現。如果使用RStudio IDE，透過ggvis套件畫的圖會呈現在右下角的Viewer視窗中。
# install.packages("ggvis")
library(ggvis)## Warning: package 'ggvis' was built under R version 3.6.3##
## Attaching package: 'ggvis'## The following object is masked from 'package:ggplot2':
##
## resolutiong <- ggvis(G20, x = ~ gdp_mil_usd, y = ~ hdi)
layer_points(g)g <- ggvis(G20, x = ~ hdi)
layer_histograms(g,
width = input_slider(0, 2, step = 0.10, label = "width"),
center = input_slider(0, 2, step = 0.5, label = "center"))## Warning: Can't output dynamic/interactive ggvis plots in a knitr document.
## Generating a static (non-dynamic, non-interactive) version of the plot.除了input_slider()外，ggvis還提供以下互動式輸入介面： >input_checkbox() input_checkboxgroup() input_numeric() input_radiobuttons() input_select() input_text()
參考 官網
googleVis
- googleVis(Gesmann and de Castillo 2019) package是基於Google Chart API開發的R套件，使用前需要先安裝與載入。
# install.packages("googleVis")
library(googleVis)## Warning: package 'googleVis' was built under R version 3.6.3## Creating a generic function for 'toJSON' from package 'jsonlite' in package 'googleVis'##
## Welcome to googleVis version 0.6.4
##
## Please read Google's Terms of Use
## before you start using the package:
## https://developers.google.com/terms/
##
## Note, the plot method of googleVis will by default use
## the standard browser to display its output.
##
## See the googleVis package vignettes for more details,
## or visit https://github.com/mages/googleVis.
##
## To suppress this message use:
## suppressPackageStartupMessages(library(googleVis))demo(googleVis)##
##
## demo(googleVis)
## ---- ~~~~~~~~~
##
## > ## ---- googleVis demo ----
## > ## ---- pauseFunction ----
## > pause <- function(){
## + invisible(readline("\nPress <return> to continue: "))
## + }
##
## > ## ---- testData ----
## > df=data.frame(country=c("US", "GB", "BR"),
## + val1=c(10,13,14),
## + val2=c(23,12,32))
##
## > ## ---- LineChart ----
## > Line <- gvisLineChart(df)
##
## > plot(Line)## starting httpd help server ...## done##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- TwoAxis ----
## > Line2 <- gvisLineChart(df, "country", c("val1","val2"),
## + options=list(
## + series="[{targetAxisIndex: 0},
## + {targetAxisIndex:1}]",
## + vAxes="[{title:'val1'}, {title:'val2'}]"
## + ))
##
## > plot(Line2)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- SettingOptions ----
## > Line3 <- gvisLineChart(df, xvar="country", yvar=c("val1","val2"),
## + options=list(
## + title="Hello World",
## + titleTextStyle="{color:'red',
## + fontName:'Courier',
## + fontSize:16}",
## + backgroundColor="#D3D3D3",
## + vAxis="{gridlines:{color:'red', count:3}}",
## + hAxis="{title:'Country', titleTextStyle:{color:'blue'}}",
## + series="[{color:'green', targetAxisIndex: 0},
## + {color: 'orange',targetAxisIndex:1}]",
## + vAxes="[{title:'val1'}, {title:'val2'}]",
## + legend="bottom",
## + curveType="function",
## + width=500,
## + height=300
## + ))
##
## > plot(Line3)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- CustomizingLines ----
## > Dashed <- gvisLineChart(df, xvar="country", yvar=c("val1","val2"),
## + options=list(
## + series="[{color:'green', targetAxisIndex: 0,
## + lineWidth: 1, lineDashStyle: [2, 2, 20, 2, 20, 2]},
## + {color: 'blue',targetAxisIndex: 1,
## + lineWidth: 2, lineDashStyle: [4, 1]}]",
## + vAxes="[{title:'val1'}, {title:'val2'}]"
## + ))
##
## > plot(Dashed)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- EditButton ----
## > Line4 <- gvisLineChart(df, "country", c("val1","val2"),
## + options=list(gvis.editor="Edit me!"))
##
## > plot(Line4)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- BarChart ----
## > Bar <- gvisBarChart(df)
##
## > plot(Bar)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- ColumnChart ----
## > Column <- gvisColumnChart(df)
##
## > plot(Column)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- AreaChart ----
## > Area <- gvisAreaChart(df)
##
## > plot(Area)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- SteppedAreaChart ----
## > SteppedArea <- gvisSteppedAreaChart(df, xvar="country",
## + yvar=c("val1", "val2"),
## + options=list(isStacked=TRUE))
##
## > plot(SteppedArea)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- ComboChart ----
## > Combo <- gvisComboChart(df, xvar="country",
## + yvar=c("val1", "val2"),
## + options=list(seriesType="bars",
## + series='{1: {type:"line"}}'))
##
## > plot(Combo)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- ScatterChart ----
## > Scatter <- gvisScatterChart(women,
## + options=list(
## + legend="none",
## + lineWidth=2, pointSize=0,
## + title="Women", vAxis="{title:'weight (lbs)'}",
## + hAxis="{title:'height (in)'}",
## + width=300, height=300))
##
## > plot(Scatter)
##
## > ## ---- ScatterChartPoints ----
## > M <- matrix(nrow=6,ncol=6)
##
## > M[col(M)==row(M)] <- 1:6
##
## > dat <- data.frame(X=1:6, M)
##
## > SC <- gvisScatterChart(dat,
## + options=list(
## + title="Customizing points",
## + legend="right",
## + pointSize=30,
## + series="{
## + 0: { pointShape: 'circle' },
## + 1: { pointShape: 'triangle' },
## + 2: { pointShape: 'square' },
## + 3: { pointShape: 'diamond' },
## + 4: { pointShape: 'star' },
## + 5: { pointShape: 'polygon' }
## + }"))
##
## > plot(SC)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- BubbleChart ----
## > Bubble <- gvisBubbleChart(Fruits, idvar="Fruit",
## + xvar="Sales", yvar="Expenses",
## + colorvar="Year", sizevar="Profit",
## + options=list(
## + hAxis='{minValue:75, maxValue:125}'))
##
## > plot(Bubble)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- CandlestickChart ----
## > Candle <- gvisCandlestickChart(OpenClose,
## + options=list(legend='none'))
##
## > plot(Candle)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- PieChart ----
## > Pie <- gvisPieChart(CityPopularity)
##
## > plot(Pie)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- Gauge ----
## > Gauge <- gvisGauge(CityPopularity,
## + options=list(min=0, max=800, greenFrom=500,
## + greenTo=800, yellowFrom=300, yellowTo=500,
## + redFrom=0, redTo=300, width=400, height=300))
##
## > plot(Gauge)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- IntensityMap ----
## > Intensity <- gvisIntensityMap(df)
##
## > plot(Intensity)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- GeoChart ----
## > Geo=gvisGeoChart(Exports, locationvar="Country",
## + colorvar="Profit",
## + options=list(projection="kavrayskiy-vii"))
##
## > plot(Geo)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- USStateData ----
## > require(datasets)
##
## > states <- data.frame(state.name, state.x77)
##
## > GeoStates <- gvisGeoChart(states, "state.name", "Illiteracy",
## + options=list(region="US",
## + displayMode="regions",
## + resolution="provinces",
## + width=600, height=400))
##
## > plot(GeoStates)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- GeoChartHurricaneAndrew ----
## > GeoMarker <- gvisGeoChart(Andrew, "LatLong",
## + sizevar='Speed_kt',
## + colorvar="Pressure_mb",
## + options=list(region="US"))
##
## > plot(GeoMarker)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- GoogleMapHurricaneAndrew ----
## > AndrewMap <- gvisMap(Andrew, "LatLong" , "Tip",
## + options=list(showTip=TRUE,
## + showLine=TRUE,
## + enableScrollWheel=TRUE,
## + mapType='terrain',
## + useMapTypeControl=TRUE))
##
## > plot(AndrewMap)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- Table ----
## > Table <- gvisTable(Stock,
## + formats=list(Value="#,###"))
##
## > plot(Table)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- TableWithPages ----
## > PopTable <- gvisTable(Population,
## + formats=list(Population="#,###",
## + '% of World Population'='#.#%'),
## + options=list(page='enable'))
##
## > plot(PopTable)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- OrgChart ----
## > Org <- gvisOrgChart(Regions,
## + options=list(width=600, height=250,
## + size='large', allowCollapse=TRUE))
##
## > plot(Org)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- TreeMap ----
## > Tree <- gvisTreeMap(Regions,
## + "Region", "Parent",
## + "Val", "Fac",
## + options=list(fontSize=16))
##
## > plot(Tree)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- AnnotationChart ----
## > Anno <- gvisAnnotationChart(Stock,
## + datevar="Date",
## + numvar="Value",
## + idvar="Device",
## + titlevar="Title",
## + annotationvar="Annotation",
## + options=list(
## + width=600, height=350,
## + fill=10, displayExactValues=TRUE,
## + colors="['#0000ff','#00ff00']")
## + )
##
## > plot(Anno)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- SankeyChart ----
## > datSK <- data.frame(From=c(rep("A",3), rep("B", 3)),
## + To=c(rep(c("X", "Y", "Z"),2)),
## + Weight=c(5,7,6,2,9,4))
##
## > Sankey <- gvisSankey(datSK, from="From", to="To", weight="Weight",
## + options=list(
## + sankey="{link: {color: { fill: '#d799ae' } },
## + node: { color: { fill: '#a61d4c' },
## + label: { color: '#871b47' } }}"))
##
## > plot(Sankey)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > # ---- CalendarChart ----
## > Cal <- gvisCalendar(Cairo,
## + datevar="Date",
## + numvar="Temp",
## + options=list(
## + title="Daily temperature in Cairo",
## + height=320,
## + calendar="{yearLabel: { fontName: 'Times-Roman',
## + fontSize: 32, color: '#1A8763', bold: true},
## + cellSize: 10,
## + cellColor: { stroke: 'red', strokeOpacity: 0.2 },
## + focusedCellColor: {stroke:'red'}}")
## + )
##
## > plot(Cal)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > # ---- Timeline ----
## > datTL <- data.frame(Position=c(rep("President", 3), rep("Vice", 3)),
## + Name=c("Washington", "Adams", "Jefferson",
## + "Adams", "Jefferson", "Burr"),
## + start=as.Date(x=rep(c("1789-03-29", "1797-02-03",
## + "1801-02-03"),2)),
## + end=as.Date(x=rep(c("1797-02-03", "1801-02-03",
## + "1809-02-03"),2)))
##
## > Timeline <- gvisTimeline(data=datTL,
## + rowlabel="Name",
## + barlabel="Position",
## + start="start",
## + end="end",
## + options=list(timeline="{groupByRowLabel:false}",
## + backgroundColor='#ffd',
## + height=350,
## + colors="['#cbb69d', '#603913', '#c69c6e']"))
##
## > plot(Timeline)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- Histogram ----
## > set.seed(123)
##
## > datHist=data.frame(A=rpois(100, 20),
## + B=rpois(100, 5),
## + C=rpois(100, 50))
##
## > Hist <- gvisHistogram(datHist, options=list(
## + legend="{ position: 'top', maxLines: 2 }",
## + colors="['#5C3292', '#1A8763', '#871B47']",
## + width=400, height=360))
##
## > plot(Hist)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- WordTree ----
## > wt1 <- gvisWordTree(Cats, textvar = "Phrase")
##
## > plot(wt1)
##
## > ## ---- gvisMerge ----
## > G <- gvisGeoChart(Exports, "Country", "Profit",
## + options=list(width=300, height=300))
##
## > T <- gvisTable(Exports,
## + options=list(width=220, height=300))
##
## > GT <- gvisMerge(G,T, horizontal=TRUE)
##
## > plot(GT)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## Flash charts
## > ## ---- GeoMap ----
## > Geo=gvisGeoMap(Exports, locationvar="Country", numvar="Profit",
## + options=list(height=350, dataMode='regions'))## Warning in gvisGeoMap(Exports, locationvar = "Country", numvar = "Profit", : GeoMap (gvisGeoMap) is Flash based, conisder using GeoChart (gvisGeoChart) instead.
## For more details visit: goo.gl/tkiEV8##
## > plot(Geo)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- GeoMap ----
## > AndrewGeo <- gvisGeoMap(Andrew,
## + locationvar="LatLong",
## + numvar="Speed_kt",
## + hovervar="Category",
## + options=list(height=350,
## + region="US",
## + dataMode="markers"))## Warning in gvisGeoMap(Andrew, locationvar = "LatLong", numvar = "Speed_kt", : GeoMap (gvisGeoMap) is Flash based, conisder using GeoChart (gvisGeoChart) instead.
## For more details visit: goo.gl/tkiEV8##
## > plot(AndrewGeo)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- AnnotatedTimeLine ----
## > AnnoTimeLine <- gvisAnnotatedTimeLine(Stock,
## + datevar="Date",
## + numvar="Value",
## + idvar="Device",
## + titlevar="Title",
## + annotationvar="Annotation",
## + options=list(displayAnnotations=TRUE,
## + width="600px", height="350px"))## Warning in gvisAnnotatedTimeLine(Stock, datevar = "Date", numvar = "Value", : AnnotatedTimeline (gvisAnnotatedTimeLine) is Flash based, conisder using AnnotationChart (gvisAnnotationChart) instead.
## For more details visit: goo.gl/tkiEV8##
## > plot(AnnoTimeLine)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## ---- MotionChart ----
## > Motion=gvisMotionChart(Fruits,
## + idvar="Fruit",
## + timevar="Year")
##
## > plot(Motion)
##
## > ## ---- pause ----
## > pause()
##
## Press <return> to continue:
##
## > ## You can change some of displaying settings via the browser,
## > ## e.g. the level of opacity of non-selected items, or the chart type.
## > ## The state string from the 'Advanced' tab can be used to set those
## > ## settings via R. Just copy and past the string from the browser into
## > ## the argument state of the options list.
## > ## Here is an example of a motion chart, with an initial line chart
## > ## displayed.
## >
## > ## ---- MotionChartSettings ----
## > myStateSettings <-'
## + {"xZoomedDataMin":1199145600000,"colorOption":"2",
## + "duration":{"timeUnit":"Y","multiplier":1},"yLambda":1,
## + "yAxisOption":"4","sizeOption":"_UNISIZE",
## + "iconKeySettings":[],"xLambda":1,"nonSelectedAlpha":0,
## + "xZoomedDataMax":1262304000000,"iconType":"LINE",
## + "dimensions":{"iconDimensions":["dim0"]},
## + "showTrails":false,"uniColorForNonSelected":false,
## + "xAxisOption":"_TIME","orderedByX":false,"playDuration":15000,
## + "xZoomedIn":false,"time":"2010","yZoomedDataMin":0,
## + "yZoomedIn":false,"orderedByY":false,"yZoomedDataMax":100}
## + '
##
## > M <- gvisMotionChart(Fruits, "Fruit", "Year", options=list(state=myStateSettings))
##
## > plot(M)
##
## > ## See demo(package='googleVis') for other available demos.- googleVis套件提供多種繪圖方式，包括：
- 一維資料做圖
- gvisHistogram
- 類別-數值資料做圖
- gvisPieChart
- gvisGauge
- gvisBarChart
- gvisColumnChart
- gvisCandlestickChart
- 數值-數值資料做圖
- gvisLineChart
- gvisAreaChart
- gvisSteppedAreaChart
- gvisScatterChart
- gvisAnnotationChart
- 數值-數值-數值資料做圖
- gvisBubbleChart
- 地圖相關
- gvisIntensityMap
- gvisGeoChart
- gvisMap
- 其他圖形
- gvisOrgChart
- gvisTreeMap
- gvisSankey
- gvisComboChart
- gvisCalendar
- gvisTimeline
require(datasets) # data: state
states <- data.frame(state.name, state.x77)
GeoStates <- gvisGeoChart(states, "state.name", "Illiteracy",
# Illiteracy 文盲(比率)
options=list(region="US",
displayMode="regions",
resolution="provinces",
width=600, height=400))
plot(GeoStates)# 1992/8/16 - 8/28安德魯颶風路徑
AndrewMap <- gvisMap(Andrew, "LatLong" , "Tip",
# LatLong 緯/經度
options=list(showTip=TRUE,
showLine=TRUE,
enableScrollWheel=TRUE,
mapType='terrain',
useMapTypeControl=TRUE))
plot(AndrewMap)Plot.ly
- Plotly是一個線上分析與視覺化的工具，如需線上作圖，可至https://plot.ly/create/ 建立帳號並開始作圖。Plotly也提供套件R使用，使用者可以透過安裝plotly(Sievert et al. 2019) package在R中畫基於Plotly.js (d3.js + stack.gl)的圖表和地圖。除了R的套件外，還有Python, MATLAB, Perl, Julia, Arduino等套件可供使用。
# install.packages("plotly")
library(plotly)## Warning: package 'plotly' was built under R version 3.6.3##
## Attaching package: 'plotly'## The following objects are masked from 'package:ggvis':
##
## add_data, hide_legend## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layout# 鑽石的價格與其他屬性之數據
d <- diamonds[sample(nrow(diamonds), 1000), ]
p <- ggplot(data = d, aes(x = carat, y = price)) +
geom_point(aes(text = paste("Clarity:", clarity))) +
geom_smooth(aes(colour = cut, fill = cut)) + facet_wrap(~ cut)## Warning: Ignoring unknown aesthetics: textggplotly(p)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'