Scatter plots with ggplot2 (plotly.com)
Line plots with R (plotly.com)
NW <- read_csv("data/interactive_bulletin_charts_agecl_median.csv") %>%
select(Category, year, Net_Worth) %>%
group_by(Category) %>%
arrange(year) %>%
ungroup()如果希望滑鼠在移到折線上時就會有浮出的資訊（tips）顯示該資料點的屬性特徵，可以採用plotly()這個套件。這個套件原本就是做線上互動圖表的，但他開發了R client讓R的使用者可以很輕易地把ggplot2的結果轉為互動圖表。但這所謂的互動也僅限於滑鼠移過去所浮出的資訊罷了，不過已經能夠達到吸引部分讀者目光、提供訊息的效果。
而plotly的設計非常簡單，就是把ggplot的結果指給一個變數後，然後用ggplotly(NW.plot)將其轉為plotly即可。但要注意的是，並不是每一個圖都可以順利轉換。例如本節最後一個例子Treemap便無法成功轉換。
設定：原本plotly會帶一個操控列，可以在ggplotly()指令後加入config()便可將其隱藏。
NW.plot <- NW %>%
ggplot() +
aes(year, Net_Worth, color=Category) +
geom_line() +
theme_minimal() +
labs(title = "Net Worth by year grouped by age groups",
x = "Year",
y = "Net Worth") + th
library(plotly)
ggplotly(NW.plot) %>%
config(displayModeBar = FALSE)<div class="plotly html-widget html-fill-item" id="htmlwidget-0b2e7be3b294afd361e2" style="width:100%;height:336px;"></div>
<script type="application/json" data-for="htmlwidget-0b2e7be3b294afd361e2">{"x":{"data":[{"x":[1989,1992,1995,1998,2001,2004,2007,2010,2013,2016,2019],"y":[112.4753,79.910499999999999,86.971344000000002,99.872505000000004,113.37223,94.152184000000005,109.43051,49.911793000000003,51.668906999999997,63.495274000000002,91.105002999999996],"text":["year: 1989<br />Net_Worth: 112.47530<br />Category: 35-44","year: 1992<br />Net_Worth: 79.91050<br />Category: 35-44","year: 1995<br />Net_Worth: 86.97134<br />Category: 35-44","year: 1998<br />Net_Worth: 99.87251<br />Category: 35-44","year: 2001<br />Net_Worth: 113.37223<br />Category: 35-44","year: 2004<br />Net_Worth: 94.15218<br />Category: 35-44","year: 2007<br />Net_Worth: 109.43051<br />Category: 35-44","year: 2010<br />Net_Worth: 49.91179<br />Category: 35-44","year: 2013<br />Net_Worth: 51.66891<br />Category: 35-44","year: 2016<br />Net_Worth: 63.49527<br />Category: 35-44","year: 2019<br />Net_Worth: 91.10500<br />Category: 35-44"],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(248,118,109,1)","dash":"solid"},"hoveron":"points","name":"35-44","legendgroup":"35-44","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1989,1992,1995,1998,2001,2004,2007,2010,2013,2016,2019],"y":[195.1163,139.97745,154.74556000000001,166.13436999999999,193.98927,196.70961,228.36722,138.03183000000001,115.69223,131.92552000000001,168.80000000000001],"text":["year: 1989<br />Net_Worth: 195.11630<br />Category: 45-54","year: 1992<br />Net_Worth: 139.97745<br />Category: 45-54","year: 1995<br />Net_Worth: 154.74556<br />Category: 45-54","year: 1998<br />Net_Worth: 166.13437<br />Category: 45-54","year: 2001<br />Net_Worth: 193.98927<br />Category: 45-54","year: 2004<br />Net_Worth: 196.70961<br />Category: 45-54","year: 2007<br />Net_Worth: 228.36722<br />Category: 45-54","year: 2010<br />Net_Worth: 138.03183<br />Category: 45-54","year: 2013<br />Net_Worth: 115.69223<br />Category: 45-54","year: 2016<br />Net_Worth: 131.92552<br />Category: 45-54","year: 2019<br />Net_Worth: 168.80000<br />Category: 45-54"],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(183,159,0,1)","dash":"solid"},"hoveron":"points","name":"45-54","legendgroup":"45-54","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1989,1992,1995,1998,2001,2004,2007,2010,2013,2016,2019],"y":[195.25554,203.44103999999999,192.82265000000001,201.16051999999999,267.58733999999998,341.63213999999999,313.16998000000001,210.55304000000001,181.98876999999999,199.20712,213.14999],"text":["year: 1989<br />Net_Worth: 195.25554<br />Category: 55-64","year: 1992<br />Net_Worth: 203.44104<br />Category: 55-64","year: 1995<br />Net_Worth: 192.82265<br />Category: 55-64","year: 1998<br />Net_Worth: 201.16052<br />Category: 55-64","year: 2001<br />Net_Worth: 267.58734<br />Category: 55-64","year: 2004<br />Net_Worth: 341.63214<br />Category: 55-64","year: 2007<br />Net_Worth: 313.16998<br />Category: 55-64","year: 2010<br />Net_Worth: 210.55304<br />Category: 55-64","year: 2013<br />Net_Worth: 181.98877<br />Category: 55-64","year: 2016<br />Net_Worth: 199.20712<br />Category: 55-64","year: 2019<br />Net_Worth: 213.14999<br />Category: 55-64"],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(0,186,56,1)","dash":"solid"},"hoveron":"points","name":"55-64","legendgroup":"55-64","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1989,1992,1995,1998,2001,2004,2007,2010,2013,2016,2019],"y":[154.34277,176.44667000000001,185.22726,230.41452000000001,257.07333,257.85091999999997,295.51790999999997,243.50864999999999,254.8853,238.24021999999999,266.07001000000002],"text":["year: 1989<br />Net_Worth: 154.34277<br />Category: 65-74","year: 1992<br />Net_Worth: 176.44667<br />Category: 65-74","year: 1995<br />Net_Worth: 185.22726<br />Category: 65-74","year: 1998<br />Net_Worth: 230.41452<br />Category: 65-74","year: 2001<br />Net_Worth: 257.07333<br />Category: 65-74","year: 2004<br />Net_Worth: 257.85092<br />Category: 65-74","year: 2007<br />Net_Worth: 295.51791<br />Category: 65-74","year: 2010<br />Net_Worth: 243.50865<br />Category: 65-74","year: 2013<br />Net_Worth: 254.88530<br />Category: 65-74","year: 2016<br />Net_Worth: 238.24022<br />Category: 65-74","year: 2019<br />Net_Worth: 266.07001<br />Category: 65-74"],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(0,191,196,1)","dash":"solid"},"hoveron":"points","name":"65-74","legendgroup":"65-74","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1989,1992,1995,1998,2001,2004,2007,2010,2013,2016,2019],"y":[144.29855000000001,155.35173,155.24635000000001,197.85765000000001,225.80571,221.11190999999999,263.17635999999999,255.56213,214.14319,281.58080999999999,254.89999],"text":["year: 1989<br />Net_Worth: 144.29855<br />Category: 75 or older","year: 1992<br />Net_Worth: 155.35173<br />Category: 75 or older","year: 1995<br />Net_Worth: 155.24635<br />Category: 75 or older","year: 1998<br />Net_Worth: 197.85765<br />Category: 75 or older","year: 2001<br />Net_Worth: 225.80571<br />Category: 75 or older","year: 2004<br />Net_Worth: 221.11191<br />Category: 75 or older","year: 2007<br />Net_Worth: 263.17636<br />Category: 75 or older","year: 2010<br />Net_Worth: 255.56213<br />Category: 75 or older","year: 2013<br />Net_Worth: 214.14319<br />Category: 75 or older","year: 2016<br />Net_Worth: 281.58081<br />Category: 75 or older","year: 2019<br />Net_Worth: 254.89999<br />Category: 75 or older"],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(97,156,255,1)","dash":"solid"},"hoveron":"points","name":"75 or older","legendgroup":"75 or older","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1989,1992,1995,1998,2001,2004,2007,2010,2013,2016,2019],"y":[16.170189000000001,16.607797999999999,20.031787999999999,14.312438,16.897516,19.250699999999998,14.454948999999999,10.957713,11.48686,11.699297,14],"text":["year: 1989<br />Net_Worth: 16.17019<br />Category: Less than 35","year: 1992<br />Net_Worth: 16.60780<br />Category: Less than 35","year: 1995<br />Net_Worth: 20.03179<br />Category: Less than 35","year: 1998<br />Net_Worth: 14.31244<br />Category: Less than 35","year: 2001<br />Net_Worth: 16.89752<br />Category: Less than 35","year: 2004<br />Net_Worth: 19.25070<br />Category: Less than 35","year: 2007<br />Net_Worth: 14.45495<br />Category: Less than 35","year: 2010<br />Net_Worth: 10.95771<br />Category: Less than 35","year: 2013<br />Net_Worth: 11.48686<br />Category: Less than 35","year: 2016<br />Net_Worth: 11.69930<br />Category: Less than 35","year: 2019<br />Net_Worth: 14.00000<br />Category: Less than 35"],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(245,100,227,1)","dash":"solid"},"hoveron":"points","name":"Less than 35","legendgroup":"Less than 35","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null}],"layout":{"margin":{"t":48.146118721461193,"r":7.3059360730593621,"b":44.56621004566211,"l":43.105022831050235},"font":{"color":"rgba(0,0,0,1)","family":"Noto Sans CJK TC","size":14.611872146118724},"title":{"text":"<b> Net Worth by year grouped by age groups <\/b>","font":{"color":"rgba(0,0,0,1)","family":"Noto Sans CJK 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可以在aes()設定要帶入圖的變數時，新增一個text變數，手動設定要呈現的動態呈現方塊。但要注意的是，要多加入一個group=1才能夠作用（WHY？）。但前例浮出視窗的原始內容所顯示的是原本的變數名稱和值，往往不易觀察。比較好的方式是在下ggplot() + aes()指令時，在aes()中指定text來作為後續浮出視窗內容。指定方法如下。要注意的是，該浮出視窗的語法是HTML，所以如果要改寫浮出視窗內容，要用paste0()將變數和HTML的標籤給銜接起來。以下例子中的<b>代表粗體的意思，<br>則是換行符號。
NW.plot <- NW %>%
ggplot() +
aes(year, Net_Worth,
color=Category,
text = paste0("<b>年(X): </b>", year, "<br>",
"<b>淨資產(Y): </b>", Net_Worth,"<br>",
"<b>年齡組: </b>", Category),
group=1) +
geom_line() +
theme_minimal() +
labs(title = "Net Worth by year grouped by age groups",
x = "Year",
y = "Net Worth") + th
ggplotly(NW.plot, tooltip = "text") %>%
config(displayModeBar = FALSE)<div class="plotly html-widget html-fill-item" id="htmlwidget-12fce42b64c35caf1b42" style="width:100%;height:336px;"></div>
<script type="application/json" data-for="htmlwidget-12fce42b64c35caf1b42">{"x":{"data":[{"x":[1989,1992,1995,1998,2001,2004,2007,2010,2013,2016,2019],"y":[112.4753,79.910499999999999,86.971344000000002,99.872505000000004,113.37223,94.152184000000005,109.43051,49.911793000000003,51.668906999999997,63.495274000000002,91.105002999999996],"text":["<b>年(X): <\/b>1989<br><b>淨資產(Y): <\/b>112.4753<br><b>年齡組: <\/b>35-44","<b>年(X): <\/b>1992<br><b>淨資產(Y): <\/b>79.9105<br><b>年齡組: <\/b>35-44","<b>年(X): <\/b>1995<br><b>淨資產(Y): <\/b>86.971344<br><b>年齡組: <\/b>35-44","<b>年(X): <\/b>1998<br><b>淨資產(Y): <\/b>99.872505<br><b>年齡組: <\/b>35-44","<b>年(X): <\/b>2001<br><b>淨資產(Y): <\/b>113.37223<br><b>年齡組: <\/b>35-44","<b>年(X): <\/b>2004<br><b>淨資產(Y): <\/b>94.152184<br><b>年齡組: <\/b>35-44","<b>年(X): <\/b>2007<br><b>淨資產(Y): <\/b>109.43051<br><b>年齡組: <\/b>35-44","<b>年(X): <\/b>2010<br><b>淨資產(Y): <\/b>49.911793<br><b>年齡組: <\/b>35-44","<b>年(X): <\/b>2013<br><b>淨資產(Y): <\/b>51.668907<br><b>年齡組: <\/b>35-44","<b>年(X): <\/b>2016<br><b>淨資產(Y): <\/b>63.495274<br><b>年齡組: <\/b>35-44","<b>年(X): <\/b>2019<br><b>淨資產(Y): <\/b>91.105003<br><b>年齡組: <\/b>35-44"],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(248,118,109,1)","dash":"solid"},"hoveron":"points","name":"35-44","legendgroup":"35-44","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1989,1992,1995,1998,2001,2004,2007,2010,2013,2016,2019],"y":[195.1163,139.97745,154.74556000000001,166.13436999999999,193.98927,196.70961,228.36722,138.03183000000001,115.69223,131.92552000000001,168.80000000000001],"text":["<b>年(X): <\/b>1989<br><b>淨資產(Y): <\/b>195.1163<br><b>年齡組: <\/b>45-54","<b>年(X): <\/b>1992<br><b>淨資產(Y): <\/b>139.97745<br><b>年齡組: <\/b>45-54","<b>年(X): <\/b>1995<br><b>淨資產(Y): <\/b>154.74556<br><b>年齡組: <\/b>45-54","<b>年(X): <\/b>1998<br><b>淨資產(Y): <\/b>166.13437<br><b>年齡組: <\/b>45-54","<b>年(X): <\/b>2001<br><b>淨資產(Y): <\/b>193.98927<br><b>年齡組: <\/b>45-54","<b>年(X): <\/b>2004<br><b>淨資產(Y): <\/b>196.70961<br><b>年齡組: <\/b>45-54","<b>年(X): <\/b>2007<br><b>淨資產(Y): <\/b>228.36722<br><b>年齡組: <\/b>45-54","<b>年(X): <\/b>2010<br><b>淨資產(Y): <\/b>138.03183<br><b>年齡組: <\/b>45-54","<b>年(X): <\/b>2013<br><b>淨資產(Y): <\/b>115.69223<br><b>年齡組: <\/b>45-54","<b>年(X): <\/b>2016<br><b>淨資產(Y): <\/b>131.92552<br><b>年齡組: <\/b>45-54","<b>年(X): <\/b>2019<br><b>淨資產(Y): <\/b>168.8<br><b>年齡組: <\/b>45-54"],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(183,159,0,1)","dash":"solid"},"hoveron":"points","name":"45-54","legendgroup":"45-54","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1989,1992,1995,1998,2001,2004,2007,2010,2013,2016,2019],"y":[195.25554,203.44103999999999,192.82265000000001,201.16051999999999,267.58733999999998,341.63213999999999,313.16998000000001,210.55304000000001,181.98876999999999,199.20712,213.14999],"text":["<b>年(X): <\/b>1989<br><b>淨資產(Y): <\/b>195.25554<br><b>年齡組: <\/b>55-64","<b>年(X): <\/b>1992<br><b>淨資產(Y): <\/b>203.44104<br><b>年齡組: <\/b>55-64","<b>年(X): <\/b>1995<br><b>淨資產(Y): <\/b>192.82265<br><b>年齡組: <\/b>55-64","<b>年(X): <\/b>1998<br><b>淨資產(Y): <\/b>201.16052<br><b>年齡組: <\/b>55-64","<b>年(X): <\/b>2001<br><b>淨資產(Y): <\/b>267.58734<br><b>年齡組: <\/b>55-64","<b>年(X): <\/b>2004<br><b>淨資產(Y): <\/b>341.63214<br><b>年齡組: <\/b>55-64","<b>年(X): <\/b>2007<br><b>淨資產(Y): <\/b>313.16998<br><b>年齡組: <\/b>55-64","<b>年(X): <\/b>2010<br><b>淨資產(Y): <\/b>210.55304<br><b>年齡組: <\/b>55-64","<b>年(X): <\/b>2013<br><b>淨資產(Y): <\/b>181.98877<br><b>年齡組: <\/b>55-64","<b>年(X): <\/b>2016<br><b>淨資產(Y): <\/b>199.20712<br><b>年齡組: <\/b>55-64","<b>年(X): <\/b>2019<br><b>淨資產(Y): <\/b>213.14999<br><b>年齡組: <\/b>55-64"],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(0,186,56,1)","dash":"solid"},"hoveron":"points","name":"55-64","legendgroup":"55-64","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1989,1992,1995,1998,2001,2004,2007,2010,2013,2016,2019],"y":[154.34277,176.44667000000001,185.22726,230.41452000000001,257.07333,257.85091999999997,295.51790999999997,243.50864999999999,254.8853,238.24021999999999,266.07001000000002],"text":["<b>年(X): <\/b>1989<br><b>淨資產(Y): <\/b>154.34277<br><b>年齡組: <\/b>65-74","<b>年(X): <\/b>1992<br><b>淨資產(Y): <\/b>176.44667<br><b>年齡組: <\/b>65-74","<b>年(X): <\/b>1995<br><b>淨資產(Y): <\/b>185.22726<br><b>年齡組: <\/b>65-74","<b>年(X): <\/b>1998<br><b>淨資產(Y): <\/b>230.41452<br><b>年齡組: <\/b>65-74","<b>年(X): <\/b>2001<br><b>淨資產(Y): <\/b>257.07333<br><b>年齡組: <\/b>65-74","<b>年(X): <\/b>2004<br><b>淨資產(Y): <\/b>257.85092<br><b>年齡組: <\/b>65-74","<b>年(X): <\/b>2007<br><b>淨資產(Y): <\/b>295.51791<br><b>年齡組: 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其他例子中使用ggplotly()都是直接照前面的方法套用即可。唯獨在Treemap中無法用這樣的方法來做互動的視覺化。想想這也正常，畢竟Treemap是用非ggplot的第三方套件（library(treemapify)）。
除此之外，可以把R Markdown中Code Cell的的設定加入include=FALSE，這樣可以讓RMD在編製為HTML檔時，不要顯示程式碼，而直接顯示互動的視覺化介面。
bw <- read_csv("data/unicef-changing-childhood-data.csv") %>%
select(country = WP5, age = WP22140, bw = WP22092) %>%
mutate(country = ordered(country,
levels=c(1, 3, 4, 10, 11, 12, 13, 14, 17,
29, 31, 33, 35, 36, 60, 61, 77,
79, 81, 87, 165),
labels=c("USA", "Morocco", "Lebanon",
"Indonesia","Bangladesh", "UK",
"France", "Germany", "Spain",
"Japan", "India", "Brazil",
"Nigeria", "Kenya", "Ethiopia",
"Mali", "Ukraine", "Cameroon",
"Zimbabwe","Argentina", "Peru"))) %>%
count(country, age, bw) %>%
group_by(country, age) %>%
mutate(perc = n/sum(n)) %>%
ungroup() %>%
filter(bw == 1) %>%
select(country, age, perc) %>%
spread(age, perc) %>%
rename(`15-24y` = `1`, `40+y` = `2`)bw.p <- bw %>%
ggplot() + aes(`40+y`, `15-24y`, label = country) +
geom_point(color = "skyblue", size = 2) +
xlim(0.2, 0.85) + ylim(0.2, 0.85) +
geom_text(hjust = -0.1, vjust = -0.5) +
geom_abline(intercept = 0, slop = 1,
color="lightgrey", alpha=0.5, linetype="dashed") +
th +
theme(aspect.ratio=1)bw.p %>% ggplotly()<div class="plotly html-widget html-fill-item" id="htmlwidget-5d8a6b38ec55a39d66cd" style="width:100%;height:336px;"></div>
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TC","size":14.611872146118724}},"hoverformat":".2f"},"shapes":[{"type":"rect","fillcolor":null,"line":{"color":null,"width":0,"linetype":[]},"yref":"paper","xref":"paper","x0":0,"x1":1,"y0":0,"y1":1}],"showlegend":false,"legend":{"bgcolor":null,"bordercolor":null,"borderwidth":0,"font":{"color":"rgba(0,0,0,1)","family":"Noto Sans CJK TC","size":11.68949771689498}},"hovermode":"closest","barmode":"relative"},"config":{"doubleClick":"reset","modeBarButtonsToAdd":["hoverclosest","hovercompare"],"showSendToCloud":false},"source":"A","attrs":{"185c07ee4b993":{"x":{},"y":{},"label":{},"type":"scatter"},"185c05a53a2fa":{"x":{},"y":{},"label":{}},"185c028f0f96a":{"intercept":{},"slope":{}}},"cur_data":"185c07ee4b993","visdat":{"185c07ee4b993":["function (y) ","x"],"185c05a53a2fa":["function (y) ","x"],"185c028f0f96a":["function (y) 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Bar charts with R (plotly.com)
county <- read_csv("data/tw_population_opendata110N010.csv") %>%
slice(-1, -(370:375)) %>%
type_convert() %>%
mutate(county = str_sub(site_id, 1, 3)) %>%
group_by(county) %>%
summarize(
area = sum(area),
people_total = sum(people_total)
) %>%
ungroup()
population.p <- county %>%
mutate(county = reorder(county, people_total)) %>%
ggplot() + aes(county, people_total) %>%
geom_col(fill="skyblue") +
coord_flip() + thpopulation.p %>% ggplotly()<div class="plotly html-widget html-fill-item" id="htmlwidget-7b1e8b83e04aebf6685d" style="width:100%;height:336px;"></div>
<script type="application/json" data-for="htmlwidget-7b1e8b83e04aebf6685d">{"x":{"data":[{"orientation":"h","width":[0.89999999999999858,0.90000000000000036,0.89999999999999858,0.90000000000000036,0.89999999999999947,0.89999999999999858,0.89999999999999858,0.89999999999999858,0.89999999999999858,0.89999999999999858,0.89999999999999858,0.90000000000000013,0.89999999999999858,0.89999999999999858,0.89999999999999858,0.90000000000000036,0.90000000000000036,0.89999999999999858,0.89999999999999991,0.90000000000000036,0.89999999999999858,0.89999999999999858],"base":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"x":[484897,264727,493316,363977,450692,804440,1255330,4008113,452640,575580,2272391,106340,2813490,2524393,1862059,213386,321358,538178,13645,141539,670132,2744691],"y":[10,5,11,7,8,15,16,22,9,13,18,2,21,19,17,4,6,12,1,3,14,20],"text":["county: 南投縣<br />people_total: 484897","county: 嘉義市<br />people_total: 264727","county: 嘉義縣<br />people_total: 493316","county: 基隆市<br />people_total: 363977","county: 宜蘭縣<br />people_total: 450692","county: 屏東縣<br />people_total: 804440","county: 彰化縣<br />people_total: 1255330","county: 新北市<br />people_total: 4008113","county: 新竹市<br />people_total: 452640","county: 新竹縣<br />people_total: 575580","county: 桃園市<br />people_total: 2272391","county: 澎湖縣<br />people_total: 106340","county: 臺中市<br />people_total: 2813490","county: 臺北市<br />people_total: 2524393","county: 臺南市<br />people_total: 1862059","county: 臺東縣<br />people_total: 213386","county: 花蓮縣<br />people_total: 321358","county: 苗栗縣<br />people_total: 538178","county: 連江縣<br />people_total: 13645","county: 金門縣<br />people_total: 141539","county: 雲林縣<br />people_total: 670132","county: 高雄市<br />people_total: 2744691"],"type":"bar","textposition":"none","marker":{"autocolorscale":false,"color":"rgba(135,206,235,1)","line":{"width":1.8897637795275593,"color":"transparent"}},"showlegend":false,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null}],"layout":{"margin":{"t":30.611872146118724,"r":7.3059360730593621,"b":44.56621004566211,"l":43.105022831050235},"font":{"color":"rgba(0,0,0,1)","family":"Noto Sans CJK TC","size":14.611872146118724},"xaxis":{"domain":[0,1],"automargin":true,"type":"linear","autorange":false,"range":[-200405.65000000002,4208518.6500000004],"tickmode":"array","ticktext":["0","1000000","2000000","3000000","4000000"],"tickvals":[0,999999.99999999988,2000000,3000000,4000000.0000000005],"categoryorder":"array","categoryarray":["0","1000000","2000000","3000000","4000000"],"nticks":null,"ticks":"","tickcolor":null,"ticklen":3.6529680365296811,"tickwidth":0,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"Noto Sans CJK 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","x"]},"highlight":{"on":"plotly_click","persistent":false,"dynamic":false,"selectize":false,"opacityDim":0.20000000000000001,"selected":{"opacity":1},"debounce":0},"shinyEvents":["plotly_hover","plotly_click","plotly_selected","plotly_relayout","plotly_brushed","plotly_brushing","plotly_clickannotation","plotly_doubleclick","plotly_deselect","plotly_afterplot","plotly_sunburstclick"],"base_url":"https://plot.ly"},"evals":[],"jsHooks":[]}</script>
Box plots with ggplot2 (plotly.com)
aqi.toplot <- read_rds("https://github.com/p4css/R4CSS/raw/master/data/AQI_Chaozhou.rds") %>%
arrange(日期)%>%
filter(測項=="PM2.5") %>%
gather("hour", "PM25", 4:28) %>%
mutate(PM25 = as.numeric(PM25)) %>%
drop_na() %>%
mutate(year = lubridate::year(日期), month = lubridate::month(日期)) %>%
filter(month %in% c(11, 12, 1, 2, 3))
aqi.plot <- aqi.toplot %>%
mutate(year = as.character(year)) %>%
ggplot() + aes(x=year, y=PM25) +
geom_boxplot(fill="skyblue", alpha=0.2) +
ylim(0, 200) +
coord_flip() +
theme_minimal()aqi.plot %>% ggplotly<div class="plotly html-widget html-fill-item" id="htmlwidget-404091cb4d0472d289df" style="width:100%;height:336px;"></div>
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其他例子中使用ggplotly()都是直接照前面的方法套用即可。唯獨在Treemap中無法用這樣的方法來做互動的視覺化。想想這也正常，畢竟Treemap是用非ggplot的第三方套件（library(treemapify)）。
totreemap <- read_csv("data/GCB2021v34_MtCO2_flat.csv") %>%
drop_na(`Total`) %>%
filter(!Country %in% c("Global", "International Transport")) %>%
filter(Year==2020) %>%
arrange(desc(`Total`)) %>%
mutate(perc = Total/sum(Total)) %>%
slice(1:20)
library(treemapify)
carbon.p <- totreemap %>%
ggplot() + aes(area = perc, fill=`Per Capita`, label=Country) +
geom_treemap() +
geom_treemap_text(color="white",
place="centre",
grow=TRUE
)# carbon.p %>% ggplotly