Yotta 資料視覺化實戰

一個例子學會畫圖：mpg

先看兩個變數：

1. displ - 引擎排氣公升
2. hwy - (油耗效率，哩/加侖)

• 大引擎的車子更耗油嗎？如果是的話，那有多耗油？
• 引擎大小和油耗效率之間的關係為何？正/負相關？線性/非線性？相關程度？

Scatterplots

library(ggplot2)
ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy))

從圖表可歸納幾個結論：

1. 兩變數為高度負相關 ── 大引擎 => 低效率
2. 有些車是離群值

Exercise: 用 mpg 資料畫不同的圖

看看不同變數之間的相關

• 畫出 scatterplot: hwy vs cyl
• 畫出 scatterplot: class vs drv

Answer-1

## hwy vs cyl
ggplot(data = mpg) +
  geom_point(mapping = aes(x = hwy, y = cyl))

Answer-2

## class vs drv
ggplot(data = mpg) +
  geom_point(mapping = aes(x = class, y = drv))

Exercise: 觀察 Aesthetic Mapping

• 有哪些變數 variables
• 分別對應到哪個 aethetic

Aesthetics 基本題 1

在 x-y 二維的 Scatterplot 加入第三個 aesthetic

• x = displ
• y = hwy
• color = class (把 class 對應到點的顏色)
• hint: ?geom_point: 查詢支援的 aesthetics

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

Aesthetics 基本題 2

• x = displ
• y = hwy
• shape = class

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, shape = class))

Plot Exercise

試試在 x-y 二維的 Scatterplot 加入第三個 aesthetic

• 把 class 對應到點的形狀

Answer

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, shape = class))

#> Warning: The shape palette can deal with a maximum of 6 discrete values
#> because more than 6 becomes difficult to discriminate; you have 7.
#> Consider specifying shapes manually if you must have them.

#> Warning: Removed 62 rows containing missing values (geom_point).

Aesthetic Mappings 小結

ggplot(data = <DATA>) + # Data
  geom_<xxx>(
     mapping = aes(<MAPPINGS>), ##  <= Aesthetic mappings
     stat = <STAT>,
     position = <POSITION>
  ) +
  scale_<xxx>() + coord_<xxx>() + facet_<xxx>()
  theme_()

• aes() 可以放在：
  • ggplot()裡面 – 有“記憶效果”(成為所有圖層預設)
  • 外面 + aes() – 有“記憶效果”(成為所有圖層預設)
  • geom_<xxx>()裡面 – 無“記憶效果”(只對該 geom 有效)
• geom_<xxx>(inherit.aes=FALSE): overrides the default aesthetics.

Static Aesthetic

有時候你可能只想要手動設定某個固定 aesthetic，這裡的設定只為了美觀， 並不會帶出多餘資料訊息。

• 將 aesthetic 放在 aes() 裡面: map aesthetic 並自動建立 legend
• 將 aesthetic 放在 aes() 之外: 手動設定某個固定 aesthetic

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue")

Aesthetics 不只這些

如何查？

• ?geom_: 各 geom 有不同支援的 aesthetics

• 用 ggplot2 官方大抄

ggplot2 起手式

ggplot(data = <DATA>) + # Data
  geom_<xxx>(
     mapping = aes(<MAPPINGS>),
     stat = <STAT>,
     position = <POSITION>
  ) + # Layers & Aesthetic mappings
  scale_<xxx>() + coord_<xxx>() + facet_<xxx>() # Position
  theme_()

Data for Plot – ETL

• 每一欄 (column) 都是一個(繪圖)變數
• 每一列 (row) 都是一筆觀察值
• Wide Format -> Long Format (tidyr)
  • 你的資料是寬的還是長的？

資料和圖表是一體兩面，先有資料才有圖表

以 mpg 為例

• mpg 共有 11 個變數 234 筆資料
• 這裡需要的繪圖變數 (aesthetic mapping)
  • x: displ
  • y: hwy

#> # A tibble: 234 x 11
#>    manufacturer model    displ  year   cyl trans   drv     cty   hwy fl   
#>    <chr>        <chr>    <dbl> <int> <int> <chr>   <chr> <int> <int> <chr>
#>  1 audi         a4         1.8  1999     4 auto(l… f        18    29 p    
#>  2 audi         a4         1.8  1999     4 manual… f        21    29 p    
#>  3 audi         a4         2    2008     4 manual… f        20    31 p    
#>  4 audi         a4         2    2008     4 auto(a… f        21    30 p    
#>  5 audi         a4         2.8  1999     6 auto(l… f        16    26 p    
#>  6 audi         a4         2.8  1999     6 manual… f        18    26 p    
#>  7 audi         a4         3.1  2008     6 auto(a… f        18    27 p    
#>  8 audi         a4 quat…   1.8  1999     4 manual… 4        18    26 p    
#>  9 audi         a4 quat…   1.8  1999     4 auto(l… 4        16    25 p    
#> 10 audi         a4 quat…   2    2008     4 manual… 4        20    28 p    
#> # ... with 224 more rows, and 1 more variable: class <chr>

Geoms

這兩張圖差在哪裡？

• Geom 決定圖表呈現的「幾何圖形物件」，也就是你眼睛看到的資料呈現方式
• geom_<xxx>()

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy))

ggplot(data = mpg) +
  geom_smooth(mapping = aes(x = displ, y = hwy))

Different geom:

• 因為 Geoms 真的太多了，通常要用的時候再去查: RStudio - ggplot2 Cheatsheet
• 如同前面所述，不同 Geoms 有不同支援的 Aesthetics

Bar Charts

• 各種車型(class)的數量？
• geom_bar()

ggplot(data = mpg) +
  geom_bar(mapping = aes(x = class))

Stats (Geom 的一體兩面)

• x: class
• y: ?? count 不在原本的 mpg 資料中

到底 count 是怎麼算出來的？

1. 原本可能在 Excel 算
2. R 幫你算 dplyr::summarise()
3. ggplot2 geom_bar 幫你算
   • ?geom_bar 的預設 stat 是 “count”

• 有些 Geom (例如 scatterplot) 畫的是 raw value (stat_identity)
• 有些 Geom (例如 geom_bar()) 會自動幫你計算新的 stat (e.g., count) 以供畫圖
  • 使用 geom_<xxx>() 時，要注意預設的 stat 是什麼
  • 若不計算 stat: stat = "identity"

Stats 是怎麼幫你算出來的？

stat_<xxx>()

d <- mpg %>% 
  group_by(class) %>% 
  summarise(n = n())

# Or use tally() the same:
d <- mpg %>% 
  group_by(class) %>% 
  tally()
d
#> # A tibble: 7 x 2
#>   class          n
#>   <chr>      <int>
#> 1 2seater        5
#> 2 compact       47
#> 3 midsize       41
#> 4 minivan       11
#> 5 pickup        33
#> 6 subcompact    35
#> 7 suv           62

ggplot(data = d) +
  geom_bar(mapping = aes(x = class, y = n),
           stat = "identity")

#> # A tibble: 7 x 2
#>   class      mean_hwy
#>   <chr>         <dbl>
#> 1 2seater        24.8
#> 2 compact        28.3
#> 3 midsize        27.3
#> 4 minivan        22.4
#> 5 pickup         16.9
#> 6 subcompact     28.1
#> 7 suv            18.1

d <- mpg %>% 
  group_by(class) %>% 
  summarise(n = n())
d
#> # A tibble: 7 x 2
#>   class          n
#>   <chr>      <int>
#> 1 2seater        5
#> 2 compact       47
#> 3 midsize       41
#> 4 minivan       11
#> 5 pickup        33
#> 6 subcompact    35
#> 7 suv           62

ggplot(data = d) +
  geom_bar(mapping = aes(x = reorder(class, -n), y = n),
           stat = "identity")

Geoms + Stats 實例

• bar charts, histograms: 計算每一組 bin 裡面的數目.

ggplot(data = mpg, aes(x = class)) +
  geom_bar() +
  geom_text(stat = "count",
            aes(label = ..count.., y =..count..),
            vjust = "bottom")

• boxplots: plot quartiles. (看各種車型的油耗分佈)

ggplot(data = mpg) +
  geom_boxplot(mapping = aes(x = class, y = hwy))

Layers 圖層觀念

要呈現多個幾何圖形物件 (Geoms) 時要怎麼做到呢？

• 一個 geom_<xxx>() 就會在圖上畫一圖層 (Layer)
• 可一層層疊加上去
• 每個圖層甚至可以用不同的 data，在畫進階圖表時很常用到
• 但要注意是否有預設的 aesthetic 不小心 mapping 到該圖層

兩層圖層

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy)) +
  geom_smooth(mapping= aes(x = displ, y = hwy))

(附錄) Positions：當圖形在位置打架時要怎麼辦？

ggplot(data = mpg) +
  geom_bar(mapping = aes(x = class, fill = class))

ggplot(data = mpg) +
  geom_bar(mapping = aes(x = class, fill = manufacturer),
           position = "stack") +
  ggtitle('Position = "stack"')

ggplot(data = mpg) +
  geom_bar(mapping = aes(x = class, fill = manufacturer),
           position = "identity", alpha = .4) +
  ggtitle('Position = "identity"')

ggplot(data = mpg) +
  geom_bar(mapping = aes(x = class, fill = manufacturer),
           position = "dodge") +
  ggtitle('Position = "dodge"')

ggplot(data = mpg) +
  geom_bar(mapping = aes(x = class, fill = manufacturer),
           position = "fill") +
  ggtitle('Position = "fill"')

Facets: Small-Multiples

Too many variables!!!!

• 看到剛才的車車油耗🚗，是不是覺得還是很難透過圖表理解資料？
• 剛才畫的圖因為多了第3個變數，所以更難理解了

• Facets 是很重要的一個呈現方式，一定要學起來
• 為什麼要用 Facets？
  1. 當同一個座標平面塞入太多變數，會造成大腦無法負荷
  2. 分拆資訊，讓大腦協助腦補更有效率

“Illustrations of postage-stamp size are indexed by category or a label, sequenced over time like the frames of a movie, or ordered by a quantitative variable not used in the single image itself.” – Edward Tufte

ggplot(data = mpg) +
  geom_bar(mapping = aes(x = class, fill = class)) +
  facet_wrap( ~ manufacturer, ncol = 4) +
  scale_fill_viridis_d() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

mpg %>% 
  group_by(manufacturer, class) %>%
  tally() %>% 
  ggplot(aes(x = class, y = manufacturer, fill = n)) +
  geom_tile() +
  geom_text(aes(label = n)) +
  scale_fill_distiller(direction = 1) +
  scale_x_discrete(position = "top") +
  ggtitle("Number of car class by manufacturers")

Labels

圖表一定要有標題，別人才知道你要講的故事是什麼

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point(mapping = aes(color = class)) +
  geom_smooth() + 
  ggtitle("Fuel efficiency vs. Engine size")

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point(mapping = aes(color = class)) +
  geom_smooth() + 
  ggtitle("Fuel efficiency vs. Engine size") +
  xlab("Engine displacement (L)") +
  ylab("Highway fuel efficiency (mpg)") 

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point(mapping = aes(color = class)) +
  geom_smooth() +
  theme_bw()

Learning from copying 從抄別人的圖表學起

• Google: “圖表名稱 + R”
• 如果要用得順手，平常就要多看別人畫的好圖，要用時才知道從哪裡找起
• Google 是學習畫圖的好朋友
• 視覺化資源整理

Export Plots 匯出圖表

p <- ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point(mapping = aes(color = class)) +
  geom_smooth() +
  theme_bw()

ggsave(p,
       filename = "my_plot.png",
       device = "png", h = 2, w = 3, type = "cairo")

Data Cleansing (ETL)

• dplyr
• tidyr
• broom

ggplot2 Cookbook and Documentation

• ggplot2 官方使用手冊: 完整範例細節
• Cookbook for R - Graphs: 此站其他學習資源也很推薦

ggplot2 輔助繪圖

• RColorBrewer: 色票
• ColorBrewer by PennState University: Web tool for guidance in choosing choropleth map color schemes, based on the reasearch of Dr. Cynthia Brewer.
• cowplot: 組合圖表
• ggrepel: labeling ggplot
• directlabels: labeling ggplot (has few bugs)
• lemon: Refresh axis lines, facets, pointpath

ggplot2 延伸套件整理

• ggplot2 Extensions & Gallery

Cheatsheet

• RStudio - ggplot2 Cheatsheet

Other Viz Packages

• sjPlot: 快速繪製統計模型和表格 (html output)
• sjmisc: Utility and recode functions for R and sjPlot. 處理問卷 variable, label, spss file, …
• dygraphs: (RStudio) 動態繪製 time-series data
• leaflet: Leaflet.js 地圖
• ggvis: (hadley) 動態圖表，可搭配 Shiny，尚未有完整應用體系
• rCharts: (停止開發很久) 動態圖表

R Plot Galleries

• R Graph Catalog - Creating More Effective Graphs: 有效圖表的最簡範例集
• R 動態圖表套件集 - htmlwidgets
• RStudio - Quick list of useful R packages
• THE R GRAPH GALLERY
• R graph gallery

Other Plot Galleries

• The Data Visualisation Catalogue: 各種圖表類型範例
• The Economist - Graphic detail: 看經濟學人如何用圖表說故事