Taiwan R User Group, 2013.07.01

Hello, ggplot2

Introduce high-level R plotting package ggplot2

Slide and sources code are on GitHub. A screencast is on Youtube.
Made by Liang Bo Wang under a CC 3.0 BY license.

← → PgUp PgDn Space to navigate, f for fullscreen and Esc for an overview.

About me

王亮博 or 亮亮
台大電機四年級
太弱所以要繼續唸第五年
專題研究 @ NTU Center
of Genomics Medicine
Bioinformatics & Biostatistics
about.me/lbwang

今天主題

除了畫圖，還是畫圖

…… 聽起來有點 Low

想像一個情境 …

Deadline 前夕

source: doingsomethingmoreforgod.blogspot.tw

等於開工的時候

source: hksilicon.com

很容易生出這種圖表

excel	matlab

其實還不少 …

不好看…… 而且老闆會生氣

如果是 ggplot2 …

Basic R Plot vs ggplot2


# basic plot
plot(mtcars$wt, mtcars$mpg)

# using ggplot2
library(ggplot2)
qplot(wt, mpg, data=mtcars)

Reasons to use R and ggplot2

預設值即提供很好的樣式組合（style and layout）
各類型圖皆能以簡單指令完成（high-level）
以圖層的方式，有系統建構複雜、整合性圖表
搭配 R 統計分析，直接呈現資料樣貌

今天的目標

如何在短時間內完成高質量的圖表
了解 ggplot2 設計，能自行閱讀官方文件
讓老闆開心，順利畢業

今天無法講到的主題

資料前處理
圖表資料結構 hack
細部調整 → 麻煩參考官網說明 or 參考書

參考書


主要參考這本	R 基礎語法	詳細介紹 ggplot2 等 R 圖形套件

臺灣應該都買得到 Ex. 天瓏書局

什麼樣的資料適合作圖？

1 row = 1 observation 的 data.frame, csv, ... 檔案
格式奇怪的資料，可以透過 Python, R, sed/awk/... 來整理
找方法，不如直接問問 Stack Overflow 大神


# in namelist.csv
# data should be one observation per row
"First", "Last", "Sex", "Birth"
"Liang Bo", "Wang", "Male", "1991-01-01"
"Otsuka", "Ai", "Female", "1999-01-01"

# read from csv file
data <- read.csv("namelist.csv", 
                 stringsAsFactors=FALSE,
                 comment.char='#')
data$Sex <- factor(data$Sex)
str(data)   # view data.frame structure

Give Data a Quick View

using basic R plot, ggplot() and qplot()

sample code: ex_quick

back to Quick

Scatter Plot


# basic
plot(mtcars$wt, mtcars$mpg)

# qplot()
qplot(mtcars$wt, mtcars$mpg, geom='point')
qplot(wt, mpg, data=mtcars)  # if they are in same data.frame

# ggplot()
ggplot(mtcars, aes(x=wt, y=mpg)) + geom_point()

basic	`qplot()` or `ggplot()`

back to Quick

Line Plot


# basic
plot(pressure$temperature, pressure$pressure, type="l")
points(pressure$temperature, pressure$pressure)

# qplot()
qplot(pressure$temperature, pressure$pressure, geom=c('line', 'point'))

# ggplot()
ggplot(pressure, aes(x=temperature, y=pressure)) + geom_line() +
  geom_point()

basic	`qplot()` or `ggplot()`

back to Quick

Bar Plot


# basic
barplot(BOD$demand, names.arg=BOD$Time)

# qplot()
qplot(factor(Time), demand, data=BOD, geom="bar", stat="identity")

# ggplot()
ggplot(BOD, aes(x=factor(Time), y=demand)) + geom_bar(stat='identity')

basic	`qplot()` or `ggplot()`

back to Quick

Bar Plot by Counting


# basic
barplot(table(mtcars$cyl))

# qplot()
qplot(factor(mtcars$cyl))   # no factor(), treat cyl as 
                            # integer(continuous) not factors

# ggplot()
ggplot(mtcars, aes(x=factor(cyl))) + geom_bar()

basic	`qplot()` or `ggplot()`

back to Quick

Histogram of 1-D data


# basic
hist(mtcars$mpg, breaks=10)

# qplot()
qplot(mpg, data=mtcars, binwidth=5)

# ggplot()
ggplot(mtcars, aes(x=mpg)) + geom_histogram(binwidth=5)

basic	`qplot()` or `ggplot()`

back to Quick

Box Plot


# basic (NOTE: outliers are different from ggplot results!)
plot(ToothGrowth$supp, ToothGrowth$len, names=levels(ToothGrowth$supp))
boxplot(len ~ supp, data=ToothGrowth)   # boxplot using formula syntax
boxplot(len ~ supp + dose, data=ToothGrowth)  # interaction: supp, dose

# qplot()
qplot(supp, len, data=ToothGrowth, geom='boxplot')
qplot(interaction(supp, dose), len, data=ToothGrowth, geom='boxplot')

# ggplot()
ggplot(ToothGrowth, aes(x=supp, y=len)) + geom_boxplot()
ggplot(ToothGrowth, aes(x=interaction(supp, dose), y=len)) + geom_boxplot()

back to Quick

Function Curve


# basic
curve(x^3 - 7*x, from=-4, to=4)

# plot a user-defined function
# in: numeric vector, out: numeric vector
cumfun <- function(xvec) 1/(1 + exp(-xvec + 5))

curve(cumfun(x), from=0, to=10)
curve(1-cumfun(x), add=TRUE, col='green')   # append to same figure

basic	basic (self-defined function)

back to Quick

Function Curve (cont'd)


# qplot()
qplot(c(0, 10), fun=cumfun, stat='function', geom='line')

# ggplot()
g <- ggplot(data.frame(x=c(0, 10)), aes(x=x))   # store it first
g + stat_function(fun=cumfun, geom='line')  # try geom='point'

ggplot() ggplot() (self-defined function)

no straight forward way

try wrapped by a function

Bar Plots

sample code: ex_bar

Fill, Color | Group (Dodge) | Color Theme (Palette) | Stack

Not familiar? Start from Quick or back to Bar

Set Fill and Color for Bar Plot


# use facters as x-axis
weekabbrv <- c('Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun')
BOD$week <- factor(BOD$Time, levels=1:7, labels=weekabbrv)

# set uniform color of fill(param: fill) or outline(param: color) 
g <- ggplot(BOD, aes(x=week, y=demand)) 
g + geom_bar(stat='identity', fill='lightpink', color='black')

# using aes() to denote variable-dependent aesthetics 
ggplot(BOD, aes(x=week, y=demand, fill=week)) + geom_bar(stat='identity')
ggplot(BOD, aes(x=week, y=demand, color=week)) + geom_bar(stat='identity')

Not familiar? Start from Quick or back to Bar

Grouping Bar Plot by Dodging


library(gcookbook)   # import required data sets

# === Grouping ===
# group var: Cultivar (determine the 'fill' color)
ggplot(cabbage_exp, aes(x=Date, y=Weight, fill=Cultivar)) + 
  geom_bar(stat='identity', position='dodge')

# === Interaction ===
ggplot(cabbage_exp, aes(x=interaction(Cultivar, Date), y=Weight, fill=Cultivar)) + 
  geom_bar(stat='identity')

group	interaction

Not familiar? Start from Quick or back to Bar

First into Color Theme using Bar Plot


library(gcookbook)
g <- ggplot(cabbage_exp, aes(x=Date, y=Weight, fill=Cultivar)) + 
  geom_bar(stat='identity', position='dodge', color='brown')

# use different color themes, i.e., palettes
g + scale_fill_brewer(palette='Pastel1')  # try 'Blues' or 'Oranges'

ggplot(cabbage_exp, aes(x=Cultivar, y=Weight, fill=Date)) + 
  geom_bar(stat='identity', position='dodge', color='black') + 
  scale_fill_brewer(palette='Blues')

Not familiar? Start from Quick or back to Bar

More on Conditional Coloring


library(gcookbook)
csub <- subset(climate, Source=='Berkeley' & Year >= 1900)
csub$pos <- csub$Anomaly10y >= 0

# define the position explicitly by "poisition='identity'"
g <- ggplot(csub, aes(x=Year, y=Anomaly10y, fill=pos)) 
g + geom_bar(stat='identity', position='identity') + guides(fill=FALSE)

# change the width of bars (var: width, default is 0.9) + custom coloring
g + geom_bar(stat='identity', position='identity', 
             width=0.8, size=0.3, color='black') +
  scale_fill_manual(values=c('#80BBFF', '#D86C82'), guide=FALSE)

Not familiar? Start from Quick or back to Bar

Stacked Bar Plot


library(gcookbook)
# for stacked bar plots (compared with Grouping by Dodging)
ggplot(cabbage_exp, aes(x=Date, y=Weight, fill=Cultivar)) + 
  geom_bar(stat='identity')

# make a proportional (or 100%) stacked bar graph
library(plyr)
ce <- ddply(cabbage_exp, 'Date', transform,
            percent_weight = Weight / sum(Weight) * 100)
ggplot(ce, aes(x=Date, y=percent_weight, fill=Cultivar)) +
  geom_bar(stat="identity", color="black") +
  scale_fill_brewer(palette='Pastel1')

小結 ggplot2 設計觀點

qplot() 適合用在簡單的繪圖上
ggplot() ggplot2 的起手式
aes(param=somevar, ...) 使用離散變數（某一欄位）來改變繪圖的屬性（顏色、大小…）
geom_xxxx() 決定每一層畫圖的類型
可以再額外疊加參數修改圖的設定

Line Plots

sample code: ex_line

y limit, log scale | Style | Group | Area | Stacked Area

Not familiar? Start from Quick or back to Line

Control Y Axis of Line Plot


g <- ggplot(BOD, aes(x=Time, y=demand))
# change y limit (they have same results)
g + geom_line() + ylim(0, max(BOD$demand))
g + geom_line() + expand_limits(y=0)

library(gcookbook)
# view y as log scale
ggplot(worldpop, aes(x=Year, y=Population)) + 
  geom_line() + geom_point() + scale_y_log10()

Not familiar? Start from Quick or back to Line

Change Line Plot Style


# change line style
ggplot(BOD, aes(x=Time, y=demand)) + 
  geom_line(linetype='dashed', size=1, colour='red')

# change point style
ggplot(BOD, aes(x=Time, y=demand)) +
  geom_point(size=5, shape=21, colour='darkred', fill='pink') +
  geom_line()

Search "ggplot2" + "linetype" or "shape" for more information

Not familiar? Start from Quick or back to Line

Grouping Line Plot


library(plyr)
tg <- ddply(ToothGrowth, c("supp", "dose"), summarize, length=mean(len))
g <- ggplot(tg, aes(x=dose, y=length, shape=supp, color=supp))
g + geom_line() + geom_point(size=4)  # supp determines both shape and color

g + geom_line(position=position_dodge(0.2)) +   # dodge lines and point
  geom_point(position=position_dodge(0.2), size=4)

ggplot(tg, aes(x=dose, y=length, shape=supp, color=supp, linetype=supp)) + 
  geom_line() + geom_point(size=4) +  # styling
  scale_color_brewer(palette='Set1') + scale_shape_manual(values=c(20, 6)) + 
  scale_linetype_manual(values=c('dotted', 'dashed'))

Not familiar? Start from Quick or back to Line

Shaded Area Line Plot


# convert sunspot.year into dataframe
sunspotyear <- data.frame(Year = as.numeric(time(sunspot.year)), 
                          Sunspots = as.numeric(sunspot.year))
# using geom_area() for area plot
ggplot(sunspotyear, aes(x=Year, y=Sunspots)) + 
  geom_area(color='black', fill='blue', alpha=.3)

# outline the area using geom_line()
ggplot(sunspotyear, aes(x=Year, y=Sunspots)) + 
  geom_area(fill='blue', alpha=.3) + geom_line(color='black')

Not familiar? Start from Quick or back to Line

Stacked Area Line Plot


library(gcookbook)
head(uspopage)  # requires long format (wide -> long)

ggplot(uspopage, aes(x=Year, y=Thousands, fill=AgeGroup)) + 
  geom_area(color='black', size=.2, alpha=.6) + 
  scale_fill_brewer(palette='Blues')

# use descending order: desc() to reorder stacking order
library(plyr)
ggplot(uspopage, aes(x=Year, y=Thousands, fill=AgeGroup, order=desc(AgeGroup))) + 
  geom_area(color=NA, alpha=.6) + scale_fill_brewer(palette='Blues') + 
  geom_line(position='stack', color='black', size=.4)

可能想要先小試身手

看太多 code 太累了嗎？不就畫個圖而已…

不如試試一個實例

Not familiar? Start from Quick or back to Facet

Facet Grid


# View(mpg)
g <- ggplot(mpg, aes(x=displ, y=hwy)) + geom_point()
g   # the original scatter plot

# what facet does here is that it treats the "original" data
# with a category variable(discrete var: drv), and plot the figure
# using same axis ranges but only "sub" data.

# now the category var: drv
g + facet_grid(drv ~ .)
# now the category var: cyl
g + facet_grid(. ~ cyl)

# now verticl var: drv, horizontal var: cyl
g + facet_grid(drv ~ cyl)

例子在下一頁

Not familiar? Start from Quick or back to Facet

Facet Wrap


# continue from grid.R
g <- ggplot(mpg, aes(x=displ, y=hwy)) + geom_point()

# they have same result
g + facet_wrap(~ class, ncol=4)
g + facet_wrap(~ class, nrow=2)

好像也沒很簡單……

希望大家能掌握 ggplot2 設計的概念

用的時候能知道查哪些關鍵字

Deadline 前能查得到最重要！

有一天走在路上

想不到要用什麼 dataset 當範例

突然看到這個

......

不如就用教育相關的統計數字當範例

補習班的還沒收集好，多是廣告文宣
可能需要影像辨識，不好爬資料

Open Data? 不如試試教育部的統計資料

教育部 → 重要教育統計資訊 (link)

感覺都蠻好戰的 XD

來個禮貌的起手式

使用的資料表

大學生就讀類科之比率 go →
近年來大學學科能力測驗（學測）總級分人數分布 go →

back to Edu Dataset

大學生就讀類科之比率

如果我們想要看 92 - 101 學年度類科變化的趨勢…

Start from begin, or back to Edu Dataset

Demonstrating Bar Plots

使用 Dodged, Stacked, Percentage Stacked 三種方式作圖


g <- ggplot(twCD, aes(x=schoolyear, y=count, fill=category))
# dodge
g + scale_fill_brewer(palette='Set2') + 
  geom_bar(stat='identity', width=0.8, alpha=.8, 
           position=position_dodge(0.7))
# stack
g + geom_bar(stat='identity', color='black') + 
  scale_fill_brewer(palette='Pastel1')

library(plyr)
# === Proportional Stacked Plot ===
twCD <- ddply(twCD, "schoolyear", transform,
              percent_count = count / sum(count) * 100)
ggplot(twCD, aes(x=schoolyear, y=percent_count, fill=category)) +
  geom_bar(stat='identity', color='black') + 
  scale_fill_brewer(palette='Set3')

完整的原始碼請參考 source code

Start from begin, or back to Edu Dataset

Dodged

Start from begin, or back to Edu Dataset

Stack

Start from begin, or back to Edu Dataset

Percentage Stack

back to Edu Dataset

大學學測總級分人數分布

ggplot2 馬上就可以派上用場

但這筆資料很奇怪，分別存成 1, 2, 3 三檔

相關整理過程放在 twPT2013.csv, read_csv.R 中

Start from begin, or back to Edu Dataset

Demonstrating Line/Area Plots

Overlapped Area Plot


twPT$scorecls <- cut(twPT$score, breaks=c(seq(from=-1, to=70, by=10), 75),
                     labels=c('<10', '10-19', '20-29', '30-39', '40-49', 
                              '50-59','60-69', '> 69'))
g <- ggplot(twPT, aes(x=score, y=count, group=schoolyear, fill=schoolyear))
g + geom_area(alpha=.8, position='identity', color='black', size=0.2) 

# Palette using 'Blues' (warning: no enough defined colors)
g + geom_area(alpha=.8, position='identity', color='black', size=0.2) +
  scale_fill_brewer(palette='Blues')

# custom gradient function
bluegrad_fnt <- colorRampPalette(c('#0C0C63', 'white'))
g + geom_area(alpha=1, position='identity', color='black', size=0.2) +
  scale_fill_manual(values=bluegrad_fnt(11))

g + geom_area(alpha=1, position='identity', color='black', size=0.2) +
  scale_fill_grey(start=0.05, end=0.9) + theme_bw() # grey

（結果在下一頁）

完整的原始碼請參考 source code

Start from begin, or back to Edu Dataset

Result

Start from begin, or back to Edu Dataset

Normalized Area Plot


library(plyr)
twPT <- ddply(twPT, "schoolyear", transform,
              percent_count = count / sum(count) * 100)
by(twPT$percent_count, twPT$schoolyear, sum)  # check the result

ggplot(twPT, aes(x=score, y=percent_count, group=schoolyear, fill=schoolyear)) + 
  geom_area(alpha=1, position='identity', color='black', size=0.2) +
  scale_fill_manual(values=bluegrad_fnt(11))

Start from begin, or back to Edu Dataset

Line Plot


ggplot(twPT, aes(x=score, y=percent_count, group=schoolyear, 
                 color=schoolyear, size=schoolyear)) + 
  geom_line(position='identity') + theme_bw() 
  scale_size_discrete(range = c(1.5, 0.2))

# combined with area plot
ggplot(twPT, aes(x=score, y=percent_count, group=schoolyear, 
                 size=schoolyear, fill=schoolyear)) +
  geom_area(position='identity', alpha = 0.8, color='#333333') +
  scale_size_discrete(range = c(1.5, 0.2)) +
  scale_fill_manual(values=bluegrad_fnt(11)) + theme_bw()

Start from begin, or back to Edu Dataset

Stacked Bar Plot


# Now we plot the stacked bar plot
ggplot(twPT, aes(x=schoolyear, y=count, fill=scorecls)) + 
  geom_bar(stat='identity') +
  scale_fill_brewer(palette='Blues', breaks=rev(levels(twPT$scorecls)))

# we define another set of colorPalette
bluegrad_inv_fnt <- colorRampPalette(c('white', '#0C0C63'))
ggplot(twPT, aes(x=schoolyear, y=count, fill=scorecls)) + 
  geom_bar(stat='identity') + 
  scale_fill_manual(values=bluegrad_inv_fnt(8), 
                    breaks=rev(levels(twPT$scorecls)))

Start from begin, or back to Edu Dataset

Percentage Bar Plot and Labeling


ggplot(twPT, aes(x=schoolyear, y=percent_count, fill=scorecls)) + 
  geom_bar(stat='identity') + 
  scale_fill_manual(values=bluegrad_inv_fnt(8), 
                    breaks=rev(levels(twPT$scorecls)))


twptcls <- ddply(twPT, c("schoolyear", "scorecls"), summarise, 
                 percent_count = sum(percent_count, na.rm=TRUE))
twptcls <- ddply(twptcls, "schoolyear", transform, 
                 label_y=cumsum(percent_count) - 0.5 * percent_count)

formatter <- function(x, ...) {   # function to format label
  x[x < 1] <- 0   # we don't want to show numbers below 1
  format(round(x, digits=1), zero.print = FALSE, ...)
}

ggplot(twptcls, aes(x=schoolyear, y=percent_count, fill=scorecls)) + 
  geom_bar(stat='identity', color=NA) + 
  scale_fill_manual(values=bluegrad_inv_fnt(8), 
                    breaks=rev(levels(twPT$scorecls))) + 
  geom_text(aes(y=label_y, label=formatter(percent_count), 
            color=scorecls), size=5) + guides(color=FALSE) +
  scale_color_manual(values=c(rep('black', 3), rep('grey', 5)))

結果在下一頁

Start from begin, or back to Edu Dataset

Result

What's Next? 今天沒提到的部份

Scatter (Bubble) plot
Annotations: text, line/arrow, shape(rectangle)
Labels
X, Y Axis, Legends
Error bar
Theme setting
Many other types of plot: pie chart, box plot, ...

What's Next? Want More

搜尋 "ggplot2 tutorial"
看 ggplot2 官網
買本參考書 R Graphics Cookbook（本投影的 mindflow）

What's Next? Beyond Static

想動態的，試試
- rCharts
- rNVD3
- googleVis
想能互動更新資料的，試試 Shiny
想告訴老闆的……等等，你為什麼要這樣做 T_T

Q & A

The set of images are made by chibird.

Thank you for listening > <

Taiwan R User Group, 2013.07.01

Hello, ggplot2

About me

今天主題

想像一個情境 …

Deadline 前夕

等於開工的時候

很容易生出這種圖表

其實還不少 …

不好看…… 而且老闆會生氣

如果是 ggplot2 …

Basic R Plot vs ggplot2

Reasons to use R and ggplot2

今天的目標

今天無法講到的主題

參考書

目錄

什麼樣的資料適合作圖？

Give Data a Quick View

Scatter Plot

Line Plot

Bar Plot

Bar Plot by Counting

Histogram of 1-D data

Box Plot

Function Curve

Function Curve (cont'd)

Bar Plots

Set Fill and Color for Bar Plot

Grouping Bar Plot by Dodging

First into Color Theme using Bar Plot

More on Conditional Coloring

Stacked Bar Plot

小結 ggplot2 設計觀點

Line Plots

Control Y Axis of Line Plot

Change Line Plot Style

Grouping Line Plot

Shaded Area Line Plot

Stacked Area Line Plot

可能想要先小試身手

Facets: Split Data into Subplots

Facet Grid

Facet Wrap

有一天走在路上

......

不如就用教育相關的統計數字當範例

感覺都蠻好戰的 XD

使用的資料表

大學生就讀類科之比率

Demonstrating Bar Plots

大學學測總級分人數分布

Demonstrating Line/Area Plots

Result

Normalized Area Plot

Line Plot

Stacked Bar Plot

Percentage Bar Plot and Labeling

Result

What's Next? 今天沒提到的部份

What's Next? Want More

What's Next? Beyond Static

Q & A