title : R Basic Visualization subtitle : Easy Way to Plot author : Ben job : Engineer, GBC logo : Taiwan_R_slidify.png license : by-nc-sa framework : io2012 # {io2012, html5slides, shower, dzslides, ...} highlighter : highlight.js # {highlight.js, prettify, highlight} hitheme : zenburn # widgets : [quiz, bootstrap] # {mathjax, quiz, bootstrap} mode : standalone # {standalone, draft} --- .segue .nobackground .dark

資料整理完了，來畫圖吧~~~

The R Graphics Package

Package: graphics

basic function for graphics
Function List

library(help = "graphics")

簡單
可快速觀察資料
自由度大，易調整版面

R basic graphic tools

Simple Plots
Add something to a plot
Adjust

Graphical Elements
Figure Margins
Multiple Figure Environment

Others
Projects

--- .segue .nobackground .dark

Simple Plots

--- &twocol

Simple Plots

*** =left

*** =right

Simple Plots

Object Class: data.frame or matrix or vector

Data: iris

	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
1	5.10	3.50	1.40	0.20	setosa
2	4.90	3.00	1.40	0.20	setosa
3	4.70	3.20	1.30	0.20	setosa
4	4.60	3.10	1.50	0.20	setosa
5	5.00	3.60	1.40	0.20	setosa
6	5.40	3.90	1.70	0.40	setosa
7	4.60	3.40	1.40	0.30	setosa
8	5.00	3.40	1.50	0.20	setosa
9	4.40	2.90	1.40	0.20	setosa
10	4.90	3.10	1.50	0.10	setosa
11	5.40	3.70	1.50	0.20	setosa
12	4.80	3.40	1.60	0.20	setosa
13	4.80	3.00	1.40	0.10	setosa
14	4.30	3.00	1.10	0.10	setosa
15	5.80	4.00	1.20	0.20	setosa
16	5.70	4.40	1.50	0.40	setosa
17	5.40	3.90	1.30	0.40	setosa
18	5.10	3.50	1.40	0.30	setosa
19	5.70	3.80	1.70	0.30	setosa
20	5.10	3.80	1.50	0.30	setosa
21	5.40	3.40	1.70	0.20	setosa
22	5.10	3.70	1.50	0.40	setosa
23	4.60	3.60	1.00	0.20	setosa
24	5.10	3.30	1.70	0.50	setosa
25	4.80	3.40	1.90	0.20	setosa
26	5.00	3.00	1.60	0.20	setosa
27	5.00	3.40	1.60	0.40	setosa
28	5.20	3.50	1.50	0.20	setosa
29	5.20	3.40	1.40	0.20	setosa
30	4.70	3.20	1.60	0.20	setosa
31	4.80	3.10	1.60	0.20	setosa
32	5.40	3.40	1.50	0.40	setosa
33	5.20	4.10	1.50	0.10	setosa
34	5.50	4.20	1.40	0.20	setosa
35	4.90	3.10	1.50	0.20	setosa
36	5.00	3.20	1.20	0.20	setosa
37	5.50	3.50	1.30	0.20	setosa
38	4.90	3.60	1.40	0.10	setosa
39	4.40	3.00	1.30	0.20	setosa
40	5.10	3.40	1.50	0.20	setosa
41	5.00	3.50	1.30	0.30	setosa
42	4.50	2.30	1.30	0.30	setosa
43	4.40	3.20	1.30	0.20	setosa
44	5.00	3.50	1.60	0.60	setosa
45	5.10	3.80	1.90	0.40	setosa
46	4.80	3.00	1.40	0.30	setosa
47	5.10	3.80	1.60	0.20	setosa
48	4.60	3.20	1.40	0.20	setosa
49	5.30	3.70	1.50	0.20	setosa
50	5.00	3.30	1.40	0.20	setosa
51	7.00	3.20	4.70	1.40	versicolor
52	6.40	3.20	4.50	1.50	versicolor
53	6.90	3.10	4.90	1.50	versicolor
54	5.50	2.30	4.00	1.30	versicolor
55	6.50	2.80	4.60	1.50	versicolor
56	5.70	2.80	4.50	1.30	versicolor
57	6.30	3.30	4.70	1.60	versicolor
58	4.90	2.40	3.30	1.00	versicolor
59	6.60	2.90	4.60	1.30	versicolor
60	5.20	2.70	3.90	1.40	versicolor
61	5.00	2.00	3.50	1.00	versicolor
62	5.90	3.00	4.20	1.50	versicolor
63	6.00	2.20	4.00	1.00	versicolor
64	6.10	2.90	4.70	1.40	versicolor
65	5.60	2.90	3.60	1.30	versicolor
66	6.70	3.10	4.40	1.40	versicolor
67	5.60	3.00	4.50	1.50	versicolor
68	5.80	2.70	4.10	1.00	versicolor
69	6.20	2.20	4.50	1.50	versicolor
70	5.60	2.50	3.90	1.10	versicolor
71	5.90	3.20	4.80	1.80	versicolor
72	6.10	2.80	4.00	1.30	versicolor
73	6.30	2.50	4.90	1.50	versicolor
74	6.10	2.80	4.70	1.20	versicolor
75	6.40	2.90	4.30	1.30	versicolor
76	6.60	3.00	4.40	1.40	versicolor
77	6.80	2.80	4.80	1.40	versicolor
78	6.70	3.00	5.00	1.70	versicolor
79	6.00	2.90	4.50	1.50	versicolor
80	5.70	2.60	3.50	1.00	versicolor
81	5.50	2.40	3.80	1.10	versicolor
82	5.50	2.40	3.70	1.00	versicolor
83	5.80	2.70	3.90	1.20	versicolor
84	6.00	2.70	5.10	1.60	versicolor
85	5.40	3.00	4.50	1.50	versicolor
86	6.00	3.40	4.50	1.60	versicolor
87	6.70	3.10	4.70	1.50	versicolor
88	6.30	2.30	4.40	1.30	versicolor
89	5.60	3.00	4.10	1.30	versicolor
90	5.50	2.50	4.00	1.30	versicolor
91	5.50	2.60	4.40	1.20	versicolor
92	6.10	3.00	4.60	1.40	versicolor
93	5.80	2.60	4.00	1.20	versicolor
94	5.00	2.30	3.30	1.00	versicolor
95	5.60	2.70	4.20	1.30	versicolor
96	5.70	3.00	4.20	1.20	versicolor
97	5.70	2.90	4.20	1.30	versicolor
98	6.20	2.90	4.30	1.30	versicolor
99	5.10	2.50	3.00	1.10	versicolor
100	5.70	2.80	4.10	1.30	versicolor
101	6.30	3.30	6.00	2.50	virginica
102	5.80	2.70	5.10	1.90	virginica
103	7.10	3.00	5.90	2.10	virginica
104	6.30	2.90	5.60	1.80	virginica
105	6.50	3.00	5.80	2.20	virginica
106	7.60	3.00	6.60	2.10	virginica
107	4.90	2.50	4.50	1.70	virginica
108	7.30	2.90	6.30	1.80	virginica
109	6.70	2.50	5.80	1.80	virginica
110	7.20	3.60	6.10	2.50	virginica
111	6.50	3.20	5.10	2.00	virginica
112	6.40	2.70	5.30	1.90	virginica
113	6.80	3.00	5.50	2.10	virginica
114	5.70	2.50	5.00	2.00	virginica
115	5.80	2.80	5.10	2.40	virginica
116	6.40	3.20	5.30	2.30	virginica
117	6.50	3.00	5.50	1.80	virginica
118	7.70	3.80	6.70	2.20	virginica
119	7.70	2.60	6.90	2.30	virginica
120	6.00	2.20	5.00	1.50	virginica
121	6.90	3.20	5.70	2.30	virginica
122	5.60	2.80	4.90	2.00	virginica
123	7.70	2.80	6.70	2.00	virginica
124	6.30	2.70	4.90	1.80	virginica
125	6.70	3.30	5.70	2.10	virginica
126	7.20	3.20	6.00	1.80	virginica
127	6.20	2.80	4.80	1.80	virginica
128	6.10	3.00	4.90	1.80	virginica
129	6.40	2.80	5.60	2.10	virginica
130	7.20	3.00	5.80	1.60	virginica
131	7.40	2.80	6.10	1.90	virginica
132	7.90	3.80	6.40	2.00	virginica
133	6.40	2.80	5.60	2.20	virginica
134	6.30	2.80	5.10	1.50	virginica
135	6.10	2.60	5.60	1.40	virginica
136	7.70	3.00	6.10	2.30	virginica
137	6.30	3.40	5.60	2.40	virginica
138	6.40	3.10	5.50	1.80	virginica
139	6.00	3.00	4.80	1.80	virginica
140	6.90	3.10	5.40	2.10	virginica
141	6.70	3.10	5.60	2.40	virginica
142	6.90	3.10	5.10	2.30	virginica
143	5.80	2.70	5.10	1.90	virginica
144	6.80	3.20	5.90	2.30	virginica
145	6.70	3.30	5.70	2.50	virginica
146	6.70	3.00	5.20	2.30	virginica
147	6.30	2.50	5.00	1.90	virginica
148	6.50	3.00	5.20	2.00	virginica
149	6.20	3.40	5.40	2.30	virginica
150	5.90	3.00	5.10	1.80	virginica

--- &twocol

Simple Plots

*** =left

*** =right

--- &twocol

Exercise

練習Formula

*** =left

data: cars

##    speed dist
## 1      4    2
## 2      4   10
## 3      7    4
## 4      7   22
## 5      8   16
## 6      9   10
## 7     10   18
## 8     10   26
## 9     10   34
## 10    11   17
## 11    11   28
## 12    12   14
## 13    12   20
## 14    12   24
## 15    12   28
## 16    13   26
## 17    13   34
## 18    13   34
## 19    13   46
## 20    14   26
## 21    14   36
## 22    14   60
## 23    14   80
## 24    15   20
## 25    15   26
## 26    15   54
## 27    16   32
## 28    16   40
## 29    17   32
## 30    17   40
## 31    17   50
## 32    18   42
## 33    18   56
## 34    18   76
## 35    18   84
## 36    19   36
## 37    19   46
## 38    19   68
## 39    20   32
## 40    20   48
## 41    20   52
## 42    20   56
## 43    20   64
## 44    22   66
## 45    23   54
## 46    24   70
## 47    24   92
## 48    24   93
## 49    24  120
## 50    25   85

*** =right

--- &radio

Exercise

練習Formula

plot(speed~dist,cars)
plot(dist~speed,cars)
plot(~dist+speed,cars)

*** .hint plot(y~x,data)

*** .explanation

plot(dist~speed,cars)
speed for x axis
dist for y axis
data: cars

Simple Plots

趨勢

Line Chart
Bar Plot

比較、組成

Bar Plot
Pie Chart

分佈

Scatter Plot
Histgram
Box Plot

Simple Plots-Line Chart

plot(sin(seq(0,2*pi,1/50)),type='l')

Exercise

Exercise

實質薪資倒退十五年？

畫出民國69年到102年實質薪資趨勢
實質薪資=每人每月平均薪資/消費者物價指數x100
每人每月平均薪資：薪資及生產力統計資料查詢系統
消費者物價指數：主計總處統計專區
data(salary,package='DSC2014Tutorial')
- salary_cpi
- salary_2013
- salary_detail

Exercise

Data: salary_cpi

	year	salary	cpi
1	69	8843	55.61
2	70	10677	60.67
3	71	11472	61.83
4	72	12122	62.17
5	73	13409	62.64
6	74	13980	62.16
7	75	15118	63.41
8	76	16496	63.69
9	77	18399	65.12
10	78	21247	67.56
11	79	24317	70.21
12	80	26881	73.59
13	81	29449	75.87
14	82	31708	78.21
15	83	33661	81.25
16	84	35389	84.69
17	85	36699	87.40
18	86	38489	86.94
19	87	39673	90.34
20	88	40842	89.53
21	89	41861	91.55
22	90	41960	90.51
23	91	41530	90.00
24	92	42065	89.58
25	93	42680	90.95
26	94	43159	93.23
27	95	43488	93.45
28	96	44392	97.94
29	97	44367	99.83
30	98	42182	98.22
31	99	44359	99.71
32	100	45508	100.74
33	101	45589	102.34
34	102	45664	103.04

Exercise

平均月薪

plot(salary_cpi[,1:2],type='l')

Exercise

實質薪資

salary_cpi$real_wage=salary_cpi$salary/salary_cpi$cpi*100
plot(real_wage~year,salary_cpi,type='l')

Exercise

--- &twocol

Simple Plots-Bar plot

x=sample(1:150,50) #從1~150中隨機挑選50個數字
plot(iris[x,5])

*** =left

觀察趨勢
比較不同類別的差異
適用於數量較小的資料

*** =right

--- &twocol

*** =left

Simple Plots-Bar plot

y=table(iris[x,5])
barplot(y,horiz=TRUE,las=1)

## 
##     setosa versicolor  virginica 
##         13         20         17

*** =right

只接受vector or matrix

barplot(y)

Simple Plots-Bar plot

Data: VADeaths

	Rural Male	Rural Female	Urban Male	Urban Female
50-54	11.70	8.70	15.40	8.40
55-59	18.10	11.70	24.30	13.60
60-64	26.90	20.30	37.00	19.30
65-69	41.00	30.90	54.60	35.10
70-74	66.00	54.30	71.10	50.00

--- &twocol

Simple Plots-Bar plot

*** =left barplot(VADeaths, beside = TRUE, legend=rownames(VADeaths))

*** =right

barplot(VADeaths, 
     legend=rownames(VADeaths))

--- &twocol

Simple Plots-Pie

*** =left

比較同類別個群體之間的差異
常見於新聞媒體
只接受正數

## 
##     setosa versicolor  virginica 
##         13         20         17

*** =right pie(y)

Exercise

油電業薪資近9萬，是教服業的4倍?

畫出薪資最低與最高的三個行業
Data: salary_2013
library(xts) a=order(salary_2013$每人每月薪資) salary_news=matrix(salary_2013$每人每月薪資[c(head(a,3),tail(a,3))],ncol = 6) colnames(salary_news)=salary_2013$行業[c(head(a,3),tail(a,3))] par(family='STKaiti') #Only for Mac!!! mp=barplot(salary_news,col='dodgerblue4') #x軸座標 text(mp,10000,salary_news,col='gold') #標註薪資

Exercise

最高薪資與最低薪資

mp=barplot(salary_news,xaxt='n',col='dodgerblue4')
text(mp,-10000,colnames(salary_news),xpd=TRUE,srt=20,cex=1.5)

但是...，有些事情新聞沒說...

若把行業別劃分更細，可以發現更高的薪水...
以'salary_detail'再畫一次

Simple Plots-Scatter Plot

plot(iris[,3:4])
plot(Petal.Width~Petal.Length,data=iris)

Simple Plots-Scatter Plot

plot(iris[,1:3])
plot(~Sepal.Length+Sepal.Width+Petal.Length,data=iris)

Simple Plots-Box plot

plot(factor,number) #Don't Run!
plot(iris[,5],iris[,1])

--- &twocol

Simple Plots-Box plot

*** =left

boxplot(iris[,1]~iris[,5])
boxplot(Sepal.Length~Species,data=iris)

*** =right boxplot(iris[,1:2])

Simple Plots-Histgram

hist(iris[, 1], breaks = 4)

--- .segue .nobackground .dark

Add Something to a Plot

--- &twocol

Add Something to a Plot- 低階繪圖

加上點、線、面、座標軸、文字說明
需要先有圖，才能畫出，無法獨立執行 *** =left

點
- points
線
- lines
- abline
- arrows
- segaments
- grid

*** =right

面
- rect
- polygon
文字說明
- text
- mtext
- title
- legend
座標軸
- axis

Add Points & Lines

Add Points

Exercise

凸顯實質薪資成長率的沉淪

Exercise

real_wage=matrix(salary_cpi$real_wage,ncol=34)
colnames(real_wage)=salary_cpi[,1]
mp=barplot(real_wage,ylim=c(-20000,60000),col='dodgerblue4',ylab='TWD',xlab='year')

Exercise

ratio=diff(salary_cpi$real_wage)/salary_cpi$real_wage[1:33] #實質薪資成長率
lines(mp[2:34],ratio*500000,typ='o',pch=20,lwd=3,col=2) 
#畫上實質薪資成長率，為配合原圖的scale，乘上500000

Exercise

axis(4,seq(-20000,60000,10000),labels=paste(seq(-4,12,2),'%',sep = ""),col=2)
# 加上右邊Y軸，須考慮比例

Exercise

legend("bottomleft",c('實質薪資','實質薪資成長率'),bty='n',
   text.col=c('dodgerblue4','red'),
   col=c('dodgerblue4','red'),pch=c(15,20))# 加上圖例說明

Exercise

mtext(side=3,'成長率',adj=1) # 在plot的周邊加上說明

Add text with locator

locator(n=1)

--- &twocol

Mathematical Annotation

*** =left

x=seq(-pi,pi,pi/1000);y=sin(x)/abs(x)
plot(x,y)
text(0,0,expression(over(cos(x)%.%
sin(x),abs(x))))

*** =right

--- &twocol

Mathematical Annotation

*** =left

*** =right

--- &twocol

Mathematical Annotation

*** =left

*** =right

Example- Batman Equation

R Blogger http://www.r-bloggers.com/batman-equationhappy-halloween-plot-the-batman-logo-in/

f1 <- function(x) {
    y1 <- 3 * sqrt(1 - (x/7)^2)
    y2 <- -3 * sqrt(1 - (x/7)^2)
    y <- c(y1, y2)
    d <- data.frame(x = x, y = y)
    d <- d[d$y > -3 * sqrt(33)/7, ]
    return(d)
}
x1 <- c(seq(3, 7, 0.001), seq(-7, -3, 0.001))
d1 <- f1(x1)
x2 <- seq(-4, 4, 0.001)
y2 <- abs(x2/2) - (3 * sqrt(33) - 7) * x2^2/112 - 3 + sqrt(1 - (abs(abs(x2) - 
    2) - 1)^2)
x3 <- c(seq(0.75, 1, 0.001), seq(-1, -0.75, 0.001))
y3 <- 9 - 8 * abs(x3)
x4 <- c(seq(-0.5, -0.75, -0.001), seq(0.75, 0.5, -0.001))
y4 <- 3 * abs(x4) + 0.75
x5 <- seq(-0.5, 0.5, 0.001)
y5 <- rep(2.25, length(x5))

x6 <- c(seq(-3, -1, 0.001), seq(1, 3, 0.001))
y6 <- 6 * sqrt(10)/7 + (1.5 - 0.5 * abs(x6)) * sqrt(abs(abs(x6) - 1)/(abs(x6) - 
    1)) - 6 * sqrt(10) * sqrt(4 - (abs(x6) - 1)^2)/14
dd = data.frame(x = c(x2, x3, x4, x5, x6), y = c(y2, y3, y4, y5, y6))
d1 = rbind(d1, dd)
plot(d1, asp = 1)  # asp: x軸與y軸的比例
text(0, 0, expression(((over(x, 7))^2 * sqrt(over(abs(abs(x) - 3), abs(x) - 
    3)) + (over(y, 3))^2 * sqrt(over(abs(y^3 - over(sqrt(33), 7)), y^3 - over(sqrt(33), 
    7))) - 1) %.% (abs(over(x, 2)) - over(3 * sqrt(33) - 7, 122) * x^2 - 3 + 
    sqrt(1 - (abs(abs(x) - 2) - 1)^2) - y) %.% (9 * sqrt(over(abs((abs(x) - 
    1) * (abs(x) - over(3, 4))), (1 - abs(x)) * (abs(x) - over(3, 4)))) - 8 * 
    abs(x) - y)))  ## 方程式的上半段
text(0, -0.8, expression((3 * abs(x) + over(3, 4) * sqrt(over(abs((abs(x) - 
    over(3, 4)) * (abs(x) - over(1, 2))), (over(3, 4) - abs(x)) * (abs(x) - 
    over(1, 2)))) - y) %.% (over(9, 4) * sqrt(over((x - over(1, 2)) * (x + over(1, 
    2)), (over(1, 2) - x) * (over(1, 2) + x))) - y) %.% (over(6 * sqrt(10), 
    7) + over(3 - abs(x), 2) * sqrt(over(abs(abs(x) - 1), abs(x) - 1)) - over(6 * 
    sqrt(10), 14) * sqrt(4 - (abs(x) - 1)^2) - y) == 0))  ##方程式的下半段

--- .segue .nobackground .dark

Adjust

Permanent Changes
par(...)
Temporary Changes (with Simple Plots)
Graphical Elements
Figure Margins
Multiple Figure Environment

Graphical Elements

http://spark.rstudio.com/kmchen/graphics/

Graphical Elements

Colors

colors() #內建的顏色
rainbow(144) #產生彩虹色
palette(rainbow(144)) #將彩虹色設定成預設顏色
colorRampPalette(c('red','green'))(10) #紅綠漸層

Example- Barnsley Fern Fractal

想要讓葉子顏色漸層

data("df_browser", package = "DSC2014Tutorial")
a = order(df[, 2])

plot(x = df[a, 2], y = df[a, 1], cex = 0.1, asp = 1,
col=colorRampPalette(c("darkgreen", "lightgreen"))(10000))

Example- 偽ggplot

plot(iris[, 3:4], bty = "n", axes = FALSE)
## 利用rect將背景顏色換掉 par('usr')：繪圖範圍的座標
rect(par("usr")[1], par("usr")[3], par("usr")[2], par("usr")[4], border = "grey89", 
    col = "grey89")
grid(col = "white", lty = 1)  ## 加上grid
axis(1, col = "lightgrey")  ## 加上X軸
axis(2, col = "lightgrey")  ## 加上Y軸
points(iris[, 3:4], pch = 20)  ## 最後畫上data

Figure Margins

par(mar = c(3, 1, 1, 1))#the number of lines of margin
par(mai = c(3, 1, 1, 1))#margin size specified in inches

Multiple Figure Environment

par(mfrow=c(3,2))

Multiple Figure Environment

nf=layout(matrix(c(2,1,0,3), 2, 2), widths=c(3,1), heights=c(1,3))
layout.show(nf)

Multiple Figure Environment

layout(matrix(c(2, 1, 0, 3), 2, 2), widths = c(3, 1), heights = c(1, 3))
xhist = hist(iris[, 3], plot = FALSE)  # get distribution
yhist = hist(iris[, 4], plot = FALSE)  # get distribution
par(mar = c(5, 5, 1, 1))  #調整邊界
plot(iris[, 3:4])
par(mar = c(0, 3, 1, 1))  #調整邊界
barplot(xhist$counts, axes = FALSE, space = 0)
par(mar = c(3, 1, 1, 0))  #調整邊界
barplot(yhist$counts, axes = FALSE, space = 0, horiz = TRUE)

--- &twocol

Multiple Figure Environment

*** =left 調整邊界前

*** =right 調整邊界後

Exercise

極端的工時與薪資

找到工時和薪資太誇張的工作
將工時和薪資在同一畫面上呈現
Data: salary_detail

time_salary=cbind(hours=salary_detail$平均.工時,TWD=salary_detail$每人每月薪資)
rownames(time_salary)=salary_detail$行業

Exercise

plot(TWD~hours,time_salary)

Exercise

ind=identify(time_salary,plot=FALSE)

identify可以偵測圖上選擇的點之index
執行上述指令後，在圖上以滑鼠點擊選擇欲觀察的點
選完後，在Console按ESC鍵

Exercise

points(time_salary[ind,],pch=20,col='red')
text(time_salary[ind,],rownames(time_salary)[ind],col='dodgerblue',font=2)

Exercise

利用layout，將Scatter plot與Bar plot結合

layout(matrix(c(1,1,2,3), 2, 2), widths=c(3,6), heights=c(2,2))
plot(TWD~hours,time_salary,xlim=c(100,260),ylim=c(2e4,1.2e5))
points(time_salary[ind,],pch=20,col='red')
text(time_salary[ind,],rownames(time_salary)[ind],pos=1:4,col='dodgerblue',font=2)
mp1=barplot(time_salary[ind,1],ylab='hours')
text(mp1,50,time_salary[ind,1])
barplot(time_salary[ind,2],ylab='TWD')
text(mp1,10000,time_salary[ind,2])

--- .segue .nobackground .dark

Others

Save image

png(file='test.png')
plot(iris[,3:4])
dev.off()

png
pdf
jpeg
bmp
tiff

wordcloud

install.packages("wordcloud")
library("wordcloud")
wordcloud(words=c(letters,LETTERS,0:9),freq=seq(1,1000,len=62))

Project

Browser market

Project

Answer

plot_color=c("gray22", "deepskyblue4", "firebrick2", "springgreen4", "orange",'gray50')
palette(plot_color) 
layout(matrix(c(3,1,2,1), 2, 2), widths=c(2,2), heights=c(2,2))
par(mar=c(4,4,0,2),cex=1.2)
plot(browser_table[,1:2],lwd=2,ylim=c(0,60),pch=20,type='o')
for (i in 3:7){
  lines(browser_table[,c(1,i)],type='o',col=i-1,lwd=2,pch=20)
}
par(mar=c(0,0,0,0.1),cex=1.2)
pie(as.numeric(tail(browser_table,1)[2:7]),col=1:6,labels="")
legend('topleft',legend=colnames(browser_table)[2:7],
  text.col=1:6,col=1:6,pch=20,bty='n')
par(mar=c(0,0,0,0),cex=1)
wordcloud(colnames(browser_table)[2:7],tail(browser_table,1)[,2:7]*10000,scale=c(6,1),
 ordered.colors = TRUE,colors =1:6) #字不見時，可調整scale

--- .segue .nobackground .dark

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資料整理完了，來畫圖吧~~~

The R Graphics Package

Package: graphics

R basic graphic tools

Simple Plots

Simple Plots

Simple Plots

Object Class: data.frame or matrix or vector

Data: iris

Simple Plots

Exercise

練習Formula

Exercise

練習Formula

Simple Plots

Simple Plots-Line Chart

Exercise

Exercise

實質薪資倒退十五年？

Exercise

Exercise

平均月薪

Exercise

實質薪資

Exercise

Simple Plots-Bar plot

Simple Plots-Bar plot

只接受vector or matrix

Simple Plots-Bar plot

Simple Plots-Bar plot

Simple Plots-Pie

Exercise

油電業薪資近9萬，是教服業的4倍?

Exercise

最高薪資與最低薪資

但是...，有些事情新聞沒說...

Simple Plots-Scatter Plot

Simple Plots-Scatter Plot

Simple Plots-Box plot

Simple Plots-Box plot

Simple Plots-Histgram

Add Something to a Plot

Add Something to a Plot- 低階繪圖

Add Points & Lines

Add Points

Exercise

凸顯實質薪資成長率的沉淪

Exercise

Exercise

Exercise

Exercise

Exercise

Add text with locator

Mathematical Annotation

Mathematical Annotation

Mathematical Annotation

Example- Batman Equation

Adjust

Adjust

Graphical Elements

Graphical Elements

Colors

Example- Barnsley Fern Fractal

Example- Barnsley Fern Fractal

想要讓葉子顏色漸層

Example- 偽ggplot

Example- 偽ggplot

Figure Margins

Multiple Figure Environment

Multiple Figure Environment

Multiple Figure Environment

Multiple Figure Environment

Exercise

極端的工時與薪資