AS7-0A ggplot2 繪圖套件

Sys.setlocale("LC_ALL","C")

[1] "C"

packages = c(
  "dplyr","ggplot2","d3heatmap","googleVis","devtools","plotly", "xgboost",
  "magrittr","caTools","ROCR","corrplot", "rpart", "rpart.plot",
  "doParallel", "caret", "glmnet", "Matrix", "e1071", "randomForest",
  "flexclust", "FactoMineR", "factoextra", "maps", "ggmap", "igraph", "rgl",
  "tm", "SnowballC", "wordcloud", "slam", "Matrix", "RColorBrewer"
  )
existing = as.character(installed.packages()[,1])
for(pkg in packages[!(packages %in% existing)]) install.packages(pkg)

rm(list=ls(all=T))
options(digits=4, scipen=12)
library(dplyr)
library(ggplot2)
library(maps)
library(ggmap)

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7.1 ggplot2 繪圖套件

7.1.1 基本點狀圖

WHO = read.csv("data/WHO.csv")
str(WHO)

'data.frame':   194 obs. of  13 variables:
 $ Country                      : Factor w/ 194 levels "Afghanistan",..: 1 2 3 4 5 6 7 8 9 10 ...
 $ Region                       : Factor w/ 6 levels "Africa","Americas",..: 3 4 1 4 1 2 2 4 6 4 ...
 $ Population                   : int  29825 3162 38482 78 20821 89 41087 2969 23050 8464 ...
 $ Under15                      : num  47.4 21.3 27.4 15.2 47.6 ...
 $ Over60                       : num  3.82 14.93 7.17 22.86 3.84 ...
 $ FertilityRate                : num  5.4 1.75 2.83 NA 6.1 2.12 2.2 1.74 1.89 1.44 ...
 $ LifeExpectancy               : int  60 74 73 82 51 75 76 71 82 81 ...
 $ ChildMortality               : num  98.5 16.7 20 3.2 163.5 ...
 $ CellularSubscribers          : num  54.3 96.4 99 75.5 48.4 ...
 $ LiteracyRate                 : num  NA NA NA NA 70.1 99 97.8 99.6 NA NA ...
 $ GNI                          : num  1140 8820 8310 NA 5230 ...
 $ PrimarySchoolEnrollmentMale  : num  NA NA 98.2 78.4 93.1 91.1 NA NA 96.9 NA ...
 $ PrimarySchoolEnrollmentFemale: num  NA NA 96.4 79.4 78.2 84.5 NA NA 97.5 NA ...

# Basic Plot in R 
plot(WHO$GNI, WHO$FertilityRate)

library(ggplot2)
# Create the ggplot object with the data and the aesthetic mapping:
#gglot2可用來繪製圖片
scatterplot = ggplot(WHO , aes(x = GNI , y = FertilityRate))

# Add the geom_point geometry
scatterplot + geom_point() 

# Make a line graph instead:
scatterplot + geom_line()

# Switch back to our points:
scatterplot + geom_point()

# Redo the plot with blue triangles instead of circles:
scatterplot + geom_point(color = "blue", size = 3, shape = 21)

#增加顏色為藍色、大小設定、shape=17(三角形)
scatterplot + geom_point(color = "blue" , size = 3 , shape= 17)

# Another option:
scatterplot + geom_point(color = "darkred", size = 3, shape = 8)

# Add a title to the plot:
scatterplot + 
  geom_point(colour = "blue", size = 3, shape = 17) + 
  ggtitle("Fertility Rate vs. Gross National Income")

7.1.2 儲存圖檔

# Save our plot:
fertilityGNIplot = scatterplot + geom_point(colour = "blue", size = 3, shape = 17) + ggtitle("Fertility Rate vs. Gross National Income")
#fertilityGNIplot = scatterplot + geom_point(color = "darkred" , size = 3 , shape= 8) + ggtitle("Fertility Rate vs. Gross National Income")
pdf("MyPlot.pdf")
print(fertilityGNIplot)
dev.off()

null device 
          1 

7.1.3 圖形元件屬性

# Color the points by region:
ggplot(WHO , aes(x= GNI , y= FertilityRate , color = Region)) + geom_point()

# Color the points according to life expectancy:
ggplot(WHO , aes(x= GNI , y= FertilityRate , color = LifeExpectancy)) + geom_point()

# Is the fertility rate of a country was a good predictor of the 
# percentage of the population under 15?
ggplot(WHO , aes(x= FertilityRate , y=Under15 )) + geom_point()

7.1.4 數值尺度比例轉換

# Let's try a log transformation:
ggplot(WHO , aes(x= log(FertilityRate) , y=Under15 )) + geom_point()

7.1.5 回歸趨勢線

# Simple linear regression model to predict the percentage of the 
# population under 15, using the log of the fertility rate:
mod = lm(Under15 ~ log(FertilityRate), data = WHO)
#顯示線性模型摘要
summary(mod)

Call:
lm(formula = Under15 ~ log(FertilityRate), data = WHO)

Residuals:
    Min      1Q  Median      3Q     Max 
-10.313  -1.774   0.045   1.744   7.717 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)           7.654      0.448    17.1   <2e-16 ***
log(FertilityRate)   22.055      0.418    52.8   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.65 on 181 degrees of freedom
  (11 observations deleted due to missingness)
Multiple R-squared:  0.939, Adjusted R-squared:  0.939 
F-statistic: 2.79e+03 on 1 and 181 DF,  p-value: <2e-16

# Add this regression line to our plot:
ggplot(WHO , aes(x= log(FertilityRate) , y=Under15 )) + geom_point() + stat_smooth(method = "lm")

7.1.6 趨勢線的信賴區間

# 99% confidence interval
ggplot(WHO , aes(x= log(FertilityRate) , y=Under15 )) + geom_point() + stat_smooth(method = "lm" , level = 0.99)

# No confidence interval in the plot
ggplot(WHO , aes(x= log(FertilityRate) , y=Under15 )) + geom_point() + stat_smooth(method = "lm" , se = FALSE )

# Change the color of the regression line:
ggplot(WHO , aes(x= log(FertilityRate) , y=Under15 )) + geom_point() + stat_smooth(method = "lm" , se = FALSE , color = "orange")

7.1.7 分群點狀圖

# quiz-1:
ggplot(WHO, aes(x = FertilityRate, y = Under15 , color = Region)) + geom_point()+scale_color_brewer(palette="Dark2")

7.1.8 分格點狀圖

# quiz-1:
ggplot(WHO, aes(x = log(Population), y = GNI, color=Region)) + 
  geom_point() + 
  stat_smooth(method='lm') +
  facet_wrap(~Region) + theme_bw()

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