AS7-0A ggplot2 繪圖套件

rr Sys.setlocale(_ALL,)

[1] \C\

rr packages = c( ,2,3heatmap,,,, , ,,,, , .plot, , , , , 1071, , , , , , , , , , , , , ,
) existing = as.character(installed.packages()[,1]) for(pkg in packages[!(packages %in% existing)]) install.packages(pkg)

rr 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

rr WHO = read.csv(/WHO.csv)

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

rr library(ggplot2) # Create the ggplot object with the data and the aesthetic mapping: scatterplot = ggplot(WHO, aes(x = GNI, y = FertilityRate))

rr # Add the geom_point geometry scatterplot + geom_point()

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

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

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

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

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

7.1.2 儲存圖檔

rr # Save our plot: fertilityGNIplot = scatterplot + geom_point(colour = , size = 3, shape = 17) + ggtitle(Rate vs. Gross National Income) pdf(.pdf) print(fertilityGNIplot) dev.off()

null device 
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7.1.3 圖形元件屬性

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

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

rr # 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

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

7.1.5 回歸趨勢線

rr # 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) 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 ***
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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

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

7.1.6 趨勢線的信賴區間

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

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

rr # Change the color of the regression line: ggplot(WHO, aes(x = log(FertilityRate), y = Under15)) + geom_point() + stat_smooth(method = , colour = )

7.1.7 分群點狀圖

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

7.1.8 分格點狀圖

rr # 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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