1 require(ggplot2)
   2 ## load the data set GaltonFamilies from the package HistData 
   3 require(HistData)
   4 
   5 ## bivariate plots
   6 ggplot(GaltonFamilies, aes(x=mother,y=father,size=children,shape=gender)) +
   7     geom_point() 
   8 
   9 
  10 ggsave("scatter.png")
  11 
  12 ## adding a trend line
  13 ggplot(GaltonFamilies, aes(x=mother,y=father)) +
  14     geom_point() +
  15     geom_smooth()
  16 
  17 ggsave("scattertrend1.png")
  18 
  19 
  20 ## adding a trend line
  21 ggplot(GaltonFamilies, aes(x=mother,y=father)) +
  22     geom_point() +
  23     geom_smooth(method="lm")
  24 
  25 ggsave("scattertrend2.png")
  26 
  27 
  28 ## Exercises
  29 ## use again the GaltonFamilies data set; produce a scatter plot
  30 ## of childHeight vs midparentHeight
  31 
  32 ggplot(GaltonFamilies, aes(x=midparentHeight,y=childHeight)) +
  33     geom_point() 
  34 
  35 
  36 ##add a trend line by using geom\_smooth() without any
  37 ## arguments. Which method is used?
  38 ggplot(GaltonFamilies, aes(x=midparentHeight,y=childHeight)) +
  39     geom_point() +
  40     geom_smooth() 
  41 
  42 
  43 ## add a second trend line, this time a linear one!
  44 ggplot(GaltonFamilies, aes(x=midparentHeight,y=childHeight)) +
  45     geom_point() +
  46     geom_smooth(se=F,colour="red",size=3) +
  47     geom_smooth(method="lm",se=F)
  48 
  49 
  50 ## now use the aesthetic colour in the first line of the plot
  51 ## definition. What happens?
  52 ggplot(GaltonFamilies, aes(x=midparentHeight,y=childHeight,colour=gender)) +
  53     geom_point() +
  54     geom_smooth() +
  55     geom_smooth(method="lm")
  56 
  57 
  58 p1 <- ggplot(GaltonFamilies, aes(x=midparentHeight,y=childHeight,shape=gender)) +
  59     geom_point() +
  60     geom_smooth() +
  61     geom_smooth(method="lm")
  62 
  63 ## dot plots
  64 ggplot(mtcars, aes(x = mpg)) + geom_dotplot()
  65 ggsave("dotplot1.png")
  66 
  67 ggplot(mtcars, aes(x = mpg)) + geom_dotplot(binwidth = 1.5)
  68 ggsave("dotplot2.png")
  69 
  70 ## Use fixed-width bins
  71 ggplot(mtcars, aes(x = mpg)) +
  72     geom_dotplot(method="histodot", binwidth = 1.5)
  73 ggsave("dotplot3.png")
  74 
  75 ## Some other stacking methods
  76 ggplot(mtcars, aes(x = mpg)) +
  77     geom_dotplot(binwidth = 1.5, stackdir = "center")
  78 ggsave("dotplot4.png")
  79 
  80 ## INBOtheme
  81 install.packages("INBOtheme", repos="http://R-Forge.R-project.org")
  82 require(INBOtheme)
  83 
  84 p1 + theme_elsevier()
  85 ggsave("testelsevier.png",width = 15, height = 10, units = "cm")
  86 
  87 p1 + theme_INBO()
  88 p1 + theme_map()
  89 p1 + theme_inbo2015()
  90 
  91 
  92 ## density legends
  93 install.packages("oaPlots", repos = "http://repos.openanalytics.eu", type = "source")
  94 
  95 require(oaPlots)
  96 scatterplotDL(GaltonFamilies$mother,GaltonFamilies$childHeight,
  97               colorVar = GaltonFamilies$childHeight,
  98               colorPalette = blues9,
  99               pch = 19)
 100 
 101 scatterplotDL(GaltonFamilies$mother,GaltonFamilies$childHeight,
 102               colorVar = GaltonFamilies$childHeight,
 103               colorPalette = blues9,
 104               pch = 19)
 105 
 106 savePlot("legendexample.png")
 107 
 108 ## membranes and tritiated water
 109 data <- data.frame(values = c(0.8,0.83,1.89,1.04,1.45,
 110                        1.38,1.91,1.64,0.73,1.46,
 111                        1.15,0.88,0.9,0.74,1.21),
 112                    membrane = rep(LETTERS[1:2],c(10,5)))
 113 
 114 ## histogram
 115 ggplot(data,aes(x=values,colour=membrane)) +
 116     geom_histogram() +
 117     facet_wrap(~ membrane,nrow=2)
 118 
 119 qqnorm(data$values[data$membrane=="A"])
 120 qqline(data$values[data$membrane=="A"])    
 121 
 122 ## test
 123 test <- wilcox.test(data$values ~ data$membrane, alternative = "greater")
 124 
 125 ggplot(data,aes(x=values,fill=membrane)) +
 126     geom_dotplot(binwidth = 0.1)
 127 
 128 ggplot(data,aes(x=values,fill=membrane)) +
 129     geom_dotplot(binwidth = 0.1) +
 130     facet_wrap(~ membrane,nrow=2)
 131 
 132 ggplot(data,aes(x=membrane,y=values)) +
 133     geom_point(size=15,shape=21,fill="red") 
 134 
 135 
 136 
 137 ## weight loss programs
 138 
 139 ## data
 140 data <- data <- data.frame(x=c(12.42,9.31,6.83,11.51,10.42,8.87,6.73,9.53,8.8,8.01,7.01,9.69),
 141                            y=c(13.8,10,8.51,11.95,10.66,8.76,7.93,11.81,11.62,9.76,9.2,11.2))
 142 
 143 
 144 ## investigate deviation from normality
 145 ## histogram
 146 dev.new()
 147 hist(data$x - data$y)
 148 ks.test(data$x - data$y,"pnorm",mean=mean(data$x - data$y))
 149 
 150 ## test
 151 t.test(data$y,data$x,paired = T)
 152 t.test(data$y-data$x,mu=0)
 153 
 154 
 155 ## visualization
 156 require(granovaGG)
 157 granovagg.ds(data,revc = T)
 158 
 159 
 160 ## magnets
 161 
 162 require(asbio)
 163 data(magnets)
 164 head(magnets)
 165 
 166 ?magnets
 167 
 168 magnets$diff <- magnets$Score_2 - magnets$Score_1
 169 
 170 table(magnets$Active)
 171 
 172 ggplot(magnets,aes(x=Active,y=diff)) +
 173     geom_boxplot()
 174 
 175 
 176 ggplot(magnets,aes(x=diff,fill=Active)) +
 177     geom_dotplot(position = position_dodge(width=0.8),alpha=0.2)
 178 
 179 ggplot(magnets,aes(x=diff,fill=Active)) +
 180     geom_dotplot(alpha=0.2)
 181 
 182 
 183 ggplot(magnets,aes(x=diff,fill=Active)) +
 184     geom_histogram(position = position_dodge(width=0.8))
 185 
 186 ggplot(magnets,aes(x=Active,y=diff)) +
 187     geom_dotplot(binaxis = "y",stackdir = "center")
 188