Monday, November 3, 2014

Graphics for Statistics - figures with ggplot2 - chapter 5 quantile-quantile plots

1 Keen Chapter 5

1.1 Figure 5.1 normal quantile-quantile plot

  • first set up the data vector
  • then we use a point layer with the quantile-quantile stat and change the size of the points: geom_point(stat="qq",shape=1,size=3.5)

require(ggplot2)
require(grid)
mass<-c(5.9,32.0,40.0,51.5,70.0,100.0,78.0,80.0,85.0,85.0,
              110.0,115.0,125.0,130.0,120.0,120.0,130.0,135.0,110.0,130.0,
              150.0,145.0,150.0,170.0,225.0,145.0,188.0,180.0,197.0,218.0,
              300.0,260.0,265.0,250.0,250.0,300.0,320.0,514.0,556.0,840.0,
              685.0,700.0,700.0,690.0,900.0,650.0,820.0,850.0,900.0,1015.0,
              820.0,1100.0,1000.0,1100.0,1000.0,1000.0)
df <- data.frame(y=mass)

ggplot(df,aes(sample=mass)) +
    geom_point(stat="qq",shape=1,size=3.5) +
    scale_x_continuous(limits=c(-3,3),breaks=-3:3,expand=c(0,0.1)) +
    scale_y_continuous(limits=c(0,1200),breaks=seq(0,1200,by=200),expand=c(0,20)) +
    xlab("Standard Normal Quantiles") +
    ylab("Mass (g)") +
          theme(
          panel.background=element_blank(),
          axis.line=element_line(colour="black"),
          axis.text=element_text(colour="black",size=14),
          axis.title=element_text(colour="black",size=14),
          axis.ticks=element_line(colour="black"),
          axis.ticks.length=unit(3,"mm")
          )


1.2 Figure 5.2 normal quantile-quantile plot

  • only use coord_flip() on the previous plot


ggplot(df,aes(sample=mass)) +
    geom_point(stat="qq",shape=1,size=3.5) +
    scale_x_continuous(limits=c(-3,3),breaks=-2:2,expand=c(0,0.5)) +
    scale_y_continuous(limits=c(0,1200),breaks=seq(0,1200,by=200),expand=c(0,20)) +
    xlab("Standard Normal Quantiles") +
    ylab("Mass (g)") +
    coord_flip() +
          theme(
          panel.background=element_blank(),
          axis.line=element_line(colour="black"),
          axis.text=element_text(colour="black",size=14),
          axis.title=element_text(colour="black",size=14),
          axis.ticks=element_line(colour="black"),
          axis.ticks.length=unit(3,"mm")
          )


1.3 Figure 5.3 normal quantile-quantile plot

  • only add geom_abline() with intercept=mean(mass) and slope=sd(mass) (the line goes through the point defined by the mean of both, x and y values which is (mean(standard normal dist)=0,mean(mass)) therefore mean(mass) is the intercept; the sd of the norm is one so the slope is sd(mass)/sd(standard normal dist)=sd(mass)) 


ggplot(df,aes(sample=mass)) +
    geom_point(stat="qq",shape=1,size=3.5) +
    geom_abline(intercept=mean(mass),slope=sd(mass)) + 
    scale_x_continuous(limits=c(-3,3),breaks=-3:3,expand=c(0,0.1)) +
    scale_y_continuous(limits=c(0,1200),breaks=seq(0,1200,by=200),expand=c(0,20)) +
    xlab("Standard Normal Quantiles") +
    ylab("Mass (g)") +
          theme(
          panel.background=element_blank(),
          axis.line=element_line(colour="black"),
          axis.text=element_text(colour="black",size=14),
          axis.title=element_text(colour="black",size=14),
          axis.ticks=element_line(colour="black"),
          axis.ticks.length=unit(3,"mm")
          )


1.4 Figure 5.4 gamma quantile-quantile plot

  • first we calculate the estimates of the scale and shape parameter of the gamma dist
  • then we only change the dist to qgamma (distribution="qgamma") and set the parameters as list (dparams=list(shape=sshape,scale=sscale))
  • last adjust the x-axis (limits and breaks)

sshape<-(mean(mass)/sd(mass))**2
sscale<-var(mass)/mean(mass)

ggplot(df,aes(sample=mass)) +
    geom_point(stat="qq",distribution="qgamma",dparams=list(shape=sshape,scale=sscale),shape=1,size=3.5) +
    scale_x_continuous(limits=c(0,1500),breaks=seq(0,1500,by=500),expand=c(0,20)) +
    scale_y_continuous(limits=c(0,1200),breaks=seq(0,1200,by=200),expand=c(0,20)) +
    xlab("Gamma Quantiles") +
    ylab("Mass (g)") +
          theme(
          panel.background=element_blank(),
          axis.line=element_line(colour="black"),
          axis.text=element_text(colour="black",size=14),
          axis.title=element_text(colour="black",size=14),
          axis.ticks=element_line(colour="black"),
          axis.ticks.length=unit(3,"mm")
          )


1.5 Figure 5.5 normal quantile-quantile plot

  • we only have to change the x-axis of plot 5.1
  • so set the limits to c(-4,4) and the breaks to the required x-values by using the qnorm() function and change the labels appropriately
  • finally change to title of the x-axis

ggplot(df,aes(sample=mass)) +
    geom_point(stat="qq",shape=1,size=3.5) +
    scale_x_continuous(limits=c(-4,4),
                       breaks=qnorm(c(0.0001,.01,.05,.25,.50,.75,.95,.99,.9999)),
                       labels=c(0.01,1,5,25,50,75,95,99,99.99),
                       expand=c(0,0.1)) +
    scale_y_continuous(limits=c(0,1200),breaks=seq(0,1200,by=200),expand=c(0,20)) +
    xlab("Normal Percentiles") +
    ylab("Mass (g)") +
          theme(
          panel.background=element_blank(),
          axis.line=element_line(colour="black"),
          axis.text=element_text(colour="black",size=14),
          axis.title=element_text(colour="black",size=14),
          axis.ticks=element_line(colour="black"),
          axis.ticks.length=unit(3,"mm")
          )


1.6 Figure 5.6 normal quantile-quantile plot

  • it is the same like plot 5.5 - only add coord_flip()

ggplot(df,aes(sample=mass)) +
    geom_point(stat="qq",shape=1,size=3.5) +
    scale_x_continuous(limits=c(-4,4),
                       breaks=qnorm(c(0.0001,.01,.05,.25,.50,.75,.95,.99,.9999)),
                       labels=c(0.01,1,5,25,50,75,95,99,99.99),
                       expand=c(0,0.1)) +
    scale_y_continuous(limits=c(0,1200),breaks=seq(0,1200,by=200),expand=c(0,20)) +
    xlab("Normal Percentiles") +
    ylab("Mass (g)") +
    coord_flip() +
          theme(
          panel.background=element_blank(),
          axis.line=element_line(colour="black"),
          axis.text=element_text(colour="black",size=14),
          axis.title=element_text(colour="black",size=14),
          axis.ticks=element_line(colour="black"),
          axis.ticks.length=unit(3,"mm")
          )


1.7 Figure 5.7 gamma quantile-quantile plot

  • from figure 5.4 we have to change the axis and flip the coordinates:
  • to calculate the breaks we use qgamma() with the appropriate values and the shape and scale parameter
  • then we change the labels accordingly
  • add coord_flip()

sshape<-(mean(mass)/sd(mass))**2
sscale<-var(mass)/mean(mass)

ggplot(df,aes(sample=mass)) +
    geom_point(stat="qq",distribution="qgamma",dparams=list(shape=sshape,scale=sscale),shape=1,size=3.5) +
    scale_x_continuous(limits=c(0,2400),
                       breaks=qgamma(c(0.01,.25,.50,.75,.95,.99,.999),shape=sshape,scale=sscale),
                       labels=c(1,25,50,75,95,99,99.9),expand=c(0,20)) +
    scale_y_continuous(limits=c(0,1200),breaks=seq(0,1200,by=200),expand=c(0,20)) +
    xlab("Gamma Quantiles") +
    ylab("Mass (g)") +
    coord_flip() +
          theme(
          panel.background=element_blank(),
          axis.line=element_line(colour="black"),
          axis.text=element_text(colour="black",size=14),
          axis.title=element_text(colour="black",size=14),
          axis.ticks=element_line(colour="black"),
          axis.ticks.length=unit(3,"mm")
          )


Posted by mvogel78 at 12:05 PM 0 comments
Labels: , , ,

Sunday, November 2, 2014

Graphics for Statistics - figures with ggplot2 - chapter 4 ecdf plots

1 Keen Chapter 4

Graphics out of the book Graphics for Statistics and Data Analysis with R by Kevin Keen (book home page)

1.1 Figure 4.17 EDF plot

  • first set up the data frame:
    • we use the ecdf() function to get create a stepfunction ecdfmass()
    • from this function we can extract the knots (which will be mapped to the x-axis)
    • using this knots as arguments in ecdfmass() we'll get the belonging probabilities (which we will map to the y-axis)
    • the end column in df contains the end points of the horizontal lines in the step function - it's only the knots vector beginning with the second element and setting the last element to NA

mass<-c(5.9,32.0,40.0,51.5,70.0,100.0,78.0,80.0,85.0,85.0,
        110.0,115.0,125.0,130.0,120.0,120.0,130.0,135.0,110.0,130.0,
        150.0,145.0,150.0,170.0,225.0,145.0,188.0,180.0,197.0,218.0,
        300.0,260.0,265.0,250.0,250.0,300.0,320.0,514.0,556.0,840.0,
        685.0,700.0,700.0,690.0,900.0,650.0,820.0,850.0,900.0,1015.0,
        820.0,1100.0,1000.0,1100.0,1000.0,1000.0)


ecdfmass <- ecdf(mass)
kn <- knots(ecdfmass)
ed <- ecdfmass(kn)

df <- data.frame(knots=kn,ed=ed,end=c(kn[-1],NA))
head(df)


  knots         ed  end
1   5.9 0.01785714 32.0
2  32.0 0.03571429 40.0
3  40.0 0.05357143 51.5
4  51.5 0.07142857 70.0
5  70.0 0.08928571 78.0
6  78.0 0.10714286 80.0

  • now we first set the aesthetics for the points: x to knots and y to ed
  • add the point layer and setting the point size to 3: geom_point(size=3)
  • add the lines using a segment layer setting the aesthetics xend and yend: geom_segment(aes(xend=end,yend=ed))
  • in the next step we add two addition segment layers, one for each arrow; you can also use annotate() to do this; inside these segments we use the arrow() function from the grid package, so we can define the appearance of our arrows
  • the next two lines change the appearance of the axes: setting the limits, the breaks and the expansion
  • then set the appropriate axes titles and customize axis elements and panel.background 



require(grid) ## for the arrow() function
ggplot(df,aes(x=knots,y=ed)) +
    geom_point(size=3) +
    geom_segment(aes(xend=end,yend=ed)) +
    geom_segment(x=min(df$knots),xend=-45,y=0,yend=0,arrow=arrow(length = unit(0.15,"cm")),size=c(0.4)) +
    geom_segment(x=max(df$knots),xend=1245,y=1,yend=1,arrow=arrow(length = unit(0.15,"cm")),size=c(0.4)) +
    scale_x_continuous(limits=c(-45,1245),breaks=seq(0,1200,by=200),expand=c(0,0)) +
    scale_y_continuous(limits=c(-0.01,1.05),breaks=seq(0,1,by=0.2),expand=c(0,0)) +
    xlab("Mass (g)") +
    ylab("Empirical Distribution Function") +
    theme(
        panel.background=element_blank(),
        axis.line=element_line(colour="black"),
        axis.text=element_text(colour="black",size=14),
        axis.title=element_text(colour="black",size=14),
        axis.ticks=element_line(colour="black"),
        axis.ticks.length=unit(3,"mm")
        )


1.2 Figure 4.18 EDF plot

  • we only have to change the axis breaks of the y-axis and add the horizontal lines
  • the first is done changing the by argument in the seq() to 0.25 in scale_y_continuous()
  • then we add a hline layer


ggplot(df,aes(x=knots,y=ed)) +
    geom_point(size=3) +
    geom_segment(aes(xend=end,yend=ed)) +
    geom_segment(x=min(df$knots),xend=-45,y=0,yend=0,arrow=arrow(length = unit(0.15,"cm")),size=c(0.4)) +
    geom_segment(x=max(df$knots),xend=1245,y=1,yend=1,arrow=arrow(length = unit(0.15,"cm")),size=c(0.4)) +
    geom_hline(yintercept=c(0.25,0.5,0.75),linetype=2) +
    scale_x_continuous(limits=c(-45,1245),breaks=seq(0,1200,by=200),expand=c(0,0)) +
    scale_y_continuous(limits=c(-0.01,1.05),breaks=seq(0,1,by=0.25),expand=c(0,0)) +
    xlab("Mass (g)") +
    ylab("Empirical Distribution Function") +
    theme(
        panel.background=element_blank(),
        axis.line=element_line(colour="black"),
        axis.text=element_text(colour="black",size=14),
        axis.title=element_text(colour="black",size=14),
        axis.ticks=element_line(colour="black"),
        axis.ticks.length=unit(3,"mm")
        )



  • we also can use the grid lines (but then we have also lines at 0 and 1


ggplot(df,aes(x=knots,y=ed)) +
    geom_point(size=3) +
    geom_segment(aes(xend=end,yend=ed)) +
    geom_segment(x=min(df$knots),xend=-45,y=0,yend=0,arrow=arrow(length = unit(0.15,"cm")),size=c(0.4)) +
    geom_segment(x=max(df$knots),xend=1245,y=1,yend=1,arrow=arrow(length = unit(0.15,"cm")),size=c(0.4)) +
    scale_x_continuous(limits=c(-45,1245),breaks=seq(0,1200,by=200),expand=c(0,0)) +
    scale_y_continuous(limits=c(-0.01,1.05),breaks=seq(0,1,by=0.25),expand=c(0,0)) +
    xlab("Mass (g)") +
    ylab("Empirical Distribution Function") +
    theme(
        panel.background=element_blank(),
        panel.grid.major.x=element_blank(),
        panel.grid.major.y=element_line(linetype = 2,colour="grey50"),
        axis.line=element_line(colour="black"),
        axis.text=element_text(colour="black",size=14),
        axis.title=element_text(colour="black",size=14),
        axis.ticks=element_line(colour="black"),
        axis.ticks.length=unit(3,"mm")
        )


1.3 Figure 4.19 EDF plot

  • replace geom_point() by geom_step()
  • get rid of the horizontal lines
  • add another little segment which connects the left arrow with the step function: geom_segment(x=min(df$knots),xend=min(df$knots),y=0,yend=min(df$ed),size=0.4)
  • leave everything as it is 


ggplot(df,aes(x=knots,y=ed)) +
    geom_step(direction = "hv") +
    geom_segment(x=min(df$knots),xend=-45,y=0,yend=0,arrow=arrow(length = unit(0.15,"cm")),size=c(0.4)) +
    geom_segment(x=max(df$knots),xend=1245,y=1,yend=1,arrow=arrow(length = unit(0.15,"cm")),size=c(0.4)) +
    geom_segment(x=min(df$knots),xend=min(df$knots),y=0,yend=min(df$ed),size=0.4) +
    scale_x_continuous(limits=c(-45,1245),breaks=seq(0,1200,by=200),expand=c(0,0)) +
    scale_y_continuous(limits=c(-0.01,1.05),breaks=seq(0,1,by=0.2),expand=c(0,0)) +
    xlab("Mass (g)") +
    ylab("Empirical Distribution Function") +
    theme(
        panel.background=element_blank(),
        axis.line=element_line(colour="black"),
        axis.text=element_text(colour="black",size=14),
        axis.title=element_text(colour="black",size=14),
        axis.ticks=element_line(colour="black"),
        axis.ticks.length=unit(3,"mm")
        )



  • instead of using the data frame created above you can use the original data (mass) and set stat to ecdf
  • but for the arrows you need to calculate the vals anyway, that's why I use the data frame above


df3 <- data.frame(mass=mass)
ggplot(df,aes(x=knots,y=ed)) +
    geom_step(inherit.aes=F,stat="ecdf",data=df3,aes(x=mass)) +
    geom_segment(x=min(df$knots),xend=-45,y=0,yend=0,arrow=arrow(length = unit(0.15,"cm")),size=c(0.4)) +
    geom_segment(x=max(df$knots),xend=1245,y=1,yend=1,arrow=arrow(length = unit(0.15,"cm")),size=c(0.4)) +
    geom_segment(x=min(df$knots),xend=min(df$knots),y=0,yend=min(df$ed),size=0.4) +
    scale_x_continuous(limits=c(-45,1245),breaks=seq(0,1200,by=200),expand=c(0,0)) +
    scale_y_continuous(limits=c(-0.01,1.05),breaks=seq(0,1,by=0.2),expand=c(0,0)) +
    xlab("Mass (g)") +
    ylab("Empirical Distribution Function") +
    theme(
        panel.background=element_blank(),
        axis.line=element_line(colour="black"),
        axis.text=element_text(colour="black",size=14),
        axis.title=element_text(colour="black",size=14),
        axis.ticks=element_line(colour="black"),
        axis.ticks.length=unit(3,"mm")
        )



1.4 Figure 4.20 EDF plot

  • the last plot only with the horizontal grid lines add the quartiles


ggplot(df,aes(x=knots,y=ed)) +
    geom_step(direction = "hv") +
    geom_segment(x=min(df$knots),xend=-45,y=0,yend=0,arrow=arrow(length = unit(0.15,"cm")),size=c(0.4)) +
    geom_segment(x=max(df$knots),xend=1245,y=1,yend=1,arrow=arrow(length = unit(0.15,"cm")),size=c(0.4)) +
    geom_segment(x=min(df$knots),xend=min(df$knots),y=0,yend=min(df$ed),size=0.4) +
    geom_hline(yintercept=c(0.25,0.5,0.75),linetype=2) +
    scale_x_continuous(limits=c(-45,1245),breaks=seq(0,1200,by=200),expand=c(0,0)) +
    scale_y_continuous(limits=c(-0.01,1.05),breaks=seq(0,1,by=0.25),expand=c(0,0)) +
    xlab("Mass (g)") +
    ylab("Empirical Distribution Function") +
    theme(
        panel.background=element_blank(),
        axis.line=element_line(colour="black"),
        axis.text=element_text(colour="black",size=14),
        axis.title=element_text(colour="black",size=14),
        axis.ticks=element_line(colour="black"),
        axis.ticks.length=unit(3,"mm")
        )



1.5 Figure 4.20 EDF plot with cumulative normal distribution function added

  • first we need to create a second data frame containing the values defining the curve, we choose to use 10000 points on the x-axis and use pnorm() to calculate to respective y values (using the empirical mean and the empirical sd of the vector mass)
  • then we add the layer (geom_line())
  • change the limits of the x-axis and the breaks inside scale_x_continuous()
  • change the length of the right arrow (setting xend to 1500)


mean_mass<-mean(mass)
sd_mass<-sd(mass)
min_mass<-min(mass)
max_mass<-1500

xx <- seq(0,10000,1)*(max_mass-min_mass)/10000.+min_mass
yy <- pnorm(xx,mean_mass,sd_mass)

df2 <- data.frame(xx=xx,yy=yy)

ggplot(df,aes(x=knots,y=ed)) +
    geom_step(direction = "hv") +
    geom_segment(x=min(df$knots),xend=-45,y=0,yend=0,arrow=arrow(length = unit(0.15,"cm")),size=c(0.4)) +
    geom_segment(x=max(df$knots),xend=1500,y=1,yend=1,arrow=arrow(length = unit(0.15,"cm")),size=c(0.4)) +
    geom_segment(x=min(df$knots),xend=min(df$knots),y=0,yend=min(df$ed),size=0.4) +
    geom_line(data=df2,aes(x=xx,y=yy)) +
    scale_x_continuous(limits=c(-45,1500),breaks=seq(0,1500,by=500),expand=c(0,0)) +
    scale_y_continuous(limits=c(-0.01,1.05),breaks=seq(0,1,by=0.2),expand=c(0,0)) +
    xlab("Mass (g)") +
    ylab("Empirical Distribution Function") +
    theme(
        panel.background=element_blank(),
        axis.line=element_line(colour="black"),
        axis.text=element_text(colour="black",size=14),
        axis.title=element_text(colour="black",size=14),
        axis.ticks=element_line(colour="black"),
        axis.ticks.length=unit(3,"mm")
        )


Posted by mvogel78 at 8:02 AM 0 comments
Labels: , , ,

Graphics for Statistics - figures with ggplot2 - Chapter 3 part 2 Bar charts with errorbars, dot-whisker charts

1 Keen Chapter 3 part 2

Graphics out of the book Graphics for Statistics and Data Analysis with R by Kevin Keen (book home page)

1.1 Figure 3.8 Bar-whisker chart

  • we will work out the graphic on flipped axes and flip them later
  • set the aesthetics:
    • x to the names of the allergenes
    • y to the percentage (prevalence)
    • ymin to y minus the standard error and
    • ymax to y plus the standard error
  • using again geom_bar() with the stat="identity" option because we have already aggregated data (so the height of the bars is set by the y aesthetic)
  • set the filling and the colour of the edges (fill and colour), finally adjust the width (width) of the bars
  • there is no axis title on the axis with the names, so set xlab("") (remember we will flip the axis later)
  • set the title of the continuous axis to Percent and
  • set the limits of the axis to 0 and 50
  • set expansion of it to c(0,0) - because we did not want to expand the axis, it should actually end at 0 and 50
  • now we flip the axes
  • and set the appearance of the text, axis and background elements 

require(ggplot2)

names<-c("Epidermals","Dust Mites","Weeds","Grasses","Molds","Trees")
prevs<-c(38.2,37.8,31.1,31.1,29.3,26.7)
se<-c(3.2,3.2,3.1,3.1,3.0,2.9)

df <- data.frame(item=factor(1:6,labels=names),prevs=prevs,se=se)

ggplot(df,aes(x=item,y=prevs,ymin=prevs-se,ymax=prevs+se)) +
    geom_bar(stat="identity",fill="transparent",colour="black",width=0.7) +
    geom_errorbar(width=0.3) +
    xlab("") +
    ylab("Percent") +
    scale_y_continuous(limits=c(0,50),expand=c(0,0)) +
    coord_flip() +
    theme(
        panel.background=element_blank(),
        axis.line=element_line(colour="black"),
        axis.line.y=element_blank(),
        axis.text=element_text(colour="black",size=14),
        axis.ticks.x=element_line(colour="black"),
        axis.ticks.y=element_blank()
        )


1.2 Figure 3.9 Bar and single whisker chart

  • the same as above, only change the filling of the bars to black (you do not need the colour argument any more) and the width of the error bars to 0

ggplot(df,aes(x=item,y=prevs,ymin=prevs-se,ymax=prevs+se)) +
    geom_bar(stat="identity",fill="black",width=0.7) +
    geom_errorbar(width=0) +
    xlab("") +
    ylab("Percent") +
    scale_y_continuous(limits=c(0,50),expand=c(0,0)) +
    coord_flip() +
    theme(
        panel.background=element_blank(),
        axis.line=element_line(colour="black"),
        axis.line.y=element_blank(),
        axis.text=element_text(colour="black",size=14),
        axis.ticks.x=element_line(colour="black"),
        axis.ticks.y=element_blank()
        )


1.3 Figure 3.10 Dot-whisker chart

  • for the dot-whisker chart we replace geom_bar() by geom_point()
  • in geom_point() we set the point size to four and the colour to black
  • in geom_errorbar() we set the the width to 0.3
  • add geom_vline() for the dotted lines and set the aesthetics xintercept to as.numeric(item) (because this aesthetic expects a numeric argument)
  • then change some elements in the theme section
    • set the colour in panel.border to black and do not forget to set fill to NA (you won't see anything if you don't)
    • remove the axis.line.y line


ggplot(df,aes(x=item,y=prevs,ymin=prevs-se,ymax=prevs+se)) +
    geom_point(colour="black",size=4) +
    geom_errorbar(width=0.25) +
    geom_vline(aes(xintercept=as.numeric(item)),linetype=3,size=0.4) +
    xlab("") +
    ylab("Percent") +
    scale_y_continuous(limits=c(0,50),expand=c(0,0)) +
    coord_flip() +
    theme(
        panel.background=element_blank(),
        panel.border=element_rect(colour="black",fill=NA),
        axis.line=element_line(colour="black"),
        axis.text=element_text(colour="black",size=14),
        axis.title=element_text(colour="black",size=14),
        axis.ticks.x=element_line(colour="black"),
        axis.ticks.y=element_blank()
        )


1.4 Figure 3.11 Dot-whisker chart

  • only minor changes to the previous plot
  • remove geom_vline()
  • adjust the widths of the error bars


ggplot(df,aes(x=item,y=prevs,ymin=prevs-se,ymax=prevs+se)) +
    geom_point(colour="black",size=4) +
    geom_errorbar(width=0.1) +
    xlab("") +
    ylab("Percent") +
    scale_y_continuous(limits=c(0,50),expand=c(0,0)) +
    coord_flip() +
    theme(
        panel.background=element_blank(),
        panel.border=element_rect(colour="black",fill=NA),
        axis.line=element_line(colour="black"),
        axis.text=element_text(colour="black",size=14),
        axis.title=element_text(colour="black",size=14),
        axis.ticks.x=element_line(colour="black"),
        axis.ticks.y=element_blank()
        )


1.5 Figure 3.12 Dot-whisker chart

  • only adjust the widths of the error bars


ggplot(df,aes(x=item,y=prevs,ymin=prevs-se,ymax=prevs+se)) +
    geom_point(colour="black",size=4) +
    geom_errorbar(width=0) +
    xlab("") +
    ylab("Percent") +
    scale_y_continuous(limits=c(0,50),expand=c(0,0)) +
    coord_flip() +
    theme(
        panel.background=element_blank(),
        panel.border=element_rect(colour="black",fill=NA),
        axis.line=element_line(colour="black"),
        axis.text=element_text(colour="black",size=14),
        axis.title=element_text(colour="black",size=14),
        axis.ticks.x=element_line(colour="black"),
        axis.ticks.y=element_blank()
        )


1.6 Figure 3.13 two-tiered dot-whisker chart

  • there are several possibilities
  • I decided to use two error bar layers so first
  • I have to move the aesthetics for ymin and ymax to geom_errorbar(), I set the width to 0.2
  • then I add a second geom_errorbar() set there also aesthetics but now ymin to prevs-1.96*se and ymax to prevs+1.96*se


ggplot(df,aes(x=item,y=prevs)) +
    geom_point(colour="black",size=4) +
    geom_errorbar(aes(ymin=prevs-se,ymax=prevs+se),width=0.2) +
    geom_errorbar(aes(ymin=prevs-1.96*se,ymax=prevs+1.96*se),width=0) +
    xlab("") +
    ylab("Percent") +
    scale_y_continuous(limits=c(0,50),expand=c(0,0)) +
    coord_flip() +
    theme(
        panel.background=element_blank(),
        panel.border=element_rect(colour="black",fill=NA),
        axis.line=element_line(colour="black"),
        axis.text=element_text(colour="black",size=14),
        axis.title=element_text(colour="black",size=14),
        axis.ticks.x=element_line(colour="black"),
        axis.ticks.y=element_blank()
        )


Posted by mvogel78 at 5:04 AM 0 comments
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