Using Built-in Plotting Functions
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1 Workshop: Graphics in R Katherine Thompson (katherine.thompson@uky.edu Department of Statistics, University of Kentucky September 15, 2016 Using Built-in Plotting Functions ## Plotting One Quantitative Variable ## Creating a histogram hist(practicedata$respvar, # what the histogram is plotting main='histogram of ', # change the main axis title xlab='' # change the x-axis label #, freq=true # histogram is of counts # breaks="sturges", # this can be changed to specify a series of points # for the breaks in the histogram # xlim=c(20,70, # sets the limits of the x-axis # ylim=c(0,25, # sets the limits of the y-axis Histogram of Frequency
2 ## Creating a boxplot: boxplot(practicedata$respvar, # what the boxplot is plotting main='default R Boxplot' # change the main title Default R Boxplot ## Creating a ''nicer'' boxplot boxplot(practicedata$respvar, # what the boxplot is plotting main='nicer R Boxplot', # change the main title ylab='', # change the y-axis label names='all Data', # change the name under the boxplot col='blue', # change the color of the boxplot outline=true # Draw outliers if there are any in the data Nicer R Boxplot
3 ## Plotting One Categorical Variable par(mfrow=c(2,1 # Put two plots in one figure ## Bar Chart plot.group<-table(practicedata$groupvar # Create table of counts barplot(plot.group, # Bar chart of the variable main='bar Chart of Groups', # change main title col=c('green','blue' # change color of each bar ## Pie Chart: pie(plot.group, # Pie chart of the variable main='pie Chart of Grouping Variable', # change main title col=c('green','blue' # change the color of each slice of the pie Bar Chart of Groups Control Treatment Pie Chart of Grouping Variable Control Treatment 3
4 ## Plotting Two Variables #### Plotting One Quantitative and One Categorical Variable: #### Side-by-side boxplots boxplot(practicedata$respvar~practicedata$groupvar, main='default R Boxplots' Default R Boxplots Control Treatment boxplot(practicedata$respvar~practicedata$groupvar, main='nicer R Boxplots', names=c('control Group', 'Treatment Group', col=c('green','blue', # change color of boxes ylab='' # change y-axis label # horizontal=true # change boxplots to horizontal Nicer R Boxplots Control Group Treatment Group 4
5 #### Two Histograms: # Create a two data subsets for control and treatment individuals n.controls=50 n.treatments=50 # Extract the first n.controls rows from the data controldata=practicedata[1:n.controls,] # Extract everything except the first n.controls rows from the data treatmentdata=practicedata[-(1:n.controls,] ## Histograms for each group hist(controldata$respvar, main='control Group', # change the main title xlab='' Control Group Frequency hist(treatmentdata$respvar, main='treatment Group', # change the main title xlab='' Treatment Group Frequency
6 #### Plotting two quantitative variables using a scatterplot: plot(practicedata$toyvar,practicedata$respvar, # x variable, y variable main="default R Scatterplot" Default R Scatterplot practicedata$respvar practicedata$toyvar plot(practicedata$toyvar,practicedata$respvar, # x variable, y variable main="plot of vs. Toy Variable", # change main label ylab='', # change y-axis label xlab='toy Variable', # change x-axis label pch=20 # change the plotting symbol #, type='l', # instead of pch, you can create a line plot # (make sure your x's are ordered if you do this. # col='black' # change color of plotting symbol Plot of vs. Toy Variable Toy Variable 6
7 ## Plots Showing More than Two Variables ## Find the range of the variables we are plotting for BOTH groups ydatarange=range(practicedata$respvar,na.rm=true xdatarange=range(practicedata$toyvar,na.rm=true ## Create scatterplot with all data plot(controldata$toyvar,controldata$respvar, # x variable, y variable main="plot of vs. Toy Variable", # change main label ylab='', # change y-axis label xlab='toy Variable', # change x-axis label pch=20, # change the plotting symbol col='green', # change color of plotting symbol xlim=xdatarange, # change the x-axis to cover the range # of both the treatment and control groups ylim=ydatarange, # change the y-axis to cover the range # of both the treatment and control groups type='n' # create the plotting window, but not the points ## Add observations for the control data to the plot points(controldata$toyvar,controldata$respvar, # x variable, y variable pch=20, # change the plotting symbol col='green' # change color of plotting symbol ## Add observations for the treatment data to the plot points(treatmentdata$toyvar,treatmentdata$respvar, # x variable, y variable pch=20, # change the plotting symbol col='blue' # change color of plotting symbol ## Adding a Legend to the Plot legend('topleft', # location of legend legend=c('control Group','Treatment Group', # lines of text in the legend pch=20, # symbol used in the legend col=c('green','blue' # colors of the symbol in the same # order as the lines of text in 'legend' # lty=1, lwd=1 # change the line type and width if lines are on the plot 7
8 Plot of vs. Toy Variable Control Group Treatment Group Toy Variable 8
9 ## Using par( # Example: Using the par function to change the number of plots in the panel par(mfrow=c(1,2 #mfrow=c(1,2 creates a plotting window with 2 rows and 1 column of plots #mfrow=c(2,1 creates a plotting window with 1 row and 2 columns of plots #### Histograms for each group hist(controldata$respvar, main='control Group', # change the main title xlab='' hist(treatmentdata$respvar, main='treatment Group', # change the main title xlab='' Control Group Treatment Group Frequency Frequency
10 Using ggplot2 Package for Graphics For a helpful guide to ggplot2, go to this website: 04/ggplot2-cheatsheet.pdf ## Plotting One Categorical Variable ## Make bar graph with ggplot2 ggplot(data=practicedata,aes(groupvar + geom_bar(stat="count",color=c('darkgreen','navy',fill=3:4 + labs(x='group',y='counts',title='bar Chart with Group Sizes' 50 Bar Chart with Group Sizes Counts Control Group Treatment 10
11 ################################ ## Plotting One Quantitative and One Categorical Variable -- Mean plot ################################ ## Calculate summary statistics for ggplot2 summary.stats<-describeby(x=practicedata$respvar,group=practicedata$groupvar,mat=true ggplot(data=summary.stats,aes(x=group1,y=mean,color=group1 + geom_errorbar(aes(ymax=mean+se,ymin=mean-se,size=1.5,width= ## Plot errorbars geom_point(size=4 + ## Plot points scale_color_hue(name="group" + labs(x='group Variable',y='', title='mean Plot' + ## Change plot labels theme_bw( +## Changes to white background with grid lines theme(legend.position="bottom" 47.5 Mean Plot Control Group Variable Treatment Group Control Treatment 11
12 ################################ ## Plotting One Quantitative and Two Categorical Variables -- Mean Plot ################################ ## Calculate summary statistics for ggplot2 summary.stats<-describeby(x=practicedata$respvar,group=practicedata[,c(4,6],mat=true ggplot(data=summary.stats,aes(x=group2,y=mean,color=group1 + geom_line(aes(group=group1,size= # Plot lines geom_errorbar(aes(ymax=mean+se,ymin=mean-se,size=1.25,width= # Plot standard error bars geom_point(size=4 + ## Plot points scale_color_hue(name="group" + labs(x='group Variable',y='', title='mean Plot' + ## Change plot labels theme_bw( + ## Changes to white background with grid lines theme(legend.position="bottom" 48 Mean Plot A Group Variable Group Control Treatment B 12
13 ######################################## ## Plotting Two Quantitative Variables and One Categorical Variable ######################################## ggplot(data=practicedata,aes(x=expvar,y=respvar,group=groupvar,color=groupvar + geom_point( + ## Plot points scale_color_hue(name="group" + ## Change legend title labs(x='explanatory Variable',y='', title='scatterplot with Two Variables' + ## Change plot labels theme_bw( + ## Changes to white background with grid lines theme(legend.position="bottom" ## Place legend at bottom of plot Scatterplot with Two Variables Explanatory Variable Group Control Treatment 13
14 ######################################## ## Plotting Two Quantitative Variables and Two Categorical Variables ######################################## ggplot(data=practicedata,aes(x=expvar,y=respvar,group=groupvar,color=groupvar + geom_point(aes(pch=groupvar2 + ## Plot points scale_color_hue(name="group" + ## Change legend title scale_shape_discrete(name="group 2" + ## Change legend title labs(x='explanatory Variable',y='', title='scatterplot with Two Variables' + ## Change plot labels theme_bw( + ## Changes to white background with grid lines theme(legend.position="bottom" ## Place legend at bottom of plot Scatterplot with Two Variables Explanatory Variable Group 2 A B Group Control Treatment 14
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