################################# ## ## ## CHAPTER 6: SCATTERPLOTS ## ## ## ################################# ## BIGGER PICTURE 6.1: SCATTERPLOT CORRELATION EXAMPLES ## par(mfrow=c(2,3)) # arrange following plots in 2 rows with 3 columns par(mar=c(4.1, 2.1, 2.1, 2.1)) # reduce bottom, left, and top margin sizes x<-c(seq(2,30)) # list x-values y<-c(4,5,3,5,6,7,6.5,8,7,8,9,8.5,9.5,10,10.5,11,11.5,11,12,11.5,12,13, 13.5,13,14,14.5,15,14.75,15.5) # list y-values that would show strong # positive correlation with x-values plot(x,y,xaxt="n",yaxt="n",xlab="",ylab="",pch=19) # plot stripped-down scatterplot mtext("Strong positive\n correlation", # add text label to margin of plot side=1, # position at the bottom of the plot line=2, # distance from the plotting area cex=0.75,font=1) # choose font size and type y<-c(4,5,3,9,7,6,5,8,7,6,7,8.5,9.5,8,7,11,13,11,10,11.5,10.5,12,13,11.5, 10,13.5,14,12.5,15) # list y-values that would show moderate # positive correlation with x-values plot(x,y,xaxt="n",yaxt="n",xlab="",ylab="",pch=19) # plot stripped-down scatterplot mtext("Moderate positive\n correlation", # add text label to margin of plot side=1,line=2,cex=0.75,font=1) y<-c(13,5,3,9,10,6,5,8,7,6,7,8.5,9.5,12,7,6,13,9,10,4,8,5,7,11.5,3.5,13.5, 4,12.5,8) # list y-values that would show # no correlation with x-values plot(x,y,xaxt="n",yaxt="n",xlab="",ylab="",pch=19) # plot stripped-down scatterplot mtext("No correlation", # add text label to margin of plot side=1,line=1,cex=0.75,font=1) y<-c(15,12.5,14,13.5,10,11.5,13,12,10.5,11.5,10,11,13,11,7,8,9.5,8.5,7,6, 7,8,5,6,7,9,3,5,4) # list y-values that would show moderate # negative correlation with x-values plot(x,y,xaxt="n",yaxt="n",xlab="",ylab="",pch=19) # plot stripped-down scatterplot mtext("Moderate negative\n correlation", # add text label to margin of plot side=1,line=2,cex=0.75,font=1) y<-c(15.5,14.75,15,14.5,14,13,13.5,13,12,11.5,12,11,11.5,11,10.5,10,9.5, 8.5,9,8,7,8,6.5,7,6,5,3,5,4) # list y-values that would show strong # negative correlation with x-values plot(x,y,xaxt="n",yaxt="n",xlab="",ylab="",pch=19) # plot stripped-down scatterplot mtext("Strong negative\n correlation", # add text label to margin of plot side=1,line=2,cex=0.75,font=1) y<-c(9,8,7,8,6.5,7,6,5,3,5,4,6,4.5,5,6.5,7,9,8,8.5,10,10.5,9,11,10.5,10, 11.5,11,12,11) # list y-values that would show # non-linear relationship with x-values plot(x,y,xaxt="n",yaxt="n",xlab="",ylab="",pch=19) # plot stripped-down scatterplot mtext("Non-linear\n relationship", # add text label to margin of plot side=1,line=2,cex=0.75,font=1) par(mar=c(5.1, 4.1, 4.1, 2.1)) # restore margins to default sizes par(mfrow=c(1,1)) # return to plotting single plot in plotting area ########################################################################### ## AN EXAMPLE OF ## ## CONTINUOUS ## ## DATA ## data(Puromycin) # load the built-in data set 'Puromycin' from R View(Puromycin) # view the data Puromycin <- read.table(file.choose(), header = T, sep = ",") # OR load the data in from the # Excel CSV. file puromycin_R_data.csv # and name it 'Puromycin' View(Puromycin) # view the data state<-Puromycin$state # assign each variable a simple name conc<-Puromycin$conc rate<-Puromycin$rate ########################################################################### ## LOOKING FOR ## ## CORRELATIONS WITH ## ## SCATTERPLOTS ## ### Simple scatterplot summary(conc) # check min and max concentrations summary(rate) # check min and max rates plot(conc,rate, # plot the data: x-axis variable, y-axis variable xlim=c(0,1.2), ylim=c(0,250), xaxs = "i",yaxs = "i", # set axis limits xlab="Concentration (mM)", ylab="Enzymatic reaction rate (DPM/min)") # set axis labels plot(-5, # create blank plot using value not on axes xlim=c(0,1.2), ylim=c(0,250), xaxs = "i",yaxs = "i", # set axis limits xlab="", ylab="", # keep axis labels blank xaxt="n",yaxt="n") # keep tick marks blank abline(h=(seq(0,250,10)), col="lightgray", lty=1) # set minor horizontal grid lines abline(h=(seq(0,250,50)), col="darkgray", lty=1) # set major horizontal grid lines abline(v=(seq(0,1.2,0.05)), col="lightgray", lty=1) # set minor vertical grid lines abline(v=(seq(0,1.2,0.2)), col="darkgray", lty=1) # set major vertical grid lines par(new=TRUE) # prepare to add to existing plot plot(conc,rate, # draw scatterplot of data xlim=c(0,1.2), ylim=c(0,250), xaxs = "i",yaxs = "i", # set axis limits xlab="Concentration (mM)", ylab="Enzymatic reaction rate (DPM/min)", # set axis labels col="purple", # choose point outline colour pch=24, # choose point type bg="yellow", # choose point fill colour cex=1.5, # choose point size lwd=2) # choose line width of point outline abline(lm(rate~conc)) # add line of linear model (rate against concentration) ## SCIENTIFIC APPROACH 6.1: MODIFYING POINTS ## resetPar <- function() { dev.new() op <- par(no.readonly = TRUE) dev.off() op } # create function to reset default par values pointoptionsfunc<-function(){ defaultpar<-par() par(font=2, mar=c(0.5,0,0,0)) y=rev(c(rep(1,6),rep(2,5), rep(3,5), rep(4,5), rep(5,5))) x=c(rep(1:5,5),6) plot(x, y, pch = 0:25, cex=3.5, ylim=c(0.5,6), xlim=c(0.5,6.5), axes=FALSE, xlab="", ylab="", bg="skyblue") text(x, y, labels=0:25, pos=3, offset=1.5) par(mar=defaultpar$mar,font=defaultpar$font ) } # create function to plot all point types pointoptionsfunc() par(resetPar()) par(mar=c(5.1, 4.1, 4.1, 2.1)) par(oma=c(0,0,0,0)) # restore par defaults ## BIGGER PICTURE 6.3: EXPLAINING LINEAR MODELS ## lm(rate~conc) # Rate = 93.92 + 105.4Conc ## SCIENTIFIC APPROACH 6.2: RESTRICTED LINE OF BEST FIT ## plot(-5, xlim=c(0,1.2), ylim=c(0,250), xaxs = "i",yaxs = "i", xlab="", ylab="",xaxt="n",yaxt="n") abline(h=(seq(0,250,10)), col="lightgray", lty=1) abline(h=(seq(0,250,50)), col="darkgray", lty=1) abline(v=(seq(0,1.2,0.05)), col="lightgray", lty=1) abline(v=(seq(0,1.2,0.2)), col="darkgray", lty=1) par(new=TRUE) plot(conc,rate, xlim=c(0,1.2), ylim=c(0,250), xaxs = "i",yaxs = "i", xlab="Concentration (mM)", ylab="Enzymatic reaction rate (DPM/min)", col="purple", pch=24, bg="yellow", cex=1.5,lwd=2) # plot full figure as above summary(conc) # check min and max concentrations x0<-0.02 # set x0 as min concentration x1<-1.1 # set x1 as max concentration a<-93.92 # set linear model intercept as a b<-105.4 # set linear model gradient as b segments(x0, a+b*x0, x1, a+b*x1, # add restricted line using x0, x1, a and b col = "seagreen3", # choose line colour lty = 5, # choose line type lwd=3) # choose line width ### Scatterplot with multiple samples/data sets summary(state) # check number of cell states/samples treated <- subset(Puromycin, state == 'treated') # create 'treated' data set untreated <- subset(Puromycin, state == 'untreated') # create 'untreated' data set plot(-5, # create blank plot using value not on axes, as above xlim=c(0,1.2), ylim=c(0,250), xaxs = "i",yaxs = "i", xlab="", ylab="",xaxt="n",yaxt="n") abline(h=(seq(0,250,10)), col="lightgray", lty=1) # add grid lines as above abline(h=(seq(0,250,50)), col="darkgray", lty=1) abline(v=(seq(0,1.2,0.05)), col="lightgray", lty=1) abline(v=(seq(0,1.2,0.2)), col="darkgray", lty=1) par(new=TRUE) # prepare to add to existing plot plot(treated$conc,treated$rate, # plot the 'treated' cells data xlim=c(0,1.2), ylim=c(0,250), xaxs = "i",yaxs = "i", # set axis limits xlab="Concentration (mM)", ylab="Enzymatic reaction rate (DPM/min)", # set axis labels pch=15, # choose point type col="orange") # choose point colour par(new=TRUE) # prepare to add to existing plot plot(untreated$conc,untreated$rate, # plot the 'untreated' cells data xlim=c(0,1.2), ylim=c(0,250), xaxs = "i",yaxs = "i", # set axis limits xlab="", ylab="", xaxt="n",yaxt="n", # do not re-draw axis or tick labels pch=19, # choose point type col="blue") # choose point colour abline(lm(treated$rate~treated$conc), # add linear model for 'treated' data col="orange", # choose line colour lwd=2) # choose line width abline(lm(untreated$rate~untreated$conc), # add linear model for 'untreated' data col="blue", # choose line colour lwd=2) # choose line width legend(0.9,70, # set coordinates for legend placement bg="white", # give legend a white background pch = c(15,19), # list point types to explain col = c("orange", "blue"), # list corresponding colours legend=c("Treated","Untreated")) # list corresponding sample names ### Refined scatterplot: adding curved lines of best fit, labelling ### individual data points, reference lines, and text plot(-5, xlim=c(0,1.2), ylim=c(0,250), # create blank plot as above xaxs = "i",yaxs = "i", xlab="", ylab="",xaxt="n",yaxt="n") abline(h=(seq(0,250,10)), col="lightgray", lty=1) # add grid lines as above abline(h=(seq(0,250,50)), col="darkgray", lty=1) abline(v=(seq(0,1.2,0.05)), col="lightgray", lty=1) abline(v=(seq(0,1.2,0.2)), col="darkgray", lty=1) par(new=TRUE) # prepare to add to existing plot plot(treated$conc,treated$rate, # plot 'treated' data as above xlim=c(0,1.2), ylim=c(0,250), xaxs = "i",yaxs = "i", xlab="Concentration (mM)", ylab="Enzymatic reaction rate (DPM/min)", pch=15, col="orange") par(new=TRUE) # prepare to add to existing plot plot(untreated$conc,untreated$rate, # plot 'untreated' data as above xlim=c(0,1.2), ylim=c(0,250), xaxs = "i",yaxs = "i", xlab="", ylab="", xaxt="n",yaxt="n", pch=19, col="blue") legend(0.9,70, bg="white", pch = c(15,19), col = c("orange", "blue"), legend=c("Treated","Untreated")) # add legend as above treatedcurve<-lowess(treated$conc,treated$rate) # create 'treated' lowess values untreatedcurve<-lowess(untreated$conc,untreated$rate) # create 'untreated' lowess values lines(treatedcurve, # add smooth 'treated' curve col="orange", # choose line colour lwd=2) # choose line width lines(untreatedcurve, # add smooth 'untreated' curve col="blue", # choose line colour lwd=2) # choose line width ## SCIENTIFIC APPROACH 6.3: SMOOTHING WITH lowess ## sub1<-subset(Theoph, Subject == '1') # create subject 1 data set sub2<-subset(Theoph, Subject == '2') # create subject 2 data set sub6<-subset(Theoph, Subject == '6') # create subject 6 data set par(oma=c(1.75,0.5,1.25,0)) # create outer margin space for shared axis labels and legend par(mar=c(2.5,4,2.5,1)) # edit margin sizes for shared axis labels and legend par(mfrow=c(3,1)) # arrange following plots in 3 rows with 1 column plot(sub1$Time,sub1$conc, # plot subject 1 data xlim=c(-0.5,25), ylim=c(-0.5,12), xaxs = "i",yaxs = "i", # set axis limits ylab="", xlab="", # keep axis labels blank pch=15, # choose point type col="orange") # choose point colour par(new=TRUE) # prepare to add to existing plot plot(sub2$Time,sub2$conc, # plot subject 2 data xlim=c(-0.5,25), ylim=c(-0.5,12), xaxs = "i",yaxs = "i", xaxt="n",yaxt="n",ylab="", xlab="", pch=16, col="blue") par(new=TRUE) # prepare to add to existing plot plot(sub6$Time,sub6$conc, # plot subject 6 data xlim=c(-0.5,25), ylim=c(-0.5,12), xaxs = "i",yaxs = "i", xaxt="n",yaxt="n",ylab="", xlab="", pch=17, col="green") mtext("Less smoothing\n(f=0.1) ", # add text explaining smoothing side=3, # position at the top of the plot line=-3, # distance from plotting area (- means within the area) adj=0.9, # shift alignment to the right cex=1) # choose font size sub1curve0.1<-lowess(sub1$Time,sub1$conc,f=0.1) sub2curve0.1<-lowess(sub2$Time,sub2$conc,f=0.1) sub6curve0.1<-lowess(sub6$Time,sub6$conc,f=0.1) # create lowess values for each subject # with little smoothing (f=0.1) lines(sub1curve0.1,col="orange",lwd=2) lines(sub2curve0.1,col="blue",lwd=2) lines(sub6curve0.1,col="green",lwd=2) # add curved lines for each subject # using lowess values (f=0.1) legend("top", # add shared legend to top of plot bg="white", # make background white bty="n", # do not encase legend with a box pch = c(15,16,17), # list point types col = c("orange","blue","green"), # list corresponding colours cex=1.2, # choose point sizes xpd=TRUE, # allow legend to be drawn outside of plotting area legend=c("1", "2", "6"), # list corresponding subjects/sample names inset=-0.4, # move legend slightly further out from the 'top' of plot horiz=TRUE) # arrange legend horizontally plot(sub1$Time,sub1$conc, xlim=c(-0.5,25), ylim=c(-0.5,12), xaxs = "i",yaxs = "i", ylab="", xlab="", pch=15, col="orange") par(new=TRUE) # prepare to add to existing plot plot(sub2$Time,sub2$conc, xlim=c(-0.5,25), ylim=c(-0.5,12), xaxs = "i",yaxs = "i", xaxt="n",yaxt="n",ylab="", xlab="", pch=16, col="blue") par(new=TRUE) # prepare to add to existing plot plot(sub6$Time,sub6$conc, xlim=c(-0.5,25), ylim=c(-0.5,12), xaxs = "i",yaxs = "i", xaxt="n",yaxt="n",ylab="", xlab="", pch=17, col="green") # plot all three subjects' data as above mtext("Default smoothing\n(f=2/3) ", # add text explaining smoothing side=3,line=-3,adj=0.9,cex=1) sub1curve<-lowess(sub1$Time,sub1$conc) sub2curve<-lowess(sub2$Time,sub2$conc) sub6curve<-lowess(sub6$Time,sub6$conc) # create lowess values for each subject # with default smoothing (f=2/3) lines(sub1curve,col="orange",lwd=2) lines(sub2curve,col="blue",lwd=2) lines(sub6curve,col="green",lwd=2) # add curved lines for each subject # using lowess values (f=2/3) mtext("Theophylline concentration (mg/L)", # add shared y-axis label side=2, # position at the left of the plot line=3, # distance from plotting area cex=1, # choose font size xpd=TRUE) # allow shared y-axis label to be drawn outside of plotting area plot(sub1$Time,sub1$conc, xlim=c(-0.5,25), ylim=c(-0.5,12), xaxs = "i",yaxs = "i", ylab="", xlab="",cex.lab=1.25,xpd=TRUE, pch=15, col="orange") par(new=TRUE) # prepare to add to existing plot plot(sub2$Time,sub2$conc, xlim=c(-0.5,25), ylim=c(-0.5,12), xaxs = "i",yaxs = "i", xaxt="n",yaxt="n",ylab="", xlab="", pch=16, col="blue") par(new=TRUE) # prepare to add to existing plot plot(sub6$Time,sub6$conc, xlim=c(-0.5,25), ylim=c(-0.5,12), xaxs = "i",yaxs = "i", xaxt="n",yaxt="n",ylab="", xlab="", pch=17, col="green") # plot all three subjects' data as above mtext("More smoothing\n(f=1.0) ", # add text explaining smoothing side=3,line=-3,adj=0.9,cex=1) sub1curve1<-lowess(sub1$Time,sub1$conc,f=1) sub2curve1<-lowess(sub2$Time,sub2$conc,f=1) sub6curve1<-lowess(sub6$Time,sub6$conc,f=1) # create lowess values for each subject # with lots of smoothing (f=1) lines(sub1curve1,col="orange",lwd=2) lines(sub2curve1,col="blue",lwd=2) lines(sub6curve1,col="green",lwd=2) # add curved lines for each subject # using lowess values (f=1) mtext("Time since drug administration (hr)", # add shared x-axis label side=1, # position at the bottom of the plot line=3, # distance from plotting area cex=1, # choose font size xpd=TRUE) # allow shared x-axis label to be drawn outside of plotting area par(mfrow=c(1,1)) # return to plotting single plot in plotting area par(oma=c(0,0,0,0)) # restore default outer margin areas par(mar=c(5.1, 4.1, 4.1, 2.1)) # restore default margin sizes ### Continuing Refined scatterplot plot(treated$conc,treated$rate, xlim=c(0,1.2), ylim=c(0,250), xaxs = "i",yaxs = "i", xlab="Concentration (mM)", ylab="Enzymatic reaction rate (DPM/min)", pch=15, col="orange") par(new=TRUE) # prepare to add to existing plot plot(untreated$conc,untreated$rate, xlim=c(0,1.2), ylim=c(0,250), xaxs = "i",yaxs = "i", xlab="", ylab="", xaxt="n",yaxt="n", pch=19, col="blue") legend(0.9,70, bg="white", pch = c(15,19), col = c("orange", "blue"), legend=c("Treated","Untreated")) treatedcurve<-lowess(treated$conc,treated$rate) untreatedcurve<-lowess(untreated$conc,untreated$rate) lines(treatedcurve,col="orange",lwd=2) lines(untreatedcurve,col="blue",lwd=2) # plot figure as above, but # without grid lines text(rate~conc, # add text labels at the data points labels=rate, # take the point labels from the reaction rate values pos=2, # position text labels to the left of data points cex= 0.8, # choose font size font=1) # choose default font face plot(treated$conc,treated$rate, xlim=c(0,1.2), ylim=c(0,250), xaxs = "i",yaxs = "i", xlab="Concentration (mM)", ylab="Enzymatic reaction rate (DPM/min)", pch=15, col="orange") par(new=TRUE) # prepare to add to existing plot plot(untreated$conc,untreated$rate, xlim=c(0,1.2), ylim=c(0,250), xaxs = "i",yaxs = "i", xlab="", ylab="", xaxt="n",yaxt="n", pch=19, col="blue") legend(0.9,70, bg="white", pch = c(15,19), col = c("orange", "blue"), legend=c("Treated","Untreated")) treatedcurve<-lowess(treated$conc,treated$rate) untreatedcurve<-lowess(untreated$conc,untreated$rate) lines(treatedcurve,col="orange",lwd=2) lines(untreatedcurve,col="blue",lwd=2) # reproduce plot again, as above interestvals<-subset(Puromycin,conc==0.56) # create data set with reaction rates # only at concentration 0.56mM text(interestvals$rate~interestvals$conc, # add text labels at the data points # from our subsetted data labels=interestvals$rate, # take the point labels from the reaction rate values # from our subsetted data pos=c(4,2,4,2), # position text labels alternately to the right and left # of data points cex= 0.8, # choose font size font=1) # choose default font face abline(v=0.56, # add vertical reference line at 0.56mM lwd=2, # choose line width lty=2, # choose line type col="purple") # choose line colour text(0.65,50, # set coordinates for text placement labels="0.56mM", # state text to position col="purple", # choose text colour font=2) # choose bold font face text(0.7,220, # set coordinates for text placement labels=paste("mean =",mean(c(191,201))), # calculate and print the mean col="orange", # choose colour font=2) # choose bold font face text(0.7,125, # set coordinates for text placement labels=paste("mean =",mean(c(144,158))), # calculate and print the mean col="blue", # choose colour font=2) # choose bold font face ########################################################################### ## PRESENTING ## ## TIME ## ## SERIES ## ### Refined time series temps <- read.table(file.choose(), header = T, sep = ",") # load the data in from the Excel CSV. file # 'land_and_ocean.csv' and name it 'temps' View(temps) # view the data summary(temps$year) # check year variable summary summary(temps$value) # check value variable summary neg<-subset(temps,value<=0) # create data set of just negative values pos<-subset(temps,value>0) # create data set of just positive values plot(400, # create blank plot using value not on axes xlim=c(1880,2020), ylim=c(-0.6,1.2), xaxs = "i",yaxs = "i", # set axis limits xlab="Year", ylab=expression(paste("Temperature Anomaly ( ",degree,"C)"))) # set axis labels abline(h=(seq(-0.6,1.2,0.2)), col="lightgray", lty=1) # add minor grid lines abline(h=0, col="darkgray", lwd=2.5) # add major grid lines par(new=TRUE) # prepare to add to existing plot plot(neg$year,neg$value, # plot data set of negative values xlim=c(1880,2020), ylim=c(-0.6,1.2),xaxs = "i",yaxs = "i", # set axis limits xlab="", ylab="", # do not re-draw axis labels xaxt="n",yaxt="n", # do not re-draw axis tick marks pch=16, # choose point type col="darkblue") # choose point colour par(new=TRUE) # prepare to add to existing plot plot(pos$year,pos$value, # plot data set of positive values xlim=c(1880,2020), ylim=c(-0.6,1.2), xaxs = "i",yaxs = "i", # set axis limits xlab="", ylab="", # do not re-draw axis labels xaxt="n",yaxt="n", # do not re-draw axis tick marks pch=16, # choose point type col="red") # choose point colour sortedvals <- temps[order(temps$year),] # re-arrange the rows of data by year lines(sortedvals$year,sortedvals$value, # add lines connecting ordered data points lty=1, # choose line type lwd=1, # choose line width col="purple") # choose line colour ### The importance of order in a time series lute <- read.table(file.choose(), header = T, sep = ",") # load the data in from the # Excel CSV. file 'LH_blood.csv' and # name it 'lute' View(lute) # view the data summary(lute$time) # check time variable summary summary(lute$LH) # check LH variable summary par(mfrow=c(1,2)) # arrange following plots in 1 row with 2 columns plot(lute$time,lute$LH, # plot unordered data points xlim=c(0,470), ylim=c(1.2,3.6),xaxs = "i",yaxs = "i", xlab="Time (mins)", ylab="LH Concentration (mlU/mL)", pch=17, col="springgreen3") lines(lute$time,lute$LH, # connect unordered data points lty=1,lwd=1.5,col="forestgreen") mtext("a.", side=3,line=1,adj=-0.2,cex=1.5,font=2) # label panel a plot(lute$time,lute$LH, # plot unordered data points xlim=c(0,470), ylim=c(1.2,3.6),xaxs = "i",yaxs = "i", xlab="Time (mins)", ylab="LH Concentration (mlU/mL)", pch=17, col="springgreen3") sortedlute <- lute[order(lute$time),] # re-arrange the rows of data by time lines(sortedlute$time,sortedlute$LH, # connect ordered data points lty=1,lwd=1.5,col="forestgreen") mtext("b.", side=3,line=1,adj=-0.2,cex=1.5,font=2) # label panel b par(mfrow=c(1,1)) # return to plotting single plot in plotting area ########################################################################### ## ## ## UNIVARIATE SCATTERPLOTS ## ## (STRIP-CHARTS) ## flight <- read.table(file.choose(), header = T, sep = ",") # load the data in from the Excel CSV. file # 'birds_and_pterosaurs.csv' and name it 'flight' # RUN THE FOLLOWING UNTIL YOU HAVE GOOD-LOOKING 'JITTER' ACROSS ALL! # par(mfrow=c(2,2)) # arrange following plots in 2 rows with 2 columns par(mar=c(2.1,4.1,2.1,2.1)) # edit margin sizes for all following plots stripchart(flight$mass_kg ~ flight$broad_group, # plot strip-charts of the data ylim=c(-20,280),yaxs="i", # set y-axis limits method="jitter", # choose jitter rather than default overplot vertical=TRUE, # draw strip-chart vertically jitter=0.2, # specify amount of jitter ylab = "Mass (kg)", # set y-axis label pch=19, # choose point type col="darkblue") # choose point colour mtext("a.", side=3,line=1,adj=-0.2,cex=1,font=2) # label panel a boxplot(flight$mass_kg ~flight$broad_group, # plot boxplots of the data ylim=c(-20,280),yaxs="i", # set axis limits ylab = "Mass (kg)", # set y-axis label xlab = "", # keep x-axis label blank col="yellow") # choose box colour mtext("b.", side=3,line=1,adj=-0.2,cex=1,font=2) # label panel b boxplot(flight$mass_kg ~flight$broad_group, # plot boxplots of the data ylim=c(-20,280),xlab = "", yaxs="i",ylab = "Mass (kg)",col="yellow", boxwex=0.3, # set box scaling factor at=1.3:2.3) # set where boxes should be drawn along x-axis stripchart(flight$mass_kg ~ flight$broad_group, # plot strip-charts alongside ylim=c(-20,280), yaxs="i",method="jitter",jitter=0.1,vertical=TRUE,ylab = "", pch=19,col="darkblue",add=TRUE) mtext("c.", side=3,line=1,adj=-0.2,cex=1,font=2) # label panel c boxplot(flight$mass_kg ~flight$broad_group, # plot boxplots of the data xlab = "",las=1, cex.axis=0.75, # reduce font size of axes log="y", # draw the y-axis on a logarithmic scale ylab = "Mass (kg)",col="yellow", boxwex=0.3, # set box scaling factor at=1.3:2.3) # set where boxes should be drawn along x-axis stripchart(flight$mass_kg ~ flight$broad_group, # plot strip-charts alongside log="y", # draw the y-axis on a logarithmic scale method="jitter",jitter=0.1,vertical=TRUE,ylab = "", pch=19,col="darkblue",add=TRUE) mtext("d.", side=3,line=1,adj=-0.2,cex=1,font=2) # label panel d par(mfrow=c(1,1)) # return to plotting single plot in plotting area par(mar=c(5.1,4.1,4.1,2.1)) # restore default margin sizes