Fertilizer Dry Moist Wet Fertilizer Dry Moist Wet Control Control N N P P NP NP 9 8 7

Transcription

1 Part 6 Bar Plots & Dot Plots Bar and dot plots are used in two ways: (1) to display proportions of categories, and (2) to compare class means, i.e. experimental treatments or sampling sites. In either case, you don t have a sensible scale on your x-axis, i.e. your independent variable is a factor, such as Control, Nitrogen, Phosphorus, Nitrogen & Phosporous fertilization in our example below. Because the bars/dots can be grouped, these graph types works well for multi-factor experiments or hierarchical sampling designs, e.g. if you also have the treatment Drought versus Irrigated, on top of your fertilization treatments (bars in 2 groups of 3 or 3 groups of 2). More factors can be handled with colors, patterns, and dividing lines. If your treatments are in an ordered sequence, e.g. Control, 1 x Nitrogen, 2 x Nitrogen, 4 x Nitrogen, you may rather want to use a line graph, where you use 0,1,2,3 as placeholders for your factors (which you can override with text labels that state your treatments as covered earlier). Multi-factor experiments can then be represented by different lines (e.g. one line for Drought and one line for Irrigated. However, a bar graph would also be acceptable even if you have a factor in an ordered sequence. Bar charts should start with a 0 value to convey the correct sense of the treatment/factor effect. If the treatment effect is very small relative to the scale, use a dot plot. Dot plots don t have a perception issue when the y-axis does not start at 0. Don t do s: (1) Bar plots are widely misused to visualize trends over time. This is not a good choice use line graphs instead. (2) Pie charts are generally not used in science because of perception problems (people are not good at estimating areas of circles). You can always use a bar or dot chart instead of a pie chart Bar plot basics Below is a dataset from a 2-factor fertilization / irrigation experiment. The numbers represent Yield in bushels/acre of your favorite agricultural crop or Yield in m³/ha of your favorite forestry tree. The numbers below are not the original measurements. Summary statistics have already been calculated and the statistics have been arranged as a matrix to generate bar and dot plots. Enter the data as shown into Excel, save as CSV and import to R (or download the datasets from the course web site): Means Standard error of the mean Fertilizer Dry Moist Wet Fertilizer Dry Moist Wet Control Control N N P P NP NP Write a three-liner that (1) re-sets your graphics parameters, (2) sets your window size to 6 wide and 4 high, and (3) sets the global graphics parameter to your preferences (size, font-size, font-type). Remember to run and re-run each graphic starting with the graphics.off command.

2 Now, let s try out basic bar plot commands. Wet is the variable that we use here, but you could also choose Dry or Moist irrigation treatments. All customizations are below all familiar stuff. windows(width=6, height=4) barplot(wet) barplot(wet, names=fertilizer) barplot(moist, names=c("what","ever","you","like")) barplot(wet, names=fertilizer, ylim=c(0,100), xlab="fertilizer", ylab="biomass (kg/ha)") Of course, now we want to plot both treatments (Fertilizer and Irrigation) in one graph. However, for this we need to create a matrix. Our data table looks like a matrix, already. However, matrices can only contain numbers, so we have to convert our variable Fertilizer to row labels (which are not recognized as a variable) and then delete the variable Fertilizer from the data columns. fix(yield) row.names(yield)=fertilizer yield=yield[,c("dry","moist","wet")] fix(yield) Now that we have the data table in a proper matrix format, we have to define it as a matrix for R. This also allows us to do matrix algebra! For a start, let s transpose the matrix with the t() function. Check the original and transposed matrix (yield_m, and yield_t) with the fix command: yield_m=as.matrix(yield) yield_t=t(yield_m) fix(yield_m) fix(yield_t) The barplot function in R is made for matrices. All we have to do are the minimal commands below. Barplot picks the labels up from the column and row names. Which plot do you like better, the original or the transposed? This is something you always want to check. Some groupings work better than others to show multiple treatment effects: barplot(yield_m, beside=t) barplot(yield_t, beside=t) barplot(yield_m) barplot(yield_t) Which one do you like best? This is a subjective choice as all four charts convey exactly the same information. You simply pick one that best displays the trends or effects that you want to discuss:

3 Let s stick with the transposed/beside version, and customize the graph. (You can out-source the xlab/ylab/main as usual). I personally don t like titles much (especially if they repeat what can already be inferred from the axis labels), so I blanked this out with #: barplot(yield_t, beside=t, col=heat.colors(3), ylim=c(0,100)) title(xlab="fertilizer Treatment") title(ylab="biomass (kg/ha)") # title(main="fertilizer Effects on Biomass") 6.2. Bar plot customization and legends OK, we know how to do all this from the line and scatter plots with legend, arrow, text, abline commands, but to position our elements we need an x-axis and we don t have one! Here is the solution: simply type a variable name, e.g. xpos= (could be anything), followed by your plot command. The plot will still be generated, but you also have a matrix of x-axis values: xpos=barplot(yield_t, beside=t, col=grey.colors(3), ylim=c(0,100)) fix(xpos) This gives the mid-point of each bar (the numbers are different for each bar graph). Now we can use the numbers to position a legend where we want. Bar plot specific is the fill= command. Here, the first bar mid-point is 1.5. To get the legend to the top-left, I use the coordinates x=1 and y=100: legend(1,100, cex=0.8, legend=row.names(yield_t), fill=grey.colors(3)) Just for fun, let s try some more customization. The first one would represent my dryness treatments well. The second one is some old-school hatch. For more, check:?barplot barplot(yield_t, beside=t, ylim=c(0,100), col=c("tan","lightseagreen","steelblue")) barplot(yield_t, beside=t, ylim=c(0,100), col="black", density=c(20,10,30), angle=c(45,-45,0)) legend(1,100, row.names(yield_t), fill="black", density=c(20,10,30), angle=c(45,-45,0))

4 6.3. Adding error bars to bar plots Last on the list are our error bars, which we do exactly in the same way as for line plots, except that we use matrices of x and y coordinates instead of vectors (or variables). For this we have to import the CSV file of standard error corresponding to each treatment and then convert it to a matrix like the main dataset of means: se=read.csv("bar_standard_errors.csv") row.names(se)=fertilizer se=se[,c("dry","moist","wet")] se_m=as.matrix(se) se_t=t(se_m) For bar-charts it s good enough to show errors above, rather than to both sides. The syntax for the arrow command is (from x, from y, to x, to y). Instead of a single coordinate, we feed a matrix for multiple arrows (just like we used vectors for multiple arrows in line plots): arrows(xpos,yield_t, xpos,yield_t+se_t, angle=90, length=0.05) In the same way I can position text. For example annotations or letters to indicate significant differences. In the caption you would have to explain that different lower-case letters indicate significant differences between irrigation treatments and different capital letters significantly different fertilizer treatments. There is lots of information packed into this graph now. text(xpos, yield_t+se_t+5, c("a","ab","b","a","b","b","a","a","b","a","b","b"), cex=0.8) text(xpos, yield_t+se_t+10, c("a","a","a","b","b","b","a","a","a","b","b","b"), cex=0.8)

5 6.4. Dot charts Dot charts can be used as an alternative to bar charts and they are generally easier to read and more compact. They are not widely known and used (because Excel can t do them) but journal editors love them! Here is a fully customized dot-chart. In this case I used the original matrix (yield_m). The y- axis values simply count up, 1,2,3 from the bottom, so we can add text labels and error bars as usual. Play with the code until you understand what it does: windows(width=6, height=5) dotchart(yield_m, xlab="yield (kg/ha)", pch=c(18,17,16,15), xlim=c(0,100), lcolor="black") text(50,5, "Wet") text(50,11, "Moist") text(50,17, "Dry") arrows(yield_m,c(13,14,15,16,7,8,9,10,1,2,3,4), yield_m+se_m, c(13,14,15,16,7,8,9,10,1,2,3,4), angle=90, length=0.05) arrows(yield_m,c(13,14,15,16,7,8,9,10,1,2,3,4), yield_m-se_m, c(13,14,15,16,7,8,9,10,1,2,3,4), angle=90, length=0.05) Here comes the same dot-chart in an extremely compact format. This is useful if you have many treatments (species, varieties, locations) that are factorial or hierarchical: windows(width=6, height=3) dotchart(yield_m[,1], xlab="yield (kg/ha)", xlim=c(0,100), lcolor="black") points(yield_m[,1],1:4,pch=21, bg="white", cex=1.2) points(yield_m[,2],1:4,pch=21, bg="grey", cex=1.2) points(yield_m[,3],1:4,pch=21, bg="black", cex=1.2) text(65,4.5,"dry Moist Wet",cex=0.8)