![Last thoughts
Can handle millions of datapoints
It’s free
Is good for having a quick look at your data and changing the display in an
easy manner
Works in all platforms ( Windows, Mac and Linux [ser ver] like it )
It’s pretty ( and did I mention is fast? )
I think it saves you a bit of Illustrator time
If you already have your scheme and it works for you is not worth it, but
if you are looking for a new plotting strategy I think is a good place to
start
If you get into it you can start making statistical analysis of your data
and plotting it all together](https://image.slidesharecdn.com/rgraphics260809-121126204700-phpapp01/85/R-graphics260809-14-320.jpg)
R graphics260809
- 1. Plotting 1.0 Selene Fernandez-Valverde Lab Meeting 26-08-09
- 2. Your scientific graphing options Ot he r s ...?
- 3. Why not only Excel ? Excel is relatively limited in its support of scientific graphing It’s options provide limited control over the output Limited selection of graph types Limited number of datapoint that can be plotted (or it dies)
- 4. What plots you can do with R? Type on your R terminal: > demo(graphics) > demo(persp) > demo(lattice) Now, that something you can’t make in Excel or Prism. ( you actually can in Matlab )
- 5. Cool, but... Steep learning cur ve Plotting is step by step Prettifying a graph takes a bit lot of effort I don’t want to script in R I just want to plot my results
- 6. How do we avoid that? We use a package made by someone who encountered these problems before “ggplot2 is a plotting system for R, based on the grammar of graphics, which tries to take the good parts of base and lattice graphics and (almost) none of the bad parts. It takes care of many of the fiddly details that make plotting a hassle (like drawing legends) as well as providing a powerful model of graphics that makes it easy to produce complex multi-layered graphics.” In summary: R graphs made easy
- 7. How do I start? First format the data into a table that looks like this: carat cut color clarity depth table price x y z 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 0.29 Premium I VS2 62.4 58 334 4.2 4.23 2.63 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48 Wait ! This looks like an Excel table! Well... it is ( it can also be a tab delimited file ) Make sure your variables (columns) are meaningful and allow you to retrieve the information that you want to plot
- 8. Read the table into R Set your working directory: > setwd (“./Documents/MyUsername/FolderWhereMyExcelFileIs/”) > getwd() > install.packages("ggplot2", dependencies=TRUE) > library(ggplot2) If your file is and Excel file: > install.packages("gdata") > library(gdata) > table <- read.xls(“MyExcelFile.xls”) If your file is a tab delimited file: > table <- read.delim(“MyExcelFile.txt”) > summary(table) We already have a loaded dataset named “diamonds” > summary(diamonds) Start plotting!
- 9. Your first plot (s) > ggplot(diamonds, aes(color)) + geom_bar() > ggplot(diamonds, aes(color, fill=cut)) + geom_bar() > ggplot(diamonds, aes(color, fill=cut)) + geom_bar(position="dodge") > ggplot(diamonds, aes(color, fill=cut)) + geom_bar(position="dodge") + scale_y_log10() > ggplot(diamonds, aes(color, fill=cut)) + geom_bar(position="fill") > ggplot(diamonds, aes(color, depth)) + geom_point() > ggplot(diamonds, aes(color, color=cut, group=cut)) + geom_freqpoly() > ggplot(diamonds, aes(color, color=cut, group=cut)) + geom_freqpoly() + xlab("Diamond Color") > ggplot(diamonds, aes(color, color=cut, group=cut)) + geom_freqpoly() + xlab("Diamond Color") + coord_flip() > ggplot(diamonds, aes(clarity, fill=color)) + geom_bar() + facet_wrap(~ cut) > ggplot(diamonds, aes(clarity, fill=color)) + geom_bar() + facet_grid(. ~ cut) > ggplot(diamonds, aes(color, depth, color=cut)) + geom_point() > ggplot(diamonds, aes(color, depth, color=cut)) + geom_jitter() > ggplot(diamonds, aes(color, depth, color=cut)) + geom_jitter() + ylim(53,70) > ggplot(diamonds, aes(color, depth, color=cut)) + geom_boxplot()
- 10. Making the graph prettier > ggplot(diamonds, aes(color, color=cut, group=cut)) + geom_freqpoly() + xlab("Diamond Color") > ggplot(diamonds, aes(color, color=cut, group=cut)) + geom_freqpoly() + theme_bw() + labs(x="Diamond color") > ggplot(diamonds, aes(color, color=cut, group=cut)) + geom_freqpoly() + theme_bw() + labs(x="Diamond color") + scale_y_continuous("Counts") > ggplot(diamonds, aes(color, color=cut, group=cut)) + geom_freqpoly() + theme_bw() + labs(x="Diamond color") + scale_y_continuous("Counts") + scale_color_hue("Cut") > ggplot(diamonds, aes(color, color=cut, group=cut)) + geom_freqpoly() + theme_bw() + labs(x="Diamond color") + scale_y_continuous("Counts") + scale_color_brewer("Cut") > ggplot(diamonds, aes(color, color=cut, group=cut)) + geom_freqpoly() + theme_bw() + labs(x="Diamond color") + scale_y_continuous("Counts") + scale_color_brewer("Cut", palette="Set1") ggplot(diamonds, aes(color, color=cut, group=cut)) + geom_freqpoly() + theme_bw() + labs(x="Diamond color") + scale_y_continuous("Counts", formatter="comma") + scale_color_brewer("Cut", palette="Set1") > ggplot(diamonds, aes(color, color=cut, group=cut)) + geom_freqpoly() + theme_bw() + labs(x="Diamond color") + scale_y_continuous("Counts") + scale_color_brewer("Cut", palette="Set1") + facet_wrap(~ clarity) > ggplot(diamonds, aes(color, color=cut, group=cut)) + geom_freqpoly() + theme_bw() + labs(x="Diamond color") + scale_y_continuous("Counts") + scale_color_brewer("Cut", palette="Set1") + facet_wrap(~ clarity, scale="free_y")
- 11. Oneliner graph example ggplot(NHANES, aes(TIBC, Hemoglobin)) + geom_hex() + facet_grid(~Sex) + opts(aspect.ratio = 0.8)
- 12. Our version F M 20 Number of 15 Hemoglobin 20 40 60 10 80 100 5 200 300 400 500 600 700 200 300 400 500 600 700 TIBC ggplot(NHANES, aes(TIBC, Hemoglobin)) + geom_hex() + facet_grid(~Sex) + opts(aspect.ratio = 0.8) + theme_bw() + scale_fill_gradient("Number of Patients")
- 13. Some things ggplot2 can’t do You can’t click on your graph to change the labels, you have to rerun the program ( work around that , edit them in illustrator ) or use opts When stacking data and setting a new limit, you’ll lose all the data that is in the group over that range
- 14. Last thoughts Can handle millions of datapoints It’s free Is good for having a quick look at your data and changing the display in an easy manner Works in all platforms ( Windows, Mac and Linux [ser ver] like it ) It’s pretty ( and did I mention is fast? ) I think it saves you a bit of Illustrator time If you already have your scheme and it works for you is not worth it, but if you are looking for a new plotting strategy I think is a good place to start If you get into it you can start making statistical analysis of your data and plotting it all together
- 15. For more info http://had.co.nz/ggplot2/ http://learnr.wordpress.com/
- 16. NHANES Data : National Health and Nutrition Examination Survey Description This is a somewhat large interesting dataset, a data frame of 15 variables (columns) on 9575 persons (rows).This data frame contains the following columns: Cancer.Incidence binary factor with levels No and Yes. Cancer.Death binary factor with levels No and Yes. Age numeric vector giving age of the person in years. Smoke a factor with levels Current, Past, Nonsmoker, and Unknown. Ed numeric vector of {0,1} codes giving the education level. Race numeric vector of {0,1} codes giving the person's race. Weight numeric vector giving the weight in kilograms BMI numeric vector giving Body Mass Index, i.e., Weight/Height^2 where Height is in meters, and missings (61% !) are coded as 0 originally. Diet.Iron numeric giving Dietary iron. Albumin numeric giving albumin level in g/l. Serum.Iron numeric giving Serum iron in ug/l. TIBC numeric giving Total Iron Binding Capacity in ug/l. Transferin numeric giving Transferin Saturation which is just 100*serum.iron/TIBC. Hemoglobin numeric giving Hemoglobin level. Sex a factor with levels F (female) and M (male).