Reshape2

After my presentation yesterday to a group of grad students on R resources, I did a presentation today on intro to R data manipulation, visualizations, and analyses/visualizations of biparite networks and community level analyses (diversity, rarefaction, ordination, etc.). As I said yesterday I’ve been playing with two ways to make reproducible presentations in R: RStudio’s presentations built in to RStudio IDE, and Slidify. Yesterday I went with RStudio’s product - today I used Slidify. See the Markdown file for the presentation here. ...

Excel pivot tables have been how I have reorganized data…up until now. These are just a couple of examples why R is superior to Excel for reorganizing data: UPDATE: I fixed the code to use ‘dcast’ instead of ‘cast’. And library(ggplot2) instead of library(plyr) [plyr is called along with ggplot2]. Thanks Bob! Also, see another post on this topic here. library(reshape2) library(ggplot2) dataset <- data.frame(var1 = rep(c("a","b","c","d","e","f"), each = 4), var2 = rep(c("level1","level1","level2","level2"), 6), var3 = rep(c("h","m"), 12), meas = rep(1:12)) Created by Pretty R at inside-R.org # simply pivot table dcast(dataset, var1 ~ var2 + var3) Using meas as value column. Use the value argument to cast to override this choice var1 level1_h level1_m level2_h level2_m 1 a 1 2 3 4 2 b 5 6 7 8 3 c 9 10 11 12 4 d 1 2 3 4 5 e 5 6 7 8 6 f 9 10 11 12 # mean by var1 and var2 dcast(dataset, var1 ~ var2, mean) Using meas as value column. Use the value argument to cast to override this choice var1 level1 level2 1 a 1.5 3.5 2 b 5.5 7.5 3 c 9.5 11.5 4 d 1.5 3.5 5 e 5.5 7.5 6 f 9.5 11.5 # mean by var1 and var3 dcast(dataset, var1 ~ var3, mean) Using meas as value column. Use the value argument to cast to override this choice var1 h m 1 a 2 3 2 b 6 7 3 c 10 11 4 d 2 3 5 e 6 7 6 f 10 11 # mean by var1, var2 and var3 (version 1) dcast(dataset, var1 ~ var2 + var3, mean) Using meas as value column. Use the value argument to cast to override this choice var1 level1_h level1_m level2_h level2_m 1 a 1 2 3 4 2 b 5 6 7 8 3 c 9 10 11 12 4 d 1 2 3 4 5 e 5 6 7 8 6 f 9 10 11 12 # mean by var1, var2 and var3 (version 2) dcast(dataset, var1 + var2 ~ var3, mean) Using meas as value column. Use the value argument to cast to override this choice var1 var2 h m 1 a level1 1 2 2 a level2 3 4 3 b level1 5 6 4 b level2 7 8 5 c level1 9 10 6 c level2 11 12 7 d level1 1 2 8 d level2 3 4 9 e level1 5 6 10 e level2 7 8 11 f level1 9 10 12 f level2 11 12 # use package plyr to create flexible data frames... dataset_plyr <- ddply(dataset, .(var1, var2), summarise, mean = mean(meas), se = sd(meas), CV = sd(meas)/mean(meas) ) > dataset_plyr var1 var2 mean se CV 1 a level1 1.5 0.7071068 0.47140452 2 a level2 3.5 0.7071068 0.20203051 3 b level1 5.5 0.7071068 0.12856487 4 b level2 7.5 0.7071068 0.09428090 5 c level1 9.5 0.7071068 0.07443229 6 c level2 11.5 0.7071068 0.06148755 7 d level1 1.5 0.7071068 0.47140452 8 d level2 3.5 0.7071068 0.20203051 9 e level1 5.5 0.7071068 0.12856487 10 e level2 7.5 0.7071068 0.09428090 11 f level1 9.5 0.7071068 0.07443229 12 f level2 11.5 0.7071068 0.06148755 # ...to use for plotting qplot(var1, mean, colour = var2, size = CV, data = dataset_plyr, geom = "point") ...