reshape2

I want to reshape a wide format dataset that has multiple tests which are measured at 3 time points: ID Test Year Fall Spring Winter 1 1 2008 15 16 19 1 1 2009 12 13 27 1 2 2008 22 22 24 1 2 2009 10 14 20 2 1 2008 12 13 25 2 1 2009 16 14 21 2 2 2008 13 11 29 2 2 2009 23 20 26 3 1 2008 11 12 22 3 1 2009 13 11 27 3 2 2008 17 12 23 3 2 2009 14 9 31 into a data set that separates the tests by column but converts the measurement time into long format, for each of the new columns like this: ID Year Time Test1 Test2 1 2008 Fall 15 22 1 2008 Spring 16 22 1 2008 Winter 19 24 1 2009 Fall 12 10 1 2009

I'm using a data frame similar to this one: df<-data.frame(student=c(rep(1,5),rep(2,5)), month=c(1:5,1:5), quiz1p1=seq(20,20.9,0.1),quiz1p2=seq(30,30.9,0.1), quiz2p1=seq(80,80.9,0.1),quiz2p2=seq(90,90.9,0.1)) print(df) student month quiz1p1 quiz1p2 quiz2p1 quiz2p2 1 1 1 20.0 30.0 80.0 90.0 2 1 2 20.1 30.1 80.1 90.1 3 1 3 20.2 30.2 80.2 90.2 4 1 4 20.3 30.3 80.3 90.3 5 1 5 20.4 30.4 80.4 90.4 6 2 1 20.5 30.5 80.5 90.5 7 2 2 20.6 30.6 80.6 90.6 8 2 3 20.7 30.7 80.7 90.7 9 2 4 20.8 30.8 80.8 90.8 10 2 5 20.9 30.9 80.9 90.9 Describing grades received by students during five months – in two quizzes

I can achieve this task, but I feel like there must be a "best" (slickest, most compact, clearest-code, fastest?) way of doing it and have not figured it out so far ... For a specified set of categorical factors I want to construct a table of means and variances by group. generate data : set.seed(1001) d <- expand.grid(f1=LETTERS[1:3],f2=letters[1:3], f3=factor(as.character(as.roman(1:3))),rep=1:4) d$y <- runif(nrow(d)) d$z <- rnorm(nrow(d)) desired output : f1 f2 f3 y.mean y.var 1 A a I 0.6502307 0.09537958 2 A a II 0.4876630 0.11079670 3 A a III 0.3102926 0.20280568 4 A b I 0.3914084 0

I have 2 df's like this ID = c('x1','x2','x5') df1 <- data.frame(ID) x1 = c(1,2,3,4,5) x2 = c(11,12,13,14,15) x3 = c(21,22,23,24,25) x4 = c(31,32,33,34,35) x5 = c(41,42,43,44,45) df2 <- data.frame(x1,x2,x3,x4,x5) Desired output x1 x2 x5 1 1 11 41 2 2 12 42 3 3 13 43 4 4 14 44 5 5 15 45 I would like my new dataset to contain only those variables that are identified in df1 as important (i.e: x1,x2,x5) with the values from df2. In this simple dataset, I know I could do this but just removing x3,x4 in df2 but ideally I would like to apply it to a larger data set where I have more than 100

Say I have a dataframe df with dozens of identifying variables (in columns) and only a few measured variables (also in columns). To avoid repetitively typing all the variables for each argument, I assign the names of the identifying and measured df columns to df_id and df_measured , respectively. It's easy enough to input these vectors to shorten the argument inputs for melt ... df.m <- melt(df, id.vars = df_id, measure.vars = df_measured) ... but I'm at a loss for how to enter the formula = argument in dcast using the same method to specify my id variables since it requires that the input

Suggestions for how to smoothly get from foo to foo2 (preferably with tidyr or reshape2 packages)? This is kind of like this question , but not exactly I think, because I don't want to auto-number columns, just widen multiple columns. It's also kind of like this question , but again, I don't think I want the columns to vary with a row value as in that answer. Or, a valid answer to this question is to convince me it's exactly like one of the others. The solution in the second question of "two dcasts plus a merge" is the most attractive right now, because it is comprehensible to me. foo: foo =

This is what I have: id<-c(1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2) measure<-c("speed","weight","time","speed","weight","time","speed","weight","time", "speed","weight","time","speed","weight","time","speed","weight","time") value<-c(1.23,10.3,33,1.44,10.4,31,1.21,10.1,33,4.25,12.5,38,1.74,10.8,31,3.21,10.3,33) testdf<-data.frame(id,measure,value) This is what I want: id<-c(1,1,1,2,2,2) speed<-c(1.23,1.44,1.21,4.25,1.74,3.21) weight<-c(10.3,10.4,10.1,12.5,10.8,10.3) time<-c(33,31,33,37,31,33) res<-data.frame(id,speed,weight,time) The issue lies in that my variables speed weight and time are