tidyr

It's shameful, but I still can't wrap my mind fully around tidyr , specifically gather() . I feel like I'm missing something fundamental. If I run this tiny snippet of code library(tidyr) x <- data.frame(var1=letters[1:3], var2=LETTERS[7:9], var3=21:23) gather(x, foo, value) I get > x var1 var2 var3 1 a G 21 2 b H 22 3 c I 23 > gather(x, foo, value) variable value 1 var1 a 2 var1 b 3 var1 c 4 var2 G 5 var2 H 6 var2 I 7 var3 21 8 var3 22 9 var3 23 Where does foo get used? Is this completely unnecessary? Am I tripping up because I'm thinking reshape style where you define the ID variables and

This question already has an answer here: Combine Multiple Columns Into Tidy Data [duplicate] 3 answers Reshaping multiple sets of measurement columns (wide format) into single columns (long format) 7 answers I have the following structure: key | category_x | 2009 | category_y | 2010 test example data as requested set.seed(24) df <- data.frame( key = 1:10, category_x = paste0("stock_", 0:9), '2008' = rnorm(10, 0, 10), category_y = paste0("stock_", 0:9), '2009' = rnorm(10, 0, 10), category_z = paste0("stock_", 0:9), '2010' = rnorm(10, 0, 10), check.names=FALSE ) how do I change that into: key |

Following my earlier question: R: reshape/gather function to create dataset ready for multilevel analysis I discovered it is a bit more complicated. My dataset is actually 'messier' than I hoped. So here's the full story: I have a big dataset, 240 cases. Each row is a case (breast cancer patient). Somewhere at the end of the dataset(say from column 417 onwards) I have partner data of the patients, that also filled in a questionnaire. In the beginning, there are demographic variables for both patients and partners, followed by test outcomes only of patients, thus followed by partner data. I

I hope that I'm not recreating the wheel, and do not think that the following can be answered using reshape . I have messy longitudinal survey data, that I want to convert from wide to long format. By messy I mean: I have a mixture of variable types (numeric, factor, logical) Not all variables have been collected at every timepoint. For example: data <- read.table(header=T, text=' id inlove.1 inlove.2 income.2 income.3 mood.1 mood.3 random 1 TRUE FALSE 87717.76 82281.25 happy happy filler 2 TRUE TRUE 70795.53 54995.19 so-so happy filler 3 FALSE FALSE 48012.77 47650.47 sad so-so filler ') I

In continuation of this issue comparison Mann-Whitney test between groups , I decided to create a new topic. Solution of Rui Barradas helped me calculate Mann-Whitney for group 1-2 and 1-3. lst <- split(mydat, mydat$group) lapply(lst[-1], function(DF) wilcox.test(DF$var, lst[[1]]$var, exact = FALSE)) So now i want get the descriptive statistics. I use library:psych describeBy(mydat$var,mydat$group) So i get the following output group: 1 vars n mean sd median trimmed mad min max range skew kurtosis se X1 1 4 23.5 0.58 23.5 23.5 0.74 23 24 1 0 -2.44 0.29 -----------------------------------------

This question already has an answer here: Reshaping multiple sets of measurement columns (wide format) into single columns (long format) 7 answers Apologies if this is a simple question, but I haven't been able to find a simple solution after searching. I'm fairly new to R, and am having trouble converting wide format to long format using either the melt (reshape2) or gather(tidyr) functions. The dataset that I'm working with contains 22 different time variables that are each 3 time periods. The problem occurs when I try to convert all of these from wide to long format at once. I have had