The reason why you are getting this warning is in the description of fun.aggregate (see ?dcast): aggregation function needed if variables do not identify a single observation for each output cell. Defaults to length (with a message) if needed but not specified So, an aggregation function is needed when there is more than one value … Read more
This approach seems pretty natural to me: df %>% gather(key, value, -id, -time) %>% extract(key, c(“question”, “loop_number”), “(Q.\\..)\\.(.)”) %>% spread(question, value) First gather all question columns, use extract() to separate into question and loop_number, then spread() question back into the columns. #> id time loop_number Q3.2 Q3.3 #> 1 1 2009-01-01 1 0.142259203 -0.35842736 #> … Read more
There are many ways to do this. This answer starts with what is quickly becoming the standard method, but also includes older methods and various other methods from answers to similar questions scattered around this site. tmp <- data.frame(x=gl(2,3, labels=letters[24:25]), y=gl(3,1,6, labels=letters[1:3]), z=c(1,2,3,3,3,2)) Using the tidyverse: The new cool new way to do this is … Read more
As of Dec 2014, this can be done using the unnest function from Hadley Wickham’s tidyr package (see release notes http://blog.rstudio.org/2014/12/08/tidyr-0-2-0/) > library(tidyr) > library(dplyr) > mydf V1 V2 2 1 a,b,c 3 2 a,c 4 3 b,d 5 4 e,f 6 . . > mydf %>% mutate(V2 = strsplit(as.character(V2), “,”)) %>% unnest(V2) V1 V2 … Read more
With the data.table package, this could easily be solved with the new rowid function: library(data.table) dcast(setDT(d1), Name ~ rowid(Name, prefix = “medication”), value.var = “MedName”) which gives: Name medication1 medication2 medication3 1 Name1 atenolol 25mg aspirin 81mg sildenafil 100mg 2 Name2 atenolol 50mg enalapril 20mg <NA> Another method (commonly used before version 1.9.7): dcast(setDT(d1)[, rn … Read more
reshape(dat, idvar=”ID”, direction=”long”, varying=list(Start=c(2,5,8), End=c(3,6,9), Value=c(4,7,10)), v.names = c(“DateRangeStart”, “DateRangeEnd”, “Value”) ) #————- ID time DateRangeStart DateRangeEnd Value 1.1 1 1 1/1/90 3/1/90 4.4 1.2 1 2 4/5/91 6/7/91 6.2 1.3 1 3 5/5/95 6/6/96 3.3 (Added the v.names per Josh’s suggestion.)
Three alternative solutions: 1) With data.table: You can use the same melt function as in the reshape2 package (which is an extended & improved implementation). melt from data.table has also more parameters that the melt-function from reshape2. You can for example also specify the name of the variable-column: library(data.table) long <- melt(setDT(wide), id.vars = c(“Code”,”Country”), … Read more
Using reshape function: reshape(dat1, idvar = “name”, timevar = “numbers”, direction = “wide”)
We just need to transpose it t(m1)