As of dplyr 0.2 (I think) rowwise() is implemented, so the answer to this problem becomes: iris %>% rowwise() %>% mutate(Max.Len= max(Sepal.Length,Petal.Length)) Non rowwise alternative Five years (!) later this answer still gets a lot of traffic. Since it was given, rowwise is increasingly not recommended, although lots of people seem to find it intuitive. … Read more
If it’s just two columns, you can also use pmin and pmax, like this: library(data.table) unique(as.data.table(df)[, c(“V1”, “V2”) := list(pmin(V1, V2), pmax(V1, V2))], by = c(“V1”, “V2″)) # V1 V2 # 1: a b # 2: b d # 3: c e A similar approach using “dplyr” might be: library(dplyr) data.frame(df, stringsAsFactors = FALSE) %>% … Read more
Following up on comments with Ben Barnes and starting with his mydf3 : DT = as.data.table(mydf3) setkey(DT,Id,Time) DT[CJ(unique(Id),seq(min(Time),max(Time)))] Id Time Value Id2 [1,] 1 1 -0.262482283 2 [2,] 1 2 -1.423935165 2 [3,] 1 3 0.500523295 1 [4,] 1 4 -1.912687398 1 [5,] 1 5 -1.459766444 2 [6,] 1 6 -0.691736451 1 [7,] 1 7 … Read more
I believe you’ve loaded plyr after dplyr, which is why you are getting an overall summary instead of a grouped summary. This is what happens with plyr loaded last. library(dplyr) library(plyr) df %>% group_by(DRUG,FED) %>% summarize(mean=mean(AUC0t, na.rm=TRUE), low = CI90lo(AUC0t), high= CI90hi(AUC0t), min=min(AUC0t, na.rm=TRUE), max=max(AUC0t,na.rm=TRUE), sd= sd(AUC0t, na.rm=TRUE)) mean low high min max sd 1 … Read more
You could do this within data.table library(data.table) data[, lag.value:=c(NA, value[-.N]), by=groups] data # time groups value lag.value #1: 1 a 0.02779005 NA #2: 2 a 0.88029938 0.02779005 #3: 3 a -1.69514201 0.88029938 #4: 1 b -1.27560288 NA #5: 2 b -0.65976434 -1.27560288 #6: 3 b -1.37804943 -0.65976434 #7: 4 b 0.12041778 -1.37804943 For multiple columns: … Read more
The data.table way is : DT[, lapply(.SD,sum), by=list(col1,col2,col3,…)] or DT[, lapply(.SD,sum), by=colnames(DT)[1:10]] where .SD is the (S)ubset of (D)ata excluding group columns. (Aside: If you need to refer to group columns generically, they are in .BY.)
In order to handle multiple variables like you want, you need to melt the data you have before casting it. library(“reshape2”) dcast(melt(my.df, id.vars=c(“ID”, “TIME”)), ID~variable+TIME) which gives ID X_1 X_2 X_3 X_4 X_5 Y_1 Y_2 Y_3 Y_4 Y_5 1 A 1 4 7 10 13 16 19 22 25 28 2 B 2 5 8 … Read more
Try this: result <- df %>% group_by(A, B) %>% filter(value == max(value)) %>% arrange(A,B,C) Seems to work: identical( as.data.frame(result), ddply(df, .(A, B), function(x) x[which.max(x$value),]) ) #[1] TRUE As pointed out in the comments, slice may be preferred here as per @RoyalITS’ answer below if you strictly only want 1 row per group. This answer will … Read more
The problem here is that you are loading dplyr first and then plyr, so plyr’s function summarise is masking dplyr’s function summarise. When that happens you get this warning: library(plyr) Loading required package: plyr —————————————————————————————— You have loaded plyr after dplyr – this is likely to cause problems. If you need functions from both plyr … 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