plyr

I am trying to write some code which identifies the greatest two values for each row and provides their column number and value. df = data.frame( car = c (2,1,1,1,0), bus = c (0,2,0,1,0), walk = c (0,3,2,0,0), bike = c(0,4,0,0,1)) I've managed to get it to do this for the maximum value using the max and max.col functions. df$max = max.col(df,ties.method="first") df$val = apply(df[ ,1:4], 1, max) As far as I know there are no equivalent functions for the second highest value so doing this has made things a little trickier. Using this code provides the second highest value but (importantly) not

My aggregation needs vary among columns / data.frames. I would like to pass the "list" argument to the data.table dynamically. As a minimal example: require(data.table) type <- c(rep("hello", 3), rep("bye", 3), rep("ok",3)) a <- (rep(1:3, 3)) b <- runif(9) c <- runif(9) df <- data.frame(cbind(type, a, b, c), stringsAsFactors=F) DT <-data.table(df) This call: DT[, list(suma = sum(as.numeric(a)), meanb = mean(as.numeric(b)), minc = min(as.numeric(c))), by= type] will have result similar to this: type suma meanb minc 1: hello 6 0.1332210 0.4265579 2: bye 6 0.5680839 0.2993667 3: ok 6 0.5694532 0

I'll apologise in advance - I know this has likely been answered elsewhere, but I don't seem to be able to find the answer I need, and can't manage to adapt other code I have found to my needs. I have a data frame: FILE | TECHNIQUE | COUNT ------------------------ A | ONE | 10 A | TWO | 25 B | ONE | 5 B | TWO | 30 C | ONE | 30 C | TWO | 50 I would like to produce a data frame of the difference of the COUNT values between ONE and TWO, with a row for each FILE, i.e. FILE | DIFFERENCE ----------------- A | 15 B | 25 C | 20 I'm convinced I should be able to do this fairly easily with base R or

I have a large data frame in which I am multiplying two columns together to get another column. At first I was running a for-loop, like so: for(i in 1:nrow(df)){ df$new_column[i] <- df$column1[i] * df$column2[i] } but this takes like 9 days. Another alternative was plyr , and I actually might be using the variables incorrectly: new_df <- ddply(df, .(column1,column2), transform, new_column = column1 * column2) but this is taking forever As Blue Magister said in comments, df$new_column <- df$column1 * df$column2 should work just fine. Of course we can never know for sure if we don't have an

Given a list: alist = list( list(name="Foo",age=22), list(name="Bar"), list(name="Baz",age=NULL) ) what's the best way to convert this into a dataframe with name and age columns, with missing values (I'll accept NA or "" in that order of preference)? Simple methods using ldply fail because it tries to convert each list element into a data frame, but the one with the NULL barfs because the lengths don't match. Best I have at the moment is: > ldply(alist,function(s){t(data.frame(unlist(s)))}) name age 1 Foo 22 2 Bar <NA> 3 Baz <NA> but that's pretty icky and the numeric variable becomes a factor

How can I add text to points rendered with geom_jittered to label them? geom_text will not work because I don't know the coordinates of the jittered dots. Could you capture the position of the jittered points so I can pass to geom_text? My practical usage would be to plot a boxplot with the geom_jitter over it to show the data distribution and I would like to label the outliers dots or the ones that match certain condition (for example the lower 10% for the values used for color the plots). One solution would be to capture the xy positions of the jittered plots and use it later in another