In a data.frame (or data.table), I would like to "fill forward" NAs with the closest previous non-NA value. A simple example, using vectors (instead of a data.frame) 开发者_开发百科is the fo
I am trying to subsample rows of a DataFrame according to a grouping.Here is an example.Say I define the following data:
I am trying to produce stats for cumulative goals by season by a particular soccer player. I have used the cut function to obtain the season from the game dates. I have data which corresponds to this
I am trying to take a very large set of records with multiple indices, calculate an aggregate statistic on groups determined by a subset of the indices, and then insert that into every row in开发者_Py
I am trying to calculate a simple ratio using data.table. Different files have different tmax values, so that is why I need ifelse. When I debug this, the dt looks good. The tmaxValue is a single valu
I have a large data.table, with many missing values scattered throughout its ~200k rows and 200 columns.I would like to re code those NA values to zeros as efficiently as possible.
I have a data.table of a and b that I\'ve partitioned into below wit开发者_StackOverflowh b < .5 and above with b > .5:
I recently discovered the data.table package and was now wondering whether or not I should replace some of my plyr-code. To summarize, I really like plyr and I basically achieved everything I wanted.
I have a data table in R: library(data.table) set.seed(1234) DT <- data.table(x=rep(c(1,2,3),each=4), y=c(\"A\",\"B\"), v=sample(1:100,12))
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