plyr

问题 I'm a power excel pivot table user who is forcing himself to learn R. I know exactly how to do this analysis in excel, but can't figure out the right way to code this in R. I'm trying to group user data by 2 different variables, while grouping the variables into ranges (or bins), then summarizing other variables. Here is what the data looks like: userid visits posts revenue 1 25 0 25 2 2 2 0 3 86 7 8 4 128 24 94 5 30 5 18 … … … … 280000 80 10 100 280001 42 4 25 280002 31 8 17 Here is what I

问题 I am trying to compute rolling means of an unbalanced data set. To illustrate my point I have produced this toy example of my data: ID year Var RollingAvg(Var) 1 2000 2 NA 1 2001 3 2 1 2002 4 2.5 1 2003 2 3 2 2001 2 NA 2 2002 5 2 2 2003 4 3.5 The column RollingAvg(Var) is what I want, but can't get. In words, I am looking for the rolling average of ALL the previous observations of Var for each ID . I have tried using rollapply and ddply in the zoo and the plyr package, but I can't see how to

问题 I have the following, somewhat large dataset: > dim(dset) [1] 422105 25 > class(dset) [1] "data.frame" > Without doing anything, the R process seems to take about 1GB of RAM. I am trying to run the following code: dset <- ddply(dset, .(tic), transform, date.min <- min(date), date.max <- max(date), daterange <- max(date) - min(date), .parallel = TRUE) Running that code, RAM usage skyrockets. It completely saturated 60GB's of RAM, running on a 32 core machine. What am I doing wrong? 回答1: If

问题 I like to write a function using ddply that outputs the summary statistics based on the name of two columns of data.frame mat . mat is a big data.frame with the name of columns "metric", "length", "species", "tree", ...,"index" index is factor with 2 levels "Short", "Long" "metric", "length", "species", "tree" and others are all continuous variables Function: summary1 <- function(arg1,arg2) { ... ss <- ddply(mat, .(index), function(X) data.frame( arg1 = as.list(summary(X$arg1)), arg2 = as

问题 I am a big fan of the data.table package and I am having trouble converting some code in ddply of the plyr package into the equivalent in a data.table. The code for ddply is: dfx <- data.frame( group = c(rep('A', 8), rep('B', 15), rep('C', 6)), sex = sample(c("M", "F"), size = 29, replace = TRUE), age = runif(n = 29, min = 18, max = 54), age2 = runif(n = 29, min = 18, max = 54) ) ddply(dfx, .(group, sex), numcolwise(sum)) What I want to do is sum across multiple columns without having to

问题 I need to use group by in levels with ddply or aggregate if that's easier. I am not really sure how to do this as I need to use cumsum as my aggregate function. This is what my data looks like: level1 level2 hour product A tea 0 7 A tea 1 2 A tea 2 9 A coffee 17 7 A coffee 18 2 A coffee 20 4 B coffee 0 2 B coffee 1 3 B coffee 2 4 B tea 21 3 B tea 22 1 expected output: A tea 0 7 A tea 1 9 A tea 2 18 A coffee 17 7 A coffee 18 9 A coffee 20 13 B coffee 0 2 B coffee 1 5 B coffee 2 9 B tea 21 3 B

问题 I regularly used d_ply to produce exploratory plots. A trivial example: require(plyr) plot_species <- function(species_data){ p <- qplot(data=species_data, x=Sepal.Length, y=Sepal.Width) print(p) } d_ply(.data=iris, .variables="Species", function(x)plot_species(x)) Which produces three separate plots, one for each species. I would like to reproduce this behaviour using functions in dplyr. This seems to require the reassembly of the data.frame within the function called by summarise, which is

问题 I have a list in which each list item is a word frequency table derived from using "table()" on a different sample text. Each table is, therefore, a different length. I want to now convert the list into a single data frame in which each column is a word each row is a sample text. Here is a dummy example of my data: t1<-table(strsplit(tolower("this is a test in the event of a real word file you would see many more words here"), "\\W")) t2<-table(strsplit(tolower("Four score and seven years ago

问题 My data is structured as follows: Individ <- data.frame(Participant = c("Bill", "Bill", "Bill", "Bill", "Bill", "Bill", "Bill", "Bill", "Bill", "Bill", "Bill", "Bill", "Harry", "Harry", "Harry", "Harry","Harry", "Harry", "Harry", "Harry", "Paul", "Paul", "Paul", "Paul"), Time = c(1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4), Condition = c("Placebo", "Placebo", "Placebo", "Placebo", "Expr", "Expr", "Expr", "Expr", "Expr", "Expr", "Expr", "Expr", "Placebo", "Placebo",

问题 I have a large dataframe which has observations from surveys from multiple states for several years. Here's the data structure: state | survey.year | time1 | obs1 | time2 | obs2 CA | 2000 | 1 | 23 | 1.2 | 43 CA | 2001 | 2 | 43 | 1.4 | 52 CA | 2002 | 5 | 53 | 3.2 | 61 ... CA | 1998 | 3 | 12 | 2.3 | 20 CA | 1999 | 4 | 14 | 2.8 | 25 CA | 2003 | 5 | 19 | 4.3 | 29 ... ND | 2000 | 2 | 223 | 3.2 | 239 ND | 2001 | 4 | 233 | 4.2 | 321 ND | 2003 | 7 | 256 | 7.9 | 387 For each state/survey.year