plyr

I have this dataframe: set.seed(50) data <- data.frame(age=c(rep("juv", 10), rep("ad", 10)), sex=c(rep("m", 10), rep("f", 10)), size=c(rep("large", 10), rep("small", 10)), length=rnorm(20), width=rnorm(20), height=rnorm(20)) data$length[sample(1:20, size=8, replace=F)] <- NA data$width[sample(1:20, size=8, replace=F)] <- NA data$height[sample(1:20, size=8, replace=F)] <- NA age sex size length width height 1 juv m large NA -0.34992735 0.10955641 2 juv m large -0.84160374 NA -0.41341885 3 juv m large 0.03299794 -1.58987765 NA 4 juv m large NA NA NA 5 juv m large -1.72760411 NA 0.09534935 6 juv

Here is a simple data frame for my real data set: df <- data.frame(ID=rep(101:102,each=9),phase=rep(1:3,6),variable=rep(LETTERS[1:3],each=3,times=2),mm1=c(1:18),mm2=c(19:36),mm3=c(37:54)) I would like to first group by ID and variable, then for values(mm1, mm2, mm3), phase 3 is subtracted from all phases(phase1 to phase3), which would make mm(1-3) in phase 1 all -2, in phase 2 all -1, and phase 3 all 0. R throws an error of "Error in Ops.data.frame(x, x[3, ]) : - only defined for equally-sized data frames" as I tried: df1 <- ddply(df, .(ID, variable), function(x) (x - x[3,])) Any advice would

My data contain time variable and chosen brand variable as below. time indicates the shopping time and chosenbrand indicates the purchased brand at the time. With this data, I would like to create rank variable as shown third column, fourth column, and so on. The rank of brands (e.g., brand1 - brand3) should be based on past 36 hours. So, to calculate the rank for the second row, which has shoptime as "2013-09-01 08:54:00 UTC" the rank should be based on all chosenbrand values within 36 hours before the time. ( brand1 in second row should not be in the 36 hours) Therefore, rank_brand1, rank

I have a data frame on which I calculate a run length encoding for a specific column. The values of the column, dir , are either -1, 0, or 1. dir.rle <- rle(df$dir) I then take the run lengths and compute segmented cumulative sums across another column in the data frame. I'm using a for loop, but I feel like there should be a way to do this more intelligently. ndx <- 1 for(i in 1:length(dir.rle$lengths)) { l <- dir.rle$lengths[i] - 1 s <- ndx e <- ndx+l tmp[s:e,]$cumval <- cumsum(df[s:e,]$val) ndx <- e + 1 } The run lengths of dir define the start, s , and end, e , for each run. The above code

I have a data frame (datadf) with 3 columns, 'x', 'y, and z. Several 'x' values are missing ( NA ). 'y' and 'z' are non measured variables. x y z 153 a 1 163 b 1 NA d 1 123 a 2 145 e 2 NA c 2 NA b 1 199 a 2 I have another data frame (imputeddf) with the same three columns: x y z 123 a 1 145 a 2 124 b 1 168 b 2 123 c 1 176 c 2 184 d 1 101 d 2 I wish to replace NA in 'x' in 'datadf' with values from 'imputeddf' where 'y' and 'z' matches between the two data sets (each combo of 'y' and 'z' has its own value of 'x' to fill in). The desired result: x y z 153 a 1 163 b 1 184 d 1 123 a 2 145 e 2 176

This question already has an answer here: How should I deal with “package 'xxx' is not available (for R version x.y.z)” warning? 14 answers I tried installing the plyr package and I got the warning message saying it isn't available for R version 3.0.2. Is this true or is no? If not, why would I be getting this message? I tried using two different CRAN mirrors and both gave the same message. The answer is that the package is available in R (just checked this on my machine). The particular error message that you are getting is very misleading. It is R's `catch-all' condition for anything that it

I have the following data.frames: a <- data.frame(id = 1:3, v1 = c('a', NA, NA), v2 = c(NA, 'b', 'c')) b <- data.frame(id = 1:3, v1 = c(NA, 'B', 'C'), v2 = c("A", NA, NA)) > a id v1 v2 1 1 a <NA> 2 2 <NA> b 3 3 <NA> c > b id v1 v2 1 1 <NA> A 2 2 B <NA> 3 3 C <NA> note: There are no ids for which v1 or v2 are defined in both tables; there is only a single unique non-NA value in each column for each id value I would like to merge these data frames on matching values of "id': ab <- merge(a, b, by = "id") but I would also like to combine the two columns v1 and v2 , so that the data.frame ab will

I often have to make stacked barplots to compare variables, and because I do all my stats in R, I prefer to do all my graphics in R with ggplot2. I would like to learn how to do two things: First, I would like to be able to add proper percentage tick marks for each variable rather than tick marks by count. Counts would be confusing, which is why I take out the axis labels completely. Second, there must be a simpler way to reorganize my data to make this happen. It seems like the sort of thing I should be able to do natively in ggplot2 with plyR, but the documentation for plyR is not very clear

I am new to plyr (and R) and looking for a little help to get started. Using the baseball dataset as an exaple, how could I calculate the year-over-year (yoy) change in "at batts" by league and team (lg and team)? library(plyr) df1 <- aggregate(ab~year+lg+team, FUN=sum, data=baseball) After doing a little aggregating to simplify the data fame, the data looks like this: head(df1) year lg team ab 1884 UA ALT 108 1997 AL ANA 1703 1998 AL ANA 1502 1999 AL ANA 660 2000 AL ANA 85 2001 AL ANA 219 I would like to end up with someting like this year lg team ab yoy 1997 AL ANA 1703 NA 1998 AL ANA 1502