plyr

I have a data set with several grouping variables on which I want to run a rolling window linear regression. The ultimate goals is to extract the 10 linear regressions with the lowest slopes and average them together to provide a mean minimum rate of change. I have found examples using rollapply to calculate rolling window linear regressions, but I have the added complication that I would like to apply these linear regressions to groups within the data set. Here is a sample data set and my current code which is close and isn't quite working. dat<-data.frame(w=c(rep(1,27), rep(2,27),rep(3,27)),

I've got this csv table for which I need to rescale data between 0 and 1 per each column. That is, the lowest value of any given column will be 0, the highest will be 1, and all other values will be linearly scaled accordingly. Here's my script: tableau <- read.csv("/tableau.csv") tableau.m <- melt(tableau) tableau.m <- ddply(tableau.m, .(variable), transform,rescale = rescale(value)) (And here's the data: https://dl.dropboxusercontent.com/u/73950/tableau.csv ) The issue is that I need the second column ("B") to be inverted. That is, for this column only and not for the others, the lowest

I have a data frame that contains dates and id's. I need to add multiple columns to this data frame based on each date. I use ddply to do this as follows: ddply(df, "dt", transform, new_column1 = myfun(column_name_1)) However,I have a bunch of column names and would like to add multiple new columns. Is there a way that I can pass a string to transform instead of new_column1? For example I tried: ddply(df, "dt", transform, get("some_column_name")=myfun(column_name_1)) but this does not work. Additionally, if I pass the column_name_1 to myfun as a string, can I just use get("column_name_1")

I have a dataset with a lot of entries. Each of these entries belongs to a certain ID (belongID), the entries are unique (with uniqID), but multiple entries can come from the same source (sourceID). It is also possible that multiple entries from the same source have a the same belongID. For the purposes of the research I need to do on the dataset I have to get rid of the entries of a single sourceID that occur more than 5 times for 1 belongID. The maximum of 5 entries that need to be kept are the ones with the highest 'Time' value. To illustrate this I have the following example dataset:

I got following data frame,df, (fragment displayed here): H2475 H2481 H2669 H2843 H2872 H2873 H2881 H2909 E1 94.470 26.481 15.120 18.490 16.189 11.422 14.886 0.512 E2 1.016 0.363 0.509 1.190 1.855 0.958 0.771 0.815 E3 9.671 0.637 0.571 0.447 0.116 0.452 0.403 0.003 E4 3.448 2.826 2.183 2.607 4.288 2.526 2.820 3.523 E5 2.548 1.916 1.126 1.553 1.089 1.228 0.887 1.065 what I want to do is to compute mean values of each row after removing two extreme values. For whole rows I used plyr: library(plyr) df.my_means <- adply(df, 1, transform, my_means = mean(as.matrix(df[i,]) ) ) It should be also OK

I would like to know if there is a simple way to achieve what I describe below using ddply . My data frame describes an experiment with two conditions. Participants had to select between options A and B , and we recorded how long they took to decide, and whether their responses were accurate or not. I use ddply to create averages by condition. The column nAccurate summarizes the number of accurate responses in each condition. I also want to know how much time they took to decide and express it in the column RT . However, I want to calculate average response times only when participants got the