plyr

I have a data set containing product prototype test data. Not all tests were run on all lots, and not all tests were executed with the same sample sizes. To illustrate, consider this case: > test <- data.frame(name = rep(c("A", "B", "C"), each = 4), var1 = rep(c(1:3, NA), 3), var2 = 1:12, var3 = c(rep(NA, 4), 1:8)) > test name var1 var2 var3 1 A 1 1 NA 2 A 2 2 NA 3 A 3 3 NA 4 A NA 4 NA 5 B 1 5 1 6 B 2 6 2 7 B 3 7 3 8 B NA 8 4 9 C 1 9 5 10 C 2 10 6 11 C 3 11 7 12 C NA 12 8 In the past, I've only had to deal with cases of mis-matched repetitions, which has been easy with aggregate(cbind(var1,

I am trying to convert the following format: mydata <- data.frame(movie = c("Titanic", "Departed"), actor1 = c("Leo", "Jack"), actor2 = c("Kate", "Leo"))) movie actor1 actor2 1 Titanic Leo Kate 2 Departed Jack Leo to binary response variables: movie Leo Kate Jack 1 Titanic 1 1 0 2 Departed 1 0 1 I tried the solution described in Convert row data to binary columns but I could get it to work for two variables, not three. I would really appreciate if there is a clean way to do this. How much spice is too much? Here is a solution via tidyr : library(dplyr) library(tidyr) mydata %>% gather(actor

I have a dataframe with one factor column with two levels, and many numeric columns. I want to split the dataframe by the factor column and do t-test on the colunm pairs. Using the example dataset Puromycin I want the result to look something like this: Variable Treated Untreated p-value Test-statistic CI of difference**** Conc 0.3450 0.2763 XXX T XX - XX Rate 141.58 110.7272 xxx T XX - XX I think I am looking for a solution using PLYR that can an output the above results in a nice dataframe. (The Puromycin only contains two numeric variables, but the solution I am looking for would work on a

Hi guys how can I use ddply function for linear model: x1 <- c(1:10, 1:10) x2 <- c(1:5, 1:5, 1:5, 1:5) x3 <- c(rep(1,5), rep(2,5), rep(1,5), rep(2,5)) set.seed(123) y <- rnorm(20, 10, 3) mydf <- data.frame(x1, x2, x3, y) require(plyr) ddply(mydf, mydf$x3, .fun = lm(mydf$y ~ mydf$X1 + mydf$x2)) Generates this error: Error in model.frame.default(formula = mydf$y ~ mydf$X1 + mydf$x2, drop.unused.levels = TRUE) : invalid type (NULL) for variable 'mydf$X1' Appreciate your help. Here is what you need to do. mods = dlply(mydf, .(x3), lm, formula = y ~ x1 + x2) mods is a list of two objects containing

I like the plyr syntax. Any time I have to use one of the *apply() commands I end up kicking the dog and going on a 3 day bender. So for the sake of my dog and my liver, what's concise syntax for doing a ddply operation on every row of a data frame? Here's an example that works well for a simple case: x <- rnorm(10) y <- rnorm(10) df <- data.frame(x,y) ddply(df,names(df) ,function(df) max(df$x,df$y)) that works fine and gives me what I want. But if things get more complex this causes plyr to get funky (and not like Bootsy Collins) because plyr is chewing on making "levels" out of all those