similar to: Using dcast with multiple functions to aggregate

according to the documentation of the cast function in the reshape function, I would expect this bit of code from the examples to calculate marginal means over only the 'diet' variable. #Chick weight example names(ChickWeight) <- tolower(names(ChickWeight)) chick_m <- melt(ChickWeight, id=2:4, na.rm=TRUE) cast(chick_m, diet + chick ~ time, mean, margins="diet") But,

Hi, In the examples from the ReShape package there is a simple example of using melt followed by cast that produces a smallish amount of output about the chicks database. Here's the code: library(reshape) names(ChickWeight) <- tolower(names(ChickWeight)) chick_m <- melt(ChickWeight, id=2:4, na.rm=TRUE) DietResults <- cast(chick_m, diet + chick ~ time) DietResults My challenge

Working with 5 subset streams from my source data frame, three of them successfully call dcast(), but two fail: jerritt.cast <- dcast(jerritt.melt, site + sampdate ~ param) Aggregation function missing: defaulting to length and winters.cast <- dcast(winters.melt, site + sampdate ~ param) Aggregation function missing: defaulting to length Yet both data frames have the values in their

I'm in the middle of my own little intellectual exercise comparing dcast() and reshape() and have successfully stumped myself. I want to melt() a data frame, then dcast() it into a new form. After doing so, I want to duplicate the process using reshape(). So far, I can do the melt and cast require(reshape2) Raw <- data.frame(site = c(1, 1, 1, 1, 2, 2, 2, 2), id =

Hello--I'm doing a simple crosstab using dcast: rawfreq <- dcast(nh11brfs, race3~CHCCOPD, length) with the results race3 Yes No NA 1 White non-Hispanic 446 5473 21 2 Other non-Hispanic 29 211 0 3 Hispanic 6 81 1 4 <NA> 10 83 1 How would I modify this call to exclude all missing values; that is, to obtain race3

I need to understand how and why dcast() adds NAs to a data frame that contained no missing values. The database table of chemical concentrations has all missing values removed because they cannot contribute to data analyses. The structure of the R data frame of these data have no NA values, and neither does the data frame resulting from applying the reshape2 melt() function to it. However,

Hi, I am trying to calculated various summary statistics using the dcast function of reshape2. This works perfectly for getting the mean, sum, length, sd. But when I want to calculate the median I get an error. I tried it with and without removing NAs: my_median <- function(x) median(x, na.rm = FALSE) median_df <- dcast(patch_stats_dfm,formula=species~input+barriers,my_median) Error in

Estimado Oliver Nuñez Envío un ejemplo reproducible. Javier Marcuzzi # de donde tomo datos, y tiene el modelo (en el pdf) library(MCMCglmm) # librería con las funciónes que voy a usar library(nlme) datos0<-ChickWeight # creo algunos datos que agrego a los origonales Factor<-as.numeric(datos0$Chick) Factor[Factor > 0 & Factor <= 10] <- 'A' Factor[Factor > 10

Hola a todos, Queria preguntaros si conoceis alguna manera para invertir la funcion dcast. Quiero transformar una matriz en un data frame de tres columnas que indiquen solo los casos donde la combinacion fila-columna sea diferente de NA. Se me habia ocurrido hacer un bucle que fuera seleccionando todos los valores para cada combinacion de fila y columna, pero el problema es que con una matriz de

An excerpt from dataset ChickWeight: ??? weight Time Chick Diet 1?????? 42??? 0???? 1??? 1 2?????? 51??? 2???? 1??? 1 3?????? 59??? 4???? 1??? 1 I am interested in the residuals of the dataset.? Specifically in saving them to another format. I have been creating text files with sink. CW.lm <- lm(weight ~ Diet, ChickWeight) resid.CW.lm <- resid(CW.lm) But when I call: resid.CW.lm The data

Suppose I have a small dataframe > aa Target Eaten ID 50 TPP 0 1 51 TPP 1 2 52 TPP 3 3 53 TPP 1 4 54 TPP 2 5 50.1 GPA 9 1 51.1 GPA 11 2 52.1 GPA 8 3 53.1 GPA 8 4 54.1 GPA 10 5 And I want to reshape it into ID TPP GPA 1 1 0 9 2 2 1 11 3 3 3 8 4 4 1 8 5 5 2 10 I realise that

I've noticed that R has a number of very useful functions for obtaining descriptive statistics on groups of variables, including summary {stats}, describe {Hmisc}, and describe {psych}, but none that I have found is able to provided weighted descriptives of subsets of a data set (ex. descriptives for both males and females for age, where accurate results require use of sampling

I have data in the following format:   person<- c(1,1,1,1,2,2,2,2,2,3,3,3,3,3,3) v2<- c("2011-01-01", "2011-02-01", "2011-03-01", "2011-04-01", "2011-01-01", "2011-02-01", "2011-03-01", "2011-04-01", "2011-05-01", "2011-01-01", "2011-02-01", "2011-03-01",

Con data.table todo puede ir muy rapido. > require(data.table) > M=matrix(c(5,NA,NA,NA,6,NA,7,NA,8),3,3) > M [,1] [,2] [,3] [1,] 5 NA 7 [2,] NA 6 NA [3,] NA NA 8 > M2=data.table(M) > M2 V1 V2 V3 1: 5 NA 7 2: NA 6 NA 3: NA NA 8 > M3=melt(M2,variable.name = "columna") > M3 columna value 1: V1 5 2: V1 NA 3: V1

Hi, I'd like to get mean values for the margins of my casted data.frame. For the casting I am using dcast() from reshape2. However, when I set the margins parameter (margins=c("grand\_row")) I get following error concerning an unrecognized escape character '\_'. So what is the correct command to get the outermost margins only in reshape2? /johannes [[alternative HTML

Hello, I implemented two functions reshape_long and reshape_wide (see full working example below) to reshape data frames. I created several small examples and the two functions seemed to work properly. However, using the reshape_wide function on my real data sets (about 200.000 to 300.000 rows) failed. What happens is set all values for X, Y and Z were set to 1. The structure of my real data

I have a simple dataset of a numerical dependent Y, a numerical independent X and a categorial variable Z with three levels. I want to do linear regression Y~X for each level of Z. How can I do this in a single command that is without using lm() applied three isolated times? -- View this message in context: http://r.789695.n4.nabble.com/Linear-regression-on-several-groups-tp2322835p2322835.html

Hi Thomas, Glad to hear the suggestion helped, and that switching to a `data.table` approach reduced the processing time and memory overhead?15 minutes for one of the smaller datasets is certainly better! Sounds like the adjustments you devised, especially keeping the multicore approach for `make_clean_names()` and ensuring that `ID_Key` values remain intact, were the missing components you

Dear R-experts! I am working on some meta-analysis using the metafor package. I would like to extract values of the confidence intervals of the effect sizes of the single studies from an rma object. Those values are printed out when plotting a forest plot using the forest function on the rma object, however I was not able to locate them. Many thanks for your help! Jokel [[alternative HTML

Dear R-experts! I am currently running a meta-analysis with the help of the great metafor package. However I have some difficulties setting up my raw data to enter it to the meta-analysis models. I have a group of subjects that have been measured in two continuous variables (A & B). I have the information about the mean of the two variables, the group size (n) and the standard deviations of