similar to: Error when using median as aggregation function in dcast

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

Dear R communitiy, I am trying to use multiple functions for aggregation within a function call for dcast. However this seems to result in an error. Also I have not managed to make dcast() work with fun.aggregate=sd. Please find attached some example code using the ChickWeight data. Many thanks for your help! Jokel #Chick weight example names(ChickWeight) <- tolower(names(ChickWeight))

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

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,

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 =

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

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

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 would like to split dataframe based on one colum and want to connect the two dataframes by rows (like rbind). Here a small example: # The orgininal dataframe df1 <- data.frame(col1 = c("A","A","B","B"),col2 = c(1:4), col3 = c(1:4)) # The datafame how it could look like df2 <- data.frame(A.col2 = c(1,2), A.col3 = c(1,2), B.col2 = c(3,4), B.col3

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",

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

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

Hi Gregg. Just wanted to follow up on the solution you proposed. I had to make some adjustments to get exactly what I wanted, but it works, and takes about 15 minutes on our server configuration: temp <- ??????open_dataset( ????????????sources = input_files, ????????????format = 'csv', ????????????unify_schema = TRUE, ????????????col_types = schema( ????????????"ID_Key"

I seem to be doing something really stupid or missing something really obvious but what? I have a simple three column data.frame that I would like to reshape to wide preferably using reshape2. An example from http://stackoverflow.com/questions/9617348/reshape-three-column-data-frame-to-matrix looked perfect except I wanted a data frame but it seemed okay. I just changed acast to dcast and it

I've successfully reformatted data frames from long to wide with reshape2, but this time I'm getting errors that I want to understand and resolve. Here's the data frame structure and the results of the melt() and dcast() functions: str(waterchem) 'data.frame': 128412 obs. of 8 variables: $ site : Factor w/ 64 levels "D-1","D-2","D-3",..: 1 1

Hello Thomas, Consider that the primary bottleneck may be tied to memory usage and the complexity of pivoting extremely large datasets into wide formats with tens of thousands of unique values per column. Extremely large expansions of columns inherently stress both memory and CPU, and splitting into 110k separate data frames before pivoting and combining them again is likely causing resource

About to try this implementation. As a follow-up, this is the exact error: Lost warning messages Error: no more error handlers available (recursive errors?); invoking 'abort' restart Execution halted Error: cons memory exhausted (limit reached?) Error: cons memory exhausted (limit reached?) Error: cons memory exhausted (limit reached?) Error: cons memory exhausted (limit reached?)

Muchas gracias, pensaba hacerlo en una sola línea pero no anda. Me quedo con dcast así no tengo que cargar más paquetes. Un abrazo! Fernando Macedo El 03/09/16 a las 14:26, Carlos J. Gil Bellosta escribió: > reshape2 + dcast > > El día 3 de septiembre de 2016, 19:23, Fernando Macedo > <fermace en gmail.com> escribió: >> Buenas, estoy intentando hacer una especie de pivot

Hi, I have a few methodological and implementation questions for ya'll. Thank you in advance for your help. I have a dataset that reflects people's preference choices. I want to see if there's any kind of clustering effect among certain preference choices (e.g. do people who pick choice A also pick choice D). I have a data set that has one record per user ID, per preference choice.