tapply

I have been provided with some customer data in Latitude, Longitude, and Counts format. All the data I need to create a ggplot heatmap is present, but I do not know how to put it into the format ggplot requires. I am trying to aggregate the data by total counts within 0.01 Lat and 0.01 Lon blocks (typical heatmap), and I instinctively thought "tapply". This creates a nice summary by block size, as desired, but the format is wrong. Furthermore, I would really like to have empty Lat or Lon block values be included as zeroes, even if there is nothing there... otherwise the heatmap ends up looking

I can't wrap my mind around the ave function. I read the help and searched the net but I still cannot understand what it does. I understand it applies some function on a subset of observation but not in the same way as for example tapply Could someone please enlighten me perhaps with a small example? Thanks, and excuse me for perhaps an unusual request. tapply returns a single result for each factor level. ave also produces a single result per factor level, but it copies this value to each position in the original data. ave is handy for producing a new column in a data frame with summary data.

I have the following data: a <- c(1,1,1,1,2,2,2,2) b <- c(2,4,6,8,2,3,4,1) c <- factor(c("A","B","A","B","A","B","A","B")) df <- data.frame( sp=a, length=b, method=c) I can use the following to get a count of the number of samples of each species by method: n <- with(df,tapply(sp,method,function(x) count(x))) How do I also get the mean length by method for each species? Personally I would use aggregate : aggregate(length ~ sp, data = df, FUN= "mean" ) # by species only # sp length #1 1 5.0 #2 2 2.5 aggregate(length ~ sp + method, data = df, FUN= "mean" ) # by species and method # sp method

I'm trying to multiply column and get its names. I have a data frame: v1 v2 v3 v4 v5 0 1 1 1 1 0 1 1 0 1 1 0 1 1 0 I'm trying to multiplying each column with other, like: v1v2 v1v3 v1v4 v1v5 and v2v3 v2v4 v2v5 etc, and v1v2v3 v1v2v4 v1v2v5 v2v3v4 v2v3v5 4 combination and 5 combination...if there is n column then n combination. I'm try to use following code in while loop, but it is not working: i<-1 while(i<=ncol(data) { results<-data.frame() v<-i results<- t(apply(data,1,function(x) combn(x,v,prod))) comb <- combn(colnames(data),v) colnames(results) <- apply(comb,v,function(x) paste(x[1],x[2]

I am trying to make a data frame with the maximum over records by a factor. I would like a data frame with 4 rows (one for each G) with the max for X in that group and the corresponding Y value. I know I could write a loop but would rather not. Data<-data.frame(X=rnorm(200), Y=rnorm(200), G=rep(c(1,2,3,4), each=50)) XMax<-tapply(Data$X, Data$G, function(x){max(x, na.rm=T)}) WhichXMax<-tapply(Data$X, Data$G, function(x){which.max(x)}) The which.max function returns the row number after the data has been subsetted by the tapply factor, where I really want the row number referencing the Data rows

My question is: I have a data frame with some factor variables. I now want to assign a new vector to this data frame, which creates an index for each subset of those factor variables. data <-data.frame(fac1=factor(rep(1:2,5)), fac2=sample(letters[1:3],10,rep=T)) Gives me something like: fac1 fac2 1 1 a 2 2 c 3 1 b 4 2 a 5 1 c 6 2 b 7 1 a 8 2 a 9 1 b 10 2 c And what I want is a combination counter which counts the occurrence of each factor combination. Like this fac1 fac2 counter 1 1 a 1 2 2 c 1 3 1 b 1 4 2 a 1 5 1 c 1 6 2 b 1 7 1 a 2 8 2 a 2 9 1 b 2 10 1 a 3 So far I thought about using tapply

I have two data frames, df1 with reference data and df2 with new data. For each row in df2 , I need to find the best (and the second best) matching row to df1 in terms of hamming distance. I used e1071 package to compute hamming distance. Hamming distance between two vectors x and y can be computed as for example: x <- c(356739, 324074, 904133, 1025460, 433677, 110525, 576942, 526518, 299386, 92497, 977385, 27563, 429551, 307757, 267970, 181157, 3796, 679012, 711274, 24197, 610187, 402471, 157122, 866381, 582868, 878) y <- c(356739, 324042, 904133, 959893, 433677, 110269, 576942, 2230, 267130,