Here is a small example of how it could be done using for loops with the mtcars dataset for(g in unique(mtcars$gear)) { f <- filter(mtcars, gear == g) p <- ggplot(f, aes(disp, hp)) + geom_point() ggsave(paste0(‘plot_’, g,’.jpg’), p) } In your case it would something like this for(s in unique(WQ_byStation$StationName)){ f <- filter(WQ_byStation, StationName == … Read more
I would suggest you next approach. If you want error bars you can compute it based on mean and standard deviation. In the next code is sketched the way to do that. I have used one standard deviation but you can set any other value. Also as you want to see different samples, I have … Read more
You can do it all in one step and get proper labeling: > aggregate(. ~ id1+id2, data = x, FUN = function(x) c(mn = mean(x), n = length(x) ) ) # id1 id2 val1.mn val1.n val2.mn val2.n # 1 a x 1.5 2.0 6.5 2.0 # 2 b x 2.0 2.0 8.0 2.0 # 3 … Read more
The general (non ggplot-specific) answer is to use reorder() to reset the factor levels in a categorical column, based on some function of the other columns. ## Examine the default factor order levels(samp.data$fullname) ## Reorder fullname based on the the sum of the other columns samp.data$fullname <- reorder(samp.data$fullname, rowSums(samp.data[-1])) ## Examine the new factor order … Read more
dplyr will have a specialized group_by function group_by_at to deal with multiple grouping variables. It would be much easier to use the new member of the _at family: # using the pre-release 0.6.0 cols <- c(“am”,”gear”) mtcars %>% group_by_at(.vars = cols) %>% summarise(mean_cyl=mean(cyl)) # Source: local data frame [4 x 3] # Groups: am [?] … Read more
A simple example of what I was talking about: m <- matrix(c(1,2,3,4,5,6,7,7,7),nrow = 3,ncol = 3,byrow = TRUE) layout(mat = m,heights = c(0.4,0.4,0.2)) for (i in 1:6){ par(mar = c(2,2,1,1)) plot(runif(5),runif(5),xlab = “”,ylab = “”) } plot(1, type = “n”, axes=FALSE, xlab=””, ylab=””) plot_colors <- c(“blue”,”black”, “green”, “orange”, “pink”) legend(x = “top”,inset = 0, legend … Read more
You can pass in a custom tokenizing function to tm‘s DocumentTermMatrix function, so if you have package tau installed it’s fairly straightforward. library(tm); library(tau); tokenize_ngrams <- function(x, n=3) return(rownames(as.data.frame(unclass(textcnt(x,method=”string”,n=n))))) texts <- c(“This is the first document.”, “This is the second file.”, “This is the third text.”) corpus <- Corpus(VectorSource(texts)) matrix <- DocumentTermMatrix(corpus,control=list(tokenize=tokenize_ngrams)) Where n in … Read more
So another answer, outside a loop : use a child process. library(shiny) library(parallel) # # reactive variables # rVal <- reactiveValues() rVal$process <- NULL rVal$msg <- NULL rVal$obs <- NULL counter <- 0 results <- list() dfEmpty <- data.frame(results = numeric(0)) # # Long computation # analyze <- function() { out <- lapply(1:5, function(x) { … Read more
Is this anything like what you’re after? With stat_summary, as requested: # function for number of observations give.n <- function(x){ return(c(y = median(x)*1.05, label = length(x))) # experiment with the multiplier to find the perfect position } # function for mean labels mean.n <- function(x){ return(c(y = median(x)*0.97, label = round(mean(x),2))) # experiment with the … Read more
A <- matrix(1:16, 4) # create an indicator for all diagonals in the matrix d <- row(A) – col(A) # use split to group on these values split(A, d) # # $`-3` # [1] 13 # # $`-2` # [1] 9 14 # # $`-1` # [1] 5 10 15 # # $`0` # [1] … Read more