magrittr

I would like to be able to print the name of a dataframe passed through the pipe. Is this possible? I can do. printname <- function(df){ print(paste(substitute(df))) } printname(mtcars) #[1] "mtcars" However, it returns "." when this function is piped using the magrittr pipe. mtcars %>% printname # [1] "." This would be helpful when writing custom error messages of functions used in logged production processes -- it's hard to know where something failed if the only thing in the log is "." It would probably be enough to return the original call, which would include the mtcars %>% piece. This is

Related to this question . I'd like to build a custom pipe %W>% that would silence warnings for one operation library(magrittr) data.frame(a= c(1,-1)) %W>% mutate(a=sqrt(a)) %>% cos will be equivalent to : w <- options()$warn data.frame(a= c(1,-1)) %T>% {options(warn=-1)} %>% mutate(a=sqrt(a)) %T>% {options(warn=w)} %>% cos These two tries don't work : `%W>%` <- function(lhs,rhs){ w <- options()$warn on.exit(options(warn=w)) options(warn=-1) lhs %>% rhs } `%W>%` <- function(lhs,rhs){ lhs <- quo(lhs) rhs <- quo(rhs) w <- options()$warn on.exit(options(warn=w)) options(warn=-1) (!!lhs) %>% (!

Github developmental version of magrittr includes some cool new function for piping but I do not exactly catch de difference between %>% and %,% . Is this only formal with %>% for value and %,% for functions, or there is some specific peculiarity? The normal piping operator is %>% . You can use %,% to create a reusable pipe, a pipe without data. Then later you can use the same pipe with various data sets. Here is an example. library(magrittr) library(dplyr) library(Lahman) Suppose you want to calculate the top 5 baseball players, according to total hits. Then you can do something like this

Since the introduction of the %>% operator in the magrittr package (and it's use in dplyr ), I have started to use this in my own work. One simple operation has me stumped, however. Specifically, this is the extraction (or subsetting) of elements from a list. An example: In base R I would use $ , [ or [[ to extract an element from a list: iris$Species iris[["Species"]] I can achieve the same using the %>% pipe: iris %>% subset(select = "Species") %>% head Species 1 setosa 2 setosa 3 setosa 4 setosa 5 setosa 6 setosa Or iris %>% `[[`("Species") %>% levels [1] "setosa" "versicolor" "virginica"

The dplyr package introduced the %.% operator to pass the left hand side as an argument of the function on the right hand side, similar to a *NIX pipe. The magrittr package is a much more lightweight package that exists to define only that pipe-like operator. Yet one uses %.% and the other %>% . Is there any difference between the two? Can I just use %>% even in dplyr code, or will that cause subtle bugs? On inspecting the code, they take very different paths early on, so simple eyeball comparison would suggest that they're different. I haven't been able to find anything documented when I

I'm getting a dplyr::bind_rows error. It's a very trivial problem, because I can easily get around it, but I'd like to understand the meaning of the error message. I have the following data of some population groups for New England states, and I'd like to bind on a copy of these same values with the name changed to "New England," so that I can group by name and add them up, giving me values for the individual states, plus an overall value for the region. df <- structure(list(name = c("CT", "MA", "ME", "NH", "RI", "VT"), estimate = c(501074, 1057316, 47369, 76630, 141206, 27464)), class = c(

I want to set the string N=xxx as the title of my figure, where xxx is the number of observations in the data frame that I pass as the data argument to ggplot() . In my current code, I explicitly pass that data frame a second time as an argument to sprintf() which I use inside of labs() : ggplot(mtcars, aes(mpg, hp)) + labs(title=sprintf("N=%i", nrow(mtcars))) + geom_point() This does produce the desired title, but it won't work with more complex tasks: I use a dplyr pipe to construct the data frame that is being plotted, and as this is a time-consuming process, I wouldn't want to repeat the