similar to: quote()/eval() question

Dear group, Is there a way I could avoid the sort of duplication illustrated below? i.e., I have the same dplyr::summarise function on different group_by arguments. So I'd like to create a single summarise function that could be applied to both. My attempt below fails. df <- data.frame(matrix(rnorm(40), 10, 4), f1 = gl(3, 10, labels = letters[1:3]), f2 =

Hi Lars, Two comments: 1. You can achieve what you want with a slight modification of your definition of s(), using the hint from the error message that you need an argument '.': s <- function(.) { dplyr::summarise(., x1m = mean(X1), x2m = mean(X2), x3m = mean(X3), x4m = mean(X4)) } 2. You have not given a great test case in

Hi Lars I am not very sure what you really want. However, I am suggesting the following code that enables (1) to obtain the full summary of your data and (2) retrieve only mean of X values as function of factors f1 and f2. library(tidyverse) library(psych) df <- data.frame(matrix(rnorm(40), 10, 4), f1 = gl(3, 10, labels = letters[1:3]), f2 = gl(3, 10, labels

Buenas para tod en s, tengo una consulta para poder optimizar tiempos. Ejemplo tengo el siguiente dataframe: distrito<-c("A","A","A","B","B","B","C","C","C","A","A","B","B","C")

Hello, Any of the two will do, the first is now preferred. library(dplyr) mtcars %>% summarise(across(everything(), sum)) mtcars %>% summarise_all(sum) # no need for `funs()` Hope this helps, Rui Barradas ?s 18:29 de 01/10/20, Jeff Reichman escreveu: > r-help Forum > > > > I'm using the dplyr:: summarize_all(funs(sum)) function and am receiving a >

Hi, I tried this: # extract date from the time stamp dt1 <- cbind(as.Date(dt$EndDate, format="%m/%d/%Y"), dt$EnergykWh) head(dt1) colnames(dt1) <- c("date", "EnergykWh") and my dt1 becomes these, the dates are replace by numbers. dt1 <- cbind(as.Date(dt$EndDate, format="%m/%d/%Y"), dt$EnergykWh) dput(head(dt1)) colnames(dt1) <-

Dear Massimo, It seems straightforward to use weighted.mean() in a dplyr context library(dplyr) mydf %>% group_by(date_time, type) %>% summarise(vel = weighted.mean(speed, n_vehicles)) Best regards, ir. Thierry Onkelinx Statisticus / Statistician Vlaamse Overheid / Government of Flanders INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST Team

It is not generally advisable to get too fancy with stat functions in ggplot... things can easily get more complicated than ggplot is ready to handle when it comes to calculations. It is better to create data that corresponds directly to the graphical representations you are mapping them to. Read [1] for more on this philosophy. [1] H. Wickham, Tidy Data, Journal of Statistical Software,

Is this what you are after? library(tidyverse) library(lubridate) input <- structure(list(StationName = c("PALO ALTO CA / CAMBRIDGE #1", "PALO ALTO CA / CAMBRIDGE #1", "PALO ALTO CA / CAMBRIDGE #1", "PALO ALTO CA / CAMBRIDGE #1", "PALO ALTO CA / CAMBRIDGE #1", "PALO ALTO CA / CAMBRIDGE #1", "PALO ALTO CA / CAMBRIDGE

Hi All, I'm planning to submit my first package to R, and although I read all the documentation, I'm not very clear on the following 2 items, from which I'd appreciate your guidance: 1)I understand it is suggested to use the R dev version to build the package. Which one specifically should I use to build a package on a Mac OS? How about package dependencies, which version should I

?s 01:49 de 03/11/2023, roslinazairimah zakaria escreveu: > Hi all, > > This is the data: > >> dput(head(dt1,20))structure(list(StationName = c("PALO ALTO CA / CAMBRIDGE #1", > "PALO ALTO CA / CAMBRIDGE #1", "PALO ALTO CA / CAMBRIDGE #1", > "PALO ALTO CA / CAMBRIDGE #1", "PALO ALTO CA / CAMBRIDGE #1", > "PALO ALTO

Hmmm... typos and thinkos ? Maybe: mean_narm<- function(x) { m <- mean(x, na.rm = T) if (is.nan (m)) NA else m } -- Bert On Mon, Sep 16, 2024 at 4:40?PM CALUM POLWART <polc1410 at gmail.com> wrote: > > Rui's solution is good. > > Bert's suggestion is also good! > > For Berts suggestion you'd make the list bit > > list(mean = mean_narm) >

Hi All, I didn't have much success through my Google search in finding any active R-related projects to create a wrapper around TensorFlow in R. Anyone know if this is on the go? Thanks, Axel. [[alternative HTML version deleted]]

Hi All, I didn't have much success through my Google search in finding any active R-related projects to create a wrapper around TensorFlow in R. Anyone know if this is on the go? Thanks, Axel. [[alternative HTML version deleted]]

Dear List, The gbm package on Win 7 produces different results for the relative importance of input variables in R 32-bit relative to R 64-bit. Any idea why? Any idea which one is correct? Based on this example, it looks like the relative importance of 2 perfectly correlated predictors is "diluted" by half in 32-bit, whereas in 64-bit, one of these predictors gets all the importance

Dear List, I'm trying to solve this simple optimization problem in R. The parameters are the exponents to the matrix mm. The constraints specify that each row of the parameter matrix should sum to 1 and their product to 0. I don't understand why the constraints are not satisfied at the solution. I must be misinterpreting how to specify the constrains somehow. library(alabama) ff <-

Dear all, I have this set of data. I would like to sum the EnergykWh according date sequences. > head(dt1,20) StationName date time EnergykWh 1 PALO ALTO CA / CAMBRIDGE #1 1/14/2016 12:09 4.680496 2 PALO ALTO CA / CAMBRIDGE #1 1/14/2016 19:50 6.272414 3 PALO ALTO CA / CAMBRIDGE #1 1/14/2016 20:22 1.032782 4 PALO ALTO CA / CAMBRIDGE #1 1/15/2016 8:25 11.004884 5

Thanks Ben, Here the reproducible example. It works without notch=TRUE, but provides an error with notch=TURE Error in `geom_boxplot()`: ! Problem while converting geom to grob. ? Error occurred in the 1st layer. Caused by error in `ans[ypos] <- rep(yes, length.out = len)[ypos]`: ! replacement has length zero Run `rlang::last_trace()` to see where the error occurred. Warning message: In

Hello an update about my question: I worked out the following solution (with the package "dplyr") library(dplyr) mydf%>% mutate(speed_vehicles=n_vehicles*mydf$speed) %>% group_by(date_time,type) %>% summarise( sum_n_times_speed=sum(speed_vehicles), n_vehicles=sum(n_vehicles), vel=sum(speed_vehicles)/sum(n_vehicles) ) In fact I was hoping to manage everything in a

Dear R users, I know this issue came up in the list several times. I’m currently running R on 32-bit on Windows and due to memory limitation problems would like to move to a 64-bit environment. I’m exploring my options and would appreciate your expertise: 1) Windows 64-bit: Prof. Brian Ripley recently posted the experimental built of R for win 64-bit. I’ll appreciate any feedback on