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Jul 18, 2018 · Let's compare the following three methods in terms of performance/runtime. We use sample data consisting of 10^6 entries for a and b. a = sample(100, 10^6, replace = T), b = sample(100, 10^6, replace = T)) method_lag = {. df <- mutate(df, c = lag(a) + lag(b) - a) }, method_base = {. df$c <- NA.
Code sample
set.seed(2017)df <- data.frame(a = sample(100, 10^6, replace = T),b = sample(100, 10^6, replace = T))library(microbenchark)...I am using the following to calculate the time interval-. site_data[1:nrow(site_data)-1,"Interval"] <- as.numeric(difftime(site_data[2:nrow(site_data),"DateTime"], site_data[1:nrow(site_data)-1,"DateTime"])) But this code is taking too long to run. Is there a faster alternative to difftime?
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Jan 25, 2013 · Speed up the loop operation in R. I have a few questions regarding loops. I know that R works faster with vectorized calculations, and I would like to change the below code to take advantage of this.
Apr 3, 2016 · There is the lobstr package by Hadley. Besides several other more or less helpful functions it includes lobstr::tree() which tries to be more predictable, compact and overall more helpful than str(). An important difference between the two is that str() is an S3 generic whereas lobstr::tree() is not.
May 28, 2015 · For the x[logical] VS x[which(logical)], x[which(x)] is 3 ms on both versions of R, but x[x] is 5 ms on R-3.2.0 VS 12 ms on R-3.1.2. Looking at R-3.1.2's logicalSubscript VS R-3.2.0's logicalSubscript, it seems that R-3.2.0 avoids extended use of "%" in returning integer indices from the logical ones (to be -subsequently- used). –
- You can vectorize using max.col indx <- names(df)[max.col(df[-1], ties.method = "first") + 1L] df$firstyear <- as.numeric(sub("in", "20", indx)) d...
- df$FirstYear <- gsub('in', '20', names(df))[apply(df, 1, match, x=1)] df id in05 in06 in07 in08 in09 FirstYear 1 a 1 0 1 0 0...
- Another answer with some notes of efficiency (although this QA is not about speed). Firstly, it could be better to avoid the conversion of a "list...
- You can use dplyr::case_when inside dplyr::mutate() along the lines of the method presented in this tweet . # Using version 0.5.0. # Dev versi...
- Here is another option: years <- as.integer(substr(names(df[-1]), 3, 4)) + 2000L cbind(df, yr=do.call(pmin.int, Map(`/`, years, df[-1]))) Produces...
- I always prefer to work with tidied data. First method filters on cumsums # Tidy df <- df %>% gather(year, present.or.not, -id) # Create df of...
- Other messy alternatives: library(tidyr) library(sqldf) newdf <- gather(df, year, code, -id) df$firstyear <- sqldf('SELECT min(rowid) rowid...
May 3, 2021 · For example, imagine that I have a dataset with columns x and y. Then, if I'd like to have a third column, you could do this: data <- data %>% mutate ( z = x + y ). Moreover, you could also use conditionals: data <- data %>% mutate ( z = ifelse (x > k, x + y, x - y)) – user7355155. May 3, 2021 at 12:25.
Feb 15, 2015 · However I'd like to find a code that is more efficient to run in R, I tried to mess around with the apply-function for quite some time now but can't seem to find a feasible way to do so.. In the end, I hope to find something like this:
