Using dplyr to summarise a dataset, I want to call n_distinct to count the number of unique occurrences in a column. However, I also want to do another summarise() for all unique occurrences in a column where a condition in another column is satisfied.
7/20/2018 · na.rm should not be included in the splicing. move bot mentioned this issue Jul 21, 2018 dbplyr: n_distinct translation does not handle na.rm tidyverse/dbplyr#133, dbplyr: n_distinct translation does not handle na.rm tidyverse/dplyr#3720 Closed hadley added feature func trans ?? verb trans ?? and removed func trans ?? labels Jan 2, 2019, 7/9/2020 · When dplyr functions involve external functions that you’re applying to columns e.g. n_distinct() in the example above, this external function is placed in the .fnd argument. For example, we would to apply n_distinct() to species, island, and sex, we would write across(c(species, island, sex), n_distinct) in the summarise parentheses.
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Error in n_distinct_multi(list(…), na.rm) : object ‘a’ not found Using uniqueN() from data.table does not work and provides the error: library(data.table) df %>% filter(!is.na(b)) %>% ggplot() + geom_bar(aes(x = a, y = b), fill = randomColor(uniqueN(a)), stat = summary, fun.y = mean), 12/7/2020 · n_distinct() gains an na_rm argument (#1052). The Progress bar used by do() now respects global option dplyr.show_progress (default is TRUE) so you can turn it off globally (@jimhester #1264, #1226). summarise() handles expressions that returning heterogenous outputs, e.g. median(), which that sometimes returns an integer, and other times a …
*collapse* and *dplyr* 1. Fast Aggregations. A key feature of collapse is it’s broad set of Fast Statistical Functions (fsum, fprod, fmean, fmedian, fmode, fvar, fsd, fmin, fmax, fnth, ffirst, flast, fNobs, fNdistinct) which are able to substantially speed-up column-wise, grouped and weighted computations on vectors, matrices or data frames.The functions are S3 generic, with a default (vector …
2/17/2015 · unique uses its own hash table implementation. it might be worth extracting it out from the R source into a package at some point. unique actually initially create a logical vector (the one we would get from duplicate), then extract the unique values afterwards. Perhaps we could skip that last step. decyphering what goes on in unique.c might be interesting in the context of vctrs.
Incase you want to add additional arguments for the functions mean and median (for example na.rm = TRUE), you can do it like the code below. summarise_at(mydata, vars(Y2011, Y2012),funs(mean, median), na.rm = TRUE) We can also use custom functions in the summarise function.
