#' iris %>% arrange(across(starts_with("Sepal"), desc))Īrrange =1.5.3 is required to arrange in a different locale. #' iris %>% arrange(pick(starts_with("Sepal")))
#' # Use `across()` or `pick()` to select columns with tidy-select #' # see ?dplyr_data_masking for more details #' # use embracing when wrapping in a function #' help page under the `Default locale` section. #' particularly when it comes to data containing a mix of upper and lower case #' The C locale is not the same as English locales, such as `"en"`, Scoped verbs ( if, at, all) have been superseded by the use of across () in an existing verb. This does not require stringi and is often much faster than Arrange rows by a selection of variables. #' - If `"C"` is supplied, then character vectors will always be sorted in the #' sort with the American English locale. #' this will be used as the locale to sort with. #' - If a single string from is supplied, then #' `dplyr.legacy_locale` global option escape hatch is active. #' - If `NULL`, the default, uses the `"C"` locale unless the locale The locale to sort character vectors in. by_group If `TRUE`, will sort first by grouping variable. Use to sort a variable in descending order. data A data frame, data frame extension (e.g. #' The following methods are currently available in loaded packages: #' individual methods for extra arguments and differences in behaviour. #' implementations (methods) for other classes. #' This function is a **generic**, which means that packages can provide #' * Data frame attributes are preserved. #' * All rows appear in the output, but (usually) in a different place. #' An object of the same type as `.data`. #' * treated differently for remote data, depending on the backend. #' * always sorted to the end for local data, even when wrapped with `desc()`. #' Unlike base sorting with `sort()`, `NA` are:
#Dplyr arrange how to#
#' once per data frame, not once per group. In this article, we will discuss how to rearrange or reorder the column of the dataframe using dplyr package in R Programming Language. #' in order to group by them, and functions of variables are evaluated #' need to explicitly mention grouping variables (or use `.by_group = TRUE`) #' Unlike other dplyr verbs, `arrange()` largely ignores grouping you #' `arrange()` orders the rows of a data frame by the values of selected These may appear in metaprogramming techniques In case we decide in the future that `pick()` with no arguments should select "everything" * Never expand across anonymous function boundaries * Use not Mention `pick()` before using it The arrange() function is used to reorder rows of a data frame according to one of the variables. * Mention the current group in `pick()` docs * Be clearer about `cur_data_all()` being a bad idea * Tweak `arrange()` and `distinct()` example docs * Replace usage of `across(.fns = NULL)` with `pick()` * NEWS bullet for `cur_data()` and `cur_data_all()`
* Soft-deprecate `cur_data()` and `cur_data_all()` * Tweak NEWS bullet to mention `cur_data()` and `cur_data_all()`
#Dplyr arrange code#
* Improve documentation based on code review feedback * Use expansion based approach in `pick()` * Use `lapply()` to avoid `as_function()` overhead of `map()` shim * Rename existing `$pick()` to `$pick_current()`
This means that `pick()` doesn't "update" sequentially, but instead we get the invariant that the number of rows returned by `pick()` is the same as the number of rows in the group, which feels more important. This greatly simplifies the caching, because we can just chop the picked columns from the original data frame, store the chops, and retrieve each group's chop on demand. We do this by evaluating it only once on the original data frame. Not particularly performant, but I think the semantics are right * Add `dplyr_new_tibble()` performance helper * Add `quo_set_env_to_data_mask_top()` wrapper See sort_vars % # Embed the `values` object in the call using !!! step_arrange ( ! ! ! syms ( sort_vars ) ) %>% prep (training = iris ) tidy ( qq_rec, number = 1 ) #> # A tibble: 2 × 2 #> terms id #> #> 1 Sepal.Length arrange_bvEzT #> 2 Petal.* Rename `$across_cols()` to `$get_current_data()` Rec % step_arrange ( desc ( Sepal.Length ), 1 / Petal.Length ) prepped % slice ( 1 : 75 ) ) tidy ( prepped, number = 1 ) #> # A tibble: 2 × 2 #> terms id #> #> 1 desc(Sepal.Length) arrange_lIRz5 #> 2 1/Petal.Length arrange_lIRz5 library ( dplyr ) dplyr_train % as_tibble ( ) %>% slice ( 1 : 75 ) %>% dplyr :: arrange ( desc ( Sepal.Length ), 1 / Petal.Length ) rec_train TRUE dplyr_test % as_tibble ( ) %>% slice ( 76 : 150 ) %>% dplyr :: arrange ( desc ( Sepal.Length ), 1 / Petal.Length ) rec_test % slice ( 76 : 150 ) ) all.equal ( dplyr_test, rec_test ) #> TRUE # When you have variables/expressions, you can create a # list of symbols with `rlang::syms()`` and splice them in # the call with `!!!`.
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