diff --git a/DESCRIPTION b/DESCRIPTION index 6251b37..7e52679 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: tidier Title: Enhanced 'mutate' -Version: 0.0.1 +Version: 0.1.0 Authors@R: person("Srikanth", "Komala Sheshachala",, diff --git a/NEWS.md b/NEWS.md index a711c9b..996676c 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,8 @@ +# tidier 0.1.0 (on github: 2023-06-01) + +* Exposed slider's `.complete` argument in `tidier::mutate` +* bugfix: `mutate` can now modify a column (same name) in sliding operation. + # tidier 0.0.1 * Added a `NEWS.md` file to track changes to the package. diff --git a/R/mutate.R b/R/mutate.R index 687affa..327ee29 100644 --- a/R/mutate.R +++ b/R/mutate.R @@ -47,6 +47,10 @@ #' @param .index (string, optional: Yes) name of index column #' @param .desc (logical_vector, default: FALSE) bool or logical vector of same #' length as `.order_by`. +#' @param .complete (flag, default: FALSE) This will be passed to +#' `slider::slide` / `slider::slide_vec`. Should the function be evaluated on +#' complete windows only? If FALSE, the default, then partial computations +#' will be allowed. #' @return data.frame #' @importFrom magrittr %>% #' @importFrom utils tail @@ -72,37 +76,27 @@ #' # Using a sample airquality dataset, #' # compute mean temp over last seven days in the same month for every row #' +#' set.seed(101) #' airquality %>% #' # create date column -#' dplyr::mutate(date_col = as.Date(paste("1973", -#' stringr::str_pad(Month, -#' width = 2, -#' side = "left", -#' pad = "0" -#' ), -#' stringr::str_pad(Day, -#' width = 2, -#' side = "left", -#' pad = "0" -#' ), -#' sep = "-" -#' ) -#' ) -#' ) %>% +#' dplyr::mutate(date_col = lubridate::make_date(1973, Month, Day)) %>% #' # create gaps by removing some days #' dplyr::slice_sample(prop = 0.8) %>% +#' dplyr::arrange(date_col) %>% #' # compute mean temperature over last seven days in the same month -#' mutate_(avg_temp_over_last_week = mean(Temp, na.rm = TRUE), -#' .order_by = "Day", -#' .by = "Month", -#' .frame = c(lubridate::days(7), # 7 days before current row -#' lubridate::days(-1) # do not include current row -#' ), -#' .index = "date_col" -#' ) +#' tidier::mutate(avg_temp_over_last_week = mean(Temp, na.rm = TRUE), +#' .order_by = "Day", +#' .by = "Month", +#' .frame = c(lubridate::days(7), # 7 days before current row +#' lubridate::days(-1) # do not include current row +#' ), +#' .index = "date_col" +#' ) #' @export -mutate_ = function(x, ..., .by, .order_by, .frame, .index, .desc = FALSE){ +mutate_ = function(x, ..., .by, .order_by, .frame, .index, + .desc = FALSE, .complete = FALSE + ){ # capture expressions -------------------------------------------------------- ddd = rlang::enquos(...) @@ -182,9 +176,11 @@ mutate_ = function(x, ..., .by, .order_by, .frame, .index, .desc = FALSE){ x_copy, .f = ~ as.list(dplyr::summarise(.x, !!!ddd)), .before = .frame[1], - .after = .frame[2] + .after = .frame[2], + .complete = .complete ) ) %>% + remove_common_nested_columns(slide_output__) %>% tidyr::unnest_wider(slide_output__) } else { x_copy = x_copy %>% @@ -194,9 +190,11 @@ mutate_ = function(x, ..., .by, .order_by, .frame, .index, .desc = FALSE){ .f = ~ as.list(dplyr::summarise(.x, !!!ddd)), .i = x_copy[[.index]], .before = .frame[1], - .after = .frame[2] + .after = .frame[2], + .complete = .complete ) ) %>% + remove_common_nested_columns(slide_output__) %>% tidyr::unnest_wider(slide_output__) } } @@ -221,7 +219,8 @@ mutate_ = function(x, ..., .by, .order_by, .frame, .index, .desc = FALSE){ chunk, .f = ~ as.list(dplyr::summarise(.x, !!!ddd)), .before = .frame[1], - .after = .frame[2] + .after = .frame[2], + .complete = .complete ) ) } else { @@ -232,7 +231,8 @@ mutate_ = function(x, ..., .by, .order_by, .frame, .index, .desc = FALSE){ .f = ~ as.list(dplyr::summarise(.x, !!!ddd)), .i = chunk[[.index]], .before = .frame[1], - .after = .frame[2] + .after = .frame[2], + .complete = .complete ) ) } @@ -252,6 +252,7 @@ mutate_ = function(x, ..., .by, .order_by, .frame, .index, .desc = FALSE){ dplyr::ungroup() %>% dplyr::mutate(data__ = furrr::future_map(data__, fun_per_chunk)) %>% tidyr::unnest(data__) %>% + remove_common_nested_columns(slide_output__) %>% tidyr::unnest_wider(slide_output__) } @@ -285,6 +286,7 @@ mutate_ = function(x, ..., .by, .order_by, .frame, .index, .desc = FALSE){ #' API](https://www.databricks.com/blog/2015/07/15/introducing-window-functions-in-spark-sql.html). #' #' +#' #' Implementation Details: #' #' - Iteration per row over the window is implemented using the versatile @@ -309,6 +311,10 @@ mutate_ = function(x, ..., .by, .order_by, .frame, .index, .desc = FALSE){ #' [interval](https://lubridate.tidyverse.org/reference/interval.html) #' objects. See examples. #' @param .index (expression, optional: Yes) index column +#' @param .complete (flag, default: FALSE) This will be passed to +#' `slider::slide` / `slider::slide_vec`. Should the function be evaluated on +#' complete windows only? If FALSE, the default, then partial computations +#' will be allowed. #' @return data.frame #' @importFrom magrittr %>% #' @importFrom utils tail @@ -335,36 +341,24 @@ mutate_ = function(x, ..., .by, .order_by, .frame, .index, .desc = FALSE){ #' # Using a sample airquality dataset, #' # compute mean temp over last seven days in the same month for every row #' +#' set.seed(101) #' airquality %>% #' # create date column -#' dplyr::mutate(date_col = as.Date(paste("1973", -#' stringr::str_pad(Month, -#' width = 2, -#' side = "left", -#' pad = "0" -#' ), -#' stringr::str_pad(Day, -#' width = 2, -#' side = "left", -#' pad = "0" -#' ), -#' sep = "-" -#' ) -#' ) -#' ) %>% +#' dplyr::mutate(date_col = lubridate::make_date(1973, Month, Day)) %>% #' # create gaps by removing some days #' dplyr::slice_sample(prop = 0.8) %>% +#' dplyr::arrange(date_col) %>% #' # compute mean temperature over last seven days in the same month -#' mutate(avg_temp_over_last_week = mean(Temp, na.rm = TRUE), -#' .order_by = Day, -#' .by = Month, -#' .frame = c(lubridate::days(7), # 7 days before current row -#' lubridate::days(-1) # do not include current row -#' ), -#' .index = date_col -#' ) +#' tidier::mutate(avg_temp_over_last_week = mean(Temp, na.rm = TRUE), +#' .order_by = Day, +#' .by = Month, +#' .frame = c(lubridate::days(7), # 7 days before current row +#' lubridate::days(-1) # do not include current row +#' ), +#' .index = date_col +#' ) #' @export -mutate = function(x, ..., .by, .order_by, .frame, .index){ +mutate = function(x, ..., .by, .order_by, .frame, .index, .complete = FALSE){ # capture expressions -------------------------------------------------------- ddd = rlang::enquos(...) @@ -462,9 +456,11 @@ mutate = function(x, ..., .by, .order_by, .frame, .index){ x_copy, .f = ~ as.list(dplyr::summarise(.x, !!!ddd)), .before = .frame[1], - .after = .frame[2] + .after = .frame[2], + .complete = .complete ) ) %>% + remove_common_nested_columns(slide_output__) %>% tidyr::unnest_wider(slide_output__) } else { x_copy = x_copy %>% @@ -474,9 +470,11 @@ mutate = function(x, ..., .by, .order_by, .frame, .index){ .f = ~ as.list(dplyr::summarise(.x, !!!ddd)), .i = x_copy[[.index]], .before = .frame[1], - .after = .frame[2] + .after = .frame[2], + .complete = .complete ) ) %>% + remove_common_nested_columns(slide_output__) %>% tidyr::unnest_wider(slide_output__) } } @@ -499,7 +497,8 @@ mutate = function(x, ..., .by, .order_by, .frame, .index){ chunk, .f = ~ as.list(dplyr::summarise(.x, !!!ddd)), .before = .frame[1], - .after = .frame[2] + .after = .frame[2], + .complete = .complete ) ) } else { @@ -510,7 +509,8 @@ mutate = function(x, ..., .by, .order_by, .frame, .index){ .f = ~ as.list(dplyr::summarise(.x, !!!ddd)), .i = chunk[[.index]], .before = .frame[1], - .after = .frame[2] + .after = .frame[2], + .complete = .complete ) ) } @@ -529,6 +529,7 @@ mutate = function(x, ..., .by, .order_by, .frame, .index){ dplyr::ungroup() %>% dplyr::mutate(data__ = furrr::future_map(data__, fun_per_chunk)) %>% tidyr::unnest(data__) %>% + remove_common_nested_columns(slide_output__) %>% tidyr::unnest_wider(slide_output__) } @@ -542,3 +543,29 @@ mutate = function(x, ..., .by, .order_by, .frame, .index){ return(x_copy) } +# remove_common_nested_columns ---- +#' @name remove_common_nested_columns +#' @title Remove non-list columns when same are present in a list column +#' @description Remove non-list columns when same are present in a list column +#' @param df input dataframe +#' @param list_column Name or expr of the column which is a list of named lists +#' @return dataframe +remove_common_nested_columns = function(df, list_column){ + + # we assume that all dfs in list_column have identical column names + new_names = df %>% + dplyr::slice(1) %>% + dplyr::pull({{ list_column }}) %>% + `[[`(1) %>% + names() + + common_names = intersect(new_names, colnames(df)) + + if (length(common_names) >= 1){ + for (a_common_name in common_names){ + df[[a_common_name]] = NULL + } + } + + return(df) +} diff --git a/README.Rmd b/README.Rmd index 075196c..41ff833 100644 --- a/README.Rmd +++ b/README.Rmd @@ -19,6 +19,8 @@ devtools::load_all() # tidier + +[![CRAN status](https://www.r-pkg.org/badges/version/tidier)](https://CRAN.R-project.org/package=tidier) `tidier` package provides '[Apache Spark](https://spark.apache.org/)' style window aggregation for R dataframes via '[mutate](https://dplyr.tidyverse.org/reference/mutate.html)' in '[dplyr](https://dplyr.tidyverse.org/index.html)' flavour. @@ -37,42 +39,57 @@ airquality |> # compute mean temperature over last seven days in the same month tidier::mutate(avg_temp_over_last_week = mean(Temp, na.rm = TRUE), .order_by = Day, - .by = Month, - .frame = c(lubridate::days(7), # 7 days before current row - lubridate::days(-1) # do not include current row - ), - .index = date_col + .by = Month, + .frame = c(lubridate::days(7), # 7 days before current row + lubridate::days(-1) # do not include current row + ), + .index = date_col ) ``` +## Features + +- `mutate` supports + - `.by` (group by), + - `.order_by` (order by), + - `.frame` (endpoints of window frame), + - `.index` (identify index column like date column), + - `.complete` (whether to compute over incomplete window). + +- `mutate` automatically uses a future backend (via [`furrr`](https://furrr.futureverse.org/)). + ## Motivation This implementation is inspired by Apache Spark's [`windowSpec`](https://spark.apache.org/docs/3.2.1/api/python/reference/api/pyspark.sql.Column.over.html?highlight=windowspec) class with [`rangeBetween`](https://spark.apache.org/docs/3.2.1/api/python/reference/api/pyspark.sql.WindowSpec.rangeBetween.html) and [`rowsBetween`](https://spark.apache.org/docs/3.2.1/api/python/reference/api/pyspark.sql.WindowSpec.rowsBetween.html). ## Ecosystem -1. [`dbplyr`](https://dbplyr.tidyverse.org/) implements this via [`dbplyr::win_over`](https://dbplyr.tidyverse.org/reference/win_over.html?q=win_over#null) enabling [`sparklyr`](https://spark.rstudio.com/) users to write window computations. Also see, [`dbplyr::window_order`/`dbplyr::window_frame`](https://dbplyr.tidyverse.org/reference/window_order.html?q=window_fr#ref-usage). +1. [`dbplyr`](https://dbplyr.tidyverse.org/) implements this via [`dbplyr::win_over`](https://dbplyr.tidyverse.org/reference/win_over.html?q=win_over#null) enabling [`sparklyr`](https://spark.rstudio.com/) users to write window computations. Also see, [`dbplyr::window_order`/`dbplyr::window_frame`](https://dbplyr.tidyverse.org/reference/window_order.html?q=window_fr#ref-usage). -2. [`tidypyspark`](https://talegari.github.io/tidypyspark/_build/html/index.html) python package implements `mutate` style window computation API for pyspark. +2. [`tidypyspark`](https://talegari.github.io/tidypyspark/_build/html/index.html) python package implements `mutate` style window computation API for pyspark. ## Installation -- dev: `remotes::install_github("talegari/tidier")` -- cran: `install.packages("tidier")` +- dev: `remotes::install_github("talegari/tidier")` +- cran: `install.packages("tidier")` ## Acknowledgements `tidier` package is deeply indebted to two amazing packages and people behind it. -1. [`dplyr`](https://cran.r-project.org/package=dplyr): -``` -Wickham H, François R, Henry L, Müller K, Vaughan D (2023). _dplyr: A -Grammar of Data Manipulation_. R package version 1.1.0, -. -``` +1. [`dplyr`](https://cran.r-project.org/package=dplyr): -2. [`slider`](https://cran.r-project.org/package=slider): +```{=html} + ``` -Vaughan D (2021). _slider: Sliding Window Functions_. R package -version 0.2.2, . + Wickham H, François R, Henry L, Müller K, Vaughan D (2023). _dplyr: A + Grammar of Data Manipulation_. R package version 1.1.0, + . + +2. [`slider`](https://cran.r-project.org/package=slider): + +```{=html} + ``` + Vaughan D (2021). _slider: Sliding Window Functions_. R package + version 0.2.2, . diff --git a/README.md b/README.md index 06d6ea7..ed66ce5 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,9 @@ # tidier + +[![CRAN +status](https://www.r-pkg.org/badges/version/tidier)](https://CRAN.R-project.org/package=tidier) `tidier` package provides ‘[Apache Spark](https://spark.apache.org/)’ @@ -26,11 +29,11 @@ airquality |> # compute mean temperature over last seven days in the same month tidier::mutate(avg_temp_over_last_week = mean(Temp, na.rm = TRUE), .order_by = Day, - .by = Month, - .frame = c(lubridate::days(7), # 7 days before current row - lubridate::days(-1) # do not include current row - ), - .index = date_col + .by = Month, + .frame = c(lubridate::days(7), # 7 days before current row + lubridate::days(-1) # do not include current row + ), + .index = date_col ) #> # A tibble: 122 × 8 #> Month Ozone Solar.R Wind Temp Day date_col avg_temp_over_last_week @@ -45,9 +48,20 @@ airquality |> #> 8 6 NA 186 9.2 84 4 1973-06-04 72.5 #> 9 8 78 NA 6.9 86 4 1973-08-04 81.3 #> 10 8 168 238 3.4 81 25 1973-08-25 76.5 -#> # … with 112 more rows +#> # ℹ 112 more rows ``` +## Features + +- `mutate` supports + - `.by` (group by), + - `.order_by` (order by), + - `.frame` (endpoints of window frame), + - `.index` (identify index column like date column), + - `.complete` (whether to compute over incomplete window). +- `mutate` automatically uses a future backend (via + [`furrr`](https://furrr.futureverse.org/)). + ## Motivation This implementation is inspired by Apache Spark’s diff --git a/docs/404.html b/docs/404.html index 90f1aa5..c52ada2 100644 --- a/docs/404.html +++ b/docs/404.html @@ -24,7 +24,7 @@ tidier - 0.0.1 + 0.1.0 + + + + + +
+
+
+

Remove non-list columns when same are present in a list column

+ Source: R/mutate.R +
remove_common_nested_columns.Rd
+
+ +
+

Remove non-list columns when same are present in a list column

+
+ +
+

Usage

+ 
remove_common_nested_columns(df, list_column)
+
+ +
+

Arguments

+
df
+

input dataframe

+ + +
list_column
+

Name or expr of the column which is a list of named lists

+ +
+
+

Value

+ + +

dataframe

+
+ +
+ + +
+

Developed by Srikanth Komala Sheshachala.

+
+ +
+

Site built with pkgdown 2.0.6.

+
+ +
+ + + + + + + diff --git a/docs/search.json b/docs/search.json index eb214bc..2012873 100644 --- a/docs/search.json +++ b/docs/search.json @@ -1 +1 @@ -[{"path":"/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU General Public License","title":"GNU General Public License","text":"Version 3, 29 June 2007Copyright © 2007 Free Software Foundation, Inc.  Everyone permitted copy distribute verbatim copies license document, changing allowed.","code":""},{"path":"/LICENSE.html","id":"preamble","dir":"","previous_headings":"","what":"Preamble","title":"GNU General Public License","text":"GNU General Public License free, copyleft license software kinds works. licenses software practical works designed take away freedom share change works. contrast, GNU General Public License intended guarantee freedom share change versions program–make sure remains free software users. , Free Software Foundation, use GNU General Public License software; applies also work released way authors. can apply programs, . speak free software, referring freedom, price. 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This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Srikanth Komala Sheshachala. Author, maintainer.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Komala Sheshachala S (2023). tidier: Enhanced 'mutate'. R package version 0.0.1, https://github.com/talegari/tidier.","code":"@Manual{, title = {tidier: Enhanced 'mutate'}, author = {Srikanth {Komala Sheshachala}}, year = {2023}, note = {R package version 0.0.1}, url = {https://github.com/talegari/tidier}, }"},{"path":"/index.html","id":"tidier","dir":"","previous_headings":"","what":"Enhanced mutate","title":"Enhanced mutate","text":"tidier package provides ‘Apache Spark’ style window aggregation R dataframes via ‘mutate’ ‘dplyr’ flavour.","code":""},{"path":"/index.html","id":"example","dir":"","previous_headings":"","what":"Example","title":"Enhanced mutate","text":"Create new column average temp last seven days month.","code":"set.seed(101) air_df = airquality %>% # create date column dplyr::mutate(date_col = as.Date(paste(\"1973\", stringr::str_pad(Month, width = 2, side = \"left\", pad = \"0\" ), stringr::str_pad(Day, width = 2, side = \"left\", pad = \"0\" ), sep = \"-\" ) ) ) %>% # create gaps by removing some days dplyr::slice_sample(prop = 0.8) %>% tibble::as_tibble() air_df #> # A tibble: 122 × 7 #> Ozone Solar.R Wind Temp Month Day date_col #> #> 1 10 264 14.3 73 7 12 1973-07-12 #> 2 NA 127 8 78 6 26 1973-06-26 #> 3 16 77 7.4 82 8 3 1973-08-03 #> 4 14 191 14.3 75 9 28 1973-09-28 #> 5 NA 138 8 83 6 30 1973-06-30 #> 6 NA 98 11.5 80 6 28 1973-06-28 #> 7 122 255 4 89 8 7 1973-08-07 #> 8 47 95 7.4 87 9 5 1973-09-05 #> 9 23 220 10.3 78 9 8 1973-09-08 #> 10 NA 286 8.6 78 6 1 1973-06-01 #> # … with 112 more rows air_df %>% # compute mean temperature over last seven days in the same month mutate(avg_temp_over_last_week = mean(Temp, na.rm = TRUE), .order_by = Day, .by = Month, .frame = c(lubridate::days(7), # 7 days before current row lubridate::days(-1) # do not include current row ), .index = date_col ) #> #> Attaching package: 'purrr' #> The following object is masked from 'package:testthat': #> #> is_null #> The following object is masked from 'package:magrittr': #> #> set_names #> # A tibble: 122 × 8 #> Month Ozone Solar.R Wind Temp Day date_col avg_temp_over_last_week #> #> 1 6 NA 332 13.8 80 14 1973-06-14 87.2 #> 2 5 28 NA 14.9 66 6 1973-05-06 66 #> 3 5 6 78 18.4 57 18 1973-05-18 65.2 #> 4 8 45 212 9.7 79 24 1973-08-24 76.5 #> 5 5 36 118 8 72 2 1973-05-02 NaN #> 6 9 24 238 10.3 68 19 1973-09-19 73 #> 7 9 16 201 8 82 20 1973-09-20 71.7 #> 8 6 NA 186 9.2 84 4 1973-06-04 72.5 #> 9 8 78 NA 6.9 86 4 1973-08-04 81.3 #> 10 8 168 238 3.4 81 25 1973-08-25 76.5 #> # … with 112 more rows"},{"path":"/index.html","id":"motivation","dir":"","previous_headings":"","what":"Motivation","title":"Enhanced mutate","text":"implementation inspired Apache Spark’s windowSpec class rangeBetween rowsBetween.","code":""},{"path":"/index.html","id":"ecosystem","dir":"","previous_headings":"","what":"Ecosystem","title":"Enhanced mutate","text":"dbplyr implements via dbplyr::win_over enabling sparklyr users write window computations. Also see, dbplyr::window_order/dbplyr::window_frame. tidypyspark python package implements mutate style window computation API pyspark.","code":""},{"path":"/index.html","id":"acknowledgements","dir":"","previous_headings":"","what":"Acknowledgements","title":"Enhanced mutate","text":"tidier package deeply indebted two amazing packages people behind . dplyr: Hadley wickham slider: Davis Vaughan","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Enhanced mutate","text":"dev: remotes::install_github(\"talegari/tidier\") cran: install.packages(\"tidier\")","code":""},{"path":"/reference/mutate.html","id":null,"dir":"Reference","previous_headings":"","what":"Drop-in replacement for mutate — mutate","title":"Drop-in replacement for mutate — mutate","text":"Provides supercharged version mutate group_by, order_by aggregation arbitrary window frame around row.","code":""},{"path":"/reference/mutate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drop-in replacement for mutate — mutate","text":"","code":"mutate(x, ..., .by, .order_by, .frame, .index)"},{"path":"/reference/mutate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Drop-in replacement for mutate — mutate","text":"x (data.frame) ... expressions passed mutate .(expression, optional: Yes) columns group .order_by (expression, optional: Yes) columns order .frame (vector, optional: Yes) Vector length 2 indicating number rows consider current row. argument .index provided (typically column type date datetime), can interval objects. See examples. .index (expression, optional: Yes) index column","code":""},{"path":"/reference/mutate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drop-in replacement for mutate — mutate","text":"data.frame","code":""},{"path":"/reference/mutate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Drop-in replacement for mutate — mutate","text":"window function returns value every input row dataframe based group rows (frame) neighborhood input row. function implements computation groups (partition_by SQL) predefined order (order_by SQL) across neighborhood rows (frame) defined (, ) /number rows corresponding row /interval objects (ex: c(days(2), days(1))) implementation inspired spark's window API. Implementation Details: Iteration per row window implemented using versatile slider. Application window aggregation can optionally run parallel multiple groups (see argument .) setting future parallel backend. implemented using furrr package. function subsumes regular usecases mutate","code":""},{"path":[]},{"path":"/reference/mutate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Drop-in replacement for mutate — mutate","text":"","code":"library(\"magrittr\") # example 1 # Using iris dataset, # compute cumulative mean of column `Sepal.Length` # ordered by `Petal.Width` and `Sepal.Width` columns # grouped by `Petal.Length` column iris %>% mutate(sl_mean = mean(Sepal.Length), .order_by = c(Petal.Width, Sepal.Width), .by = Petal.Length, .frame = c(Inf, 0), ) %>% dplyr::slice_min(n = 3, Petal.Width, by = Species) #> #> Attaching package: ‘purrr’ #> The following object is masked from ‘package:magrittr’: #> #> set_names #> # A tibble: 15 × 6 #> Petal.Length Sepal.Length Sepal.Width Petal.Width Species sl_mean #> #> 1 1.1 4.3 3 0.1 setosa 4.3 #> 2 1.4 4.9 3.6 0.1 setosa 4.85 #> 3 1.4 4.8 3 0.1 setosa 4.8 #> 4 1.5 4.9 3.1 0.1 setosa 4.9 #> 5 1.5 5.2 4.1 0.1 setosa 5.05 #> 6 4.1 5.8 2.7 1 versicolor 5.8 #> 7 3.3 5 2.3 1 versicolor 5 #> 8 3.3 4.9 2.4 1 versicolor 4.95 #> 9 3.7 5.5 2.4 1 versicolor 5.5 #> 10 3.5 5 2 1 versicolor 5 #> 11 4 6 2.2 1 versicolor 6 #> 12 3.5 5.7 2.6 1 versicolor 5.35 #> 13 5.6 6.1 2.6 1.4 virginica 6.1 #> 14 5 6 2.2 1.5 virginica 6 #> 15 5.1 6.3 2.8 1.5 virginica 6.3 # example 2 # Using a sample airquality dataset, # compute mean temp over last seven days in the same month for every row airquality %>% # create date column dplyr::mutate(date_col = as.Date(paste(\"1973\", stringr::str_pad(Month, width = 2, side = \"left\", pad = \"0\" ), stringr::str_pad(Day, width = 2, side = \"left\", pad = \"0\" ), sep = \"-\" ) ) ) %>% # create gaps by removing some days dplyr::slice_sample(prop = 0.8) %>% # compute mean temperature over last seven days in the same month mutate(avg_temp_over_last_week = mean(Temp, na.rm = TRUE), .order_by = Day, .by = Month, .frame = c(lubridate::days(7), # 7 days before current row lubridate::days(-1) # do not include current row ), .index = date_col ) #> # A tibble: 122 × 8 #> Month Ozone Solar.R Wind Temp Day date_col avg_temp_over_last_week #> #> 1 9 96 167 6.9 91 1 1973-09-01 NaN #> 2 7 20 81 8.6 82 26 1973-07-26 84.2 #> 3 5 45 252 14.9 81 29 1973-05-29 61.2 #> 4 8 44 190 10.3 78 20 1973-08-20 79 #> 5 8 9 36 14.3 72 22 1973-08-22 78 #> 6 9 46 237 6.9 78 16 1973-09-16 75.6 #> 7 7 77 276 5.1 88 7 1973-07-07 83.2 #> 8 7 NA 258 9.7 81 22 1973-07-22 83.3 #> 9 8 NA 153 5.7 88 27 1973-08-27 78.2 #> 10 8 118 225 2.3 94 29 1973-08-29 80.4 #> # … with 112 more rows"},{"path":"/reference/mutate_.html","id":null,"dir":"Reference","previous_headings":"","what":"Drop-in replacement for mutate — mutate_","title":"Drop-in replacement for mutate — mutate_","text":"Provides supercharged version mutate group_by, order_by aggregation arbitrary window frame around row. function allows arguments passed strings instead expressions.","code":""},{"path":"/reference/mutate_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drop-in replacement for mutate — mutate_","text":"","code":"mutate_(x, ..., .by, .order_by, .frame, .index, .desc = FALSE)"},{"path":"/reference/mutate_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Drop-in replacement for mutate — mutate_","text":"x (data.frame) ... expressions passed mutate .(character vector, optional: Yes) columns group .order_by (character vector, optional: Yes) columns order .frame (vector, optional: Yes) Vector length 2 indicating number rows consider current row. argument .index provided (typically column type date datetime), can interval objects. See examples. .index (string, optional: Yes) name index column .desc (logical_vector, default: FALSE) bool logical vector length .order_by.","code":""},{"path":"/reference/mutate_.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drop-in replacement for mutate — mutate_","text":"data.frame","code":""},{"path":"/reference/mutate_.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Drop-in replacement for mutate — mutate_","text":"window function returns value every input row dataframe based group rows (frame) neighborhood input row. function implements computation groups (partition_by SQL) predefined order (order_by SQL) across neighborhood rows (frame) defined (, ) /number rows corresponding row /interval objects (ex: c(days(2), days(1))) implementation inspired spark's window API. Implementation Details: Iteration per row window implemented using versatile slider. Application window aggregation can optionally run parallel multiple groups (see argument .) setting future parallel backend. implemented using furrr package. function subsumes regular usecases mutate","code":""},{"path":[]},{"path":"/reference/mutate_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Drop-in replacement for mutate — mutate_","text":"","code":"library(\"magrittr\") # example 1 # Using iris dataset, # compute cumulative mean of column `Sepal.Length` # ordered by `Petal.Width` and `Sepal.Width` columns # grouped by `Petal.Length` column iris %>% mutate_(sl_mean = mean(Sepal.Length), .order_by = c(\"Petal.Width\", \"Sepal.Width\"), .by = \"Petal.Length\", .frame = c(Inf, 0), ) %>% dplyr::slice_min(n = 3, Petal.Width, by = Species) #> # A tibble: 15 × 6 #> Petal.Length Sepal.Length Sepal.Width Petal.Width Species sl_mean #> #> 1 1.4 4.8 3 0.1 setosa 4.8 #> 2 1.4 4.9 3.6 0.1 setosa 4.85 #> 3 1.1 4.3 3 0.1 setosa 4.3 #> 4 1.5 4.9 3.1 0.1 setosa 4.9 #> 5 1.5 5.2 4.1 0.1 setosa 5.05 #> 6 3.5 5 2 1 versicolor 5 #> 7 3.5 5.7 2.6 1 versicolor 5.35 #> 8 3.3 4.9 2.4 1 versicolor 4.95 #> 9 3.3 5 2.3 1 versicolor 5 #> 10 4.1 5.8 2.7 1 versicolor 5.8 #> 11 4 6 2.2 1 versicolor 6 #> 12 3.7 5.5 2.4 1 versicolor 5.5 #> 13 5.6 6.1 2.6 1.4 virginica 6.1 #> 14 5.1 6.3 2.8 1.5 virginica 6.3 #> 15 5 6 2.2 1.5 virginica 6 # example 2 # Using a sample airquality dataset, # compute mean temp over last seven days in the same month for every row airquality %>% # create date column dplyr::mutate(date_col = as.Date(paste(\"1973\", stringr::str_pad(Month, width = 2, side = \"left\", pad = \"0\" ), stringr::str_pad(Day, width = 2, side = \"left\", pad = \"0\" ), sep = \"-\" ) ) ) %>% # create gaps by removing some days dplyr::slice_sample(prop = 0.8) %>% # compute mean temperature over last seven days in the same month mutate_(avg_temp_over_last_week = mean(Temp, na.rm = TRUE), .order_by = \"Day\", .by = \"Month\", .frame = c(lubridate::days(7), # 7 days before current row lubridate::days(-1) # do not include current row ), .index = \"date_col\" ) #> # A tibble: 122 × 8 #> Month Ozone Solar.R Wind Temp Day date_col avg_temp_over_last_week #> #> 1 5 NA NA 8 57 27 1973-05-27 62.2 #> 2 6 39 323 11.5 87 10 1973-06-10 82 #> 3 7 97 272 5.7 92 9 1973-07-09 85.8 #> 4 6 NA 259 10.9 93 11 1973-06-11 84.9 #> 5 8 44 192 11.5 86 12 1973-08-12 88.8 #> 6 7 82 213 7.4 88 28 1973-07-28 82.3 #> 7 9 24 238 10.3 68 19 1973-09-19 73.8 #> 8 7 108 223 8 85 25 1973-07-25 82.8 #> 9 7 63 220 11.5 85 20 1973-07-20 82.6 #> 10 8 66 NA 4.6 87 6 1973-08-06 83.2 #> # … with 112 more rows"},{"path":"/news/index.html","id":"tidier-001","dir":"Changelog","previous_headings":"","what":"tidier 0.0.1","title":"tidier 0.0.1","text":"Added NEWS.md file track changes package.","code":""}] +[{"path":"/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU General Public License","title":"GNU General Public License","text":"Version 3, 29 June 2007Copyright © 2007 Free Software Foundation, Inc.  Everyone permitted copy distribute verbatim copies license document, changing allowed.","code":""},{"path":"/LICENSE.html","id":"preamble","dir":"","previous_headings":"","what":"Preamble","title":"GNU General Public License","text":"GNU General Public License free, copyleft license software kinds works. licenses software practical works designed take away freedom share change works. contrast, GNU General Public License intended guarantee freedom share change versions program–make sure remains free software users. , Free Software Foundation, use GNU General Public License software; applies also work released way authors. can apply programs, . speak free software, referring freedom, price. 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This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Srikanth Komala Sheshachala. Author, maintainer.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Srikanth Komala Sheshachala (2023). tidier: Enhanced 'mutate'. Provides ‘Apache Spark’ style window aggregation R dataframes via ‘mutate’ ‘dplyr’ flavour. R package version 4.2.0. https://CRAN.R-project.org/package=tidier","code":"@Manual{, title = {tidier: Enhanced 'mutate'}, author = {Srikanth Komala Sheshachala}, year = {2023}, url = {https://CRAN.R-project.org/package=tidier}, }"},{"path":"/index.html","id":"tidier","dir":"","previous_headings":"","what":"Enhanced mutate","title":"Enhanced mutate","text":"tidier package provides ‘Apache Spark’ style window aggregation R dataframes via ‘mutate’ ‘dplyr’ flavour.","code":""},{"path":"/index.html","id":"example","dir":"","previous_headings":"","what":"Example","title":"Enhanced mutate","text":"Create new column average temp last seven days month.","code":"set.seed(101) airquality |> # create date column dplyr::mutate(date_col = lubridate::make_date(1973, Month, Day)) |> # create gaps by removing some days dplyr::slice_sample(prop = 0.8) |> # compute mean temperature over last seven days in the same month tidier::mutate(avg_temp_over_last_week = mean(Temp, na.rm = TRUE), .order_by = Day, .by = Month, .frame = c(lubridate::days(7), # 7 days before current row lubridate::days(-1) # do not include current row ), .index = date_col ) #> # A tibble: 122 × 8 #> Month Ozone Solar.R Wind Temp Day date_col avg_temp_over_last_week #> #> 1 6 NA 332 13.8 80 14 1973-06-14 87.2 #> 2 5 28 NA 14.9 66 6 1973-05-06 66 #> 3 5 6 78 18.4 57 18 1973-05-18 65.2 #> 4 8 45 212 9.7 79 24 1973-08-24 76.5 #> 5 5 36 118 8 72 2 1973-05-02 NaN #> 6 9 24 238 10.3 68 19 1973-09-19 73 #> 7 9 16 201 8 82 20 1973-09-20 71.7 #> 8 6 NA 186 9.2 84 4 1973-06-04 72.5 #> 9 8 78 NA 6.9 86 4 1973-08-04 81.3 #> 10 8 168 238 3.4 81 25 1973-08-25 76.5 #> # ℹ 112 more rows"},{"path":"/index.html","id":"features","dir":"","previous_headings":"","what":"Features","title":"Enhanced mutate","text":".(group ), .order_by (order ), .frame (endpoints window frame), .index (identify index column like date column), .complete (whether compute incomplete window). mutate automatically uses future backend (via furrr).","code":""},{"path":"/index.html","id":"motivation","dir":"","previous_headings":"","what":"Motivation","title":"Enhanced mutate","text":"implementation inspired Apache Spark’s windowSpec class rangeBetween rowsBetween.","code":""},{"path":"/index.html","id":"ecosystem","dir":"","previous_headings":"","what":"Ecosystem","title":"Enhanced mutate","text":"dbplyr implements via dbplyr::win_over enabling sparklyr users write window computations. Also see, dbplyr::window_order/dbplyr::window_frame. tidypyspark python package implements mutate style window computation API pyspark.","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Enhanced mutate","text":"dev: remotes::install_github(\"talegari/tidier\") cran: install.packages(\"tidier\")","code":""},{"path":"/index.html","id":"acknowledgements","dir":"","previous_headings":"","what":"Acknowledgements","title":"Enhanced mutate","text":"tidier package deeply indebted two amazing packages people behind . dplyr: slider:","code":"Wickham H, François R, Henry L, Müller K, Vaughan D (2023). _dplyr: A Grammar of Data Manipulation_. R package version 1.1.0, . Vaughan D (2021). _slider: Sliding Window Functions_. R package version 0.2.2, ."},{"path":"/reference/mutate.html","id":null,"dir":"Reference","previous_headings":"","what":"Drop-in replacement for mutate — mutate","title":"Drop-in replacement for mutate — mutate","text":"Provides supercharged version mutate group_by, order_by aggregation arbitrary window frame around row.","code":""},{"path":"/reference/mutate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drop-in replacement for mutate — mutate","text":"","code":"mutate(x, ..., .by, .order_by, .frame, .index, .complete = FALSE)"},{"path":"/reference/mutate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Drop-in replacement for mutate — mutate","text":"x (data.frame) ... expressions passed mutate .(expression, optional: Yes) columns group .order_by (expression, optional: Yes) columns order .frame (vector, optional: Yes) Vector length 2 indicating number rows consider current row. argument .index provided (typically column type date datetime), can interval objects. See examples. .index (expression, optional: Yes) index column .complete (flag, default: FALSE) passed slider::slide / slider::slide_vec. function evaluated complete windows ? FALSE, default, partial computations allowed.","code":""},{"path":"/reference/mutate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drop-in replacement for mutate — mutate","text":"data.frame","code":""},{"path":"/reference/mutate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Drop-in replacement for mutate — mutate","text":"window function returns value every input row dataframe based group rows (frame) neighborhood input row. function implements computation groups (partition_by SQL) predefined order (order_by SQL) across neighborhood rows (frame) defined (, ) /number rows corresponding row /interval objects (ex: c(days(2), days(1))) implementation inspired spark's window API. Implementation Details: Iteration per row window implemented using versatile slider. Application window aggregation can optionally run parallel multiple groups (see argument .) setting future parallel backend. implemented using furrr package. function subsumes regular usecases mutate","code":""},{"path":[]},{"path":"/reference/mutate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Drop-in replacement for mutate — mutate","text":"","code":"library(\"magrittr\") # example 1 # Using iris dataset, # compute cumulative mean of column `Sepal.Length` # ordered by `Petal.Width` and `Sepal.Width` columns # grouped by `Petal.Length` column iris %>% mutate(sl_mean = mean(Sepal.Length), .order_by = c(Petal.Width, Sepal.Width), .by = Petal.Length, .frame = c(Inf, 0), ) %>% dplyr::slice_min(n = 3, Petal.Width, by = Species) #> #> Attaching package: ‘purrr’ #> The following object is masked from ‘package:magrittr’: #> #> set_names #> # A tibble: 15 × 6 #> Petal.Length Sepal.Length Sepal.Width Petal.Width Species sl_mean #> #> 1 1.1 4.3 3 0.1 setosa 4.3 #> 2 1.4 4.9 3.6 0.1 setosa 4.85 #> 3 1.4 4.8 3 0.1 setosa 4.8 #> 4 1.5 4.9 3.1 0.1 setosa 4.9 #> 5 1.5 5.2 4.1 0.1 setosa 5.05 #> 6 4.1 5.8 2.7 1 versicolor 5.8 #> 7 3.3 5 2.3 1 versicolor 5 #> 8 3.3 4.9 2.4 1 versicolor 4.95 #> 9 3.7 5.5 2.4 1 versicolor 5.5 #> 10 3.5 5 2 1 versicolor 5 #> 11 4 6 2.2 1 versicolor 6 #> 12 3.5 5.7 2.6 1 versicolor 5.35 #> 13 5.6 6.1 2.6 1.4 virginica 6.1 #> 14 5 6 2.2 1.5 virginica 6 #> 15 5.1 6.3 2.8 1.5 virginica 6.3 # example 2 # Using a sample airquality dataset, # compute mean temp over last seven days in the same month for every row set.seed(101) airquality %>% # create date column dplyr::mutate(date_col = lubridate::make_date(1973, Month, Day)) %>% # create gaps by removing some days dplyr::slice_sample(prop = 0.8) %>% dplyr::arrange(date_col) %>% # compute mean temperature over last seven days in the same month tidier::mutate(avg_temp_over_last_week = mean(Temp, na.rm = TRUE), .order_by = Day, .by = Month, .frame = c(lubridate::days(7), # 7 days before current row lubridate::days(-1) # do not include current row ), .index = date_col ) #> # A tibble: 122 × 8 #> Month Ozone Solar.R Wind Temp Day date_col avg_temp_over_last_week #> #> 1 7 64 175 4.6 83 5 1973-07-05 83.5 #> 2 8 16 77 7.4 82 3 1973-08-03 81 #> 3 5 36 118 8 72 2 1973-05-02 NaN #> 4 6 NA 286 8.6 78 1 1973-06-01 NaN #> 5 8 78 NA 6.9 86 4 1973-08-04 81.3 #> 6 5 12 149 12.6 74 3 1973-05-03 72 #> 7 9 78 197 5.1 92 2 1973-09-02 NaN #> 8 6 NA 242 16.1 67 3 1973-06-03 78 #> 9 7 77 276 5.1 88 7 1973-07-07 83.4 #> 10 8 35 NA 7.4 85 5 1973-08-05 82.5 #> # ℹ 112 more rows"},{"path":"/reference/mutate_.html","id":null,"dir":"Reference","previous_headings":"","what":"Drop-in replacement for mutate — mutate_","title":"Drop-in replacement for mutate — mutate_","text":"Provides supercharged version mutate group_by, order_by aggregation arbitrary window frame around row. function allows arguments passed strings instead expressions.","code":""},{"path":"/reference/mutate_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drop-in replacement for mutate — mutate_","text":"","code":"mutate_( x, ..., .by, .order_by, .frame, .index, .desc = FALSE, .complete = FALSE )"},{"path":"/reference/mutate_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Drop-in replacement for mutate — mutate_","text":"x (data.frame) ... expressions passed mutate .(character vector, optional: Yes) columns group .order_by (character vector, optional: Yes) columns order .frame (vector, optional: Yes) Vector length 2 indicating number rows consider current row. argument .index provided (typically column type date datetime), can interval objects. See examples. .index (string, optional: Yes) name index column .desc (logical_vector, default: FALSE) bool logical vector length .order_by. .complete (flag, default: FALSE) passed slider::slide / slider::slide_vec. function evaluated complete windows ? FALSE, default, partial computations allowed.","code":""},{"path":"/reference/mutate_.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drop-in replacement for mutate — mutate_","text":"data.frame","code":""},{"path":"/reference/mutate_.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Drop-in replacement for mutate — mutate_","text":"window function returns value every input row dataframe based group rows (frame) neighborhood input row. function implements computation groups (partition_by SQL) predefined order (order_by SQL) across neighborhood rows (frame) defined (, ) /number rows corresponding row /interval objects (ex: c(days(2), days(1))) implementation inspired spark's window API. Implementation Details: Iteration per row window implemented using versatile slider. Application window aggregation can optionally run parallel multiple groups (see argument .) setting future parallel backend. implemented using furrr package. function subsumes regular usecases mutate","code":""},{"path":[]},{"path":"/reference/mutate_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Drop-in replacement for mutate — mutate_","text":"","code":"library(\"magrittr\") # example 1 # Using iris dataset, # compute cumulative mean of column `Sepal.Length` # ordered by `Petal.Width` and `Sepal.Width` columns # grouped by `Petal.Length` column iris %>% mutate_(sl_mean = mean(Sepal.Length), .order_by = c(\"Petal.Width\", \"Sepal.Width\"), .by = \"Petal.Length\", .frame = c(Inf, 0), ) %>% dplyr::slice_min(n = 3, Petal.Width, by = Species) #> # A tibble: 15 × 6 #> Petal.Length Sepal.Length Sepal.Width Petal.Width Species sl_mean #> #> 1 1.4 4.8 3 0.1 setosa 4.8 #> 2 1.4 4.9 3.6 0.1 setosa 4.85 #> 3 1.1 4.3 3 0.1 setosa 4.3 #> 4 1.5 4.9 3.1 0.1 setosa 4.9 #> 5 1.5 5.2 4.1 0.1 setosa 5.05 #> 6 3.5 5 2 1 versicolor 5 #> 7 3.5 5.7 2.6 1 versicolor 5.35 #> 8 3.3 4.9 2.4 1 versicolor 4.95 #> 9 3.3 5 2.3 1 versicolor 5 #> 10 4.1 5.8 2.7 1 versicolor 5.8 #> 11 4 6 2.2 1 versicolor 6 #> 12 3.7 5.5 2.4 1 versicolor 5.5 #> 13 5.6 6.1 2.6 1.4 virginica 6.1 #> 14 5.1 6.3 2.8 1.5 virginica 6.3 #> 15 5 6 2.2 1.5 virginica 6 # example 2 # Using a sample airquality dataset, # compute mean temp over last seven days in the same month for every row set.seed(101) airquality %>% # create date column dplyr::mutate(date_col = lubridate::make_date(1973, Month, Day)) %>% # create gaps by removing some days dplyr::slice_sample(prop = 0.8) %>% dplyr::arrange(date_col) %>% # compute mean temperature over last seven days in the same month tidier::mutate(avg_temp_over_last_week = mean(Temp, na.rm = TRUE), .order_by = \"Day\", .by = \"Month\", .frame = c(lubridate::days(7), # 7 days before current row lubridate::days(-1) # do not include current row ), .index = \"date_col\" ) #> # A tibble: 122 × 8 #> Month Ozone Solar.R Wind Temp Day date_col avg_temp_over_last_week #> #> 1 5 36 118 8 72 2 1973-05-02 NaN #> 2 5 12 149 12.6 74 3 1973-05-03 72 #> 3 5 18 313 11.5 62 4 1973-05-04 73 #> 4 5 NA NA 14.3 56 5 1973-05-05 69.3 #> 5 5 28 NA 14.9 66 6 1973-05-06 66 #> 6 5 23 299 8.6 65 7 1973-05-07 66 #> 7 5 19 99 13.8 59 8 1973-05-08 65.8 #> 8 5 8 19 20.1 61 9 1973-05-09 64.9 #> 9 5 NA 194 8.6 69 10 1973-05-10 63.3 #> 10 5 16 256 9.7 69 12 1973-05-12 62.7 #> # ℹ 112 more rows"},{"path":"/reference/remove_common_nested_columns.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove non-list columns when same are present in a list column — remove_common_nested_columns","title":"Remove non-list columns when same are present in a list column — remove_common_nested_columns","text":"Remove non-list columns present list column","code":""},{"path":"/reference/remove_common_nested_columns.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove non-list columns when same are present in a list column — remove_common_nested_columns","text":"","code":"remove_common_nested_columns(df, list_column)"},{"path":"/reference/remove_common_nested_columns.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove non-list columns when same are present in a list column — remove_common_nested_columns","text":"df input dataframe list_column Name expr column list named lists","code":""},{"path":"/reference/remove_common_nested_columns.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove non-list columns when same are present in a list column — remove_common_nested_columns","text":"dataframe","code":""},{"path":"/news/index.html","id":"tidier-010-on-github-2023-06-01","dir":"Changelog","previous_headings":"","what":"tidier 0.1.0 (on github: 2023-06-01)","title":"tidier 0.1.0 (on github: 2023-06-01)","text":"Exposed slider’s .complete argument tidier::mutate bugfix: mutate can now modify column (name) sliding operation.","code":""},{"path":"/news/index.html","id":"tidier-001","dir":"Changelog","previous_headings":"","what":"tidier 0.0.1","title":"tidier 0.0.1","text":"CRAN release: 2023-04-27 Added NEWS.md file track changes package.","code":""}] diff --git a/docs/sitemap.xml b/docs/sitemap.xml index fc443c6..c5205ab 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -24,4 +24,7 @@ /reference/mutate_.html + + /reference/remove_common_nested_columns.html + diff --git a/man/mutate.Rd b/man/mutate.Rd index c8c2f97..77e1aee 100644 --- a/man/mutate.Rd +++ b/man/mutate.Rd @@ -4,7 +4,7 @@ \alias{mutate} \title{Drop-in replacement for \code{\link[dplyr]{mutate}}} \usage{ -mutate(x, ..., .by, .order_by, .frame, .index) +mutate(x, ..., .by, .order_by, .frame, .index, .complete = FALSE) } \arguments{ \item{x}{(data.frame)} @@ -23,6 +23,11 @@ and after can be objects. See examples.} \item{.index}{(expression, optional: Yes) index column} + +\item{.complete}{(flag, default: FALSE) This will be passed to +\code{slider::slide} / \code{slider::slide_vec}. Should the function be evaluated on +complete windows only? If FALSE, the default, then partial computations +will be allowed.} } \value{ data.frame @@ -77,34 +82,22 @@ iris \%>\% # Using a sample airquality dataset, # compute mean temp over last seven days in the same month for every row +set.seed(101) airquality \%>\% # create date column - dplyr::mutate(date_col = as.Date(paste("1973", - stringr::str_pad(Month, - width = 2, - side = "left", - pad = "0" - ), - stringr::str_pad(Day, - width = 2, - side = "left", - pad = "0" - ), - sep = "-" - ) - ) - ) \%>\% + dplyr::mutate(date_col = lubridate::make_date(1973, Month, Day)) \%>\% # create gaps by removing some days dplyr::slice_sample(prop = 0.8) \%>\% + dplyr::arrange(date_col) \%>\% # compute mean temperature over last seven days in the same month - mutate(avg_temp_over_last_week = mean(Temp, na.rm = TRUE), - .order_by = Day, - .by = Month, - .frame = c(lubridate::days(7), # 7 days before current row - lubridate::days(-1) # do not include current row - ), - .index = date_col - ) + tidier::mutate(avg_temp_over_last_week = mean(Temp, na.rm = TRUE), + .order_by = Day, + .by = Month, + .frame = c(lubridate::days(7), # 7 days before current row + lubridate::days(-1) # do not include current row + ), + .index = date_col + ) } \seealso{ mutate_ diff --git a/man/mutate_.Rd b/man/mutate_.Rd index c8ffbb6..f2953e4 100644 --- a/man/mutate_.Rd +++ b/man/mutate_.Rd @@ -4,7 +4,16 @@ \alias{mutate_} \title{Drop-in replacement for \code{\link[dplyr]{mutate}}} \usage{ -mutate_(x, ..., .by, .order_by, .frame, .index, .desc = FALSE) +mutate_( + x, + ..., + .by, + .order_by, + .frame, + .index, + .desc = FALSE, + .complete = FALSE +) } \arguments{ \item{x}{(data.frame)} @@ -26,6 +35,11 @@ objects. See examples.} \item{.desc}{(logical_vector, default: FALSE) bool or logical vector of same length as \code{.order_by}.} + +\item{.complete}{(flag, default: FALSE) This will be passed to +\code{slider::slide} / \code{slider::slide_vec}. Should the function be evaluated on +complete windows only? If FALSE, the default, then partial computations +will be allowed.} } \value{ data.frame @@ -81,34 +95,22 @@ iris \%>\% # Using a sample airquality dataset, # compute mean temp over last seven days in the same month for every row +set.seed(101) airquality \%>\% # create date column - dplyr::mutate(date_col = as.Date(paste("1973", - stringr::str_pad(Month, - width = 2, - side = "left", - pad = "0" - ), - stringr::str_pad(Day, - width = 2, - side = "left", - pad = "0" - ), - sep = "-" - ) - ) - ) \%>\% + dplyr::mutate(date_col = lubridate::make_date(1973, Month, Day)) \%>\% # create gaps by removing some days dplyr::slice_sample(prop = 0.8) \%>\% + dplyr::arrange(date_col) \%>\% # compute mean temperature over last seven days in the same month - mutate_(avg_temp_over_last_week = mean(Temp, na.rm = TRUE), - .order_by = "Day", - .by = "Month", - .frame = c(lubridate::days(7), # 7 days before current row - lubridate::days(-1) # do not include current row - ), - .index = "date_col" - ) + tidier::mutate(avg_temp_over_last_week = mean(Temp, na.rm = TRUE), + .order_by = "Day", + .by = "Month", + .frame = c(lubridate::days(7), # 7 days before current row + lubridate::days(-1) # do not include current row + ), + .index = "date_col" + ) } \seealso{ mutate diff --git a/man/remove_common_nested_columns.Rd b/man/remove_common_nested_columns.Rd new file mode 100644 index 0000000..7a49d94 --- /dev/null +++ b/man/remove_common_nested_columns.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/mutate.R +\name{remove_common_nested_columns} +\alias{remove_common_nested_columns} +\title{Remove non-list columns when same are present in a list column} +\usage{ +remove_common_nested_columns(df, list_column) +} +\arguments{ +\item{df}{input dataframe} + +\item{list_column}{Name or expr of the column which is a list of named lists} +} +\value{ +dataframe +} +\description{ +Remove non-list columns when same are present in a list column +} diff --git a/tests/testthat/tests_tidier.R b/tests/testthat/tests_tidier.R index fc6a16c..e7aa4d0 100644 --- a/tests/testthat/tests_tidier.R +++ b/tests/testthat/tests_tidier.R @@ -3,12 +3,12 @@ test_that("basic mutate", { res = iris %>% mutate(sl_pl_1 = Sepal.Length + 1) - testthat::expect(inherits(res, "data.frame")) + testthat::expect(inherits(res, "data.frame"), "not a df") res = iris %>% mutate_(sl_pl_1 = Sepal.Length + 1) - testthat::expect(inherits(res, "data.frame")) + testthat::expect(inherits(res, "data.frame"), "not a df") }) test_that("order_by without by", { @@ -19,7 +19,7 @@ test_that("order_by without by", { ) %>% dplyr::arrange(Petal.Width) - testthat::expect(inherits(res, "data.frame")) + testthat::expect(inherits(res, "data.frame"), "not a df") # involved order_by res = iris %>% @@ -28,7 +28,7 @@ test_that("order_by without by", { ) %>% dplyr::arrange(desc(Petal.Width), Sepal.Length) - testthat::expect(inherits(res, "data.frame")) + testthat::expect(inherits(res, "data.frame"), "not a df") res = iris %>% mutate_(sl_pl_1 = cumsum(Sepal.Length), @@ -36,7 +36,7 @@ test_that("order_by without by", { ) %>% dplyr::arrange(Petal.Width) - testthat::expect(inherits(res, "data.frame")) + testthat::expect(inherits(res, "data.frame"), "not a df") res = iris %>% mutate_(sl_pl_1 = cumsum(Sepal.Length), @@ -45,7 +45,7 @@ test_that("order_by without by", { ) %>% dplyr::arrange(desc(Petal.Width), Sepal.Length) - testthat::expect(inherits(res, "data.frame")) + testthat::expect(inherits(res, "data.frame"), "not a df") }) test_that("order_by with by", { @@ -59,7 +59,7 @@ test_that("order_by with by", { dplyr::select(-rn) %>% dplyr::slice_min(n = 3, Petal.Width, by = Species) - testthat::expect(inherits(res, "data.frame")) + testthat::expect(inherits(res, "data.frame"), "not a df") res = iris %>% dplyr::mutate(rn = dplyr::row_number()) %>% @@ -70,7 +70,7 @@ test_that("order_by with by", { dplyr::select(-rn) %>% dplyr::slice_min(n = 3, Petal.Width, by = Species) - testthat::expect(inherits(res, "data.frame")) + testthat::expect(inherits(res, "data.frame"), "not a df") }) test_that("order_by, with by, with frame", { @@ -83,7 +83,7 @@ test_that("order_by, with by, with frame", { ) %>% dplyr::slice_min(n = 3, Petal.Width, by = Species) - testthat::expect(inherits(res, "data.frame")) + testthat::expect(inherits(res, "data.frame"), "not a df") res = iris %>% mutate_(sl_mean = mean(Sepal.Length), @@ -93,7 +93,7 @@ test_that("order_by, with by, with frame", { ) %>% dplyr::slice_min(n = 3, Petal.Width, by = Species) - testthat::expect(inherits(res, "data.frame")) + testthat::expect(inherits(res, "data.frame"), "not a df") }) test_that("order_by, with by, with frame, with index", { @@ -126,7 +126,7 @@ test_that("order_by, with by, with frame, with index", { ), .index = date_col ) - testthat::expect(inherits(res, "data.frame")) + testthat::expect(inherits(res, "data.frame"), "not a df") res = airquality %>% # create date column @@ -156,5 +156,114 @@ test_that("order_by, with by, with frame, with index", { ), .index = "date_col" ) - testthat::expect(inherits(res, "data.frame")) + testthat::expect(inherits(res, "data.frame"), "not a df") +}) + +test_that("order_by, with by, with frame, same column name", { + + res = iris %>% + mutate(Sepal.Length = mean(Sepal.Length), + .frame = c(Inf, 0), + .order_by = Petal.Width, + .by = c(Petal.Length, Sepal.Width) + ) %>% + dplyr::slice_min(n = 3, Petal.Width, by = Species) + + testthat::expect(inherits(res, "data.frame"), "not a df") + + res = iris %>% + mutate_(Sepal.Length = mean(Sepal.Length), + .frame = c(Inf, 0), + .order_by = "Petal.Width", + .by = c("Species", "Petal.Length") + ) %>% + dplyr::slice_min(n = 3, Petal.Width, by = Species) + + testthat::expect(inherits(res, "data.frame"), "not a df") +}) + +test_that("order_by, with by, with frame, same column name", { + + res = iris %>% + mutate(Sepal.Length = mean(Sepal.Length), + .frame = c(Inf, 0), + .order_by = Petal.Width, + .by = c(Petal.Length, Sepal.Width) + ) %>% + dplyr::slice_min(n = 3, Petal.Width, by = Species) + + testthat::expect(inherits(res, "data.frame"), "not a df") + + res = iris %>% + mutate_(Sepal.Length = mean(Sepal.Length), + .frame = c(Inf, 0), + .order_by = "Petal.Width", + .by = c("Species", "Petal.Length") + ) %>% + dplyr::slice_min(n = 3, Petal.Width, by = Species) + + testthat::expect(inherits(res, "data.frame"), "not a df") +}) + +test_that("order_by, with by, with frame, with index, with same column", { + + res = airquality %>% + # create date column + dplyr::mutate(date_col = as.Date(paste("1973", + stringr::str_pad(Month, + width = 2, + side = "left", + pad = "0" + ), + stringr::str_pad(Day, + width = 2, + side = "left", + pad = "0" + ), + sep = "-" + ) + ) + ) %>% + # create gaps by removing some days + dplyr::slice_sample(prop = 0.8) %>% + # compute mean temperature over last seven days in the same month + mutate(Temp = mean(Temp, na.rm = TRUE), + .order_by = Day, + .by = Month, + .frame = c(lubridate::days(7), # 7 days before current row + lubridate::days(-1) # do not include current row + ), + .index = date_col + ) + testthat::expect(inherits(res, "data.frame"), "not a df") + + res = airquality %>% + # create date column + dplyr::mutate(date_col = as.Date(paste("1973", + stringr::str_pad(Month, + width = 2, + side = "left", + pad = "0" + ), + stringr::str_pad(Day, + width = 2, + side = "left", + pad = "0" + ), + sep = "-" + ) + ) + ) %>% + # create gaps by removing some days + dplyr::slice_sample(prop = 0.8) %>% + # compute mean temperature over last seven days in the same month + mutate_(Temp = mean(Temp, na.rm = TRUE), + .order_by = "Day", + .by = "Month", + .frame = c(lubridate::days(7), # 7 days before current row + lubridate::days(-1) # do not include current row + ), + .index = "date_col" + ) + testthat::expect(inherits(res, "data.frame"), "not a df") })