{tidypivot} allows you to create tables by describing them (like ggplot2 plot description/declaration)

{tidypivot} allows you to create tables by describing them (like ggplot2 plot description/declaration)
declarative table creation with ggplot2
- Status quo table creation: Harder than it should be?
  - pivotr function: toward declarative table generation
toward a piped workflow
examples/derivative
filling cells with examples from data.
proportions helpers

{tidypivot} allows you to create tables by describing them (like ggplot2 plot description/declaration)

note: see original discussion here: https://evamaerey.github.io/mytidytuesday/2022-02-14-tables/tables.html and thoughtful contributions from @shannonpileggi and @brshallow EvaMaeRey/mytidytuesday#3

And, you know, I’d get a dataset. And, in my head I could very clearly kind of picture, I want to put this on the x-axis. Let’s put this on the y-axis, draw a line, put some points here, break it up by this variable. And then, like, getting that vision out of my head, and into reality, it’s just really, really hard. Just, like, felt harder than it should be. Like, there’s a lot of custom programming involved, where I just felt, like, to me, I just wanted to say, like, you know, this is what I’m thinking, this is how I’m picturing this plot. Like you’re the computer ‘Go and do it’. … and I’d also been reading about the Grammar of Graphics by Leland Wilkinson, I got to meet him a couple of times and … I was, like, this book has been, like, written for me. - https://www.trifacta.com/podcast/tidy-data-with-hadley-wickham/

declarative table creation with ggplot2

library(ggplot2)
StatSum$default_aes <- aes(label = after_stat(n))

# I want to put this on the x-axis (cols)
tidytitanic::tidy_titanic |>
  ggplot(
      # I want to put this on the x-axis (cols)
  aes(x = sex, 
      # I want to put this on the y- axis (rows)
      y = survived)
  )

# grouping and computation happen in one step, filling in 'table'
last_plot() + 
  stat_sum(geom = "text")

Status quo table creation: Harder than it should be?

1. grouping
1. compute
1. pivot

pivotr function: toward declarative table generation

Under the hood:

group by rows and columns
value in data to consider (1 if not specified)
wt, weight the value (1 if not specified)
fun - do an operation (on value) within group

But API:

describe layout of table (rows and cols) and compute (default to count records)

data_filter <- function(data, filter = TRUE){

  data <- data |>
      dplyr::filter({{filter}})
  
  data

}

data_define_value <- function(data, value = NULL, wt = NULL){
  
    value_quo <- rlang::enquo(value)
    wt_quo    <- rlang::enquo(wt)

      if(rlang::quo_is_null(value_quo) ){

      ## adding a value as 1 if there is none
      data <- data |>
        dplyr::mutate(value = 1)
      
    }else{
      
      data <- data |> 
          dplyr::mutate(value = {{value}})
        
    }
    
    #### weighting ####
    
    if(!rlang::quo_is_null(wt_quo) ){
     
      data <- data |>
        dplyr::mutate(value = .data$value * {{wt}}) 
   }
    
    data
  
}


data_to_grouped <- function(data, cols, rows){
  
    ### grouping by tabulation vars col and row
    data |>
      dplyr::group_by(dplyr::across(c({{cols}}, {{rows}})),
                      .drop = TRUE)
  
  
}


data_grouped_to_summarized <- function(data, fun = NULL){
  
      if(is.null(fun))  {fun <- sum}

    ## adding a value as 1 if there is none
  
    ### summarizing ####
    
    data |>
        dplyr::summarise(summary = fun(.data$value))
  
  
}


data_summarized_to_proportioned <- function(data, prop = F, percent = F, within = NULL, round = 2){ 
    # proportion case or percent
  
    within_quo        <- rlang::enquo(within)
    # totals_within_quo <- rlang::enquo(totals_within)

    if(is.null(prop)) {prop <- FALSE}

    if(prop|percent){
      
      mult <- ifelse(percent, 100, 1)
      if(is.null(round)){round <- ifelse(percent, 1, 3)}

      # prop is across all data
        if(rlang::quo_is_null(within_quo) ){

            data <- data |>
              dplyr::ungroup() |>
              dplyr::mutate(prop = round(.data$summary*mult/sum(.data$summary), round))

        # prop is within categories specified by within variable
        }else{

              data <- data |>
                dplyr::ungroup() |>
                dplyr::group_by(dplyr::across(c({{within}})),
                                .drop = FALSE) |>
                dplyr::mutate(prop = round(.data$summary*mult/sum(.data$summary), round))

        }
    }
    
  if(prop|percent){data$display <- data$prop}else{data$display <- data$summary}
  
  data

}


data_proportioned_to_pivoted <- function(data, pivot = TRUE, cols = NULL){
  
    cols_quo  <- rlang::enquo(cols)

    tidy <- data |>
      dplyr::ungroup()

    # do not pivot if argument pivot false or if no columns specified
    if(pivot == F){

      tidy 

      # otherwise pivot by columns
    }else
    
    
    if(rlang::quo_is_null(cols_quo) & pivot){
      
      tidy <- tidy |> dplyr::select(-summary)
      if(!is.null(data$prop)|!is.null(data$percent)){
        tidy <- tidy |>  dplyr::select(-prop)
      }
        
      tidy |>
        dplyr::rename(value = display)
      
    } else
      
    
    if(!rlang::quo_is_null(cols_quo) & pivot){
      # keep only display column, and tabulation vars
      tidy <- tidy |> dplyr::select(-summary)
      if(!is.null(data$prop)|!is.null(data$percent)){
        tidy <- tidy |>  dplyr::select(-prop)
      }

      tidy |>
        tidyr::pivot_wider(names_from = {{cols}}, values_from = display)

    }

  }

tidytitanic::flat_titanic |> 
  dplyr::filter(freq > 30) |>
  data_define_value(value = freq) |> 
  data_to_grouped(rows = survived, cols = sex) |>
  data_grouped_to_summarized() |>
  data_summarized_to_proportioned(percent = T, within = survived) |>
  data_proportioned_to_pivoted(cols = sex)
#> # A tibble: 2 × 3
#>   survived  Male Female
#>   <fct>    <dbl>  <dbl>
#> 1 No        93.9   6.13
#> 2 Yes       52.3  47.7

pivotr <- function(data,
                   rows = NULL,
                   cols = NULL,
                   
                   value = NULL,
                   wt = NULL,
                       
                   fun = NULL,
                       
                   filter = TRUE,
                   prop = FALSE,
                   percent = FALSE,
                   round = NULL,
                       
                   within = NULL,

                   pivot = TRUE
){

  
  data |> 
  data_filter({{filter}}) |>
  data_define_value(value = {{value}}, wt = {{wt}}) |> 
  data_to_grouped(rows = {{rows}}, cols = {{cols}}) |>
  data_grouped_to_summarized(fun = fun) |>
  data_summarized_to_proportioned(prop = prop, percent = percent, within = {{within}}, round = round) |>
  data_proportioned_to_pivoted(pivot = pivot, cols = {{cols}})
  
}

tidytitanic::flat_titanic |> 
  pivotr(value = freq, rows = survived, cols = sex, percent = T, within = survived)
#> # A tibble: 2 × 3
#>   survived  Male Female
#>   <fct>    <dbl>  <dbl>
#> 1 No        91.5    8.5
#> 2 Yes       51.6   48.4

tidytitanic::flat_titanic |> 
  pivotr(value = freq, rows = survived, cols = sex, filter = sex == "Female")
#> # A tibble: 2 × 2
#>   survived Female
#>   <fct>     <dbl>
#> 1 No          126
#> 2 Yes         344

library(tidytitanic)

tidy_titanic |> pivotr()
#> # A tibble: 1 × 1
#>   value
#>   <dbl>
#> 1  2201

tidy_titanic |> pivotr(rows = sex, cols = survived)
#> # A tibble: 2 × 3
#>   sex       No   Yes
#>   <fct>  <dbl> <dbl>
#> 1 Male    1364   367
#> 2 Female   126   344

tidy_titanic |> pivotr(rows = c(sex, age), cols = survived)
#> # A tibble: 4 × 4
#>   sex    age      No   Yes
#>   <fct>  <fct> <dbl> <dbl>
#> 1 Male   Child    35    29
#> 2 Male   Adult  1329   338
#> 3 Female Child    17    28
#> 4 Female Adult   109   316

tidy_titanic |> pivotr(rows = sex, cols = survived, pivot = F)
#> # A tibble: 4 × 4
#>   survived sex    summary display
#>   <fct>    <fct>    <dbl>   <dbl>
#> 1 No       Male      1364    1364
#> 2 No       Female     126     126
#> 3 Yes      Male       367     367
#> 4 Yes      Female     344     344

flat_titanic |> pivotr(rows = sex, value = freq, prop = TRUE)
#> # A tibble: 2 × 2
#>   sex    value
#>   <fct>  <dbl>
#> 1 Male   0.786
#> 2 Female 0.214

flat_titanic |> pivotr(rows = sex, cols = survived, value = freq, prop = TRUE)
#> # A tibble: 2 × 3
#>   sex       No   Yes
#>   <fct>  <dbl> <dbl>
#> 1 Male   0.62  0.167
#> 2 Female 0.057 0.156

flat_titanic |> pivotr(rows = sex, cols = survived, value = freq, prop = TRUE, within = sex)
#> # A tibble: 2 × 3
#>   sex       No   Yes
#>   <fct>  <dbl> <dbl>
#> 1 Male   0.788 0.212
#> 2 Female 0.268 0.732

toward a piped workflow

https://evamaerey.github.io/mytidytuesday/2024-07-02-s3-tables/s3-tables-tidypivot.html

new_tidypivot <- function(data = data.frame(),
                          rows = NULL,
                          cols = NULL,
                          value = NULL,
                          wt = NULL,
                          fun = NULL,
                          filter = TRUE,
                          prop = FALSE,
                          percent = FALSE,
                          round = NULL,
                          within = NULL,
                          pivot = TRUE) {

  # table specification components !
  tp <- list(
    data = data,
    rows = rows,
    cols = cols,
    value = value,
    wt = wt,
    fun = fun,
    prop = prop,
    percent = percent,
    round = round,
    within = within,
    pivot = pivot
    # more 'slots' to be added
  )

  # declare class 'tidypivot'
  class(tp) <- "tidypivot"

  # Return the created object
  invisible(tp)

}


print.tidypivot <- function(tp){
  
  print(do.call(pivotr, tp))
  
  invisible(tp)
  
}

#' @export
ggtable <- function(data = NULL){
  
  # thedata <<- data # don't love this
  data <- data %||% data.frame()
  
  tp <- new_tidypivot()
  
  tp$data <- data
  
  last_tp <<- tp
  
  tp

}


#' @export
last_table <- function(){
  
  last_tp
  
}

ggtable() 
#> # A tibble: 1 × 1
#>   value
#>   <dbl>
#> 1     0


tidytitanic::tidy_titanic |> head()
#>   id class  sex   age survived
#> 1  1   3rd Male Child       No
#> 2  2   3rd Male Child       No
#> 3  3   3rd Male Child       No
#> 4  4   3rd Male Child       No
#> 5  5   3rd Male Child       No
#> 6  6   3rd Male Child       No

ggtable(tidytitanic::tidy_titanic) 
#> # A tibble: 1 × 1
#>   value
#>   <dbl>
#> 1  2201


last_table()
#> # A tibble: 1 × 1
#>   value
#>   <dbl>
#> 1  2201

#' @export
set_rows <- function(tp, rows = NULL){
  
  tp$rows <- enquo(rows)
  
  last_tp <<- tp
  
  tp

  
}


#' @export
set_cols <- function(tp, cols = NULL){
  
  tp$cols <- enquo(cols)

  last_tp <<- tp
  
  tp
  

}

#' @export
set_filter <- function(tp, filter = TRUE){
  
  if(!filter){tp$filter <- !!rlang::enquo(filter)}
  
  last_tp <<- tp
  
  tp
  

}

ggtable(tidytitanic::tidy_titanic) |>
  set_rows(sex) |>
  set_cols(survived) |>
  set_filter(TRUE)
#> # A tibble: 2 × 3
#>   sex       No   Yes
#>   <fct>  <dbl> <dbl>
#> 1 Male    1364   367
#> 2 Female   126   344

# last_table |>
#   set_filter(sex == "Female")

#' @export
set_fun <- function(tp, fun = sum){

  tp$fun <- fun

  last_tp <<- tp

  tp
  
}


#' @export
set_value <- function(tp, value = NULL){
  
  tp$value <- enquo(value)

  last_tp <<- tp
  
  tp
  
}


#' @export
set_wt <- function(tp, wt = NULL){
  
  tp$wt <- enquo(wt)

  last_tp <<- tp
  
  tp
  
}


#' @export
set_weight <- function(tp, weight = NULL){
  
  tp$weight <- enquo(weight)
  
  last_tp <<- tp
  
  tp
  
}


#' @export
set_prop <- function(tp, within = NULL){
  
  tp$percent <- FALSE
  tp$prop <- TRUE
  tp$within <- enquo(within)
  
  last_tp <<- tp
  
  tp
  
}

#' @export
set_percent <- function(tp, within = NULL){
  
  tp$prop <- FALSE
  tp$percent <- TRUE
  tp$within <- enquo(within)
  
  last_tp <<- tp
  
  tp
  
}


#' @export
set_within <- function(tp, within = NULL){
  
  tp$within <- enquo(within)
  
  last_tp <<- tp
  
  tp
  
}

#' @export
no_pivot <- function(tp){
  
  tp$pivot <- FALSE

  last_tp <<- tp

  tp  
  
}

collect <- function(tp){
  
  do.call(pivotr, tp)
  
}

tidytitanic::flat_titanic 
#>    class    sex   age survived freq
#> 1    1st   Male Child       No    0
#> 2    2nd   Male Child       No    0
#> 3    3rd   Male Child       No   35
#> 4   Crew   Male Child       No    0
#> 5    1st Female Child       No    0
#> 6    2nd Female Child       No    0
#> 7    3rd Female Child       No   17
#> 8   Crew Female Child       No    0
#> 9    1st   Male Adult       No  118
#> 10   2nd   Male Adult       No  154
#> 11   3rd   Male Adult       No  387
#> 12  Crew   Male Adult       No  670
#> 13   1st Female Adult       No    4
#> 14   2nd Female Adult       No   13
#> 15   3rd Female Adult       No   89
#> 16  Crew Female Adult       No    3
#> 17   1st   Male Child      Yes    5
#> 18   2nd   Male Child      Yes   11
#> 19   3rd   Male Child      Yes   13
#> 20  Crew   Male Child      Yes    0
#> 21   1st Female Child      Yes    1
#> 22   2nd Female Child      Yes   13
#> 23   3rd Female Child      Yes   14
#> 24  Crew Female Child      Yes    0
#> 25   1st   Male Adult      Yes   57
#> 26   2nd   Male Adult      Yes   14
#> 27   3rd   Male Adult      Yes   75
#> 28  Crew   Male Adult      Yes  192
#> 29   1st Female Adult      Yes  140
#> 30   2nd Female Adult      Yes   80
#> 31   3rd Female Adult      Yes   76
#> 32  Crew Female Adult      Yes   20

ggtable(tidytitanic::flat_titanic) |>
  set_value(freq) |>
  set_rows(sex) |>
  set_cols(survived)
#> # A tibble: 2 × 3
#>   sex       No   Yes
#>   <fct>  <dbl> <dbl>
#> 1 Male    1364   367
#> 2 Female   126   344

last_table() |>
  set_percent()
#> # A tibble: 2 × 3
#>   sex       No   Yes
#>   <fct>  <dbl> <dbl>
#> 1 Male    62    16.7
#> 2 Female   5.7  15.6

last_table() |>
  set_prop()
#> # A tibble: 2 × 3
#>   sex       No   Yes
#>   <fct>  <dbl> <dbl>
#> 1 Male   0.62  0.167
#> 2 Female 0.057 0.156

last_table() |>
  set_prop(within = sex)
#> # A tibble: 2 × 3
#>   sex       No   Yes
#>   <fct>  <dbl> <dbl>
#> 1 Male   0.788 0.212
#> 2 Female 0.268 0.732

# a null table...
ggtable(tidytitanic::flat_titanic) |>
  set_value(NA) |>
  set_rows(sex)
#> # A tibble: 2 × 2
#>   sex    value
#>   <fct>  <int>
#> 1 Male      NA
#> 2 Female    NA

ggtable(tidytitanic::flat_titanic) |>
  set_wt(freq) |>
  set_rows(sex) |>
  set_cols(survived)
#> # A tibble: 2 × 3
#>   sex       No   Yes
#>   <fct>  <dbl> <dbl>
#> 1 Male    1364   367
#> 2 Female   126   344

last_table() |>
  set_fun(mean)
#> # A tibble: 2 × 3
#>   sex       No   Yes
#>   <fct>  <dbl> <dbl>
#> 1 Male   170.   45.9
#> 2 Female  15.8  43

last_table() |> 
  no_pivot()
#> # A tibble: 4 × 4
#>   survived sex    summary display
#>   <fct>    <fct>    <dbl>   <dbl>
#> 1 No       Male     170.    170. 
#> 2 No       Female    15.8    15.8
#> 3 Yes      Male      45.9    45.9
#> 4 Yes      Female    43      43


last_table() |>
  collect()
#> # A tibble: 4 × 4
#>   survived sex    summary display
#>   <fct>    <fct>    <dbl>   <dbl>
#> 1 No       Male     170.    170. 
#> 2 No       Female    15.8    15.8
#> 3 Yes      Male      45.9    45.9
#> 4 Yes      Female    43      43

# knitrExtra::chunk_names_get()

knitrExtra::chunk_to_dir("helpers")
knitrExtra::chunk_to_dir("pivotr")
knitrExtra::chunk_to_dir("piping")

library(tidyverse)
library(tidypivot)
ext_exports <- read_csv("https://raw.githubusercontent.com/EvaMaeRey/mytidytuesday/refs/heads/main/2024-11-19-gg-prefixes/exported_funs_exts_ggplot2_tidyverse_org.csv") %>% 
  mutate(prefix = str_extract(fun_exported, ".*?_")) %>% 
  mutate(prefix_long = str_extract(fun_exported, ".+_")) %>% 
  mutate(ind_classic_prefix = prefix %in% c("stat_", "geom_", "theme_", "scale_", "coord_", "facet_"))


ggtable(ext_exports)
#> # A tibble: 1 × 1
#>   value
#>   <dbl>
#> 1  5527

ggtable(ext_exports |> filter(ind_classic_prefix))
#> # A tibble: 1 × 1
#>   value
#>   <dbl>
#> 1  1992

last_table() |>
  set_rows(user) 
#> # A tibble: 86 × 2
#>    user                 value
#>    <chr>                <dbl>
#>  1 AckerDWM                 2
#>  2 AllanCameron            32
#>  3 Ather-Energy             6
#>  4 IndrajeetPatil           1
#>  5 LCBC-UiO                34
#>  6 LKremer                  1
#>  7 ProjectMOSAIC            8
#>  8 PursuitOfDataScience     4
#>  9 Ryo-N7                  54
#> 10 Selbosh                  7
#> # ℹ 76 more rows

last_table() |>
  set_cols(prefix)
#> # A tibble: 86 × 7
#>    user          coord_ facet_ geom_ scale_ stat_ theme_
#>    <chr>          <dbl>  <dbl> <dbl>  <dbl> <dbl>  <dbl>
#>  1 AllanCameron       1     NA    24      6     1     NA
#>  2 cidm-ph            1     NA     4     NA     2     NA
#>  3 davidchall         1     NA     1     NA     1      2
#>  4 easystats          1     NA    14     78    NA      6
#>  5 hrbrmstr           1     NA    11     10     6      9
#>  6 stefanedwards      5      2     3      2    NA     NA
#>  7 teunbrand          2      5    11     47     7      1
#>  8 davidgohel        NA      2    50     94    NA     NA
#>  9 earowang          NA      1     1     NA     1     NA
#> 10 erocoar           NA      1    12     NA     5     NA
#> # ℹ 76 more rows

# last_table() |>
#   set_rows(c(user, repo))


read_csv("https://raw.githubusercontent.com/EvaMaeRey/mytidytuesday/refs/heads/main/2024-12-10-ggplot2-layer-composition/ggplot2_exported_layer_fun_composition.csv") %>% 
  rename(prefix = fun_prefix) ->
ggplot2_layers_definers

ggplot2_layers_definers |>
  ggtable()
#> # A tibble: 1 × 1
#>   value
#>   <dbl>
#> 1   254

last_table() |>
  set_rows(type)
#> # A tibble: 3 × 2
#>   type     value
#>   <chr>    <dbl>
#> 1 geom        84
#> 2 position    85
#> 3 stat        85

last_table() |>
  set_rows(type) |>
  set_cols(default_or_fixed) |>
  set_rows(c(prefix, type))
#> # A tibble: 6 × 4
#>   prefix type     default fixed
#>   <chr>  <chr>      <dbl> <dbl>
#> 1 geom_  geom           2    50
#> 2 geom_  position      51     2
#> 3 geom_  stat          47     6
#> 4 stat_  geom          32    NA
#> 5 stat_  position      32    NA
#> 6 stat_  stat          NA    32

devtools::check()
devtools::install(pkg = ".", upgrade = "never")

examples/derivative

Here are some examples where you might have derivative functions

pivot_count <- function(...){

  # maybe a wt version...
  pivotr(fun = length, ...) 

  }

pivot_average <- function(...){
  
  mean_na_rm <- function(x){mean(x, na.rm = T)}
  
  pivotr(fun = mean_na_rm, ...) 
  
}

pivot_sum <- function(...){
  
  pivotr(fun = sum, ...)
  
}

pivot_empty <- function(...){
  
nar <- function(x) return(NA)

  pivotr(fun = nar, ...)

}

library(magrittr)
library(tidytitanic)

passengers <- readr::read_csv("https://raw.githubusercontent.com/clauswilke/dviz.supp/master/data-raw/titanic/Titanic.csv")

head(passengers)
#> # A tibble: 6 × 7
#>    ...1 Name                                 PClass   Age Sex   Survived SexCode
#>   <dbl> <chr>                                <chr>  <dbl> <chr>    <dbl>   <dbl>
#> 1     1 Allen, Miss Elisabeth Walton         1st    29    fema…        1       1
#> 2     2 Allison, Miss Helen Loraine          1st     2    fema…        0       1
#> 3     3 Allison, Mr Hudson Joshua Creighton  1st    30    male         0       0
#> 4     4 Allison, Mrs Hudson JC (Bessie Wald… 1st    25    fema…        0       1
#> 5     5 Allison, Master Hudson Trevor        1st     0.92 male         1       0
#> 6     6 Anderson, Mr Harry                   1st    47    male         1       0

tidy_titanic |> pivot_count(rows = sex)
#> # A tibble: 2 × 2
#>   sex    value
#>   <fct>  <int>
#> 1 Male    1731
#> 2 Female   470
tidy_titanic |> pivot_count(rows = sex, col = survived)
#> # A tibble: 2 × 3
#>   sex       No   Yes
#>   <fct>  <int> <int>
#> 1 Male    1364   367
#> 2 Female   126   344
flat_titanic |> pivot_sum(rows = survived, value = freq)
#> # A tibble: 2 × 2
#>   survived value
#>   <fct>    <dbl>
#> 1 No        1490
#> 2 Yes        711
flat_titanic |> pivot_sum(rows = sex,  cols = survived, value = freq)
#> # A tibble: 2 × 3
#>   sex       No   Yes
#>   <fct>  <dbl> <dbl>
#> 1 Male    1364   367
#> 2 Female   126   344

flat_titanic |> pivot_average(rows = sex,  cols = survived, value = freq)
#> # A tibble: 2 × 3
#>   sex       No   Yes
#>   <fct>  <dbl> <dbl>
#> 1 Male   170.   45.9
#> 2 Female  15.8  43
flat_titanic |> pivot_empty(rows = survived, cols = age)
#> # A tibble: 2 × 3
#>   survived Child Adult
#>   <fct>    <lgl> <lgl>
#> 1 No       NA    NA   
#> 2 Yes      NA    NA

passengers |> pivot_average(rows = c(Sex, PClass), cols = Survived, value = Age)
#> # A tibble: 7 × 4
#>   Sex    PClass   `0`   `1`
#>   <chr>  <chr>  <dbl> <dbl>
#> 1 female 1st     35.2  37.9
#> 2 female 2nd     31.4  26.9
#> 3 female 3rd     22.8  22.7
#> 4 male   *      NaN    NA  
#> 5 male   1st     44.8  34.3
#> 6 male   2nd     31.7  14.8
#> 7 male   3rd     27.1  22.1

filling cells with examples from data.

pivot_example <- function(...){

  sample1 <- function(x) sample(x, 1)
  pivotr(fun = sample1, ...)

}


pivot_samplen <- function(..., n = 3, sep = "; "){

  samplen <- function(x) paste(sample(x, n, replace = F), collapse = sep)

  pivotr(fun = samplen, ...) 

}

pivot_list <- function(..., sep = "; "){

  paste_collapse <- function(x) paste (x, collapse = sep)
  pivotr(fun = paste_collapse, ...) 
  
}

flat_titanic |> pivot_example(rows = sex, value = freq)
#> # A tibble: 2 × 2
#>   sex    value
#>   <fct>  <dbl>
#> 1 Male       0
#> 2 Female   140

flat_titanic |> pivot_samplen(rows = sex, value = freq)
#> # A tibble: 2 × 2
#>   sex    value      
#>   <fct>  <chr>      
#> 1 Male   154; 11; 57
#> 2 Female 14; 0; 1

flat_titanic |> pivot_list(rows = sex, cols = survived, value = freq)
#> # A tibble: 2 × 3
#>   sex    No                              Yes                          
#>   <fct>  <chr>                           <chr>                        
#> 1 Male   0; 0; 35; 0; 118; 154; 387; 670 5; 11; 13; 0; 57; 14; 75; 192
#> 2 Female 0; 0; 17; 0; 4; 13; 89; 3       1; 13; 14; 0; 140; 80; 76; 20

set.seed(12345)
passengers |> pivot_example(rows = Survived, cols = Sex, value = Name)
#> # A tibble: 2 × 3
#>   Survived female                     male                          
#>      <dbl> <chr>                      <chr>                         
#> 1        0 Solvang, Mrs Lena Jacobsen Meyer, Mr August              
#> 2        1 Gibson, Miss Dorothy       Williams, Mr Richard Norris II
passengers |> pivot_samplen(rows = Survived, cols = Sex, value = Name, n = 2, sep = "; ") 
#> # A tibble: 2 × 3
#>   Survived female                                                          male 
#>      <dbl> <chr>                                                           <chr>
#> 1        0 McGowan, Miss Katherine; Klasen, Miss Gertrud Emilia            Smar…
#> 2        1 Ware, Mrs John James (Florence Louise Long); Dyker, Mrs Adolf … Mock…

passengers |> pivot_samplen(rows = Survived, cols = Sex, value = Age, n = 7) 
#> # A tibble: 2 × 3
#>   Survived female                    male                      
#>      <dbl> <chr>                     <chr>                     
#> 1        0 NA; 44; 20; NA; 18; 2; NA NA; NA; 28; NA; 19; NA; NA
#> 2        1 22; 5; 59; 12; 13; 26; NA 32; 9; 35; 60; NA; NA; NA

passengers |> dplyr::sample_n(20) |> pivot_list(rows = Sex, cols = Survived, value = Age)
#> # A tibble: 2 × 3
#>   Sex    `0`                                                `1`       
#>   <chr>  <chr>                                              <chr>     
#> 1 female NA; 30                                             NA; 45; 22
#> 2 male   NA; 24; 26; 29; 21; 29; 19; 46; 54; NA; 21; 22; NA 19; 2

proportions helpers

library(tidytitanic)
# pivot_prop
flat_titanic |> pivotr(rows = sex, 
                       value = freq, 
                       prop = TRUE) # pivot_prop
#> # A tibble: 2 × 2
#>   sex    value
#>   <fct>  <dbl>
#> 1 Male   0.786
#> 2 Female 0.214

flat_titanic |> 
  pivotr(rows = sex, cols = survived, 
         value = freq, prop = TRUE)
#> # A tibble: 2 × 3
#>   sex       No   Yes
#>   <fct>  <dbl> <dbl>
#> 1 Male   0.62  0.167
#> 2 Female 0.057 0.156

flat_titanic |> 
  pivotr(rows = sex, cols = survived, 
         value = freq, prop = TRUE, within = sex)
#> # A tibble: 2 × 3
#>   sex       No   Yes
#>   <fct>  <dbl> <dbl>
#> 1 Male   0.788 0.212
#> 2 Female 0.268 0.732

# pivot_percent
flat_titanic |> 
  pivotr(rows = sex, cols = survived, 
         value = freq, percent = TRUE, within = sex)
#> # A tibble: 2 × 3
#>   sex       No   Yes
#>   <fct>  <dbl> <dbl>
#> 1 Male    78.8  21.2
#> 2 Female  26.8  73.2

knitr::knit_exit()

Name		Name	Last commit message	Last commit date
Latest commit History 50 Commits
R		R
data-raw		data-raw
data		data
docs		docs
man		man
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
DESCRIPTION		DESCRIPTION
LICENSE		LICENSE
LICENSE.md		LICENSE.md
NAMESPACE		NAMESPACE
README.Rmd		README.Rmd
README.md		README.md
_pkgdown.yml		_pkgdown.yml
tidypivot.Rproj		tidypivot.Rproj

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{tidypivot} allows you to create tables by describing them (like ggplot2 plot description/declaration)

declarative table creation with ggplot2

Status quo table creation: Harder than it should be?

pivotr function: toward declarative table generation

toward a piped workflow

examples/derivative

filling cells with examples from data.

proportions helpers

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{tidypivot} allows you to create tables by describing them (like ggplot2 plot description/declaration)

declarative table creation with ggplot2

Status quo table creation: Harder than it should be?

pivotr function: toward declarative table generation

toward a piped workflow

examples/derivative

filling cells with examples from data.

proportions helpers

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