Introduction to Respectables

Introduction

The respecatlbes package provides a framework to

  • create recipes define a way to derive interdependent variables
  • variables are created using generating functions

For this vignette we load the respecatbles and dplyr package:

library(respectables)
library(dplyr)

Note the respectables package is still under development.

Simple Dataset

Lets start defining a simple dataset dm with a single variable id.

gen_id <- function(n) {
  paste0("id-", 1:n)
}

dm_recipe <- tribble(
  ~variables, ~dependencies,  ~func,   ~func_args,
  "id",       no_deps,        gen_id,  no_args
)

gen_table_data(N = 2, recipe = dm_recipe)
##     id
## 1 id-1
## 2 id-2

Note that the argument n is defined by respectables, in this case it is equal N.

We can use the recepie dm_recepie again to create a different dataset:

gen_table_data(N = 5, recipe = dm_recipe)
##     id
## 1 id-1
## 2 id-2
## 3 id-3
## 4 id-4
## 5 id-5

Adding Multiple Variables

We will now specify the variables height and weight to the dm recipe:

gen_hw <- function(n) {
  bmi <- 17 + abs(rnorm(n, mean = 3, sd = 3))
  
  data.frame(height = runif(n, min = 1.5, 1.95)) %>%
    mutate(weight = bmi * height^2)
}

dm_recipe <- tribble(
  ~variables,               ~dependencies,  ~func,   ~func_args,
  "id",                     no_deps,        gen_id,  no_args,
   c("height", "weight"),   no_deps,        gen_hw,  no_args
)

gen_table_data(N = 2, recipe = dm_recipe)
##     id   height   weight
## 1 id-1 1.835681 61.37383
## 2 id-2 1.584224 44.35310

Note that we used random number generators in gen_hw, hence rerunning gen_table_data will give different values

gen_table_data(N = 2, recipe = dm_recipe)
##     id   height   weight
## 1 id-1 1.704511 69.89425
## 2 id-2 1.673766 47.69598

Variable Dependencies

We will now continue our dm example by defining the variable age which for illustrative purposes is dependent on the height.

gen_age <- function(n, .df) {
  .df %>%
    transmute(age = height*25)
}

dm_recipe <- tribble(
  ~variables,               ~dependencies,  ~func,   ~func_args,
  "id",                     no_deps,        gen_id,  no_args,
   c("height", "weight"),   no_deps,        gen_hw,  no_args,
  "age",                    "height",       gen_age, no_args
)

gen_table_data(N = 2, recipe = dm_recipe)
##     id   height   weight      age
## 1 id-1 1.668165 48.96687 41.70413
## 2 id-2 1.547495 43.62934 38.68739

Note that respectables creates the arguments n and .df on the fly. Also, respectables determines the evaluation order of the variables based on the dependency structure. That is, respectables does not guarantee to build the resulting data frame using the recipe row by row.

Configurable Arguments

If we plan to make configurable variable generating functions we can specify the arguments in the recipe

gen_color <- function(n, colors = colors()) {
  data.frame(color = sample(colors, n, replace = TRUE))
}

dm_recipe <- tribble(
  ~variables,               ~dependencies,  ~func,   ~func_args,
  "id",                     no_deps,        gen_id,     no_args,
   c("height", "weight"),   no_deps,        gen_hw,     no_args,
  "age",                    "height",       gen_age,    no_args,
  "color",                  no_deps,        gen_color,  list(color = c("blue", "red"))
)

gen_table_data(N = 4, recipe = dm_recipe)
##     id   height   weight      age color
## 1 id-1 1.766169 59.86787 44.15423   red
## 2 id-2 1.918571 86.07405 47.96427  blue
## 3 id-3 1.548031 50.23885 38.70077  blue
## 4 id-4 1.697221 54.30586 42.43053   red

Injecting Missing Data

The miss_recipe argument in gen_table_data can be used to inject missing values in the last step when creating data with gen_table_data. That is, first the data generation recipe is executed and then the missing data is injected. Hence, all variables are available at execution time and the .df argument is supplied to the func.

gen_alternate_na <- function(.df) {
  n <- nrow(.df)
  rep(c(TRUE, FALSE), length.out = n)
}

dm_na_recipe <- tribble(
  ~variables,       ~func,             ~func_args,
  "age",            gen_alternate_na,  no_args
)

gen_table_data(N = 4, recipe = dm_recipe, miss_recipe = dm_na_recipe)
##     id   height   weight      age color
## 1 id-1 1.913277 73.47471       NA   red
## 2 id-2 1.676949 60.91223 41.92373  blue
## 3 id-3 1.561641 51.86744       NA   red
## 4 id-4 1.756622 81.29693 43.91554  blue

Note that this currently only works with one variable per row in the missing recipe. This is a feature that we are still working on to allow for more complex missing structure definition.

Scaffolding

For this example we create a data frame aseq with the variable seqterm being c("step 1", ..., "step i"), where i is extracted from the variable id.

dm <- gen_table_data(N = 3, recipe = dm_recipe)

# grow dataset
gen_seq <- function(.db) {
  
  dm <- .db$dm
  
  ni <- as.numeric(substring(dm$id, 4))
  
  df_grow <- data.frame(
    id = rep(dm$id, ni),
    seq = unlist(sapply(ni, seq, from = 1))
  )
  
  left_join(dm, df_grow, by = "id")
}

aseq_scf_recipe <- tribble(
  ~foreign_tbl, ~foreign_key, ~func,     ~func_args,
  "dm",         "id",         gen_seq,   no_args     
)

gen_seq_term <- function(.df, ...) {
  data.frame(seqterm = paste("step", .df$seq))
}

aseq_recipe <- tribble(
  ~variables,      ~dependencies,  ~func,            ~func_args,
  "seqterm",       "seq",          gen_seq_term,     no_args
)

gen_reljoin_table(joinrec = aseq_scf_recipe, tblrec = aseq_recipe, db = list(dm = dm))
##     id   height   weight      age color seq seqterm
## 1 id-1 1.874792 62.68412 46.86981  blue   1  step 1
## 2 id-2 1.777130 53.97085 44.42826  blue   1  step 1
## 3 id-2 1.777130 53.97085 44.42826  blue   2  step 2
## 4 id-3 1.702817 75.99336 42.57044  blue   1  step 1
## 5 id-3 1.702817 75.99336 42.57044  blue   2  step 2
## 6 id-3 1.702817 75.99336 42.57044  blue   3  step 3

The steps here are:

  1. use joinrec to grow a new data frame, say A, possibly from db
  2. call gen_table_data with the following arguments
    • A for df
    • tblrec for recipe
    • forward miss_recipe

Note that this functionality is under development. Currently aseq_scf_recipe needs to be a tibble with one row, and the foreign_key is currently not used.

Compare dplyr

This section needs further work.

Let’s map the following code into respectible recipes:

iris %>% 
  mutate(SPECIES = toupper(Species)) %>%
  head()
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species SPECIES
## 1          5.1         3.5          1.4         0.2  setosa  SETOSA
## 2          4.9         3.0          1.4         0.2  setosa  SETOSA
## 3          4.7         3.2          1.3         0.2  setosa  SETOSA
## 4          4.6         3.1          1.5         0.2  setosa  SETOSA
## 5          5.0         3.6          1.4         0.2  setosa  SETOSA
## 6          5.4         3.9          1.7         0.4  setosa  SETOSA

There are multiple solutions to map this to the respectables framework.

gen_toupper <- function(varname, .df, ...) {
   toupper(.df[[varname]])
}

rcp <- tribble(
    ~variables, ~dependencies,  ~func,          ~func_args,
    "SPECIES",  "Species",       gen_toupper,   list(varname = "Species") 
)

gen_table_data(recipe = rcp, df = iris) %>%
  head()
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species SPECIES
## 1          5.1         3.5          1.4         0.2  setosa  SETOSA
## 2          4.9         3.0          1.4         0.2  setosa  SETOSA
## 3          4.7         3.2          1.3         0.2  setosa  SETOSA
## 4          4.6         3.1          1.5         0.2  setosa  SETOSA
## 5          5.0         3.6          1.4         0.2  setosa  SETOSA
## 6          5.4         3.9          1.7         0.4  setosa  SETOSA

Note in gen_toupper we use the ellipsis ... to absorb not used arguments such as n.