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 1.592136 44.67546
## 2 id-2 1.799889 68.45025

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 1.660847 51.21497
## 2 id-2 1.843456 81.48764

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 1.509350 41.21258 37.73374
## 2 id-2 1.678266 53.80386 41.95664

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 1.547743 47.35653 38.69357  blue
## 2 id-2 1.674113 53.12697 41.85283  blue
## 3 id-3 1.525821 42.22603 38.14552  blue
## 4 id-4 1.921139 76.14339 48.02846   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 1.823601 59.43860       NA  blue
## 2 id-2 1.568291 53.79496 39.20728  blue
## 3 id-3 1.663088 53.69008       NA   red
## 4 id-4 1.908176 66.95732 47.70439   red

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 1.796400 69.69563 44.91000   red   1  step 1
## 2 id-2 1.610831 51.08810 40.27077   red   1  step 1
## 3 id-2 1.610831 51.08810 40.27077   red   2  step 2
## 4 id-3 1.764510 65.95831 44.11276   red   1  step 1
## 5 id-3 1.764510 65.95831 44.11276   red   2  step 2
## 6 id-3 1.764510 65.95831 44.11276   red   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.