vignette - chunkfactory

author: irene cruz

date: 27-05-25

This package was developed in the context of the project Igualdad de género en los usos del tiempo: Cambios, resistencias y continuidades, GENERA Proyecto PID2021-122515NB-I00.

Objectives

The package chunkfactoryis meant to minimize the coding for markdown documents where a handful of functions need to be executed iteratively.

It solves 2 problems:

repetitive code when performing the same operations on multiple combinations of variables
presenting figures and tables in RMD when repeating functions on a list

The chunk factory can be applied to custom functions, or can be used with the built-in descriptive statistics functions for weighted data. Let's see the two cases.

library(chunkfactory)
library(tidyverse)

utils::data(package = "palmerpenguins", "penguins")
penguins$pes <- 1

Execute any function as a chunk factory {.tabset}

We can use the chunk factory on any custom function that generates the collection of results we need. The only restriction is that the data argument needs to be passed as a text string. Then the function needs to evaluate the text string.

For example, we'll build a function that, for every pair of variables returns:

a linear regression model
a formatted table of model results
a tibble of model coefficients
a predicted means plot

myfunc <- function(data_name, v1, v2){
  data <- get(data_name)
  
  myformula <- as.formula(glue::glue("{v1} ~ {v2}"))
  res1 <- lm(myformula, data = data)
  res2 <- sjPlot::tab_model(res1, use.viewer = F)
  res3 <- broom::tidy(res1)
  res4 <- sjPlot::plot_model(res1, type = "eff", terms = v2)
  
  return(list(res1, res2, res3, res4))
}

If we were to simply execute myfunc()and get the list of results it produces as is, we'd find each element is preceded by the aesthetically unpleasant [[x]], and table outputs are not printed following document guidelines (i.e. paged data frames):

myfunc("penguins", "flipper_length_mm", "species")

## [[1]]
## 
## Call:
## lm(formula = myformula, data = data)
## 
## Coefficients:
##      (Intercept)  speciesChinstrap     speciesGentoo  
##           189.95              5.87             27.23  
## 
## 
## [[2]]
## 
## [[3]]
## # A tibble: 3 × 5
##   term             estimate std.error statistic   p.value
##   <chr>               <dbl>     <dbl>     <dbl>     <dbl>
## 1 (Intercept)        190.       0.540    351.   0        
## 2 speciesChinstrap     5.87     0.970      6.05 3.79e-  9
## 3 speciesGentoo       27.2      0.807     33.8  1.84e-110
## 
## [[4]]

chunkfactory will help us reproduce results in an optimal way for a rmd document, while minimising the iteration of code.

In order to create a chunk factory, first we'll need to define a list of parameters to iterate upon. Remember that the parameters need to be named exactly as the function arguments' names, and follow the same order:

myfunc_params <- list(
  data_name = "penguins", 
  v1        = c("flipper_length_mm", "bill_length_mm"),
  v2        = c("species", "island", "sex")
  )

Next we'll only need to execute our custom function with our list of parameters:

myres <- fabrica_chunks_myfunc(
  myfunc = myfunc, 
  param_list = myfunc_params, 
  title_level = 2)

This generates a character vector containing the markdown, chunks and code to be evaluated. See a sample:

head(myres)

## [1] "\n\n## penguins.flipper_length_mm.species\n\n```{r echo = TRUE}\n\neval(parse(text = 'reslist[[1]][[1]]'))\neval(parse(text = 'reslist[[1]][[2]]'))\neval(parse(text = 'reslist[[1]][[3]]'))\neval(parse(text = 'reslist[[1]][[4]]'))\n```\n\n"
## [2] "\n\n## penguins.bill_length_mm.species\n\n```{r echo = TRUE}\n\neval(parse(text = 'reslist[[2]][[1]]'))\neval(parse(text = 'reslist[[2]][[2]]'))\neval(parse(text = 'reslist[[2]][[3]]'))\neval(parse(text = 'reslist[[2]][[4]]'))\n```\n\n"   
## [3] "\n\n## penguins.flipper_length_mm.island\n\n```{r echo = TRUE}\n\neval(parse(text = 'reslist[[3]][[1]]'))\neval(parse(text = 'reslist[[3]][[2]]'))\neval(parse(text = 'reslist[[3]][[3]]'))\neval(parse(text = 'reslist[[3]][[4]]'))\n```\n\n" 
## [4] "\n\n## penguins.bill_length_mm.island\n\n```{r echo = TRUE}\n\neval(parse(text = 'reslist[[4]][[1]]'))\neval(parse(text = 'reslist[[4]][[2]]'))\neval(parse(text = 'reslist[[4]][[3]]'))\neval(parse(text = 'reslist[[4]][[4]]'))\n```\n\n"    
## [5] "\n\n## penguins.flipper_length_mm.sex\n\n```{r echo = TRUE}\n\neval(parse(text = 'reslist[[5]][[1]]'))\neval(parse(text = 'reslist[[5]][[2]]'))\neval(parse(text = 'reslist[[5]][[3]]'))\neval(parse(text = 'reslist[[5]][[4]]'))\n```\n\n"    
## [6] "\n\n## penguins.bill_length_mm.sex\n\n```{r echo = TRUE}\n\neval(parse(text = 'reslist[[6]][[1]]'))\neval(parse(text = 'reslist[[6]][[2]]'))\neval(parse(text = 'reslist[[6]][[3]]'))\neval(parse(text = 'reslist[[6]][[4]]'))\n```\n\n"

The list of results is then interpreted by knit_child(), like in the previous example:

penguins.flipper_length_mm.species

eval(parse(text = 'reslist[[1]][[1]]'))

## 
## Call:
## lm(formula = myformula, data = data)
## 
## Coefficients:
##      (Intercept)  speciesChinstrap     speciesGentoo  
##           189.95              5.87             27.23

eval(parse(text = 'reslist[[1]][[2]]'))

	flipper length mm
Predictors	Estimates	CI	p
(Intercept)	189.95	188.89 – 191.02	<0.001
species [Chinstrap]	5.87	3.96 – 7.78	<0.001
species [Gentoo]	27.23	25.65 – 28.82	<0.001
Observations	342
R² / R² adjusted	0.778 / 0.777

eval(parse(text = 'reslist[[1]][[3]]'))

## # A tibble: 3 × 5
##   term             estimate std.error statistic   p.value
##   <chr>               <dbl>     <dbl>     <dbl>     <dbl>
## 1 (Intercept)        190.       0.540    351.   0        
## 2 speciesChinstrap     5.87     0.970      6.05 3.79e-  9
## 3 speciesGentoo       27.2      0.807     33.8  1.84e-110

eval(parse(text = 'reslist[[1]][[4]]'))

penguins.bill_length_mm.species

eval(parse(text = 'reslist[[2]][[1]]'))

## 
## Call:
## lm(formula = myformula, data = data)
## 
## Coefficients:
##      (Intercept)  speciesChinstrap     speciesGentoo  
##           38.791            10.042             8.713

eval(parse(text = 'reslist[[2]][[2]]'))

	bill length mm
Predictors	Estimates	CI	p
(Intercept)	38.79	38.32 – 39.27	<0.001
species [Chinstrap]	10.04	9.19 – 10.89	<0.001
species [Gentoo]	8.71	8.01 – 9.42	<0.001
Observations	342
R² / R² adjusted	0.708 / 0.706

eval(parse(text = 'reslist[[2]][[3]]'))

## # A tibble: 3 × 5
##   term             estimate std.error statistic   p.value
##   <chr>               <dbl>     <dbl>     <dbl>     <dbl>
## 1 (Intercept)         38.8      0.241     161.  2.47e-322
## 2 speciesChinstrap    10.0      0.432      23.2 4.23e- 72
## 3 speciesGentoo        8.71     0.360      24.2 5.33e- 76

eval(parse(text = 'reslist[[2]][[4]]'))

penguins.flipper_length_mm.island

eval(parse(text = 'reslist[[3]][[1]]'))

## 
## Call:
## lm(formula = myformula, data = data)
## 
## Coefficients:
##     (Intercept)      islandDream  islandTorgersen  
##          209.71           -16.63           -18.51

eval(parse(text = 'reslist[[3]][[2]]'))

	flipper length mm
Predictors	Estimates	CI	p
(Intercept)	209.71	208.01 – 211.40	<0.001
island [Dream]	-16.63	-19.23 – -14.04	<0.001
island [Torgersen]	-18.51	-22.02 – -15.00	<0.001
Observations	342
R² / R² adjusted	0.376 / 0.372

eval(parse(text = 'reslist[[3]][[3]]'))

## # A tibble: 3 × 5
##   term            estimate std.error statistic  p.value
##   <chr>              <dbl>     <dbl>     <dbl>    <dbl>
## 1 (Intercept)        210.      0.862     243.  0       
## 2 islandDream        -16.6     1.32      -12.6 4.20e-30
## 3 islandTorgersen    -18.5     1.78      -10.4 4.04e-22

eval(parse(text = 'reslist[[3]][[4]]'))

penguins.bill_length_mm.island

eval(parse(text = 'reslist[[4]][[1]]'))

## 
## Call:
## lm(formula = myformula, data = data)
## 
## Coefficients:
##     (Intercept)      islandDream  islandTorgersen  
##          45.257           -1.090           -6.307

eval(parse(text = 'reslist[[4]][[2]]'))

	bill length mm
Predictors	Estimates	CI	p
(Intercept)	45.26	44.49 – 46.02	<0.001
island [Dream]	-1.09	-2.26 – 0.08	0.069
island [Torgersen]	-6.31	-7.89 – -4.72	<0.001
Observations	342
R² / R² adjusted	0.154 / 0.149

eval(parse(text = 'reslist[[4]][[3]]'))

## # A tibble: 3 × 5
##   term            estimate std.error statistic   p.value
##   <chr>              <dbl>     <dbl>     <dbl>     <dbl>
## 1 (Intercept)        45.3      0.390    116.   4.68e-275
## 2 islandDream        -1.09     0.597     -1.83 6.88e-  2
## 3 islandTorgersen    -6.31     0.806     -7.83 6.44e- 14

eval(parse(text = 'reslist[[4]][[4]]'))

penguins.flipper_length_mm.sex

eval(parse(text = 'reslist[[5]][[1]]'))

## 
## Call:
## lm(formula = myformula, data = data)
## 
## Coefficients:
## (Intercept)      sexmale  
##     197.364        7.142

eval(parse(text = 'reslist[[5]][[2]]'))

	flipper length mm
Predictors	Estimates	CI	p
(Intercept)	197.36	195.29 – 199.44	<0.001
sex [male]	7.14	4.22 – 10.07	<0.001
Observations	333
R² / R² adjusted	0.065 / 0.062

eval(parse(text = 'reslist[[5]][[3]]'))

## # A tibble: 2 × 5
##   term        estimate std.error statistic    p.value
##   <chr>          <dbl>     <dbl>     <dbl>      <dbl>
## 1 (Intercept)   197.        1.06    187.   0         
## 2 sexmale         7.14      1.49      4.80 0.00000239

eval(parse(text = 'reslist[[5]][[4]]'))

penguins.bill_length_mm.sex

eval(parse(text = 'reslist[[6]][[1]]'))

## 
## Call:
## lm(formula = myformula, data = data)
## 
## Coefficients:
## (Intercept)      sexmale  
##      42.097        3.758

eval(parse(text = 'reslist[[6]][[2]]'))

	bill length mm
Predictors	Estimates	CI	p
(Intercept)	42.10	41.31 – 42.88	<0.001
sex [male]	3.76	2.65 – 4.87	<0.001
Observations	333
R² / R² adjusted	0.118 / 0.116

eval(parse(text = 'reslist[[6]][[3]]'))

## # A tibble: 2 × 5
##   term        estimate std.error statistic   p.value
##   <chr>          <dbl>     <dbl>     <dbl>     <dbl>
## 1 (Intercept)    42.1      0.400    105.   2.18e-256
## 2 sexmale         3.76     0.564      6.67 1.09e- 10

eval(parse(text = 'reslist[[6]][[4]]'))

Examples with built-in functions

The package includes built-in functions in order to perform bivariate analyses with weighted data.

If the dependent variable is numeric, results will show:

grouped descriptive statistics
grouped boxplots

If the dependent variable is categorical, results will show:

a cross-tab in tidy format, including adjusted-standardized residuals [citation]
stacked bar chart

Results are organized into tabsets, like in the following example:

Bivariates by sex {.tabset}

myres <- fabrica_chunks(
  vd = c("bill_length_mm", "bill_depth_mm", "flipper_length_mm"), 
  vi = c("sex"), 
  d = "penguins", 
  w = "pes")

myres generates a vector character containing the code that will be later evaluated with knit_child

bill_length_mm x sex

eval(parse(text = (reslist_mytab[[1]][[1]])))

## # A tibble: 3 × 9
##   sex        n     N mitjana mediana desv_tip margin lower upper
##   <fct>  <int> <dbl>   <dbl>   <dbl>    <dbl>  <dbl> <dbl> <dbl>
## 1 female   165   165    42.1    42.8     4.90  0.754  41.3  42.9
## 2 male     168   168    45.9    46.8     5.37  0.817  45.0  46.7
## 3 <NA>      11    11    41.3    42       4.63  3.07   38.2  44.4

eval(parse(text = (reslist_mytab[[1]][[2]])))

bill_depth_mm x sex

eval(parse(text = (reslist_mytab[[2]][[1]])))

## # A tibble: 3 × 9
##   sex        n     N mitjana mediana desv_tip margin lower upper
##   <fct>  <int> <dbl>   <dbl>   <dbl>    <dbl>  <dbl> <dbl> <dbl>
## 1 female   165   165    16.4    17       1.80  0.276  16.1  16.7
## 2 male     168   168    17.9    18.4     1.86  0.284  17.6  18.2
## 3 <NA>      11    11    16.6    17.1     2.24  1.48   15.2  18.1

eval(parse(text = (reslist_mytab[[2]][[2]])))

flipper_length_mm x sex

eval(parse(text = (reslist_mytab[[3]][[1]])))

## # A tibble: 3 × 9
##   sex        n     N mitjana mediana desv_tip margin lower upper
##   <fct>  <int> <dbl>   <dbl>   <dbl>    <dbl>  <dbl> <dbl> <dbl>
## 1 female   165   165    197.    193      12.5   1.92  195.  199.
## 2 male     168   168    205.    200.     14.5   2.22  202.  207.
## 3 <NA>      11    11    199     193      16.5  10.9   188.  210.

eval(parse(text = (reslist_mytab[[3]][[2]])))

Bivariates by island {.tabset}

myres <- fabrica_chunks(
  vd = c("sex", "species"), 
  vi = c("island"), 
  d = "penguins", 
  w = "pes")

sex x island

eval(parse(text = (reslist_mytab[[1]][[1]])))

## # A tibble: 6 × 7
##   VI        VD         N     n    TT    PP  ASres
##   <chr>     <chr>  <dbl> <int> <dbl> <dbl>  <dbl>
## 1 Biscoe    female    80    80   163  49.1 -0.167
## 2 Biscoe    male      83    83   163  50.9  0.167
## 3 Dream     female    61    61   123  49.6  0.012
## 4 Dream     male      62    62   123  50.4 -0.013
## 5 Torgersen female    24    24    47  51.1  0.225
## 6 Torgersen male      23    23    47  48.9 -0.224

eval(parse(text = (reslist_mytab[[1]][[2]])))

species x island

eval(parse(text = (reslist_mytab[[2]][[1]])))

## # A tibble: 5 × 7
##   VI        VD            N     n    TT    PP  ASres
##   <chr>     <chr>     <dbl> <int> <dbl> <dbl>  <dbl>
## 1 Biscoe    Adelie       44    44   168  26.2 -6.57 
## 2 Biscoe    Gentoo      124   124   168  73.8 14.3  
## 3 Dream     Adelie       56    56   124  45.2  0.273
## 4 Dream     Chinstrap    68    68   124  54.8 12.3  
## 5 Torgersen Adelie       52    52    52 100    8.80

eval(parse(text = (reslist_mytab[[2]][[2]])))

Name		Name	Last commit message	Last commit date
Latest commit History 32 Commits
R		R
inst/rmarkdown/templates/plantilla_chunkfactory		inst/rmarkdown/templates/plantilla_chunkfactory
man		man
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
DESCRIPTION		DESCRIPTION
LICENSE		LICENSE
LICENSE.md		LICENSE.md
NAMESPACE		NAMESPACE
README.md		README.md
chunkfactory.Rproj		chunkfactory.Rproj

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vignette - chunkfactory

author: irene cruz

date: 27-05-25

Objectives

Execute any function as a chunk factory {.tabset}

penguins.flipper_length_mm.species

penguins.bill_length_mm.species

penguins.flipper_length_mm.island

penguins.bill_length_mm.island

penguins.flipper_length_mm.sex

penguins.bill_length_mm.sex

Examples with built-in functions

Bivariates by sex {.tabset}

bill_length_mm x sex

bill_depth_mm x sex

flipper_length_mm x sex

Bivariates by island {.tabset}

sex x island

species x island

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vignette - chunkfactory

author: irene cruz

date: 27-05-25

Objectives

Execute any function as a chunk factory {.tabset}

penguins.flipper_length_mm.species

penguins.bill_length_mm.species

penguins.flipper_length_mm.island

penguins.bill_length_mm.island

penguins.flipper_length_mm.sex

penguins.bill_length_mm.sex

Examples with built-in functions

Bivariates by sex {.tabset}

bill_length_mm x sex

bill_depth_mm x sex

flipper_length_mm x sex

Bivariates by island {.tabset}

sex x island

species x island

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