index.RMD

---
title:  "R.U.M - Publication Ready Tables"
author:
- name: Lucy Njoki Njuki
  affiliation: Centre for Epidemiology VS Arthritis, UoM, Manchester
subtitle: User-defined functions to create summary tables
date: "`r Sys.Date()`"
output:
  rmdformats::readthedown:
    highlight: espresso
    use_bookdown: TRUE
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
categories: ["R"]
---


```{r style, echo=FALSE, message=FALSE, warning=FALSE, results="hide"}
suppressPackageStartupMessages({
  library(knitr) # A General-Purpose Package for Dynamic Report Generation in R, CRAN v1.41
  library(rmarkdown) # Dynamic Documents for R, CRAN v2.19
  library(bookdown) # Authoring Books and Technical Documents with R Markdown, CRAN v0.33
  library(tidylog) # Logging for 'dplyr' and 'tidyr' Functions, CRAN v1.0.2
  library(tidyverse) # Easily Install and Load the 'Tidyverse', CRAN v1.3.2
  library(janitor) # Simple Tools for Examining and Cleaning Dirty Data, CRAN v2.2.0
  library(tidyr) # Tidy Messy Data, CRAN v1.2.1
  library(palmerpenguins) # Palmer Archipelago (Antarctica) Penguin Data, CRAN v0.1.1
  library(summarytools) # Tools to Quickly and Neatly Summarize Data, CRAN v1.0.1
})

options(width = 100)
```


\newpage

# Introduction

- Occasionally, we encounter environments like Microsoft Azure that do not support the default viewer mode. 
- Consequently, utilising packages like `gtsummary` becomes impractical.
- Therefore, it would be preferable to develop user-defined functions for data summarisation.

```{r penguins_df, results='hide'}
penguins = penguins
```

## Structure of the dataframe

```{r skim_data_fn, warning=FALSE, message=FALSE}
skim_data <- function(df, vars=NULL) {
  df<-dplyr::as_tibble(df)
  if (is.null(vars) == TRUE) vars <- names(df)
  
  variable_type <- sapply(vars,
                          function(x) is(df[, x][[1]])[1])
  missing_count <- sapply(vars,
                          function(x) sum(!complete.cases(df[, x])))
  unique_count <- sapply(vars,
                         function(x) dplyr::n_distinct(df[, x]))
  data_count <- nrow(dplyr::as_tibble(df))
  Example <- sapply(vars,
                    function(x) (df[1, x]))
  
  dplyr::tibble(variables = vars, types = variable_type,
                example = Example,
                missing_count = missing_count,
                missing_percent = (missing_count / data_count) * 100,
                unique_count = unique_count,
                total_data = data_count - missing_count)
}
```

- An example: Assess the structure of `penguins` data

```{r warning=FALSE, message=FALSE}
skim_data(penguins) |> knitr::kable(caption = "Structure of the penguin species dataset")
```


# Summary tables for numeric variables

```{r explore_numeric_fn, warning=FALSE, message=FALSE}
explore_numeric <- function(df, ...) {
  df<-dplyr::as_tibble(df)
  df %>%
    summarise(across(
      .cols = where(is.numeric), # checks if a variable si numeric
      .fns = list(Min = min, Max = max, Median = median, Mean = mean, SD = sd), na.rm = TRUE, 
      .names = "{col}_{fn}"
    )) 
}
```

- An example: Summarise penguins data.frame

```{r explore_numeric_fn_example, warning=FALSE, message=FALSE, results='hide'}
(table1 = explore_numeric(penguins))
```

```{r warning=FALSE, message=FALSE}
table1 |> knitr::kable(caption = 'Summary statistics for numerical variables in a DF for penguin species')
```

# Summary tables for categorical variables

```{r explore_factors_fn}
explore_factors <- function(df, ...){
  df<-dplyr::as_tibble(df)
  
  df%>%
    dplyr::select(...)%>%
    tidyr::gather(., "variable", "variable_level") %>%
    dplyr::count(variable, variable_level) %>%
    dplyr::group_by(variable) %>%             
    dplyr::mutate(proportion = round(prop.table(n)*(100), digits=2))%>%
    mutate(propotion_count = paste(n,"(",proportion,"%)")) %>%
    dplyr::group_by(variable)%>%
    dplyr::arrange(desc(n),.by_group = TRUE)%>%
    rename("frequency" = "n")
}
```

- An example: Summarise penguins data.frame

```{r explore_factors_fn_example, warning=FALSE, message=FALSE, results='hide'}
(table2 = explore_factors(penguins, species, island, sex))
```

```{r warning=FALSE, message=FALSE}
table2 |> knitr::kable(caption = 'Summary statistics for factor variables in a DF for penguin species', align = "c")
```

# Combine the two summary tables

- The utilisation of `knitr::kable()` is significant when it comes to conveniently visualizing datasets like these two tables in a platform like Microsoft Azure.

```{r warning=FALSE, message=FALSE}
knitr::kable(
  list(table2, table1),
  caption = 'Summary statistics for penguins DF',
  booktabs = TRUE, valign = 't'
)
```

\newpage

# Other functions

- Sometimes, it becomes necessary for us to determine the mode, like finding the most common International Statistical Classification of Diseases, 10th Revision (ICD-10) codes associated with a patient. 
  - To accomplish this, we need to calculate the mode of the variable.
- Regrettably, the default mode function is not available in R. - Therefore, creating our own custom function to calculate the mode becomes a solution.

```{r get_mode, warning=FALSE, message=FALSE}
getmode <- function(v) {
  uniqv <- unique({{v}})
  tab <- tabulate(match(v, uniqv))
  uniqv[tab == max(tab)]
}

```

- An example: What is the common `Petal.Length` and `Sepal.Length` for the different species?

```{r iris_df, results='hide', warning=FALSE, message=FALSE}
iris = iris
```

```{r warning=FALSE, message=FALSE}
(mode_example = iris %>% 
  group_by(Species) %>% 
  summarise(sepal_length_mode = getmode(Sepal.Length), petal_length_mode = getmode(Petal.Length)) %>% 
  kable(caption = "Example of mode", align = "c"))
```

# R package: `summarytools`

- The function `summarytools::dfSummary` proves to be valuable in performing basic descriptive statistics for both numeric variables and categorical variables.
- Additionally, it attempts to generate visual representations of the variable distributions, but <p style="color:red">these plots lack utility.</p>
- Furthermore, the function also identifies duplicate values and missing values within the dataset.

> No need for Viewer mode! `r emojifont::emoji("smiley")` `r emojifont::emoji("raised_hands")`

```{r df_summary_tables, warning=FALSE, message=FALSE}
# create a summary table using dfSummary function
(table_stat = dfSummary(penguins))
```


# Acknowledgement

1. Dr. Belay Birlie Yimer, Centre for Epidemiology VS Arthritis, UoM, major contributor in writing the functions `skim_data`, `explore_numeric` and `explore_factors`. 

2. Lana Bojanic, Centre for Mental Health and Safety, UoM, Manchester

# More resources

1. [Deep Exploratory Data Analysis (EDA) in R](https://yuzar-blog.netlify.app/posts/2021-01-09-exploratory-data-analysis-and-beyond-in-r-in-progress/#summarytools)

2. [A sufficient Introduction to R](https://dereksonderegger.github.io/570L/12-user-defined-functions.html)