exploring_mtpl2_dataset.Rmd

---
title: "Exploring the MTPL2 Dataset"
author: "Mick Cooney <mickcooney@gmail.com>"
date: "`r Sys.Date()`"
output:
  rmdformats::readthedown:
    fig_caption: yes
    toc_depth: 3
    use_bookdown: yes

  html_document:
    fig_caption: yes
    theme: spacelab
    highlight: pygments
    number_sections: TRUE
    toc: TRUE
    toc_depth: 3
    toc_float:
      smooth_scroll: FALSE

  pdf_document: default
---


```{r import_libraries, echo=FALSE, message=FALSE}
knitr::opts_chunk$set(tidy       = FALSE,
                      cache      = FALSE,
                      warning    = FALSE,
                      message    = FALSE,
                      fig.height =     8,
                      fig.width  =    11
                     )

library(conflicted)
library(tidyverse)
library(scales)
library(cowplot)
library(magrittr)
library(rlang)
library(purrr)
library(vctrs)
library(fs)
library(forcats)
library(snakecase)
library(lubridate)

source("custom_functions.R")

resolve_conflicts(c("magrittr", "rlang", "dplyr", "readr", "purrr", "ggplot2"))


options(width = 80L,
        warn  = 1,
        mc.cores = parallel::detectCores()
        )

theme_set(theme_cowplot())

set.seed(42)
```

```{r custom_functions, echo=FALSE}
### Checks if variable is a date/time
is_date <- function(x)
  x %>% inherits(c("POSIXt", "POSIXct", "POSIXlt", "Date", "hms"))


### Returns the category of data type passed to it
categorise_datatype <- function(x) {
  if (all(are_na(x))) return("na")

  if (is_date(x))                          "datetime"
  else if (!is_null(attributes(x)) ||
           all(is_character(x)))          "discrete"
  else if (all(is_logical(x)))            "logical"
  else                                    "continuous"
}


### create_coltype_list() splits columns into various types
create_coltype_list <- function(data_tbl) {
  coltypes  <- data_tbl %>% map_chr(categorise_datatype)
  cat_types <- coltypes %>% unique() %>% sort()

  split_lst <- cat_types %>% map(~ coltypes[coltypes %in% .x] %>% names())

  names(split_lst) <- coltypes %>% unique() %>% sort()

  coltype_lst <- list(
    split   = split_lst,
    columns = coltypes
  )

  return(coltype_lst)
}

```


This workbook was created using the "dataexpks" template:

https://github.com/DublinLearningGroup/dataexpks



# Introduction

This workbook performs the basic data exploration of the dataset.

```{r set_exploration_params, echo=TRUE}
level_exclusion_threshold <- 100

cat_level_count <- 40
hist_bins_count <- 50
```


# Load Data

First we load the dataset.

```{r load_dataset, echo=TRUE}
rawdata_tbl <- read_rds("data/modelling2_data_tbl.rds") %>% select(-sev_data)

rawdata_tbl %>% glimpse()
```



## Perform Quick Data Cleaning


```{r perform_simple_datatype_transforms, echo=TRUE}
cleaned_names <- rawdata_tbl %>% names()

data_tbl <- rawdata_tbl %>% set_colnames(cleaned_names)

data_tbl %>% glimpse()
```



```{r, echo=FALSE}
#knitr::knit_exit()
```


## Create Derived Variables

We now create derived features useful for modelling. These values are
new variables calculated from existing variables in the data.

```{r construct_derived_values, echo=FALSE}
data_tbl <- data_tbl

data_tbl %>% glimpse()
```





## Check Missing Values

Before we do anything with the data, we first check for missing values
in the dataset. In some cases, missing data is coded by a special
character rather than as a blank, so we first correct for this.

```{r replace_missing_character, echo=TRUE}
### _TEMPLATE_
### ADD CODE TO CORRECT FOR DATA ENCODING HERE
```

With missing data properly encoded, we now visualise the missing data in a
number of different ways.

### Univariate Missing Data

We first examine a simple univariate count of all the missing data:

```{r missing_data_univariate_count, echo=TRUE}
row_count <- data_tbl %>% nrow()

missing_univariate_tbl <- data_tbl %>%
  summarise_all(list(~sum(are_na(.)))) %>%
  gather("variable", "missing_count") %>%
  mutate(missing_prop = missing_count / row_count)

ggplot(missing_univariate_tbl) +
  geom_bar(aes(x = fct_reorder(variable, -missing_prop),
               weight = missing_prop)) +
  xlab("Variable") +
  ylab("Missing Value Proportion") +
  theme(axis.text.x = element_text(angle = 90))
```

We remove all variables where all of the entries are missing

```{r remove_entirely_missing_vars, echo=TRUE}
remove_vars <- missing_univariate_tbl %>%
  filter(missing_count == row_count) %>%
  pull(variable)

lessmiss_data_tbl <- data_tbl %>%
  select(-one_of(remove_vars))
```

With these columns removed, we repeat the exercise.

```{r missing_data_univariate_count_redux, echo=TRUE}
missing_univariate_tbl <- lessmiss_data_tbl %>%
  summarise_all(list(~sum(are_na(.)))) %>%
  gather("variable", "missing_count") %>%
  mutate(missing_prop = missing_count / row_count)

ggplot(missing_univariate_tbl) +
  geom_bar(aes(x = fct_reorder(variable, -missing_prop),
               weight = missing_prop)) +
  xlab("Variable") +
  ylab("Missing Value Proportion") +
  theme(axis.text.x = element_text(angle = 90))
```


To reduce the scale of this plot, we look at the top twenty missing data
counts.

```{r missing_data_univariate_top10_count, echo=TRUE}
missing_univariate_top_tbl <- missing_univariate_tbl %>%
  arrange(desc(missing_count)) %>%
  top_n(n = 50, wt = missing_count)

ggplot(missing_univariate_top_tbl) +
  geom_bar(aes(x = fct_reorder(variable, -missing_prop),
               weight = missing_prop)) +
  xlab("Variable") +
  ylab("Missing Value Proportion") +
  theme(axis.text.x = element_text(angle = 90))
```



### Multivariate Missing Data

It is useful to get an idea of what combinations of variables tend to have
variables with missing values simultaneously, so to construct a visualisation
for this we create a count of all the times given combinations of variables
have missing values, producing a heat map for these combination counts.

```{r missing_data_matrix, echo=TRUE}
row_count <- rawdata_tbl %>% nrow()

count_nas <- ~ .x %>% are_na() %>% vec_cast(integer())

missing_plot_tbl <- rawdata_tbl %>%
  mutate_all(count_nas) %>%
  mutate(label = pmap_chr(., str_c)) %>%
  group_by(label) %>%
  summarise_all(list(sum)) %>%
  arrange(desc(label)) %>%
  select(-label) %>%
  mutate(label_count = pmap_int(., pmax)) %>%
  gather("col", "count", -label_count) %>%
  mutate(miss_prop   = count / row_count,
         group_label = sprintf("%6.4f", round(label_count / row_count, 4))
        )

ggplot(missing_plot_tbl) +
  geom_tile(aes(x = col, y = group_label, fill = miss_prop), height = 0.8) +
  scale_fill_continuous() +
  scale_x_discrete(position = "top") +
  xlab("Variable") +
  ylab("Missing Value Proportion") +
  theme(axis.text.x = element_text(angle = 90))
```

This visualisation takes a little explaining.

Each row represents a combination of variables with simultaneous missing
values. For each row in the graphic, the coloured entries show which particular
variables are missing in that combination. The proportion of rows with that
combination is displayed in both the label for the row and the colouring for
the cells in the row.

## Inspect High-level-count Categorical Variables

With the raw data loaded up we now remove obvious unique or near-unique
variables that are not amenable to basic exploration and plotting.

```{r find_highlevelcount_categorical_variables, echo=TRUE}
coltype_lst <- create_coltype_list(data_tbl)

count_levels <- ~ .x %>% unique() %>% length()

catvar_valuecount_tbl <- data_tbl %>%
  summarise_at(coltype_lst$split$discrete, count_levels) %>%
  gather("var_name", "level_count") %>%
  arrange(-level_count)

print(catvar_valuecount_tbl)

row_count <- nrow(data_tbl)

cat(str_c("Dataset has ", row_count, " rows\n"))
```

Now that we a table of the counts of all the categorical variables we can
automatically exclude unique variables from the exploration, as the level
count will match the row count.

```{r remove_id_variables, echo=TRUE}
unique_vars <- catvar_valuecount_tbl %>%
  filter(level_count == row_count) %>%
  pull(var_name)

print(unique_vars)

explore_data_tbl <- data_tbl %>%
  select(-one_of(unique_vars))
```

Having removed the unique identifier variables from the dataset, we
may also wish to exclude categoricals with high level counts also, so
we create a vector of those variable names.

```{r collect_highcount_variables, echo=TRUE}
highcount_vars <- catvar_valuecount_tbl %>%
  filter(level_count >= level_exclusion_threshold,
         level_count < row_count) %>%
  pull(var_name)

cat(str_c(highcount_vars, collapse = ", "))
```

We now can continue doing some basic exploration of the data. We may
also choose to remove some extra columns from the dataset.

```{r drop_variables, echo=TRUE}
### You may want to comment out these next few lines to customise which
### categoricals are kept in the exploration.
drop_vars <- c(highcount_vars)

if (length(drop_vars) > 0) {
  explore_data_tbl <- explore_data_tbl %>%
      select(-one_of(drop_vars))

  cat(str_c(drop_vars, collapse = ", "))
}
```




```{r, echo=FALSE}
#knitr::knit_exit()
```


# Univariate Data Exploration

Now that we have loaded the data we can prepare it for some basic data
exploration. We first exclude the variables that are unique
identifiers or similar, and tehen split the remaining variables out
into various categories to help with the systematic data exploration.


```{r separate_exploration_cols, echo=TRUE}
coltype_lst <- create_coltype_list(explore_data_tbl)

print(coltype_lst)
```


## Logical Variables

Logical variables only take two values: TRUE or FALSE. It is useful to see
missing data as well though, so we also plot the count of those.

```{r create_univariate_logical_plots, echo=TRUE, warning=FALSE}
logical_vars <- coltype_lst$split$logical %>% sort()

for (plot_varname in logical_vars) {
  cat("--\n")
  cat(str_c(plot_varname, "\n"))

  na_count <- explore_data_tbl %>% pull(!! plot_varname) %>% are_na() %>% sum()

  explore_plot <- ggplot(explore_data_tbl) +
    geom_bar(aes(x = !! sym(plot_varname))) +
    xlab(plot_varname) +
    ylab("Count") +
    scale_y_continuous(labels = label_comma()) +
    ggtitle(str_c("Barplot of Counts for Variable: ", plot_varname,
                  " (", na_count, " missing values)")) +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5))

  plot(explore_plot)
}
```


## Numeric Variables

Numeric variables are usually continuous in nature, though we also have
integer data.

```{r create_univariate_numeric_plots, echo=TRUE, warning=FALSE}
numeric_vars <- coltype_lst$split$continuous %>% sort()

for (plot_varname in numeric_vars) {
  cat("--\n")
  cat(str_c(plot_varname, "\n"))

  plot_var <- explore_data_tbl %>% pull(!! plot_varname)
  na_count <- plot_var %>% are_na() %>% sum()

  plot_var %>% summary %>% print

  explore_plot <- ggplot(explore_data_tbl) +
    geom_histogram(aes(x = !! sym(plot_varname)),
                   bins = hist_bins_count) +
    geom_vline(xintercept = mean(plot_var, na.rm = TRUE),
               colour = "red",   size = 1.5) +
    geom_vline(xintercept = median(plot_var, na.rm = TRUE),
               colour = "green", size = 1.5) +
    xlab(plot_varname) +
    ylab("Count") +
    scale_y_continuous(labels = label_comma()) +
    ggtitle(str_c("Histogram Plot for Variable: ", plot_varname,
                  " (", na_count, " missing values)"),
            subtitle = "(red line is mean, green line is median)")

  explore_std_plot <- explore_plot + scale_x_continuous(labels = label_comma())
  explore_log_plot <- explore_plot + scale_x_log10     (labels = label_comma())

  plot_grid(explore_std_plot,
            explore_log_plot, nrow = 2) %>% print()
}
```

## Categorical Variables

Categorical variables only have values from a limited, and usually fixed,
number of possible values

```{r create_univariate_categorical_plots, echo=TRUE, warning=FALSE}
categorical_vars <- coltype_lst$split$discrete %>% sort()

for (plot_varname in categorical_vars) {
  cat("--\n")
  cat(str_c(plot_varname, "\n"))

  na_count <- explore_data_tbl %>% pull(!! plot_varname) %>% are_na() %>% sum()

  plot_tbl <- explore_data_tbl %>%
    pull(!! plot_varname) %>%
    fct_lump(n = cat_level_count) %>%
    fct_count() %>%
    mutate(f = fct_relabel(f, str_trunc, width = 15))

  explore_plot <- ggplot(plot_tbl) +
    geom_bar(aes(x = fct_reorder(f, -n), weight = n)) +
    xlab(plot_varname) +
    ylab("Count") +
    scale_y_continuous(labels = label_comma()) +
    ggtitle(str_c("Barplot of Counts for Variable: ", plot_varname,
                  " (", na_count, " missing values)")) +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5))

  plot(explore_plot)
}
```


## Date/Time Variables

Date/Time variables represent calendar or time-based data should as time of the
day, a date, or a timestamp.

```{r create_univariate_datetime_plots, echo=TRUE, warning=FALSE}
datetime_vars <- coltype_lst$split$datetime %>% sort()

for (plot_varname in datetime_vars) {
  cat("--\n")
  cat(str_c(plot_varname, "\n"))

  plot_var <- explore_data_tbl %>% pull(!! plot_varname)
  na_count <- plot_var %>% are_na() %>% sum()

  plot_var %>% summary() %>% print()

  explore_plot <- ggplot(explore_data_tbl) +
    geom_histogram(aes(x = !! sym(plot_varname)),
                   bins = hist_bins_count) +
    xlab(plot_varname) +
    ylab("Count") +
    scale_y_continuous(labels = label_comma()) +
    ggtitle(str_c("Barplot of Dates/Times in Variable: ", plot_varname,
                  " (", na_count, " missing values)"))

  plot(explore_plot)
}
```


```{r, echo=FALSE}
#knitr::knit_exit()
```


# Bivariate Data Exploration

We now move on to looking at bivariate plots of the data set.

## Facet Plots on Variables

A natural way to explore relationships in data is to create univariate
visualisations facetted by a categorical value.

```{r bivariate_facet_data, echo=TRUE}
facet_varname <- "region"

facet_count_max <- 3
```


### Logical Variables

For logical variables we facet on barplots of the levels, comparing TRUE,
FALSE and missing data.

```{r create_bivariate_logical_plots, echo=TRUE}
logical_vars <- logical_vars[!logical_vars %in% facet_varname] %>% sort()


for (plot_varname in logical_vars) {
  cat("--\n")
  cat(str_c(plot_varname, "\n"))

  plot_tbl <- data_tbl %>% filter(!are_na(!! plot_varname))

  explore_plot <- ggplot(plot_tbl) +
    geom_bar(aes(x = !! sym(plot_varname))) +
    facet_wrap(facet_varname, scales = "free") +
    xlab(plot_varname) +
    ylab("Count") +
    scale_y_continuous(labels = label_comma()) +
    ggtitle(str_c(facet_varname, "-Faceted Barplots for Variable: ",
                  plot_varname)) +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5))

  plot(explore_plot)
}
```


### Numeric Variables

For numeric variables, we facet on histograms of the data.

```{r create_bivariate_numeric_plots, echo=TRUE}
for (plot_varname in numeric_vars) {
  cat("--\n")
  cat(str_c(plot_varname, "\n"))

  plot_tbl <- data_tbl %>% filter(!are_na(!! plot_varname))

  explore_plot <- ggplot(plot_tbl) +
    geom_histogram(aes(x = !! sym(plot_varname)),
                   bins = hist_bins_count) +
    facet_wrap(facet_varname, scales = "free") +
    xlab(plot_varname) +
    ylab("Count") +
    scale_y_continuous(labels = label_comma()) +
    ggtitle(str_c(facet_varname, "-Faceted Histogram for Variable: ",
                  plot_varname)) +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5))

  print(explore_plot + scale_x_continuous(labels = label_comma()))
  print(explore_plot + scale_x_log10     (labels = label_comma()))
}
```

### Categorical Variables

We treat categorical variables like logical variables, faceting the barplots
of the different levels of the data.

```{r create_bivariate_categorical_plots, echo=TRUE}
categorical_vars <- categorical_vars[!categorical_vars %in% facet_varname] %>% sort()

for (plot_varname in categorical_vars) {
  cat("--\n")
  cat(str_c(plot_varname, "\n"))

  plot_tbl <- data_tbl %>%
    filter(!are_na(!! plot_varname)) %>%
    mutate(
      varname_trunc = fct_relabel(!! sym(plot_varname), str_trunc, width = 10)
      )

  explore_plot <- ggplot(plot_tbl) +
    geom_bar(aes(x = varname_trunc)) +
    facet_wrap(facet_varname, scales = "free") +
    xlab(plot_varname) +
    ylab("Count") +
    scale_y_continuous(labels = label_comma()) +
    ggtitle(str_c(facet_varname, "-Faceted Histogram for Variable: ",
                  plot_varname)) +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5))

  plot(explore_plot)
}
```


### Date/Time Variables

Like the univariate plots, we facet on histograms of the years in the dates.

```{r create_bivariate_datetime_plots, echo=TRUE}
for (plot_varname in datetime_vars) {
  cat("--\n")
  cat(str_c(plot_varname, "\n"))

  plot_tbl <- data_tbl %>% filter(!are_na(!! plot_varname))

  explore_plot <- ggplot(plot_tbl) +
    geom_histogram(aes(x = !! sym(plot_varname)),
                   bins = hist_bins_count) +
    facet_wrap(facet_varname, scales = "free") +
    xlab(plot_varname) +
    ylab("Count") +
    scale_y_continuous(labels = label_comma()) +
    ggtitle(str_c(facet_varname, "-Faceted Histogram for Variable: ",
                  plot_varname))

  plot(explore_plot)
}
```

```{r free_memory_facetplot, echo=FALSE}
rm(plot_var, plot_tbl)
```


```{r, echo=FALSE}
#knitr::knit_exit()
```


# Custom Explorations

In this section you can add your own multivariate visualations such as
boxplots and so on.





# R Environment

```{r show_session_info, echo=TRUE, message=TRUE}
sessioninfo::session_info()
```