ae-11a-data-visualisation.qmd

---
title: "Application Exercise 11 - Data Visualisation"
format: html
editor: visual
editor_options: 
  chunk_output_type: console
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---

```{r}

# R Packages that are required for this script are loaded in the beginning

library(readr)
library(dplyr)
library(ggplot2)

# An R package to access the World Bank World Development Indicator timeseries

library(WDI)

# An R package for working with country codes

library(countrycode)

# An R Package to work with model outputs

library(broom)

```

# Data import

## What a waste!

What a Waste is a global project to aggregate data on solid waste management from around the world. This database features the statistics collected through the effort, covering nearly all countries and over 330 cities. Beyond waste generation, there are a long list of additina indicators in this dataset.

-   Source: https://datacatalog.worldbank.org/search/dataset/0039597

```{r}

# This code is copied from application exercise week 10

global_waste_data <- read_csv("data/raw_data/country_level_data_0.csv")
```

## World Development Indicators (WDI)

World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates.

-   World Bank: https://databank.worldbank.org/source/world-development-indicators
-   GitHub repo: https://github.com/vincentarelbundock/WDI
-   R Package Documentation: http://vincentarelbundock.github.io/WDI/

```{r}

# Only walk through. No live coding.

WDIsearch("GDP per capita ")

gdp_data <- WDI(indicator = "NY.GDP.PCAP.CD", start = 2019, end = 2019) 

```

# Data tidying

## Waste data

```{r}

# Only walk through. No live coding.
# This code is copied from application exercise week 10

global_waste_data_small <- global_waste_data %>% 
  select(country_name, 
         iso3c, 
         income_id, 
         total_msw_total_msw_generated_tons_year, 
         population_population_number_of_people) %>%  
  rename(msw_tons_year = total_msw_total_msw_generated_tons_year,
         population = population_population_number_of_people) 

```

## GDP data

```{r}

# Only walk through. No live coding.

gdp_data_tidy <- gdp_data %>% 
  as_tibble() %>% 
  rename(gdp_capita = NY.GDP.PCAP.CD)

```

# Data transformation

## Waste data

```{r}

# This code is copied from application exercise week 10
# Only walk through. No live coding.

global_waste_data_kg_year <- global_waste_data_small %>% 
  mutate(capita_kg_year = msw_tons_year / population * 1000) %>% 
  mutate(income_id = factor(income_id, 
                            levels = c("HIC", "UMC", "LMC", "LIC"))) %>% 
  # select relevant columns
  select(country_name:income_id, capita_kg_year)

```

## GDP data

```{r}

# Only walk through. No live coding.

gdp_data_join <- gdp_data_tidy %>%
  mutate(iso3c = countrycode(iso2c, origin = "iso2c", destination = "iso3c")) %>% 
  filter(!is.na(iso3c)) 

```

## Joining two datasets

```{r}

# Only walk through. No live coding.

waste_gdp_data <- global_waste_data_kg_year %>% 
  left_join(gdp_data_join) %>% 
  select(country_name:capita_kg_year, gdp_capita, year)

```

# Data visualisation

## Aesthetic mappings

Main aesthetic mappings:

-   x
-   y
-   color/fill
-   shape
-   size
-   alpha (Transparency)


## Scaling

All functions that adapt the scale start with `scale_` followed by the aesthetic to be adapted, e.g. `x_`, and then followed by the type of variable, e.g. `continouous`. So to adapt a continuous scale on an x-axis, the function to use is: `scale_x_continuous()`

### Axis scales



### Color scales

-   scale_color_brewer()



-   scale_color_manual()

```{r}

my_colors <- c("#ED6D37", "#901C6C", "#18A5C5", "#A6CE8F")


```

## Geoms

Aesthetic mappings are typically defined within the `ggplot()` function. Everyone geom that follows will use the same aesthetic mappings to visualise the data.

```{r}



```

If we want to define a mapping for one geom only, then the aesthetic mappings must be included in the function of that geom, here `geom_point()`. We can even use additional data in the same plot for an individual geom, see `geom_text()`. - defined for a geom only - geom_text

```{r}

waste_gdp_data_filter <- waste_gdp_data %>% 
  filter(capita_kg_year > 850 | 
           country_name == "Switzerland" |
           country_name == "Canada")



```

## Labels



## Theme

A reference to @fig-plot-waste-gdp.

```{r}

#| label: fig-plot-waste-gdp
#| fig-cap: "Global Municipal Solid Waste generation over GPD per capita, illustrated by defined income groups."


```

# Data modeling

```{r}



```

Learn more about modeling in R:

-   R4DS chapter - https://r4ds.had.co.nz/model-intro.html
-   Book: Statistical Inference via Data Science: A ModernDive into R and the Tidyverse! - https://moderndive.com/
-   R Packages: Tidymodels - https://www.tidymodels.org/
-   R Package broom

https://broom.tidymodels.org/articles/broom.html


# Data communication

Let's add a table of contents a date and an author to the document.

You can learn more about the options for HTML files in the Quarto documentation: https://quarto.org/docs/reference/formats/html.html

And to learn more about creating citeable articles, read here: https://quarto.org/docs/authoring/create-citeable-articles.html