Statistical graphics with ggplot2

.title[
# Statistical graphics with <code>ggplot2</code>
]
.subtitle[
## Programming for Statistical Science
]
.author[
### Dr. Zulfiqar Ali
]
.institute[
### College of Statistcal Sciences, University of the Punjab, Lahore
]
.date[
### 15 June 2023
]

---

## Supplementary materials

Additional resources

- [Chapter 3](https://r4ds.had.co.nz/data-visualisation.html), R for Data Science
- `ggplot2` [Reference](https://ggplot2.tidyverse.org/reference/index.html)
- `ggplot2` [cheat sheet](https://github.com/rstudio/cheatsheets/raw/master/data-visualization-2.1.pdf)
- [color brewer 2](http://colorbrewer2.org/)

---

## `What is ggplot2?`

- `ggplot2` is a tool used in R for creating plots. It follows the grammar of graphics, which means it has a structured way of creating visualizations. It combines the best features from the base and lattice plotting systems.

- `Base Graphics:` Base graphics is the default plotting system in R. It offers a wide range of basic plotting functions, such as `plot()`, `hist()`, `boxplot()`, and more. These functions allow users to create various types of plots, customize colors, add labels, and modify axes. Base graphics are known for their simplicity and ease of use.
  
  - ` Lattice Graphics:` Lattice graphics is an advanced plotting system in R that builds on top of base graphics. It provides additional features for creating more complex and structured plots. Lattice graphics are particularly useful for visualizing multivariate data, where multiple variables are involved. The main function in the lattice package is `xyplot()`, which allows users to create scatter plots, line plots, bar plots, and more. Lattice graphics also support features like faceting (dividing plots into multiple panels) and conditioning (plotting subsets of data based on specific conditions)
---

## `What is ggplot2?`

- One of the advantages of `ggplot2` is that it handles many of the intricate aspects of plotting automatically. For example, it takes care of tasks like creating legends and dividing plots into multiple panels (faceting). This saves users from dealing with these complex tasks themselves 
  
- When working with data that has multiple variables, `ggplot2` is especially useful. It simplifies the process of visualizing relationships between different variables, making it easier to understand complex datasets.
  
Package `ggplot2` is available in package `tidyverse`. Let's load that now.

```r
library(tidyverse)
```

---

## Tidyverse Package

The **tidyverse** package is a collection of R packages designed for data manipulation, visualization, and analysis. It provides a cohesive and consistent set of functions that work together seamlessly. Some of the packages included in the tidyverse are:

- **ggplot2**: For creating elegant and customizable data visualizations.
- **dplyr**: For data manipulation tasks, such as filtering, summarizing, and joining datasets.
- **tidyr**: For tidying messy data by reshaping and organizing it.
- **readr**: For reading and writing data in various formats.
- **purrr**: For working with functional programming paradigms.

---
## The Grammar of Graphics

- The Grammar of Graphics is a visualization concept that was introduced by Leland Wilkinson in 1999.

- Its purpose is to define the fundamental elements of a statistical graphic.
In 2009, Hadley Wickham adapted the concept for the R programming language.

- The adaptation provided a consistent and concise syntax for describing statistical graphics.
  - It offers a highly modular approach, breaking graphs into semantic components.
  - It's important to note that The Grammar of Graphics does not serve as a guide for selecting the most appropriate graph type or how to effectively convey your data.

- This aspect will be explored further in subsequent discussions.
  - The Grammar of Graphics provides a powerful framework for creating visualizations in R, emphasizing the structure and principles behind statistical graphics.

---

## Today's data: MLB

```r
teams <- read_csv("http://www2.stat.duke.edu/~sms185/data/mlb/teams.csv")
```

Object `teams` is a data frame that contains yearly statistics and standings
for Majore League Ball (MLB) https://www.mlb.com/stats/team  teams from 2009 to 2018.

The data has 300 rows and 56 variables.

---

```r
teams
```

```
#> # A tibble: 300 × 56
#>    yearID lgID  teamID franchID divID  Rank     G Ghome     W     L DivWin WCWin
#>     <dbl> <chr> <chr>  <chr>    <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>  <chr>
#>  1   2009 NL    ARI    ARI      W         5   162    81    70    92 N      N    
#>  2   2009 NL    ATL    ATL      E         3   162    81    86    76 N      N    
#>  3   2009 AL    BAL    BAL      E         5   162    81    64    98 N      N    
#>  4   2009 AL    BOS    BOS      E         2   162    81    95    67 N      Y    
#>  5   2009 AL    CHA    CHW      C         3   162    81    79    83 N      N    
#>  6   2009 NL    CHN    CHC      C         2   161    80    83    78 N      N    
#>  7   2009 NL    CIN    CIN      C         4   162    81    78    84 N      N    
#>  8   2009 AL    CLE    CLE      C         4   162    81    65    97 N      N    
#>  9   2009 NL    COL    COL      W         2   162    81    92    70 N      Y    
#> 10   2009 AL    DET    DET      C         2   163    81    86    77 N      N    
#> # ℹ 290 more rows
#> # ℹ 44 more variables: LgWin <chr>, WSWin <chr>, R <dbl>, AB <dbl>, H <dbl>,
#> #   X2B <dbl>, X3B <dbl>, HR <dbl>, BB <dbl>, SO <dbl>, SB <dbl>, CS <dbl>,
#> #   HBP <dbl>, SF <dbl>, RA <dbl>, ER <dbl>, ERA <dbl>, CG <dbl>, SHO <dbl>,
#> #   SV <dbl>, IPouts <dbl>, HA <dbl>, HRA <dbl>, BBA <dbl>, SOA <dbl>, E <dbl>,
#> #   DP <dbl>, FP <dbl>, name <chr>, park <chr>, attendance <dbl>, BPF <dbl>,
#> #   PPF <dbl>, teamIDBR <chr>, teamIDlahman45 <chr>, teamIDretro <chr>, …
```
]

---

# Plot comparison

---

## scatter plot using `ggplot()`

---
## Using simple `plot()`

---

## Diffrence

The main difference between the scatter plot in ggplot2 and the simple plot command lies in the underlying philosophy and syntax of the two approaches.

#ggplot2 Scatter Plot:
- ggplot2 is a part of the tidyverse package and follows the grammar of graphics framework.
- It provides a more structured and layered approach to creating plots.
- In ggplot2, a scatter plot is created using the geom_point() function.
- You can customize various aspects of the scatter plot, such as color, size, and shape of the points, using aesthetics mapping (aes()).
- Additional layers, such as trend lines or smoothing curves, can be added using separate geom_* functions.
---

## Diffrence 
#Simple Plot Command:
- The simple plot command, such as plot() in base R, is a quick and straightforward way to create basic plots.
- It follows a more traditional and direct approach.
- A scatter plot can be created by passing the x and y variables as arguments to the plot() function.
Basic customization options, like point color and size, can be adjusted using additional parameters.

Overall, ggplot2 offers a more flexible and customizable approach to creating scatter plots, with a focus on the grammar of graphics. 
On the other hand, the simple plot command provides a quick and easy way to generate basic scatter plots with fewer customization options.
The choice between the two approaches depends on the level of complexity and customization required for your visualization.

---

## More understanding on ggplot2 type graphs - Spatial graphs as an example

```
#>         coordinates Stations      Jan      Feb     Mar      Apr      May
#> 1 (74.8624, 35.357)    badin 1.178969 1.166179 1.10908 1.061619 1.012896
#>        Jun     Jul      Aug      Sep     Oct     Nov      Dec
#> 1 1.009722 1.01368 1.025683 1.038332 1.07704 1.11487 1.155086
#> [using ordinary kriging]
```

---

---

## Scatter plot

Using `plot()`

```r
teams$RD <- teams$R - teams$RA
teams_div <- teams[teams$DivWin == "Y", ]
teams_no_div <- teams[teams$DivWin == "N", ]

mod1 <- lm(WinPct ~ RD, data = teams_div)
mod2 <- lm(WinPct ~ RD, data = teams_no_div)

plot(x = (teams$R - teams$RA), y = teams$WinPct,
     col = adjustcolor(as.integer(factor(teams$DivWin))),
     pch = 16, 
     xlab = "Run Differential",
     ylab = "Win Percentage")
abline(mod1, col = 2, lwd=2)
abline(mod2, col = 1, lwd=2)
```

---

# What's in a `ggplot()`?

---

## Terminology

A statistical graphic is a...

- mapping of **data**

- which may be **statistically transformed** (summarized, log-transformed, etc.)

- to **aesthetic attributes** (color, size, xy-position, etc.)

- using **geometric objects** (points, lines, bars, etc.)

- and mapped onto a specific **facet** and **coordinate system.**

---

## What do I "need"?

1) Some data (preferably in a data frame)

```r
*ggplot(data = teams)
```

---

2) A set of variable mappings

```r
*ggplot(data = teams, mapping = aes(x = attendance / 1000, y = W))
```

---

3) A geom with arguments, or multiple geoms with arguments connected by `+`

```r
ggplot(data = teams, mapping = aes(x = attendance / 1000, y = W)) +
* geom_point(color = "blue")
```

---

4) Some options on changing scales or adding facets

```r
ggplot(data = teams, mapping = aes(x = attendance / 1000, y = W)) +
  geom_point(color = "blue") +
* facet_wrap(~yearID, nrow = 2)
```

---

5) Some labels

```r
ggplot(data = teams, mapping = aes(x = attendance / 1000, y = W)) +
  geom_point(color = "blue") +
  facet_wrap(~yearID, nrow = 2) +
* labs(x = "Attendance", y = "Wins", caption = "Attendance in thousands")
```

---

6) Other options

```r
ggplot(data = teams, mapping = aes(x = attendance / 1000, y = W)) +
  geom_point(color = "blue") +
  facet_wrap(~yearID, nrow = 2) +
  labs(x = "Attendance", y = "Wins", caption = "Attendance in thousands") +
* theme_bw(base_size = 16) +
* theme(axis.text.x = element_text(angle = 45, hjust = 1))
```

---

## Anatomy of a ggplot

```r
ggplot(
  data = [dataframe],
  
  aes(
    x = [var_x], y = [var_y], 
    color = [var_for_color], 
    fill  = [var_for_fill], 
    shape = [var_for_shape],
    size  = [var_for_size],
    alpha = [var_for_alpha],
    ...#other aesthetics
  )
) +
  geom_<some_geom>([geom_arguments]) +
  ... # other geoms
  scale_<some_axis>_<some_scale>() +
  facet_<some_facet>([formula]) +
  ... # other options
```

To visualize multivariate relationships we can add variables to our 
visualization by specifying aesthetics: color, size, shape, linetype, 
alpha, or fill; we can also add facets based on variable levels.

---

# Scatter plots

---

## Base plot

```r
ggplot(data = teams, mapping = aes(x = (R ^ 2 / (R ^ 2 + RA ^2 )), y = WinPct)) +
* geom_point()
```

<img src="lec_08_files/figure-html/unnamed-chunk-17-1.png" style="display: block; margin: auto;" />
]

---

## Altering aesthetic color

```r
ggplot(data = teams, mapping = aes(x = (R ^ 2 / (R ^ 2 + RA ^2 )), y = WinPct)) +
* geom_point(color = "#E81828")
```

<img src="lec_08_files/figure-html/unnamed-chunk-18-1.png" style="display: block; margin: auto;" />
]

---

## Altering aesthetic color

```r
*ggplot(data = teams, mapping = aes(x = (R ^ 2 / (R ^ 2 + RA ^2 )), y = WinPct, color = lgID)) +
  geom_point(show.legend = FALSE)
```

<img src="lec_08_files/figure-html/unnamed-chunk-19-1.png" style="display: block; margin: auto;" />
]

---

## Altering aesthetic color

```r
ggplot(data = teams, mapping = aes(x = (R ^ 2 / (R ^ 2 + RA ^2 )), y = WinPct, color = lgID)) +
* geom_point()
```

<img src="lec_08_files/figure-html/unnamed-chunk-20-1.png" style="display: block; margin: auto;" />
]

---

## Base plot

```r
ggplot(data = teams[teams$yearID == 2018, ], mapping = aes(x = BB + H, y = SO)) +
  geom_point()
```

<img src="lec_08_files/figure-html/unnamed-chunk-21-1.png" style="display: block; margin: auto;" />
]

---

## Altering multiple aesthetics

```r
ggplot(data = teams[teams$yearID == 2018, ], mapping = aes(x = BB + H, y = SO)) +
* geom_point(size = 3, shape = 2, color = "#E81828")
```

<img src="lec_08_files/figure-html/unnamed-chunk-22-1.png" style="display: block; margin: auto;" />
]

---

## Altering multiple aesthetics

```r
ggplot(data = teams[teams$yearID == 2018, ], mapping = aes(x = BB + H, y = SO, 
*                    color = factor(Rank), shape = factor(Rank))) +
  geom_point(size = 4, alpha = .8, show.legend = FALSE)
```

<img src="lec_08_files/figure-html/unnamed-chunk-23-1.png" style="display: block; margin: auto;" />
]

---

## Altering multiple aesthetics

```r
ggplot(data = teams[teams$yearID == 2018, ], mapping = aes(x = BB + H, y = SO, 
*                    color = factor(Rank), shape = factor(Rank))) +
  geom_point(size = 4, alpha = .8)
```

<img src="lec_08_files/figure-html/unnamed-chunk-24-1.png" style="display: block; margin: auto;" />
]

---

## Inside or outside `aes()`?

When does an aesthetic go inside function `aes()`?

- If you want an aesthetic to be reflective of a variable's values, it must
  go inside aes.

- If you want to set an aesthetic manually and not have it convey
  information about a variable, use the aesthetic's name outside of aes
  and set it to your desired value.

Aesthetics for continuous and discrete variables are measured on
continuous and discrete scales, respectively.

---

## Faceting

```r
ggplot(data = teams, mapping = aes(x = R, y = WinPct, color = DivWin)) +
  geom_point(alpha = .8) +
* facet_grid(lgID~ .)
```

<img src="lec_08_files/figure-html/unnamed-chunk-25-1.png" style="display: block; margin: auto;" />
]

---

## Faceting

```r
ggplot(data = teams, mapping = aes(x = R, y = WinPct, color = DivWin)) +
  geom_point(alpha = .8) +
* facet_grid(. ~lgID)
```

<img src="lec_08_files/figure-html/unnamed-chunk-26-1.png" style="display: block; margin: auto;" />
]

---

## Faceting

```r
ggplot(data = teams, mapping = aes(x = R, y = WinPct, color = DivWin)) +
  geom_point(alpha = .8) +
* facet_grid(divID~lgID)
```

<img src="lec_08_files/figure-html/unnamed-chunk-27-1.png" style="display: block; margin: auto;" />
]

---

## Faceting

```r
ggplot(data = teams, mapping = aes(x = R, y = WinPct, color = DivWin)) +
  geom_point(alpha = .8) +
* facet_wrap(~yearID)
```

<img src="lec_08_files/figure-html/unnamed-chunk-28-1.png" style="display: block; margin: auto;" />
]

---

## Facet grid or wrap?

- Use `facet_wrap()` to wrap a one dimensional sequence into two dimensional
  panels.
  
- Use `facet_grid()` when you have two discrete variables and you want panels
  of plots to represent all possible combinations.
  
---

## Exercise

Let's explore the relationship between runs and strikeouts for division
winners and non-division winners. 
Use tibble `teams` to re-create the plot below.

<br/>

**How can we improve this visualization?**

???

```r
ggplot(data = teams, mapping = aes(x = SO, y = R, color = factor(DivWin))) +
  geom_point(size = 3, alpha = .8) +
  facet_wrap(~yearID, nrow = 2) +
  labs(x = "Strike outs", y = "Runs", color = "Division winner")
```
]

---

## A more effective visualization

???

```r
ggplot(data = teams, mapping = aes(x = SO, y = R, color = factor(DivWin))) +
  geom_point(size = 2, alpha = .8) +
  geom_hline(yintercept = 750, lty = 2, alpha = .5, color = "blue") +
  geom_vline(xintercept = 1250, lty = 2, alpha = .5, color = "blue") +
  facet_wrap(~yearID, nrow = 2) +
  labs(x = "Strike outs", y = "Runs", color = "Division winner",
       title = "Division winners generally score more runs",
       subtitle = "and have fewer strike outs") +
  scale_color_manual(values = c("grey", "red")) +
  scale_x_continuous(limits = c(750, 1750), breaks = seq(900, 1700, 350),
                     labels = seq(900, 1700, 350)) +
  scale_y_continuous(limits = c(500, 1000), breaks = seq(500, 1000, 100),
                     labels = seq(500, 1000, 100)) +
  theme_bw(base_size = 16) +
  theme(legend.position = "bottom")
```

---

# Other geoms

---

## Caution

- The following plots are not well-polished. They are designed to demonstrate
  the various geoms and options that exist within `ggplot2`.
  
- You should always have a well-labelled and polished visualization if it will
  be seen by an outside audience.

---

## Box plots

```r
ggplot(teams, mapping = aes(x = factor(yearID), y = kpg)) +
* geom_boxplot(color = "#E81828", fill = "#002D72", alpha = .7)
```

<img src="lec_08_files/figure-html/unnamed-chunk-33-1.png" style="display: block; margin: auto;" />
]

---

## Box plots: flipped coordinates

```r
ggplot(teams, mapping = aes(x = factor(yearID), y = kpg)) +
  geom_boxplot(color = "#E81828", fill = "#002D72", alpha = .7) +
* coord_flip()
```

<img src="lec_08_files/figure-html/unnamed-chunk-34-1.png" style="display: block; margin: auto;" />
]

---

## Box plots: custom colors

```r
ggplot(teams, mapping = aes(x = factor(yearID), y = kpg, fill = lgID)) +
  geom_boxplot(color = "grey", alpha = .7) +
* scale_fill_manual(values = c("#E81828", "#002D72")) +
  coord_flip() +
* theme_bw()
```

<img src="lec_08_files/figure-html/unnamed-chunk-35-1.png" style="display: block; margin: auto;" />
]

---

## Bar plots

```r
ggplot(teams[teams$yearID == 2018, ], mapping = aes(y = W, x = franchID)) +
  geom_bar(stat = "identity")
```

<img src="lec_08_files/figure-html/unnamed-chunk-36-1.png" style="display: block; margin: auto;" />
]

---

## Bar plots: angled text

```r
ggplot(teams[teams$yearID == 2018, ], mapping = aes(y = W, x = franchID)) +
  geom_bar(stat = "identity") +
* theme(axis.text.x = element_text(angle = 45, hjust = 1))
```

<img src="lec_08_files/figure-html/unnamed-chunk-37-1.png" style="display: block; margin: auto;" />
]

---

## Bar plots: sorted

```r
*ggplot(teams[teams$yearID == 2018, ], mapping = aes(y = W, x = reorder(franchID, W))) +
  geom_bar(stat = "identity", color = "#E81828", fill = "#002D72", alpha = .2) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
```

<img src="lec_08_files/figure-html/unnamed-chunk-38-1.png" style="display: block; margin: auto;" />
]

---

## Bar plots

```r
ggplot(teams[teams$yearID == 2018, ], mapping = aes(y = W, x = reorder(franchID, W))) +
  geom_bar(stat = "identity", color = "#E81828", fill = "#002D72", alpha = .3) +
* scale_y_continuous(breaks = seq(0, 120, 15), labels = seq(0, 120, 15), limits = c(0, 120)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
```

<img src="lec_08_files/figure-html/unnamed-chunk-39-1.png" style="display: block; margin: auto;" />
]

---

## Histograms

```r
ggplot(teams, mapping = aes(x = WinPct)) +
  geom_histogram(binwidth = .025, fill = "#E81828", color = "#002D72", alpha = .7)
```

<img src="lec_08_files/figure-html/unnamed-chunk-40-1.png" style="display: block; margin: auto;" />
]

---

## Density plots

```r
ggplot(teams, mapping = aes(x = WinPct)) +
  geom_density(fill = "#E81828", color = "#002D72", alpha = .7)
```

<img src="lec_08_files/figure-html/unnamed-chunk-41-1.png" style="display: block; margin: auto;" />
]

---

## Density plots: custom colors

```r
ggplot(teams, mapping = aes(x = WinPct, fill = lgID)) +
  geom_density(alpha = .5) +
* scale_fill_manual(values = c("#E81828", "#002D72"))
```

<img src="lec_08_files/figure-html/unnamed-chunk-42-1.png" style="display: block; margin: auto;" />
]

---

## Heat maps

```r
ggplot(teams[teams$yearID == 2018, ], mapping = aes(x = Rank, y = divID, fill = RD)) +
* geom_raster()
```

<img src="lec_08_files/figure-html/unnamed-chunk-43-1.png" style="display: block; margin: auto;" />
]

---

## Heat maps: color palette

```r
ggplot(teams[teams$yearID == 2018, ], mapping = aes(x = Rank, y = divID, fill = RD)) +
  geom_raster() +
* scale_fill_gradientn(colours = terrain.colors(100))
```

<img src="lec_08_files/figure-html/unnamed-chunk-44-1.png" style="display: block; margin: auto;" />
]

---

## Heat maps: color palette

```r
ggplot(teams[teams$yearID == 2018, ], mapping = aes(x = Rank, y = divID, fill = RD)) +
  geom_raster() +
* scale_fill_gradient(low = "#fef0d9", high = "#b30000")
```

<img src="lec_08_files/figure-html/unnamed-chunk-45-1.png" style="display: block; margin: auto;" />
]

---

## Choosing colors

[Color Brewer 2](http://colorbrewer2.org/)

---

## Effective visualization tips
  
- Provide a title that tells a story.
  
- Strive to have your visualization function in a closed environment.

- Be mindful of color and scale choices.

- Generally, color is better than shape to make things "pop".

- Not everything has to have a color, shape, transparency, etc.

- Add labels and annotation.

- Use your visualization to support your story.

- Use chunk options `fig.width`, `fig.height`, `fig.align`, and `fig.show` to 
  manipulate your plot's size and placement.

---

# Exercise

---

## Energy data

```r
energy <- read_csv("http://www2.stat.duke.edu/~sms185/data/energy/energy.csv")
```
]

```r
energy
```

```
#> # A tibble: 105 × 6
#>    MWhperDay name                                 type  location     note    boe
#>        <dbl> <chr>                                <chr> <chr>        <chr> <dbl>
#>  1         3 Chernobyl Solar                      Solar Ukraine      "On …     0
#>  2       637 Solarpark Meuro                      Solar Germany       <NA>    55
#>  3       920 Tesla's proposed virtual power plant Solar South Austr… "50,…    79
#>  4      1280 Quaid-e-Azam                         Solar Pakistan     "Nam…   110
#>  5      1760 Topaz                                Solar USA           <NA>   152
#>  6      2025 Agua Caliente                        Solar USA          "Ari…   175
#>  7      2466 Kamuthi                              Solar India        "\"1…   213
#>  8      2720 Longyangxia                          Solar China         <NA>   234
#>  9      3840 Kurnool                              Solar India         <NA>   331
#> 10      4950 Tengger Desert                       Solar China        "Cov…   427
#> # ℹ 95 more rows
```
]

---

## Data dictionary

The power sources represent the amount of energy a power source generates
each day as represented in daily MWh.

- `MWhperDay`: MWh of energy generated per day
- `name`: energy source name
- `type`: type of energy source
- `location`: country of energy source
- `note`: more details on energy source
- `boe`: barrel of oil equivalent

<br>

- **Daily megawatt hour (MWh)** is a measure of energy output.
- **1 MWh** is, on average, enough power for 28 people in the USA

---

## Objective

Re-create the plot on the following slide.

A few notes:

- base font size is 18

- hex colors: `c("#9d8b7e", "#315a70", "#66344c", "#678b93", "#b5cfe1", "#ffcccc")`

- use function `order()` to help get the top 30

Starter code:

```r
energy_top_30 <- energy[order(energy$MWhperDay, decreasing = T)[1:30], ]
```

---

???

```r
ggplot(energy_top_30, mapping = aes(x = reorder(name, MWhperDay), 
                                    y = MWhperDay / 1000, 
                                    fill = type)) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = c("#9d8b7e", "#315a70", "#66344c",
                               "#678b93", "#b5cfe1", "#ffcccc")) +
  theme_bw(base_size = 18) +
  labs(y       = "Daily MWh (in thousands)", x = "Power Source",
       title   = "Top 30 power source energy generators",
       fill    = "Power Source",
       caption = "1 MWh is, on average, enough power for 28 people in the USA") +
  coord_flip()
```
]

---

## References

1. Grolemund, G., & Wickham, H. (2019). R for Data Science. R4ds.had.co.nz.
   https://r4ds.had.co.nz/data-visualisation.html

2. https://ggplot2.tidyverse.org/reference/

3. Lahman, S. (2019) Lahman's Baseball Database, 1871-2018, Main page,
   http://www.seanlahman.com/baseball-archive/statistics/
  
4. https://www.visualcapitalist.com/worlds-largest-energy-sources/