Using the following Chapter 6 in Fundamentals of Data Visualization (Wilke 2018) https://clauswilke.com/dataviz/visualizing-amounts.html, Chapter 6 in The Truthful Art (Cairo 2016) https://datavizs25.classes.andrewheiss.com/content/04-content.html#ref-Cairo:2016, Chapter 10 in Fundamentals of Data Visualization (Wilke 2018) https://datavizs25.classes.andrewheiss.com/content/04-content.html#ref-Wilke:2018, Engaging Readers with Square Pie/Waffle Charts https://eagereyes.org/blog/2008/engaging-readers-with-square-pie-waffle-charts, Understanding Pie Charts https://eagereyes.org/techniques/pie-charts, Video from the Financial Times about the design decisions behind their COVID-19 tracking charts https://www.youtube.com/watch?v=4lm3MWTVAK0, answer the following questions Which three lessons from the session were the most fascinating or thrilling? Why? Which three aspects of the session were the most ambiguous or unclear? Are there questions someone might still want to ask about?

The list of construction sites designated as "essential" under the city's shelter-in-place pandemic order is kept up to date by the New York City Department of Buildings (DOB). view the many projects on the interactive map at https://www1.nyc.gov/assets/buildings/html/essential-active-construction.html with this link. Utilize this information to illustrate the quantities or ratios of several vital projects across New York City's five boroughs (Brooklyn, Manhattan, the Bronx, Queens, and Staten Island). Respond to the following inquiries: What condensed data was used to determine the number of construction sites by several criteria. Utilizing `group_by()` and `summarize()`,

```{r}

| label: summarize-data-borough

essential_by_borough <- essential |>

group_by(BOROUGH) |>

summarize(total = n()) |>

mutate(proportion = total / sum(total))

```

```{r}

| label: plot-borough-summary

Add plot with geom_col() here

Approved projects by category

```{r}

| label: summarize-data-category. a summarized dataset of projects by category needs to be shown (big hint though: copy the code for the borough summary and change just one thing)

```{r}

| label: plot-category-summary

Add a lollipop chart here

```

Approved projects across boroughs and category

```{r}

| label: summarize-data-heatmap

Build a dataset that summarizes the projects by category and borough. To create the summary, one must group by two variables. Importantly, onewill also need to add another group_by() inbetween summarize() and mutate(). Before calculating the proportion, group by borough if you want the percentages of boroughs to add up to 100% in each category. Show how to do all these. Put the codes in.

```{r}

| label: plot-heatmap

Add a heatmap here with geom_tile()