visualizing data

# visualizing data
## <br>🔗 <a href="https://bit.ly/dataviz-ncpma">bit.ly/dataviz-ncpma</a>
### <br> dr. mine çetinkaya-rundel <br> duke university

---

# .hand[why] visualize

---

> *"The simple graph has brought more information to the data analyst's mind than any other device."*
>
>John Tukey

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- discover patterns that may not be obvious from numerical summaries

---

.pull-left-narrow[
.task[
We have 13 datasets, each with 142 observations. For each observation we have values on two variables recorded: an X and a Y.

Summary statistics for these two variables for each of the datasets is given on the right.

How, if at all, are these 13 datasets different from each other?
]
]
.pull-right-wide[

|dataset    |   n| Average x| Average y|
|:----------|---:|---------:|---------:|
|Dataset 1  | 142|      54.3|      47.8|
|Dataset 2  | 142|      54.3|      47.8|
|Dataset 3  | 142|      54.3|      47.8|
|Dataset 4  | 142|      54.3|      47.8|
|Dataset 5  | 142|      54.3|      47.8|
|Dataset 6  | 142|      54.3|      47.8|
|Dataset 7  | 142|      54.3|      47.8|
|Dataset 8  | 142|      54.3|      47.8|
|Dataset 9  | 142|      54.3|      47.8|
|Dataset 10 | 142|      54.3|      47.8|
|Dataset 11 | 142|      54.3|      47.8|
|Dataset 12 | 142|      54.3|      47.8|
|Dataset 13 | 142|      54.3|      47.8|
]

---

How, if at all, are these 13 datasets different from each other?
]
]
.pull-right-wide[

|dataset    |   n| Average x| Average y| St Dev x| St Dev y|
|:----------|---:|---------:|---------:|--------:|--------:|
|Dataset 1  | 142|      54.3|      47.8|     16.8|     26.9|
|Dataset 2  | 142|      54.3|      47.8|     16.8|     26.9|
|Dataset 3  | 142|      54.3|      47.8|     16.8|     26.9|
|Dataset 4  | 142|      54.3|      47.8|     16.8|     26.9|
|Dataset 5  | 142|      54.3|      47.8|     16.8|     26.9|
|Dataset 6  | 142|      54.3|      47.8|     16.8|     26.9|
|Dataset 7  | 142|      54.3|      47.8|     16.8|     26.9|
|Dataset 8  | 142|      54.3|      47.8|     16.8|     26.9|
|Dataset 9  | 142|      54.3|      47.8|     16.8|     26.9|
|Dataset 10 | 142|      54.3|      47.8|     16.8|     26.9|
|Dataset 11 | 142|      54.3|      47.8|     16.8|     26.9|
|Dataset 12 | 142|      54.3|      47.8|     16.8|     26.9|
|Dataset 13 | 142|      54.3|      47.8|     16.8|     26.9|
]

---

How, if at all, are these 13 datasets different from each other?
]
]
.pull-right-wide[

|dataset    |   n| Average x| Average y| St Dev x| St Dev y| Correlation|
|:----------|---:|---------:|---------:|--------:|--------:|-----------:|
|Dataset 1  | 142|      54.3|      47.8|     16.8|     26.9|        -0.1|
|Dataset 2  | 142|      54.3|      47.8|     16.8|     26.9|        -0.1|
|Dataset 3  | 142|      54.3|      47.8|     16.8|     26.9|        -0.1|
|Dataset 4  | 142|      54.3|      47.8|     16.8|     26.9|        -0.1|
|Dataset 5  | 142|      54.3|      47.8|     16.8|     26.9|        -0.1|
|Dataset 6  | 142|      54.3|      47.8|     16.8|     26.9|        -0.1|
|Dataset 7  | 142|      54.3|      47.8|     16.8|     26.9|        -0.1|
|Dataset 8  | 142|      54.3|      47.8|     16.8|     26.9|        -0.1|
|Dataset 9  | 142|      54.3|      47.8|     16.8|     26.9|        -0.1|
|Dataset 10 | 142|      54.3|      47.8|     16.8|     26.9|        -0.1|
|Dataset 11 | 142|      54.3|      47.8|     16.8|     26.9|        -0.1|
|Dataset 12 | 142|      54.3|      47.8|     16.8|     26.9|        -0.1|
|Dataset 13 | 142|      54.3|      47.8|     16.8|     26.9|        -0.1|
]

---

How, if at all, are these 13 datasets different from each other?
]
]
.pull-right-wide[
<img src="index_files/figure-html/unnamed-chunk-6-1.png" width="100%" />
]

---

- discover patterns that may not be obvious from numerical summaries

- convey information in a way that is otherwise difficult/impossible to convey

---

.small[
Source: [Financial Times](https://www.ft.com/content/a2901ce8-5eb7-4633-b89c-cbdf5b386938), 27 Aug 2021.
]
]
.pull-right-wide[
<img src="images/ft-covid-excess-deaths.png" width="100%" />
]

---

.small[
Source: [New York Times](https://www.nytimes.com/interactive/2020/us/covid-19-vaccine-doses.html), 27 Aug 2021.
]
]
.pull-right-wide[
<img src="images/nytimes-us-covid-vaccine.png" width="100%" />
]

---

.small[
Source: Kieran Healy - [Excess Deaths in 2020](https://kieranhealy.org/blog/archives/2021/10/21/excess-deaths-in-2020/), 21 Oct 2021.
]
]
.pull-right-wide[
<img src="images/healy-excess_all_cause_by_jurisdiction.png" width="50%" style="display: block; margin: auto;" />
]

---

# .hand[how] visualize

## and .hand[how not]

---

## Case study 1:

## Trends instructional staff employees in universities

---

The American Association of University Professors (AAUP) is a nonprofit membership association of faculty and other academic professionals. 
[This report](https://www.aaup.org/sites/default/files/files/AAUP_Report_InstrStaff-75-11_apr2013.pdf) by the AAUP shows trends in instructional staff employees between 1975 and 2011, and contains the following image.

.pull-left-narrow[
.task[
What trends are apparent in the visualization on the right?
]
]
.pull-right-wide[
<img src="images/staff-employment.png" width="85%" style="display: block; margin: auto;" />
]

---

## Data

Each row in this dataset represents a faculty type, and the columns are the years for which we have data. 
The values are percentage of hires of that type of faculty for each year.

|Faculty type                       | 1975| 1989| 1993| 1995| 1999| 2001| 2003| 2005| 2007| 2009| 2011|
|:----------------------------------|----:|----:|----:|----:|----:|----:|----:|----:|----:|----:|----:|
|Full-Time Tenured Faculty          | 29.0| 27.6| 25.0| 24.8| 21.8| 20.3| 19.3| 17.8| 17.2| 16.8| 16.7|
|Full-Time Tenure-Track Faculty     | 16.1| 11.4| 10.2|  9.6|  8.9|  9.2|  8.8|  8.2|  8.0|  7.6|  7.4|
|Full-Time Non-Tenure-Track Faculty | 10.3| 14.1| 13.6| 13.6| 15.2| 15.5| 15.0| 14.8| 14.9| 15.1| 15.4|
|Part-Time Faculty                  | 24.0| 30.4| 33.1| 33.2| 35.5| 36.0| 37.0| 39.3| 40.5| 41.1| 41.3|
|Graduate Student Employees         | 20.5| 16.5| 18.1| 18.8| 18.7| 19.0| 20.0| 19.9| 19.5| 19.4| 19.3|

---

## Recreate

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## Represent percentages as parts of a whole

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## Place time on x-axis

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## Represent time as time

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## Use an accessible color scale

This is how the previous plot might look like to someone with Deuteranopia (a type of red-green confusion)

---

## Use an accessible color scale

This is it might look like to someone with Protanopia (also a type of red-green confusion)

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## Use an accessible color scale

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## Use direct labeling

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## Use color to draw attention

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## Pick a purpose

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## Use labels to communicate the message

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## Simplify

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## Summary

- Represent percentages as parts of a whole

--
- Place variables representing time on the x-axis when possible

--
- Pay attention to data types, e.g., represent time as time on a continuous scale, not years as levels of a categorical variable

--
- Prefer direct labeling over legends

--
- Use accessible colors

--
- Use color to draw attention

--
- Pick a purpose and label, color, annotate for that purpose

--
- Communicate your main message directly in the plot labels

--
- Simplify before you call it done (a.k.a. "Before you leave the house, look in the mirror and take one thing off")

---

## Case study 2:

## Bachelor's degrees

---

## Data

Each row in this dataset represents a field / year combination.
For each combination we know the number and the percentage of graduates.
Only the most popular three fields are identified, the remaining fields are lumped into "Other".

| year|field                       |      perc|
|----:|:---------------------------|---------:|
| 1971|Business                    | 0.1374204|
| 1971|Health professions          | 0.0300370|
| 1971|Social sciences and history | 0.1849690|
| 1971|Other                       | 0.6475736|
| 1976|Business                    | 0.1546547|
| 1976|Health professions          | 0.0582071|
| 1976|Social sciences and history | 0.1365342|
| 1976|Other                       | 0.6506039|
| 1981|Business                    | 0.2144289|
| 1981|Health professions          | 0.0680807|

---

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      <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="19" style="font-weight: bold;">
        <span class="gt_column_spanner">Popular Bachelor's degrees over the years</span>
      </th>
    </tr>
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">Field</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">1971</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">1976</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">1981</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">1986</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">1991</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">1996</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2001</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2005</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2006</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2007</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2008</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2009</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2010</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2011</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2012</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2013</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2014</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2015</th>
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    <tr><td class="gt_row gt_left">Business</td>
<td class="gt_row gt_right">14%</td>
<td class="gt_row gt_right">15%</td>
<td class="gt_row gt_right">21%</td>
<td class="gt_row gt_right">24%</td>
<td class="gt_row gt_right">23%</td>
<td class="gt_row gt_right">19%</td>
<td class="gt_row gt_right">21%</td>
<td class="gt_row gt_right">22%</td>
<td class="gt_row gt_right">21%</td>
<td class="gt_row gt_right">21%</td>
<td class="gt_row gt_right">21%</td>
<td class="gt_row gt_right">22%</td>
<td class="gt_row gt_right">22%</td>
<td class="gt_row gt_right">21%</td>
<td class="gt_row gt_right">20%</td>
<td class="gt_row gt_right">20%</td>
<td class="gt_row gt_right">19%</td>
<td class="gt_row gt_right">19%</td></tr>
    <tr><td class="gt_row gt_left">Health professions</td>
<td class="gt_row gt_right">3%</td>
<td class="gt_row gt_right">6%</td>
<td class="gt_row gt_right">7%</td>
<td class="gt_row gt_right">7%</td>
<td class="gt_row gt_right">5%</td>
<td class="gt_row gt_right">7%</td>
<td class="gt_row gt_right">6%</td>
<td class="gt_row gt_right">6%</td>
<td class="gt_row gt_right">6%</td>
<td class="gt_row gt_right">7%</td>
<td class="gt_row gt_right">7%</td>
<td class="gt_row gt_right">8%</td>
<td class="gt_row gt_right">8%</td>
<td class="gt_row gt_right">8%</td>
<td class="gt_row gt_right">9%</td>
<td class="gt_row gt_right">10%</td>
<td class="gt_row gt_right">11%</td>
<td class="gt_row gt_right">11%</td></tr>
    <tr><td class="gt_row gt_left">Social sciences and history</td>
<td class="gt_row gt_right">18%</td>
<td class="gt_row gt_right">14%</td>
<td class="gt_row gt_right">11%</td>
<td class="gt_row gt_right">9%</td>
<td class="gt_row gt_right">11%</td>
<td class="gt_row gt_right">11%</td>
<td class="gt_row gt_right">10%</td>
<td class="gt_row gt_right">11%</td>
<td class="gt_row gt_right">11%</td>
<td class="gt_row gt_right">11%</td>
<td class="gt_row gt_right">11%</td>
<td class="gt_row gt_right">11%</td>
<td class="gt_row gt_right">10%</td>
<td class="gt_row gt_right">10%</td>
<td class="gt_row gt_right">10%</td>
<td class="gt_row gt_right">10%</td>
<td class="gt_row gt_right">9%</td>
<td class="gt_row gt_right">9%</td></tr>
    <tr><td class="gt_row gt_left">Other</td>
<td class="gt_row gt_right">65%</td>
<td class="gt_row gt_right">65%</td>
<td class="gt_row gt_right">61%</td>
<td class="gt_row gt_right">60%</td>
<td class="gt_row gt_right">60%</td>
<td class="gt_row gt_right">62%</td>
<td class="gt_row gt_right">62%</td>
<td class="gt_row gt_right">62%</td>
<td class="gt_row gt_right">62%</td>
<td class="gt_row gt_right">61%</td>
<td class="gt_row gt_right">61%</td>
<td class="gt_row gt_right">60%</td>
<td class="gt_row gt_right">60%</td>
<td class="gt_row gt_right">60%</td>
<td class="gt_row gt_right">60%</td>
<td class="gt_row gt_right">61%</td>
<td class="gt_row gt_right">61%</td>
<td class="gt_row gt_right">61%</td></tr>
  </tbody>
  
  
</table>
</div>

---

## Tables vs. plots

- To look up or compare individual values
- To display precise values
- To include detail and summary values
- To display quantitative values including more than one unit of measure
]
.pull-right[
**Plots:**

- To reveal relationships among whole sets of values
- To display a message that is contained in the shape of the values (e.g., patterns, trends, exceptions)
]

---

## Add visualizations to your table

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    <tr>
      <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="20" style="font-weight: bold;">
        <span class="gt_column_spanner">Popular Bachelor's degrees over the years</span>
      </th>
    </tr>
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">Field</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1">Trend</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">1971</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">1976</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">1981</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">1986</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">1991</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">1996</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2001</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2005</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2006</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2007</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2008</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2009</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2010</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2011</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2012</th>
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      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2014</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">2015</th>
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    <tr><td class="gt_row gt_left" style="color: #9D6C06;">Business</td>
<td class="gt_row gt_center"><img 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2xsUDdLbn9gVukn+bySg/1xN+aV3f3QlDlJVJ5TtmiO2VNDuPvh2mMYl3yOSXNcet54/HrGUVl7jPKSdrpsxOQ7TdrprhEz3ymzkUTaGbmNuCU73W1ErhPMdJKk1wf9FWC8fNUUrWjrH93EaysAng0DOgAAdcQlG+7JvdPlV0n609A9qIoHZX7RjIdKn6+FJwkANI6h3uxpcq2WNDN0C/bgujOx6PSF/aObQ6cAAOrbxlOUevzoKcdkktLcJLFmM2uWq1n2myfIj5WUCluJg7TNpDtcdrtHvqljVfxTk3z4TDX51ClzZOU5smiO5HPc7XDZb4dwSXt+OyTsLwMYXyb9h7tWPDAn/hJvTgXwTBjQAQCoE2uXZF4dua6S2ztCt6AqnjDpiumZuG/+NdodOgZAYxpe1tSclP0myd8cugV7cO0wU1d7f3xT6BQAQO3aeIpSO+dMOdpSpebK0wO5NFfuzRr78+MkpQMmovqe0Ni/8+mhQ4Aa8X8u/2wlLt2weI12hY4BUDsY0AEAqHHrz256vqeSyyR9KHQLqqJoUl+Sia/ovEaPhY4BgHxe6eMezV4g6WLxFFpNMemWUhx3LV6jJ0O3AACqL59XNPehKUeV0qVms6jZXM0umytLmseOWdfxGrtTGgDwbMwetcT7UooHWge0LXQOgPAY0AEAqFHrztFhFmcultnZ4kWPRuCSPu8efbJzYPS+0DEA8LvW9mbeELndLOn5oVuwl/uUJKd3rC5/K3QIAGB8uWRrlk09Ml2Kmy2KmpNEzWY215Q0u6xZ0gmSskEjAWBy2WXy9RWlPseVSUBjY0AHAKDGuGRD3dkPmvm1kh0eugdV8S9JRecvvC7+cegQAHg2N/VqZuzZVS61hm7BXhJJl5Xj+NLFa1QKHQMA2Dcu2bql0w5PleNmmTUnruZINtfNn7qLXCdIagrdCQANKJG00dw/0z5Quid0DIDqY0AHAKCGDC6dcnyqUrnOpL8O3YKq+L6iaHnHqtFNoUMAYH+s78l+0KVBSYeEbsFevpMyO+3MvuL/hg4BAIwN5MO9mm0QZBEAACAASURBVG2VTHMlGruD3KW55t4s6elvU0I2AgCei3898WhF50Dx6zZ2eiCABsCADgBAjRjuTb818egfJM0K3YIJ938mXXj/7PjWfF5J6BgAOBCDS6ccn64kn5f8baFbsJed7t7TMVDawAt8ADCxXLIN3TosTmWao8SaZdYsJXM1dv94s48N5NOCRgIAxoVLP4xMK+6fFd+az6scugfAxGJABwCgBgz1ZhbIbZ2463ySs61uumTmrOKa+XnFoWsA4GBtPEWp7Ydnl5vpEknp0D3Yg2ljsRyf1XWdHg+dAgD1bPXZel42nWm2xJolmytLms2t2U1PH7M+I3QjAKCq7jOzz6VzxaEFV2skdAyAicGADgBAYEM92YskXRK6AxNql6TP5iy++vQ+bQ8dAwDjbbgr89okslskvTB0C/bygFlyRntf+Y7QIQBQqwYX6ZB0bmwgT8yaIyVzE7Nm898csc51JQCAZ/KYpIFMOdu/4Pqdj4SOATC+GNABAAhoXW+ux9z7QndgwlTMbV0pk/rU4pW7fh06BgAm0kCXpjdFuZWSd4RuwV7cpCtLcXzx4jUqhY4BgGobWq4Z0a5sc8WsOZLmJp40m6xZ0tPHrD8vbCEAoM6NutmwKvpc5+riL0LHABgfDOgAAAQy1JP9gKRbxe/Hk5JJX/KUXdhxbfHnoVsAoJqGerIfMGktg0TN+X4U2altq4r/EzoEAMbTQJemZyx7QhRZs6S5liTNst/cQT5X0mFhCwEADSKR6wtRJvpE28rRLaFjABwcXrAHACCA9UvSb/IkKkjKhW7BODP7lpLK8o6B8l2hUwAglLW9U46NkvIGmc0L3YK97JJ8SXt/ab1JHjoGAPbF4CJNjdJjA7krmWtuzWYau4NcNlfus0M3AgCwh1jm/Z4uXdZ5jR4LHQPgwDCgAwBQZcNd045MotI9ko4K3YJx9Z9udn5HX/ErjBIAIOXzio7blvuo3C+TlAndg98y6Uspjxe1Dmhb6BYA2NPQcs3wXdl3SHqNSa+U9KeSjg2cBQDAgXjMpY7O/vjLoUMA7D8GdAAAqmhwkTLpbO7rkr8tdAvGza8k/+QDs0s35vMqh44BgFqzdknm1VHFbpbpj0K3YC+/NEUL2vtHC6FDADS2G7s1qxRl/tZkJ8vVIikbugkAgPFi8n7bWfpY2w0aDd0CYN8xoAMAUEVD3ZmrZfbR0B0YF9vddWWlFF+7eI12hY4BgFq24VxNK+/OfdbNF4duwV5crqtnzIk/MT+vOHQMgMaxMa/szq3Zv3JTq6T3iJNKAACTmEs/dEXvX9g/ujl0C4B9w4AOAECVrO/N/q27bg3dgYNWkvlAuVi6bPEaPRo6BgDqybqe7MkmrZM0K3QL9vKDSDq1rT/+r9AhACYvl2y4O/Mql1oVRadydzkAoLHYVosqJ7evKt8ZugTAc2NABwCgCoZ7si9JpO9Jmh66BQfBdXNi0UW8YxgADtxQz9SjTaUbXPYXoVuwl91ufk5HX2mNSR46BsDkMbhs6lGpcuU0k7dKennoHgAAAoplau/oi28OHQLg2TGgAwAwwQa6NL0pyn5X0h+HbsGBMfnXEtN5nX2lH4RuAYDJIJ9XdNzW3DKZXyHuuq0pbrqtUow7OWUFwMEYPlNNPj17kkutkv5SUhS6CQCAWuGmSzr64jxvXAVqFwM6AAATyCUb6sl+waQPhm7BAflB4nbewoHi10KHAMBkNNyVeWUS2S3iTWa15qHEbQG//wHYHy7Z8JL0Gz1JtUr+QUmHhG4CAKCGfXFGJm6ff412hw4B8PsY0AEAmEDrenJLTb4ydAf2k2uLmz7+4Oz4i/m8ktA5ADCZDS7S1HQ29xnJu0K34HeYf25EpQuX9KkYOgVA7VrX3XSCWbJA0gJJLwzdAwBAvTDp7koqc/LCa0ceDt0CYG8M6AAATJB1Pek3m6LbJaVDt2CfbZPbp0ei4nWMBQBQXeu7c+9x03rJ54RuwR5MPzbpw+198X+GTgFQOwa6ND1nmQ+Y1CqzeaF7AACoY/d5pPd0rorvDR0C4LcY0AEAmADDXdOOTKLSPZKOCt2CfbJbrmvKpXjF4jV6MnQMADSq4a5pRyap0rBc7wrdgr2MuvyjHf2l67inEWhc+byi4x9tOtGVtEr6gKRpoZsAAJgUXDtk9sGO/uK/hE4BMIYBHQCAcZbPK33co7mvS/720C14Tombrc+Uonzr9bt/GToGAPDUQLMt1+PuKyTlQvdgL/+UKWfbF1y/85HQIQCqZ0137sWR+YJIOsOl40P3AAAwSSWSLevoL/aFDgHAgA4AwLhb35P9jEvnhu7Ac/pHM13AkbQAUJuGe7J/kki3SHp56BbsyR+RojN5OgaY3IaX6dBKJfNBc2uV9MbQPQAANAzzgQdmlZbl8yqHTgEaGQM6AADjaKgn+wFJfx+6A8/qLiXJ8o7V5W+FDgEAPLuN52jKjjhzpcyWhG7B7/K+aGdpedsNGg1dAmB85PNKn7A1984k8la5ThKngAAAEMpXy3H8Qa4ZBMJhQAcAYJwMLc39kcr+PZlmhG7BM3D9XNIF7QPxl7m/FQDqy1BP7q+kZFiyI0K3YC/3RtKpbf3xT0KHADhwa8/OviKKfIHMTpN0ZOgeAAAgSfrPRNF7FvaPbg4dAjQiBnQAAMbBQJemN0XZ70h6aegW/J6H3Dz/4KzSEMdfAUD92nDW9MNL6XhI0ntCt2AvRZed19FfXMUb1ID6sb5XcxLPnWryVkmvCt0DAACeiW21qHJy+6rynaFLgEbDgA4AwEFyydb3ZG+W9OHQLdiDa4dHWpHNxdcsuFojoXMAAAfPJRvqyZxtss9Kagrdgz2Y/jWqZNraVo88FDoFwDNb1avcFGXfHblaJf21pHToJgAA8JyKLrV39se3hA4BGgkDOgAAB2moJ9cr+arQHfiNkuTXm5Uube/T1tAxAIDxt6Y7+7JUpFvkekXoFuzB7FFL1NY+UPxK6BQAY1yyG7oyr0lSapXbhyUdFroJAAAckE+198ef4tQnoDoY0AEAOAjrl6Tf5El0h3h6oya49Hdps4+f2Vf839AtAICJtapXuWnKXC63j4Ruwe+y1eW4+LHFa7QrdAnQqG48a8oxpVTldDO1Svrj0D0AAGBcfCHaGbe33aDR0CHAZMeADgDAAVq7dNoRUaV0j6SjQ7fAC5LO6+gvfT90CQCguoZ6c++U+42Sjgzdgr38LEr81LbVpR+GDgEaxeAiTU1lsyeb1CrpzyVFoZsAAMD4MunuSipz8sJrRx4O3QJMZgzoAAAcgHxe6eMfzXzNZSeGbmloph97xc7rWF38KkdYAUDjGlyk2alcdp25Tgrdgr3EcrvggTnFlfm8ktAxwGTkkt3Qm35L4qlWuc+XaUboJgAAMOHu80jv6VwV3xs6BJisGNABADgA63qyV5m0PHRHozLpfje/aMZDpZvn36pK6B4AQHgu2XBPZqHLVkqaEroHv2Xyr7kyZ3b07/pV6BZgsljb0zQ3UrJA0gJJzw/dAwAAqsy1w93md64u/mvoFGAyYkAHAGA/DXVn/0amL4XuaEQmPe5ml0U7igPc9wQAeCbDPdmXJNItkl4VugV72eZSZ2d//OXQIUC9uqlXM0eV+VvzqFXyt4XuAQAAwSVutrSzr9gfOgSYbBjQAQDYD2u6cy9Oyb/P0YhVV3Tp2rgSX9l1nR4PHQMAqG0b88pufzR7qUkfE1/31hhbk2kqfmTB1RoJXQLUg42nKPXk4bmWSN4q0/vFCRsAAOB3mQ88MKu0LJ9XOXQKMFnwQgIAAPtow7maVhrN3i3p5aFbGohLusGS1MXtq3c/EDoGAFBf1vc0tbiSDZKOCd2CPbh+bvJT2wdK94ROAWrVU6dptEo6Q3wMAwAAz8X0r+Vi/KHFa/Rk6BRgMmBABwBgH4zdq5q9yaVTQ7c0kH/ySOd3rorvDR0CAKhfN3ZrVsWya1x6f+gW7KVk0ifunx1fnc8rCR0D1IJ15+gwlTIfMlmrpNeF7gEAAPXFpJ9aOnpP28rRLaFbgHrHgA4AwD5Y15vrMfe+0B0N4rtmyfL2vvIdoUMAAJODSzbUm2kzt1WSpoXuwR7cNyVResHCvt0Phk4BQhhcpEwql3uXubdKeq+kbOgmAABQz2yrvHJSx0D5rtAlQD1jQAcA4DkMdaffKIvukJQJ3TLJ/cKlCzv647+3saPbAQAYV8NLci9KEr9F0mtCt+C3THrcpYUd/fH/C90CVMtwV+aVSUqt8ug0yeeE7gEAAJNK0Uxt7X3xF0KHAPWKAR0AgGex4azph5fS8T3i3sEJ5I+49KlKXFq7eI1KoWsAAJPb4CJl0plsXqYLxNfENcVc63d7vLR7tXaGbgEmwtql046IyuXTFHmrXK8I3QMAACa9fHt/fAkPqgD7jxcLAAD4A/J5pY/bmvk3mc0L3TJJjUi6WlPjz3as0I7QMQCAxrK+N/129+jzko4L3YK9/CJK/NS21aXvhQ4BxsPwmWqqTM++16RWSe+SlArdBAAAGsoXop1xe9sNGg0dAtQTBnQAAP6A9b3ZK911XuiOSajiZmtSlfQlbatHHgodAwBoXKvP1vNy6ez1cs0P3YK9lGW6eMZD8VXzb1UldAywv1yydb2Z10cetUr+IUmHhm4CAAAN7a5MOXvygut3PhI6BKgXDOgAADyDdT3Zk036h9Adk47r7yuyjy8aKP536BQAAKSxoWuoO7PAzPolTQ/dgz3ZN8qp6IzF1+6+P3QJsC/Wd005zlOVM+RqlfTi0D0AAAC/4driKb23c1V8b+gUoB4woAMA8DuGl+RelCT+fUkzQ7dMHvYN82R5+0DpO6FLAAB4Jjf05l5Qcb9Z0utDt2AvT5q0uL0//rvQIcAz2XCupsW7M++3SK1yaxGvtQEAgFrl2uFu8ztXF/81dApQ6/ikHgCAPWw4V9NKo9m7Jb08dMtkYNJP5XZ+20Dxn0zy0D0AADybwUXKpLPZiyR9XFIUugd72aCpcU/HCu0IHQLk84qOebTpbZEnrTL9rTi9AgAA1I9Ebks6BooDoUOAWsaADgDAU1yy9d3Zz8t0WuiWSeBBk39y+sOlDdxdCgCoN0Nd6bcoim6SdELoFuzl/xLz0xb2le4OHYLGtK4r90KLfIGkBeLjAwAAqGMm779/dumcfF7l0C1ALWJABwDgKUPduW6Z94fuqHNPuuuKmdl41fxrtDt0DAAAB2pwkQ7JZLOrXTo1dAv2UjHXJffPiS/nxT5Uw+AiHZLOZebLo1bJ3xy6BwCA/eCS7nazH5orK/cmmZpcmmLyJrk1ydQkPeO3KWI/agT/krP4Q6f3aXvoEKDW8AEQAABJa3szb4jcviEpE7qlTsVy70urdEXrgLaFjgEAYLwM9WZPU6LrZJoRugV7sm9HaTu9beXoltAlmHw2nqLU9jm5v7DIWyWdrLEhAQCAejBqrq+5+W1JKvuVhdeOPHwgP4hLtmaR0ulD1WS71aTKlCZLJ01KvKliPkWJN1mUapKrSe5NirzJXE2yaOyvXU1uPttkp7v0vPH+RWL8mPRTS0fv4fNqYG8M6ACAhre+V3Pcsz+QdEzoljrkMt3kSXRR58DofaFjAACYCGt7muZGntwk05tCt2Av22Xq6uiLbw4dgslhTXf2ZSmpVabTJR0VugcAgH1i9qgS/4qbbss2xV9bcLVGQic9baBL06ekcu3u/hFx/UkN80fkfnLHQPmu0CVArWBABwA0tI2nKLXjyMxX5faO0C116KsyP6+jr/Sj0CEAAEy0fF7p47dmL3TTJyWlQvdgD66by6W4e/EaPRk6BfVncJFmp7O5D5u81aVXh+4BAGAf/Y9Jt3mS3DZja/mu+beqEjro2eTzSh+/LXtK4lpu0itD9+AZFd11ZudA/MXQIUAtYEAHADS09T3Zy126IHRHnbnHPVreOTD676FDAACotqHu9Btl0c2S5oZuwV7ucyWndfaXvx06BLVvY17Z7Y9m/9qkVknvFtc4Adg/j0naImmmpBeGTUEDcUl3S/rHJNFtnavj/7Kx76srLtm67tyfR5Ysl+zPQ/fgGbid0zFQXBk6AwiNAR0A0LDWd2dPctOXQ3fUkc3uuvDBOfHGfF5J6BgAAEK5qVczi57tk7QgdAv2krjp0w/Oii/N51UOHYPa4pKtW5L5syhRq2SnSpoVuglAzXrCpS0mbXb5lsiiLe7aEsk3Zyy+7/Q+bX/6b1zbO+XYlFdOlGuem06U9Pxw2ZiERiV93dxvM89+pW31yEOhg8bT+u7Mn7nZxyTNlxSF7sFvuemijr74snp8kwYwXhjQAQANaV1X7oUW+X9o7B3jeBYmPS6z/PRZxevn5xWH7gEAoFas685+yEzXSzokdAt+y6S7I7PTz+wr/m/oFoQ3uGzqUelK5XRzb3XpZaF7ANQA1w5F2uzS5sh9i3u0xc23mPnmKFW6r22lnjjQH3pw6ZTj00n5RCU2T6Z54s5n7K+n7jOX9I+ZKfG/1dJ95hNlbU/T3EiVcyTrlDQldA/GuLSioz8+nxEdjYoBHQDQcAYXaWo6l71LrleEbqkDlciSeW195W+GDgEAoBYNLp1yfKqS3GTyt4ZuwV52yry7va/0eV70azwbz9GU7XH2JIvUKtc7xVNtQKMZkbTZXJvdfIs82pJEvsXkm+NyacvZ1+mJav3eMLysqdnLlXluNk+ueZKOrcbPi7rzC7nfFkV+27SHynfW+n3mE2VwkWZnMtkuj6xX7rND90Bys+senFXs4SRKNCIGdABAQ3HJ1vdmb5TrjNAt9cBkH2nvL14TugMAgFq28RSldhyZPU+uSySlQvfgt1z6u7gSn911nR4P3YKJ5ZIN9aTfZEq1Sj5fnAwBTGa7JG1xaYuZb3aPtkTmWyrmm7OV0pYFA3qsFt885ZLd2Jt7fpIk82Q2z6V5ko4K3YUg3KTvuHRbPd9nPlEGF2lqKps502QfFdci1IIND8yOO7giCY2GAR0A0FDWd2fOdrPVoTvqgmlje1/8Ib6IAwBg36zrzbxObreY9ILQLdjLA2bJGe195TtCh2D8DS9rak7KyRmSFkh6YegeAONiVNIWmba47Klj1rXFI98SqbS5rU+PToavU12ytd25F0VRMs/8/2fv3uPjquv8j78/Z2bOpCStIOUigqvrXVd+WgRdVwXE+4r3dlWQNpm0lSYpsAK6uuq4roqKAs2k0OZGykUtgiJe8LK6q+INwdVVV3fXJVBUoAW1aWnmzMz5/P4orFRBUprkO2fm9fyn5fHA5FVUksx7vt9jx0l+vGSHhO7CnKlK+orMPxs14mta7Xnmc6FcVv6IrfFrzXS2S0eF7mlnJl21w5I3rR1UNXQLMF8Y0AEAbWOsr/AsN/umpELolgz4ufZLjil9WFOhQwAAyJKhNepaYPEFbuoJ3YI9uFwfrNeS8uqNqoWOwb4ZPVsLtavwOnMtd9lxoXsA7LWqpJslTZrbTW5/GMgVxTf1XrDzjlYYyPeWSzayJn5Szvy43SfU7TjJDwrdhX1yp0mfc9fV7fI887ngko2t7ThOnp4t10tD97Qt07X1avK61Rt1d+gUYD4woAMA2sLYgA5yj28UzxubiR1pqmNWrk/+M3QIAABZNTYQv16ujS4dELoF92G6PjI7qXtd9b9Dp2DvlMuKDt/acbxF6XK5Xidpv9BNAB5QTdLNkk9K0U26ZyCPonSyFsU3/Wb/u2/neboPbvcJ9fgpUWTHR+7Hu3SspANDd+2jRNJWl7ZG8q3udoci3yrZVsm3Sqpaqk6Poi65d8q9SxZ1urzLpE6Td7lbpyJ1ue/+a8k6JXWqWbYO1y8k/5zLP3vrQfVvc+317BodKPw/dzvTpDeKRycFYN8oWvXEkwe1PXQJMNea44sKAABzaPNS5aYOKVwr2QtDt2SBS8t6K8kVoTsAAMi6sTULjlBU38QJ2aaz083XlgZr4+14wjFrNvYVn5CTL5fpzZKOCN0DQJJUl3TLvQO5m09GrklP08l8WrjppkN3/YaBfPaVy4oeeXv8V5az401+nEnHNsEb9aYlbTXpDjdtlbR7CPd7B/Foa2rpHQVFW/Oqbj1pUFNz8bXXJdu4SgsWRF1d1Xy9M6+0q27emVOuS2na6ZG6XN5ladRp8q7U1GmyLpN3utQlU6dku0d7qUu7B/l7f32gkfZuSdtl+ne5vuepfVfF6vd7z9Nds/3nw58a6ev4C4sap8ttpXb/94T5Yrre88lL+d86Wh0DOgCg5Y32xe+X6R2hO7LA5ef1Vmp/H7oDAIBWsXmpcjsOid/q0vsl5UP3YA9XeiFZxYt/zWf9qTqgmCv8ncmWu/Ts0D1AG2rItcXMJ112k91zvbqnPhl54aaurbt+vewKNUJHtrvNS5XbfmjhyMij4+R+vJueKekR+/hhd8q1VZG2umtrpHtHcW118ztc0VYp3eqe25qk01vXrNfOVn4zmkt28QoVo051JtGCBWl117Sku1dt1K5W/nNnycgZenhUi091+VrJDg7d00Z+EqWFF3Wv33lb6BBgrjCgAwBa2mh/8ZWSXx26IxPMvlWvVl/Ac0EBAJh9w2sLR0WpXS7pCaFbsIdfKYreXFo3/fXQIe2uXFb+8DuLL4ncl7v0SknF0E1AC0tNutVlk7tPkesmmU+mnpvM5zV58/7Tt3LtdDatP1UHxLn8UyLZUyU9JbVoj5O55nIzv8tlW6V0q/wPg3ijNr2VZxsjq8ZXqMO7Cqe47ExJjw/d0yb+u57LvXD1BbtuCR0CzAUGdABAyxpZU3ycRf4DSQ8L3ZIBt9Xz+SWrz7/7N6FDAABoVZvOVGdtuvgxyVeFbsEe3KWPLFqcvGtZWUnomHYzfGp8ZJT35XKdJNkhoXuAFuGSfiWzSXefNOmea9Zzk2postaY3sIbpwG0os1Llfv9ofGrItfbJB0TuqfVmXRLmtoJveur/xO6BZhtDOgAgJa0YZX2yxfj78h1ZOiWDGhElh7fPVj/ZugQAADawWhf/BqZRiQ9PHQL9nCjcvam0gXVX4QOaXWb3tJ1cC1Xe5ObLzfp6aF7gIz6tVyTZppMpZsi90lFucm04ZOLDq7ewhuCALQzl2y8v+N4V3qJpMNC97S42zzSi3rXJT8JHQLMJgZ0AEDLccnG+uOLJZ0SuiUT3N5aGqp+LHQGAADtZLR/v8Ok2oRkLwzdgj3sMvnp3ZXaMM82nV3rBlTs9PgV5lruppdJyoduAprcbSZNujRp0k2ST7pFk41Uk4Wd1Vu6L9Z06EAAaHYTb1nwyHq+cY2kZ4RuaXF3Sf6SUqX2g9AhwGxhQAcAtJzR/sJbJLswdEcmuD7VM5Qs4wViAADmX7ms6Ig7i2fI/YOSCqF7sIfP1JNk5eqN2hY6JMtcstGBwtHm0XLJ3yBuXQDuw++QbFK2eyB390lPo8lc5JOdheTmZedpV+hCAGgFux+jFF8q6dWhW1qaayqK0r/lhku0CgZ0AEBLGRkoHGNu35QUh25peq5fqDM5uvRhTYVOAQCgnY2vKTw9jexySU8O3YI9/CZ1W75yqPqV0CFZMzyw4PDIGydLWi7pSaF7gCDMtsl9Uq5J3fMM8pxHkx75ZL6Y3HzKudoZOhEA2kW5rOjwbfEHTTo7dEuL2yWzV5cGq18OHQLsKwZ0AEDL2LBKi/NxfKOkI0K3ZMBOMx3TM5j8LHQIAACQNqzSfrli8VxzPzV0C/6I+cd2qvaOtYOqhk5pZhtWab98MX6NuS/33Y8m4DUntLq7tPt69Ul3v8mjaDJKNZnmfLJaTyb71mtH6EAAwJ5G+wslyS4Sj5KZS4lcbygNJZ8OHQLsC36YAQC0hM1LlZs6pHAtzxGdGZPe0FNJPhm6AwAA7GlkoHiiSWNyXxy6BXv4kZnexJsP91QuKzp8a/65kaLlLi2VaWHoJmAW/c7vHcjlN0UWTbprMm345IJ8MnnyoLaHDgQA7L3h/o7jckqvcumA0C0trGHSip5KcmnoEOChYkAHALSEkf74n016Z+iOLDD5BT2V2umhOwAAwP0bX9N5aBrVLpb0ktAt2MO0y99aqtQuNMlDx4Q0dmrHX3ouPUXSKZIeE7oHeEhcU4p0k1yTcp+Uot3XrKc+qbg22X2+fhc6EQAwNzb2FZ+QM/+cpMeHbmlh7uan9g7WNoQOAR4KBnQAQOaNDBRPNPfPhu7IBruunlSPX71RtdAlAADgge1+TmNxwOQflhSH7sEePleox6VTLtpxR+iQ+XTpgBZVvbDUFS03+fNC9wAzsFPSTZImTT7pHt2URrsH8jSuTZbO02/b/c0wANDORs7Qw61WvEryY0O3tDKXndVbqZ4bugPYWwzoAIBMu+f0y42SHha6pfn57VJhSaly969DlwAAgJkZPjU+MpfT5S49NXQL7stv9zRa0bu+em3okrm0ealyU4cWT9j9XHO9RtKC0E3AA7jJXJcp0o/cfbKe1CZXbdSdDOQAgD9nc1nxjq3xhW7qCd3Sytz0T6XBpMzXZWQJAzoAILM2n6EF22vxt016euiWDGiYpSf0DNb/LXQIAADYO5vP0IKpWuFDkg2EbsEfcV8X7ay9rftiTYdOmU3Da+In53Ja7q6TJT0ydA9wv1xTJl2RKp249aD6t8plpaGTAADZ45KN98dnuXSO2MzmjMvPK1Vqb2VER1bwLwMAQCa5ZGN98ZhMK0K3ZAHXJQEAkH3jfcWXp5aOS3Zw6Bbs4Sce6Y2965KfhA7ZFxN9OrCu4hsU+XK5jg7dAzwAN/lX3WyiXk0+vXqj7g4dBABoDaN98WtkukzcuDOHbHjh7dVTl12hRugS4MEwoAMAMmmsv7DKZRtCd2TElT2VZCnv8AQAIPs2vaXr4CSfjJv08tAt2ENVsrN6KtVKlr7n2rBKhXxcfJnJl7t0oqRC6CbgAfxc0kRquUtXDu66NXQMAKA1iwegHQAAIABJREFUDa8tHBWl9llJh4VuaVUmXV5LkhWrN6oWugX4cxjQAQCZM76mcHQa2bckxaFbmp7rF8UoOebkQW0PnQIAAGbH7pt4imtkfq6kjtA9uA/TtWlUWLHygp23h055IC7Z6EDh6ZFruSt6k+QHhW4C7o9Jv3XZx93SidJg7fosvTkFAJBdE29Z8Mh6vnGNpGeEbmlVbro6N5W8odUeg4TWwoAOAMiUDau0uBDHN7j0qNAtGXB3w3XMqqHkp6FDAADA7NvYFz81F+lyuY4M3YL7sq1u6u4drH4+dMl9ja/pPDTN1U+S+3JJTwvdAzyAurm+6KaJnZZ8bu2gqqGDAADtZ2iNuoq5+FJzvSp0S6sy+VfyHbXXnHKudoZuAe4PAzoAIDM2L1Vu6pD4C5JeHLolC9z1xt6h5BOhOwAAwNwZX6GORlfhAyY7I3QL/oj50MJ87axl52lXqITxFepIO+NXumm5SS+RlAvVAvw5Lv27uU0UGoXLT7loxx2hewAAKJcVHbE1Pkems0K3tC67rp5U/3b1Rv0+dAnwxxjQAQCZMdof/5Okd4XuyAT3daWh2mmhMwAAwPwYHSi+WO4Tkg4N3YI9/EzmbyoN1n40X59w9xX/+Webcsvd/O8k7T9fnxvYO367TJeldZtYeWHy49A1AADcn9H+QkmyiyTlQ7e0qBvrSfKS1Ru1LXQIcF8M6ACATBgZKP6tuX8udEcmuL698KDk+GVlJaFTAADA/Bkb0EHu8YikV4ZuwR4Sk739lsXVC8plpXP1STactuBRhUbjzS6dIukJc/V5gH2UmHR1ajZx64HVL5XLqocOAgDgwYyu7Tje0vRKlw4I3dKifiblX1Sq3P3r0CHAvRjQAQBNb+zUjr/0XHqDOD0zA35Hvp5fsvyiXb8KXQIAAOafSzY6UFhlbudJWhC6B39g8q+4Citm84XBoTXq6sgVXivXcsmOF6/zoEmZ9F2XT1QbtU+uuVC/Dd0DAMDe2thXfELO/POSHhe6pRW59MtcPnph9/nTk6FbAIkfrAAATW7zGVqwvRZ/26Snh27JgEaq6IUrK9P/GjoEAACENbwmfrJFupzvoZrOneYq9QwlVz/UD1AuKzriro5jLU2Xu/R6SZ2z2AfMpi1yXaK8bSpdUP1F6BgAAPbVRJ8OrFvxSsmPDd3Som5Vzl7I9w1oBgzoAICm5ZKN9cejkrpDt2SC2dmlwepHQmcAAIDmsG5Axc40fp9MZ4VuwZ7MbUN+QfWtp5yrnTP9z4yvLT7eG36Km94s6S/mMA/YF3fLdKWn0cStB01/fS4fWwAAQAiby4qntsUXidcr54jfIdOLS4O1H4UuQXtjQAcANK3RgcJKuW0M3ZEFJl3VXUleb5KHbgEAAM1lpK/jBLN0k6TDQrfgPly/MPmbeoZqNz7Q3zJ+uvb3emGZuy2X6TnzmQfsDZP/q5smtKB2ZenDmgrdAwDAXHLJRvvis810TuiWFvU7c39pz1Dte6FD0L4Y0AEATWm0v/BMya6TFIduyYD/Klpy9MmD2h46BAAANKfd103Gw5JeE7oFe6jJ7J1bDqx+9N6TuuWy8kdsK77I5ctNerWkYuBG4H659EuTJqJ8dAnPKwUAtKPhgfi1ketSSQtCt7SgHamiE3lUJUJhQAcANJ17XuC9QVxNORN3e6Rn9a5LfhI6BAAANDeXbLyvUHKzCyTtF7oH9+VfM/N/kkevcOlkSYeGLgIewHbJPulqTJQq9W9zAxYAoN0Nry0cFaV2jaRHhG5pQdOR2+u6h6pfCB2C9sOADgBoKpuXKjd1SPx5SS8J3ZIFLp3UW0kuD90BAACyY2Nf8Ql588tdOip0C4BMSGX6sqeaWBQnVy87T7tCBwEA0EyGBxYcHnnjs5KeEbqlBdXM9KaeweRToUPQXhjQAQBNZWQgfq+53h26IwtMXump1AZCdwAAgOzZXFY8tS1+r6S3idcGANwPk37qZhPy3GWlyt2/Dt0DAEAzG1qjrmIuvtRcrwrd0oJSN19z64G14XsfewTMNX5IBgA0jfG+4stT88+H7siI7yxcnBy3rKwkdAgAAMiu4f6O4yKll0g6PHQLgKZwp+SXp5EmetfVbuSKdgAAZm7zUuWmDo4/KNNZoVta1H+49L5bFydXMqRjrjGgAwCawnB/x2NySm9w6YDQLc3PtqYWLVk5uOvW0CUAACD71p+qAzpy8QaXloZuARBETdLnXZpYtDj5Am/SBQBg34z1FXrd7EJJ+dAtLepn7nrfojuSK5ZdoUboGLQmBnQAQHCbz9CCqVp8nXhO0EykpuhFPZXpr4UOAQAArcMlGxsoLJfboKSu0D0A5p5JN7hsop5UP756o7aF7gEAoJWM9Xe8wJVeKWn/0C0t7Ocy/fPC25JPMKRjtjGgAwCCG+uLR93UE7ojC9z19t6h5EOhOwAAQGsaWVN8nEV+maRjQrcAmBO/kevShjSxaij5aegYAABa2ca+4hNyux9X+bjQLS3uv2T+/i0H1i4vl1UPHYPWwIAOAAhqtL9QkmwkdEdGfKankryW5xACAIC5tGGVCvk4fpekd0qKQvcA2GfTkj7jqU3cenD1q7ywDADA/Jno04F1K14p+bGhW1qdS7+M5O+vJbVLV29ULXQPso0BHQAQzPDawlFRatdJKoZuyYD/rifJ0as36vehQwAAQHsYH8g/zz261KVHhW4B8FDYdbJ0ol6tbebnCAAAwtlcVjy1Nd4g04rQLW3iJsnfX09qmxjS8VAxoAMAghg5Qw+3JL5BpkeHbsmAXZH0rO5K8h+hQwAAQHsZP137p/V4vaQ3hm4BMCM3S9rkqW3qXV/9n9AxAABgN5dstC8+20znhG5pIzeb/ANdi2sXLysrCR2DbGFABwDMu3JZ0RHb4s9JelnolkwwnVwaTC4LnQEAANqTSzbeH5/krvUyLQzdA+BP7JDrU6lFE79aPP2Ncllp6CAAAHD/RvriN5jpEkn50C1tZIu5f3BHV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<td class="gt_row gt_right">14%</td>
<td class="gt_row gt_right">15%</td>
<td class="gt_row gt_right">21%</td>
<td class="gt_row gt_right">24%</td>
<td class="gt_row gt_right">23%</td>
<td class="gt_row gt_right">19%</td>
<td class="gt_row gt_right">21%</td>
<td class="gt_row gt_right">22%</td>
<td class="gt_row gt_right">21%</td>
<td class="gt_row gt_right">21%</td>
<td class="gt_row gt_right">21%</td>
<td class="gt_row gt_right">22%</td>
<td class="gt_row gt_right">22%</td>
<td class="gt_row gt_right">21%</td>
<td class="gt_row gt_right">20%</td>
<td class="gt_row gt_right">20%</td>
<td class="gt_row gt_right">19%</td>
<td class="gt_row gt_right">19%</td></tr>
    <tr><td class="gt_row gt_left" style="color: #077DAA;">Health professions</td>
<td class="gt_row gt_center"><img 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style="height:15px;"></td>
<td class="gt_row gt_right">3%</td>
<td class="gt_row gt_right">6%</td>
<td class="gt_row gt_right">7%</td>
<td class="gt_row gt_right">7%</td>
<td class="gt_row gt_right">5%</td>
<td class="gt_row gt_right">7%</td>
<td class="gt_row gt_right">6%</td>
<td class="gt_row gt_right">6%</td>
<td class="gt_row gt_right">6%</td>
<td class="gt_row gt_right">7%</td>
<td class="gt_row gt_right">7%</td>
<td class="gt_row gt_right">8%</td>
<td class="gt_row gt_right">8%</td>
<td class="gt_row gt_right">8%</td>
<td class="gt_row gt_right">9%</td>
<td class="gt_row gt_right">10%</td>
<td class="gt_row gt_right">11%</td>
<td class="gt_row gt_right">11%</td></tr>
    <tr><td class="gt_row gt_left" style="color: #026D4E;">Social sciences and history</td>
<td class="gt_row gt_center"><img 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style="height:15px;"></td>
<td class="gt_row gt_right">18%</td>
<td class="gt_row gt_right">14%</td>
<td class="gt_row gt_right">11%</td>
<td class="gt_row gt_right">9%</td>
<td class="gt_row gt_right">11%</td>
<td class="gt_row gt_right">11%</td>
<td class="gt_row gt_right">10%</td>
<td class="gt_row gt_right">11%</td>
<td class="gt_row gt_right">11%</td>
<td class="gt_row gt_right">11%</td>
<td class="gt_row gt_right">11%</td>
<td class="gt_row gt_right">11%</td>
<td class="gt_row gt_right">10%</td>
<td class="gt_row gt_right">10%</td>
<td class="gt_row gt_right">10%</td>
<td class="gt_row gt_right">10%</td>
<td class="gt_row gt_right">9%</td>
<td class="gt_row gt_right">9%</td></tr>
    <tr><td class="gt_row gt_left" style="color: #A39A09;">Other</td>
<td class="gt_row gt_center"><img 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" style="height:15px;"></td>
<td class="gt_row gt_right">65%</td>
<td class="gt_row gt_right">65%</td>
<td class="gt_row gt_right">61%</td>
<td class="gt_row gt_right">60%</td>
<td class="gt_row gt_right">60%</td>
<td class="gt_row gt_right">62%</td>
<td class="gt_row gt_right">62%</td>
<td class="gt_row gt_right">62%</td>
<td class="gt_row gt_right">62%</td>
<td class="gt_row gt_right">61%</td>
<td class="gt_row gt_right">61%</td>
<td class="gt_row gt_right">60%</td>
<td class="gt_row gt_right">60%</td>
<td class="gt_row gt_right">60%</td>
<td class="gt_row gt_right">60%</td>
<td class="gt_row gt_right">61%</td>
<td class="gt_row gt_right">61%</td>
<td class="gt_row gt_right">61%</td></tr>
  </tbody>
  
  
</table>
</div>

---

## Summary

- A table may be preferable to a visualization

--
- A table can be enhanced with a visualization

---

## Case study 3:

## COVID-19 Deaths

---

## Data

Each row represents a country date combination.
For each combination we have the total number of cases, the cumulative cases, and the days elapsed since 10th confirmed COVID-19 case in that country.

|country |date       | tot_cases| cumulative_cases| days_elapsed|
|:-------|:----------|---------:|----------------:|------------:|
|China   |2020-01-22 |        17|               17|            0|
|China   |2020-01-23 |         1|               18|            1|
|China   |2020-01-24 |         8|               26|            2|
|China   |2020-01-25 |        16|               42|            3|
|China   |2020-01-26 |        14|               56|            4|
|China   |2020-01-27 |        26|               82|            5|
|China   |2020-01-28 |        49|              131|            6|
|China   |2020-01-29 |         2|              133|            7|
|China   |2020-01-30 |        38|              171|            8|
|China   |2020-01-31 |        42|              213|            9|

---

## Plot 1: Linear scale

---

## Plot 2: Logged scale

---

---

---

## Summary

- Your data scale matters!

--
- Keep in mind not just best practices, but also your audience and the amount of supplementary information you can provide

---

# visualizion .hand[resources]

---

## Resources

- Books:
  - [Data Visualization: A practical introduction](https://socviz.co/) by Kieran Healy
  - [Fundamentals of Data Visualization](https://clauswilke.com/dataviz/) by Claus O. Wilke
  - [How charts lie](http://www.thefunctionalart.com/p/reviews.html) by Alberto Cairo
  - [Presenting Data Effectively](https://stephanieevergreen.com/books/) by Stephanie Evergreen
  - [Datavision](https://www.amazon.com/Datavision-2-David-McCandless/dp/2221145925) by David McCandless
  
- Community: [Data Visualization Society](https://www.datavisualizationsociety.org/resources)

- Tools: All visualizations presented have been created with [R](https://cran.r-project.org/) and [ggplot2](https://ggplot2.tidyverse.org/)
  - For those who are interested, the source code can be found [here](https://github.com/mine-cetinkaya-rundel/2021-data-viz-nc)