add new draft post
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Kyle Belanger
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5 changed files with 496 additions and 0 deletions
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cspell.json
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.lintr
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.lintr
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linters: linters_with_defaults(
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line_length_linter(120)
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, commented_code_linter = NULL
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, object_usage_linter = NULL
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, indentation_linter(
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) # see vignette("lintr")
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encoding: "UTF-8"
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posts/2024-05-15-US-NHL-Birthrate/cdc_wonder_request.xml
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posts/2024-05-15-US-NHL-Birthrate/cdc_wonder_request.xml
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<?xml version="1.0" encoding="UTF-8"?><request-parameters>
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<name>B_1</name>
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<value>D66.V20</value>
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<value>*None*</value>
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<name>F_D66.V21</name>
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<value>*All*</value>
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<name>F_D66.V22</name>
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<value>*All*</value>
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<name>F_D66.V37</name>
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<value>*All*</value>
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<name>I_D66.V21</name>
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<value>*All* (The United States)
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<parameter>
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<name>I_D66.V22</name>
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<value>*All* (The United States)
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<name>I_D66.V37</name>
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<value>*All* (The United States)
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<parameter>
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<name>M_1</name>
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<value>D66.M1</value>
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<name>O_V21_fmode</name>
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<name>O_age</name>
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<value>D66.V1</value>
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<name>O_gestation</name>
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<value>D66.V23</value>
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<name>O_javascript</name>
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<value>on</value>
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<name>O_location</name>
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<value>D66.V21</value>
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<name>O_oc-sect1-request</name>
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<value>close</value>
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<name>O_oe_gestation</name>
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<value>D66.V33</value>
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<name>O_precision</name>
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<value>2</value>
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<name>O_rate_per</name>
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<value>1000</value>
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<name>O_show_totals</name>
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<value>true</value>
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<name>O_timeout</name>
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<name>O_title</name>
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<name>O_urban</name>
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<name>O_weight</name>
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<value>D66.V9</value>
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<name>V_D66.V1</name>
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<value>*All*</value>
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<name>V_D66.V10</name>
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<value>*All*</value>
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<name>V_D66.V14</name>
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<value>*All*</value>
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<name>V_D66.V154</name>
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<value>*All*</value>
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<value>*All*</value>
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<name>V_D66.V16</name>
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<name>V_D66.V17</name>
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<value>*All*</value>
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<name>V_D66.V18</name>
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<value>*All*</value>
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<name>V_D66.V2</name>
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<value>*All*</value>
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<name>V_D66.V20</name>
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<value>*All*</value>
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<name>V_D66.V23</name>
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<value>*All*</value>
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<name>V_D66.V24</name>
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<value>*All*</value>
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<name>V_D66.V25</name>
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<value>*All*</value>
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<name>V_D66.V26</name>
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<value>*All*</value>
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<name>V_D66.V27</name>
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<value>*All*</value>
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<name>V_D66.V28</name>
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<value>*All*</value>
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<name>V_D66.V29</name>
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<value>*All*</value>
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<name>V_D66.V3</name>
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<value>*All*</value>
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<name>V_D66.V30</name>
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<value>*All*</value>
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<name>V_D66.V31</name>
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<value>*All*</value>
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<name>V_D66.V32</name>
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<value>*All*</value>
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<name>V_D66.V33</name>
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<value>*All*</value>
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<name>V_D66.V34</name>
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<value>*All*</value>
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<name>V_D66.V35</name>
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<value>*All*</value>
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<name>V_D66.V37</name>
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<value/>
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<parameter>
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<name>V_D66.V38</name>
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<value>*All*</value>
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<parameter>
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<name>V_D66.V39</name>
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<value>*All*</value>
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<name>V_D66.V4</name>
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<value>*All*</value>
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<name>V_D66.V41</name>
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<value>*All*</value>
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<name>V_D66.V42</name>
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<value>*All*</value>
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<name>V_D66.V43</name>
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<value>*All*</value>
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<name>V_D66.V5</name>
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<value>*All*</value>
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<name>V_D66.V6</name>
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<value>*All*</value>
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<name>V_D66.V7</name>
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<value>*All*</value>
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<name>V_D66.V8</name>
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<value>*All*</value>
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<name>V_D66.V9</name>
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<value>*All*</value>
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<parameter>
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<name>action-Send</name>
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<value>Send</value>
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<parameter>
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<name>dataset_code</name>
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<value>D66</value>
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</parameter>
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<parameter>
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<name>dataset_label</name>
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<value>Natality, 2007-2022</value>
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</parameter>
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<parameter>
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<name>dataset_vintage</name>
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<value>2022</value>
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<parameter>
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<name>finder-stage-D66.V21</name>
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<value>codeset</value>
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<name>finder-stage-D66.V22</name>
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<value>codeset</value>
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<name>finder-stage-D66.V37</name>
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<value>codeset</value>
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<name>saved_id</name>
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<value/>
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<parameter>
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<name>stage</name>
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<value>request</value>
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</parameter>
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</request-parameters>
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57
posts/2024-05-15-US-NHL-Birthrate/index.qmd
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posts/2024-05-15-US-NHL-Birthrate/index.qmd
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---
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title: "Does a US Born Players Birthdate affect their shot at the NHL"
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description: "Inspired by TidyTuesday Week 2 - 2024 dataset about Candian Players, lets look at the same anaylyis for American Born Players"
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date: "5/15/2024" #Update when live
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categories:
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- tidytuesday
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- R
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- dataViz
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draft: TRUE
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---
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```{r}
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#| include: false
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knitr::read_chunk(here::here("posts", "2024-05-15-US-NHL-Birthrate", "nhl_usa_births.R"))
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```
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```{r }
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#| label: Setup
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#| echo: FALSE
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#| warning: false
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#| message: false
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```
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This post is inspired by this fantastic [blog post](https://jlaw.netlify.app/2023/12/04/are-birth-dates-still-destiny-for-canadian-nhl-players/) on Jlaws Blog. In it they explore how in the first chapter Malcolm Gladwell’s Outliers he discusses how in Canadian Junior Hockey there is a higher likelihood for players to be born in the first quarter of the year. As it appears cutoff dates for USA hockey are different and they are currently using June 1st (if my internet searches are to be believed), I wondered if the same analysis would hold true for American Born Players.
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## Distribution of Births by Month in the United States
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The data for US Birth Rates can be pulled from [CDC Wonder](https://wonder.cdc.gov/). The particular table of interest is the Natality, 2007 - 2022. CDC Wonder has a quite interesting API that requires a request with quite a few XML parameters. Thankfully you can build the request on the website and a nice package already exists to send the query. Check out the [Wonderapi Page](https://socdatar.github.io/wonderapi/index.html) for more info.
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```{r}
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#| label: USA_Birth_Data
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```
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### Graph Distribution of Births Compared to Expected
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note use of month.name to sort
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```{r}
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#| label: USA_Distro
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```
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## Hockey Data
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```{r}
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#| label: Roster_Data
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```
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## Graph It
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```{r}
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#| label: Graph_It
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#| fig-height: 7
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#| fig-width: 7
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```
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116
posts/2024-05-15-US-NHL-Birthrate/nhl_usa_births.R
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posts/2024-05-15-US-NHL-Birthrate/nhl_usa_births.R
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#These first few lines run only when the file is run in RStudio, !!NOT when an Rmd/Rnw file calls it!!
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rm(list = ls(all = TRUE)) #Clear the variables from previous runs.
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cat("\f") # clear console
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# cSpell:disable disable spell checking for this document
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## ---- Setup ----
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library(magrittr)
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library(ggplot2)
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library(ggtext)
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library(ggimage)
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## ---- USA_Birth_Data ----
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usa_raw <- wonderapi::send_query("D66", here::here("posts", "2024-05-15-US-NHL-Birthrate", "cdc_wonder_request.xml"))
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usa_births <- usa_raw %>%
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dplyr::group_by(Month) %>%
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dplyr::summarise(country_births = sum(Births), .groups = "drop") %>%
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dplyr::mutate(country_pct = country_births / sum(country_births))
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## ---- USA_Distro ----
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usa_births %>%
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dplyr::mutate(expected_births = dplyr::case_when(
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Month %in% c("April", "June", "September", "November") ~ 30 / 365
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, Month == "February" ~ 28 / 365
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, .default = 31 / 365
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)
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, difference = country_pct - expected_births
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, dplyr::across(Month, ~factor(., levels = month.name))
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, dplyr::across(c(country_pct, expected_births, difference), ~scales::percent(., accuracy = .1))
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) %>%
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dplyr::arrange(Month) %>%
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dplyr::rename_with(~stringr::str_replace_all(., "_", " ")) %>%
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dplyr::rename_with(stringr::str_to_title) %>%
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kableExtra::kbl() %>%
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kableExtra::kable_styling()
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## ---- Roster_Data ----
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teams <- httr::GET("https://api.nhle.com/stats/rest/en/team") %>%
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httr::content() %>%
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.[["data"]] %>%
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tibble::tibble(data = .) %>%
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tidyr::unnest_wider(data)
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get_roster <- function(team){
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httr::GET(glue::glue("https://api-web.nhle.com/v1/roster/{team}/20232024")) %>%
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httr::content() %>%
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purrr::flatten() %>%
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tibble::tibble(data = .) %>%
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tidyr::hoist(
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.col = "data"
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, "firstName" = list("firstName", 1L)
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, "lastName" = list("lastName", 1L)
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, "positionCode"
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, "birthDate"
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, "birthCountry"
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)
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}
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usa_roster <- purrr::map(teams$triCode, get_roster) %>%
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purrr::list_rbind() %>%
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dplyr::filter(!is.na(firstName)) %>%
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dplyr::filter(birthCountry == "USA") %>%
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dplyr::mutate(
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mob = lubridate::month(lubridate::ymd(birthDate), label = TRUE, abbr = FALSE)
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, mob_id = lubridate::month(lubridate::ymd(birthDate))
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) %>%
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dplyr::count(mob_id, mob, name = "players") %>%
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dplyr::mutate(player_pct = players / sum(players))
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## ---- Graph_It ----
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nhl_icon <- "https://pbs.twimg.com/media/F9sTTAYakAAkRv6.png"
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usa_icon <- "https://cdn-icons-png.flaticon.com/512/197/197484.png"
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combined <- usa_roster %>%
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dplyr::left_join(usa_births, by = c("mob" = "Month")) %>%
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dplyr::mutate(
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random = dplyr::case_when(
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mob_id %in% c(4, 6, 9, 11) ~ 30 / 365,
|
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mob_id %in% c(1, 3, 5, 7, 8, 10, 12) ~ 31 / 365,
|
||||
mob_id == 2 ~ 28 / 365
|
||||
)
|
||||
)
|
||||
|
||||
g1 <- combined %>%
|
||||
ggplot(aes(x = forcats::fct_reorder(mob, -mob_id))) +
|
||||
geom_line(aes(y = random, group = 1), linetype = 2, color = "grey60") +
|
||||
geom_linerange(aes(ymin = country_pct, ymax = player_pct)) +
|
||||
geom_image(aes(image = nhl_icon, y = player_pct)) +
|
||||
geom_image(aes(image = usa_icon, y = country_pct), size = 0.04) +
|
||||
geom_text(aes(label = scales::percent(player_pct, accuracy = .1),
|
||||
y = dplyr::if_else(player_pct > country_pct, player_pct + .005, player_pct - .005))) +
|
||||
geom_text(aes(label = scales::percent(country_pct, accuracy = .1),
|
||||
y = dplyr::if_else(country_pct > player_pct, country_pct + .005, country_pct - .005))) +
|
||||
scale_y_continuous(labels = scales::percent) +
|
||||
coord_flip() +
|
||||
labs(
|
||||
x = "Month of Birth"
|
||||
, y = "Percentage of Births"
|
||||
, title = "Are United States Born NHL Players More Likely to be Born Early in the Year?"
|
||||
, subtitle = "Comparing the distribution of birth months between Canadian NHL players and Canada in general"
|
||||
, caption = glue::glue(
|
||||
"<img src = {nhl_icon} width = '15' height=' 15' /> - US NHL Players Birth Month Distribution <br />
|
||||
<img src = {usa_icon} width = '15' height=' 15' /> - US Birth Month (2007-2022) Distribution"
|
||||
)
|
||||
) +
|
||||
theme_minimal() +
|
||||
theme(
|
||||
plot.caption = element_markdown()
|
||||
,plot.title.position = "plot"
|
||||
)
|
||||
|
||||
g1
|
||||
Loading…
Reference in a new issue