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Tried to fix the dumb pandoc change that broke lib_dir. Adding day 9 stuff
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day_10/day_10.ipynb

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day_11/day_11.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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"version": "3.8.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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"nbformat_minor": 4
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}

day_6/lecture/day_6.Rmd

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theme: white
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progress: true
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transition: convex
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self_contained: false
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self_contained: true
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css: ../../libs/css/custom_css.css
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lib_dir: ../../libs
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---

day_6/lecture/day_6.html

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day_7/day_7.ipynb

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"\n",
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"One of the first things we want to do is look at the distributions of data and the relationships between them. Here are a few common approaches.\n",
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"\n",
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"### Distplot\n",
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"### Displot\n",
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"\n",
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"This is the workhorse for univariate distributions."
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]
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"metadata": {},
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"outputs": [],
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"source": [
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"sns.distplot(iris.sepal_length); # The semicolon at the end suppresses the matplotlib output"
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"sns.displot(iris.sepal_length); # The semicolon at the end suppresses the matplotlib output"
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]
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},
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{
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"outputs": [],
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"source": [
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"# There are some additional options for this plot, e.g.\n",
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"sns.distplot(iris.sepal_length, kde=False);"
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"sns.displot(iris.sepal_length, kde=False);"
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]
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},
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{
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"source": [
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"# Or add a fit distribution to a plot\n",
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"from scipy import stats\n",
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"sns.distplot(iris.sepal_length, kde=False, fit=stats.norm);"
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"sns.displot(iris.sepal_length, kde=False, fit=stats.norm);"
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]
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},
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{
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"### Exercise 5\n",
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"\n",
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"Now take your swarm plot and make a facet for each day of the week.\n",
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"Hint: To make it look nice, you need to figure out how to wrap it."
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"\n",
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"Hint: In order to use facets, you need to use one of the higher-level functions - `relplot()`, `displot()`, or `catplot()`.\n",
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"\n",
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"Bonus: See if you can figure out how to wrap the facets into a 2x2 grid"
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]
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},
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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"version": "3.8.8"
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}
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},
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"nbformat": 4,

day_7/lecture/day_7.Rmd

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theme: white
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progress: true
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transition: convex
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self_contained: false
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self_contained: true
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css: ../../libs/css/custom_css.css
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lib_dir: ../../libs/
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---

day_7/lecture/day_7.html

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day_8/day_8.ipynb

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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Use input to request width/height and print an image\n"
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]
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"outputs": [],
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"source": [
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"public_tweets = api.search('\"data science\"', count=20)\n",
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"public_tweets = api.search('\"from:@jdfoote\"', count=20)\n",
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"\n",
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"for tweet in public_tweets:\n",
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" print(tweet.user.screen_name + \"\\t\" + str(tweet.created_at) + \"\\t\" + tweet.text)"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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"version": "3.8.8"
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}
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"nbformat": 4,

day_8/day_8_solutions.ipynb

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day_8/lecture/day_8.Rmd

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---
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pagetitle: Intro to APIs
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title: Intro to APIs
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output:
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revealjs::revealjs_presentation:
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theme: white
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progress: true
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transition: convex
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self_contained: true
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css: ../../resources/css/custom_css.css
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css: ../../libs/css/custom_css.css
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lib_dir: ../../libs/
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---
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```{r setup, include=FALSE}
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## Housekeeping
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> - Self-assessment reflection and planning document
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> - Day 9 and Day 10 due on Friday
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> - Feel free to keep working on Day 8, too
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> - Breakout rooms?
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## Paper Discussion
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Kieran Healy and James Moody (2014). “Data Visualization in Sociology.” American Review of Sociology. 40: 105-28.
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Discussant: Pearlynne
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## Scraping Data
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> - APIs
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> - Scraping
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## API Notebook
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## Homework
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> - Project Planning Document and self-reflection
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> - Coding assignments due on Monday
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## Review
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> - Faceting
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## HW Review

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