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---
title: "gganimate cookbook code"
output:
  html_document:
    df_print: tibble
    highlight: kate
    css: gganimate_cookbook.css
---

```{r setup}

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# somewhat hackish solution to:
# https://twitter.com/EamonCaddigan/status/646759751242620928
# based mostly on copy/pasting from ggplot2 geom_violin source:
# https://github.com/hadley/ggplot2/blob/master/R/geom-violin.r

library(ggplot2)
library(dplyr)


"%||%" <- function(a, b) {

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Classifiers

Introduction

Looking at the decision boundary a classifier generates can give us some geometric intuition about the decision rule a classifier uses and how this decision rule changes as the classifier is trained on more data.

Plotting Functions

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read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv") %>%
  gather(date, cases, 5:ncol(.)) %>%
  mutate(date = as.Date(date, "%m/%d/%y")) %>%
  group_by(country = `Country/Region`, date) %>%
  summarise(cases = sum(cases)) %>%
  filter(country != "Others" & country != "Mainland China") %>%
  bind_rows(
    tibble(country = "Republic of Korea", date = as.Date("2020-03-11"), cases = 7755)
  ) %>%
  group_by(country) %>%

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library(tidyverse)

# Download Fira Sans Condensed from 
# https://fonts.google.com/specimen/Fira+Sans+Condensed

high_mean <- 12
high_sd <- 4
flat_mean <- 35
flat_sd <- 12

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---
title: "ggplot2 examples and exercises"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

This document contains all the code that is displayed during the workshop. The

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library(tidyverse)
library(scales)
library(broom)
library(gganimate)

# Original by David Robinson at http://varianceexplained.org/files/loess.html


# Basic static plot -------------------------------------------------------

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library(tidyverse)
library(patchwork)

dat_wide <- tibble(
  x = 1:3,
  top = c(4.5, 4, 5.5),
  middle = c(4, 4.75, 5),
  bottom = c(3.5, 3.75, 4.5)
)

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library(tidyverse)
library(lubridate)
library(broom)
library(scales)
library(gganimate)

# Load and clean data
# This data comes from Dark Sky's API
weather_provo_raw <- read_csv("https://andhs.co/provoweather")

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library(tidyverse)
library(ggridges)
library(patchwork)

p1 <- ggplot(mpg, aes(x = cty, y = hwy)) +
  geom_point() +
  geom_smooth() +
  labs(title = "1: geom_point() + geom_smooth()") +
  theme(plot.title = element_text(face = "bold"))