perlatex

library(ggplot2)
library(dplyr)
library(gganimate)

heartdf = tibble(
	t = seq(0, 2*pi, pi/60),
	x = 16*sin(t)^3,
	y = 13*cos(t) - 5*cos(2*t) - 2*cos(3*t) - cos(4*t)
)

perlatex

# learn from https://rpubs.com/dgrtwo/valentine
library(ggplot2)
library(dplyr)
library(gganimate)

heartdf <- tibble(
  t = seq(0, 2 * pi, pi / 60),
  x = 16 * sin(t)^3,
  y = 13 * cos(t) - 5 * cos(2 * t) - 2 * cos(3 * t) - cos(4 * t)
)

perlatex

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

```{r setup}

perlatex

# 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) {

perlatex

library(tidyverse)
library(brms)
library(tidybayes)
library(gganimate)
library(magick)

tb <- tibble(
  index = 1:15,
  a = runif(n = 15, min = 0, max = 2),
  b = rnorm(15, mean = 2 * a, sd = 1),

perlatex

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

perlatex

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) %>%

perlatex

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

perlatex

---
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

perlatex

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

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


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