wang minjie perlatex

## Kernel estimation animation
## Code by Walker Harrison @walkwearscrocs
## From https://www.walker-harrison.com/posts/2021-02-13-visualizing-how-a-kernel-draws-a-smooth-line/

library(tidyverse)

theme_set(theme_bw())
set.seed(0)

n <- 100
x <- runif(n, 0, 4*pi)

## rotate-axis-labels-ggplot2.R
# Adapted from https://stackoverflow.com/a/7267364/1036500 by Andrie de Vries

# This is it: theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

library(ggplot2)

td <- expand.grid(
  hjust=c(0, 0.5, 1),
  vjust=c(0, 0.5, 1),
  angle=c(0, 45, 90),

## tidy-rolling-regression.r
library(dplyr)
library(slider)
library(lubridate)
library(tsibbledata)

# Google, Apple, Facebook, Amazon stock
gafa_stock <- as_tibble(gafa_stock)
gafa_stock <- select(gafa_stock, Symbol, Date, Close, Volume)

head(gafa_stock, 2)

## keep_three_ggplot.R
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"))

## animated_humidity_regression.md

      
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                perlatex
                / animated_humidity_regression.md
            
            
              Created
              April 2, 2020 10:27
                — forked from andrewheiss/animated_humidity_regression.md
            
          
    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")

  
## 2020-03-29_sane-legend.R
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)
)

## gganimate_loess.R
library(tidyverse)
library(scales)
library(broom)
library(gganimate)

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


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

## celebration2020.Rmd
---
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

## flatten_the_curve.R
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

## coronavirus_cases_trajectories.R
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) %>%
	## Code by Walker Harrison @walkwearscrocs
	## From https://www.walker-harrison.com/posts/2021-02-13-visualizing-how-a-kernel-draws-a-smooth-line/

	library(tidyverse)

	theme_set(theme_bw())
	set.seed(0)

	n <- 100
	x <- runif(n, 0, 4*pi)
	# Adapted from https://stackoverflow.com/a/7267364/1036500 by Andrie de Vries

	# This is it: theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

	library(ggplot2)

	td <- expand.grid(
	hjust=c(0, 0.5, 1),
	vjust=c(0, 0.5, 1),
	angle=c(0, 45, 90),
	library(dplyr)
	library(slider)
	library(lubridate)
	library(tsibbledata)

	# Google, Apple, Facebook, Amazon stock
	gafa_stock <- as_tibble(gafa_stock)
	gafa_stock <- select(gafa_stock, Symbol, Date, Close, Volume)

	head(gafa_stock, 2)
	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"))
	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)
	)
	library(tidyverse)
	library(scales)
	library(broom)
	library(gganimate)

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


	# Basic static plot -------------------------------------------------------
	---
	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
	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
	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) %>%