exploring_data.Rmd

---
title: "Exploring Data with R"
author: "Tony Yao-Jen Kuo"
output:
  revealjs::revealjs_presentation:
    highlight: pygments
    reveal_options:
      slideNumber: true
      previewLinks: true
---

```{r include=FALSE}
knitr::opts_chunk$set(results = "hold", message = FALSE)
```

# Overview

## Introducing the tidyverse system

- Picked by RStudio
- `dplyr` for data manipulation
- `ggplot` for data visualization
- And more...

## We are gonna talk about 3 packages

- `gapminder` for a data gapminder
- `dplyr` for grammar of data manipulation
- `ggplot2` for grammar of graphics

# gapminder

## Getting data

```{r eval=FALSE}
install.packages("gapminder")
```

```{r}
library(gapminder)
```

## The story of Hans Rosling and Gapminder

<https://youtu.be/jbkSRLYSojo>

# dplyr

## Installing `dplyr`

```{r eval=FALSE}
install.packages("dplyr")
```

## Basic functions

>- `filter()`
>- `select()`
>- `arrange()`
>- `mutate()`
>- `summarise()`
>- `group_by()`

## `filter()` for subsetting rows

```{r}
library(dplyr)

gapminder %>% 
  filter(country == "Taiwan")
```

## `select()` for extracting columns

```{r}
gapminder %>%
  filter(country == "Taiwan") %>% 
  select(year, gdpPercap, lifeExp)
```

## `arrange()` for sorting

```{r}
gapminder %>% 
  filter(continent == "Asia") %>% 
  filter(year == 2007) %>% 
  arrange(gdpPercap)
```

## `mutate()` for creating new columns

```{r}
gapminder %>% 
  filter(country == "Taiwan") %>% 
  mutate(gdp_million = (gdpPercap * pop / 1000000))
```

## `summarise()` for...a summary

```{r}
gapminder %>% 
  summarise(median(gdpPercap))
```

## `group_by()` for a grouped summary

```{r}
gapminder %>% 
  group_by(continent) %>% 
  summarise(medianGdpPercap = median(gdpPercap))
```

## Going further

<https://dplyr.tidyverse.org/>

# ggplot2

## gg stands for...

> The grammar of graphics.

## Installing `ggplot2`

```{r eval=FALSE}
install.packages("ggplot2")
```

## Basic concepts

- `ggplot(aes(x = , y = , color = , fill = , ...))` for data mapping
- `geom_OOO()` for different charts`
- Using `+` to add different layers

## `geom_point()` for exploring correlations

Making a scatter plot

```{r}
library(ggplot2)

gapminder %>% 
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point()
```

## `geom_histogram()` for exploring distributions

```{r}
gapminder %>% 
  ggplot(aes(x = gdpPercap)) +
  geom_histogram(bins = 40)
```

## `geom_bar()` for exploring row counts

```{r}
gapminder %>% 
  filter(year == 2007) %>%
  ggplot(aes(x = continent)) +
  geom_bar()
```

## `geom_bar()` for grouped summary

```{r}
gapminder %>% 
  filter(year == 2007) %>% 
  group_by(continent) %>% 
  summarise(medianGdpPercap = median(gdpPercap)) %>% 
  ggplot(aes(x = continent, y = medianGdpPercap)) +
  geom_bar(stat = "identity")
```

## Going further

<https://ggplot2.tidyverse.org/>

# Animated plot for inspirations

## Installing `plotly`

```{r eval=FALSE}
install.packages("plotly")
```

## Plotting a gapminder replica

```{r eval=FALSE}
library(plotly)
radius <- sqrt((gapminder$pop)/pi)

p <- gapminder %>%
  plot_ly(
    x = ~gdpPercap, 
    y = ~lifeExp, 
    size = ~pop, 
    color = ~continent, 
    frame = ~year, 
    text = ~country, 
    hoverinfo = "text",
    type = 'scatter',
    mode = 'markers',
    sizes = c(min(radius), max(radius))
  ) %>%
  layout(
    xaxis = list(
      type = "log"
    )
  )
```

## The gapminder replica

```{r eval=FALSE}
p
```