jhu_eda_w1.Rmd

---
title: "Exploratory Data Analysis: Electric Power Consumption"
author: 郭耀仁
date: "`r Sys.Date()`"
output: 
  revealjs::revealjs_presentation:
    theme: black
    highlight: zenburn
    center: true
---

# 案例來源

## Exploratory Data Analysis by Johns Hopkins University on Coursera

<https://www.coursera.org/learn/exploratory-data-analysis>

# 資料

## 下載資料

[Electric power consumption](https://storage.googleapis.com/jhu_coursera_data/household_power_consumption.zip)

## 注意

- 變數分隔符號為 ';'
- 2,075,259 個觀測值與 9 個變數
- 只需要使用 2007-02-01 與 2007-02-02 這兩天的資料
- 遺漏值被記錄為 '?'

## 變數資訊上

- Date: Date in format dd/mm/yyyy
- Time: time in format hh:mm:ss
- Global_active_power: household global minute-averaged active power (in kilowatt)
- Global_reactive_power: household global minute-averaged reactive power (in kilowatt)
- Voltage: minute-averaged voltage (in volt)
- Global_intensity: household global minute-averaged current intensity (in ampere)

## 變數資訊下

- Sub_metering_1: energy sub-metering No. 1 (in watt-hour of active energy). It corresponds to the kitchen, containing mainly a dishwasher, an oven and a microwave (hot plates are not electric but gas powered).
- Sub_metering_2: energy sub-metering No. 2 (in watt-hour of active energy). It corresponds to the laundry room, containing a washing-machine, a tumble-drier, a refrigerator and a light.
- Sub_metering_3: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner.

# 練習繪製四個圖形

## 圖形一

```{r echo=FALSE, message=FALSE}
library(ggplot2)

file_url <- "https://storage.googleapis.com/jhu_coursera_data/household_power_consumption.csv"
exdata <- read.csv(file_url, stringsAsFactors = FALSE)
ggplot(exdata, aes(x = Global_active_power)) +
  geom_histogram(bins = 15, fill = rgb(1, 0, 0, 0.5)) +
  theme_minimal() +
  ggtitle("Global Active Power") +
  xlab("Global Active Power (kilowatts)") +
  ylab("Frequency")
```

## 圖形二

```{r echo=FALSE, message=FALSE}
exdata$Datetime <- paste(exdata$Date, exdata$Time)
exdata$Datetime <- as.POSIXct(exdata$Datetime)
ggplot(exdata, aes(x = Datetime, y = Global_active_power)) +
  geom_line() +
  ylab("Global Active Power (kilowatts)") +
  scale_x_datetime(date_labels = "%A") +
  xlab("") +
  theme_minimal()
```

## 圖形三

```{r echo=FALSE, message=FALSE}
library(tidyr)

long_exdata <- gather(exdata[, c("Datetime", "Sub_metering_1", "Sub_metering_2", "Sub_metering_3")], key = submeter, value = metering, Sub_metering_1, Sub_metering_2, Sub_metering_3)
ggplot(long_exdata, aes(x = Datetime, y = metering, color = submeter)) +
  geom_line() +
  ylab("Energy sub metering") +
  scale_x_datetime(date_labels = "%A") +
  xlab("") +
  theme_minimal()
```

## 圖形四

```{r echo=FALSE, message=FALSE}
library(gridExtra)

p1 <- ggplot(exdata, aes(x = Datetime, y = Global_active_power)) +
  geom_line() +
  ylab("Global Active Power (kilowatts)") +
  scale_x_datetime(date_labels = "%A") +
  xlab("") +
  theme_minimal()

p2 <- ggplot(long_exdata, aes(x = Datetime, y = metering, color = submeter)) +
  geom_line() +
  ylab("Energy sub metering") +
  scale_x_datetime(date_labels = "%A") +
  xlab("") +
  theme_minimal() +
  theme(legend.position = c(0.8, 0.8))

p3 <- ggplot(exdata, aes(x = Datetime, y = Voltage)) +
  geom_line() +
  ylab("Voltage") +
  scale_x_datetime(date_labels = "%A") +
  xlab("") +
  theme_minimal()

p4 <- ggplot(exdata, aes(x = Datetime, y = Global_reactive_power)) +
  geom_line() +
  ylab("Global Reactive Power") +
  scale_x_datetime(date_labels = "%A") +
  xlab("") +
  theme_minimal()

grid.arrange(p1, p3, p2, p4, ncol=2)
```