rbasic.Rmd

---
title: "rbasic"
author: "知了新聞團隊"
# date: "3/19/2017"
date: "`r Sys.Date()`"
output:
  ioslides_presentation:
    incremental: true
---

```{r setup 1, include=FALSE}
knitr::opts_chunk$set(echo = TRUE )
```

## 回頭來看 R 基礎語法 {.build}

- 向量式的運算
```{r vectorize}
c(1, 2, 3, 4) + 1
c(1, 2, 3, 4) + c(2, 3, 4, 5)
c(1, 2, 3, 4) + c(2, 10)
```
- ％＆＠＄＾＊％＠！
- 「阿鬼你還是說中文吧！」


## 回頭來看 R 基礎語法 {.build}

*To understand computations in R, two slogans are helpful:*

- *Everything that exists is an object.*
- *Everything that happens is a function call."*

*— John Chambers*

<div class="columns-2">

```{r function call, warning=FALSE, message=FALSE}
`+`
`<-`
`[`
`c`
```

</div>

## 回頭來看 R 基礎語法 {.build}

- 什麼是 `function call`？
```{r function call 1}
1 + 1
`+`(1, 1)
```

## 回頭來看 R 基礎語法 {.build}

- 沒關係不重要，搞懂 `Dataframe` 就好
- （直翻）資料框，就是表格的意思啦
- 看起來大概差不多就是長這種樣子（？）
```{r dataframe 0, echo=FALSE, warning=FALSE, message=FALSE}
# install.packages("Lahman")
library(Lahman)
library(dplyr)
```

```{r dataframe 1}
wang <- Pitching %>% filter(playerID == "wangch01") %>% arrange(desc(yearID))
wang
```

## 新潮好用的玩意兒 R {.build}

- 好用工具`懶人包` 🎒👝👛👜💼 
- [`tidyverse`](http://tidyverse.org/)

![](http://forcats.tidyverse.org/logo.png)
![](http://ggplot2.tidyverse.org/logo.png)
![](http://haven.tidyverse.org/logo.png)
![](http://readr.tidyverse.org/logo.png)
![](http://stringr.tidyverse.org/logo.png)
![](http://tidyr.tidyverse.org/logo.png)
```{r tidyverse}
# install.packages("tidyverse")
library(tidyverse)
```

## 畫圖之前先把資料讀進來 {.build}

- `readr::read_csv()`
- 跟原生方法比起來，效率較高，也不會將文字格式轉成 `factor`（因子）來處理
- 比 `data.table::fread()` 略慢，不過語法較簡單
```{r read_csv 0}
traffic <- read_csv("ggplot2-slides/traffic_eng.csv")
```

## 畫圖之前先把資料讀進來 {.build}

- `readr::read_csv()`
```{r read_csv 1}
head(traffic, n=3)
```

## 畫圖之前先把資料讀進來 {.build}

- `readr::read_csv()`
```{r read_csv 2}
traffic <- read_csv("ggplot2-slides/traffic_eng.csv", col_types = 
                      cols(
                        time.year = col_integer(),
                        time.month = col_integer(),
                        time.day = col_integer(),
                        time.hour = col_integer(),
                        time.minute = col_integer(),
                        event.level = col_character(),
                        location.district = col_character(),
                        location.address = col_character(),
                        number.dead = col_integer(),
                        number.injury = col_integer(),
                        party.sn = col_integer(),
                        vehicle.type = col_character(),
                        party.gender = col_character(),
                        party.age = col_integer(),
                        party.injury = col_character(),
                        location.weather = col_character(),
                        location.speed.limit = col_integer(),
                        location.road.type = col_character(),
                        location.type = col_character()
                      ))
```

## 畫圖之前先把資料讀進來 {.build}

- `readr::read_csv()`
```{r read_csv 3}
head(traffic, n=3)
```

## 畫圖之前先把資料讀進來 {.build}

- `dplyr::data_frame()`
- 跟原生方法比起來，效率較高，也不會將文字格式轉成 `factor`（因子）來處理
- 比 `data.table::data.table()` 略慢，不過語法較簡單
```{r data_frame}
traffic <- data_frame(time.year = c(104), time.month = c(1), 
                      time.day = c(1), time.hour = c(0), 
                      time.minute = c(0, 1, 2), event.level = c("一般"))
head(traffic, n=3)
```

## 畫圖之前先把資料讀進來 {.build}

- `magrittr` (pipe `%>%`)
- 把工作流程的每一站`串接`起來，簡化開發邏輯跟程式碼
```{r pipe 0}
head(traffic, n=3)
```

## 畫圖之前先把資料讀進來 {.build}

- `magrittr` (pipe `%>%`)
- 把工作流程的每一站`串接`起來，簡化開發邏輯跟程式碼
```{r pipe 1}
traffic %>% head(3)
```

## 畫圖之前先把資料讀進來 {.build}

- `dplyr::select()`
```{r dplyr 0}
wang %>% select(yearID, teamID, W, L, ERA)
```

## 畫圖之前先把資料讀進來 {.build}

- `dplyr::filter()`
```{r dplyr 1}
wang %>% filter(GS > 10)
```

## 畫圖之前先把資料讀進來 {.build}

- `dplyr::summarise()`
```{r dplyr 2}
wang %>% group_by(lgID) %>% summarise(mean(ERA))
```