Lab02_R-Basic_Tutorial

class: center, middle, inverse, title-slide

# Lab02_R-Basic_Tutorial
## vector, list, and dataframe
### 曾子軒 Dennis Tseng
### 台大新聞所 NTU Journalism
### 2021/03/02

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# Vectors 向量

- **原子型態向量(Atomic vectors)**, 有六種類型: 邏輯logical, 整數integer, 浮點數double, 字符character, 複數complex, and 原始raw
- 最常用的是前四種 logical, integer, double, character，atomic vectors 內的元素類型都相同(homogeneous)
- **列表(Lists)**, 可以包含不同類型(heterogeneous)的元素

---
# Atomic Vectos

- `c(5,6)` 是 vectors, `5` 也是，等同於 `c(5)`
- 用 `c()` 創建與拼接, 像是 `x = c(1,2)` 以及 `y = c(x, x[1], x[2])`
- 用 ` length()` 獲取長度, 像是 `length(x) = 2`
- 幾個特性: 再利用(recycling), 篩選(filtering), 向量化(vectorization)
- arithmetic: 可以加減乘除, e.g. `c(2,5) + c(1,4)`

---
# Vector Operations

- 常用函數 `seq(from, to, by)`, 分別代表開始, 終結, 間距 
- 常用函數 `rep(x, times)`, 分別代表要重複的向量, 重複的次數

```r
seq(from=5,to=30,by=5)
```

```
## [1]  5 10 15 20 25 30
```

```r
seq(from=5,to=29,by=5)
```

```
## [1]  5 10 15 20 25
```

```r
seq(from=1,to=2,by=0.2)
```

```
## [1] 1.0 1.2 1.4 1.6 1.8 2.0
```

---

# Vector Operations

- 常用函數 `seq(from, to, by)`, 分別代表開始, 終結, 間距 
- 常用函數 `rep(x, times)`, 分別代表要重複的向量, 重複的次數

```r
rep(2,3)
```

```
## [1] 2 2 2
```

```r
rep(c(1,3,5),3)
```

```
## [1] 1 3 5 1 3 5 1 3 5
```

```r
rep(1:3, 2)
```

```
## [1] 1 2 3 1 2 3
```

---
# Vectorized/Vectorization

- 向量化的特性有點難用文字說明
- 原文: functions are applied element-wise to vectors

```r
# 第一個跟第一個比
c(1,2,4) > c(0,0,4)
```

```
## [1]  TRUE  TRUE FALSE
```

```r
# 第一個跟第一個相加
c(1,2,4) + c(0,0,4)
```

```
## [1] 1 2 8
```

```r
# 取四捨五入
round(c(1.2, 3.9, 0.4))
```

```
## [1] 1 4 0
```

```r
sqrt(c(2,5,10))
```

```
## [1] 1.414214 2.236068 3.162278
```

---
# Recycling

- R 會自動回收長度不同的向量

```r
# 自動延長前者
c(1,2) + c(1,2,3,4,5) 
```

```
## [1] 2 4 4 6 6
```

```r
# 等同 c(1,2,1,2,1) + c(1,2,3,4,5)

# 自動延長前者
2*c(1,2,3,4,5)
```

```
## [1]  2  4  6  8 10
```

```r
# 等同 c(2,2,2,2,2) + c(1,2,3,4,5) 
```

---
# Filtering

```r
a <- c(1,2,-6,8)
a[a > 0]
```

```
## [1] 1 2 8
```

```r
a > 0
```

```
## [1]  TRUE  TRUE FALSE  TRUE
```

```r
a[c(T, T, F, T)]
```

```
## [1] 1 2 8
```

---
# Filtering

```r
# Assigment
b <- c(3,5,9)
b[b == 3] <- -1
# subset()
subset(b, b > 3)
```

```
## [1] 5 9
```

```r
# which(): 告訴你 index
which(b > 1)
```

```
## [1] 2 3
```

---
# Conversion/Coercion

- 型態轉換
 - Explicit coercion: `as.logical()`, `as.integer()`, `as.double()`, `as.character()`
 - Implicit coercion: 因為 vector 內的元素具有相同型態，所在某些情況下，若元素型態不同，會被強制轉換
---

# Explicit coercion

```r
as.logical(c(1,0))
```

```
## [1]  TRUE FALSE
```

```r
as.integer(c(T,F))
```

```
## [1] 1 0
```

```r
as.logical(c("T",F))
```

```
## [1]  TRUE FALSE
```

```r
as.character(c(1,0))
```

```
## [1] "1" "0"
```

---

# Implicit coercion

```r
c(T,0)
```

```
## [1] 1 0
```

```r
c(T,'F')
```

```
## [1] "TRUE" "F"
```

---
# Atomic Vectos: Summary

- 創建向量: `c()`, `seq()`, `rep()`
- 獲取元素: index with `[]`, `subset()`, `which()`的輔助
- 操作向量: 加減乘除, 轉變型態 e.g. `as.logical()`
- 重要特性
  - 再利用(recycling): 長度不同會補上元素
  - 篩選(filtering): 使用`[]`
  - 向量化(vectorization): 函數與元素, 元素對元素

---

# List

- 包含不同型態元素的 vectors
- 創建 list: `list()`

```r
i <- list(name="Chen", salary=50000, boss=F)
j <- list("Lin", 70000, T)
i
```

```
## $name
## [1] "Chen"
## 
## $salary
## [1] 50000
## 
## $boss
## [1] FALSE
```

```r
j
```

```
## [[1]]
## [1] "Lin"
## 
## [[2]]
## [1] 70000
## 
## [[3]]
## [1] TRUE
```

---
# Lists Opreation

- list 可以有很多層，變得比較複雜，離我們習慣的 table 很遠，但它很常見，知道怎麼用會比較好

```r
k <- list("a", list(1, list("3", "g")))
k
```

```
## [[1]]
## [1] "a"
## 
## [[2]]
## [[2]][[1]]
## [1] 1
## 
## [[2]][[2]]
## [[2]][[2]][[1]]
## [1] "3"
## 
## [[2]][[2]][[2]]
## [1] "g"
```

---
# Visualizing Lists

```r
# source: R4DS
x1 <- list(c(1, 2), c(3, 4))
x2 <- list(list(1, 2), list(3, 4))
x3 <- list(1, list(2, list(3)))   
```

---
# Subestting

- `[` 抓的是 sub-list，一樣是 list
- `[[` 抓的是單一的 component，它消除了 list 的 hierarchy
- `$` 用來抓有名字的 component，抓出來的東西跟 `[[` 一樣

---

# Subsetting

```r
a <- list(a = 1:3, b = "a string", c = pi, d = list(-1, -5))
a[1]
```

```
## $a
## [1] 1 2 3
```

```r
str(a[1])
```

```
## List of 1
##  $ a: int [1:3] 1 2 3
```

---

# Subsetting

```r
a <- list(a = 1:3, b = "a string", c = pi, d = list(-1, -5))
a[[2]]
```

```
## [1] "a string"
```

```r
str(a[2])
```

```
## List of 1
##  $ b: chr "a string"
```

---

# Subsetting

```r
a <- list(a = 1:3, b = "a string", c = pi, d = list(-1, -5))
a$a
```

```
## [1] 1 2 3
```

```r
a[["a"]]
```

```
## [1] 1 2 3
```
---

# Subsetting

```r
a <- list(a = 1:3, b = "a string", c = pi, d = list(-1, -5))
```

---
# Visualizing Lists

<img src="photo/Lab02_pepper_01.jpg" width="20%" height="20%" /><img src="photo/Lab02_pepper_02.jpg" width="20%" height="20%" /><img src="photo/Lab02_pepper_03.jpg" width="20%" height="20%" /><img src="photo/Lab02_pepper_04.jpg" width="20%" height="20%" />
- x
- x[1]
- x[[1]]
- x[[1]][[1]]

source: [R4DS](https://r4ds.had.co.nz/vectors.html#lists-of-condiments)

---

# List Operation

- 加新的元素
- 刪掉舊的元素

```r
i[4] <- "LV2"
i[[5]] <- "Blue"
i$friends <- "Huang"
i$name <- NULL
i$salary <- NULL
names(i)
```

```
## [1] "boss"    ""        ""        "friends"
```
---

# List Operation

- 加新的元素
- 刪掉舊的元素

```r
i
```

```
## $boss
## [1] FALSE
## 
## [[2]]
## [1] "LV2"
## 
## [[3]]
## [1] "Blue"
## 
## $friends
## [1] "Huang"
```
---

# Dataframe

- 資料框，平常用的 excel, spreadsheet 都是 dataframe
- 先 row 再 column

```r
head(iris, 3)
```

```
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
```

---

# tidyverse

- 有一個宇宙叫做 tidyverse = tidy + universe
- 在這個宇宙裡有個叫做 tidy data 的 priciple
- 由許多遵循共通 philosophy 的套件所組成
- The tidyverse is a coherent system of packages for data manipulation, exploration and visualization that share a common design philosophy. 
- 參考他們的 [manifesto](https://tidyverse.tidyverse.org/articles/manifesto.html)

---
# tibble

- 也是 dataframe，但多了不同於原始 dataframe 的性質
- 性質一: 列印的時候很舒適，不會全部印出來

```r
# install.packages("tidyverse")
library("tidyverse")
```

```
## Warning: package 'tidyverse' was built under R version 4.0.2
```

```
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
```

```
## ✓ ggplot2 3.3.3     ✓ purrr   0.3.4
## ✓ tibble  3.0.6     ✓ dplyr   1.0.4
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.1
```

```
## Warning: package 'ggplot2' was built under R version 4.0.2
```

```
## Warning: package 'tibble' was built under R version 4.0.2
```

```
## Warning: package 'tidyr' was built under R version 4.0.2
```

```
## Warning: package 'readr' was built under R version 4.0.2
```

```
## Warning: package 'purrr' was built under R version 4.0.2
```

```
## Warning: package 'dplyr' was built under R version 4.0.2
```

```
## Warning: package 'stringr' was built under R version 4.0.2
```

```
## Warning: package 'forcats' was built under R version 4.0.2
```

```
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
```

```r
# iris
as_tibble(iris)
```

```
## # A tibble: 150 x 5
##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##           <dbl>       <dbl>        <dbl>       <dbl> <fct>  
##  1          5.1         3.5          1.4         0.2 setosa 
##  2          4.9         3            1.4         0.2 setosa 
##  3          4.7         3.2          1.3         0.2 setosa 
##  4          4.6         3.1          1.5         0.2 setosa 
##  5          5           3.6          1.4         0.2 setosa 
##  6          5.4         3.9          1.7         0.4 setosa 
##  7          4.6         3.4          1.4         0.3 setosa 
##  8          5           3.4          1.5         0.2 setosa 
##  9          4.4         2.9          1.4         0.2 setosa 
## 10          4.9         3.1          1.5         0.1 setosa 
## # … with 140 more rows
```

---
# tibble

- 性質二: subset 的時候不一樣

```r
df <- data.frame(a = 1:3, bc = 4:6)
tbl <- tibble(a = 1:3, bc = 4:6)
df[, "a"]
```

```
## [1] 1 2 3
```

```r
tbl[, "a"]
```

```
## # A tibble: 3 x 1
##       a
##   <int>
## 1     1
## 2     2
## 3     3
```

---
# tibble

- 性質二: subset 的時候不一樣

```r
# Accessing non-existent columns:
df$b
```

```
## [1] 4 5 6
```

```r
#> [1] 4 5 6
tbl$b
```

```
## Warning: Unknown or uninitialised column: `b`.
```

```
## NULL
```

```r
#> Warning: Unknown or uninitialised column: `b`.
#> NULL
```

---
# tibble

- 創建 tibble 跟 dataframe 一樣

```r
tibble(
  x = 1:5, 
  y = 1, 
  z = x ^ 2 + y
)
```

```
## # A tibble: 5 x 3
##       x     y     z
##   <int> <dbl> <dbl>
## 1     1     1     2
## 2     2     1     5
## 3     3     1    10
## 4     4     1    17
## 5     5     1    26
```

---
# tibble

- `View()`
- `glimse()`

```r
# View(iris)
glimpse(iris)
```

```
## Rows: 150
## Columns: 5
## $ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4…
## $ Sepal.Width  <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3…
## $ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1…
## $ Petal.Width  <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0…
## $ Species      <fct> setosa, setosa, setosa, setosa, setosa, setosa, setosa, …
```

---
# tibble

- 之後主要資料都是用 dataframe
- 推薦都把 dataframe 變成 tibble (純推薦，不換也可以)