.hi-grey[原住民族資料分析線上訓練工作坊：R的基礎與應用]

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# .hi-grey[原住民族資料分析線上訓練工作坊：R的基礎與應用]
## .smallest.hi-slate[第二週]
### .hi-slate[Kacing 廖彥傑]
### .smallest[英國艾賽克斯大學博士候選人]

---

exclude: true

---
layout: true
# 結構 (Data Structure)
---
name:
&nbsp;

&nbsp;

##### 參考書目：R Cookbook Proven Recipes for Data Analysis, Statistics, and Graphics by J. D. Long, Paul Teetor

---
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# 向量 (Vectors)
---
name:

建立一個向量，命名(`names()`) 它。

```r
v <- c(20, 20, NA)
names(v) <- c("Kacing", "Labak", "Truku")
print(v)
```

```
#> Kacing  Labak  Truku 
#>     20     20     NA
```

提取名稱為

```r
v[["Kacing"]]
```

```
#> [1] 20
```

```r
v["Kacing"]
```

```
#> Kacing 
#>     20
```

---
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# 列表 (Lists)
---
name:

用`list()`建立list 物件

```r
a.list <- list(x = 1:6, 
               y = "a",
               z = c(TRUE, FALSE))
```

提取它

```r
a.list$x
```

```
#> [1] 1 2 3 4 5 6
```

另個方式提取它

```r
a.list[["x"]]
```

```
#> [1] 1 2 3 4 5 6
```

---

比較一下`a.list[["x"]]`與`a.list["x"]`

```r
a.list[["x"]]
```

```
#> [1] 1 2 3 4 5 6
```

```r
a.list["x"]
```

```
#> $x
#> [1] 1 2 3 4 5 6
```

用`c()`提取，挑取特定的**vector**

```r
a.list[c(1,3)]
```

```
#> $x
#> [1] 1 2 3 4 5 6
#> 
#> $z
#> [1]  TRUE FALSE
```

---
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# 矩陣 (matrix)
---
name:

`ncol = 1`的matrix

```r
one.col.matrix <- matrix(1:6, ncol = 1)
```

`ncol = 2`的matrix

```r
two.col.matrix <- matrix(1:6, ncol = 2)
```

構面

```r
dim(two.col.matrix)
```

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

---

建立4個col的matrix

```r
A <- matrix(1:20, ncol = 4)
```

--
matrix的遞遺性

```r
A + 1
```

```
#>      [,1] [,2] [,3] [,4]
#> [1,]    2    7   12   17
#> [2,]    3    8   13   18
#> [3,]    4    9   14   19
#> [4,]    5   10   15   20
#> [5,]    6   11   16   21
```

---
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# factor (類化)
---
name:

```r
my.vector <- c(1, 1, 0, 0, 0, 1)
my.factor <- factor(x = my.vector,
                    levels = c(1, 0), 
                    labels = c("treated", "control")) 
```

用`levels()`看㔌幾種類別

```r
levels(my.factor)
```

```
#> [1] "treated" "control"
```

---
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# 內建函數 (build-in function)
---
name:

用`str()`查看**a.list**內容

```r
str(a.list)
```

```
#> List of 3
#>  $ x: int [1:6] 1 2 3 4 5 6
#>  $ y: chr "a"
#>  $ z: logi [1:2] TRUE FALSE
```

用`append()`增加更多list 於之前的list

```r
another.list <- append(a.list, list(yy = 1:10, zz = letters[5:1]))
```

用`str()`查看

```r
str(another.list)
```

```
#> List of 5
#>  $ x : int [1:6] 1 2 3 4 5 6
#>  $ y : chr "a"
#>  $ z : logi [1:2] TRUE FALSE
#>  $ yy: int [1:10] 1 2 3 4 5 6 7 8 9 10
#>  $ zz: chr [1:5] "e" "d" "c" "b" ...
```

---

刪除某個list 中的vector

```r
a.list$y <- NULL
```

鳥巢list (nested list)

```r
nested.list <- list(A = list("a", "aa", "aaa"), 
                    B = list("b", "bb")) 
```

用`str()`查看

```r
str(nested.list, max.level = 1)
```

```
#> List of 2
#>  $ A:List of 3
#>  $ B:List of 2
```

---

用`is.list()`，判斷**nested.list**是否屬於list 性質

```r
is.list(nested.list)
```

```
#> [1] TRUE
```

用`unlist()`去**list**特質

```r
c.vec <- unlist(nested.list)
```

用`mode()`，判斷**nested.list**是否屬於list 性質

```r
mode(nested.list)
```

```
#> [1] "list"
```

```r
names(nested.list)
```

```
#> [1] "A" "B"
```

```r
mode(c.vec)
```

```
#> [1] "character"
```

---
layout: true
# 練習：提取鳥巢序列(nested list)
---
name:

首先，建立一個鳥巢list，嘗試提換別的數值。

```r
nested.list <- list(A = list("a", "aa", "aaa"), 
                    B = list("b", "bb")) 
```

```r
str(nested.list)
```

```
#> List of 2
#>  $ A:List of 3
#>   ..$ : chr "a"
#>   ..$ : chr "aa"
#>   ..$ : chr "aaa"
#>  $ B:List of 2
#>   ..$ : chr "b"
#>   ..$ : chr "bb"
```

用以下範例自己跑看看

```r
nested.list[1]
nested.list[[1]][2]
nested.list[[1]][[2]]
nested.list[2]
nested.list[2][[1]]
```

---
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# 資料架構 (Dataframe)
---
name:

創建一個資料框架

```r
a.df <- data.frame(x = 1:6, 
                   y = "a", 
                   z = c(TRUE, FALSE))
```

用`is.data.frame()`判斷**a.df**是否屬於**資料框架**

```r
is.data.frame(a.df) 
```

```
#> [1] TRUE
```
--

用`is.list()`判斷**a.df**是否屬於**list序列**

```r
is.list(a.df)
```

```
#> [1] TRUE
```

---

`length()`看有多少變數

```r
length(a.df)
```

```
#> [1] 3
```

`colnames()`看變數名稱

```r
colnames(a.df)
```

```
#> [1] "x" "y" "z"
```

提取變數裡的資料

```r
a.df$x
```

```
#> [1] 1 2 3 4 5 6
```
--

另一個方式，提取變數裡的資料

```r
a.df[["x"]]
```

```
#> [1] 1 2 3 4 5 6
```

**a.df["x"]** 有何不一樣，自己跑看看。

```r
a.df["x"]
```

---

選取第一個column

```r
a.df[ , 1]
```

```
#> [1] 1 2 3 4 5 6
```

```r
a.df[[1]]
```

```
#> [1] 1 2 3 4 5 6
```

選取第一個row

```r
a.df[1, ]
```

---

選取1至2 row

```r
a.df[1:2, ]
```

取帶a.df[1, 1]特為99

```r
a.df[1, 1] <- 99
```
--

把a.df[ , 1] 所有值變成-99

```r
a.df[ , 1] <- -99
```

---

用`subset()` 取中x大於3

```r
subset(a.df,  x> 3)
```

```r
subset(a.df, x > 5)[ , -3]
```

```r
subset(a.df, x > 3)$x
```

```
#> numeric(0)
```

---

用`which()`來找出特定值

```r
which(colnames(a.df) == "y")
```

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

不要選取用`-`，選區用`+`

```r
a.df[ , -which(colnames(a.df) == "y")]
```

<table class="huxtable" style="border-collapse: collapse; border: 0px; margin-bottom: 2em; margin-top: 2em; ; margin-left: auto; margin-right: auto;  " id="tab:unnamed-chunk-51">
<col><col><tr>
<th style="vertical-align: top; text-align: right; white-space: normal; border-style: solid solid solid solid; border-width: 0.4pt 0pt 0.4pt 0.4pt;    padding: 6pt 6pt 6pt 6pt; font-weight: bold;">x</th><th style="vertical-align: top; text-align: left; white-space: normal; border-style: solid solid solid solid; border-width: 0.4pt 0.4pt 0.4pt 0pt;    padding: 6pt 6pt 6pt 6pt; font-weight: bold;">z</th></tr>
<tr>
<td style="vertical-align: top; text-align: right; white-space: normal; border-style: solid solid solid solid; border-width: 0.4pt 0pt 0pt 0.4pt;    padding: 6pt 6pt 6pt 6pt; background-color: rgb(242, 242, 242); font-weight: normal;">-99</td><td style="vertical-align: top; text-align: left; white-space: normal; border-style: solid solid solid solid; border-width: 0.4pt 0.4pt 0pt 0pt;    padding: 6pt 6pt 6pt 6pt; background-color: rgb(242, 242, 242); font-weight: normal;">TRUE</td></tr>
<tr>
<td style="vertical-align: top; text-align: right; white-space: normal; border-style: solid solid solid solid; border-width: 0pt 0pt 0pt 0.4pt;    padding: 6pt 6pt 6pt 6pt; font-weight: normal;">-99</td><td style="vertical-align: top; text-align: left; white-space: normal; border-style: solid solid solid solid; border-width: 0pt 0.4pt 0pt 0pt;    padding: 6pt 6pt 6pt 6pt; font-weight: normal;">FALSE</td></tr>
<tr>
<td style="vertical-align: top; text-align: right; white-space: normal; border-style: solid solid solid solid; border-width: 0pt 0pt 0pt 0.4pt;    padding: 6pt 6pt 6pt 6pt; background-color: rgb(242, 242, 242); font-weight: normal;">-99</td><td style="vertical-align: top; text-align: left; white-space: normal; border-style: solid solid solid solid; border-width: 0pt 0.4pt 0pt 0pt;    padding: 6pt 6pt 6pt 6pt; background-color: rgb(242, 242, 242); font-weight: normal;">TRUE</td></tr>
<tr>
<td style="vertical-align: top; text-align: right; white-space: normal; border-style: solid solid solid solid; border-width: 0pt 0pt 0pt 0.4pt;    padding: 6pt 6pt 6pt 6pt; font-weight: normal;">-99</td><td style="vertical-align: top; text-align: left; white-space: normal; border-style: solid solid solid solid; border-width: 0pt 0.4pt 0pt 0pt;    padding: 6pt 6pt 6pt 6pt; font-weight: normal;">FALSE</td></tr>
<tr>
<td style="vertical-align: top; text-align: right; white-space: normal; border-style: solid solid solid solid; border-width: 0pt 0pt 0pt 0.4pt;    padding: 6pt 6pt 6pt 6pt; background-color: rgb(242, 242, 242); font-weight: normal;">-99</td><td style="vertical-align: top; text-align: left; white-space: normal; border-style: solid solid solid solid; border-width: 0pt 0.4pt 0pt 0pt;    padding: 6pt 6pt 6pt 6pt; background-color: rgb(242, 242, 242); font-weight: normal;">TRUE</td></tr>
<tr>
<td style="vertical-align: top; text-align: right; white-space: normal; border-style: solid solid solid solid; border-width: 0pt 0pt 0.4pt 0.4pt;    padding: 6pt 6pt 6pt 6pt; font-weight: normal;">-99</td><td style="vertical-align: top; text-align: left; white-space: normal; border-style: solid solid solid solid; border-width: 0pt 0.4pt 0.4pt 0pt;    padding: 6pt 6pt 6pt 6pt; font-weight: normal;">FALSE</td></tr>
</table>