STA 326 2.0 Programming and Data Analysis with R

# STA 326 2.0 Programming and Data Analysis with R
## Lesson 2: Data Structures
### Dr Thiyanga Talagala
### 2020-02-18

---

background-image: url('dataStructures.png')
background-position: center
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# Recap

.footer-note[.tiny[.green[Image Credit: ][venus.ifca.unican.es](http://venus.ifca.unican.es/Rintro/dataStruct.html)]]

---

background-image: url('vectors.jpg')
background-position: center
background-size: contain

# Recap

.footer-note[.tiny[.green[Image Credit: ][venus.ifca.unican.es](http://venus.ifca.unican.es/Rintro/dataStruct.html)]]

---
# Recap

```
 [1] 1 2 3 4 5 5 4 3 2 1
```

- Method 1

```r
c(1, 2, 3, 4, 5, 5, 4, 3, 2, 1)

```
--

- Method 2

```r
c(1:5, 5:1)
```

- Name elements

```r
a <- c(1:5, 5:1)
a
```

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

```r
names(a) <- c("a1", "a2", "a3", "a4", "a5", "b1", "b2", "b3", "b4", "b5")
a
```

```
## a1 a2 a3 a4 a5 b1 b2 b3 b4 b5 
##  1  2  3  4  5  5  4  3  2  1
```

---

## Operations between vectors

```r
a 
```

```
a1 a2 a3 a4 a5 b1 b2 b3 b4 b5 
 1  2  3  4  5  5  4  3  2  1 
```

```r
a * c(10, 100)
```

```
 a1  a2  a3  a4  a5  b1  b2  b3  b4  b5 
 10 200  30 400  50 500  40 300  20 100 
```

- Types of vectors

---
## Vectors: Subsetting

```r
myvec <- 1:20; myvec
```

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

```r
myvec[1]
```

```
[1] 1
```

```r
myvec[5:10]
```

```
[1]  5  6  7  8  9 10
```

```r
myvec[-1]
```

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

```r
myvec[myvec > 3]
```

```
 [1]  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
```

---

## Vectors: Subsetting

Extract elements present in vector `a`

```r
a
```

```
a1 a2 a3 a4 a5 b1 b2 b3 b4 b5 
 1  2  3  4  5  5  4  3  2  1 
```

```r
myvec
```

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

```r
myvec %in% a
```

```
 [1]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
```

```r
myvec[myvec %in% a]
```

```
[1] 1 2 3 4 5
```
--

```r
b <- 100:105
myvec[myvec %in% b]
```

```
## integer(0)
```

---
class: duke-orange, center, middle

# Your turn
---

1. Generate a sequence using the code `seq(from=1, to=10, by=1)`.

2. What other ways can you generate the same sequence?

3. Using the function `rep` , create the below sequence 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4

---
background-image: url('horse.png')
background-position: center
background-size: cover

---

# 2. Matrices
---

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background-size: contain

---

Matrix is a 2-dimentional and a homogeneous data structure

**Syntax to create a matrix**

```r
matrix_name <- matrix(vector_of_elements, 
                      nrow=number_of_rows,
                      ncol=number_of_columns,
                      byrow=logical_value, # If byrow=TRUE, then the matrix is filled in by row.
                      dimnames=list(rnames, cnames)) # To assign row names and columns
```

**Example**

```r
matrix(1:6, nrow=2, ncol=3)
```

```
     [,1] [,2] [,3]
[1,]    1    3    5
[2,]    2    4    6
```

```r
matrix(1:6, nrow=2)
```

```
     [,1] [,2] [,3]
[1,]    1    3    5
[2,]    2    4    6
```

```r
matrix(1:6, ncol=3)
```

```
     [,1] [,2] [,3]
[1,]    1    3    5
[2,]    2    4    6
```

---

# Matrix fill by rows/ columns

```r
values <- c(10, 20, 30, 40)
matrix1 <- matrix(values, nrow=2) # Matrix filled by columns (default option)
matrix1
```

```
     [,1] [,2]
[1,]   10   30
[2,]   20   40
```

```r
matrix2 <- matrix(values, nrow=2, byrow=TRUE) # Matrix filled by rows
matrix2
```

```
     [,1] [,2]
[1,]   10   20
[2,]   30   40
```

- byrow=TRUE: matrix is filled in by row

- byrow=FALSE: matrix is filled in by column

- Default is by column

---

# Naming matrix rows and columns

```r
rnames <- c("R1", "R2")
cnames <- c("C1", "C2")
matrix_with_names <- matrix(values, nrow=2, dimnames=list(rnames, cnames))
matrix_with_names
```

```
   C1 C2
R1 10 30
R2 20 40
```

---
# Matrix subscript

`matraix_name[i, ]` gives the ith row of a matrix

```r
matrix1[1, ]
```

```
[1] 10 30
```

`matraix_name[, j]` gives the jth column of a matrix

```r
matrix1[, 2]
```

```
[1] 30 40
```

`matraix_name[i, j]` gives the ith row and jth column element

```r
matrix1[1, 2]
```

```
[1] 30
```

```r
matrix1[1, c(1, 2)] 
```

```
[1] 10 30
```

---

# `cbind` and `rbind`

Matrices can be created by column-binding and row-binding with `cbind()` and `rbind()`

```r
x <- 1:3
y <- c(10, 100, 1000)
cbind(x, y) # binds matrices horizontally
```

```
     x    y
[1,] 1   10
[2,] 2  100
[3,] 3 1000
```

```r
rbind(x, y) #binds matrices vertically
```

```
  [,1] [,2] [,3]
x    1    2    3
y   10  100 1000
```

---
# Matrix operations

Transpose

```r
t(x)
```

```
     [,1] [,2] [,3]
[1,]    1    2    3
```

Matrix multiplication

```r
y <- matrix(seq(10, 60, by=10), nrow=3)
z <- x %*% y
z
```

```
     [,1] [,2]
[1,]  140  320
```

Find x in: m*x=n

```r
solve(m, n)
```
---
class: duke-orange, center, middle

# Your turn

---

background-image: url('china.png')
background-position: center
background-size: contain

---

background-image: url('china.png')
background-position: center
background-size: contain

.content-box-yellow[Calculate distance between Hubei and surrounding cities on map  using the [distance calculator](https://www.distancecalculator.net/) and enter it to a vector. Name the vector as distance_to_hubei.

We want to look at the relationship between the number of confirmed cases and distance to other cities/regions/province from Hubei.
]

Help:
`$$\hat{\beta}=(X^TX)^{-1}X^TY$$`

---

background-image: url('slr.png')
background-position: center
background-size: contain

---

```r
count_corona <- c(29631, 1151, 1104, 1073, 879, 830, 771,
           492, 468, 459, 405, 337, 331, 295, 261, 218, 213, 210, 141, 136, 119, 109, 107, 91, 85, 80, 58, 49, 49, 36, 18, 18, 10, 1)

distance_to_hubei <- c(0, 929.05, 807, 7680, 426, 482, 564, 679, 571, 837, 896, 1044, 2338, 872, 827, 992, 609,
                       923, 1215, 1354, 722, 725, 1424, 993, 1252, 1886, 1581, 896, 2709, 1005, 1560, 1132, 1005, 1133)
#plot(distance_to_hubei, count_corona)

count_corona <- count_corona[c(-1, -4)]
distance_to_hubei <- distance_to_hubei[c(-1, -4)]
#plot(distance_to_hubei, count_corona)
```

---

```r
count_corona <- c(29631, 1151, 1104, 1073, 879, 830, 771, 492, 468, 459, 405, 337, 331, 295, 261, 218, 213, 210, 141, 136, 119, 109, 107, 91, 85, 80, 58, 49, 49, 36, 18, 18, 10, 1)

distance_to_hubei <- c(0, 929.05, 807, 7680, 426, 482, 564, 679, 571, 837, 896, 1044, 2338, 872, 827, 992, 609,
923, 1215, 1354, 722, 725, 1424, 993, 1252, 1886, 1581, 896, 2709, 1005, 1560, 1132, 1005, 1133)
plot(distance_to_hubei, count_corona)
```

![](l2_files/figure-html/unnamed-chunk-24-1.png)

---

```r
count_corona <- count_corona[c(-1, -4)]
distance_to_hubei <- distance_to_hubei[c(-1, -4)]
plot(distance_to_hubei, count_corona)
```

![](l2_files/figure-html/unnamed-chunk-25-1.png)
---

# 3. Arrays

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---

## Array

- data structures for storing **higher** dimensional data.

- a **homogeneous** data structure.

- a special case of the array is the matrix.

```r
array(vector, dimensions, dimnames) #dimnames-as a list
```

```r
a <-  array(c(10, 20, 30, 40, 50, 60), c(1, 2, 3))
a
```

```
, , 1

[,1] [,2]
[1,]   10   20

, , 2

[,1] [,2]
[1,]   30   40

, , 3

[,1] [,2]
[1,]   50   60
```

---

background-image: url('array.png')
background-position: center
background-size: contain

## Array
---

## Subsetting arrays

```r
a
```

```
, , 1

[,1] [,2]
[1,]   10   20

, , 2

[,1] [,2]
[1,]   30   40

, , 3

[,1] [,2]
[1,]   50   60
```

```r
a[, , 1] # Extract first entry
```

```
[1] 10 20
```

```r
a[1, ,] # All rows in each entry
```

```
     [,1] [,2] [,3]
[1,]   10   30   50
[2,]   20   40   60
```

---
class: duke-orange, center, middle

# Your turn

---

1. Create the following matrix using the `array` function

```r
matrix(1:20, ncol=5)
```

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

---

## Array with dimnames

```r
dim1 <- c("A1", "A2"); dim2 <- c("B1", "B2", "B3"); dim3 <- c("c1", "c2", "c3", "c4")
z <- array(1:24, c(2, 3, 4), dimnames = list(dim1, dim2, dim3))
z
```

```
, , c1

B1 B2 B3
A1  1  3  5
A2  2  4  6

, , c2

B1 B2 B3
A1  7  9 11
A2  8 10 12

, , c3

B1 B2 B3
A1 13 15 17
A2 14 16 18

, , c4

B1 B2 B3
A1 19 21 23
A2 20 22 24
```

---

# 4. Data Frames

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---
# Data frames

- Rectangular arrangement of data with rows corresponding to observational units and columns corresponding to variables.

- More general than a matrix in that different columns can contain different modes of data.

- It’s similar to the datasets you’d typically see in SPSS and MINITAB.

- Data frames are the most common data structure you’ll deal with in R.

![Figure 1: Components of a dataframe.](tidy-1.png)

.footer-note[.tiny[.green[Image Credit: ][Hadley Wickham and Garrett Grolemund](https://r4ds.had.co.nz/tidy-data.html)]]

---
# Create a data frame

**Syntax**

```r
name_of_the_dataframe <- data.frame(
                          var1_name=vector of values of the first variable,
                          var2_names=vector of values of the second variable)

```

**Example**

```r
corona <- data.frame(ID=c("C001", "C002", "C003", "C004"),
                     Location=c("Beijing", "Wuhan", "Shanghai", "Beijing"),
                     Test_Results=c(FALSE, TRUE, FALSE, FALSE))
corona
```

```
    ID Location Test_Results
1 C001  Beijing        FALSE
2 C002    Wuhan         TRUE
3 C003 Shanghai        FALSE
4 C004  Beijing        FALSE
```

To check if it is a dataframe

```r
is.data.frame(corona)
```

```
[1] TRUE
```

---

# Some useful functions with dataframes

```r
colnames(corona)
```

```
[1] "ID"           "Location"     "Test_Results"
```

```r
length(corona)
```

```
[1] 3
```

```r
dim(corona)
```

```
[1] 4 3
```

```r
nrow(corona)
```

```
[1] 4
```

```r
ncol(corona)
```

```
[1] 3
```
---

# Some useful functions with dataframes (cont.)

```r
summary(corona)
```

```
      ID              Location         Test_Results   
 Length:4           Length:4           Mode :logical  
 Class :character   Class :character   FALSE:3        
 Mode  :character   Mode  :character   TRUE :1        
```

```r
str(corona)
```

```
'data.frame':	4 obs. of  3 variables:
 $ ID          : chr  "C001" "C002" "C003" "C004"
 $ Location    : chr  "Beijing" "Wuhan" "Shanghai" "Beijing"
 $ Test_Results: logi  FALSE TRUE FALSE FALSE
```

---

## Convert a matrix to a dataframe

```r
mat <- matrix(1:16, ncol=4)
mat
```

```
     [,1] [,2] [,3] [,4]
[1,]    1    5    9   13
[2,]    2    6   10   14
[3,]    3    7   11   15
[4,]    4    8   12   16
```

```r
mat_df <- as.data.frame(mat)
mat_df
```

```
  V1 V2 V3 V4
1  1  5  9 13
2  2  6 10 14
3  3  7 11 15
4  4  8 12 16
```

---

## Subsetting data frames

**Select rows**

```r
head(mat_df) # default it shows 5 rows
```

```
  V1 V2 V3 V4
1  1  5  9 13
2  2  6 10 14
3  3  7 11 15
4  4  8 12 16
```

```r
head(mat_df, 3) # To extract only the first three rows 
```

```
  V1 V2 V3 V4
1  1  5  9 13
2  2  6 10 14
3  3  7 11 15
```

```r
tail(mat_df)
```

```
  V1 V2 V3 V4
1  1  5  9 13
2  2  6 10 14
3  3  7 11 15
4  4  8 12 16
```

---

## Subsetting data frames

```r
mat_df
```

```
  V1 V2 V3 V4
1  1  5  9 13
2  2  6 10 14
3  3  7 11 15
4  4  8 12 16
```

**To select some specific rows**

```r
mat_df[4, ]
```

```
  V1 V2 V3 V4
4  4  8 12 16
```

```r
index <- c(1, 3)
mat_df[index, ]
```

```
  V1 V2 V3 V4
1  1  5  9 13
3  3  7 11 15
```

---
## Subsetting data frames

```r
mat_df
```

```
  V1 V2 V3 V4
1  1  5  9 13
2  2  6 10 14
3  3  7 11 15
4  4  8 12 16
```

**Select columns**

1. Select column(s) by variable names

```r
mat_df$V1 # Method 1
```

```
[1] 1 2 3 4
```

```r
mat_df[, "V1"] # Method 2
```

```
[1] 1 2 3 4
```

2. Select column(s) by index

```r
mat_df[, 2]
```

```
[1] 5 6 7 8
```

---

# Your turn
---
background-image: url('datafr.png')
background-position: center
background-size: contain

---
background-image: url('iris.jpg')
background-position: center
background-size: cover

---
# Built-in dataframes

```r
data(iris)
```

---
background-image: url('helpiris.png')
background-position: center
background-size: contain

---
# `iris` dataset

```r
head(iris)
```

```
  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
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa
```

<img src="iris_flower_dataset.png" width="800">
---
# `iris` dataset

```r
head(iris)
```

<img src="images.jpeg" width="400"><img src="iris_petal_sepal.png" height="300">
---
# `iris` dataset

```r
head(iris)
```

- Introduced by Sir Ronald A. Fisher, 1936

---

Use	the	R	dataset	“iris”	to	answer	the	following	questions:

1. How many rows and columns does iris have?

2. Select the first 4 rows.

3. Select the last 6 rows.

4. Select rows 10 to 20, with all columns in the iris dataset.

5. Select rows 10 to 20 with only the Species, Petal.Width and Petal.Length.

6. Create a single vector (a new object) called ‘width’ that is the Sepal.Width column of iris.

7.  What are the column names and data types of the different columns in iris?

8. How many rows in the iris dataset have `Petal.Length` larger than 5 and `Sepal.Width` smaller than 3?

<div class="countdown" id="timer_5eeca24f" style="right:0;bottom:0;" data-warnwhen="0">
<code class="countdown-time"><span class="countdown-digits minutes">05</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code>
</div>
---

# 5. List

---

background-image: url('dataStructures.png')
background-position: center
background-size: contain

---

# List

- Lists are heterogeneous

**Syntax**

```r
list_name <- list(entry1, entry2, entry3, ...)

```

**Example**

```r
first_list <-list(1:3, matrix(1:6, nrow=2), iris)
first_list
```

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

[[2]]
     [,1] [,2] [,3]
[1,]    1    3    5
[2,]    2    4    6

[[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
4            4.6         3.1          1.5         0.2     setosa
5            5.0         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            5.0         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
11           5.4         3.7          1.5         0.2     setosa
12           4.8         3.4          1.6         0.2     setosa
13           4.8         3.0          1.4         0.1     setosa
14           4.3         3.0          1.1         0.1     setosa
15           5.8         4.0          1.2         0.2     setosa
16           5.7         4.4          1.5         0.4     setosa
17           5.4         3.9          1.3         0.4     setosa
18           5.1         3.5          1.4         0.3     setosa
19           5.7         3.8          1.7         0.3     setosa
20           5.1         3.8          1.5         0.3     setosa
21           5.4         3.4          1.7         0.2     setosa
22           5.1         3.7          1.5         0.4     setosa
23           4.6         3.6          1.0         0.2     setosa
24           5.1         3.3          1.7         0.5     setosa
25           4.8         3.4          1.9         0.2     setosa
26           5.0         3.0          1.6         0.2     setosa
27           5.0         3.4          1.6         0.4     setosa
28           5.2         3.5          1.5         0.2     setosa
29           5.2         3.4          1.4         0.2     setosa
30           4.7         3.2          1.6         0.2     setosa
31           4.8         3.1          1.6         0.2     setosa
32           5.4         3.4          1.5         0.4     setosa
33           5.2         4.1          1.5         0.1     setosa
34           5.5         4.2          1.4         0.2     setosa
35           4.9         3.1          1.5         0.2     setosa
36           5.0         3.2          1.2         0.2     setosa
37           5.5         3.5          1.3         0.2     setosa
38           4.9         3.6          1.4         0.1     setosa
39           4.4         3.0          1.3         0.2     setosa
40           5.1         3.4          1.5         0.2     setosa
41           5.0         3.5          1.3         0.3     setosa
42           4.5         2.3          1.3         0.3     setosa
43           4.4         3.2          1.3         0.2     setosa
44           5.0         3.5          1.6         0.6     setosa
45           5.1         3.8          1.9         0.4     setosa
46           4.8         3.0          1.4         0.3     setosa
47           5.1         3.8          1.6         0.2     setosa
48           4.6         3.2          1.4         0.2     setosa
49           5.3         3.7          1.5         0.2     setosa
50           5.0         3.3          1.4         0.2     setosa
51           7.0         3.2          4.7         1.4 versicolor
52           6.4         3.2          4.5         1.5 versicolor
53           6.9         3.1          4.9         1.5 versicolor
54           5.5         2.3          4.0         1.3 versicolor
55           6.5         2.8          4.6         1.5 versicolor
56           5.7         2.8          4.5         1.3 versicolor
57           6.3         3.3          4.7         1.6 versicolor
58           4.9         2.4          3.3         1.0 versicolor
59           6.6         2.9          4.6         1.3 versicolor
60           5.2         2.7          3.9         1.4 versicolor
61           5.0         2.0          3.5         1.0 versicolor
62           5.9         3.0          4.2         1.5 versicolor
63           6.0         2.2          4.0         1.0 versicolor
64           6.1         2.9          4.7         1.4 versicolor
65           5.6         2.9          3.6         1.3 versicolor
66           6.7         3.1          4.4         1.4 versicolor
67           5.6         3.0          4.5         1.5 versicolor
68           5.8         2.7          4.1         1.0 versicolor
69           6.2         2.2          4.5         1.5 versicolor
70           5.6         2.5          3.9         1.1 versicolor
71           5.9         3.2          4.8         1.8 versicolor
72           6.1         2.8          4.0         1.3 versicolor
73           6.3         2.5          4.9         1.5 versicolor
74           6.1         2.8          4.7         1.2 versicolor
75           6.4         2.9          4.3         1.3 versicolor
76           6.6         3.0          4.4         1.4 versicolor
77           6.8         2.8          4.8         1.4 versicolor
78           6.7         3.0          5.0         1.7 versicolor
79           6.0         2.9          4.5         1.5 versicolor
80           5.7         2.6          3.5         1.0 versicolor
81           5.5         2.4          3.8         1.1 versicolor
82           5.5         2.4          3.7         1.0 versicolor
83           5.8         2.7          3.9         1.2 versicolor
84           6.0         2.7          5.1         1.6 versicolor
85           5.4         3.0          4.5         1.5 versicolor
86           6.0         3.4          4.5         1.6 versicolor
87           6.7         3.1          4.7         1.5 versicolor
88           6.3         2.3          4.4         1.3 versicolor
89           5.6         3.0          4.1         1.3 versicolor
90           5.5         2.5          4.0         1.3 versicolor
91           5.5         2.6          4.4         1.2 versicolor
92           6.1         3.0          4.6         1.4 versicolor
93           5.8         2.6          4.0         1.2 versicolor
94           5.0         2.3          3.3         1.0 versicolor
95           5.6         2.7          4.2         1.3 versicolor
96           5.7         3.0          4.2         1.2 versicolor
97           5.7         2.9          4.2         1.3 versicolor
98           6.2         2.9          4.3         1.3 versicolor
99           5.1         2.5          3.0         1.1 versicolor
100          5.7         2.8          4.1         1.3 versicolor
101          6.3         3.3          6.0         2.5  virginica
102          5.8         2.7          5.1         1.9  virginica
103          7.1         3.0          5.9         2.1  virginica
104          6.3         2.9          5.6         1.8  virginica
105          6.5         3.0          5.8         2.2  virginica
106          7.6         3.0          6.6         2.1  virginica
107          4.9         2.5          4.5         1.7  virginica
108          7.3         2.9          6.3         1.8  virginica
109          6.7         2.5          5.8         1.8  virginica
110          7.2         3.6          6.1         2.5  virginica
111          6.5         3.2          5.1         2.0  virginica
112          6.4         2.7          5.3         1.9  virginica
113          6.8         3.0          5.5         2.1  virginica
114          5.7         2.5          5.0         2.0  virginica
115          5.8         2.8          5.1         2.4  virginica
116          6.4         3.2          5.3         2.3  virginica
117          6.5         3.0          5.5         1.8  virginica
118          7.7         3.8          6.7         2.2  virginica
119          7.7         2.6          6.9         2.3  virginica
120          6.0         2.2          5.0         1.5  virginica
121          6.9         3.2          5.7         2.3  virginica
122          5.6         2.8          4.9         2.0  virginica
123          7.7         2.8          6.7         2.0  virginica
124          6.3         2.7          4.9         1.8  virginica
125          6.7         3.3          5.7         2.1  virginica
126          7.2         3.2          6.0         1.8  virginica
127          6.2         2.8          4.8         1.8  virginica
128          6.1         3.0          4.9         1.8  virginica
129          6.4         2.8          5.6         2.1  virginica
130          7.2         3.0          5.8         1.6  virginica
131          7.4         2.8          6.1         1.9  virginica
132          7.9         3.8          6.4         2.0  virginica
133          6.4         2.8          5.6         2.2  virginica
134          6.3         2.8          5.1         1.5  virginica
135          6.1         2.6          5.6         1.4  virginica
136          7.7         3.0          6.1         2.3  virginica
137          6.3         3.4          5.6         2.4  virginica
138          6.4         3.1          5.5         1.8  virginica
139          6.0         3.0          4.8         1.8  virginica
140          6.9         3.1          5.4         2.1  virginica
141          6.7         3.1          5.6         2.4  virginica
142          6.9         3.1          5.1         2.3  virginica
143          5.8         2.7          5.1         1.9  virginica
144          6.8         3.2          5.9         2.3  virginica
145          6.7         3.3          5.7         2.5  virginica
146          6.7         3.0          5.2         2.3  virginica
147          6.3         2.5          5.0         1.9  virginica
148          6.5         3.0          5.2         2.0  virginica
149          6.2         3.4          5.4         2.3  virginica
150          5.9         3.0          5.1         1.8  virginica
```

---

## Structure of a list

```r
str(first_list)
```

```
List of 3
 $ : int [1:3] 1 2 3
 $ : int [1:2, 1:3] 1 2 3 4 5 6
 $ :'data.frame':	150 obs. of  5 variables:
  ..$ Sepal.Length: num [1:150] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
  ..$ Sepal.Width : num [1:150] 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
  ..$ Petal.Length: num [1:150] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
  ..$ Petal.Width : num [1:150] 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
  ..$ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
```

--
## Extract elements

```r
first_list[[1]]
```

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

```r
first_list[[3]]$Species
```

```
  [1] setosa     setosa     setosa     setosa     setosa     setosa    
  [7] setosa     setosa     setosa     setosa     setosa     setosa    
 [13] setosa     setosa     setosa     setosa     setosa     setosa    
 [19] setosa     setosa     setosa     setosa     setosa     setosa    
 [25] setosa     setosa     setosa     setosa     setosa     setosa    
 [31] setosa     setosa     setosa     setosa     setosa     setosa    
 [37] setosa     setosa     setosa     setosa     setosa     setosa    
 [43] setosa     setosa     setosa     setosa     setosa     setosa    
 [49] setosa     setosa     versicolor versicolor versicolor versicolor
 [55] versicolor versicolor versicolor versicolor versicolor versicolor
 [61] versicolor versicolor versicolor versicolor versicolor versicolor
 [67] versicolor versicolor versicolor versicolor versicolor versicolor
 [73] versicolor versicolor versicolor versicolor versicolor versicolor
 [79] versicolor versicolor versicolor versicolor versicolor versicolor
 [85] versicolor versicolor versicolor versicolor versicolor versicolor
 [91] versicolor versicolor versicolor versicolor versicolor versicolor
 [97] versicolor versicolor versicolor versicolor virginica  virginica 
[103] virginica  virginica  virginica  virginica  virginica  virginica 
[109] virginica  virginica  virginica  virginica  virginica  virginica 
[115] virginica  virginica  virginica  virginica  virginica  virginica 
[121] virginica  virginica  virginica  virginica  virginica  virginica 
[127] virginica  virginica  virginica  virginica  virginica  virginica 
[133] virginica  virginica  virginica  virginica  virginica  virginica 
[139] virginica  virginica  virginica  virginica  virginica  virginica 
[145] virginica  virginica  virginica  virginica  virginica  virginica 
Levels: setosa versicolor virginica
```

---

# Name entries in a list

```r
first_list_with_names <-list(a=1:3, b=matrix(1:6, nrow=2), c=iris)
first_list_with_names
```

```
$a
[1] 1 2 3

$b
     [,1] [,2] [,3]
[1,]    1    3    5
[2,]    2    4    6

$c
    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
4            4.6         3.1          1.5         0.2     setosa
5            5.0         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            5.0         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
11           5.4         3.7          1.5         0.2     setosa
12           4.8         3.4          1.6         0.2     setosa
13           4.8         3.0          1.4         0.1     setosa
14           4.3         3.0          1.1         0.1     setosa
15           5.8         4.0          1.2         0.2     setosa
16           5.7         4.4          1.5         0.4     setosa
17           5.4         3.9          1.3         0.4     setosa
18           5.1         3.5          1.4         0.3     setosa
19           5.7         3.8          1.7         0.3     setosa
20           5.1         3.8          1.5         0.3     setosa
21           5.4         3.4          1.7         0.2     setosa
22           5.1         3.7          1.5         0.4     setosa
23           4.6         3.6          1.0         0.2     setosa
24           5.1         3.3          1.7         0.5     setosa
25           4.8         3.4          1.9         0.2     setosa
26           5.0         3.0          1.6         0.2     setosa
27           5.0         3.4          1.6         0.4     setosa
28           5.2         3.5          1.5         0.2     setosa
29           5.2         3.4          1.4         0.2     setosa
30           4.7         3.2          1.6         0.2     setosa
31           4.8         3.1          1.6         0.2     setosa
32           5.4         3.4          1.5         0.4     setosa
33           5.2         4.1          1.5         0.1     setosa
34           5.5         4.2          1.4         0.2     setosa
35           4.9         3.1          1.5         0.2     setosa
36           5.0         3.2          1.2         0.2     setosa
37           5.5         3.5          1.3         0.2     setosa
38           4.9         3.6          1.4         0.1     setosa
39           4.4         3.0          1.3         0.2     setosa
40           5.1         3.4          1.5         0.2     setosa
41           5.0         3.5          1.3         0.3     setosa
42           4.5         2.3          1.3         0.3     setosa
43           4.4         3.2          1.3         0.2     setosa
44           5.0         3.5          1.6         0.6     setosa
45           5.1         3.8          1.9         0.4     setosa
46           4.8         3.0          1.4         0.3     setosa
47           5.1         3.8          1.6         0.2     setosa
48           4.6         3.2          1.4         0.2     setosa
49           5.3         3.7          1.5         0.2     setosa
50           5.0         3.3          1.4         0.2     setosa
51           7.0         3.2          4.7         1.4 versicolor
52           6.4         3.2          4.5         1.5 versicolor
53           6.9         3.1          4.9         1.5 versicolor
54           5.5         2.3          4.0         1.3 versicolor
55           6.5         2.8          4.6         1.5 versicolor
56           5.7         2.8          4.5         1.3 versicolor
57           6.3         3.3          4.7         1.6 versicolor
58           4.9         2.4          3.3         1.0 versicolor
59           6.6         2.9          4.6         1.3 versicolor
60           5.2         2.7          3.9         1.4 versicolor
61           5.0         2.0          3.5         1.0 versicolor
62           5.9         3.0          4.2         1.5 versicolor
63           6.0         2.2          4.0         1.0 versicolor
64           6.1         2.9          4.7         1.4 versicolor
65           5.6         2.9          3.6         1.3 versicolor
66           6.7         3.1          4.4         1.4 versicolor
67           5.6         3.0          4.5         1.5 versicolor
68           5.8         2.7          4.1         1.0 versicolor
69           6.2         2.2          4.5         1.5 versicolor
70           5.6         2.5          3.9         1.1 versicolor
71           5.9         3.2          4.8         1.8 versicolor
72           6.1         2.8          4.0         1.3 versicolor
73           6.3         2.5          4.9         1.5 versicolor
74           6.1         2.8          4.7         1.2 versicolor
75           6.4         2.9          4.3         1.3 versicolor
76           6.6         3.0          4.4         1.4 versicolor
77           6.8         2.8          4.8         1.4 versicolor
78           6.7         3.0          5.0         1.7 versicolor
79           6.0         2.9          4.5         1.5 versicolor
80           5.7         2.6          3.5         1.0 versicolor
81           5.5         2.4          3.8         1.1 versicolor
82           5.5         2.4          3.7         1.0 versicolor
83           5.8         2.7          3.9         1.2 versicolor
84           6.0         2.7          5.1         1.6 versicolor
85           5.4         3.0          4.5         1.5 versicolor
86           6.0         3.4          4.5         1.6 versicolor
87           6.7         3.1          4.7         1.5 versicolor
88           6.3         2.3          4.4         1.3 versicolor
89           5.6         3.0          4.1         1.3 versicolor
90           5.5         2.5          4.0         1.3 versicolor
91           5.5         2.6          4.4         1.2 versicolor
92           6.1         3.0          4.6         1.4 versicolor
93           5.8         2.6          4.0         1.2 versicolor
94           5.0         2.3          3.3         1.0 versicolor
95           5.6         2.7          4.2         1.3 versicolor
96           5.7         3.0          4.2         1.2 versicolor
97           5.7         2.9          4.2         1.3 versicolor
98           6.2         2.9          4.3         1.3 versicolor
99           5.1         2.5          3.0         1.1 versicolor
100          5.7         2.8          4.1         1.3 versicolor
101          6.3         3.3          6.0         2.5  virginica
102          5.8         2.7          5.1         1.9  virginica
103          7.1         3.0          5.9         2.1  virginica
104          6.3         2.9          5.6         1.8  virginica
105          6.5         3.0          5.8         2.2  virginica
106          7.6         3.0          6.6         2.1  virginica
107          4.9         2.5          4.5         1.7  virginica
108          7.3         2.9          6.3         1.8  virginica
109          6.7         2.5          5.8         1.8  virginica
110          7.2         3.6          6.1         2.5  virginica
111          6.5         3.2          5.1         2.0  virginica
112          6.4         2.7          5.3         1.9  virginica
113          6.8         3.0          5.5         2.1  virginica
114          5.7         2.5          5.0         2.0  virginica
115          5.8         2.8          5.1         2.4  virginica
116          6.4         3.2          5.3         2.3  virginica
117          6.5         3.0          5.5         1.8  virginica
118          7.7         3.8          6.7         2.2  virginica
119          7.7         2.6          6.9         2.3  virginica
120          6.0         2.2          5.0         1.5  virginica
121          6.9         3.2          5.7         2.3  virginica
122          5.6         2.8          4.9         2.0  virginica
123          7.7         2.8          6.7         2.0  virginica
124          6.3         2.7          4.9         1.8  virginica
125          6.7         3.3          5.7         2.1  virginica
126          7.2         3.2          6.0         1.8  virginica
127          6.2         2.8          4.8         1.8  virginica
128          6.1         3.0          4.9         1.8  virginica
129          6.4         2.8          5.6         2.1  virginica
130          7.2         3.0          5.8         1.6  virginica
131          7.4         2.8          6.1         1.9  virginica
132          7.9         3.8          6.4         2.0  virginica
133          6.4         2.8          5.6         2.2  virginica
134          6.3         2.8          5.1         1.5  virginica
135          6.1         2.6          5.6         1.4  virginica
136          7.7         3.0          6.1         2.3  virginica
137          6.3         3.4          5.6         2.4  virginica
138          6.4         3.1          5.5         1.8  virginica
139          6.0         3.0          4.8         1.8  virginica
140          6.9         3.1          5.4         2.1  virginica
141          6.7         3.1          5.6         2.4  virginica
142          6.9         3.1          5.1         2.3  virginica
143          5.8         2.7          5.1         1.9  virginica
144          6.8         3.2          5.9         2.3  virginica
145          6.7         3.3          5.7         2.5  virginica
146          6.7         3.0          5.2         2.3  virginica
147          6.3         2.5          5.0         1.9  virginica
148          6.5         3.0          5.2         2.0  virginica
149          6.2         3.4          5.4         2.3  virginica
150          5.9         3.0          5.1         1.8  virginica
```
---

## Extract elements using names

```r
str(first_list_with_names)
```

```
List of 3
 $ a: int [1:3] 1 2 3
 $ b: int [1:2, 1:3] 1 2 3 4 5 6
 $ c:'data.frame':	150 obs. of  5 variables:
  ..$ Sepal.Length: num [1:150] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
  ..$ Sepal.Width : num [1:150] 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
  ..$ Petal.Length: num [1:150] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
  ..$ Petal.Width : num [1:150] 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
  ..$ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
```

```r
first_list_with_names$a
```

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

```r
first_list_with_names$c$Species
```

---

# Your turn
---

```r
c("Jan","Feb","Mar")
```

```
[1] "Jan" "Feb" "Mar"
```

```r
matrix(c(3,9,5,1,-2,8), nrow = 2)
```

```
     [,1] [,2] [,3]
[1,]    3    5   -2
[2,]    9    1    8
```

```r
list("green",12.3)
```

```
[[1]]
[1] "green"

[[2]]
[1] 12.3
```

1. Create a list containing the above vector, matrix and the list.

2. Name the elements as `first`, `second` and `third`.

---
class: center, middle

Slides available at: hellor.netlify.com