Data Structures

class: center, middle, inverse, title-slide

# Data Structures
### Haley Jeppson, Sam Tyner

---

## Data Frames

- Data Frames are the work horse of R objects

- Structured by rows and columns and can be indexed

- Each column is a variable of one type

- Column names can be used to index a variable

- Advice for naming variables applys to naming columns

- Can be specified by grouping vectors of equal length as columns

---
## Data Frame Indexing

- Elements indexed similar to a vector using `[` `]`

- `df[i,j]` will select the element in the `$i^{th}$` row and `$j^{th}$` column

- `df[ ,j]` will select the entire `$j^{th}$` column and treat it as a vector

- `df[i ,]` will select the entire `$i^{th}$` row and treat it as a vector

- Logical or integer vectors can also be used in place of i and j used to subset the row and columns

---
## Adding a new Variable to a Data Frame

- Create a new vector that is the same length as other columns

- Append new column to the data frame using the `$` operator

- The new data frame column will adopt the name of the vector

---
## Data Frame Demo

Use [Edgar Anderson's Iris Data](https://en.wikipedia.org/wiki/Iris_flower_data_set):

```r
flower <- iris
```

Select Species column (5th column):

```r
flower[,5]
```

```
##   [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
```

---
## Demo (Continued)

Select Species column with the `$` operator:

```r
flower$Species
```

---
## Demo (Continued)

```r
flower$Species == "setosa"
```

```
##   [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [12]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [23]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [34]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [45]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
##  [56] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [67] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [78] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [89] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [100] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [111] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [122] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [144] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
```

---
## Demo (Continued)

```r
flower[flower$Species=="setosa", ]
```

```
##    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
```

---
## Creating our own Data Frame

Create our own data frame using `data_frame` function

```r
library(tidyverse)

mydf <- data_frame(NUMS = 1:5, 
                   lets = letters[1:5],
                   vehicle = c("car", "boat", "car", "car", "boat"))
mydf
```

```
## # A tibble: 5 x 3
##    NUMS lets  vehicle
##   <int> <chr> <chr>  
## 1     1 a     car    
## 2     2 b     boat   
## 3     3 c     car    
## 4     4 d     car    
## 5     5 e     boat
```

---
## Renaming columns

We can use the `names` function to set that first column to lowercase:

```r
names(mydf)[1] <- "nums"
mydf
```

```
## # A tibble: 5 x 3
##    nums lets  vehicle
##   <int> <chr> <chr>  
## 1     1 a     car    
## 2     2 b     boat   
## 3     3 c     car    
## 4     4 d     car    
## 5     5 e     boat
```

---
class: inverse
## Your Turn

1. Make a data frame with column 1: `1,2,3,4,5,6` and column 2: `a,b,a,b,a,b`

2. Select only rows with value `"a"` in column 2 using logical vector

3. `mtcars` is a built-in data set like `iris`: Extract the 4th row of the `mtcars` data.

---
## Lists

- Lists are a structured collection of R objects

- R objects in a list need not be the same type

- Create lists using the `list` function

- Lists indexed using double square brackets `[[ ]]` to select an object

- Use single square brackets to select two or more list elements. e.g. `[c(2,4)]`

- For named lists, can select a list element with `$` like data frames

---
## List Example

Creating a list containing a vector and a matrix:

```r
mylist <- list(matrix(letters[1:10], nrow = 2, ncol = 5),
               seq(0, 49, by = 7))
mylist
```

```
## [[1]]
##      [,1] [,2] [,3] [,4] [,5]
## [1,] "a"  "c"  "e"  "g"  "i" 
## [2,] "b"  "d"  "f"  "h"  "j" 
## 
## [[2]]
## [1]  0  7 14 21 28 35 42 49
```

Use indexing to select the second list element:

```r
mylist[[2]]
```

```
## [1]  0  7 14 21 28 35 42 49
```

---
class: inverse
## Your Turn

1. Create a list containing a vector and a 2x3 data frame

2. Use indexing to select the data frame from your list

3. Use further indexing to select the first row from the data frame in your list

---
## Examining Objects

- `head(x)` - View top 6 rows of a data frame

- `tail(x)` - View bottom 6 rows of a data frame

- `summary(x)` - Summary statistics

- `str(x)` - View structure of object

- `dim(x)` - View dimensions of object

- `length(x)` - Returns the length of a vector

---
## Examining Objects Example

We can examine the first two values of an object by passing the `n` parameter to the `head` function:

```r
head(iris, n = 2)
```

```
##   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
```

What's its structure?

```r
str(iris)
```

```
## 'data.frame':	150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  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 ...
```

---
class: inverse
## Your Turn

1. View the top 8 rows of mtcars data

2. What type of object is the mtcars data set?

3. How many rows are in iris data set? (try finding this using `dim` or indexing + length)

4. Summarize the values in each column in iris data set

---
## Working with Output from a Function

- The output from a function  can be saved as an object

- The object can be any type (data frame, vector, etc.) but is often a list object

- Items from the output can be extracted for further computing

- The output object can be examined using functions like `str(x)`

---
## Saving Output Demo

- t-test using iris data to see if petal lengths for setosa and versicolor are the same

- `t.test` function can only handle two groups, so we subset out the virginica species

```r
t.test(Petal.Length ~ Species, data = iris[iris$Species != "virginica", ])
```

```
## 
## 	Welch Two Sample t-test
## 
## data:  Petal.Length by Species
## t = -39.493, df = 62.14, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -2.939618 -2.656382
## sample estimates:
##     mean in group setosa mean in group versicolor 
##                    1.462                    4.260
```

---
## Demo (Continued)

Save the output of the t-test to an object

```r
tout <- t.test(Petal.Length ~ Species, data = iris[iris$Species != "virginica", ])
```

Let's look at the structure of this object:

```r
str(tout)
```

```
## List of 9
##  $ statistic  : Named num -39.5
##   ..- attr(*, "names")= chr "t"
##  $ parameter  : Named num 62.1
##   ..- attr(*, "names")= chr "df"
##  $ p.value    : num 9.93e-46
##  $ conf.int   : atomic [1:2] -2.94 -2.66
##   ..- attr(*, "conf.level")= num 0.95
##  $ estimate   : Named num [1:2] 1.46 4.26
##   ..- attr(*, "names")= chr [1:2] "mean in group setosa" "mean in group versicolor"
##  $ null.value : Named num 0
##   ..- attr(*, "names")= chr "difference in means"
##  $ alternative: chr "two.sided"
##  $ method     : chr "Welch Two Sample t-test"
##  $ data.name  : chr "Petal.Length by Species"
##  - attr(*, "class")= chr "htest"
```

---
## Demo: Extracting the P-Value

Since this is simply a list, we can use our regular indexing:

```r
tout$p.value
```

```
## [1] 9.934433e-46
```

```r
tout[[3]]
```

```
## [1] 9.934433e-46
```

---
## Importing Data

We often need to import in our own data rather than just using built-in datasets.

- First need to tell R where the data is saved using `setwd()`

- Data read in using R functions such as:

- `read_table()` for reading in .txt files

- `read_csv()` for reading in .csv files

- Assign the data to new R object when reading in the file

---
## Importing Data Demo

We first create a csv file (We can use a text editor or MS Excel)

Then we load it in:

```r
littledata <- read_csv("PretendData.csv")
```

---
class: inverse
## Your Turn

- Make 5 rows of data in an excel spreadsheet and save it as a *tab-delimited txt file*.

- Import this new .txt file into R with `read_table`. You may need to look at the help page for `read_table` in order to properly do this.