Exercises R Data Structure

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.title[
# Exercises R Data Structure
]
.subtitle[
## Elements of R e R Markdown
]
.author[
### Claudio Zandonella
]

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# Operators

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#### For Coding Pinguins 🐧

1. Compute `$\frac{(45+21)^3+\frac{3}{4}}{\sqrt{32-\frac{12}{17}}}$`.
1. Compute `$\frac{\sqrt{7-\pi}}{3\ (45-34)}$`.
1. Define a proposition to evaluate if a number is even.
1. Define a proposition to evaluate if a number is between -4 and -2 or between 2 and 4.
1. Compare the returned results by `4 ^ 3 %in% c(2,3,4)` and `4 * 3 %in% c(2,3,4)`.
]

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#### For Coding Monkeys 🐒

1. Which is the difference between `==` and `all.equal()`?
1. Compare the returned results by `TRUE & NA` and `FALSE & NA`
1. Compare the returned results by `TRUE | NA` and `FALSE | NA`
1. Which is the difference between `&` (o `|`) and `&&` (o `||`)?
1. Do you know the functions `isTRUE()` and `isFALSE()`?
]

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# Vectors

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#### For Coding Pinguins 🐧

Creation (`?seq()` and `?rep()`)

1. Create `x` with all even numbers between 1 e 25 .
1. Create `y` with the first 10 multiples of 7 starting from 14.
1. Create `a` with the letters `"A"`,`"B"` e `"C"` repeated in the same order 4 times.
1. Create `b` with the letters `"A"`,`"B"` e `"C"` each one repeated 4 times.

Selection

1. From `x`, select the values 8, 12, and 16.
1. From `y`, select values smaller than 36 or grater than 54.
1. Create `c` from `a` removing all `"B"` elements.
1. Crate `d` from `b` substituting all `"B"` with `"D"`.

]

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#### For Coding Monkeys 🐒

1. What do you know about named vectors?
1. Why we have both integer and double vectors?
1. Do you know the function `which()`?
1. Which is the meaning of `mean()` and `sum()` on logical vectors.
1. Do you know the functions `any()` and `all()`?
1. Which data type is `NA`?
1. Check the length of vectors with `NA` or with `NULL` values.
]

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# Factors

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#### For Coding Pinguins 🐧

1. Create the factor `gender` as follows,
    
    ```
    ## [1] M F M F M F F F M
    ## Levels: F M
    ```
1. Rename levels in `"Males"` and `"Females"`
1. Create the factor `size` as follows,
    
    ```r
    c("Small", "Large", "Medium", 
      "X-Large", "X-Small")
    ```
1. Make `size` an ordered factor, with appropriate label order.
]

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#### For Coding Monkeys 🐒

1. How can you get an object attributes?
1. What do you know about classes?
1. Check the data type of a factor?
1. Why a factor vector is better than a string vector?
1. Create a factor `grade` as follows,
    
    ```
    ## [1] 5 4 3 3 5 4 4
    ```
    and convert back to numeric.
]

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# Matrices

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#### For Coding Pinguins 🐧

#### Creating

1. Create the matrix `A` as follows,

$$
`\begin{matrix}
2 & 34 & 12 & 7\\
46 & 93 & 27 & 99\\
23  & 38 & 7 & 04
\end{matrix}`
$$
1. Create the matrix `B`4X3 in which `"A"`,`"B"` e `"C"` are repeated at each row.

#### Selecting

1. From `A`, select the value 27.
1. From `B`, select the elements between the second and forth row, second and third column.
1. Form `A`, select only odd values.
1. From `B`, select all values different from `"C"`. 
]

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#### For Coding Monkeys 🐒

1. Create a 5x5 matrix with named columns and rows.
1. Try different selections using names. 
1. A vector is a column vector or a row vector?
1. Selecting a column returns a vector or a matrix?
1. Check the different uses of `diag()`.
1. Do you know the function `array()`?

]

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# Dataframe

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#### For Coding Pinguins 🐧

Creating

1. Create the following dataframe

```
##       Id age gender item_1 item_2 item_3
## 1 subj_1  21      F      2      0      2
## 2 subj_2  23      M      1      2      0
## 3 subj_3  19      F      1      1      1
```

Selecting

1. Select data of `"subj_3"`.
1. Select only the answers to the items.
1. Select `Id` and `gender` for subject with `1` at `item_1`.
]

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#### For Coding Monkeys 🐒

1. Check the options `stringsAsFactors` in the function `data.frame()`.
1. Transform the dataframe from the wide-format to the long-format.
1. Comment the different results obtained by `data_wide$Id`, `class(data_wide[, "Id"])`, and `class(data_wide["Id"])`

]

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# Lists

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#### For Coding Pinguins 🐧

Creating

1. Create a list with the elements form the previous exercises:
  - vector `x`
  - matrix `A`
  - dataframe

Selecting

1. Select the vector and the matrix from the list.
1. Extract the dataframe from the list.
1. Add the sting `"Hello World!"` to the list.
]

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#### For Coding Monkeys 🐒

1. Select the age value `23` from the dataframe in the list in  single command. 
1. Crete an unnamed list.
1. Add names to an unnamed list.
1. Comment the different results obtained by `length(c(list(a = 1), b = NULL))` and
`length(list(a = 1, b = NULL))`.

]

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# Thanks!