**R-exercises**, and kindly contributed to R-bloggers)

The `Reshape 2`

package is based on differentiating between identification variables, and measurement variables. The functions of the `Reshape 2`

package then “`melt`

” datasets from wide to long format, and “`cast`

” datasets from long to wide format.

Required package:

`library(reshape2)`

Answers to the exercises are available here.

**Exercise 1**

Set a variable called “`moltenMtcars`

“, by using the `melt()`

function to format “`mtcars`

” to long format using the id variables, “`cyl`

” and “`gear`

“.

**Exercise 2**

Set a variable, “`CarSurvey`

“, using `dcast()`

to reformat “`moltenMtcars`

” to wide format, with “`cyl`

” and “`gear`

” in the first two columns. The aggregation function is “`mean`

“.

**Exercise 3**

Using the `melt()`

function, format “`airquality`

” with 1 measurement per Month/Day date. Set a variable called “`weatherSurvey`

“.

**Exercise 4**

Specify the name of “`weatherSurvey`

” column 4 as “`Condition`

“, and the name of column 5 as “`Measurement`

“, using the `melt()`

formula in Exercise 3.

**Exercise 5**

Use `dcast()`

to format “`weatherSurvey`

” from long to wide, with `Month`

and `Day`

as the first 2 columns. Set a new variable, “`airqualityEdit`

“.

**Exercise 6**

`acast()`

converts a long-format “`molten`

” data frame into a wide-format vector/matrix/array.

Set a new variable, “`AirQualityArray`

“, via using `acast()`

to re-format, “`weatherSurvey`

“, by `Day`

, `Month`

, and `Condition`

.

**Exercise 7**

Use the `acast()`

function to get the means of “`weatherSurvey`

” measurement variables by month. Also, remove not available values.

**Exercise 8**

Use the “`margins =`

” parameter of `acast()`

in order to include the means of all measurement variables in the formula from Exercise 7.

**Exercise 9**

Use the `recast()`

function to combine the `melt()`

operation from Exercise 1, and the `dcast()`

operation from Exercise 2.

**Exercise 10**

Use the `recast()`

function to combine the `melt()`

operation from Exercise 4, and the `dcast()`

operation from Exercise 5. Return the first 5 rows.

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