# Accessing Dataframe Objects Exercises

**R-exercises**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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The `attach()`

function alters the R environment search path by making dataframe variables into global variables. If incorrectly scripted, the `attach()`

function might create symantic errors. To prevent this possibility, `detach()`

is needed to reset the dataframe objects in the search path.

The `transform()`

function allows for transformation of dataframe objects. The `within()`

function creates a new dataframe, when modifying dataframe variables.

Answers to the exercises are available here.

**Exercise 1**

`attach()`

– Attach a set of R Objects to Search Path

Required Dataframe:

```
```

```
buildingSurvey <- data.frame(name=c("bldg1", "bldg2", "bldg3",
"bldg4", "bldg5", "bldg6"),
survey=c(1,1,1,2,2,2),
location=c(1,2,3,2,3,1),
floors=c(5, 10, 10, 11, 8, 12),
efficiency=c(51,64,70,71,80,58))
```

Use the `attach()`

function to make the variables in `"buildingSurvey"`

independently searchable. Then, use “`summary(location)`

” to create a summary of the “`floors`

” variable.

**Exercise 2**

Using the “`summary()`

” function, find the median “`efficiency`

” value of “`buildingSurvey`

“, using objects in the R environment search path.

**Exercise 3**

Once attached, in order to change the dataframe variable, use the assignment operator “`<<-`

“. For example: `variable1 <<- log(variable1)`

Use “`<<-`

” to divide the “`efficiency`

” category by `100`

.

**Exercise 4**

`detach()`

– Detach Objects from the Search Path

After detaching, modified `attach()`

dataframes are restored to their pre-`attach()`

values. and the R environment search path is restored. `detach()`

is needed to prevent symantec errors in programming.

Therefore, use the `detach()`

function to restore the search paths of the dataframe, “`buildingSurvey`

“.

**Exercise 5**

The “`transform()`

” function performs a transformation on a dataframe object.

Use `transform()`

to replace the “`efficiency`

” column’s values with the starting values divided by `100`

.

**Exercise 6**

First, re-attach the dataframe, “`buildingSurvey`

“.

Then, use `transform()`

to evaluate the log of the “`efficiency`

” variable. Set the result to the dataframe, “`efficiencyL`

“. The column names of the dataframe “`efficiencyL`

” should be “`X_data`

“, and “`efficiencyLog`

“.

**Exercise 7**

Next, use `transform()`

to round the “`efficiencyLog`

” variable of “`efficiencyL`

” to one decimal place.

**Exercise 8**

The `within()`

function creates a modified copy of a dataframe.

For this exercise, use `within()`

to append the “`buildingSurvey`

” dataframe with a variable called, “`efficiency10`

“. The new variable contains “`efficiency`

” multiplied by `10`

.

**Exercise 9**

Use the `within()`

function to set `efficiency[4]`

to “`85`

“. This will also create a copy of “`buildingSurvey`

“. Setting a new dataframe isn’t required for this exercise.

**Exercise 10**

For the final exercise, restore the R environment search path.

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