$.$ Data Inspection:

$($a$)(1$pt$)$ How many observations are there in the dataset? How many variables are

there in the dataset?

$($b$)(1\mathrm{.}5$pt$)$ For each variable, how many missing entries are there? Also, give the

line or lines of R code used to find this answer.

$($c$)(0\mathrm{.}5$pt$)$ Using the sort$()$ function, find the largest income for an individual in

the dataset. Also give the line or lines of R code to find this answer.

$2.$ Data Cleaning:

$($a$)(1$pt$)$ Remove all individuals from the dataset with $0$ income. Give the line or

lines of R code for this step.

$($b$)(1$pt$)$ Afterward, replace all missing incomes with the median of the non$-$missing

incomes. Give the line or lines of R code for this step.

$($c$)(1$pt$)$ Apply the log transformation $($either base e or base $10)$ to the incomes and

add this as a column to the NLS$2023$ data frame. Give the line or lines of R code

to perform these steps.

$($d$)(1$pt$)$ Apply the square root transformation to weights and add this as a column

to the NLS data frame. Give the line or lines of R code to perform these steps.

$($e$)(1$pt$)$ Change the fourth entry of the Urban variable to your name. Give the line

or lines of R code to perform these steps.

$($f$)(2$pt$)$ After performing steps $2($a$),($b$),($c$),($d$),$ and $($e$),$ export this cleaned

dataset as a $.$csv file called CleanNLS.csv$.$ Give the line or lines of R code to

perform this step. Also, upload this file onto Canvas.

$1$