ALL in R studio

Q1.$1.$ Display the contents of the dataframe.

# Do not display the dataframe, just its contents.

#Q2.$2.$ How many cases were sampled in the dataframe?

#Q3.$3.$ How many variables are in the dataframe?

#Q4.$4.$ Display the top and bottom six rows of the dataframe.

#Q5.($5.($Respond with code and short written response)$)$

#a.$.$ Which variables are quantitative?

#b.$.$ Which variables are categorical?

#Q6.$6.$ Create a frequency table per the state and employment sectors variables.

#a.$.$ State

#b.$.$ Employment Sector

#Q7.$7.$ Create a new variable with categories of the household variable as a factor, and assign levels and labels to its values.

# Decide on the levels and labels according to what you believe to be interpretable and meaningful for analysis.

# ($($There'" id="MathJax-Element-14-Frame" role="presentation" style="font-size: 121%; position: relative;" tabindex="0">'$\text{'}$s no right or wrong answer, just provide your rationale)$)$

#Q8.$8.$ Filter observations that have missing data on the age variable in a new dataframe.

#Q9.$9.$ How many observations were dropped, based on missing values in the age variable?

#Q10.$10.$ Create a histogram of the age variable of the filter_$\_$dat dataframe.

#Q11.$11.$ Draw a random sample of 10$10$ observations from your filter_$\_$dat dataframe in a new dataframe.

#Q12.$12.$ Create a histogram of the age variable from the random sample.

#Q13.$13.$ What do you notice is different between the random sample and dataset histograms? ($($Respond with code and short written response)$)$

#Q14.$14.$ Create a new variable of the income variable, called income3$3$ with 3$3$ levels, and assign levels and labels to its values.

#Q15.$15.$ Use the aggregate()$()$ function to compute the mean and standard deviation of three quantitative variables, by one categorical variable