Rstudio Homework

# First, download "county.csv" from Blackboard and then load it into memory # using the following code: d=read.csv("county.csv") View(d) # Name of the dataset is "d". # View the dataset. This is about US counties, # such as county's state, population, income, etc. # QUESTIONS # 1) What percentage of the dataset is populated with NA values? # Hint: Calculate total NAs then divide by (number of rows X number of columns) # 2) Show the county name and population in 2017 for the state of Connecticut # where unemployment rate is greater than 5.0. # 3) Show the county names, states and per capita income whose population # change is positive and unemployement rate is less than 2. # 4) We are wondering about population change from 2010 to 2017 # in counties where poverty rate is greater than 20 and # unemployment rate is greater than 8. # For this purpose, create a new variable using the following formula # as a new column of the dataset d. # popChng17_20 = (pop2017 - pop2010)/pop2010. # # 5) The population in "Hoonah Angoon Census Area" of Alaska in 2017 # is missing (i.e. NA). However, a quick Google search showed that # this population is actually 2139. Now replace the NA in this # spot with 2139. # 6) We are wondering about mean poverty level for metro and non-metro counties # in the state of Connecticut. Calculate them. Which one is higher? # (NOTE: To ignore NAs, use na.rm = TRUE when calculating means) # 7) Which year has the highest variation in terms of county populations? # Is it 2000, 2010 or 2017? (NOTE: To ignore NAs, use na.rm = TRUE ) # 8) Create a histogram for homeownership variable # with 40 bars. Comment on the skewness of the distribution. # 9) Create a boxplot showing poverty variable for categories metro and non-metro areas # (HINT: use y ~ x notation in the boxplot.) Comment on the plot # as to which location has higher poverty overall? # 10) Assume that you are investigating the variables that # could be associated with 'poverty' variable. Create scatter # plots on a 2 x 2 panel for poverty vs. unemployment_rate, # homeownership, per_capita_income, and pop_change. # Which variables seem to be associated with poverty? # (NOTE: If you get "figure margins too large", enlarge the plotting # window to the left and upward) ################################################# # In this part, use ggplot2 and dplyr packages ################################################# library(ggplot2) library(dplyr) # 11) We wonder about the change in unemployement rate # as education level changes. # For this purpose, create a boxplot for unemployment_rate vs. median_edu. # Label x-axis as "Education Level" # Comment on the chart. How does the unemployement rate change? # 12) Using dplyr and ggplot2, find population change from 2010 to 2017 # in counties where poverty rate is greater than 20 and # unemployment rate is greater than 8. # For this purpose, mutate a new variable # called popChng17_20 = (pop2017 - pop2010)/pop2010. # Then, create a ggplot boxplot showing popChng17_20 vs metro. # 13) Group the dataset by State, and then summarize using # count, mean unemployement rate, and mean per capita income. # Sort the result by mean unemployment rate. # (Hint: Remove NAs when calculating means: na.rm = TRUE)