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Rstudio,dplyr,ggplot2

14/10/2020 Client: bingyanj Deadline: 3 days

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)











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