Harvard Business School Rstudio Programming Questions 1.Data clean up: Remove duplicates by keeping the distinct combinations of the variables state, name

Harvard Business School Rstudio Programming Questions 1.Data clean up:

Remove duplicates by keeping the distinct combinations of the variables state, name, county, overall, mmr, lat, lng, and enroll.
Filter out schools outside the continent (use lng < 0). Use filtering to keep observations where mmr >= 0 or overall >= 0.
Using the function ifelse(), create a new variable rate where the value is equal to the value of mmr when mmr >= 0 is true and equal to overall when mmr >= 0 is false.
Save this data as measles_ne

2.Load the county map data using the function map_data() and save as us_counties

3.Additional data cleaning and data joining:

Begin with the measle_one data created in question 3.
Use the function str_to_lower() from the stringr package, to create a variable subregion that is the lowercase version of county.
Use the function str_to_lower() from the stringr package, to create a variable region that is the lowercase version of state.
For each region – subregion pair, use summarise() to calculate the mean of the variable rate.
Create a new variable threshold that is TRUE when rate > 0 and FALSE otherwise.
Use full_join() to join in the us_counties data by region and subregion.
Save this data as measles_two

4.Draw a map of the us counties and map the variable threshold to fill. Set the outline of the counties to the color “#f4f4f8” and a size of 0.1. Apply the palette “Dark2” from RColorBrewer, name the scale “Rate > 95%”, and set an na.value of “grey80”. Apply the theme theme_minimal().

5.Use geom_polygon()to draw a map of the counties (don’t use the measles_one data yet). Apply the following parameters (outside of aes()): fill = “#f5f5f2”, color = “black”, and size = 0.1. Save this plot as plot1 and print.

I have attach all the question below in R format.