Instructions
- Homework assignment is here with all questions commented out.
- Complete all of the coding tasks in the
.qmd file
- Upload your individual exercise to your
GitHub repo by Wed 11:59pm.
- Remember to clean your github repo and sort hw submissions by weeks. Each week should have one folder.
##-------------------------------------------------------------------------
## R code for Lab, week 10: Individual Exercises
## Written by: Howard Liu
##-------------------------------------------------------------------------
# # Creating a data frame object --------------------------------------------
#
# # Create a vector called ID that contains a number from 1 through 6
#
# Write your command here.
#
# # Create a vector called Race that contains the following:
# # "black", "white", "black", "hispanic", "white", "white"
#
# Write your command here.
#
# # What is the type of the Race vector?
# # Is it a factor? Is it a character? Is it a logical vector?
# # Find out by using some commands.
#
# Write your command here.
#
#
# # Create a vector called Voted_For_Obama that contains thefollowing:
# # TRUE, TRUE, TRUE, FALSE, FALSE, TRUE
#
# Write your command here.
#
# # Find out the data type of the Voted_For_Obama vector
#
# Write your command here.
#
#
# # Create a vector called Party_ID that contains the following:
# # "Dem", "Dem", "Dem", "Rep", "Ind", "Rep"
#
# Write your command here.
#
# # Create a vector called Income_Level that contains the following:
# # "High", "Low", "Low", "High", "High", "Low"
#
# Write your command here.
#
# # Create a vector called Approval that contains the following:
# # 70, 80, 68, 20, 10, 60
#
# Write your command here.
#
# # Create a data frame called vote.data by combining the six vectors
# # you have created above.
#
# Write your command here.
#
# # We have seen above that the Race vector was a character vector.
# # We have learned in the joint exercise that we should treat a
# # vector like this one as a factor, not as a character.
#
# # A nice thing about data frame objects is that, it will
# # convert vectors like the Race vector into factors automatically.
# # To convince yourself that the Race vector included in the vote.data
# # is indeed a factor vector, apply the is.factor function on it.
#
# Write your command here.
#
#
# # Notice that is.factor(Race) returns FALSE but the command you wrote
# # above should return TRUE.
#
#
#
# # How many people in this data set voted for Obama?
# # That is, for how many observations does the variable Voted_For_Obama
# # take the value of TRUE? Write R commands that gives you the answer.
# # Note that I know the answer is 4. What you need to give me is the command
# # that gives us the answer 4.
#
# # Hint 1: there is a function called length that returns the length of
# # a vector. One way to do this task is to measure the length of
# # a subset of the Voted_For_Obama vector where the values are
# # TRUE.
#
# # Hint 2: there is a function called nrow that returns the number of
# # rows of a matrix or a data frame. Another way to do this task
# # is to measure the number of rows of a subset of the vote.data
# # object for which Voted_For_Obama is equal to TRUE.
#
#
# Write your command here.
#
#
# # There is one person in this data set who identifies himself as "Ind"
# # (Independent). Did he vote for Obama?
# #
# # Again, I know he didn't. Give me the command that gives us the answer
# # FALSE
#
#
# Write your command here.
#
#
# # Create a subset of the data set that contains only "white" people.
# # Store this smaller data set into an object called vote.white
#
# Write your command here.
#
#
# # Show the third column of the newly created data set vote.white
#
# Write your command here.
#
#
# # How many white voters in this mini data set voted for Obama?
# # Again, I know the answer is 2. Write a command that gives the answer 2.
#
# Write your command here.
#
#
#
#
# ##### Some extra exercises ######
#
# # Modifying a data frame object -------------------------------------------
#
# # We have created a data frame called my.data in gv900-week5-Review.R.
# # Let's see how we can add variables to an existing data frame.
#
#
# # To add a new variable, you also use the $ symbol.
# # Specifically, we write DATAFRAME $ NEW_VARIABLE_NAME <- VALUES
#
# # For example, in order to add a new variable called Population
# # that contains the following values:
# # 318946000, 64105654, 127090000, 1367420000, 203322000, 80781000, 87354300
#
# # We write
#
# my.data $ Population <- c(318946000, 64105654, 127090000, 1367420000,
# 203322000, 80781000, 87354300)
#
# my.data
#
#
# # If the Console window is wide enough, it should be showing up like this:
# # > my.data
# # Country_ID Country_Name Regime_Type GDP_PC EU_Member Population
# # 1 1 United States Democracy 51163 FALSE 318946000
# # 2 2 United Kingdom Democracy 39367 TRUE 64105654
# # 3 3 Japan Democracy 46838 FALSE 127090000
# # 4 4 China Dictatorship 6070 FALSE 1367420000
# # 5 5 Brazil Democracy 11347 FALSE 203322000
# # 6 6 Germany Democracy 41376 TRUE 80781000
# # 7 7 Egypt Dictatorship 3115 FALSE 87354300
#
# # We can see that a new column is now added at the end (far right).
#
#
# # If the Console window is not wide enough, it may show up like this:
# #
# # > my.data
# # Country_ID Country_Name Regime_Type GDP_PC EU_Member
# # 1 1 United States Democracy 51163 FALSE
# # 2 2 United Kingdom Democracy 39367 TRUE
# # 3 3 Japan Democracy 46838 FALSE
# # 4 4 China Dictatorship 6070 FALSE
# # 5 5 Brazil Democracy 11347 FALSE
# # 6 6 Germany Democracy 41376 TRUE
# # 7 7 Egypt Dictatorship 3115 FALSE
# # Population
# # 1 318946000
# # 2 64105654
# # 3 127090000
# # 4 1367420000
# # 5 203322000
# # 6 80781000
# # 7 87354300
#
# # You may want to adjust the width and ask R to show it again.
#
# my.data
#
# # If you want to browse a data frame object, use the View function
#
# View(my.data)
#
#
# # We can creaate a new variable that is an answer to some operations.
# # For example, GDP_PC measures per capita GDP (in 2013 US dollars).
# # This was calculated as a country's GDP divided by its population:
# # GDP_PC = GDP / Population
# # Therefore, if we multiply GDP_PC and Population, we can obtain its
# # GDP:
# # GDP = GDP_PC * Population
#
#
# # Create a variable within the my.data object called GDP which is equal to
# # the product of GDP_PC and Population (GDP_PC times Population).
#
# Write your commands here
#
#
#
# my.data
#
#
# # It is a little bit difficult to read numbers like 1.631823e+13, which
# # means 1.631823 * 10^13. Let's create another variable that shows
# # re-scaled GDPs by dividing the raw GDP by 1000000 (on million).
#
# my.data $ GDP_mil <- my.data $ GDP / 1000000
#
# my.data
#
#
# # So, now this new variable GDP_mil is shown in 1 million dollars.
# # For example, the value of GDP_mil is 16318234.2 for the United
# # States, which means that GDP of the US is 16318234.2 million dollars.
#
#
# # Create a new variable within my.data called Is_Democracy that
# # is a logical vector that tells us whether or not a country is democratic.
#
# # Hint: utilize the Regime_Type variable included in the my.data
# # object.
#
#
# Write your commands here