[2.5 points; +0.5 extra credit point available]

Question 0 (0 points)

Require tidyverse and load the nba_players_2018.Rds data to an object called nba.

require()

## Loading required package:

nba <- read_rds()

## Error in read_rds(): could not find function "read_rds"

Question 1 [0.5 points]

Plot the distribution of field goals attempted by all NBA players in the 2018-2019 season. Explain why you chose the visualization that you did. Then add a vertical line indicating the mean and median number of points in the data. Color the median line blue and the mean line red. Why is the median lower than the mean?

nba %>%
  ggplot() + # Put the fga variable on the x-axis of a ggplot.
   geom_...() + # Choose the appropriate geom function to visualize.
  labs() + # Add labels
      geom_vline() + # Median vertical line (blue)
      geom_vline() # Mean vertical line (red)

## Error in nba %>% ggplot(): could not find function "%>%"

Write answer here.

Question 2 [0.5 points]

Now examine the country variable. Visualize this variable using the appropriate geom_..., and justify your reason for choosing it. Tweak the plot to put the country labels on the y-axis, ordered by frequency. Which country are most NBA players from? What is weird about your answer, and what might explain it?

nba %>%
  count() %>% # count the number of players by country
  ggplot() + # place the country on the y-axis, reordered by the number of players. Put the number of players on the x-axis
  geom_...() + # Choose the best geom
  labs() # Add labels

## Error in nba %>% count() %>% ggplot(): could not find function "%>%"

Write answer here

Question 3 [1.5 points]

Let’s pretend we are consulting for an NBA organization. The owner and GM tell us they are interested in the relationship between the player’s age (agePlayer) and the amount of points they score (pts). Please answer the following research question and provide a theory supporting your answer: “Do older NBA players score more points than younger players?”

Write answer here

Based on your answer above, what is the outcome / dependent / \(Y\) variable and what is the explanatory / independent / \(X\) variable? Why?

Write answer here

Create a univariate visualization of both the \(X\) and \(Y\) variables. Choose the best geom_...() based on the variable type, and make sure to label your plots!

# X variable
nba %>%
  ggplot() + # Put the X variable on the x-axis
  geom_...() +  # Choose the best geom given the variable type (make sure to look at it if you aren't sure)
  labs()     # Add labels

## Error in nba %>% ggplot(): could not find function "%>%"

# Y variable
nba %>%
  ggplot(...) + # Put the Y variable on the x-axis
  geom_...() +  # Choose the best geom given the variable type (make sure to look at it if you aren't sure)
  labs(...)     # Add labels

## Error in nba %>% ggplot(...): could not find function "%>%"

Question 4 [0.5 points]

Now analyze the data by creating a multivariate visualization that shows the relationship between age and points. Add a STRAIGHT line of best fit with geom_smooth().

nba %>%
  ggplot() + # Put the X variable on the x-axis, and the Y variable on the y-axis
  geom_...() +  # Choose the best geom given both variable types
  geom_smooth() + # Add a STRAIGHT line of best fit
  labs()     # Add labels

## Error in nba %>% ggplot(): could not find function "%>%"

Based on your analysis, does the data support or reject your hypothesis from Question 3?

Write answer here

Extra Credit 1 [0.5 point]

Let’s look for evidence of a “curvelinear” relationship between player age and points scored. To do so, first calculate the average points scored by age. Then plot this relationship using a multivariate visualization. Add a line of best fit with geom_smooth() but DON’T use method = "lm". What do you conclude? Why?

# INSERT CODE HERE

Write answer here

Question 5 (0 points)

Require tidyverse and load the Pres2020_PV.Rds data to an object called pres.

require()

## Loading required package:

pres <- read_rds()

## Error in read_rds(): could not find function "read_rds"

Question 6 [1 point]

Consider the following hypothesis: “Most Americans don’t pay very much attention to politics, and don’t know who they will vote for until very close to the election. Therefore polling predictions should be more accurate closer to the election.” Based on this hypothesis and theoretical intuition, which variable is the \(X\) variable and which is the \(Y\) variable(s)?

• Write answer here

Now let’s first look at each variable by itself using univariate visualization. First, plot the total number of polls per start date in the data. NB: you will have convert StartDate to a date class with as.Date(). If you need help, see this post. Do you observe a pattern in the number of polls over time? Why do you think this is?

pres %>%
  mutate(StartDate = as.Date(StartDate,'%m/%d/%Y')) %>% # Convert to date
  ggplot(aes(x = StartDate)) + # Visualize the variable using univariate principles
  geom_...() + # Choose the correct `geom`
  labs() # Make sure it is clearly labeled

## Error in pres %>% mutate(StartDate = as.Date(StartDate, "%m/%d/%Y")) %>% : could not find function "%>%"

• Write answer here

Question 7 [1 point]

Next, let’s look at the other variables. Calculate the prediction error for Biden (call this variable demErr) and Trump (call this variable repErr) such that positive values mean that the poll overestimated the candidate’s popular vote share (DemCertVote for Biden and RepCertVote for Trump).

pres <- pres %>%
  mutate() # Create the two new variables 

## Error in pres %>% mutate(): could not find function "%>%"

Plot the Biden and Trump prediction errors on a single plot using geom_bar(), with red indicating Trump and blue indicating Biden (make sure to set alpha to some value less than 1 to increase the transparency!). Add vertical lines for the average prediction error for both candidates (colored appropriately) as well as a vertical line indicating no prediction error.

pres %>%
  ggplot() + # Instantiate an EMPTY ggplot object
  geom_bar(aes(...), # Put the first variable in the first `geom_bar()`
           ...) + # Set the color and opacity
  geom_bar(aes(...), # Put the second variable in the second `geom_bar()`
           ...) + # Set the color and opacity
  labs(...) + # Make sure it is clearly labeled
  geom_vline(...) + # Put a black vertical line at 0
  geom_vline() + # Put a dashed blue vertical line at the Democrat prediction error
  geom_vline() + # Put a dashed red vertical line at the Republican prediction error

## Error in parse(text = input): <text>:11:0: unexpected end of input
## 9:   geom_vline() + # Put a dashed blue vertical line at the Democrat prediction error
## 10:   geom_vline() + # Put a dashed red vertical line at the Republican prediction error
##    ^

Do you observe a systematic bias toward one candidate or the other?

• Write answer here

Question 8 [1 point]

Plot the average prediction error for Trump (red) and Biden (blue) by start date using geom_point() and add two curvey lines of best fit using geom_smooth(). Make sure that the curvey line for Trump is also red, and the curvey line for Biden is also blue!

pres %>%
  mutate(...) %>% # Convert to date
  group_by(...) %>% # Calculate the average error for Biden and Trump by date
  summarise(...,
            ...) %>%
  ggplot() + # Instantiate an empty ggplot
  geom_point(aes(x = ...,y = ...), # Put the first variable in the first `geom_point()`
           ...) + # Set the color
  geom_point(aes(x = ...,y = ...), # Put the second variable in the second `geom_point()`
           ...) + # Set the color
  geom_smooth(aes(x = ...,y = ...), # Put the first variable in the first geom_smooth()
              ...) + # Set the color
  geom_smooth(aes(x = ...,y = ...), # Put the second variable in the second geom_smooth()
              ...) + # Set the color
  labs(...) + # Make sure it is clearly labeled
  geom_hline(...) # Add a horizontal dashed line at 0

## Error in pres %>% mutate(...) %>% group_by(...) %>% summarise(..., ...) %>% : could not find function "%>%"

What pattern do you observe over time, if any? Does this support the hypothesis presented in Question 1 above?

• Write answer here

Question 9 [1 point]

Can we do better by aggregating state-level polls? Load the [Pres2020_StatePolls.Rds] to an object called state. First, create two new variables demErr and repErr just as you did in Question 2. Then recreate the same overtime plot comparing Biden and Trump prediction errors as you did in Question 3. What do you observe?

state <- read_rds(...) # Read in the data

## Error in read_rds(...): could not find function "read_rds"

state <- state %>%
  mutate(...) # Create the two new variables for Democrat and Republican prediction errors

## Error in state %>% mutate(...): could not find function "%>%"

state %>%
  mutate(...) %>% # Convert to date
  group_by(...) %>% # Calculate the average error for Biden and Trump by date
  summarise(...,
            ...) %>%
  ggplot() + # Instantiate an empty ggplot
  geom_point(aes(x = ...,y = ...), # Put the first variable in the first `geom_point()`
           ...) + # Set the color
  geom_point(aes(x = ...,y = ...), # Put the second variable in the second `geom_point()`
           ...) + # Set the color
  geom_smooth(aes(x = ...,y = ...), # Put the first variable in the first geom_smooth()
              ...) + # Set the color
  geom_smooth(aes(x = ...,y = ...), # Put the second variable in the second geom_smooth()
              ...) + # Set the color
  labs(...) + # Make sure it is clearly labeled
  geom_hline(...) # Add a horizontal dashed line at 0

## Error in state %>% mutate(...) %>% group_by(...) %>% summarise(..., ...) %>% : could not find function "%>%"

• Write answer here

Question 10 [1 point]

One other explanation for inaccurate state polls is that some states do not have many polls run. Calculate the anti-Trump/pro-Biden bias for each state by subtracting the repErr from the demErr (call this new variable bidenBias). Then calculate the average bias by state AND calculate the number of polls in that state. Finally, plot the relationship between the number of polls and the extent of bias. Does the data support the theory that states with more polls were predicted more accurately?

state <- state %>%
  mutate(...) # Create the bidenBias variable here

## Error in state %>% mutate(...): could not find function "%>%"

state %>%
  group_by(...) %>%
  summarise(...,   # Calculate the average bidenBias by state
            ...) %>% # Calculate the number of polls by state
  ungroup() %>%
  ggplot(aes(x = ...,      # Put the correct variable on the x-axis
             y = ...)) +   # Put the correct variable on the y-axis
  geom_...() + # Choose the correct geom
  geom_...(...) + # Add a straight line of best fit
  labs(...) # Give it some good labels

## Error in state %>% group_by(...) %>% summarise(..., ...) %>% ungroup() %>% : could not find function "%>%"

• Write answer here

Extra Credit 2 [0.5 points]

Do polls that underestimate Trump’s support overestimate Biden’s support? Investigate this question using both the national data (pres) and the state data (state). Use a scatterplot to test, combined with a (straight) line of best fit. Then, calculate the proportion of polls that (1) underestimate both Trump and Biden, (2) underestimate Trump and overestimate Biden, (3) overestimate Trump and underestimate Biden, (4) overestimate both candidates. In these analyses, define “overestimate” as prediction errors greater than or equal to zero, whereas “underestimate” should be prediction errors less than zero. What do you conclude? Is there any evidence of an anti-Trump bias in national polling? What about state polling?

# National scatterplot
# INSERT CODE HERE

# National proportions: 4 different types of polls
# INSERT CODE HERE

# State scatterplot
# INSERT CODE HERE

# State proportions: 4 different types of polls
# INSERT CODE HERE