Solve in R

 

Part A: Enter code to produce the structure of your dataframe 

Part B: 1. Indicate the number of rows and columns: Rows =     Columns =     

2. Which variables would be appropriate for examination by histogram? 

3. Why are these variables appropriate for histograms?   ```{r} #Question 1 Part A code goes here!  ```  

 

QUESTION 2. Part A: Generate descriptive statistics (fav_stats) for one of the variables you listed in Question 1:PartB2, and save them to a new object. Name your object using the format 'VariableName.stats'  Part B: Provide the min, mean, median, max, and IQR for this variable.   ```{r} #Your code goes here! #1) Generate descriptive stats for your categorical variable from question 1.   #2) Print these new objects to view the output  ```  

 

QUESTION 3 . Part A: Write code to produce a histogram of the variable you indicated in the last question.   Part B: Describe the shape of the histogram This is a histogram of the Age variable, which displays a somewhat normal distrubution. The value with the most instances appears to be ~50 or 51 and the least, appears to be ~95. The distribution is slightly skewed. Kurtosis = light tailed.  ```{r} #Your code goes here!  ```  

 

 

QUESTION 4. Part A: Recode Sex into new variable called "Gender" where 1 = Male and 0 = Female.  ```{r} #Your code goes here!  ```  

 

QUESTION 5 Part A: Check that your code worked by viewing the first and last six rows of your new variable  ```{r} #Your code goes here!  ```  

 

QUESTION6: Part A:  1. Rewrite your histogram from Question 5 and this time fill it in using the 'Gender' variable from question 4. (Note: This will allow you to differentiate between male and female instances.)  2. Add 10 bins  3. Label the title and x-axis of the histogram using gf_labs=title"", x="" 4. separate the histogram by gender using gf_facet_grid(variable ~.)  Part B:  What can you determine from the new histogram output?  ```{r} #Your code goes here!  ```  

 

 

QUESTION 7:Part A: Create a new categorical variable called Age3Cat from the Age variable. Part B:  1.How many instances of each category are there in your new variable? (*hint: tally) 2. What is the proportion of each category?  ```{r} #Your code goes here!   #Check your work   ```  

 

 

QUESTION 8:Part A: Evaluate Income and Age by your Age3Cat variable. 1. Create a box plot of Age by Age3Cat. 2. Create a box plot of Income by Age3Cat.  Part B:  1. Which group appears to have the greatest spread for age?  2. Which group appears to have the most outliers for income? (Estimate the max income based on the chart.) ______  ```{r} #Your code goes here!  ```  

 

QUESTION 9:Part A: Your boss has asked you to investigate whether there is an association between age and income among employees. Make a chart that displays how much employees make (Income) based on their age. Visualize this using a scatterplot   - Include these arguments: size = 3, color=~Age  Part B: Do you notice anything?  ```{r} #Your code goes here!  ```  

 

 

QUESTION 10:There's some data that doesnt seem to make sense. It looks like a cube on the left side of your scatterplot! We want to remove that so we can visualize Income by Age the correct way.  Part A: Filtering exercise.      Step 1. Create a new data frame object called MyData_Filtered          AND     Step 2: Using the filter function, capture into your new dataframe, only the rows in which Income > 10 AND Income < 200 AND Age > 17      Part B: How many rows remain in your filtered dataframe?  **Extra Credit (5pts)** If you can perform complete this using one filter function!          ```{r} #Your code goes here.  ``` 

 

 

QUESTION 11:Part A:  1. Let's try our scatterplot visualization from Question 9 again.           - Remember to include these arguments: size = 3, color=~Age            Part B: What do you notice from our new scatterplot?   Extra Credit: See if you can add a regression line through the scatter plot    ```{r} #Your code goes here!   ``` 

 

QUESTION 12: Part A: Create another scatter plot, but this time using Income by Age3Cat.    - Remember to include these arguments: size = 3, color=~Age     Part B: Based on your investigation, does there appear to be an association between age and income? Why or why not?  Part C:  Because there is an association, does this mean that Age is the cause of higher income? Why or why not?  ```{r} #Your code goes here!