The document discusses visualizing loan data to analyze trends in the number of lenders and loan amounts. It describes filtering data to the United States, creating a histogram or density plot of lender counts, and using a scatter plot to examine the relationship between loan amount and number of lenders for manufacturing sector loans. True statements about the potential results are identified. The task of visualizing monthly trends in loan funding durations for different repayment intervals in the Philippines is also outlined.
The document discusses visualizing loan data to analyze trends in the number of lenders and loan amounts. It describes filtering data to the United States, creating a histogram or density plot of lender counts, and using a scatter plot to examine the relationship between loan amount and number of lenders for manufacturing sector loans. True statements about the potential results are identified. The task of visualizing monthly trends in loan funding durations for different repayment intervals in the Philippines is also outlined.
The document discusses visualizing loan data to analyze trends in the number of lenders and loan amounts. It describes filtering data to the United States, creating a histogram or density plot of lender counts, and using a scatter plot to examine the relationship between loan amount and number of lenders for manufacturing sector loans. True statements about the potential results are identified. The task of visualizing monthly trends in loan funding durations for different repayment intervals in the Philippines is also outlined.
The document discusses visualizing loan data to analyze trends in the number of lenders and loan amounts. It describes filtering data to the United States, creating a histogram or density plot of lender counts, and using a scatter plot to examine the relationship between loan amount and number of lenders for manufacturing sector loans. True statements about the potential results are identified. The task of visualizing monthly trends in loan funding durations for different repayment intervals in the Philippines is also outlined.
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By visualizing the distribution of variable lender_count, we can find out which
numerical range has the highest frequency of data occurrence.
Say we are interested in finding out the range of the highest frequency of lender_count for all loans in the United States. Therefore, the first thing we need to do is to filter the data so it only contain all data from United States. After the data is ready, you can start to make a plot to visualize the data. You may need to check the distribution of the lender_count using a histogram or density chart.
# your code here
At what range does the lender_count in United States has the highest frequency
Say we are interested in analyzing the loans posted in the Manufacturing sector. We would like to see the relationship or pattern between the amount of loan (loan_amount) and number of lenders (lender_count). To do that, we can use a scatter plot.
# your code here
How would you describe the relationship between the amount of loan and the number of lenders from all loans within the Manufacturing sector?
[ ] The higher the loan amount, the lower the lender count [ ] The higher the loan amount, the higher the lender count [ ] Loan amount and lender count don't have any meaningful relationship
Which statement is true based on the scatterplot you have created?
[ ] There are some loan that has big loan amount but little number of lender count [ ] There are some loan that has big lender count but little loan amount [ ] Most of the loan request has loan amount more than 7500
Consider the following case: One of the data analysts in Kiva is tasked to analyze the time duration of a loan from the first time being posted to be fully funded in the Philippines according to each repayment interval types. The analyst then tried to visualize the monthly trend of the average funded time duration in hourly units each month.
Pay attention to the resulting plot of the analyst’s task in Guatemala.png. Now your task is to recreate the previous plot for country of Philippines using your data.
In order to analyze the trend, first we need to subset the data for the country of Philippines. We will also need to convert any date data into a proper date format.
What is the earliest and latest posted time of any loan in 2015?
Now we are set to calculate the duration from a loan is posted until it is fully funded. We need to create a new column that contains the difference between the funded time and the posted time. We will call it funding duration. This column will have a data type of time and presented in unit of minutes. We need to convert them into numeric and divide by 60, so the time would be in hourly value.
# your code here
Since we want to visualize the monthly average funding duration, you need to create a new column which contains the month of the posted time before aggregating the data. # your code here Finally, we will start to aggregate the data based on the month of the posted time and the repayment interval to get the average funding duration.
# your code here
Which repayment interval has the longest fund duration and at what month did it happen?
[ ] monthly repayment interval in April
[ ] bullet repayment interval in January [ ] monthly repayment interval in March
The data has been properly prepared. Now it is your time to create the line plot to visualize the trend. Fill in the code below to produce the plot.
# ggplot(loan_agg, aes(x = ........, y = ........., color = ......, group =
repayment_interval))+ # geom_line()+ # geom_point()+ # labs(title = "Funding Duration Trend on Philippines, 2015")+ # theme_minimal()+ # theme(legend.position = "top") Which statement is TRUE based on the line plot?
[ ] Monthly repayment interval has almost the same funding duration with Irregular repayment interval in August [ ] Bullet repayment interval has longer funding duration than Irregular repayment interval in June [ ] Monthly repayment interval never funded faster than Irregular repayment interval
