1. Allena Keller DSI 101 Section 2
Google Play Store Apps
Allena Keller
DSI 101
Section 2

2. Project Goal What is the most popular app category?
Possible factors investigated
Average Ratings
Average Installs

3. My Data Google play store apps from Kaggle.com
Data was originally obtained from the google play store
Information on 10.8k apps
Originally a CSV file
1.29MB
Saved as an Excel file
890KB

4. Data Used Only used 3 fields Category: Text field
Ex. Tools
Ratings: Numerical field
Ex. 3.5
Installs: Numerical field
Ex. 10,000+
The category the app belongs to.
The overall user rating of the app
The number of user downloads/installs for the app.

5. Problems With Data
Record 10474

6. How I FIXED IT Copied the records Cleared their cell contents
Repasted the information one space to the right.

7. Google play store
Typed lifestyle into the two missing fields

8. Installs Field Rounded numbers with + at the end
Highlighted all the install fields from F1:F10842.
I then did a find and replace.
I simply replaced the +’s with nothing

9. Average App Category Ratings
Advanced search of the category field
Countif function
Sumif function divided by the number of each category
Countif = (the range of category field, the criteria which was the category from the advance search).
Sumif = ((the range of category field, the criteria which was the category from the advance search, and then the range of the ratings field)

10. Average App Category Installs
I repeated the same exact steps
I inserted the installs data in the sumif function.
I repeated the same exact steps that I used to compute the average rating; however, I inserted the installs data where I had previously inserted the ratings data in the sumif function.

11. Analyzing average rating data
Create a table of the average rating data
Copied keeping text only
Highlighted and inserted table
Sort the table from largest to smallest
First, I made a table of the derived categories and average ratings data. I simply copied these to a new location keeping the text only. I then Highlighted them and went to insert where I clicked table.
This allowed me to easily sort the ratings from largest to smallest to see what app category had the highest average ratings.
The filtered table showed that apps under the education category have the highest average rating

12. I chose a clustered bar graph in order to compare the average ratings of the categories. Here it was also visibly clear that education had the highest average rating. Note that there are too many categories for them to all be listed on the axis on this display. If the graph is enlarged they are visible; however, that is not necessary because we are looking at education.

13. Analyzing Average Installs data
Repeated the same steps
Made a sorted table
I repeated the same exact steps that I used to analyze the average rating; however, I inserted the installs data where I had previously inserted the ratings data.
The filtered data shows that apps under the communications category have the highest average installments; therefore, that category is the most installed:

14. I also chose a clustered bar graph in order to compare the average installments of the categories. Here it was visibly clear that apps under the communication category had the highest average installment.
Note that there are too many categories for them to all be listed on the axis on this display. If the graph is enlarged they are visible; however, that is not necessary because we are looking at communications.

15. Discoveries
Based off of rating it can be concluded that education is the most popular app category.
Based off of installments it could be said that communication is the most popular app category.

16. Future work
What app category had the highest rating average and install average at the same time.
I would like to have seen what app category had the highest rating average and install average at the same time. This category could have also been an answer because it is simultaneously downloaded frequently and rated highly.

17. Questions