Exam 1
Brian Mochtyak
The purpose of this dociment is to look into political contribution data.
The data shows who donated how much money and when to different political parties from Cincinnati contributors. We will use this data and analysis to see if Cincinnati contributors had a potential effect on political outcomes.
I will be using the tidyverse to clean and visualize data and use the DT package to create interactive tables.
The lubridate package will also be used to manipulate dates.
Missing Values
Check for any missing values and then replace all the missing values for the categorical variables with “Not Reported” and all of the missing values in the numeric columns with the mean of that column. Fortunately, there were not any missing values in the numeric variables.
Get rid of all contributions that are equal to zero
Then get rid of all dates that are before 2015
Replace misreported values with “Not Reported”
Combine all of the different categories of “Retired” into one category.
Create new column called “qualified” that has a 1 if the committee-type is qualified and 0 if it is not.
There are 57361 observations of 12 different variables in the dataset showing information on who contributed how much money to each political compaigin on what day and for what amount.
## Warning in instance$preRenderHook(instance): It seems your data is too
## big for client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.htmlThe variables we interested in the data is the amount being donated at what point in time and who it is going to. We want to see if this data could be potentially linked to the outcome of elections.
Below are two graphs one showing total contributions year over year and one showing contributions year over year to each party.
Based off of the graphs above we can see that people tend to donate more during election years and the republican party received more in the 2015-2016 cycle but the democratic party received more in the most recent cycle.
Below is a graph showing how much contributed to each committee type.
It appears that the highest contributors are those who are not actively employed and of the ones who are actively employed they are typically executives or higher ups at their respective employer meaning they most likely have a high income.
It also appears most of the money goes to the republican party.
A total of 25 different Xavier employees have made at least one contribution.
Based off of the graphs above most contributions were made on Tuesdays which I found to be interesting because elections are held on Tuesdays as well and most refunds were made on Fridays which makes sense because it is the end of the week.
I want to know which month has the highest amount of contributions. My initial though is it would be either October or November because this is right around election time and I imagine there would be an influx in donations during that time.
After looking at the graph above it looks like contributions spike most during June I would assume this is because of the primaries that take place during those times however I do find it interesting that the lowest months are November through January which is immediately after the elections.