little moderate quite rich rich
603 103 63 48
little moderate rich
274 269 63 `summarise()` has grouped output by 'Checking account'. You can override using
the `.groups` argument.German Finacial Status
By Shanteé Enitencio
Introduction
This report analyzes the amount Germans have in their savings and checking accounts. Additionally it explores the relationship between the amount in their savings accounts, the credit amount they maintained and their age. The data that were utilized for this report were sourced from the datasetsICR package in R.
PART 1: Practice using pipes (dplyr) to summarize data: Two Categorical Variables
The two categorical variables that were chosen to do this analysis were Savings and Checking accounts in order to see the difference between what people saving and what was more liquid to them. The following table indicates the distribution of the variables.Visualizing the data indicated that the majority of the people fell into the little savings and checking division. However, there was another group of people that had moderate checking amounts but little savings. Both of these could be explained by the propensity to consume and to save that these people maintain in relation to their income, it seems that even those that had more still struggled to save.
PART 2: Create stacked and dodged bar charts: Two Categorical Variables
To visualize this difference, the following stacked bar chart was created. Initially the resulting chart seemed to support the assumption that those that had little in their savings also had little in their checking accounts. However, across all levels of checking accounts, people seemed to have little savings in around the same proportion, regardless of the amount in their checking. Despite this, the people with rich savings accounts tended to have moderate or rich savings accounts.
Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
of ggplot2 3.3.4.
Likewise, the vertical and horizontal dodged bar charts seem to indicate the same story, that at all levels people have very little savings. This realization is a concerning one for the global economy and for Germany especially. Germany is seeing an ever growing older population which will begin to take effect on a lot of the social resources avalible. Because of this, having personal savings and increasing it is an every growing necessity.
PART 3: Practice using pipes (dplyr) to summarize data: Two Continuous Variables and One Categorical
The next portion of this analysis took the Savings account, the credit amount and Age variables to see the difference in what people owed and saved in comparision to how old they were. This indicated that savings accounts that were rich and quite rich were very similar, they maintained lower credit amounts and higher ages than the lower ones. This could be indicative of an economic shift in which the older more established generation has more money and less debt in comparison to their younger counterparts on average.
PART 4: Create a scatterplot: Two Continuous Variables and One Categorical
Creating a scatterplot of the credit amount vs age and amount in savings account suggested that there was not a linear relationship with these variables. Like the conclusion in the other parts, this suggested that most people have little regardless of their age and have less than 5000 as their credit amount. Additionally, there were some observations like the ones highlighted that showcased individuals with little savings but high ages, more investigation would need to take place to understand the placement of these data points and understand if they are related to some economic or physical issue in the older population.
##PART 6: Data Labels
Lastly data point labels were created to observe the values that were below age 40 and had above 10000 as their credit amount to better visualize the data.