Data preparation and cleaning for my datasets entailed merging the
reformatting the datasets and combining them. To prepare the
CHR&R dataset for data merge, the most relevant variables were
selected for analysis and all other remaining variables were dropped.
Because the data is organized by county and the state level data is
needed, the county level data was aggregated by state. There was not
much that needed to be done to prepare the SAMHSA dataset for
merge, since it was already properly formatted. The two datasets
were merged using R which facilitates joining two data frames. In this
case, a left join was used to join the SAMHSA (on the left) and
CHR&R (on the right) datasets based on state. This means that the
final output table contains all the rows and columns of data in the
SAMHSA dataset and the corresponding matching column data in the
CHR&R dataset. Data cleaning including handling missing data and
removing outliers, all of which was done using R.
The analytical tools used in my project are Tableau, R and Python. Tableau is
a great user-friendly tool for creating visualizations of geographic data in the
form of data maps. The drawback to using Tableau is that it does not offer
users many customization options. Overall, however, Tableau’s click-and-
drag functionality make it a good choice for creating quick and informative
maps. Mapped visualizations are important in this exploration because the
mental health landscape will be compared across states. Initially, it will be
important to explore various health and environmental parameters by state to
gain a better understanding of the data.
R is also great tool choice for visualizations and is used in this project for data
preparation, cleaning, and initial data exploration. R was specifically built for
statistical analysis and is particularly well known for generating some of the
most attractive data visualizations (IBM, 2021). In R it is easy to generate
statistical summaries in one line of code by using simple commands like str()
and summary(). It is also easy to create visualizations for univariate and
multivariate analysis of each field in the dataset. It is also capable of handling
large datasets.
f f f f f f f f f Python is the tool of choice for the predictive model development since it
is a general-purpose tool widely used for big data analysis and machine
learning. As of 2022, it is considered the top programming languages by
popularity (Luna, 2022). Although R is also useful for modeling large
datasets, Python is more intuitive to use since the code is more readable. Since
Python is open source, it will also be easy to find solutions to any potential
issues through online forums, analytics libraries, and other online
documentation. Python will make it is easy to model the data and generate
easy to interpret summaries of the results. Several machine learning methods
will be employed in this project and the performance of each will be
compared. Since the target variable is categorical, neural networks, SVM,
random forest, and ensemble models will be used which can all be
implemented using Python.
Sources:
IBM Cloud Team & IBM Cloud (2021). Python vs. R: what’s the difference?
IBM. https://www.ibm.com/cloud/blog/python-vs-r
Luna, J. (2022). Top programming languages for data scientists in 2022.
Datacamp. https://www.datacamp.com/blog/top-programming-languages-for-
data-scientists-in-2022
