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.
h h h h h h h h 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
