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.
j j j j j j j j j 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
