While working on the project on gun violence in the United States of
America, the three data sets that will be collected including gun
violence data of the U.S.A, gun laws of the U.S.A and demography of
the U.S.A. R tool will be used for the data preparation purpose and
RStudio will be used for its development. For preparing the data, in the
initial stage, the data frames will be cleaned in order to remove null
values in the rows. The date fields will be reformatted and the
standard format of ‘mm/dd/yyyy’ will be followed. R has been chosen
as the main tool that will be used for the processing of the collected
data since its extensive library can be used to manipulate data to
uncover it in an in-depth manner.
The data cleansing process will be done for removing incomplete, or
corrupted data that will not add value to the study. R tool will be used
for data cleansing. Some of the key fields that will be cleaned using the
R library tidy R function include incident characteristics, participant
age, participant age group, participant gender, participant status, and
participant type (McCarthy et al., 2022). As the data in these fields
appear as pipe delimited values, the split operation will be used to
segregate the information in a comprehensible manner. The cleansing
activity will be critical in the project work since it will help in making
available the data in a presentable manner that can be analyzed and
examined. j
Both data preparation as well as data cleaning processes are of
cardinal importance in the project context. They will set the
foundation for the entire research process and ensure that the
available raw data can undergo further processing and it can shed light
on gun violence in the nation between 1992 and 2022.
References
McCarthy, R. V., McCarthy, M. M., & Ceccucci, W. (2022). Know Your
Data: Data Preparation. In
Applying Predictive Analytics
(pp. 27-54).
Springer, Cham.
