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.
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.
