Data Visualization With R Assignment Sample

Introduction of Data Visualization With R Assignment

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The term “Visualization of data” has been taken into consideration as the technique that has been used in the graphical representation of the data using R programming. This has been using the charts, graphs, scatter plots, as well other visuals that have been used in the making the provided data more understandable. It has also been responsible for the easy recognition of the trends, patterns, as well as expectations based on the provided data that has been enabling the user for conveying the information related to the results as well as information in a visual way. The environment of R programming has been responsible for providing the comprehensive toolset along with the use of in-built functions.

 Research Background

The research has been based on the visualization of data using the R programming language which has been providing an effective way for communicating with the information based on a universal manner (Gould et al. 2019). This practice has also been helping the identification factors in the dataset provided for affecting the behavior of the customers within the pinpoint area that must be improved for gaining more attention. The research has also been based upon the ability to absorb the information as well as improvement of the quick insights for making a faster decision with the increased understanding of the improved ability for the maintenance with respect to the interests of the audience along with the understandable information that has been provided to them (Neagoe et al. 2021). It has also been dealing with the elimination of the requirements based on the data science by making it understandable and accessible with respect to the requirement of the users. The research has also been dealing with the increment of the ability of quick findings that can be achieved with a great speed as well as making fewer mistakes. 

Aim and Objectives

Aim

The main aim of the research is to identify the data and visualize it as per the requirements using various charts and graphs designed in the R programming language in R Studio. 

Objectives

The objectives on the basis of the research have mainly been dealing with the following objectives. These objectives are:

• To improve the quality of the data and information that has been maintained as per the user's interest using data visualization in R.
• To determine the ability to absorb the information in a quick way for the improvement of insights and decision making (Mowinckel et al. 2020).
• To use various data visualization techniques with respect to the provided dataset.
• To provide the easy distribution of the related information for increasing the opportunity of sharing the insights involved with the users.

 Design of codes

The use of R programming language has been used for creation and representation of the data visualization using the R programming language with reference to R studio application.

“barplot(X3700049_uofp_2016_prepped2916$REPORTING_AREA, main = 'BAR PLOT', xlab = 'Reporting Area', horiz = TRUE) hist(X3700049_uofp_2016_prepped2916$REPORTING_AREA , main ="Histogram", xlab ="Reporting Area", xlim = c(990, 1020), col ="yellow", freq = TRUE) boxplot(X3700049_uofp_2016_prepped2916$REPORTING_AREA, main = "BOX PLOT", xlab = "Reporting Area", ylab = "Officer ID", col = "orange", border = "red", horizontal = TRUE, notch = TRUE) data(X3700049_uofp_2016_prepped2916) plot(X3700049_uofp_2016_prepped2916$REPORTING_AREA , X3700049_uofp_2016_prepped2916$OFFICER_ID, main ="Scatterplot", xlab ="Reporting Area", ylab =" Officer ID ", pch = 19)”

Table 1: Table representing the used codes for data visualization in R

(Source: Created by the learner)

The table mentioned above has been representing the relevant code that has been designed using the R programming language with respect to the graphical representation of the dataset provided. It has been observed that four major plots have been used that has been including the bar plots, box plots, scatter plots as well as a histogram (Meneghini et al. 2018). The plots have been made in the R Studio with respect to the given dataset using the necessary variables required for visualization.

 Analysis

 Histogram plot representing the reporting area

(Source: Developed by the learner)

The figure mentioned above has been representing the box plot with respect to the development of the histogram (Chawla et al. 2018). This histogram has been created on the basis of the variable, “REPORTING_AREA” provided in the given dataset. It has been observed from the above-represented graph that the reporting area has been plotted against the frequency of 990 to 1020 units.

 Bar plot showing the representation of reporting area

(Source: Self-developed)

The figure mentioned above has been showing the graphical representation of the bar plot that has been designed for the visualization of the reporting area with respect to the provided data for the variable, “REPORTING_AREA”. It has been observed from the given figure that data has been plotted on the horizontal bar plot for better visualization.

 Box Plot describing the Officer ID against the reporting area

(Source: Self-created)

The figure mentioned above has been dealing with the graphical representation of the box plot with respect to two variables provided in the dataset (Seoighe et al. 2020). These variables include the reporting area as well as the office ID for the visualization of the box plot.

 Figure representing Scatter Plot for reporting area against officer ID

(Source: Self-generated)

The figure shown above has been representing the scatter plot for reporting area against Officer ID. It has been observed that the graph has been showing the decrement in the reporting areas as per the officer id.

 Findings

Measuring justice and solving problem of racism 

Trying to change "defective hearts and minds" or "combat ignorance" will not solve racism. When it is focused on hearts and minds, it is distracted by trying to rehabilitate potentially racist actors and ignoring the accumulation of harms that are happening to vulnerable communities . In order to change racist behaviour, whether in a police force or a corporation, it is necessary to measure it and take action. Racism is a system that structures opportunity and assigns value to people based on physical characteristics like skin colour and hair texture. This "system" unfairly disadvantages some people and groups, harming their physical and mental well-being. Its effects range from race-based opportunities for good education, housing, and employment to daily interpersonal interactions shaped by race. It manifests itself in health, wealth, income, justice, and voting inequities, to name a few. Individuals who belong to socially and politically prominent racial groupings are also unfairly favoured.

 Conclusion

The research has mainly been involving the techniques used in the visualization of the provided dataset along with the help of the R programming language. The coding has been used for the visuals generated for the better visualizations using the R programming language. It has also been containing the packaged ranges that have been used for performing the data analysis, the building of visualization, as well as representation of data. The analysis of the big data has been mainly dealing with the techniques used in the visualization of the data. R programming has been required for the collection of the raw data and visualizes it as per the requirement of the power system.

References

Chawla, G., Bamal, S. and Khatana, R., 2018. Big data analytics for data visualization: Review of techniques. International Journal of Computer Applications, 182(21), pp.37-40.

Gould, S.J., 2019, May. Bespoke Data Visualization using R and ggplot2. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-4).

Meneghini, I.R., Koochaksaraei, R.H., Guimaraes, F.G. and Gaspar-Cunha, A., 2018, July. Information to the eye of the beholder: Data visualization for many-objective optimization. In 2018 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE.

Mowinckel, A.M. and Vidal-Piñeiro, D., 2020. Visualization of brain statistics with R packages ggseg and ggseg3d. Advances in Methods and Practices in Psychological Science, 3(4), pp.466-483.

Neagoe, I.C., Coca, M., Vaduva, C. and Datcu, M., 2021. Cross-Bands Information Transfer to Offset Ambiguities and Atmospheric Phenomena for Multispectral Data Visualization. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, pp.11297-11310.

O’Sullivan, B. and Seoighe, C., 2020. vcfView: An Extensible Data Visualization and Quality Assurance Platform for Integrated Somatic Variant Analysis. Cancer Informatics, 19, p.1176935120972377.