Fadhlul Mubarak, Atilla Aslanargun, Vinny Yuliani Sundara

Village Potential Statistics (PODES): Visualization of Schools in Jambi Province with Statistical Programming (R)

Introduction

Village potential statistics (podes): visualization of schools in jambi province with statistical programming (r). Visualize Jambi Province school percentages using R programming and BPS PODES data (2014, 2019). Explore custom graphic creation and analyze changes in education levels.

Abstract

One of the primary data that can be used in research is village potential statistics (PODES). The data was obtained based on research in a certain period by the Statistics Indonesia (BPS). This study aims to visualize the percentage of schools in each city/district in Jambi Province using R programming based on PODES data in 2014 and 2019. In this study, we not only visualize but also how to build attractive graphics and arrange them starting from windows, graphic size, dimensions, color, horizontal axis, vertical axis, and others. Of course, the graph produced in this study is different from the basic plot found in the R program, although the process carried out is also more complicated. From 2014 to 2019, in general, within a period of 5 years there has been an increase in the number of schools in each city/district in Jambi Province. However, from the university level, the number decreased. In 2014 the number of universities in Jambi City was 32 but in 2019 the number decreased to 24. There are even interesting things in Kerinci and Tebo district. In 2014 there were no universities listed, while in 2019 there were 3. This also affects the percentage of education level in each city/district.

Review

The paper "Village Potential Statistics (PODES): Visualization of Schools in Jambi Province with Statistical Programming (R)" presents a valuable application of statistical programming to a crucial regional dataset. It leverages the Village Potential Statistics (PODES) from Statistics Indonesia (BPS) to visualize the educational landscape, specifically focusing on the percentage of schools in various city/districts within Jambi Province for the years 2014 and 2019. This initiative to analyze and present official statistics using R is highly relevant for regional planning, educational policy development, and demonstrating the practical utility of data science tools in a governmental context. The abstract highlights a methodological ambition that goes beyond basic data presentation. The authors state their intent to not only visualize but also to demonstrate "how to build attractive graphics," meticulously considering parameters like window settings, graphic size, dimensions, color, and axis customization. This suggests an effort to create visually impactful and informative outputs, claiming their generated graphs differ from standard R program plots, albeit requiring a "more complicated" process. The study also offers specific findings, noting a general increase in school numbers from 2014 to 2019, but a counter-intuitive decrease in universities in Jambi City, alongside an emergence of universities in previously unlisted districts like Kerinci and Tebo, all of which significantly influence regional education level percentages. This work promises a useful demonstration of data visualization techniques applied to a pertinent socioeconomic issue. The emphasis on detailed graphic customization for improved clarity and aesthetic appeal is a commendable goal for effective data communication. However, the abstract could be strengthened by briefly outlining the specific methodologies or R packages that constitute the "more complicated" process and distinguish their approach from basic plotting. While the identified trends in school and university numbers are compelling, the full paper would benefit from a deeper analytical discussion of the potential underlying factors driving these changes and their implications for regional development and educational policy in Jambi Province. Overall, the paper offers a practical insight into leveraging R for regional statistical analysis and visualization of educational potential.

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