By Sharihan.NB
For data analysis, there are numerous programming languages available, but we chose R for this project. We examined the data set while playing with the R Markdown package, which is widely used for producing interactive documents.
Goal of this project is not just to analyze the data
- Using the exploratory technique, comprehend the data set and potential insights it contains.
- Determine which brand dominates the market globally and represents the spatial variation with frequency using a choropleth map, and map the locations of each shop using latitude/longitude information.
- Investigate the R Markdown package, usability, and the transformation of analyses into presentable reports.
- Discover R Markdown’s ability to convey a story in comparison to other data visualization software that we are familiar with.
The coffee brand data set was chosen for this project simply because we love coffee and….genuinely think it is fun to explore the data and be surprised by the results
We’ve started with exploring the data to understand more on what we’re dealing with. Mostly want to know how big is, the structure and starting to get the idea and built up questions which been answered along the way.
The geographic data plotted using mapview package and sf package for Starbucks coffee shop in US and Canada.
Top 3 countries in Asia with most Starbucks coffee shops are China, Japan and South Korea. Location of each shop can be seen on the map.
According to our data analysis
- Starbucks controls the global market.
- The sequence and height in the pareto and color density in the choropleth map demonstrate this.
- The locations of each shop in the top five countries have been mapped using latitude/longitude. We can see that the store locations are spread out across the continents as well countries.
According to our data analysis
- R Markdown package is fairly easy to use. We’ve encountered some errors during knitting such as “Error in dirname(name): path too long” which we solved by restarting R.
- R Markdown is neat and organized. Just write the notes and write th R code in the chunks. We can run the code together or separately, there’s options to publish the code and results and other useful formatting up to your preference.
- R Markdown is indeed able to convey the insights pretty well. We like how the beautiful and interactive final report of coffee brand analysis turned out.
Contact our HUJANPERA team for professional data-driven statistical services. Visit our projects page for further example on completed jobs.
Ref Links :
https://rmarkdown.rstudio.com/articles_intro.html
https://rmarkdown.rstudio.com/