Learning R — Wrangling & Visualizing Data
This week I learned how to wrangling and visualizing Data using R. Though the wrangling part is a bit dry, it is useful for data clean up and analysis. R is more powerful than I thought in terms of the different ways it offers to manipulate data. Functions such as filter, select, arrange, mutate, summarize, etc. all provides ways to work with data based on the need. And when all of them combined together through pipe, it became even more powerful.
Tidying and joining data add allows more possibility to organize and analyze data on the basis provided before. Besides the technical things I’ve learned from this chapter, I think one important thing I’ve learned is that we always need to examine our data before using it. As the author went through examples, he always took a brief look at the data to make sure everything make sense. For example when he dealt with the murders data set, he pointed out that the offenders age should not be 999. That’s something could be omitted if we are not careful, especially when we have a huge data set like this one. Check for data that are out of the norm and filter them before hand is important in analyzing and designing process.
Fun started with chapter 4 when it gets into actually visualize data. Though I’ve learned a little bit from R for Data Science book, it is nice to review some of the things again and learn more about what’s possible with R. I am always amazed how powerful R is and how much possibilities it provides. It reminds me of iNZight, a data visualization software I liked a lot. R in a way is like a coding version of iNZight, which is great. From simple bar graph to scatterplot and heat map, and even the beautiful flat circle graph, I am constantly astonished. More than creating those graphs, R also allows customization on coloring and positioning, which could potentially save a lot of time for designers. Now I can’t wait to maybe actually use it for my project and I am excited to see how that will turn out.