Publications by Sharp Sight
How to map geospatial data: USA rivers
R Code Here’s the R code to produce the map: #=============== # LOAD PACKAGES #=============== library(tidyverse) library(maptools) #=============== # GET RIVER DATA #=============== #========== # LOAD DATA #========== #DEFINE URL # - this is the location of the file url.river_data <- url("http://sharpsightlabs.com/wp-content/da...
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One thing critical for success in the age of AI (and who has it)
Data science, artificial intelligence, automation, and other advanced technologies are reshaping the world. We’re starting to see glimpses of it, for example the rapid emergence of self driving cars. Moreover, the changes brought about by technology will likely become more frequent and more dramatic in the next few years. In fact, two MIT econ...
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How to make a global map in R, step by step
In the last several blog posts at Sharp Sight, I’ve created several different maps. Maps are great for practicing data visualization. First of all, there’s a lot of data available on places like Wikipedia that you can map. Moreover, creating maps typically requires several essential skills in combination. Specifically, you commonly need t...
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How to annotate a plot in ggplot2
“Master the basics.” That’s a common mantra here at Sharp Sight. Loyal readers know what I mean by “master the basics.” To master data science, you need to master the foundational tools. That means knowing how to create essential plots like: Bar charts Line charts Histograms And performing data manipulations like: Creating new variab...
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Mapping “France at night” with the new sf package
A few days ago, I was inspired by a set of photographs of Earth from space, at night. The images are amazing, so I decided to try to replicate them using R. To be a little more specific, I found a single dataset – a data set with French population data – and I’m using it to replicate the images for a single country: France. A few notes and ...
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How to choose a project to practice data science
Here at Sharp Sight, I’ve derided the “jump in and build something” method of learning data science for quite some time. Learning data science by “jumping in” and starting a big project is highly inefficient. However, projects can be extremely useful for practicing data science and refining your skillset, if you know how to select the ...
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The one thing you need to master data science
When you ask people what makes a person great – what makes someone an elite performer – they commonly say “talent.” Most people believe that elite performers are born with their talent. Most people believe that top performers come into the world with an innate talent that makes them special. You see something like this in data science...
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Are you fluent in R?
A few weeks ago, I wrote an article saying that you should master R. The basic argument, is that if you want to actually work as a data scientist, you need to know the essential tools backwards and forwards. In response, a reader left a comment. I have to say that it’s unfortunate to read something like this, but sadly it’s very common. ...
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Simple practice: basic maps with the Tidyverse
To master data science, you need to practice. This sounds easy enough, but in reality, many people have no idea how to practice. A full explanation of how to practice is beyond the scope of this blog post, but I can give you a quick tip here: You need to master the most important techniques, and then practice those techniques in small scripts unt...
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Simple practice: data wrangling the iris dataset
In last weeks post, I emphasized the importance of practicing R and the Tidyverse with small, simple problems, drilling them until you are competent. In that post, I gave you a few very small scripts to practice (which I suggest that you memorize). This week, I want to give you another small example. We’re going to clean up the iris dataset. M...
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