We see our boxplots and it looks like we could reasonably compare them. We can change the look of the Y tick labels by scaling the axis, scale Y continuous here and setting the labels argument. First I'm going to filter out boating group and then I'm going to keep effort and trips in here even though we're only looking at the one. At the moment they're in scientific notation, this really isn't very friendly for reading. Also, let's change those tick labels on the y-axis. Let's get rid of boat fishing to make this a little bit more interesting as a demonstration. The Great Lakes are huge and never freeze over completely, so you can fish on them all the time with a boat, basically whenever you want to, while the rest of the modes they have limitations and we would expect to see these limitations come up in a given month skewing the number of hours anglers can put in. We can see that by far boat fishing is the most common. Now we can check it out with that basic boxplot. We're going to do a quick mutation on the mode and I'm going to change it all to lower. For this work though, I think we want to have to use either the two lower or two upper, which you guessed it converts a string to either lowercase or uppercase. Paste is another similar function, but it works and allows you to concatenate all objects together. We've already seen STR_C, which concatenate string values together. ![]() This is handy when you want to separate out things like a username from the rest of an email address. Another handy function is string split, which will allow you to split a string based on some value and return parts of it. For instance, there's one called N char, which will count the number of characters in a string. Many of the functions you might want to use are actually included in base R. Now I actually haven't shown you much about cleaning up string values in R so this seems like a perfect opportunity to do that. We need to clean this up in our data cleaning. It looks like a data has some different ways it was coded for a given mode, either an all caps or entitled case where each word is capitalized. I'm just going to take our data, I'm going to group it by year and mode, run our summarized function and sum of those two columns across the groups, toss it into a GG plot and build our geom boxplot. I want to be able to see, on a yearly basis, what does the distribution of fishing mode look like when it comes to a total amount of effort and angler trips? I'll start just by looking at the effort levels. I'm going to start by bringing in our dataset and grouping it by year and modes so that we can get a sense of the distributions through boxplots. ![]() I think a quick demonstration will make this really clear so let's get back to fishing. We can apply the same approach to comparing multiple distributions in a way very similar to boxplots, and the result is something called a violin plot. But last week we actually saw how we can plot the distributions themselves with histograms and kernel density estimation plots. We saw how we can plot some summary statistics of distributions. It is also ideal for current data analytics professionals or students looking to enter the public sector. The series is ideal for current or early-career professionals working in the public sector looking to gain skills in analyzing public data effectively. This is the second of four courses within the Data Analytics in the Public Sector with R Specialization. Upon completing this course, you will understand the layered grammar of graphics and its implementation in ggplot2, all while exploring a diverse set of authentic public datasets.Īll coursework is completed in RStudio in Coursera without the need to install additional software. These skills are enhanced with lessons on best practices for good information visualization design. Technical skills in this course will focus on the ggplot2 library of the tidyverse, and include developing bar, line, and scatter charts, generating trend lines, and understanding histograms, kernel density estimations, violin plots, and ridgeplots. Learn analytical and technical skills using the R programming language to explore, visualize, and present data, with a focus on equity and the administrative functions of planning and reporting. Learn about the core pillars of the public sector and the core functions of public administration through statistical Exploratory Data Analysis (EDA).
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