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As noted in several recent posts, when you’re learning R and R’s Tidyverse packages, it’s important to break everything down into small units that you can learn.

By putting the individual piece together, you solidify your knowledge of how they work individually but also begin to learn how you can combine small tools together to create novel effects.

Here, we’re going to use a fairly small set of functions to create a map of the largest cities in Europe.

We are using the minus sign (‘-‘) in front of the names of the variables that we want to remove.

To add these new variable names, we can simply assign them by using the colnames() function.

Essentially, there were some leading digits and special characters that appear to be useless artifacts of the scraping process.

This is a quick way to get the numbers at the end of the string, but we actually don’t want to keep the ‘♠’

For the sake of making the data a little easier to explain, we’re going to filter the data to records where the population is over 1,000,000.

After obtaining the geo data, we will join it back to the original data using cbind().

we would go back to an earlier part of the analysis and modify our code to correct any problems in the data.

You’ll change your data-wrangling code as you work with the data and identify new items you need to change or fix.

You’ll also change your ggplot() visualization code multiple times as you try different colors, fonts, and settings.

Creating this visualization is actually not terribly hard to do, but if you’re somewhat new to R, it might seem rather challenging.

If you look at this, and it seems difficult then you need to understand: once you master the basics, the hard things never seem hard.

What I mean by that, is that this visualization is nothing more than a careful application of a few dozen simple tools, arranged in a way to create something new.

Once you master individual tools from ggplot2, dplyr, and the rest of the Tidyverse, projects like this become very easy to execute.

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As noted in several recent posts, when you’re learning R and R’s Tidyverse packages, it’s important to break everything down into small units that you can learn.

By putting the individual piece together, you solidify your knowledge of how they work individually but also begin to learn how you can combine small tools together to create novel effects.

Here, we’re going to use a fairly small set of functions to create a map of the largest cities in Europe.

When we do this, we are extracting everything from the ‘♠’ character to the end of the string (note: to do this, we are using a regular expression in str_extract()).

This is a quick way to get the numbers at the end of the string, but we actually don’t want to keep the ‘♠’ character.

So, after we extract the population numbers (along with the ‘♠’), we then strip off the ‘♠’ character by using str_replace().

For the sake of making the data a little easier to explain, we’re going to filter the data to records where the population is over 1,000,000.

If we had found anything “out of line,” we would go back to an earlier part of the analysis and modify our code to correct any problems in the data.

You’ll change your data-wrangling code as you work with the data and identify new items you need to change or fix.

You’ll also change your ggplot() visualization code multiple times as you try different colors, fonts, and settings.

Creating this visualization is actually not terribly hard to do, but if you’re somewhat new to R, it might seem rather challenging.

If you look at this, and it seems difficult then you need to understand: once you master the basics, the hard things never seem hard.

What I mean by that, is that this visualization is nothing more than a careful application of a few dozen simple tools, arranged in a way to create something new.

Once you master individual tools from ggplot2, dplyr, and the rest of the Tidyverse, projects like this become very easy to execute.

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As a quick followup to last week’s mapping exercise (where we mapped the largest European cities), I want to map the largest cities in Asia.

When we did this last week, we used a variety of tools from the Tidyverse to scrape and wrangle the data, and we ultimately mapped the data using base ggplot2.

In this blog post, we’re going to scrape and wrangle the data in a very similar way, but we will visualize with a combination of ggmap() and ggplot().

Explaining exactly how readr works is beyond the scope of this post, but notice that we’re using several functions in series by using the pipe operator (%>%).

After executing the web scraping code and inspecting the resulting data, we are going to begin some data wrangling.

In fact, one of the reasons that I strongly recommend using tools from the Tidyverse (like dplyr::select()) is that they are easy to learn, easy to memorize, and ultimately easy to use.

You also typically want your variable names to start with lower case letters (they are easier to type that way).

We are using the c() function to create a vector of strings (the new names), and we are assigning that vector of names as the column names of df.asia_cities by using the colnames() function.

The problem is that if we geocode based on only the city name (without the country), the geocoding process can encounter some errors due to ambiguity.

refer to Naples, Florida or Naples, Italy?) To make sure that we don’t have any problems, we will create a variable that contains both city and country information.

essentially, we’ve been just getting the country shapes, plotting them with ggplot(), and then plotting data points on top of the polygons.

I won’t explain ggmap and get_map() completely here, but essentially, these tools allow you to get maps from Google Maps and other sources.

we just want to plot the data to make sure that the points are aligned properly, and that our data is properly “cleaned.”

However, when I initially ran this code, I found a few things amiss, and had to go back and make some adjustments to the previous data wrangling code.

As you progress through a project, you may find things that are wrong with your data, and you need to iteratively go back and adjust your code until you get everything just right.

This is where we will modify the theme elements of the plot, add a title and subtitles, remove extraneous non-data elements (like the axis ticks, etc).

want to emphasize that after you’ve mastered the essential syntax of the most important R functions, small projects like this are excellent practice.

Visualize Machine Learning Data in Python With Pandas

You must understand your data in order to get the best results from machine learning algorithms.

This dataset describes the medical records for Pima Indians and whether or not each patient will have an onset of diabetes within five years.

It is a good dataset for demonstration because all of the input attributes are numeric and the output variable to be predicted is binary (0 or 1).

The plots look like an abstracted histogram with a smooth curve drawn through the top of each bin, much like your eye tried to do with the histograms.

Boxplots summarize the distribution of each attribute, drawing a line for the median (middle value) and a box around the 25th and 75th percentiles (the middle 50% of the data).

The whiskers give an idea of the spread of the data and dots outside of the whiskers show candidate outlier values (values that are 1.5 times greater than the size of spread of the middle 50% of the data).

This is useful to know, because some machine learning algorithms like linear and logistic regression can have poor performance if there are highly correlated input variables in your data.

We can also see that each variable is perfectly positively correlated with each other (as you would expected) in the diagonal line from top left to bottom right.

Scatter plots are useful for spotting structured relationships between variables, like whether you could summarize the relationship between two variables with a line.

Because there is little point oi drawing a scatterplot of each variable with itself, the diagonal shows histograms of each attribute.

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