1624340760

# Recursion Tutorial Example Explained

recursion tutorial example explained

``````
// recursion = When a thing is defined in terms of itself. - Wikipedia
//      Apply the result of a procedure, to a procedure.
//      A recursive method calls itself. Can be a substitute for iteration.
//      Divide a problem into sub-problems of the same type as the original.
//      Commonly used with advanced sorting algorithms and navigating trees

//      ----------
//      easier to debug

//      ----------
//      sometimes slower
//      uses more memory

``````

#recursion

1603537200

## While You Don't Understand Recursion, Read Recursion: by Randy Taylor

Recursion is the one idea I constantly use while I solve coding problems. Most of the time I don’t start by thinking “RECURSION WILL SOLVE THIS!”. However recursion just ends up being the logical way to reach an answer. In my professional opinion recursion is the purest form of coding; write a function that will call itself until you get what you want! To implement recursion we will create a helper algorithm. 1) Identify what the smallest input is. 2) Continually break down a larger input into smaller inputs and pass those smaller inputs back into itself until you get the desired answer. 3) Define a “base case” that will stop the Recursion should the answer not be found.

Let’s look at the idea of Recursion first. We are writing code that will execute itself until we get a desired answer or reach a base case. Essentially we are creating a loop. I will illustrate this with pseudo code:

``````for (let recursion =()=>{ …answer? answer = true : false} ; answer === false; recursion())
``````

Much like a traditional for loop the above pseudo code will continue while the second condition is true; the recursion will continue until answer === true. At this point the second statement of the for loop is false terminating the loop. Above if answer === false recursion will call itself again. This is the general idea of recursion. This is why creating a base case is essential to prevent an infinite loop. The “answer” we are looking for might not be present causing recursion to run until the sun burns out.

#algorithms #javascript #recursion #tutorial-for-beginners #iteration #recursion-explained #what-is-recursion #programming

1596728880

## Tutorial: Getting Started with R and RStudio

In this tutorial we’ll learn how to begin programming with R using RStudio. We’ll install R, and RStudio RStudio, an extremely popular development environment for R. We’ll learn the key RStudio features in order to start programming in R on our own.

If you already know how to use RStudio and want to learn some tips, tricks, and shortcuts, check out this Dataquest blog post.

#data science tutorials #beginner #r tutorial #r tutorials #rstats #tutorial #tutorials

1596513720

## 1. Characteristics of Clean Data and Messy Data

What exactly is clean data? Clean data is accurate, complete, and in a format that is ready to analyze. Characteristics of clean data include data that are:

• Free of duplicate rows/values
• Error-free (e.g. free of misspellings)
• Relevant (e.g. free of special characters)
• The appropriate data type for analysis
• Free of outliers (or only contain outliers have been identified/understood), and
• Follows a “tidy data” structure

Common symptoms of messy data include data that contain:

• Special characters (e.g. commas in numeric values)
• Numeric values stored as text/character data types
• Duplicate rows
• Misspellings
• Inaccuracies
• White space
• Missing data
• Zeros instead of null values

## 2. Motivation

In this blog post, we will work with five property-sales datasets that are publicly available on the New York City Department of Finance Rolling Sales Data website. We encourage you to download the datasets and follow along! Each file contains one year of real estate sales data for one of New York City’s five boroughs. We will work with the following Microsoft Excel files:

• rollingsales_bronx.xls
• rollingsales_brooklyn.xls
• rollingsales_manhattan.xls
• rollingsales_queens.xls
• rollingsales_statenisland.xls

As we work through this blog post, imagine that you are helping a friend launch their home-inspection business in New York City. You offer to help them by analyzing the data to better understand the real-estate market. But you realize that before you can analyze the data in R, you will need to diagnose and clean it first. And before you can diagnose the data, you will need to load it into R!

Benefits of using tidyverse tools are often evident in the data-loading process. In many cases, the tidyverse package `readxl` will clean some data for you as Microsoft Excel data is loaded into R. If you are working with CSV data, the tidyverse `readr` package function `read_csv()` is the function to use (we’ll cover that later).

Let’s look at an example. Here’s how the Excel file for the Brooklyn borough looks:

The Brooklyn Excel file

Now let’s load the Brooklyn dataset into R from an Excel file. We’ll use the `readxl`package. We specify the function argument `skip = 4` because the row that we want to use as the header (i.e. column names) is actually row 5. We can ignore the first four rows entirely and load the data into R beginning at row 5. Here’s the code:

``````library(readxl) # Load Excel files
brooklyn <- read_excel("rollingsales_brooklyn.xls", skip = 4)
``````

Note we saved this dataset with the variable name `brooklyn` for future use.

## 4. View the Data with tidyr::glimpse()

The tidyverse offers a user-friendly way to view this data with the `glimpse()` function that is part of the `tibble` package. To use this package, we will need to load it for use in our current session. But rather than loading this package alone, we can load many of the tidyverse packages at one time. If you do not have the tidyverse collection of packages, install it on your machine using the following command in your R or R Studio session:

``````install.packages("tidyverse")
``````

Once the package is installed, load it to memory:

``````library(tidyverse)
``````

Now that `tidyverse` is loaded into memory, take a “glimpse” of the Brooklyn dataset:

``````glimpse(brooklyn)
## Observations: 20,185
## Variables: 21
## \$ BOROUGH <chr> "3", "3", "3", "3", "3", "3", "…
## \$ NEIGHBORHOOD <chr> "BATH BEACH", "BATH BEACH", "BA…
## \$ `BUILDING CLASS CATEGORY` <chr> "01 ONE FAMILY DWELLINGS", "01 …
## \$ `TAX CLASS AT PRESENT` <chr> "1", "1", "1", "1", "1", "1", "…
## \$ BLOCK <dbl> 6359, 6360, 6364, 6367, 6371, 6…
## \$ LOT <dbl> 70, 48, 74, 24, 19, 32, 65, 20,…
## \$ `EASE-MENT` <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## \$ `BUILDING CLASS AT PRESENT` <chr> "S1", "A5", "A5", "A9", "A9", "…
## \$ ADDRESS <chr> "8684 15TH AVENUE", "14 BAY 10T…
## \$ `APARTMENT NUMBER` <chr> NA, NA, NA, NA, NA, NA, NA, NA,…
## \$ `ZIP CODE` <dbl> 11228, 11228, 11214, 11214, 112…
## \$ `RESIDENTIAL UNITS` <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1…
## \$ `COMMERCIAL UNITS` <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## \$ `TOTAL UNITS` <dbl> 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1…
## \$ `LAND SQUARE FEET` <dbl> 1933, 2513, 2492, 1571, 2320, 3…
## \$ `GROSS SQUARE FEET` <dbl> 4080, 1428, 972, 1456, 1566, 22…
## \$ `YEAR BUILT` <dbl> 1930, 1930, 1950, 1935, 1930, 1…
## \$ `TAX CLASS AT TIME OF SALE` <chr> "1", "1", "1", "1", "1", "1", "…
## \$ `BUILDING CLASS AT TIME OF SALE` <chr> "S1", "A5", "A5", "A9", "A9", "…
## \$ `SALE PRICE` <dbl> 1300000, 849000, 0, 830000, 0, …
## \$ `SALE DATE` <dttm> 2020-04-28, 2020-03-18, 2019-0…
``````

The `glimpse()` function provides a user-friendly way to view the column names and data types for all columns, or variables, in the data frame. With this function, we are also able to view the first few observations in the data frame. This data frame has 20,185 observations, or property sales records. And there are 21 variables, or columns.

#data science tutorials #beginner #r #r tutorial #r tutorials #rstats #tidyverse #tutorial #tutorials

1599097440

## Data Visualization in R with ggplot2: A Beginner Tutorial

A famous general is thought to have said, “A good sketch is better than a long speech.” That advice may have come from the battlefield, but it’s applicable in lots of other areas — including data science. “Sketching” out our data by visualizing it using ggplot2 in R is more impactful than simply describing the trends we find.

This is why we visualize data. We visualize data because it’s easier to learn from something that we can see rather than read. And thankfully for data analysts and data scientists who use R, there’s a tidyverse package called ggplot2 that makes data visualization a snap!

In this blog post, we’ll learn how to take some data and produce a visualization using R. To work through it, it’s best if you already have an understanding of R programming syntax, but you don’t need to be an expert or have any prior experience working with ggplot2

#data science tutorials #beginner #ggplot2 #r #r tutorial #r tutorials #rstats #tutorial #tutorials

1596584126

## R Tutorial: Better Blog Post Analysis with googleAnalyticsR

In my previous role as a marketing data analyst for a blogging company, one of my most important tasks was to track how blog posts performed.

On the surface, it’s a fairly straightforward goal. With Google Analytics, you can quickly get just about any metric you need for your blog posts, for any date range.

But when it comes to comparing blog post performance, things get a bit trickier.

For example, let’s say we want to compare the performance of the blog posts we published on the Dataquest blog in June (using the month of June as our date range).

But wait… two blog posts with more than 1,000 pageviews were published earlier in the month, And the two with fewer than 500 pageviews were published at the end of the month. That’s hardly a fair comparison!

My first solution to this problem was to look up each post individually, so that I could make an even comparison of how each post performed in their first day, first week, first month, etc.

However, that required a lot of manual copy-and-paste work, which was extremely tedious if I wanted to compare more than a few posts, date ranges, or metrics at a time.

But then, I learned R, and realized that there was a much better way.

In this post, we’ll walk through how it’s done, so you can do my better blog post analysis for yourself!

## What we’ll need

To complete this tutorial, you’ll need basic knowledge of R syntax and the tidyverse, and access to a Google Analytics account.

Not yet familiar with the basics of R? We can help with that! Our interactive online courses teach you R from scratch, with no prior programming experience required. Sign up and start today!

You’ll also need the `dyplr``lubridate`, and `stringr` packages installed — which, as a reminder, you can do with the `install.packages()` command.

Finally, you will need a CSV of the blog posts you want to analyze. Here’s what’s in my dataset:

`post_url`: the page path of the blog post

`post_date`: the date the post was published (formatted m/d/yy)

`category`: the blog category the post was published in (optional)

`title`: the title of the blog post (optional)

Depending on your content management system, there may be a way for you to automate gathering this data — but that’s out of the scope of this tutorial!

For this tutorial, we’ll use a manually-gathered dataset of the past ten Dataquest blog posts.

#data science tutorials #promote #r #r tutorial #r tutorials #rstats #tutorial #tutorials