This is a second tutorial on using FLTK’s Rust bindings.
It shows using input widgets to deal with user input thru creating a temperature converter app.
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.
[tidyverse](https://www.dataquest.io/blog/tutorial-getting-started-with-r-and-rstudio/#tve-jump-173bb264c2b)Packages into Memory
#data science tutorials #beginner #r tutorial #r tutorials #rstats #tutorial #tutorials
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:
Common symptoms of messy data include data that contain:
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:
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
readxlpackage. 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.
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:
Once the package is installed, load it to memory:
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…
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
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
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!
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
stringr packages installed — which, as a reminder, you can do with the
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
In this digital world, online businesses aspire to catch the attention of users in a modern and smarter way. To achieve it, they need to traverse through new approaches. Here comes to spotlight is the user-generated content or UGC.
What is user-generated content?
“ It is the content by users for users.”
Generally, the UGC is the unbiased content created and published by the brand users, social media followers, fans, and influencers that highlight their experiences with the products or services. User-generated content has superseded other marketing trends and fallen into the advertising feeds of brands. Today, more than 86 percent of companies use user-generated content as part of their marketing strategy.
In this article, we have explained the ten best ideas to create wonderful user-generated content for your brand. Let’s start without any further ado.
Generally, social media platforms help the brand to generate content for your users. Any user content that promotes your brand on the social media platform is the user-generated content for your business. When users create and share content on social media, they get 28% higher engagement than a standard company post.
Furthermore, you can embed your social media feed on your website also. you can use the Social Stream Designer WordPress plugin that will integrate various social media feeds from different social media platforms like Facebook, Twitter, Instagram, and many more. With this plugin, you can create a responsive wall on your WordPress website or blog in a few minutes. In addition to this, the plugin also provides more than 40 customization options to make your social stream feeds more attractive.
In general, surveys can be used to figure out attitudes, reactions, to evaluate customer satisfaction, estimate their opinions about different problems. Another benefit of customer surveys is that collecting outcomes can be quick. Within a few minutes, you can design and load a customer feedback survey and send it to your customers for their response. From the customer survey data, you can find your strengths, weaknesses, and get the right way to improve them to gain more customers.
Additionally, it is the best way to convert your brand leads to valuable customers. The key to running a successful contest is to make sure that the reward is fair enough to motivate your participation. If the product is relevant to your participant, then chances are they were looking for it in the first place, and giving it to them for free just made you move forward ahead of your competitors. They will most likely purchase more if your product or service satisfies them.
Furthermore, running contests also improve the customer-brand relationship and allows more people to participate in it. It will drive a real result for your online business. If your WordPress website has Google Analytics, then track contest page visits, referral traffic, other website traffic, and many more.
The business reviews help your consumers to make a buying decision without any hurdle. While you may decide to remove all the negative reviews about your business, those are still valuable user-generated content that provides honest opinions from real users. Customer feedback can help you with what needs to be improved with your products or services. This thing is not only beneficial to the next customer but your business as a whole.
Reviews are powerful as the platform they are built upon. That is the reason it is important to gather reviews from third-party review websites like Google review, Facebook review, and many more, or direct reviews on a website. It is the most vital form of feedback that can help brands grow globally and motivate audience interactions.
However, you can also invite your customers to share their unique or successful testimonials. It is a great way to display your products while inspiring others to purchase from your website.
Moreover, Instagram videos create around 3x more comments rather than Instagram photo posts. Instagram videos generally include short videos posted by real customers on Instagram with the tag of a particular brand. Brands can repost the stories as user-generated content to engage more audiences and create valid promotions on social media.
Similarly, imagine you are browsing a YouTube channel, and you look at a brand being supported by some authentic customers through a small video. So, it will catch your attention. With the videos, they can tell you about the branded products, especially the unboxing videos displaying all the inside products and how well it works for them. That type of video is enough to create a sense of desire in the consumers.
#how to get more user generated content #importance of user generated content #user generated content #user generated content advantages #user generated content best practices #user generated content pros and cons