Do you know how long EDA (exploratory data analysis) used to take me? Not hours, not days... A full week! Today I'm going to show you how to use dataxray. With this new R package I'm about to show you, you'll cut your EDA time into 5 minutes.
How to make a plot with two different y-axis in R with ggplot2? (a secret ggplot2 hack)
Your company lives off them... Excel files. Why not automate them & save some time? Here's an Excel File you're going to make in this tutorial from R.
ggradar: radar plots with ggplot in R
Need to quickly compare multiple groups in your data? Radar plots are the perfect way to analyze groups across many numeric metrics.
explore: simplified exploratory data analysis (EDA) in R
Did you know most Data Scientists spend 80% of their time just trying to understand and prepare data for analysis? R has an Insane Exploratory Data Analysis productivity-enhancer. It's called Explore.
ggdensity: A new R package for plotting high-density regions
As data scientists, it can be downright impossible to drill into messy data. Fortunately, there's a new R package that helps us focus on a "high-density region". It's called ggdensity.
modelDown: Automate Explainable AI (Machine Learning) in R
Machine learning is great... until you have to explain it. Stakeholders are normally non-technical, C-suites that ultimately want to know what the model does for the business. And how it helps increase revenue or decrease costs. A new R package, modelDown can help.
Survival Analysis in R (in under 10-minutes)
Learn how to do survival analysis in R in under 10-minutes. Plus get 3 bonuses to take your survival plots to the NEXT LEVEL. Let's go!
The Most Overlooked R Package (That Can Get You Through A Data Science Job Interview)
If you are looking to learn about the most overlooked R package that can help you get through a job interview AND you probably don't know it yet, you've come to the right place, my friend! And, if you want a job in data science, I'm going to show you how THIS R package can help you get through an interview with 5 lines of code.
How I analyze 100+ ggplots at once
Big data? Lot's of time series? Traditionally you'd use ggplot facets. But that only works for a few datasets. Enter trelliscopejs. It's a game changer!
What is the Career Path for a Data Scientist? (From $75,000 to $150,000 salary in 1-year)
The data science career path is demystified in this article showing you 2 case studies and a ton of research on how to set your career up for going from $75,000 to $150,000 in 1-year.
The 14 most important data science skills (To get a $50,000 increase in salary)
Which skills are important to becoming a data scientist? How to pick a language? How to learn the skills? These questions and many more are answered in this post.
My 4 most important explainable AI visualizations (modelStudio)
The modelStudio library offers an interactive studio for developing and exploring explainable AI visualizations.
A new R package for Business Analytics... radiant
I'm super impressed by the radiant R package. With no prior exposure to radiant, I was able to complete a short business analytics report in under 10-minutes.
Introducing portfoliodown: The Data Science Portfolio Website Builder
I'm super excited to introduce a new R package that makes it painless for data scientists to create a professional.
How to Make a Heatmap of Customers in R
The ggplot2 package is an essential tool in every data scientists toolkit. Today we show you how to use ggplot2 to make a professional heatmap that organizes customers by their sales purchasing habits.
Tidy Parallel Processing in R with furrr
ggalt: Make a Lollipop Plot to Compare Categories in ggplot2
ggalt is a ggplot2 extension that adds many new ggplot geometries. In this tutorial, we'll learn how to make lollipop plots for comparing categories within our data using geom_lollipop().
ggalt: Make a Dumbbell Plot to Visualize Change in ggplot2
ggalt is a ggplot2 extension that adds many new ggplot geometries. In this tutorial, we'll learn how to make dumbbell plots for visualizing change within our data using geom_dumbbell().
ggforce: Make a Hull Plot to Visualize Clusters in ggplot2
ggforce is a ggplot2 extension that adds many exploratory data analysis features. In this tutorial, we'll learn how to make hull plots for visualizing clusters or groups within our data.
ggdist: Make a Raincloud Plot to Visualize Distribution in ggplot2
The ggdist package is a ggplot2 extension that is made for visualizing distributions and uncertainty. We'll show see how ggdist can be used to make a raincloud plot.
easystats: Quickly investigate model performance
The easystats performance R package makes it easy to investigate the relevant assumptions for regression models. Simply use the check_model() function to produce a visualization that combines 6 tests for model performance.
R is for Research, Python is for Production
Both R and Python are great. We’ll showcase some of the strengths of each language in this article by showcasing where the major development efforts are within each ecosystem.
Gentle Introduction to Forecasting with Modeltime [Video Tutorial]
A gentle introduction to our forecasting package, Modeltime. Modeltime extends the Tidymodels ecosystem for time series forecasting. Learn how to forecast with ARIMA, Prophet, and linear regression time series models.
grafify: Make 5 powerful ggplot2 graphs quickly with R
Siuba: Data wrangling with dplyr in Python
The siuba python library brings the power of R's dplyr and the tidyverse to Python. Gain access to functions like group_by(), mutate(), summarize(), and more!
gghalves: Make Half Boxplot | Half Dotplot Visualizations with ggplot2
DataEditR: The GUI for Interactive Dataframe Editing in R
ggside: A new R package for plotting distributions in side-plots
patchwork: ggplot2 plot combiner
Now you can make publication-ready storyboards. Patchwork makes it simple to combine separate ggplots into the same graphic.
mmtable2: ggplot2 for tables
I love ggplot2 for plotting. The grammar of graphics allows us to add elements to plots. Tables seem to be forgotten in terms of an intuitive grammar with tidy data philosophy - Until now.
ggplot2 Extension: corrmorrant for Flexible Correlation Plots in R
Productivity is essential in data science. Businesses need value quickly so they can make decisions. Corrmorrant gets this.
Webscraping Tables in R: Datapasta Copy-and-Paster
Enhance Your Storytelling - Interactive Slide Decks with Rmarkdown
Slide Decks are so important for storytelling in business. We can use Rmarkdown to tell our story with engaging interactivity thanks to the xaringan library. Here's how to make PowerPoint-style Slide Presentations that are interactive straight from R.
Make PDF Data Analysis Reports with R | Rmarkdown Visual Editor
Let's make a professional business report in 5-minutes in HTML and PDF formats, and incorporates your data analysis in R. Reporting used to take me much longer and is now faster with the new Rmarkdown Visual Editor.
Build GGPLOT Code with Tableau Drag-and-Drop (R esquisse)
Tableau-users rejoice! The esquisse R package is here to make you life much easier - make ggplot2 plot code using a drag-and-drop Tableau interface. Here's what you need to do.
A Gentle Introduction to R Shiny
What if you could turn your #datascience analysis into a web application? You can do exactly that with R Shiny. R Shiny is an amazing framework built to convert your data analysis into a web app - FAST! Create amazing applications your business can use in hours (not months!).
Assess Your DATA QUALITY in R with skimr
Skimr is my go-to R package for fast data quality assessment, and Skimr is my first step in exploratory data analysis. Before I do anything else, I check data quality with skimr.
Hierarchical Time Series Forecasting [Full Code Tutorial]
In Learning Labs PRO Episode 50, Matt tackles an in-depth tutorial on Hierarchical Forecasting using the M5 Forecasting Competition.
DataExplorer: Exploratory Data Analysis in R
Did you know most Data Scientists spend 80% of their time just trying to understand and prepare data for analysis? R has an Insane Exploratory Data Analysis productivity-enhancer. It's called DataExplorer.
5 Reasons You Should Learn Shiny
Many data scientists struggle with distributing their work, however, you can make that a problem of the past thanks to Shiny. Here are five reasons you should learn Shiny and why it is a game-changer for upskilling your career.
How to Add Shiny to Rmarkdown
Shiny is an R web framework with a HUGE ECOSYSTEM of interactive widgets, themes, and customizable user interfaces called the Shinyverse. In this article, we use Shiny to make our R Markdown Report interactive.
R is for Research, Python is for Production
Both R and Python are great. We’ll showcase some of the strengths of each language in this article by showcasing where the major development efforts are within each ecosystem.
Predictive Power Score vs CorrelationFunnel
Exploratory Data Analysis is what every data scientist does to understand actionable insights from the data. This process used to take forever. Not anymore...
Full Feature Engineering Tutorial with Max Kuhn
Max Kuhn, from RStudio, discusses in-depth feature engineering for customer analytics. Watch Max and Matt tackle a tough feature engineering problem for customer analytics prediction.
Make Awesome Statistical Plots in R
I never thought I'd be able to make publication-ready statistical plots so easily. Seriously. Thanks to ggstatsplot.
Should I Become a Data Scientist or Data Analyst?
In order to determine where you wish to set your career trajectory, you need to understand the grey area and differences between data scientists and data analysts.
4 Ways to make Data Frames in R!
Data frames (like Excel tables) are the main way for storing, organizing, and analyzing data in R. Here are 4 ways using the tidyverse.
Should you learn Python or R in 2021?
For years Python and R have been pitted as mortal enemies in the world of data science, enticing its practitioners to choose a side and never look back - not anymore. It's time for these two titans to join forces through reticulate which allows us to use Python and R together!
Learn How to Write SQL From R
What kind of job can you get if you master machine learning?
One reason interest in machine learning jobs will continue to grow is how lucrative the pay is. Another is how interesting the work is. If you're looking to plant your foot in a growing industry, then machine learning could be for you. The average machine learning salary, according to Indeed's research, can be anywhere between $96,00 - $146,085.
How to Handle Missing Data in R with simputation
In 10-minutes, learn how to visualize and impute in R using ggplot dplyr and 3 more packages to simple imputation. Here are the links to get set up.
R/Python Teams Course Announcement
Add value as part of an R/Python Collaborative Team, be confident working with Python Users as part of a Team and working with Python.
How to Make 3D Plots in R
6 Life-Altering RStudio Keyboard Shortcuts
The RStudio IDE is amazing. You can enhance your R productivity even more with these simple keyboard shortcuts.
Plotting Time Series in R (New Cyberpunk Theme)
One of the most common data science visualization is a Time Series plot. In this tutorial we'll learn how to plot time series using ggplot, plotly and timetk.
Build and Evaluate A Logistic Regression Classifier
Logistic regression is a simple, yet powerful classification model. In this tutorial, learn how to build a predictive classifier that classifies the age of a vehicle.
6 Reasons To Learn R For Business [2021]
Learn R for business - Data science for business is the future of business analytics. Here are 6 reasons why R is the right choice.
Interactive Principal Component Analysis in R
Identify Clusters in your Data. We'll make an Interactive PCA visualization to investigate clusters and learn why observations are similar to each other.
How To Make Geographic Map Visualizations In R
If you are explaining data related to geography or just want to visualize by latitude / longitude location, you need to know ggplot2 and the tidyverse for making maps.
Top 5 Best Articles on R for Business [November 2020]
Each month, we release tons of great content on R for Business. These are the 5 Top Articles in R for Business over the past month. We have some great ones in November 2020.
Analyzing Solar Power Energy (IoT Analysis)
Solar power is a form of renewable clean energy that is created when photons from the sun excite elections in a photovoltaic panel, generating electricity. The power generated is usually tracked via sensor with measurements happening on a time based cadence.
Time Series Demand Forecasting
Demand Forecasting is a technique for estimation of probable demand for a product or services. It is based on the analysis of past demand for that product or service in the present market condition.
Forecasting Time Series ARIMA Models (10 Must-Know Tidyverse Functions #5)
Making multiple ARIMA Time Series models in R used to be difficult. But, with the purrr nest() function and modeltime, forecasting has never been easier. Learn how to make many ARIMA models in this tutorial.
Detect Relationships With Linear Regression (10 Must-Know Tidyverse Functions #4)
Group Split and Map are SECRET TOOLS in my data science arsenal. Combining them will help us scale up to 15 linear regression summaries to assess relationship strength and combine in a GT table.
10 Must-Know Tidyverse Functions: #3 - Pivot Wider and Longer
Pivoting wider is essential for making summary tables that go into reports and help humans understand key information.
Top 5 Best Articles on R for Business [October 2020]
Each month, we release tons of great content on R for Business. These are the 5 Top Articles in R for Business over the past month. We have some great ones in October. Let's dive in.
10 Must-Know Tidyverse Functions: #2 - across()
The across() function was released in dplyr 1.0.0. It's a new tidyverse function that extends group_by and summarize for multiple column and function summaries.
10 Must-Know Tidyverse Functions: #1 - relocate()
relocate() is like arrange() for columns. It keeps all of the columns, but provides much more flexibility for reordering. Notice how all of the columns are returned.
How to Visualize Time Series Data: Tidy Forecasting in R
How to Automate PDF Reporting with R
Why create PDF's manually when you can automate PDFs with R? That's exactly what I show you how to do in this video showcasing parameterized Rmarkdown.
How to Make Publication-Quality Excel Pivot Tables with R
The biggest thing I missed when I transititioned from Excel to R was PIVOT TABLES! Seriously, Pivot Tables are so useful. You can summarize and reshape (aka Pivot) data so easily with them in Excel.
How to Automate Exploratory Analysis Plots
A eatter way to do your EDA, and with less unnecessary coding and more flexibility using GGPLOT2 + PURRR. When you are plotting different charts during your exploratory data analysis, you sometimes end up doing a lot of repeated coding...
How to Automate Excel with R
Your company lives off them... Excel files. Why not automate them & save some time? Here's an Excel File you're going to make in this tutorial from R.
Top 5 Best Articles on R for Business [September 2020]
Finance in R - Evaluating American Funds Portfolio
Active funds have done poorly over the last ten years, and in most cases, struggled to justify their fees. In the post, there is a supporting chart showing a group of American Funds funds compared to the Vanguard Total Market index.
Using Drake for ETL - Building A Shiny Real Estate App
The drake plan organizes the project work flow according to targets, which are generated by scripts of functions and often functions of functions. The natural flow for our ETL was to check if the raw data was available on the local disc...
How to Automate PowerPoint Slidedecks with R
Here's a common situation, you have to make a Monday Morning Slide Deck. It's the same deck each week, just date ranges for your data change. Here's how to automate this process with R!
How to Scrape Word Documents with R
Your company has tons of them - Microsoft Word Documents! Scraping word documents is a powerful technique for extracting data. Let's learn how with R, officer, & tidyverse.
How To Get My Company To Pay For My Data Science Courses
Win Data Science Competitions with Shiny
The secret to accelerating your career - SHOW THAT YOU CAN PROVIDE BUSINESS VALUE! Check out the story of Raj, who won a Shiny data science competition using Shiny.
From No-Shiny Experience to Deploying My First Shiny App in 3-Months
Data science doesnt have to take years to learn. Here's an inspiring use-case from one of our students & how data science education helped add value to his company by creating a decision-making application.
How One Student Landed a VP-Level Analytics Role at a Major Bank
Data Science is the perfect field for those who are naturally curious and aspire to learn continuously throughout their career.
How to Set Up TensorFlow 2 in R in 5 Minutes (BONUS Image Recognition Tutorial)
Python can be run from R to leverage the strengths of both R and Python Data Science langauges. Learn how to set up Python's TensorFlow Library in 5 minutes.
How to Set Up Python's Scikit-Learn in R in 5 minutes
Python can be run from R to leverage the strengths of both R and Python Data Science langauges. Learn how to set up Python's Scikit-Learn Library in 5 minutes.
Increase Your Salary With Data Science Skills
The majority of us have experienced the average pay increase, because this is what most people receive. How would it feel to save your organization money or increase revenue for your organization and receive more compensation because of your work?
3 Free Resources to Learn R - Now Open
Business Science is offering free educational resources as a response to the coronavirus outbreak and social distancing measures.
Time Series Machine Learning (and Feature Engineering) in R
Machine learning is a powerful way to forecast Time Series. Feature Engineering is critical. A new innovation is coming in timetk - to help generate 200+ time-series features.
Part 6 - R Shiny vs Tableau (3 Business Application Examples)
Shiny is much more than just a dashboarding tool. Here we illustrate 3 powerful use cases for R Shiny Apps in business.
tidyquant v1.0.0: Pivot Tables, VLOOKUPs in R
The NEW tidyquant package (v1.0.0) makes popular Excel functions like Pivot Tables, VLOOKUP(), SUMIFS(), and much more possible in R.
R for Excel Users: Pivot Tables, VLOOKUPs in R
Learn how to use popular Excel functions in R like Pivot Tables, VLOOKUP(), SUMIFS(), and much more.
Tidy Discounted Cash Flow Analysis in R (for Company Valuation)
Learn how to use the Tidy Data Principles to perform a discounted cash flow analysis for Saudi Aramco, an oil giant with a value listed of 1.7 Trillion USD
Shiny Real Estate with Zillow API (Free Course)
Zillow is a FREE TOOL with an API that allows Data Scientists to connect to a massive repository of Home Prices and Features. In this lab, we use Shiny, Crosstalk, and Zillow API to create an ML-powered Zillow Home Price Explanation tool.
Google Trends Email Automation with Shiny
Google Trends is a FREE tool to gain insights about Google Search Terms your organization cares about. What if you could streamline the process of analyzing keyword search trends and emailing a report in 3-4 seconds? Learn how to do just that.
Product Price Prediction: A Tidy Hyperparameter Tuning and Cross Validation Tutorial
Learn how to model product prices using the tune library for hyperparameter tuning and cross-validation.
Part 5 - Five Reasons to Learn H2O for High-Performance Machine Learning
H2O is the scalable, open-source ML library that features AutoML. Here's why it's an essential library for me (and you).
NEW BOOK - The Shiny Production with AWS Book
The enterprise-grade process for deploying, hosting, and maintaining Shiny web applications using AWS, Docker, and Git.
Part 4 - Git for Data Science Applications (A Top Skill for 2020)
Moving into 2020, three things are clear - Organizations want Data Science, Cloud, and Apps. A key skill that companies need is Git for application development (I call this Full Stack Data Science). Here's what is driving Git's growth, and why you should learn Git for data science application development.
Part 1 - Five Full Stack Data Science Technologies for 2020 (and Beyond)
Moving into 2020, three things are clear - Organizations want Data Science, Cloud, and Apps. Here are the essential skills for Data Scientists that need to build and deploy applications in 2020 and beyond.
How I Landed My Data Science Job
Getting a job in Data Science is difficult. Here's how one Business Science student aced his Data Science Interview and landed a job at a top Management Consulting Firm.
Part 3 - Docker for Data Scientists (A Top Skill for 2020)
Moving into 2020, three things are clear - Organizations want Data Science, Cloud, and Apps. Here's how Docker plays a part in the essential skills of 2020.
Customer Churn Modeling using Machine Learning with parsnip
Learn how to perform a tidy approach to classification problem with the new parsnip R package for machine learning.
Is Your Job's Data Science Tech Stack Intimidating?
A data science team has many tools that all need to be integrated. And, this can be INTIMIDATING. Here are some tips to deal with the complexity of a data science tech stack.
Part 2 - Data Science with AWS (A Top Skill for 2020)
Organizations depend on the Data Science team to build distributed applications that solve business needs. AWS provides an infrastructure to host data science products for stakeholder to access.
Apply Data Science to Improve Addiction Treatment
Learn how one Business Science student created a data product that aims to help his organization improve the quality of care while reducing cost.
Web Scraping Product Data in R with rvest and purrr
Learn how to web scrape HTML, wangle JSON, and visualize product data from the Bicycle Manufacturer, Specialized Bicycles.
Cleaning Anomalies to Reduce Forecast Error by 9% with anomalize
We can often improve forecast performance by cleaning anomalous data prior to forecasting. This is the perfect use case for integrating the clean_anomalies() function from anomalize into your forecast workflow.
PDF Scraping in R with tabulizer
Learn how to scrape and wrangle PDF tables of a Report on Endangered Species with the tabulizer R package and visualize trends with ggplot2.
Big Data: Wrangling 4.6M Rows with dtplyr (the NEW data.table backend for dplyr)
Wrangling Big Data is one of the best features of the R programming language - which boasts a Big Data Ecosystem that contains fast in-memory tools (e.g. data.table) and distributed computational tools (sparklyr). With the NEW dtplyr package, data scientists with dplyr experience gain the benefits of data.table backend. We saw a 3X speed boost for dplyr!
Introducing correlationfunnel v0.1.0 - Speed Up Exploratory Data Analysis by 100X
I'm pleased to announce the introduction of correlationfunnel version 0.1.0, which officially hit CRAN yesterday. The correlationfunnel package is something I've been using for a while to efficiently explore data, understand relationships, and get to business insights as fast as possible.
How I Started My Data Science Business
This is a true story based on how I created my data science company from scratch. It's a detailed documentation of my personal journey along with the company I founded, Business Science.
Excel to R, Part 2 - Speed Up Exploratory Data Analysis 100X (R Code!)
Learn how going from Excel to R can speed up Exploratory Data Analysis, getting business insights 100X FASTER.
Build A R Shiny App (Tutorial) - Wedding Risk Model
Introducing the Ultimate R Cheat Sheet Version 2.0: The Shinyverse
The Ultimate R Cheat Sheet now covers the Shinyverse - An Ecosystem of R Packages for Shiny Web Application Development, Deployment, and putting Machine Learning into Production. Download the Cheat Sheet for Free!
How To Become A Financial Data Scientist (Or A Data Scientist In Any Domain)
Becoming a data scientist in Finance can be a lofty challenge... unless you know how to streamline the path.
A/B Testing with Machine Learning - A Step-by-Step Tutorial
New Cheat Sheet - Customer Segmentation and Clustering Workflow
Student feedback led to a BRAND NEW CHEAT SHEET on customer segmentation and a major overhaul to our Week 6 Modeling Chapter in our Business Analysis with R Course.
Excel to R, Part 1 - The 10X Productivity Boost
The first article in a 3-part series on Excel to R, this article walks the reader through a Marketing Case Study exposing the 10X productivity boost from switching from Excel to R.
Data Science In R - The Ultimate R Cheat Sheet - The Ultimateness Just Doubled!
The ultimate R cheat sheet links to the documentation and cheat sheets for every major R package. It just got even better with a brand new second page containing special topics and R packages!
Time Series Analysis for Business Forecasting with Artificial Neural Networks
This article demonstrates a real-world case study for business forecasting with regression models including artificial neural networks (ANNs) with Keras
R Cheat Sheet: Data Science Workflow with R
R and Python: How to Integrate the Best of Both into Your Data Science Workflow
R and Python - learn how to integrate both R and Python into your data science workflow. Use the strengths of the two dominant data science languages.
How To Complete A Kaggle Competition In 30 Minutes - Home Credit Default Challenge
Real world data science - Learn how to compete in a Kaggle Competition using Machine Learning with R.
DALEX: Interpretable Machine Learning Algorithms with Dalex and H2O
New Course Content: DS4B 201 Chapter 7, The Expected Value Framework For Modeling Churn With H2O
I’m pleased to announce that we released brand new content for our flagship course, Data Science For Business (DS4B 201). Over the course of 10 weeks, the DS4B 201 course teaches students and end-to-end data science project solving Employee Churn with R, H2O, & LIME. The latest content is focused on transitioning from modeling Employee Churn with H2O and LIME to evaluating our binary classification model using Return-On-Investment (ROI), thus delivering business value. We do this through application of a special tool called the Expected Value Framework. Let’s learn about the new course content available now in DS4B 201, Chapter 7, which covers the Expected Value Framework for modeling churn with H2O!
Data Science For Business: Course Now Open!
We are pleased to announce that our Data Science For Business (#DS4B) Course (HR 201) is OFFICIALLY OPEN! This course is for intermediate to advanced data scientists looking to apply H2O and LIME to a real-world binary classification problem in an organization: Employee Attrition. If you are interested applying data science for business in a real-world setting with advanced tools using a client-proven system that delivers ROI to the organization, then this is the course for you. For a limited time we are offering 15% off enrollment.
Data Science For Business: Course Launch In 5 Days!!!
Last November, our data science team embarked on a journey to build the ultimate Data Science For Business (DS4B) learning platform. We saw a problem: A gap exists in organizations between the data science team and the business. To bridge this gap, we’ve created Business Science University, an online learning platform that teaches DS4B, using high-end machine learning algorithms, and organized in the fashion of an on-premise workshop but at a fraction of the price. I’m pleased to announce that, in 5 days, we will launch our first course, HR 201, as part of a 4-course Virtual Workshop. We crafted the Virtual Workshop after the data science program that we wished we had when we began data science (after we got through the basics of course!). Now, our data science process is being opened up to you. We guide you through our process for solving high impact business problems with data science!
How To Learn R, Part 1: Learn From A Master Data Scientist's Code
The R programming language is a powerful tool used in data science for business (DS4B), but R can be unnecessarily challenging to learn. We believe you can learn R quickly by taking an 80/20 approach to learning the most in-demand functions and packages. In this article, we seek to ultimately understand what techniques are most critical to a beginners success through analyzing a master data scientist’s code base. Half of this article covers the web scraping procedure (using rvest
and purrr
) we used to collect our data (if new to R, you can skip this). The second half covers the insights gained from analyzing a master’s code base. In the next article in our series, we’ll develop a strategic learning plan built on our knowledge of the master. Last, there’s a bonus at the end of the article that shows how you can analyze your own code base using the new fs
package. Enjoy.
The Tidy Time Series Platform: tibbletime 0.1.0
We’re happy to announce the third release of the tibbletime
package. This is a huge update, mainly due to a complete rewrite of the package. It contains a ton of new functionality and a number of breaking changes that existing users need to be aware of. All of the changes have been well documented in the NEWS file, but it’s worthwhile to touch on a few of them here and discuss the future of the package. We’re super excited so let’s check out the vision for tibbletime
and its new functionality!
6 Reasons To Learn R For Business
Learn R for business - Data science for business is the future of business analytics. Here are 6 reasons why R is the right choice.
Demo Week: Time Series Machine Learning with h2o and timetk
Demo Week: Tidy Time Series Analysis with tibbletime
We’re into the fourth day of Business Science Demo Week. We have a really cool one in store today: tibbletime
, which uses a new tbl_time
class that is time-aware!! For those that may have missed it, every day this week we are demo-ing an R package: tidyquant
(Monday), timetk
(Tuesday), sweep
(Wednesday), tibbletime
(Thursday) and h2o
(Friday)! That’s five packages in five days! We’ll give you intel on what you need to know about these packages to go from zero to hero. Let’s take tibbletime
for a spin!
LIVE DataTalk on HR Analytics Tonight: Using Machine Learning to Predict Employee Turnover
Tonight at 7PM EST, we will be giving a LIVE #DataTalk on Using Machine Learning to Predict Employee Turnover. Employee turnover (attrition) is a major cost to an organization, and predicting turnover is at the forefront of needs of Human Resources (HR) in many organizations. Until now the mainstream approach has been to use logistic regression or survival curves to model employee attrition. However, with advancements in machine learning (ML), we can now get both better predictive performance and better explanations of what critical features are linked to employee attrition. We used two cutting edge techniques: the h2o
package’s new FREE automatic machine learning algorithm, h2o.automl()
, to develop a predictive model that is in the same ballpark as commercial products in terms of ML accuracy. Then we used the new lime
package that enables breakdown of complex, black-box machine learning models into variable importance plots. The talk will cover HR Analytics and how we used R, H2O, and LIME to predict employee turnover.
Demo Week: Tidy Forecasting with sweep
We’re into the third day of Business Science Demo Week. Hopefully by now you’re getting a taste of some interesting and useful packages. For those that may have missed it, every day this week we are demo-ing an R package: tidyquant
(Monday), timetk
(Tuesday), sweep
(Wednesday), tibbletime
(Thursday) and h2o
(Friday)! That’s five packages in five days! We’ll give you intel on what you need to know about these packages to go from zero to hero. Today is sweep
, which has broom
-style tidiers for forecasting. Let’s get going!
Demo Week: Time Series Machine Learning with timetk
We’re into the second day of Business Science Demo Week. What’s demo week? Every day this week we are demoing an R package: tidyquant
(Monday), timetk
(Tuesday), sweep
(Wednesday), tibbletime
(Thursday) and h2o
(Friday)! That’s five packages in five days! We’ll give you intel on what you need to know about these packages to go from zero to hero. Second up is timetk
, your toolkit for time series in R. Here we go!
Demo Week: class(Monday) <- tidyquant
We’ve got an exciting week ahead of us at Business Science: we’re launching our first ever Business Science Demo Week. Every day this week we are demoing an R package: tidyquant
(Monday), timetk
(Tuesday), sweep
(Wednesday), tibbletime
(Thursday) and h2o
(Friday)! That’s five packages in five days! We’ll give you intel on what you need to know about these packages to go from zero to hero. First up is tidyquant
, our flagship package that’s useful for financial and time series analysis. Here we go!
It's tibbletime v0.0.2: Time-Aware Tibbles, New Functions, Weather Analysis and More
Today we are introducing tibbletime v0.0.2
, and we’ve got a ton of new features in store for you. We have functions for converting to flexible time periods with the ~period formula~
and making/calculating custom rolling functions with rollify()
(plus a bunch more new functionality!). We’ll take the new functionality for a spin with some weather data (from the weatherData
package). However, the new tools make tibbletime
useful in a number of broad applications such as forecasting, financial analysis, business analysis and more! We truly view tibbletime
as the next phase of time series analysis in the tidyverse
. If you like what we do, please connect with us on social media to stay up on the latest Business Science news, events and information!
It's tibbletime: Time-Aware Tibbles
We are very excited to announce the initial release of our newest R package,
tibbletime
. As evident from the name, tibbletime
is built on top of the
tibble
package (and more generally on top of the tidyverse
) with the main
purpose of being able to create time-aware tibbles through a one-time
specification of an “index” column (a column containing timestamp information). There are a ton of useful time functions that we can now use such as time_filter()
, time_summarize()
, tmap()
, as_period()
and time_collapse()
. We’ll walk through the basics in this post.
alphavantager: An R interface to the Free Alpha Vantage Financial Data API
We’re excited to announce the alphavantager
package, a lightweight R interface to the Alpha Vantage API! Alpha Vantage is a FREE API for retreiving real-time and historical financial data. It’s very easy to use, and, with the recent glitch with the Yahoo Finance API, Alpha Vantage is a solid alternative for retrieving financial data for FREE! It’s definitely worth checking out if you are interested in financial analysis. We’ll go through the alphavantager
R interface in this post to show you how easy it is to get real-time and historical financial data. In the near future, we have plans to incorporate the alphavantager
into tidyquant
to enable scaling from one equity to many.
BizSci Package Updates: Formerly timekit... Now timetk :)
We have several announcements regarding Business Science R packages. First, as of this week the R package formerly known as timekit
has changed to timetk
for time series tool kit. There are a few “breaking” changes because of the name change, and this is discussed further below. Second, the sweep
and tidyquant
packages have several improvements, which are discussed in detail below. Finally, don’t miss a beat on future news, events and information by following us on social media.
sweep: Extending broom for time series forecasting
We’re pleased to introduce a new package, sweep
, now on CRAN! Think of it like broom
for the forecast
package. The forecast
package is the most popular package for forecasting, and for good reason: it has a number of sophisticated forecast modeling functions. There’s one problem: forecast
is based on the ts
system, which makes it difficult work within the tidyverse
. This is where sweep
fits in! The sweep
package has tidiers that convert the output from forecast
modeling and forecasting functions to “tidy” data frames. We’ll go through a quick introduction to show how the tidiers can be used, and then show a fun example of forecasting GDP trends of US states. If you’re familiar with broom
it will feel like second nature. If you like what you read, don’t forget to follow us on social media to stay up on the latest Business Science news, events and information!
timekit: New Documentation, Function Improvements, Forecasting Vignette
We’ve just released timekit
v0.3.0 to CRAN. The package updates include changes that help with making an accurate future time series with tk_make_future_timeseries()
and we’ve added a few features to tk_get_timeseries_signature()
. Most important are the new vignettes that cover both the making of future time series task and forecasting using the timekit
package. If you saw our last timekit post, you were probably surprised to learn that you can use machine learning to forecast using the time series signature as an engineered feature space. Now we are expanding on that concept by providing two new vignettes that teach you how to use ML and data mining for time series predictions. We’re really excited about the prospects of ML applications with time series. If you are too, I strongly encourage you to explore the timekit
package important links below. Don’t forget to check out our announcements and to follow us on social media to stay up on the latest Business Science news, events and information! Here’s a summary of the updates.
tidyquant: New Tools for Performing Financial Analysis within the Tidy Ecosystem
In advance of upcoming Business Science talks on tidyquant
at R/Finance and EARL San Francisco, we are releasing a technical paper entitled “New Tools For Performing Financial Analysis within the ‘Tidy’ Ecosystem”. The technical paper covers an overview of the current R financial package landscape, the independent development of the “tidyverse” data science tools, and the tidyquant
package that bridges the gap between the two underlying systems. Several usage cases are discussed. We encourage anyone interested in financial analysis and financial data science to check out the technical paper. We will be giving talks related to the paper at R/Finance on May 19th in Chicago and EARL on June 7th in San Francisco. If you can’t make it, I encourage you to read the technical paper and to follow us on social media to stay up on the latest Business Science news, events and information.
timekit: Time Series Forecast Applications Using Data Mining
The timekit
package contains a collection of tools for working with time series in R. There’s a number of benefits. One of the biggest is the ability to use a time series signature to predict future values (forecast) through data mining techniques. While this post is geared toward exposing the user to the timekit
package, there are examples showing the power of data mining a time series as well as how to work with time series in general. A number of timekit
functions will be discussed and implemented in the post. The first group of functions works with the time series index, and these include functions tk_index()
, tk_get_timeseries_signature()
, tk_augment_timeseries_signature()
and tk_get_timeseries_summary()
. We’ll spend the bulk of this post introducing you to these. The next function deals with creating a future time series from an existing index, tk_make_future_timeseries()
. The last set of functions deal with coercion to and from the major time series classes in R, tk_tbl()
, tk_xts()
, tk_zoo()
(and tk_zooreg()
), and tk_ts()
.
tidyquant 0.5.0: select, rollapply, and Quandl
We’ve got some good stuff cooking over at Business Science. Yesterday, we had the fifth official release (0.5.0) of tidyquant
to CRAN. The release includes some great new features. First, the Quandl integration is complete, which now enables getting Quandl data in “tidy” format. Second, we have a new mechanism to handle selecting which columns get sent to the mutation functions. The new argument name is… select
, and it provides increased flexibility which we show off in a rollapply
example. Finally, we have added several PerformanceAnalytics
functions that deal with modifying returns to the mutation functions. In this post, we’ll go over a few of the new features in version 5.
tidyquant Integrates Quandl: Getting Data Just Got Easier
Today I’m very pleased to introduce the new Quandl API integration that is available in the development version of tidyquant
. Normally I’d introduce this feature during the next CRAN release (v0.5.0 coming soon), but it’s really useful and honestly I just couldn’t wait. If you’re unfamiliar with Quandl, it’s amazing: it’s a web service that has partnered with top-tier data publishers to enable users to retrieve a wide range of financial and economic data sets, many of which are FREE! Quandl has it’s own R package (aptly named Quandl
) that is overall very good but has one minor inconvenience: it doesn’t return multiple data sets in a “tidy” format. This slight inconvenience has been addressed in the integration that comes packaged in the latest development version of tidyquant
. Now users can use the Quandl API from within tidyquant
with three functions: quandl_api_key()
, quandl_search()
, and the core function tq_get(get = "quandl")
. In this post, we’ll go through a user-contributed example, How To Perform a Fama French 3 Factor Analysis, that showcases how the Quandl integration fits into the “Collect, Modify, Analyze” financial analysis workflow. Interested readers can download the development version using devtools::install_github("business-science/tidyquant")
. More information is available on the tidyquant GitHub page including the updated development vignettes.
tidyquant 0.4.0: PerformanceAnalytics, Improved Documentation, ggplot2 Themes and More
I’m excited to announce the release of tidyquant
version 0.4.0!!! The release is yet again sizable. It includes integration with the PerformanceAnalytics
package, which now enables full financial analyses to be performed without ever leaving the “tidyverse” (i.e. with DATA FRAMES). The integration includes the ability to perform performance analysis and portfolio attribution at scale (i.e. with many stocks or many portfolios at once)! But wait there’s more… In addition to an introduction vignette, we created five (yes, five!) topic-specific vignettes designed to reduce the learning curve for financial data scientists. We also have new ggplot2
themes to assist with creating beautiful and meaningful financial charts. We included tq_get
support for “compound getters” so multiple data sources can be brought into a nested data frame all at once. Last, we have added new tq_index()
and tq_exchange()
functions to make collecting stock data with tq_get
even easier. I’ll briefly touch on several of the updates. The package is open source, and you can view the code on the tidyquant github page.
tidyquant 0.3.0: ggplot2 Enhancements, Real-Time Data, and More
tidyquant
, version 0.3.0, is a pretty sizable release that includes a little bit for everyone, including new financial charting and moving average geoms for use with ggplot2
, a new tq_get
get option called "key.stats"
for retrieving real-time stock information, and several nice integrations that improve the ease of scaling your analyses. If your not already familiar with tidyquant
, it integrates the best quantitative resources for collecting and analyzing quantitative data, xts
, zoo
, quantmod
and TTR
, with the tidyverse
allowing for seamless interaction between each. I’ll briefly touch on some of the updates by going through some neat examples. The package is open source, and you can view the code on the tidyquant github page.
Speed Up Your Code Part 2: Parallel Processing Financial Data with multidplyr + tidyquant
Since my initial post on parallel processing with multidplyr
, there have been some recent changes in the tidy
eco-system: namely the package tidyquant
, which brings financial analysis to the tidyverse
. The tidyquant
package drastically increase the amount of tidy financial data we have access to and reduces the amount of code needed to get financial data into the tidy format. The multidplyr
package adds parallel processing capability to improve the speed at which analysis can be scaled. I seriously think these two packages were made for each other. I’ll go through the same example used previously, updated with the new tidyquant
functionality.
tidyquant 0.2.0: Added Functionality for Financial Engineers and Business Analysts
tidyquant
, version 0.2.0, is now available on CRAN. If your not already familiar, tidyquant
integrates the best quantitative resources for collecting and analyzing quantitative data, xts
, zoo
, quantmod
and TTR
, with the tidy data infrastructure of the tidyverse
allowing for seamless interaction between each. I’ll briefly touch on some of the updates. The package is open source, and you can view the code on the tidyquant github page.
tidyquant: Bringing Quantitative Financial Analysis to the tidyverse
My new package, tidyquant
, is now available on CRAN. tidyquant
integrates the best quantitative resources for collecting and analyzing quantitative data, xts
, quantmod
and TTR
, with the tidy data infrastructure of the tidyverse
allowing for seamless interaction between each. While this post aims to introduce tidyquant
to the R community, it just scratches the surface of the features and benefits. We’ll go through a simple stock visualization using ggplot2
, which which shows off the integration. The package is open source, and you can view the code on the tidyquant github page.