Written by Matt Dancho on October 20, 2024
Effectively managing inventory is critical to a well-functioning supply chain. Learn how to project inventories using the planr package in R to optimize your supply chain operations.
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Written by Matt Dancho on October 3, 2024
Enhance your data analysis workflow with these top 10 R packages for exploratory data analysis (EDA). Discover how to gain deeper insights into your data using tools like skimr, psych, corrplot, and more.
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Written by Matt Dancho on September 22, 2024
In today's fast-paced data science environment, speeding up exploratory data analysis (EDA) is more critical than ever. This is where gt_summarytools() comes in. A new function I’ve developed, gt_summarytools(), combines the best features of gt and summarytools, allowing you to create detailed, interactive data summaries faster and with more flexibility than ever.
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Written by Matt Dancho on September 15, 2024
Getting quick insights into your data is absolutely critical to data understanding, predictive modeling, and production. Learn how to use the summarytools package in R to analyze your data faster.
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Written by Matt Dancho on August 25, 2024
Hey guys, welcome back to my R-tips newsletter. In today's lesson, I'm sharing a curated list of the top 25 R packages that you aren't using (but you need to learn in 2024). Let's go!
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Written by Matt Dancho on August 9, 2024
Hey guys, welcome back to my R-tips newsletter. In today's lesson, I'm sharing how to do data exploration in R for visualization tool called GWalkR that is like Tableau for $0. Let's go!
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Written by Matt Dancho on July 19, 2024
Hey guys, welcome back to my R-tips newsletter. In today's lesson, I'm sharing how to use Polars in R for shockingly-fast data manipulation. Let's go!
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Written by Matt Dancho on June 14, 2024
Hey guys, welcome back to my R-tips newsletter. In today's lesson, I'm sharing how to use Large Language Models (LLMs) in R with tidychatmodels. Let's go!
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Written by Matt Dancho on May 11, 2024
Hey guys, welcome back to my R-tips newsletter. In today's lesson, I'm sharing how to get ChatGPT in R with chattr. Let's go!
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Written by Matt Dancho on April 14, 2024
Hey guys, welcome back to my R-tips newsletter. In today's lesson, I'm sharing how to make Mobile Web Apps with R Shiny using shinyMobile. Let's go!
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Written by Matt Dancho on March 31, 2024
Hey guys, welcome back to my R-tips newsletter. In today's lesson, I'm sharing how to scrape a PDF financial statement. Then I'll show you how to summarize it with OpenAI LLMs in R. Let's go!
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Written by Matt Dancho on February 24, 2024
What's the one thing that will impress your company? A professional business report. And Microsoft Word is the defacto standard (NOT Jupyter Notebooks, HTML web-reports). Even PDFs aren't ideal, especially if they need to review them.
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Written by Matt Dancho on January 12, 2024
Hey guys, welcome back to my R-tips newsletter. In today's lesson, I'm sharing how I tune XGBoost Hyperparameters in R. Let's go!
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Written by Matt Dancho on December 23, 2023
Hey guys, welcome back to my R-tips newsletter. In today's lesson, I'm sharing how to do Time Series Analysis in R. Let's go!
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Written by Matt Dancho on December 16, 2023
Hey guys, welcome back to my R-tips newsletter. In today's video, I'm sharing how to do A/B testing in R. Let's go!
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Written by Arben Kqiku (Intro by Matt Dancho) on December 2, 2023
Hey guys, welcome back to my R-tips newsletter. In today's lesson, we're sharing how to use R in production, with Mage.ai and Google Cloud. Let's go!
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Written by Matt Dancho on November 4, 2023
Hey guys, welcome back to my R-tips newsletter. In today's video, I'm sharing how to make a professional data science portfolio in under 15 minutes. Let's go!
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Written by Matt Dancho on October 29, 2023
Hey guys, welcome back to my R-tips newsletter. In today's video, I'm sharing the cheat code to detecting anomalizes. We'll cover a full financial analysis. Let's go!
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Written by Matt Dancho on October 15, 2023
Hey guys, welcome back to my R-tips newsletter. In today's video, I'm sharing the cheat code to making an R shiny web app that automatically analyzes your Excel Files. Plus, I'm sharing exactly how I made it in under 15 minutes. AND how you can do it for ANY company. Let's go!
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Written by Matt Dancho on September 30, 2023
In this article, I share 9 R packages that have helped me the most. Let's go!
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Written by Matt Dancho on September 5, 2023
In this article, I share the cheat code to automating Google Sheets. Plus, I'm sharing exactly how I made it in under 2 minutes. AND how you can do it for ANY company (using ChatGPT and R). Let's go!
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Written by Matt Dancho on August 27, 2023
Quit trying to tell stories with data visualization. Do this instead. If you're a data scientist or data analyst who wants to get an executive to stop from looking at his phone and to start looking at your slide deck OR someone on LinkedIn to stop mindlessly scrolling and start reading your post, then this tutorial will help.
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Written by Matt Dancho on August 6, 2023
Transitioning from Excel to R for data analysis enhances efficiency and enables more complex operations, and R's capability to convert Excel tables simplifies this transition. This article illustrates the importance of this shift and guides readers through the process of converting Excel tables into R.
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Written by Matt Dancho on July 20, 2023
Writing code is a slow process especially when you are first learning time series. What if you could speed it up? You can and this is how.
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Written by Matt Dancho on July 6, 2023
Need to cluster faster? I've been playing around with a new R package that makes it super simple. It's called Tidyclust.
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Written by Matt Dancho on June 5, 2023
Storytelling is critical to your success as a Data Scientist. Your career hinges on whether or not you can pursuade management, executives, and leadership to make decisions. But how do you do this effectively?
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Written by Matt Dancho on April 30, 2023
Writing code is a slow process especially when you are first learning data science. What if you could speed it up? You can and this is how.
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Written by Matt Dancho on April 2, 2023
Want to learn how to build a shiny app in under 10 minutes. You can with chatgpt!
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Written by Matt Dancho on March 19, 2023
Want to impact your business? Learn how to use geospatial data... And the first step is Geocoding addresses and latitude/longitude.
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Written by Matt Dancho on January 5, 2023
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.
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Written by Matt Dancho on December 1, 2022
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.
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Written by Matt Dancho on October 4, 2022
Need to quickly compare multiple groups in your data? Radar plots are the perfect way to analyze groups across many numeric metrics.
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Written by Matt Dancho on September 23, 2022
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.
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Written by Matt Dancho on July 21, 2022
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.
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Written by Matt Dancho on July 6, 2022
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.
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Written by Matt Dancho on June 9, 2022
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!
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Written by Matt Dancho on June 2, 2022
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.
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Written by Matt Dancho on March 30, 2022
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!
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Written by Matt Dancho on March 15, 2022
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.
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Written by Matt Dancho on March 11, 2022
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.
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Written by Matt Dancho on February 22, 2022
The modelStudio library offers an interactive studio for developing and exploring explainable AI visualizations.
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Written by Matt Dancho on February 1, 2022
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.
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Written by Matt Dancho on December 20, 2021
I'm super excited to introduce a new R package that makes it painless for data scientists to create a professional.
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Written by Matt Dancho on October 12, 2021
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.
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Written by Matt Dancho on September 14, 2021
furrr is a critical package to speed up iterative calculations using tidyverse purrr syntax.
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Written by Matt Dancho on August 24, 2021
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().
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Written by Matt Dancho on August 12, 2021
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().
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Written by Matt Dancho on July 27, 2021
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.
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Written by Matt Dancho on July 22, 2021
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.
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Written by Matt Dancho on July 13, 2021
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.
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Written by Matt Dancho and Jarrell Chalmers on July 12, 2021
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.
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Written by Matt Dancho on June 29, 2021
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.
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Written by Matt Dancho on June 15, 2021
grafify offers 19 plotting functions that make it quick and easy to make great-looking plots in R.
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Written by Matt Dancho on June 8, 2021
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!
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Written by Matt Dancho on May 25, 2021
gghalves is a new R package that makes it easy to compose your own half-plots using ggplot2
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Written by Matt Dancho on May 18, 2021
Now you can edit data in R using a GUI that is reminiscent of Excel.
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Written by Matt Dancho on May 18, 2021
Marginal distributions can now be made in R using ggside, a new ggplot2 extension.
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Written by Matt Dancho on May 11, 2021
Now you can make publication-ready storyboards. Patchwork makes it simple to combine separate ggplots into the same graphic.
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Written by Matt Dancho on May 4, 2021
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.
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Written by Matt Dancho on April 27, 2021
Productivity is essential in data science. Businesses need value quickly so they can make decisions. Corrmorrant gets this.
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Written by Matt Dancho on April 20, 2021
Datapasta is an amazing package that allows us to copy-and-paste any HTML or Excel Tables into R.
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Written by Matt Dancho on April 6, 2021
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.
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Written by Matt Dancho on March 30, 2021
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.
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Written by Matt Dancho on March 23, 2021
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.
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Written by Matt Dancho on March 16, 2021
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!).
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Written by Matt Dancho on March 9, 2021
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.
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Written by Matt Dancho on March 4, 2021
In Learning Labs PRO Episode 50, Matt tackles an in-depth tutorial on Hierarchical Forecasting using the M5 Forecasting Competition.
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Written by Matt Dancho on March 2, 2021
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.
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Written by Matt Dancho and Jarrell Chalmers on February 25, 2021
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.
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Written by Matt Dancho on February 24, 2021
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.
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Written by Matt Dancho and Jarrell Chalmers on February 18, 2021
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.
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Written by Matt Dancho on February 16, 2021
Exploratory Data Analysis is what every data scientist does to understand actionable insights from the data. This process used to take forever. Not anymore...
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Written by Matt Dancho on February 11, 2021
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.
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Written by Matt Dancho on February 9, 2021
I never thought I'd be able to make publication-ready statistical plots so easily. Seriously. Thanks to ggstatsplot.
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Written by Matt Dancho and Jarrell Chalmers on February 5, 2021
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.
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Written by Matt Dancho on February 2, 2021
Data frames (like Excel tables) are the main way for storing, organizing, and analyzing data in R. Here are 4 ways using the tidyverse.
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Written by Matt Dancho on January 28, 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!
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Written by Matt Dancho on January 26, 2021
SQL queries getting you down? Let R write SQL queries for you!
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Written by Matt Dancho on January 21, 2021
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.
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Written by Matt Dancho on January 19, 2021
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.
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Written by Matt Dancho on January 14, 2021
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.
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Written by Matt Dancho on January 12, 2021
Learn how to make AMAZING 3D Plots in R by combining ggplot2 and rayshader.
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Written by Matt Dancho on January 5, 2021
The RStudio IDE is amazing. You can enhance your R productivity even more with these simple keyboard shortcuts.
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Written by Matt Dancho on December 30, 2020
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.
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Written by Matt Dancho on December 22, 2020
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.
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Written by Matt Dancho on December 17, 2020
Learn R for business - Data science for business is the future of business analytics. Here are 6 reasons why R is the right choice.
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Written by Matt Dancho on December 15, 2020
Identify Clusters in your Data. We'll make an Interactive PCA visualization to investigate clusters and learn why observations are similar to each other.
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Written by Matt Dancho on December 8, 2020
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.
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Written by Matt Dancho on December 4, 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.
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Written by Nathaniel Whitlock on December 1, 2020
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.
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Written by Luciano Oliveira Batista on November 25, 2020
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.
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Written by Matt Dancho on November 24, 2020
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.
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Written by Matt Dancho on November 17, 2020
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.
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Written by Matt Dancho on November 13, 2020
Pivoting wider is essential for making summary tables that go into reports and help humans understand key information.
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Written by Matt Dancho on November 6, 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.
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Written by Matt Dancho on November 3, 2020
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.
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Written by Matt Dancho on October 27, 2020
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.
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Written by Joon Im on October 22, 2020
Plot time series data using the fpp2, fpp3, and timetik forecasting frameworks.
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Written by Matt Dancho on October 21, 2020
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.
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Written by Matt Dancho on October 14, 2020
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.
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Written by Luciano Oliveira Batista on October 8, 2020
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...
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Written by Matt Dancho on October 7, 2020
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.
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Written by Matt Dancho on October 2, 2020
The top 5 best articles on R for Business from last month.
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Written by David Lucey on September 30, 2020
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.
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Written by David Lucey on September 24, 2020
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...
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Written by Matt Dancho on September 22, 2020
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!
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Written by Matt Dancho on September 16, 2020
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.
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Written by Matt Dancho on September 7, 2020
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Written by Matt Dancho on August 12, 2020
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.
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Written by Matt Dancho on August 5, 2020
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.
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Written by Matt Dancho on May 26, 2020
Data Science is the perfect field for those who are naturally curious and aspire to learn continuously throughout their career.
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Written by Matt Dancho on May 15, 2020
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.
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Written by Matt Dancho on April 20, 2020
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.
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Written by Matt Dancho on April 13, 2020
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?
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Written by Matt Dancho on March 20, 2020
Business Science is offering free educational resources as a response to the coronavirus outbreak and social distancing measures.
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Written by Matt Dancho on March 18, 2020
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.
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Written by Matt Dancho on March 9, 2020
Shiny is much more than just a dashboarding tool. Here we illustrate 3 powerful use cases for R Shiny Apps in business.
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Written by Matt Dancho on March 4, 2020
The NEW tidyquant package (v1.0.0) makes popular Excel functions like Pivot Tables, VLOOKUP(), SUMIFS(), and much more possible in R.
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Written by Matt Dancho on February 26, 2020
Learn how to use popular Excel functions in R like Pivot Tables, VLOOKUP(), SUMIFS(), and much more.
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Written by Rafael Nicolas Fermin Cota on February 21, 2020
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
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Written by Matt Dancho on February 10, 2020
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.
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Written by Matt Dancho on January 24, 2020
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.
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Written by Matt Dancho on January 21, 2020
Learn how to model product prices using the tune library for hyperparameter tuning and cross-validation.
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Written by Matt Dancho on January 13, 2020
H2O is the scalable, open-source ML library that features AutoML. Here's why it's an essential library for me (and you).
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Written by Matt Dancho on January 2, 2020
The enterprise-grade process for deploying, hosting, and maintaining Shiny web applications using AWS, Docker, and Git.
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Written by Matt Dancho on December 9, 2019
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.
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Written by Matt Dancho on December 9, 2019
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.
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Written by Matt Dancho on November 27, 2019
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.
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Written by Matt Dancho on November 22, 2019
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.
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Written by Diego Usai on November 18, 2019
Learn how to perform a tidy approach to classification problem with the new parsnip R package for machine learning.
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Written by Matt Dancho on November 15, 2019
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.
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Written by Matt Dancho on November 13, 2019
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.
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Written by Matt Dancho on November 11, 2019
Learn how one Business Science student created a data product that aims to help his organization improve the quality of care while reducing cost.
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Written by Joon Im on October 7, 2019
Learn how to web scrape HTML, wangle JSON, and visualize product data from the Bicycle Manufacturer, Specialized Bicycles.
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Written by Matt Dancho on September 30, 2019
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.
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Written by Jennifer Cooper on September 23, 2019
Learn how to scrape and wrangle PDF tables of a Report on Endangered Species with the tabulizer R package and visualize trends with ggplot2.
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Written by Matt Dancho on August 15, 2019
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!
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Written by Matt Dancho on August 7, 2019
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.
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Written by Matt Dancho on July 22, 2019
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.
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Written by Matt Dancho on July 8, 2019
Learn how going from Excel to R can speed up Exploratory Data Analysis, getting business insights 100X FASTER.
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Written by Bryan Clark on June 9, 2019
Learn step-by-step how to built a wedding risk model Shiny app.
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Written by Matt Dancho on June 3, 2019
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!
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Written by Matt Dancho on May 23, 2019
Becoming a data scientist in Finance can be a lofty challenge... unless you know how to streamline the path.
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Written by Matt Dancho on March 11, 2019
Experience how to implement Machine Learning for A/B Testing step-by-step
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Written by Matt Dancho on February 24, 2019
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.
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Written by Matt Dancho on February 20, 2019
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.
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Written by Matt Dancho on January 7, 2019
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!
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Written by Blaine Bateman on December 4, 2018
This article demonstrates a real-world case study for business forecasting with regression models including artificial neural networks (ANNs) with Keras
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Written by Matt Dancho on November 4, 2018
Get the new R Cheat Sheet that makes learning data science with R quick and efficient.
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Written by Matt Dancho on October 8, 2018
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.
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Written by Matt Dancho on August 7, 2018
Real world data science - Learn how to compete in a Kaggle Competition using Machine Learning with R.
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Written by Brad Boehmke on July 23, 2018
Interpret machine learning algorithms with R to explain why one prediction is made over another.
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Written by Matt Dancho on July 16, 2018
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!
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Written by Matt Dancho on April 30, 2018
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.
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Written by Matt Dancho on April 25, 2018
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!
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Written by Matt Dancho on March 3, 2018
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.
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Written by Davis Vaughan on January 4, 2018
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!
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Written by Matt Dancho on December 27, 2017
Learn R for business - Data science for business is the future of business analytics. Here are 6 reasons why R is the right choice.
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Written by Matt Dancho on October 28, 2017
Learn R in this time series using H2O machine learning demonstration.
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Written by Matt Dancho on October 26, 2017
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!
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Written by Matt Dancho on October 26, 2017
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.
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Written by Matt Dancho on October 25, 2017
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!
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Written by Matt Dancho on October 24, 2017
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!
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Written by Matt Dancho on October 23, 2017
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!
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Written by Davis Vaughan on October 8, 2017
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!
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Written by Davis Vaughan on September 7, 2017
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.
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Written by Matt Dancho on September 3, 2017
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.
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Written by Matt Dancho on July 27, 2017
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.
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Written by Matt Dancho on July 9, 2017
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!
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Written by Matt Dancho on May 17, 2017
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.
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Written by Matt Dancho on May 11, 2017
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Written by Matt Dancho on May 2, 2017
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()
.
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Written by Davis Vaughan on April 4, 2017
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.
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Written by Matt Dancho on March 19, 2017
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.
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Written by Matt Dancho on March 4, 2017
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.
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Written on January 22, 2017
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.
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Written on January 21, 2017
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.
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Written on January 8, 2017
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.
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Written on January 1, 2017
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.
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Written on December 18, 2016
Use parallel processing to speed up your R code, using tidyverse multidplyr.
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