A key component in data acquisition or reporting is the ability to trigger your script to run at a set time each day. Whether you are attempting to download the latest stock prices or update corporate earnings reports, once you’ve created the script to do the actual work, you need to find a way for it to be run on the correct schedule.
PowerBI is Microsoft’s data exploration and dashboarding tool. While it hasn’t risen to desktop prominence like Excel and Outlook have for the majority of knowledge workers, it is an incredibly capable tool which allows you to quickly visualize data from a number of data sources and explore the data using a graphical interface.
Previously we looked at how you can combine R and Markdown to create reports directly from your R scripts, and also how to send email from R using Microsoft Outlook. In this post, we’ll take these concepts a step further and look at how we can use R to embed images in email messages or even use Markdown to create entire messages.
Robotic process automation (or RPA) is transforming the way many businesses handle their repetitious, labour intensive tasks such as reporting, making basic decisions, and providing services. Using software these tasks can be automated; reducing the time to complete tasks while also improving their accuracy and consistency. If you want to get started down the RPA path without incurring licensing costs, there are free tools you can start using today.
One underappreciated feature in R is the ability to easily create beautiful reports using Markdown. Markdown files contain a combination of code and text, allowing you to write your analysis alongside your code and publish both the analysis document and code in a wide variety of formats with little effort.
One of the biggest benefits from creating an automatic reporting framework is that you no longer need to directly supervise the creation and distribution of reports. However, when things go wrong it can be difficult to understand what went wrong and why. Luckily, R’s tryCatchLog package makes it easy to trap and log errors as they occur.
One of the first steps in any data science project is to acquire and analyze the raw data. Since this data will commonly be stored in databases, understanding Structured Query Language (SQL) will enable you to get the data you need and start working quickly. This post summarizes the basics of SQL’s SELECT statement, which is how you retrieve information from the database.
A successful data scientist needs to draw on skills from many disciplines, and one of the core skill sets is knowledge of relational databases and querying using structured query language (SQL). Relational databases are the most common way to store structured data, so a firm understanding of databases is key to obtaining performing simple analysis and reporting quickly.
Hiring is one of the most challenging tasks in a manager’s portfolio. Selecting individuals for a long-term commitment based on a few comparatively short interviews and conversations is not easy. Luckily, there are a number of personality traits to look for which can help you select promising individuals for your data science or analytics team.
When setting up automated reporting workflows, a key component is distributing the report to the various stakeholders so they can review it. One useful technique is to send a “push notification” by email to alert everyone that a report or analysis task has just been completed. Luckily, it isn’t hard to send out email from R by using Microsoft Outlook.