Day: April 14, 2020

Apple was the most imitated brand for phishing attacks in Q1 2020

Phishing is one of the most common attacking methods used by cybercriminals to dupe people into giving up data or money. Hackers often imitate popular brands as a front to trick people into believing that an email is sent by a genuine company.

According to a report from cybersecurity firm Checkpoint, Apple is the most imitated brand for phishing scams in Q1 2020 followed by Netflix, Yahoo, and WhatsApp. Researchers at the firm said attackers tried to take advantage of the buzz created by the Cupertino-based tech giants anticipated product launches.

[Read: Scientists don’t know if viral load is linked

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Dplyr across: First look at a new Tidyverse function

Analyzing a data frame by column is one of R’s great strengths. But what if you’re a Tidyverse user and you want to run a function across multiple columns?

As of dplyr 1.0, there will be a new function for this: across(). Let’s take a look.

When this article was published, dplyr 1.0 wasn’t yet available on CRAN. However, you can get access to all the new functions by downloading the development version of dplyr with this command:

remotes::install_github("tidyverse/dplyr")

For this demonstration, I’ll use some data showing COVID-19 spread: USA Facts’ confirmed U.S. cases by day and county. If you

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5 ways to manage a cloud migration using a remote team

The number one question I’ve gotten during the past few weeks: How to run cloud migration projects using a completely remote workforce?

The reasons are obvious. Considering the new world order, companies have accelerated demand for tactical cloud migrations at the same time they have been forced into a remote worker paradigm. Some organizations already had some remote workers, other very few, and many had none at all until recent events. 

There are two types of companies here. First, those that already have become comfortable with a remote workforce over the years and understand how to manage projects with staffers

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Dplyr across: First look at a new Tidyverse function

Analyzing a data frame by column is one of R’s great strengths. But what if you’re a Tidyverse user and you want to run a function across multiple columns?

As of dplyr 1.0, there will be a new function for this: across(). Let’s take a look.

When this article was published, dplyr 1.0 wasn’t yet available on CRAN. However, you can get access to all the new functions by downloading the development version of dplyr with this command:

remotes::install_github("tidyverse/dplyr")

For this demonstration, I’ll use some data showing COVID-19 spread: USA Facts’ confirmed U.S. cases by day and county. If you

Read More