Nowadays, most data scientists use either Python or R as their main programming language. That was also my case until I met Julia earlier this year. Julia promises performance comparable to statically typed compiled languages (like C) while keeping the rapid development features of interpreted languages (like Python, R or Matlab). This performance is achieved by just-in-time (JIT) compilation. Instead of interpreting code, Julia compiles code in runtime. While JIT compilation has…

In the last couple of years, real estate companies have shifted their focus to the digital world, and now almost all investments have an online system showing what apartments are available. This is very convenient for their potential clients, as they can easily become familiar with the apartments on offer. Things become interesting when all available data is monitored on a weekly basis, and sales progress is analysed.

Why is this so important? As sales…

Here is how we can use the maps, mapdata and ggplot2 libraries to create maps in R.



In this particular example, we’re going to create a world map showing the points of Beijing and Shanghai, both cities in China. For this particular map, we will be displaying the Northern Hemisphere from Europe to Asia.





require(maps)

require(mapdata)

library(ggplot2)

library(ggrepel)

cities =…

Last Sunday at Trivadis Tech Event, I talked about R for Hackers. It was the first session slot on Sunday morning, it was a crazy, nerdy topic, and yet there were, like, 30 people attending! An emphatic thank you to everyone who came!

R a crazy, nerdy topic, - why that, you'll be asking? What's so nerdy about using R?

Well, it was about R. But it was neither an introduction ("how to get things done quickly with R"), nor was it even about data science. True, you…

Original post is published at DataScience+

Recently, I become interested to grasp the data from webpages, such as Wikipedia, and to visualize it with R. As I did in my previous post, I use rvest package to get the data from webpage and…

In this post I will present a simple way how to export your regression results (or output) from R into Microsoft Word. Previously, I have written a tutorial how to create Table 1 with study characteristics and to export into Microsoft Word. These posts are especially useful for researchers who prepare their manuscript for publication in peer-reviewed journals.

Get the results…

In research, especially in medical research, we describe characteristics of our study populations through Table 1. The Table 1 contain information about the mean for continue/scale variable, and proportion for categorical variable. For example: we say that the mean of systolic blood pressure in our study population is 145 mmHg, or 30% of participants are smokers. Since is called Table 1, means that is the first table in the manuscript.

To create the Table 1…

In statistics, a outlier is defined as a observation which stands far away from the most of other observations. Often a outlier is present due to the measurements error. Therefore, one of the most important task in data analysis is to identify and (if is necessary) to remove the outliers.

There are different methods to detect the outliers, including standard deviation approach and Tukey’s method which use interquartile (IQR) range approach. In this post I will use…

Learn how to utilize multi-core, high-memory machines to dramatically accelerate your computations in R and Python, without any complex or time-consuming setup.

You'll learn:

1. How to…

We’ve created a Domino project with starter code in R and Python for participating in the Data Science Bowl. 

Get a jump start in the competition with our starter project by training your models on massive hardware and running multiple experiments in parallel while keeping track of…