This tutorial will be about iteration in R. More specifically, I’ll focus on some functions of the purrr package. Feedback is welcomed. The dataset I will manipulate is from the week 22 of TidyTuesday and it’s called Wine Ratings. This dataset is very informative about types of wines and its origins as well as the respective prices and points attributed. Moreover, it’s very detailed in regard to critical reviews and information about critics.

In this post I’ll work with this dataset from Kaggle which is related to the number of suicides in several countries across many years. However, I won’t make any kind of inferential analysis about the data. My main goal is to make a tutorial about how to work with factors in R by showing the powerful tidyverse package called forcats. I will explore some variables that can be turned into factors and show you the main functions of forcats to help you wrangle data.

Welcome to the blog. In this new post I’ll do a short tutorial on how to work with strings in R. I’ll show you some of the main functions of the stringr package and the amazing power of the rebus package. The data frame I will be using is from the week 13 of TidyTuesday. This data frame seemed to be the perfect opportunity to build this tutorial given the importance of strings for its understanding.

Welcome to this new post about the Euro versus Dollar historical exchange rate since 1999 to the present day. This post will deal with dates, so I will use mainly the lubridate package and some of its most important functions. I will do my best to show you the power and simplicity of this truly magnificent tool within the R universe. Nevertheless, I won’t be restricted only to lubridate and will use some other packages to deal with this type of data.

In this post I will use two of the most popular clustering methods, hierarchical clustering and k-means clustering, to analyse a data frame related to the financial variables of some pharmaceutical companies. Clustering is an unsupervised learning technique where we segment the data and identify meaningful groups that have similar characteristics. In our case, the goal will be to find these groups within the pharmaceutical companies data. Like we did in the previous posts we will start by loading the required packages to our analysis.

Welcome to a new exciting post! Today I have decided to bring you text mining applied to two of my favorite novels: Crime and Punishment by Dostoyevsky and Anna Karenina by Tolstoy. We will use mainly the incredible tidytext package developed by Julia Silge and David Robinson. You can read more about this package in the book of the same authors Text Mining with R: A Tidytext Approach. Let us start the analysis of “Crime and Punishment” and “Anna Karenina” by loading the required packages.

In this post, we will fit a multiple logistic regression model to predict the probability of a bank customer accepting a personal loan based on multiple variables to be described later. Logistic regression is a supervised learning algorithm were the independent variable has a qualitative nature. In this case, corresponding to the acceptance or rejection of a personal loan. This tutorial will build multiple logistic regression models and assess them.

In this blogpost, we will come back to the subject of the German Elections. We will try to show, mostly visually, the changes in election results during the 21st century. Thus, we will use data from the elections in 2002 to the last ones in 2017. The main focus will be mapping the results of the parties represented in the current Bundestag (German Parliament) during this time span. Let’s start our coding.

This post will talk about multiple linear regression in the context of machine learning. Linear regression is one of the simplest and most used approaches for supervised learning. This tutorial will try to help you in how to use the linear regression algorithm. I am also new to the machine learning approach, but I’m very interested in this area given the predictive ability that you can gain from this. Let’s hope I can help you.

In R missing values are usually, but not always, represented by letters NA. How to deal with missing values is very important in the data analytics world. Missing data can be sometimes tricky while analyzing a data frame, since it should be handled correctly for our statistical analysis. Before diving into more complex details about missing data, the first question that should be asked in any exploratory data analysis is: Do I have missing values in my database?