Since I started learning R, my main motivation has been to get amazing visualizations like the ones made by Cédric Scherer, Georgios Karamanis, and Thomas Lin Pedersen among many others. What mesmerizes me about them is that to achieve that goal you need to deal with code, data handling and proper graphical selection, at the same time that you check for aesthetic design. Finally, all this process will pay off turning flat data into a great piece of visualization

Since I realized all the possibilities about coding in R -mostly- but also in other language, I wanted to create a blog to share some of the things I have learned. However, I also like the idea of a blog as a platform to share incoming knowledge as a learning-teaching process.

Since I started learning R, my main motivation has been to get amazing visualizations like the ones made by Cédric Scherer, Georgios Karamanis, and Thomas Lin Pedersen among many others. What mesmerizes me about them is that to achieve that goal you need to deal with code, data handling and proper graphical selection, at the same time that you check for aesthetic design. Finally, all this process will pay off turning flat data into a great piece of visualization

Since I realized all the possibilities about coding in R -mostly- but also in other language, I wanted to create a blog to share some of the things I have learned. However, I also like the idea of a blog as a platform to share incoming knowledge as a learning-teaching process.

Many times the reason people approach the R language is because they have seen that it is a great tool for achieving better visualizations. However, since R is still a programming language, it can be frustrating that to do so you have to deal with a few lines of code to load, and preprocess your data before you start creating the simplest visualization. So the main goal of this post is to provide an easier way to deal with the steps from loading your data to visualization. So you will follow a step-by-step beginner’s guide to handle your data to visualization.

I have often seen that people are interested in learning R because of the visualizations that can be generated with packages like {ggplot2}. However, R is a programming language, so its power is not only limited to that, but it is also a powerful tool for statistical analysis. Therefore, the main goal of this post is to provide tools to generate descriptive statistical analysis, and associated visualizations. This will allow us to better explore our data in search of patterns. Thus, here you will follow a step-by-step guide to perform descriptive statistical analysis and generate visualizations of your data to help you understand it better.

Many times the reason people approach the R language is because they have seen that it is a great tool for achieving better visualizations. However, since R is still a programming language, it can be frustrating that to do so you have to deal with a few lines of code to load, and preprocess your data before you start creating the simplest visualization. So the main goal of this post is to provide an easier way to deal with the steps from loading your data to visualization. So you will follow a step-by-step beginner’s guide to handle your data to visualization.

Since I started learning R, my main motivation has been to get amazing visualizations like the ones made by Cédric Scherer, Georgios Karamanis, and Thomas Lin Pedersen among many others. What mesmerizes me about them is that to achieve that goal you need to deal with code, data handling and proper graphical selection, at the same time that you check for aesthetic design. Finally, all this process will pay off turning flat data into a great piece of visualization

I have often seen that people are interested in learning R because of the visualizations that can be generated with packages like {ggplot2}. However, R is a programming language, so its power is not only limited to that, but it is also a powerful tool for statistical analysis. Therefore, the main goal of this post is to provide tools to generate descriptive statistical analysis, and associated visualizations. This will allow us to better explore our data in search of patterns. Thus, here you will follow a step-by-step guide to perform descriptive statistical analysis and generate visualizations of your data to help you understand it better.

Many times the reason people approach the R language is because they have seen that it is a great tool for achieving better visualizations. However, since R is still a programming language, it can be frustrating that to do so you have to deal with a few lines of code to load, and preprocess your data before you start creating the simplest visualization. So the main goal of this post is to provide an easier way to deal with the steps from loading your data to visualization. So you will follow a step-by-step beginner’s guide to handle your data to visualization.

I have often seen that people are interested in learning R because of the visualizations that can be generated with packages like {ggplot2}. However, R is a programming language, so its power is not only limited to that, but it is also a powerful tool for statistical analysis. Therefore, the main goal of this post is to provide tools to generate descriptive statistical analysis, and associated visualizations. This will allow us to better explore our data in search of patterns. Thus, here you will follow a step-by-step guide to perform descriptive statistical analysis and generate visualizations of your data to help you understand it better.

Since I realized all the possibilities about coding in R -mostly- but also in other language, I wanted to create a blog to share some of the things I have learned. However, I also like the idea of a blog as a platform to share incoming knowledge as a learning-teaching process.

Since I started learning R, my main motivation has been to get amazing visualizations like the ones made by Cédric Scherer, Georgios Karamanis, and Thomas Lin Pedersen among many others. What mesmerizes me about them is that to achieve that goal you need to deal with code, data handling and proper graphical selection, at the same time that you check for aesthetic design. Finally, all this process will pay off turning flat data into a great piece of visualization

Since I realized all the possibilities about coding in R -mostly- but also in other language, I wanted to create a blog to share some of the things I have learned. However, I also like the idea of a blog as a platform to share incoming knowledge as a learning-teaching process.

I have often seen that people are interested in learning R because of the visualizations that can be generated with packages like {ggplot2}. However, R is a programming language, so its power is not only limited to that, but it is also a powerful tool for statistical analysis. Therefore, the main goal of this post is to provide tools to generate descriptive statistical analysis, and associated visualizations. This will allow us to better explore our data in search of patterns. Thus, here you will follow a step-by-step guide to perform descriptive statistical analysis and generate visualizations of your data to help you understand it better.

I have often seen that people are interested in learning R because of the visualizations that can be generated with packages like {ggplot2}. However, R is a programming language, so its power is not only limited to that, but it is also a powerful tool for statistical analysis. Therefore, the main goal of this post is to provide tools to generate descriptive statistical analysis, and associated visualizations. This will allow us to better explore our data in search of patterns. Thus, here you will follow a step-by-step guide to perform descriptive statistical analysis and generate visualizations of your data to help you understand it better.

Since I realized all the possibilities about coding in R -mostly- but also in other language, I wanted to create a blog to share some of the things I have learned. However, I also like the idea of a blog as a platform to share incoming knowledge as a learning-teaching process.

Since I started learning R, my main motivation has been to get amazing visualizations like the ones made by Cédric Scherer, Georgios Karamanis, and Thomas Lin Pedersen among many others. What mesmerizes me about them is that to achieve that goal you need to deal with code, data handling and proper graphical selection, at the same time that you check for aesthetic design. Finally, all this process will pay off turning flat data into a great piece of visualization