Specify additional aesthetics for points
ggplot2 implements the grammar of graphics to map attributes from a data set to plot features through aesthetics. This framework can be used to adjust the point size, color and...continue reading.
ggplot2 implements the grammar of graphics to map attributes from a data set to plot features through aesthetics. This framework can be used to adjust the point size, color and...continue reading.
Make your first steps with the ggplot2 package to create a scatter plot. Use the grammar-of-graphics to map data set attributes to your plot and connect different layers using the...continue reading.
Data visualization is not only important to communicate results but also a powerful technique for exploratory data analysis. Each plot type like scatter plots, line graphs, bar charts and histograms...continue reading.
All data transformation functions in dplyr can be connected through the pipe %>% operator to create powerful and yet expressive data transformation pipelines. Use the pipe operator %>% to combine...continue reading.
To select areas of interest in a data frame they often need to be ordered by specific columns. The dplyr arrange() function supports data frame orderings by multiple columns in...continue reading.
We often want to operate only on a specific subset of rows of a data frame. The dplyr filter() function provides a flexible way to extract the rows of interest...continue reading.
To select only a specific set of interesting data frame columns dplyr offers the select() function to extract columns by names, indices and ranges. You can even rename extracted columns...continue reading.
Learn what dplyr does Get an overview of Select, Filter and Sort Learn what Joins, Aggregations and Pipelines are There’s the joke that 80 percent of data science is cleaning...continue reading.
We often do not need to look at all the contents of a data frame in the console. Instead, only parts of it are sufficient like the top or bottom...continue reading.
The size of a data frame, like the number of rows or columns, is often required and can be determined in various ways. Get number of rows of a data...continue reading.
The size of a data frame, like the number of rows or columns, is often required and can be determined in various ways. Get number of rows of a data...continue reading.
Columns in a data frame can be easily extracted and manipulated with the $ operator. Even new columns can be added by assigning a vector. Extract columns from a data...continue reading.
Today we are excited to announce the QBits Workspace to run and deploy R code in the browser. QBits enable you to run R in a serverless cloud environment and...continue reading.
Tibbles are the modern reimagination of data frames and share many commonalities with their ancestors. The most visible difference is how tibble contents are printed to the console. Tibbles are...continue reading.
Tabular data is the most common format used by data scientists. In R, tables are respresented through data frames. They can be inspected by printing them to the console. Understand...continue reading.
Packages give you access to a huge set of functions and datasets, most of which are provided by the generous R community. They are the secret sauce which makes it...continue reading.
When you write code, functions are your best friends. They can make hard things very easy or provide new functionality in a nice way. Through functions you gain access to...continue reading.
R is not only good for analysing and visualizing data, but also for solving maths problems or comparing data with each other. Plus you can use it just like a...continue reading.
Usually you want to store vectors and other objects into variables so you can work with them more easily. Variables are like a box with a name. You can then...continue reading.
R always creates lists of values—even when there is only one value in a list. These lists are called vectors and they make working with data much easier. Everything is...continue reading.