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 + operator. Define a dataset for the plot using the ggplot() function ...

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 has its own purpose and can be leveraged in a powerf...

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 multiple dplyr functions into one pipeline %__% filter(___) %__% select(___) %__% arrange(___) Using the %__% operator The pipe operator %__% is a special part of the tidyverse ...

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 ascending and descending order. Use the arrange() function to sort data f...

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 based on multiple conditions. Use the filter() function to sort out the rows of a data frame that fulfill a ...

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 with select(). Learn to use the select() function Select columns...

Learn what dplyr does Get an overview of Select, Filter and Sort Learn what Joins, Aggregations and Pipelines are What is dplyr There’s the joke that 80 percent of data science is cleaning the data and 20 percent is complaining about cleaning the data. Anthony Goldbloom, Founder and CEO of Kaggle ...

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 retrieved through the head() and tail() functions. Select the top of a data frame Select the bottom of a ...

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 frame Get number of columns of a data frame Get dimensions of a data frame nrow(___) ncol(___) dim(__...

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 frame with the $. Replace values of existing columns in a data frame. Add new columns to a data frame. ___$___ ___$___ Extract columns ...

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 part of the tidyverse and used for their more consistent ...

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 why data frames are important Interpret console output created by a data frame Create a new data frame using the ...

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 possible to use R for pretty much anything you can imagine. Additionally, lots of packag...

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 all the powerful features R has to offer. Call functions with function names an...

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 pocket calculator. Use R as a pocket calculator Use arithmetic operators on vectors Use relational operators on vectors Use logical ...

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 refer to the name to see what is stored inside. Learn how to create a variable Use variables to store ...

Combine values into a vector Everything is a vector Get to know different data types in R Learn how to create vectors Use the : operator to create numeric sequences Use the concatenate function c() to create vectors of different data types 1:100 c(...

R is everywhere Learn what R is all about Get an overview of why R is useful Submit your first code exercise Introduction to R The most powerful statistical computing language on the planet. Norman Nie, Founder of SPSS R is a programming languag...

R is everywhere Learn what R is all about Get an overview of why R is useful Submit your first code exercise Introduction to R The most powerful statistical computing language on the planet. Norman Nie, Founder of SPSS R is a programming languag...