Level-up your R programming skills! Learn how to work with common data structures, optimize code, and write your own functions.
这个过程就涉及到重复抽样了resampling!Resampling methods, such as cross-validation and the bootstrap, are empirical simulation systems. They create a series of data sets similar to the training/testing split 首先理解过拟合 写重复抽样前我们先回顾过拟合的概念，数据划分后，我们会在训练集中训练好模型，...
Master the basics of data analysis in R, including vectors, lists, and data frames, and practice R with real data sets. Track R Programming Fundamentals 22 hours 12KLevel-up your R programming skills! Learn how to work with common data structures, optimize code, and write your own ...
To learn to do statistical analysis and computations, one may start by considering the R programming language as a simple calculator. In financial investigations, after preparing the collected data sets to undertake financial analysis, the first step is to enter the data sets into the R ...
Load a built-in R data set: data(“dataset_name”) Inspect the data set: head(dataset_name) Related articles Previous chapters What’is R and why learning R? Installing R and RStudio Running RStudio and setting up your working directory R programming basics Getting help with functions in...
Data manipulation involves modifying data to make it easier to read and to be more organized. We manipulate data for analysis and visualization. It is also used with the term ‘data exploration’ which involves organizing data using available sets of variables. At times, the data collection proce...
. # Your task is to create a new variable called 'prop_initiated' # in the Pseudo-Facebook data set. The variable should contain # the proportion of friendships that the user initiated. # This programming assignment WILL BE automatically graded. # DO NOT DELETE THIS NEXT LINE OF CODE # ...
R programming provides comprehensive sets of tools such as in-built functions and a wide range of packages to perform data analysis, represent data and build visualizations. Data visualization in R can be performed in the following ways:
Your role in data As a Data Scientist, you are an expert at deriving valuable insights for large and varied data sets. You are responsible for: Building and deploying end-to-end solutions leveraging machine learning and advanced analytics. Building and testing models in R, Python, or other ...
In this post we bring you all the FREE books that combine R programming with Data Science that we've found (so far), categorised by sub-topic so you can find what you're looking for easily. We'll be adding more in to this page regularly, so bookmark the page and keep popping in...