Data Science includes, not explicitly, R and Python programming tuturials, statistical data analysis, maching learning using Python, statistical modeling using R and Python, clustering using R, etc.
Statistical data analysis focus one both the correlation analysis, and causal analysis between different factor variables. Linear regression, Generalized linear models, discrete choice models are the most fundamental modeling techniques.
Machine learning and deep learning using tensorflow package in Python. These models are important for artificial intelligence application. CNN and LSTM are the two most fundamental such models.
read.table() function in R is often used when a delimited ASCII file (e.g. text file or csv file) is to be imported to generate a data frame. The basic syntax is
df <- read.table(input, options)
Data frames are the most widely used data structures in R programming. Unlike each element in vector/matrix/array must have same data mode, a data frame can store data elements with different mode or type in one object. For example, a data frame of family information can have numeric (e.g. age, income), character (e.g. name), and logical (work/not work) data types. Data frames in R act somewhat similar as a spredsheet in Microsoft Excel, where each row represents each observation or subject and each column refers to each variable or attribute.
When it is needed to store many elements of same type or mode into one data object in R, you can use array. Actually, vector and matrix are special types of array with one and two dimensions respectively.
If we want to store data of same mode or type in a two-dimensional array in R data analysis, matrix may be the most appropriate data structure to use. A matrix is just coming from a vector expanded in two dimensions with rows and columns, this is why the input subject is often a vector when a matrix is generated.
Vector in R programming is one-dimensional array. Its element can be numeric, character, or logital data. To create a vector in R, you can use function c().
Like in many other programming languages, packages in R are just collections of built-in functions and datasets are combined with R installation in a well-defined format. Library is directories where packages, either come with basic R installation or being manually installed, are stored on computer. In this post, we provide several important and useful functions associated with R package management.
RStudio acts as an computer-app for using R in a more freindly manner than the default R command window. Before using RStudio, R has to be installed first. In this post , we shortly introduce how to install R and RStudio on Windows.
R is one of the most popular programming languages and framework for statistical data analysis and data science. R has a relatively easy learning curve, and is suitable for anyone who has basic knowledge of using computer and is eager to learn programming skills.