This section introduces R data structures: One-dimensional data structure Vector; 2-dimensional data structure Matrix; n-dimensional data structure Array; tabular data structure Data Frame; and data structure List which can store any other type data objects. And this section also introduce how to create R data structure: using read.table() function to read text or csv files to create a data frame; using read.csv() function to read csv file to create a data frame. This section also introduces several functions: with().
We provide effective and economically affordable training courses for R and Python, click here for more details and course registration ! Data frames in R are the mostly widely used object type in R data analysis, due to the fact that a data frame can afford to have different variables Read more…
We provide effective and economically affordable training courses for R and Python, click here for more details and course registration ! Both R and Python are popular programming languages for data analysts. R is designed mainly for statistical data analysis, and Python may have more usage in deep learning and Read more…
We provide effective and economically affordable training courses for R and Python, click here for more details and course registration ! List is a type of data objects in R. It can store different types of data objects into one object. You can combine scalars, vectors, matrices, data frames, and Read more…
n this post, we show how to generate random numbers into vector and matrix in R programming, from various statistical distributions. Specifically, we focus several basic and widely used statistical distributions here, namely, normal distribution, continuous uniform distribution, binomial distribution and Poisson distribution.
It is not uncommon to generate random sequences in R programming. sample() function provides the feasibility of generating such random objects from given vectors, either with or without replacement. The following code shows an example that 32 numbers with replacement drawn from 50 integers from 1 to 50.
R language provides several useful functions for importing delimited files and creating data frames. These delimited files are often stored in local computer directory, with extensions ‘txt’, ‘csv’, ‘dat’. The mostly widely used functions for importing these files in R are read.table() and read.csv().
Categorical variables, including nominal and ordinal variables in R programming language are called factor variables. For example, gender(male/female) is nominal, and survey results (excellent, good, normal, bad) have ordinal values. Categorical variables are useful because many data analysis operations are related to values in different categories, such as contingency tables between two categorical variables for independence analysis, hypothesis testing of homogeneity of variances, just name a few.
list is a type of data structure in R programming language. Unlike other data structures, especially matrix and vector, in which each element must has the same type of data, a list provides the flexibility of storing various information in one object.
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.