R Advanced Data Management
Aggregating data using aggregate() in R
In R programming, function aggregate() provides an easy way to calculate summary statistics of variables by specific groups in a data frame.
Linear Regression with R
Assessing Normality Assumption for Linear Regression in R
The normality assumption in linear regression is necessary to ensure the estimates of parameters are unbiased and the hypothesis testing is correct. It states that for the fixed or given values of explanatory variables, the dependent variables are normally distributed around the mean 0. It is equivalent to say that the residuals after model estimation follow a normal distribution with the mean 0.
Python
Returning a dictionary with functions in Python
Dictionary is a data structure type in Python. One reason for why Python is so popular among programmers is that dictionary provides an useful and effective way to store key-value pairs information. When functions in Python carry out some tasks, it can return required information to a dictionary.
R Basic Data Management
Dealing with missing values in R
In R programming, the value ‘NA’ is used to represent a missing value. Say we try to read a csv file from working directory and generate a data frame. There several places in the csv file have value ‘999’, which means missing value due to various circumstances during data survey and collection.
R Basic Data Management
Recode variables in R
In data analysis it is often needed to set new values to a variable based on one or several conditions, and these kinds of operations are called recode variables. The most frequently applied recoding variables in R may be the setting some values to missing values (NA), and recoding a continuous to the values of a categorical variable.
R Basic Data Management
Working with date values in R
R has rich resources of functions dealing with date values in data analysis. In this post, we introduce and show how to use as.Date() function for working with data values in R. as.Date() accepts a string input with specific format and transform the value to a date object in R.
R Data Structure Dataset
How to generate random numbers from various statistical distributions using R
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.
R Data Structure Dataset
Creating a random sequence using sample() in R
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.
Linear Regression with R
Estimating simple linear regression model using lm() in R
Linear regression is widely used to model the relationship between response or dependent variable and explanatory or independent variables. The parameter in the model has linear form. When there is only one explanatory assumed in the model, it is called simple linear regression.
Python
Using exponential distributions in Python
The exponential distribution is modeling the probability distribution of the random time until next event occur in a Poisson event process. A Poisson event process has a constant occurring rate during an interval. Exponential distribution is a particular case of the gamma distribution when the shape parameter (alpha) equals 1.