R is a reliable programming language for Statistical Analysis. It has a wide range of statistical library support like T-test, linear regression, logistic regression, and time-series data analysis. R comes with very good data visualization features supporting potting and graphs using graphical packages like ggplot2.
Statistical Analysis with R is one of the best practices which statisticians, data analysts, and data scientists do while analyzing statistical data. The r language is a popular open-source programming language that extensively supports built-in packages and external packages for statistical analysis.
Calculating Descriptive Statistics by Group Using R
As you have seen in the previous sections, our dataset groups the observations by three flower species: setosa, versicolor, and virginica.
Therefore, it might be interesting to compare the descriptive statisticsof the different flower species.
The following R code uses the aggregate and mean functions to calculate the mean by group (i.e. flower spe.
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Creating A Correlation Matrix with R Programming
Understanding the relationships between variables provides additional useful information.
To gain this information, we can create a correlation matrix (i.e., a table showing the correlation coefficientsbetween multiple variables at the same time) by applying the cor function to the numeric variables of our data: In the correlation matrix, you can s.
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Example Data For The R Programming Language
In the first section of this article, we’ll load the iris dataset into R.
Ronald Fisher, biologist and statistician, introduced the iris flower dataset in 1936.
It contains flower measurements.
After downloading the dataset, load it into R by executing the following code: Next, we can inspect the structure of the iris flower data using the head fun.
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Generating Random Numbers with R Programming
So far, we have used R to analyze the iris flower dataset.
However, the R programming language also provides powerful functions to generate random data.
Whenever random processes are involved, it is useful to set a random seed.
A random seed is a number that initializes a pseudorandom number generator and allows other analysts to reproduce our “ran.
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How do you estimate a statistical model in R?
The R programming language also provides functions to estimate statistical models.
One of the most commonly used model types is linear regression.
Using the lm and summary functions in R, we can estimate and evaluate these models.
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Is R a good tool for statistical research?
The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.
One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including:
- mathematical symbols and formulae where needed
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Using R Functions to Calculate Basic Descriptive Statistics For A Dataset
The following syntax illustrates how to calculate a set of descriptive statisticsfor all variables in a dataset.
For this task, we can apply the summary function as shown below: This table contains the minimum, 1st quantile, median, mean, 3rd quantile, and the maximum for the numeric columns in our data, and the count of each category for the non-n.
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Using The R Programming Language to Estimate A Linear Regression Model
The R programming language also provides functions to estimate statistical models.
One of the most commonly used model types is linear regression.
Using the lm and summary functions in R, we can estimateand evaluate these models.
The following R syntax uses the variable Sepal.Length as the dependent variable and the remaining variables in the datas.
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What is the R system for statistical computing?
The R system for statistical computing consists of two major parts:
- the base system and a collection of user contributed add-on packages
The R language is implemented in the base system.
Implementations of statistical and graphical procedures are separated from the base system and are organised in the form of packages.
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Why should I learn statistical inference in R?
We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R.
We provide R programming examples in a way that will help make the connection between concepts and implementation.
