#### Overview of this book

This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.
R for Data Science Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
Functions in R
Data Preprocessing and Preparation
Visualizing Data with ggplot2
Making Interactive Reports
Simulation from Probability Distributions
Statistical Inference in R
Time Series Mining with R
Index

## Summarizing linear model fits

The `summary` function can be used to obtain the formatted coefficient, standard errors, degree of freedom, and other summarized information of a fitted model. This recipe introduces how to obtain overall model information by using the `summary` function.

You need to have completed the previous recipe by fitting the house rental data into a regression model, and have the fitted model assigned to the variable `lmfit`.

### How to do it…

Perform the following steps to summarize the linear regression model:

1. Compute a detailed summary of the fitted model, `lmfit`:

```> summary(lmfit)
Call:
lm(formula = Price ~ Sqft, data = house)

Residuals:
Min     1Q Median     3Q    Max
-76819 -12388  -3093  10024 112227

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3425.133   1766.646   1.939    0.053 .
Sqft          38.334      1.034  37.090   <2e-16 ***
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error...```