--- title: "Introduction to R Notebook" output: html_document: default html_notebook: default pdf_document: default --- This notebook demonstrates how to plot data. # R Markdown This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. Try executing code chunks below by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*. Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Ctrl+Alt+I*. When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Ctrl+Shift+K* to preview the HTML file). LaTeX math notation also works: $Y \approx \beta_0 + \beta_1 \times X$. Or if you want to have an equation on a line all by itself: \[ Y \approx \beta_0 + \beta_1 \times X \] # Plotting **Step 1**: Download data for the "Introduction to Statistical Learning" (*you may need to do this manually on non-linux operating systems*) ```{bash} cd /tmp wget http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv ``` **Step 2**: Load the dataset from the CSV ```{r} ads <- read.csv("/tmp/Advertising.csv") ``` **Step 3**: Summarize the data ```{r} summary(ads) ``` **Step 5**: Plot the dataset ```{r} #pdf("/tmp/sales_tv.pdf",7,5) plot(ads$TV, ads$Sales,col='red',pch=20,xlab = "TV", ylab = "Sales") #dev.off() ``` **Step 6**: Or prettier plots can be generated with `ggplot2` ```{r} if("ggplot2" %in% rownames(installed.packages()) == FALSE) {install.packages("ggplot2")} ``` ```{r} library(ggplot2) ggplot2::qplot(TV, Sales,data=ads,xlab="TV",ylab="Sales") ```