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knitr
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<<setup, include=FALSE, cache=FALSE>>= 
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library(knitr) 
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options(replace.assign=TRUE,width=50) 
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opts_chunk$set(fig.path='figure/graphics-', cache.path='cache/graphics-',
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@
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\begin_layout Chapter
Chapter title
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Here are some basic R operations and data structures that will be good to
 know if you do not have prior experience with R.
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Section title
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R can be used as an ordinary calculator.
 Here are a few examples:
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<<>>= 
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2 + 3 * 5       # Note the order of operations.
 
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log(10)        # Natural logarithm with base e=2.718282 
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4^2            # 4 raised to the second power 
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3/2            # Division 
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sqrt(16)      # Square root 
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abs(3-7)      # Absolute value of 3-7 
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pi             # The number 
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exp(2)        # exponential function 
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Subsection title 2
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R has great support for plotting and customizing plots.
 We will show only a few below.
 Let us sample 50 values from normal distribution and plot them as a histogram
 (See Figure
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reference "mar:hist1"

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).
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<<hist1, eval=FALSE,tidy=FALSE>>= 
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# sample 50 values from normal distribution 
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# and store them in vector x 
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x<-rnorm(50)  
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hist(x) # plot the histogram of those values 
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@
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Histogram of 50 sampled values 
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We can modify all the plots by providing certain arguments to the plotting
 function.
 Now let's give a title to the plot using 
\emph on
'main'
\emph default
 argument.
 We can also change the color of the bars using 
\emph on
'col'
\emph default
 argument.
 You can simply provide the name of the color.
 Below, we are using '
\emph on
red
\emph default
' for the color.
 See Figure 
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reference "mar:hist_title"

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 for the result this chunk.
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<<hist2, eval=FALSE>>= 
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hist(x,main="Hello histogram!!!",col="red")  
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@
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Histogram with a title
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Next, we will plot a scatter plot.
 Scatter plots are one os the most common plots you will encounter in data
 analysis.
 We will sample another set of 50 values and plotted those against the ones
 we sampled earlier.
 Scatterplot shows values of two variables for a set of data points.
 It is useful to visualize relationships between two variables.
 It is frequently used in connection with correlation and linear regression.
 Ther are other variants of scatter plots which show density of the points
 with different colors.
 We will show examples of those that in following chapters.
 The scatter plot from our sampling experimentt is shown in Figure 
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reference "fig:scatter-plot-of"

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<<scatter,eval=TRUE,out.width='2in',out.height='2in',tidy=FALSE,small.mar=FALSE
  >>= 
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# randomly sample 50 points from normal distribution 
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y<-rnorm(50)  
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#plot a scatter plot 
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plot(x,y,main="scatterplot of random samples")
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@
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\begin_inset Caption

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scatter plot of two random variables
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name "fig:scatter-plot-of"

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Session Info
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<<>>=
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sessionInfo()
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\begin_layout Section
Acknowledgements
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People who have contributed at least 2 figures and/or code-chunks with accompany
ing text are acknowledged as authors.
 Code and text must fit the context of the chapter and should improve the
 chapter in quality and context.
 
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\begin_layout Subsection
Other contributions
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Any other contribution that does not fit the above description must be acknowled
ged here.
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