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To use this transitions pdflatex needs to be used to create the document as a pdf file and the presentation needs to be viewed in full screen mode to observe the transitions. Adding a transition on a slide is as simple as adding a command like:

\transdissolve

to the slide. This transition disolves the contents of the slide. The LaTeX beamer user guide has further information about the transitions that are available.

Other useful resources are provided on the Supplementary Material page.

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The \pause command is the easiest way to create simple overlays that reveal consecutive parts of a slide.

\begin{itemize}
\item Total Runs Scored.
\pause
\item Number of Innings Batted.
\pause
\item Number of Not Out Innings.
\pause
\item Number of 100s.
\pause
\item Number of 50s.
\end{itemize}

Commands such as <1-> can be added after various beamer environments to allow them to appear on different slides of the overlay set to provide greater control over the display.

\begin{tabular}{cccccccc} \\ \hline
Opposition & Match & Inns & Runs & NO & Inns & Wicket & Catches \\ \hline
\onslide<2-> Australia & 36 & 49 & 1673 & 2 & 66 & 148 & 57 \\
\onslide<3-> India & 14 & 16 & 1201 & 0 & 23 & 59 & 14 \\
\onslide<4-> New Zealand & 15 & 22 & 846 & 2 & 28 & 64 & 14 \\
\onslide<5-> Pakistan & 14 & 20 & 647 & 1 & 18 & 40 & 14 \\
\onslide<6-> Sri Lanka & 3 & 3 & 41 & 0 & 6 & 11 & 2 \\
\onslide<7-> West Indies & 20 & 37 & 792 & 1 & 27 & 61 & 19 \\ \hline
\end{tabular}

The \onslide command is used to indicate a range of slides in the overlay where the information on that line should be displayed.

There are ways to create more complicated overlays to reveal information on a slide.

Other useful resources are provided on the Supplementary Material page.

As an initial example we will consider the English legend Sir Ian Botham who played 102 test matches for England between his debut in 1977 until his final game in 1992.

The first obvious breakdown is to consider how Botham performed against the six countries that he played against during his test career. A summary of his statistics are shown here:

 Opposition Matches Bat Inns Runs NO Bowl Inns Wicket Catch
  Australia      36       49 1673  2       66     148    57
      India      14       16 1201  0       23      59    14
New Zealand      15       22  846  2       28      64    14
   Pakistan      14       20  647  1       18      40    14
  Sri Lanka       3        3   41  0        6      11     2
West Indies      20       37  792  1       27      61    19

Botham only played three matches against Sri Lanka so it is difficult to properly assess his performance against them. If the above table is stored in a data frame itb.opp then we can create a histogram of the total runs (or wickets) by opposition country:

ggplot(itb.opp, aes(Opposition, Runs)) + geom_bar() + xlab("Country") +
  ylab("Total Runs")

This code produces the following graph:

IT Botham Total Runs by Opposition

The total wickes graph is produced by the next code:

ggplot(itb.opp, aes(Opposition, Wicket)) + geom_bar() + xlab("Country") +
  ylab("Total Wickets")

IT Botham Total Wickets by Opposition

We may now want to delve deeper into the performance against different nations to take into account the number of games or innings where Botham batted or bowled. The traditional way to assess performance is to calculate batting and bowling averages and we can do this by opposition which provides the following data frame:

> itb.opp.sum
 Opposition Discipline  Average
  Australia    Batting 29.35088
      India    Batting 70.64706
New Zealand    Batting 42.30000
   Pakistan    Batting 32.35000
  Sri Lanka    Batting 13.66667
West Indies    Batting 21.40541
  Australia    Bowling 27.65541
      India    Bowling 26.40678
New Zealand    Bowling 23.43750
   Pakistan    Bowling 31.77500
  Sri Lanka    Bowling 28.18182
West Indies    Bowling 35.18033

This can be converted into a dot plot so we can see whether Botham had a high batting average than bowling average, which is often taken to be one of the signs of an all-rounder.

ggplot(itb.opp.sum, aes(Average, Opposition, colour = Discipline)) +
  geom_point()+ xlab("Average") + ylab("")

The graph is shown here:

IT Botham Batting and Bowling Averages by Opposition

We can see the differences in performance based on the opposition. Botham’s performance against the West Indies, by far the strongest team during most of his international career, were worse than against the other countries. However, his averages were far from embarassing when compared to other players at the time. The graph also shows that Botham enjoyed batting and bowling against India.

We can divide this data further based on whether the matches were played in England or outside of England and this data is shown here:

> itb.opp.ha.sum
  Opposition Venue Discipline  Average
   Australia  Away    Batting 30.22581
       India  Away    Batting 61.55556
 New Zealand  Away    Batting 50.44444
    Pakistan  Away    Batting 16.00000
   Sri Lanka  Away    Batting 13.00000
 West Indies  Away    Batting 14.17647
   Australia  Home    Batting 28.30769
       India  Home    Batting 80.87500
 New Zealand  Home    Batting 35.63636
    Pakistan  Home    Batting 34.16667
   Sri Lanka  Home    Batting 14.00000
 West Indies  Home    Batting 27.55000
   Australia  Away    Bowling 28.44928
       India  Away    Bowling 25.53333
 New Zealand  Away    Bowling 27.44444
    Pakistan  Away    Bowling 45.00000
   Sri Lanka  Away    Bowling 21.66667
 West Indies  Away    Bowling 39.50000
   Australia  Home    Bowling 26.96203
       India  Home    Bowling 27.31034
 New Zealand  Home    Bowling 20.51351
    Pakistan  Home    Bowling 31.07895
   Sri Lanka  Home    Bowling 30.62500
 West Indies  Home    Bowling 31.97143

A dot plot is created from this data with a separate panel for each of the six opposition countries and the averages divided into batting and bowling performances. The coloured dots in the graph indicated whether the average is for matches at home or away.

ggplot(itb.opp.ha.sum, aes(Average, Discipline, colour = Venue)) +
  geom_point() + facet_wrap( ~ Opposition) +
  xlab("Batting Average") + ylab("")

This graph is shown below:

IT Botham Batting and Bowling Averages by Country and Home/Away

We can see that the difference between home and away peformance is, in general, not very large for bowling averages but in some cases there is a noticeable difference in batting averages. When looking at Botham’s performances against the West Indies his statistics at home are much better than his away performance, suggesting that his main struggles against the strong West Indies team were in the Caribbean. This might be due to his swing bowling being more suitable to English conditions compared to pitches in the West Indies.

To round off this brief look at the career of IT Botham let us consider some other important statistics, in particular games where he performed with the bat and ball.

• Overall Botham scored 14 hundreds and 22 fifties out of 161 innings so he reached fifty runs every five innings or so.
• He also took 27 five wicket hauls and 17 four wicket hauls so he took four or more wickets every four innings or so.
• He took 120 catches.

Individual matches of excellence include five games with a century and at least five wickets:

Year  Opposition       Ground Venue Runs Wicket
1978 New Zealand Christchurch  Away  133      8
1978    Pakistan       Lord's  Home  108      8
1980       India       Mumbai  Away  114     13
1981   Australia        Leeds  Home  199      7
1984 New Zealand   Wellington  Away  138      6

These performances and others show why Botham was considered such a great player as he produced some sustained periods of excellent all-round cricket rather than having one discipline more dominant for a long period of time.

For example, after loading data from a text file we might want to view the first few lines of a set of data. The functions head and tail return the first or last parts of a vector, matrix, table, data frame or function.

Consider the Orange data set that is available in R. We can view the first few lines

> head(Orange)
  Tree  age circumference
1    1  118            30
2    1  484            58
3    1  664            87
4    1 1004           115
5    1 1231           120
6    1 1372           142

or the last few lines:

> tail(Orange)
   Tree  age circumference
30    5  484            49
31    5  664            81
32    5 1004           125
33    5 1231           142
34    5 1372           174
35    5 1582           177

Another useful function is str, which compactly displays the internal structure of an R object. On this set of data we get:

> str(Orange)
Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and 'data.frame':      35 obs. of  3 variables:
 $ Tree         : Ord.factor w/ 5 levels "3"<"1"<"5"<"2"<..: 2 2 2 2 2 2 2 4 4 4 ...
 $ age          : num  118 484 664 1004 1231 ...
 $ circumference: num  30 58 87 115 120 142 145 33 69 111 ...
 - attr(*, "formula")=Class 'formula' length 3 circumference ~ age | Tree
  .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv> 
 - attr(*, "labels")=List of 2
  ..$ x: chr "Time since December 31, 1968"
  ..$ y: chr "Trunk circumference"
 - attr(*, "units")=List of 2
  ..$ x: chr "(days)"
  ..$ y: chr "(mm)"

There is quite a bit of additional information attached to this data frame, mainly due to it having more than one class.

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The graphicx is useful for including graphics files in a presentation and this package has a command \includegraphics where we specify the name of the image file to be included and (optionally) some formatting information such as the dimensions of the image in our document.

As an example if we wanted to include a jpg file then we would include a command like this:

\includegraphics{plot}

and if we wanted to scale in the horizontal or vertical dimension then we can specify the height and width to use:

\includegraphics[height=8cm,width=12cm]{plot}

There are other options as part of the graphicx package that can be used to adjust the display of the image as required.

Other useful resources are provided on the Supplementary Material page.

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The examples on this page have been inspired by the good examples on global nodes detailed on texample.net. The example can be adapted to apply to equations for various statistical models and we will consider the model for a general row-column experiment design.

To create this example we need to make tikz available in our tex file by adding the following code to the preamble:

\usepackage{tikz}
\usetikzlibrary{arrows,shapes}

The second and third lines refer to particular elements of tikz that will be used in the example.

If we want to access nodes in different areas of our latex document we need to make use of the remember picture style and the easiest way to do this is to make a global declaration rather than on each specific picture:

\tikzstyle{every picture}+=[remember picture]

The example will be the model for a row-column experiment design and we will have four elements in a bullet list that are linked by arrows to different parts of the equation, which in turn are highlighted in their own box.

We use the itemize environment to add the first bullet point:

\begin{itemize}
\item Overall mean \tikz[na] \node[coordinate] (s1) {};
\end{itemize}

At the end of the item we add a tikz node so that we can draw a line from the end of this line to one of the boxes in the equation. We also provide a name s1 so that it can be identified by tikz. This node uses a style to shift the location where the line starts from, that we need to define in our document using this code:

\tikzstyle{na} = [baseline=-.5ex]

The next step is to create the equation and the nodes and background boxes for each element of the equation that will be linked to the bullet list. The code for the equation is shown here:

\begin{equation}
y_{ijk} = \tikz[baseline]{ \node[fill=blue!20,anchor=base,rounded corners=2pt]
  (d1) {$\mu$}; }
+ \tikz[baseline]{ \node[fill=red!20,anchor=base,rounded corners=2pt]
  (d2) {$r_{i}$}; }
+ \tikz[baseline]{ \node[fill=green!20,anchor=base,rounded corners=2pt]
  (d3) {$c_{j}$}; }
+ \tikz[baseline]{ \node[fill=yellow!20,anchor=base,rounded corners=2pt]
  (d4) {$t_{k}$}; }
+ \epsilon_{ijk}
\end{equation}

If we look closely at this code the latex for the equation itself is very straightforward and the complication comes from adding a tikz node to four of the elements of the equation. The nodes themselves have colours (blue/red/green/yellow) and rounded corners. They each have a label d1 to d4 so that tikz can draw lines between the bullet list and the elements of the equation.

We then finish off the bullet list with three more elements:

\begin{itemize}
\item Effect of row $i$ \tikz[na] \node[coordinate] (s2) {};
\item Effect of column $j$ \tikz[na] \node[coordinate] (s3) {};
\item Effect of treatment $k$ \tikz[na] \node[coordinate] (s4) {};
\end{itemize}

Each of the items in this bullet list have their own identifier s2 to s4 that we will use to draw arrows.

We create a separate picture environment for the four arrows linking the bullet list to the equation. Each element is defined as a path between two nodes with additional information about the shape of the arrow.

\begin{tikzpicture}[overlay]
\path[->] (s1) edge [bend left] (d1);
\path[->] (s2) edge [bend right] (d2);
\path[->] (s3) edge [out=0, in=-90] (d3);
\path[->] (s4) edge [out=0, in=-90] (d4);
\end{tikzpicture}

There are many variants on this example that could be incorporated in a presentation.

Other useful resources are provided on the Supplementary Material page. Also head over to texample.net for more examples of using the tikz package.

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The easiest way to create a box is to use the various pre-defined environments such as definition:

\begin{definition}
The simple linear regression model describes the relationship between
a response variable and a single explanatory variable.
\end{definition}

We might want to create a box with our own title in which case the exampleblock environment can be used with the new title as an argument to this environment:

\begin{exampleblock}{Linear Regression Model}
$Y_{i} = \beta_{0} + \beta_{1} X_{i} + \epsilon_{i}$
\end{exampleblock}

Other useful resources are provided on the Supplementary Material page.

For a BIBD there are v treatments repeated r times in b blocks of k observations. There is a fifth parameter lambda that records the number of blocks where every pair of treatment occurs in the design.

We first load the crossdes package in our sessions:

require(crossdes)

The function find.BIB is used to generate a block design with specific number of treatments, blocks (rows of the design) and elements per block (columns of the design).

Consider an example with five treatments in four blocks of three elements. We can create a block design via:

> find.BIB(5, 4, 3)
     [,1] [,2] [,3]
[1,]    1    3    4
[2,]    2    4    5
[3,]    2    3    5
[4,]    1    2    5

This design is not a BIBD because the treatments are not all repeated the same number of times in the design and we can check this with the isGYD function. For this example:

> isGYD(find.BIB(5, 4, 3))
 
[1] The design is neither balanced w.r.t. rows nor w.r.t. columns.

This confirms what we can see from the design.

Let us instead consider a design with seven treatments and seven blocks of three elements to see whether we can create a BIBD with these parameters:

> my.design = find.BIB(7, 7, 3)
> my.design
     [,1] [,2] [,3]
[1,]    1    2    5
[2,]    3    4    5
[3,]    1    3    6
[4,]    2    3    7
[5,]    2    4    6
[6,]    1    4    7
[7,]    5    6    7
> isGYD(my.design)
 
[1] The design is a balanced incomplete block design w.r.t. rows.

In this situation we are able to generate a valid BIBD experiment with the specified parameters.

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For example, if we wanted to add a table of some of the common geom elements for ggplot2 to a slide we could use the following code:

\begin{frame}{Common geoms}
 
\begin{tabular}{cc}
geom & Description \\ \hline
geom_bar & Bar chart \\
geom_boxplot & Box and Whisker plot \\
geom_contour & Contour plot \\
geom_point & Scatter plot \\
geom_smooth & Smoothed conditional mean \\
\end{tabular}
 
\end{frame}

This is a basic example of creating a table and there are many examples of how the appearance of a table can be enhanced in LaTeX, see for example here or here.

Other useful resources are provided on the Supplementary Material page.

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The bullet lists can be created using the itemize or enumerate environments depending on the type of list that we want to appear on the slides. The itemize list uses symbols and is not numbered while the enumerate list is a numbered list so the choice will depend on the elements covered by the list.

Within our slide we would create the list environment in exactly the same was as over LaTeX documents and the \item command. An example of creating a bullet list of assumptions for a linear statistical model is shown here:

\begin{itemize}
\item A linear the relationship between response and explanatory variables.
\item Independence of the model errors.
\item Constant variance for the model errors (a) versus time and (b) versus
  the predictions (or versus any independent variable)
\item Normality of the errors.
\end{itemize}

This can be easily changed to an enumerate environment to make it a numbered list.

Other useful resources are provided on the Supplementary Material page.