====== Pictorial displays ======

Pictorial displays are among the most important techniques that help you describing and analyzing your data. The **''R''** graphics system is very powerful and lets you produce professional-looking graphics. There are high-level plotting functions that are best suited for simple graphs, while low-level functions provide you with advanced tools to edit details and add annotations.

These examples are based on the high-level plotting functions. Most of the times getting the right result is a matter of playing with some of the many graphical parameters. General graphical options are handled by the ''par()'' function, while each specific function has its own parameters.


===== Barcharts =====

Barcharts are good if you have to depict a non-numeric variable, such as nominal and ordinal variables. Bars in the graph are physically **separated** meaning that this is not a cartesian graph.

<code C>> barplot(table(Cond))</code>
{{  diggingnumbers:barplot1.png?300  |barplot(table(Cond))}}
<code C>> barplot(table(Mat,Cond), beside=FALSE)</code>
{{  diggingnumbers:barplot2.png?300  |barplot(table(Mat,Cond), beside=FALSE)}}
<code C>> barplot(table(Mat,Cond), beside=TRUE)</code>
{{  diggingnumbers:barplot3.png?300  |barplot(table(Mat,Cond), beside=TRUE)}}


===== Histograms =====
Histograms are as much different from barcharts as ratio variables differ from nominal. An histogram represents the density distribution of values on a continuous axis.

<code C>> hist(Socle, freq=TRUE)</code>

{{ diggingnumbers:histogram1.png?300 |hist(Socle, freq=TRUE)}}



===== Boxplots =====

These are known also as "box-and-whisker plots", because of their shape. Boxplots are useful to compare visually the same variable in different datasets because they provide a quick way to represent the most common measures of position (mean, quartiles, outliers).

<code C>> boxplot(Socle)</code>

In this graph the central line in the box represents the average mean value, the alone point is an outlier.

{{ diggingnumbers:boxplot1.png?300 |boxplot(Socle)}}

===== Stem-and-leaf plots =====

Stem-and-leaf plots are useful if you need not only to represent the distribution of your variable, but also to keep your original data available without losing too much space.

<code C>> stem(Socle)

  The decimal point is at the |

   2 | 40114455
   4 | 23556812489
   6 | 01466258
   8 | 011466726
  10 | 2
  12 | 5
  14 | 4
</code>

<code C>> stem(Socle, scale=2)

  The decimal point is at the |

   2 | 4
   3 | 0114455
   4 | 235568
   5 | 12489
   6 | 01466
   7 | 258
   8 | 0114667
   9 | 26
  10 | 2
  11 | 
  12 | 
  13 | 5
  14 | 4

</code>



===== Scatterplots =====

Scatterplots are a mean to compare one variable against another, plotting on a cartesian surface. The values of one variable are used as X values, and the other's as Y values.

<code C>> plot(Maxwi,Maxle, col=Mat, pch=19)</code>

{{  diggingnumbers:scatterplot0.png?300 |plot(Maxwi,Maxle, col=Mat, pch=19)}}

<code C>> plot(Maxwi,Maxle, col=Mat, pch=Mat)</code>

{{  diggingnumbers:scatterplot1.png?300 |plot(Maxwi,Maxle, col=Mat, pch=Mat)}}


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[[Start]] · [[Data description]] · [[Transforming variables]] · [[Tables]] · **Pictorial displays** · [[Measures of position and variability]] · [[Sampling]] · [[Tests of difference]] · [[Tests of distribution]] · [[Correlation]] · [[Tests of association]]