baseplot.Rmd

# Basic plots in R

R-base package graphics offers functions for producing many plots, for example:

* scatter plots - plot()
* bar plots - barplot()
* pie charts - pie()
* box plots - boxplot()
* histograms - hist()

## Scatter plots

*A scatter plot has points that show the **relationship** between two sets of data.*

* Simple scatter plot

```{r}
# Create 2 vectors
x <- 1:10
y <- x^2

# Plot x against y
plot(x, y)
```
<img src="images/plots/scatter1.png" width="450"/>

*Note that if one vector only is given as an input, it will be plotted against the indices of each element*

* Add arguments:
	* col: color
	* pch: type of point
	* type: "l" for line, "p" for point, "b" for both point and line
	* main: title of the plot

```{r}
plot(x, y, 
	col="red", 
	pch=2, 
	type="b", 
	main="a pretty scatter plot")
```

<img src="images/plots/scatter2.png" width="450"/>

* You can play a bit:

```{r}
plot(x, y, 
	col=1:10, 
	pch=1:10, 
	cex=1:10, 
	type="b", 
	main="an even prettier scatter plot")
```
<img src="images/plots/scatter3.png" width="450"/>

<h4>Different type of points that you can use:</h4>

<img src="images/plots/pointtype.png" width="450"/>

<h4>About colors</h4>

* Color codes 1 to 8 are taken from the **palette()** function and respectively code for: "black", "red", "green3", "blue", "cyan", "magenta", "yellow", "gray".

```{r}
# see the 8-color palette:
palette()
```

* There is a larger set of build-in colors that you can use:

```{r}
# see all 657 possible build-in colors:
colors()

# looking for blue only? You can pick from 66 blueish options:
grep("blue", colors(), value=TRUE)
``` 

You can also find them [here](http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf).

## Bar plots

*A bar chart or bar plot displays rectangular bars with **lengths proportional to the values that they represent.***

* A simple bar plot :

```{r}
# Create a vector
x <- rep(c("PhDstudent", "Postdoc", "Technician", "PI"), c(8,10,5,2))

# Count number of occurences of each character string
mytable <- table(x)

# Bar plot using that table
barplot(mytable)
```

<img src="images/plots/barplot1.png" width="450"/>

* Customize a bit :
	* col : color
	* main : title of the plot
	* las : orientation of x-axis labels: "2": perpendicular to axis

```{r}
barplot(mytable,
	col=1:4,
	main="bar plot",
	las=2)
```

<img src="images/plots/barplot2.png" width="450"/>

* Customize the ordering of the bars :

By default, the bars are organized in alphabetical order. You can change it with the factors.

```{r}
# Create an ordered factor out of x
xfact <- factor(x, 
	levels=c("PhDstudent", "Postdoc", "Technician", "PI"), 
	ordered=TRUE)

# Produce the table
xfacttable <- table(xfact)

# Plot the same way
barplot(xfacttable,
	col=1:4,
        main="reorganized bar plot",
        las=2)
```

<img src="images/plots/barplot3.png" width="450"/>

* Let's make a stacked barplot :

```{r}
# Create a matrix of number of type of employees per research program :
barmat <- matrix(c(8, 10, 9, 2, 6, 4, 5, 3, 14, 13, 16, 4, 11, 10, 8, 5),
	nrow=4,
	dimnames=list(c("Technician", "PhDstudent", "PostDoc", "PI"), c("BG", "CDB", "GRSCC", "SB")))

# Plot barplot
barplot(barmat, col=sample(colors(), 4))
```

* Add some parameters:

```{r}
# set a random color vector
mycolors <- sample(colors(), 4)

# plot barplot
# ylim sets the lower and upper limit of the y-axis: here it allows us to fit the legend !
barplot(barmat, 
	col=mycolors, 
	ylim=c(0,50),
	main="stacked barplot")

# add a legend
# first argument is the legend position
legend("topleft", 
	legend=c("Technician", "PhDstudent", "PostDoc", "PI"),
	fill=mycolors)
```

<img src="images/plots/barplot4.png" width="450"/>


## Pie charts

*A pie chart is a circular charts which is divided into slices, illustrating proportions.*

* Using our previous vector, build a simple pie chart:

```{r}
# Create a vector
x <- rep(c("PhDstudent", "Postdoc", "Technician", "PI"), c(8,10,5,2))

# Count number of occurences of each string
mytable <- table(x)

pie(mytable,
	main="pie chart",
	col=c("lightblue", "lightgreen", "salmon", "maroon"))
```

<img src="images/plots/pie1.png" width="450"/>

## Box plots

*A boxplot is a convenient way to describe the **distribution** of the data.*

* A simple boxplot:

```{r}
# Create a matrix of 1000 random values from the normal distribution (4 columns, 250 rows)
x <- matrix(rnorm(1000), ncol=4)

# Basic boxplot
boxplot(x)
```

<img src="images/plots/boxplot1.png" width="450"/>

* Add some arguments :
	* xlab: x-axis label
	* ylab: y-axis label
	* at: position of each box along the x-axis: here we skip position 3 to allow more space between boxes 1/2 and 3/4

```{r}
boxplot(x, 
	xlab="sample",
	ylab="expression",
	at=c(1, 2, 4, 5))
```

<img src="images/plots/boxplot2.png" width="450"/>

* Add an horizontal line at y=0 with **abline()**; arguments of abline :
	* h : y-axis starting point of horizontal line (v for a vertical line)
	* col : color
	* lwd : line thickness
	* lty : line type

```{r}
# First plot the box plot as before:
boxplot(x, 
	xlab="sample",
	ylab="expression",
	at=c(1, 2, 4, 5),
	 main="my boxplot")
	
# Then run the abline function
abline(h=0, col="red", lwd=3, lty="dotdash")
```

<img src="images/plots/boxplot3.png" width="450"/>

* Line types in R:

<img src="images/linetypes-in-r-line-types.png" width="250"/>


## Histograms

*A histogram graphically summarizes the **distribution** of the data.*

* A simple histogram

```{r}
# Vector of 200 random values from the normal distribution
x <- rnorm(200)

# Plot histogram
hist(x)
```

<img src="images/plots/histogram1.png" width="450"/>

* Add parameters:
	* border: color of bar borders
	* breaks: number of bars the data is divided into
	* cex.main: size of title
	* cex.lab: size of axis labels

```{r}
hist(x,
	border="blue",
	breaks=50,
	main="Histogram",
	xlab="",
	cex.main=2.5,
	cex.lab=2)
```

<img src="images/plots/histogram2.png" width="450"/>