How to do it...

1. Make sure that axes labels stand for full names by using the xlab() and ylab() functions:

> h1 <- core_plot + xlab('petal length (centimeters)') +
    ylab(' petal width (centimeters)')

1. Pick better shapes, and inside and outside colors using the scale_*_manual() functions:

> h2 <- h1 +
    scale_colour_manual(values = c('setosa' = 'magenta4', 
                                   'versicolor' = 'darkorange4',
                                   'virginica' = 'steelblue4'), 
                        name = 'Iris species') +
    scale_fill_manual(values = c('setosa' = 'magenta', 
                                 'versicolor' = 'darkorange',
                                 'virginica' = 'steelblue'), 
                      name = 'Iris species') +
    scale_shape_manual(values = c('setosa' = 21, 
                                  'versicolor' = 22, 
                                  'virginica' = 24),
                       name = 'Iris species')

1. Grow the axes bigger, while accordingly labeling each break with scale_*_continuous():

> h3 <- h2 + 
    scale_x_continuous(labels = 1:7, breaks = 1:7, minor_breaks = 0) +
    scale_y_continuous(labels = sprintf('%.2f',seq(0,2.5,.25)), 
                       breaks = seq(0,2.5,.25),
                       minor_breaks = 0)

1. Resize the text and relocate the legends using theme():

> h4 <- h3 +
    theme(legend.justification = c('left', 'top'), 
          legend.position = c(.65,.25),
          legend.background = element_rect(color = "black", 
                                           size = 1, 
                                           linetype = "solid"),
          legend.text = element_text(size = 13), 
          legend.title = element_text(size = 13),
          axis.text = element_text(size = 15), 
          axis.title = element_text(size = 15))
> h4

Finally, we have got something that can be considered publication quality:

Figure 2.14 - Publication quality scatterplot.