This notebook was authored using the Jupyter R kernel, running in a Docker container generated by the Dockerfile https://github.com/cfljam/pyRat/blob/master/Dockerfile
Sys.info()
options(jupyter.plot_mimetypes = 'image/png')
library(dplyr,quietly = TRUE)
library(ggplot2)
We get HTML tables 'out of the box'
diamonds %>% head
carat | cut | color | clarity | depth | table | price | x | y | z | |
---|---|---|---|---|---|---|---|---|---|---|
1 | 0.23 | Ideal | E | SI2 | 61.5 | 55 | 326 | 3.95 | 3.98 | 2.43 |
2 | 0.21 | Premium | E | SI1 | 59.8 | 61 | 326 | 3.89 | 3.84 | 2.31 |
3 | 0.23 | Good | E | VS1 | 56.9 | 65 | 327 | 4.05 | 4.07 | 2.31 |
4 | 0.29 | Premium | I | VS2 | 62.4 | 58 | 334 | 4.2 | 4.23 | 2.63 |
5 | 0.31 | Good | J | SI2 | 63.3 | 58 | 335 | 4.34 | 4.35 | 2.75 |
6 | 0.24 | Very Good | J | VVS2 | 62.8 | 57 | 336 | 3.94 | 3.96 | 2.48 |
...and we get inline graphics
ggplot(diamonds,aes(x=carat,y=price)) + geom_point(aes(color=cut))
plot(diamonds$carat,diamonds$price)
Read some documentation inline
?geom_point
geom_point {ggplot2} | R Documentation |
The point geom is used to create scatterplots.
geom_point(mapping = NULL, data = NULL, stat = "identity", position = "identity", na.rm = FALSE, ...)
mapping |
The aesthetic mapping, usually constructed with
|
data |
A layer specific dataset - only needed if you want to override the plot defaults. |
stat |
The statistical transformation to use on the data for this layer. |
position |
The position adjustment to use for overlapping points on this layer |
na.rm |
If |
... |
other arguments passed on to |
The scatterplot is useful for displaying the relationship between two
continuous variables, although it can also be used with one continuous
and one categorical variable, or two categorical variables. See
geom_jitter
for possibilities.
The bubblechart is a scatterplot with a third variable mapped to the size of points. There are no special names for scatterplots where another variable is mapped to point shape or colour, however.
The biggest potential problem with a scatterplot is overplotting: whenever
you have more than a few points, points may be plotted on top of one
another. This can severely distort the visual appearance of the plot.
There is no one solution to this problem, but there are some techniques
that can help. You can add additional information with
stat_smooth
, stat_quantile
or
stat_density2d
. If you have few unique x values,
geom_boxplot
may also be useful. Alternatively, you can
summarise the number of points at each location and display that in some
way, using stat_sum
. Another technique is to use transparent
points, geom_point(alpha = 0.05)
.
geom_point
understands the following aesthetics (required aesthetics are in bold):
x
y
alpha
colour
fill
shape
size
scale_size
to see scale area of points, instead of
radius, geom_jitter
to jitter points to reduce (mild)
overplotting
p <- ggplot(mtcars, aes(wt, mpg)) p + geom_point() # Add aesthetic mappings p + geom_point(aes(colour = qsec)) p + geom_point(aes(alpha = qsec)) p + geom_point(aes(colour = factor(cyl))) p + geom_point(aes(shape = factor(cyl))) p + geom_point(aes(size = qsec)) # Change scales p + geom_point(aes(colour = cyl)) + scale_colour_gradient(low = "blue") p + geom_point(aes(size = qsec)) + scale_size_area() p + geom_point(aes(shape = factor(cyl))) + scale_shape(solid = FALSE) # Set aesthetics to fixed value p + geom_point(colour = "red", size = 3) qplot(wt, mpg, data = mtcars, colour = I("red"), size = I(3)) # Varying alpha is useful for large datasets d <- ggplot(diamonds, aes(carat, price)) d + geom_point(alpha = 1/10) d + geom_point(alpha = 1/20) d + geom_point(alpha = 1/100) # You can create interesting shapes by layering multiple points of # different sizes p <- ggplot(mtcars, aes(mpg, wt)) p + geom_point(colour="grey50", size = 4) + geom_point(aes(colour = cyl)) p + aes(shape = factor(cyl)) + geom_point(aes(colour = factor(cyl)), size = 4) + geom_point(colour="grey90", size = 1.5) p + geom_point(colour="black", size = 4.5) + geom_point(colour="pink", size = 4) + geom_point(aes(shape = factor(cyl))) # These extra layers don't usually appear in the legend, but we can # force their inclusion p + geom_point(colour="black", size = 4.5, show_guide = TRUE) + geom_point(colour="pink", size = 4, show_guide = TRUE) + geom_point(aes(shape = factor(cyl))) # Transparent points: qplot(mpg, wt, data = mtcars, size = I(5), alpha = I(0.2)) # geom_point warns when missing values have been dropped from the data set # and not plotted, you can turn this off by setting na.rm = TRUE mtcars2 <- transform(mtcars, mpg = ifelse(runif(32) < 0.2, NA, mpg)) qplot(wt, mpg, data = mtcars2) qplot(wt, mpg, data = mtcars2, na.rm = TRUE) # Use qplot instead qplot(wt, mpg, data = mtcars) qplot(wt, mpg, data = mtcars, colour = factor(cyl)) qplot(wt, mpg, data = mtcars, colour = I("red"))