7.7 Histogram

geom_histogram(mapping = NULL, data = NULL, stat = "bin", position = "stack", ..., binwidth = NULL, origin = NULL, breaks = NULL, bins = NULL, na.rm = FALSE, orientation = NA, show.legend = NA, inherit.aes = TRUE)

  • binwidth The width of the bins. Can be specified as a numeric value or as a function that calculates width from unscaled x. Here, “unscaled x” refers to the original x values in the data, before application of any scale transformation. When specifying a function along with a grouping structure, the function will be called once per group.

    • The default is to use the number of bins in bins, covering the range of the data. stat_bin() using bins=30; this is not a good default, but the idea is to get you experimenting with different number of bins. You can also experiment modifying the binwidth with center or boundary arguments. binwidth overrides binsso you should do one change at a time.
    • You should always override this value, exploring multiple widths to find the best to illustrate the stories in your data.

  • center, boundary numeric values specify bin positions. One value for either center or boundary is adequate, other values will be automatically filled using binwidth.

    • center specifies the center of one of the bins. Default figure will use center position to identify bins.
    • boundary specifies the boundary between two bins. Boundary values are more informative. [suggest to specify one boundary value; just easier to say boundaries]
    • Worth noting that center and boundary can be either above or below the range of the data, in this case the value provided will be shifted of a multiple number of binwidth.

  • bins Number of bins. Overridden by binwidth. Defaults to 30.

  • breaks Actual breaks to use. Intervals are created as left open, right closed. But specifying inside geom_histogram might show weird breaks in y-axis labels.

    Specifying breaks using scale_x_continuous is a better practice.

p <- ggplot(data=data, aes(tmp) ) + 
    geom_histogram(fill="#BDBCBC", color="black", binwidth = 2, boundary=0) +
    labs(x="Average temperature [ºC]")
p

geom_histogram(aes(..density..)) surrounding the variable names with .. means to call after_stat function. It delays the mapping until later in the rendering process when summary statistics have been calculated. The expression ..density.. is deprecated; use after_stat() in stead.

Most aesthetics are mapped directly from variables found in the data, called direct input (stage1). Sometimes, however, you want to delay the mapping until later stages of the data that you can map aesthetics from, and three functions to control at which stage aesthetics should be evaluated.

after_stat(x) and after_scale(x) can be used inside the aes() function, used as the mapping argument in layers.

  • after_stat(x) uses variables calculated after the transformation by the layer stat (stage 2);

    E.g., the height of bars in geom_histogram() can be density probability;

    # this shows the count frequency
ggplot(faithful, aes(x = waiting)) +
  geom_histogram(fill="#BDBCBC", color="black") 

# this shows the density plot, can replace after_stat(density) with ..density..
# surrounding the variable name with two dots
ggplot(faithful, aes(x = waiting)) +
  geom_histogram(aes(y = after_stat(density)),
                fill="#BDBCBC", color="black") +
  geom_density() # empirical density

    • after_stat(count) show frequncy count; after_stat(ncount) count, scaled to a maximum of 1;

    • after_stat(density) show density; after_stat(ndensity) density, scaled to a maximum of 1;

  • after_scale uses variables calculated after the scale transformation (stage 3); see documents here.

    • could be used to label a bar plot;

Add fitted density from a distribution

# fit a lognormal distribution
library(MASS)
fit_params <- fitdistr(prices_monthly$AdjustedPrice,"lognormal")
fit_params$estimate

ggplot(prices_monthly, aes(x=AdjustedPrice)) +
    geom_histogram(bins=40,
                   aes(y=..density..),
                   fill="#BDBCBC", color="black") +
    stat_function(fun=dlnorm, 
                  args=list(meanlog = fit_params$estimate['meanlog'], 
                            sdlog = fit_params$estimate['sdlog']),
                  colour = "red"
                  ) +
    scale_x_continuous(limits=c(0, 170))

Histogram of a vector

dice_results <- c(1,3,2,4,5,6,5,3,2,1,6,2,6,5,6,4)
ggplot(aes(x=dice_results)) + geom_bar()

This returns an error: data must be a data.frame. If you don’t provide argument name explicitly, sequential rule is used – data arg is used for aes(x=dice_results).

To correct it – use arg name explicitly:

ggplot(mapping = aes(x=dice_results)) + geom_bar()

Alternatively, you may use it inside geom_ functions familiy without explicit naming mapping argument since mapping is the first argument unlike in ggplot function case where data is the first function argument.

ggplot() + geom_bar(aes(dice_results))
# or use the `aes` function
ggplot() + 
        aes(dice_results) +
        geom_bar()

Vertical histogram

https://stackoverflow.com/a/13334294/10108921

geom_bar and geom_col plots bar charts.

  • geom_bar makes the height of the bar proportional to the number of cases in each group.
  • geom_col the heights of the bars to represent values in the data

geom_ribbon(data=sim_obs_quantile, aes(ymin=`17%`, ymax=`83%`), alpha=0.2, fill="#F8766D") plot confidence interval (CI) in shaded areas.

geom_segment(aes(x = x1, y = y1, xend = x2, yend = y2), col = "red", arrow = arrow(length = unit(0.3, "cm")) draws a straight line between points (x, y) and (xend, yend) in the plot.

  • arrow specification for arrow heads, as created by grid::arrow().

annotate("segment", x=12, y=-0.05, xend=12, yend=0, col="red", arrow=arrow(length=unit(0.3, "cm"))) draw arrows outside the plot.