layer_bars {ggvis}

Display data with bars (a barchart).

Description

This will add bars to a plot. The exact behavior is complicated because the term bar chart is used to describe four important variations on a theme. The action of layer_bars depends on two factors: whether or not a y prop has been specified, and whether the x props is continuous or categorical.

Usage

layer_bars(vis, ..., stack = TRUE, width = NULL)

Arguments

vis

Visualisation to modify

...

Visual properties used to override defaults.

stack

If there are multiple bars to be drawn at an x location, should the bars be stacked? If FALSE, the bars will be overplotted on each other.

width

Width of each bar. When x is continuous, this controls the width in the same units as x. When x is categorical, this controls the width as a proportion of the spacing between items (default is 0.9).

Visualisations

If no y prop has been specified, then this will count the number of entries at each unique x value. There will be one bar at each unique x value, and the y value (or height) of each bar will represent the count at that x value.

If a y prop has been specified, then those y values will be used as weights for a weighted count at each unique x value. If no x values appear more than once in the data, then the end result is a plot where the height of the bar at each x value is simply the y value. However, if an x value appear more than once in the data, then this will sum up the y values at each x.

If the x variable is continuous, then a continuous x axis will be used, and the width of each bar is by default equal to the resolution of the data – that is, the smallest difference between any two x values.

If the x variable is categorical, then a categorical x axis will be used. By default, the width of each bar is 0.9 times the space between the items.

See Also

layer_histograms For bar graphs of counts at each unique x value, in contrast to a histogram's bins along x ranges.

compute_count and compute_tabulate for more information on how data is transformed.

Examples

# Discrete x: bar graph of counts at each x value
cocaine %>% ggvis(~state) %>% layer_bars()
# Continuous x: bar graph of counts at unique locations
cocaine %>% ggvis(~month) %>% layer_bars()

# Use y prop to weight by additional variable. This is also useful
# if you have pretabulated data
cocaine %>% ggvis(~state, ~weight) %>% layer_bars()
cocaine %>% ggvis(~month, ~weight) %>% layer_bars()

# For continuous x, layer_bars is useful when the variable has a few
# unique values that you want to preserve. If you have many unique
# values and you want to bin, use layer_histogram
cocaine %>% ggvis(~price) %>% layer_bars()
cocaine %>% ggvis(~price) %>% layer_histograms(width = 100)

# If you have unique x values, you can use layer_bars() as an alternative
# to layer_points()
pressure %>% ggvis(~temperature, ~pressure) %>% layer_points()
pressure %>% ggvis(~temperature, ~pressure) %>% layer_bars()

# When x is continuous, width controls the width in x units
pressure %>% ggvis(~temperature, ~pressure) %>% layer_bars(width = 10)
# When x is categorical, width is proportional to spacing between bars
pressure %>% ggvis(~factor(temperature), ~pressure) %>%
  layer_bars(width = 0.5)

# Stacked bars
# If grouping var is continuous, you need to manually specify grouping
ToothGrowth %>% group_by(dose) %>%
  ggvis(x = ~supp, y = ~len, fill = ~dose) %>% layer_bars()
# If grouping var is categorical, grouping is done automatically
cocaine %>% ggvis(x = ~state, fill = ~as.factor(month)) %>%
  layer_bars()