| Title: | Product Plots for R |
| Description: | Framework for visualising tables of counts, proportions and probabilities. The framework is called product plots, alluding to the computation of area as a product of height and width, and the statistical concept of generating a joint distribution from the product of conditional and marginal distributions. The framework, with extensions, is sufficient to encompass over 20 visualisations previously described in fields of statistical graphics and 'infovis', including bar charts, mosaic plots, 'treemaps', equal area plots and fluctuation diagrams. |
| Version: | 0.1.1 |
| Imports: | plyr, ggplot2 |
| Suggests: | reshape2, testthat, covr |
| License: | GPL-2 |
| LazyData: | true |
| RoxygenNote: | 5.0.1 |
| URL: | https://github.com/hadley/productplots |
| BugReports: | https://github.com/hadley/productplots/issues |
| NeedsCompilation: | no |
| Packaged: | 2016-07-01 21:51:47 UTC; hadley |
| Author: | Hadley Wickham [aut, cre], Heike Hofmann [aut] |
| Maintainer: | Hadley Wickham <hadley@rstudio.com> |
| Repository: | CRAN |
| Date/Publication: | 2016-07-02 07:38:04 |
Calculate column labels.
Description
Calculate column labels.
Usage
col_labels(df)
Arguments
df |
data frame produced by |
For ggplot2: colour by weight.
Description
For ggplot2: colour by weight.
Usage
colour_level()
For ggplot2: colour by weight.
Description
For ggplot2: colour by weight.
Usage
colour_weight()
Template for a double decker plot. A double decker plot is composed of a sequence of spines in the same direction, with the final spine in the opposite direction.
Description
Template for a double decker plot. A double decker plot is composed of a sequence of spines in the same direction, with the final spine in the opposite direction.
Usage
ddecker(direction = "h")
Arguments
direction |
direction of first split |
Find the first level which has columns.
Description
Returns NA if no columns at any level.
Usage
find_col_level(df)
Arguments
df |
data frame of rectangle positions |
Find the first level which has rows.
Description
Returns NA if no rows at any level.
Usage
find_row_level(df)
Arguments
df |
data frame of rectangle positions |
Fluctation partitioning.
Description
Fluctation partitioning.
Usage
fluct(data, bounds, offset = 0.05, max = NULL)
Arguments
data |
bounds data frame |
bounds |
bounds of space to partition |
offset |
space between spines |
max |
maximum value |
Template for a fluctuation diagram.
Description
Template for a fluctuation diagram.
Usage
flucts(direction = "h")
Arguments
direction |
direction of first split |
Data related to happiness from the general social survey.
Description
The data is a small sample of variables related to happiness from the general social survey (GSS). The GSS is a yearly cross-sectional survey of Americans, run from 1976. We combine data for 25 years to yield 51,020 observations, and of the over 5,000 variables, we select nine related to happiness:
Usage
data(happy)
Format
A data frame with 51020 rows and 10 variables
Details
age. age in years: 18–89.
degree. highest education: lt high school, high school, junior college, bachelor, graduate.
finrela. relative financial status: far above, above average, average, below average, far below.
happy. happiness: very happy, pretty happy, not too happy.
health. health: excellent, good, fair, poor.
marital. marital status: married, never married, divorced, widowed, separated.
sex. sex: female, male.
wtsall. probability weight. 0.43–6
Horizontal bar partition: width constant, height varies.
Description
Horizontal bar partition: width constant, height varies.
Usage
hbar(data, bounds, offset = 0.02, max = NULL)
Arguments
data |
bounds data frame |
bounds |
bounds of space to partition |
offset |
space between spines |
max |
maximum value |
Horizontal spine partition: height constant, width varies.
Description
Horizontal spine partition: height constant, width varies.
Usage
hspine(data, bounds, offset = 0.01, max = NULL)
Arguments
data |
bounds data frame |
bounds |
bounds of space to partition |
offset |
space between spines |
max |
maximum value |
Template for a mosaic plot. A mosaic plot is composed of spines in alternating directions.
Description
Template for a mosaic plot. A mosaic plot is composed of spines in alternating directions.
Usage
mosaic(direction = "v")
Arguments
direction |
direction of first split |
Template for a nested barchart. A nested bar is just a sequence of bars in the same direction.
Description
Template for a nested barchart. A nested bar is just a sequence of bars in the same direction.
Usage
nested(direction = "h")
Arguments
direction |
direction of first split |
Parse product formula into component pieces
Description
Parse product formula into component pieces
Usage
parse_product_formula(f)
Arguments
f |
function to parse into component pieces |
Value
wt |
the weighting variable |
cond |
condition variables |
margin |
margining variables |
Calculate frequencies.
Description
Calculate frequencies.
Usage
prodcalc(data, formula, divider = mosaic(), cascade = 0, scale_max = TRUE,
na.rm = FALSE)
Arguments
data |
input data frame |
formula |
formula specifying display of plot |
divider |
divider function |
cascade |
cascading amount, per nested layer |
scale_max |
Logical vector of length 1. If |
na.rm |
Logical vector of length 1 - should missing levels be silently removed? |
Examples
prodcalc(happy, ~ happy, "hbar")
prodcalc(happy, ~ happy, "hspine")
Create a product plot
Description
Create a product plot
Usage
prodplot(data, formula, divider = mosaic(), cascade = 0, scale_max = TRUE,
na.rm = FALSE, levels = -1L, ...)
Arguments
data |
input data frame |
formula |
formula specifying display of plot |
divider |
divider function |
cascade |
cascading amount, per nested layer |
scale_max |
Logical vector of length 1. If |
na.rm |
Logical vector of length 1 - should missing levels be silently removed? |
levels |
an integer vector specifying which levels to draw. |
... |
other arguments passed on to |
Examples
if (require("ggplot2")) {
prodplot(happy, ~ happy, "hbar")
prodplot(happy, ~ happy, "hspine")
prodplot(happy, ~ sex + happy, c("vspine", "hbar"))
prodplot(happy, ~ sex + happy, stacked())
prodplot(happy, ~ happy + sex | health, mosaic("h")) + aes(fill=happy)
# The levels argument can be used to extract a given level of the plot
prodplot(happy, ~ sex + happy, stacked(), level = 1)
prodplot(happy, ~ sex + happy, stacked(), level = 2)
}
Calculate row labels.
Description
Calculate row labels.
Usage
row_labels(df)
Arguments
df |
data frame produced by |
Generate an x-scale for ggplot2 graphics.
Description
Generate an x-scale for ggplot2 graphics.
Usage
scale_x_product(df)
Arguments
df |
list of data frame produced by |
Generate a y-scale for ggplot2 graphics.
Description
Generate a y-scale for ggplot2 graphics.
Usage
scale_y_product(df)
Arguments
df |
list of data frame produced by |
Spine partition: divide longest dimesion.
Description
Spine partition: divide longest dimesion.
Usage
spine(data, bounds, offset = 0.01, max = NULL)
Arguments
data |
bounds data frame |
bounds |
bounds of space to partition |
offset |
space between spines |
max |
maximum value |
Template for a stacked bar chart. A stacked bar chart starts with a bar and then continues with spines in the opposite direction.
Description
Template for a stacked bar chart. A stacked bar chart starts with a bar and then continues with spines in the opposite direction.
Usage
stacked(direction = "h")
Arguments
direction |
direction of first split |
Tree map partitioning.
Description
Adapated from SquarifiedLayout in http://www.cs.umd.edu/hcil/treemap-history/Treemaps-Java-Algorithms.zip
Usage
tile(data, bounds, max = 1)
Arguments
data |
bounds data frame |
bounds |
bounds of space to partition |
max |
maximum value |
Vertical bar partition: height constant, width varies.
Description
Vertical bar partition: height constant, width varies.
Usage
vbar(data, bounds, offset = 0.02, max = NULL)
Arguments
data |
bounds data frame |
bounds |
bounds of space to partition |
offset |
space between spines |
max |
maximum value |
Vertical spine partition: width constant, height varies.
Description
Vertical spine partition: width constant, height varies.
Usage
vspine(data, bounds, offset = 0.01, max = NULL)
Arguments
data |
bounds data frame |
bounds |
bounds of space to partition |
offset |
space between spines |
max |
maximum value |