Package 'ggHoriPlot'

Average temperature in Copenhagen

Description

A dataset containing the average temperature in degrees Celsius for Copenhagen between the year 1995 and 2019.

Usage

climate_CPH

Format

A data frame with 9,132 rows and 9 variables:

Region

geographic region

Country

country

State

state or territory

City

city or town

Month

month

Day

day

Year

year

AvgTemperature

average temperature in Fahrenheit

date_mine

data in yyyy-mm-dd format

Source

https://www.kaggle.com/sudalairajkumar/daily-temperature-of-major-cities

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Average temperature in major cities of the US

Description

A dataset containing the average temperature in Fahrenheit for major cities in the US for the year 2000.

Usage

climate_US

Format

A data frame with 57,828 rows and 9 variables:

Region

geographic region

Country

country

State

state or territory

City

city or town

Month

month

Day

day

Year

year

AvgTemperature

average temperature in Fahrenheit

date_mine

data in yyyy-mm-dd format

Source

https://www.kaggle.com/sudalairajkumar/daily-temperature-of-major-cities

https://benschmidt.org/2014/06/05/optimally-ordering-geographical-entities-in-linear-space/

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Distribution of COVID-19 cases in Asia

Description

A dataset containing the geographic distribution of COVID-19 cases in Asia during 2020.

Usage

COVID

Format

A data frame with 12,695 rows and 3 variables:

date_mine

date of the measurement in yyyy-mm-dd format

y

standardized number of cases

countriesAndTerritories

countries and territories in Asia

Source

https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide

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Horizon plots

Description

This function builds horizon plots in ggplot2. It allows for the customization of the origin and the horizon scale.

Usage

geom_horizon(
  mapping = NULL,
  data = NULL,
  position = "identity",
  ...,
  na.rm = FALSE,
  show.legend = TRUE,
  origin = "midpoint",
  horizonscale = 6,
  rm.outliers = FALSE,
  reverse = FALSE,
  mirror = FALSE,
  inherit.aes = TRUE
)

stat_horizon(
  mapping = NULL,
  data = NULL,
  geom = "ribbon",
  position = "identity",
  ...,
  na.rm = FALSE,
  show.legend = TRUE,
  inherit.aes = TRUE,
  origin = "midpoint",
  horizonscale = 6,
  rm.outliers = FALSE,
  reverse = FALSE,
  mirror = FALSE
)

Arguments

mapping	Set of aesthetic mappings created by aes() or aes_(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.
data	The data to be displayed in this layer. There are three options: If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot(). A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created. A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)).
position	Position adjustment, either as a string, or the result of a call to a position adjustment function.
...	Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.
na.rm	If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.
show.legend	logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.
origin	Origin of the horizon plot. It can either be a string, namely "midpoint" (the default), "median", "mean", "min" or "quantiles", or a user-specified number.
horizonscale	Cutpoints of the horizon plot. It can either be an integer specifying the number of ranges (default is 6), or a user-supplied numeric vector with the cutpoints defining the different ranges.
rm.outliers	If TRUE, all the values below quantile(y, 0.25)-1.5*IQR(y) and above quantile(y, 0.75)+1.5*IQR(y) are excluded from the origin and cutpoint calculations (default is FALSE). @param reverse If TRUE, the horizon peaks for the values below the origin are reversed (default is FALSE).
reverse	IF TRUE, the horizon peaks for all the values below the origin are reversed (default is FALSE).
mirror	If TRUE, the horizon peaks for all the values are reversed (default is FALSE).
inherit.aes	If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders().
geom	Used geom, default to 'ribbon'

Details

A horizon plot is a special type of area plot in which the original data is transformed based on an origin and a horizon scale. The data is cut in different intervals, and the further the data is from the origin, the deeper its color usually is. All the intervals above the origin are then stacked on top of one another, keeping the intervals closest to the origin in the bottom and the furthest away ones on top. Likewise, the intervals below the origin are normally given a different color palette and they are stacked in a similar manner in the same area as the intervals above the origin. You can learn more about how horizon plots are built in vignette('ggHoriPlot') or at https://bernatgel.github.io/karyoploter_tutorial/Tutorial/PlotHorizon/PlotHorizon.html.

Value

'ggplot2' layer for building a horizon plot.

Orientation

This geom treats each axis differently and, thus, can thus have two orientations. Often the orientation is easy to deduce from a combination of the given mappings and the types of positional scales in use. Thus, ggplot2 will by default try to guess which orientation the layer should have. Under rare circumstances, the orientation is ambiguous and guessing may fail. In that case the orientation can be specified directly using the orientation parameter, which can be either "x" or "y". The value gives the axis that the geom should run along, "x" being the default orientation you would expect for the geom.

Aesthetics

geom_horizon() understands the following aesthetics (required aesthetics are in bold):

• x

• y

• xend

• alpha

• colour

• fill

Examples

# Generate data
huron <- data.frame(year = 1875:1972, level = as.vector(LakeHuron))

# Basic plot with default colors
h <- ggplot(huron) + geom_horizon(aes(year, level))

# Add color scheme
h + theme_void() + scale_fill_hcl()

# Add cupoints
ggplot(huron) +
  geom_horizon(aes(year, level, fill = ..Cutpoints..)) +
  theme_void() +
  scale_fill_hcl()

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Distribution of repeats along the human genome

Description

A dataset containing the percentage of simple repeats in 100 kb windows along the human genome (hg38).

Usage

rmsk

Format

A data frame with 30,885 rows and 4 variables:

genoName

chromosome name

bin

starting coordinate of window

bin_2

end coordinate of window

p_repeat

percentage of repeats

Source

https://genome.ucsc.edu/cgi-bin/hgTrackUi?g=rmsk

---

Create your own discrete scale

Description

These functions allow you to specify your own set of mappings from levels in the data to aesthetic values.

Usage

scale_fill_hcl(..., palette = "RdYlBu", reverse = FALSE)

Arguments

...	Arguments passed on to ggplot2::discrete_scale scale_name The name of the scale that should be used for error messages associated with this scale. name The name of the scale. Used as the axis or legend title. If waiver(), the default, the name of the scale is taken from the first mapping used for that aesthetic. If NULL, the legend title will be omitted. breaks One of: NULL for no breaks waiver() for the default breaks (the scale limits) A character vector of breaks A function that takes the limits as input and returns breaks as output. Also accepts rlang lambda function notation. labels One of: NULL for no labels waiver() for the default labels computed by the transformation object A character vector giving labels (must be same length as breaks) A function that takes the breaks as input and returns labels as output. Also accepts rlang lambda function notation. limits One of: NULL to use the default scale values A character vector that defines possible values of the scale and their order A function that accepts the existing (automatic) values and returns new ones. Also accepts rlang lambda function notation. na.translate Unlike continuous scales, discrete scales can easily show missing values, and do so by default. If you want to remove missing values from a discrete scale, specify na.translate = FALSE. na.value If na.translate = TRUE, what aesthetic value should the missing values be displayed as? Does not apply to position scales where NA is always placed at the far right. drop Should unused factor levels be omitted from the scale? The default, TRUE, uses the levels that appear in the data; FALSE uses all the levels in the factor. guide A function used to create a guide or its name. See guides() for more information. super The super class to use for the constructed scale
palette	the name of the palette to generate colors from. A list of all available palettes can be found by running colorspace::hcl_palettes() (default is "RdYlBu").
reverse	If TRUE, the order of the colors is reversed (default is FALSE)'

Details

The functions 'scale_colour_manual()', 'scale_fill_manual()', 'scale_size_manual()', etc. work on the aesthetics specified in the scale name: 'colour', 'fill', 'size', etc. However, the functions 'scale_colour_manual()' and 'scale_fill_manual()' also have an optional 'aesthetics' argument that can be used to define both 'colour' and 'fill' aesthetic mappings via a single function call (see examples). The function 'scale_discrete_manual()' is a generic scale that can work with any aesthetic or set of aesthetics provided via the 'aesthetics' argument.

Value

Scale layer for the fill aesthetic.

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Peaks times for sports and leisure activities

Description

A dataset containing the peak time for doing 29 sports and leisure activities.

Usage

sports_time

Format

A data frame with 8,092 rows and 3 variables:

activity

name of the activity

time

time of the day, in hhmm format

p

standardized peak

Source

https://github.com/halhen/viz-pub/blob/master/sports-time-of-day/activity.tsv

https://eagereyes.org/blog/2017/joy-plots

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