Package 'lvplot'

County demographics based on 1980 US Census

Description

County level statistics based on the 1980 US Census.

Usage

census

Format

A data frame with 10 variables

county

County name

FIPS

FIPS county code

Latitude,Longitude

Geographic location of county centers

JanTmp,JulTmp

(normalized) Temperatures in January & July

JanSun,JulSun

(normalized) Sunshine measurment in January & July

Elevtn

Elevation above sea level

totalpop

Population

---

Determine depth of letter values needed for n observations.

Description

Determine depth of letter values needed for n observations.

Usage

determineDepth(n, k = NULL, alpha = NULL, perc = NULL)

Arguments

n	number of observation to be shown in the LV boxplot
k	number of letter value statistics used
alpha	if supplied, depth k is calculated such that (1-alpha)100 intervals of an LV statistic do not extend into neighboring LV statistics.
perc	if supplied, depth k is adjusted such that perc percent outliers are shown

Details

Supply one of k, alpha or perc.

---

Side-by-side LV boxplots with ggplot2.

Description

An extension of standard boxplots which draws k letter statistics. Conventional boxplots (Tukey 1977) are useful displays for conveying rough information about the central 50% of the data and the extent of the data. For moderate-sized data sets ($n < 1000$), detailed estimates of tail behavior beyond the quartiles may not be trustworthy, so the information provided by boxplots is appropriately somewhat vague beyond the quartiles, and the expected number of “outliers” and “far-out” values for a Gaussian sample of size $n$ is often less than 10 (Hoaglin, Iglewicz, and Tukey 1986). Large data sets ($n \approx 10,000-100,000$) afford more precise estimates of quantiles in the tails beyond the quartiles and also can be expected to present a large number of “outliers” (about $0.4 + 0.007 n$). The letter-value box plot addresses both these shortcomings: it conveys more detailed information in the tails using letter values, only out to the depths where the letter values are reliable estimates of their corresponding quantiles (corresponding to tail areas of roughly $2^{-i}$); “outliers” are defined as a function of the most extreme letter value shown. All aspects shown on the letter-value boxplot are actual observations, thus remaining faithful to the principles that governed Tukey's original boxplot.

Usage

geom_lv(
  mapping = NULL,
  data = NULL,
  stat = "lv",
  position = "dodge",
  outlier.colour = "black",
  outlier.shape = 19,
  outlier.size = 1.5,
  outlier.stroke = 0.5,
  na.rm = TRUE,
  varwidth = FALSE,
  width.method = "linear",
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

GeomLv

scale_fill_lv(...)

stat_lv(
  mapping = NULL,
  data = NULL,
  geom = "lv",
  position = "dodge",
  na.rm = TRUE,
  conf = 0.95,
  percent = NULL,
  k = NULL,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

StatLv

Arguments

mapping	Set of aesthetic mappings created by 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 naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.
outlier.colour	Override aesthetics used for the outliers. Defaults come from geom_point().
outlier.shape	Override aesthetics used for the outliers. Defaults come from geom_point().
outlier.size	Override aesthetics used for the outliers. Defaults come from geom_point().
outlier.stroke	Override aesthetics used for the outliers. Defaults come from geom_point().
na.rm	If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.
varwidth	if FALSE (default) draw boxes that are the same size for each group. If TRUE, boxes are drawn with widths proportional to the square-roots of the number of observations in the groups (possibly weighted, using the weight aesthetic).
width.method	character, one of 'linear' (default), 'area', or 'height'. This parameter determines whether the width of the box for letter value LV(i) should be proportional to i (linear), proportional to $2^-i$ (height), or whether the area of the box should be proportional to $2^-i$ (area).
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.
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().
...	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.
geom, stat	Use to override the default connection between geom_lv and stat_lv.
conf	confidence level
percent	numeric value: percent of data in outliers
k	number of letter values shown

Format

An object of class GeomLv (inherits from Geom, ggproto, gg) of length 6.

An object of class StatLv (inherits from Stat, ggproto, gg) of length 5.

Computed/reported variables

k

Number of Letter Values used for the display

LV

Name of the Letter Value

width

width of the interquartile box

References

McGill, R., Tukey, J. W. and Larsen, W. A. (1978) Variations of box plots. The American Statistician 32, 12-16.

See Also

stat_quantile to view quantiles conditioned on a continuous variable.

Examples

library(ggplot2)
p <- ggplot(mpg, aes(class, hwy))
p + geom_lv(aes(fill = after_stat(LV))) + scale_fill_brewer()
p + geom_lv() + geom_jitter(width = 0.2)
p + geom_lv(aes(fill = after_stat(LV))) + scale_fill_lv()

# Outliers
p + geom_lv(varwidth = TRUE, aes(fill = after_stat(LV))) + scale_fill_lv()
p + geom_lv(fill = "grey80", colour = "black")
p + geom_lv(outlier.colour = "red", outlier.shape = 1)

# Plots are automatically dodged when any aesthetic is a factor
p + geom_lv(aes(fill = drv))

# varwidth adjusts the width of the boxes according to the number of observations
ggplot(ontime, aes(UniqueCarrier, TaxiIn + TaxiOut)) +
  geom_lv(aes(fill = after_stat(LV)), varwidth=TRUE) +
  scale_fill_lv() +
  scale_y_sqrt() +
  theme_bw()

ontime$DayOfWeek <- as.POSIXlt(ontime$FlightDate)$wday
ggplot(ontime, aes(factor(DayOfWeek), TaxiIn + TaxiOut)) +
  geom_lv(aes(fill = after_stat(LV))) +
  scale_fill_lv() +
  scale_y_sqrt() +
  theme_bw()

---

Side-by-side LV boxplots with base graphics

Description

An extension of standard boxplots which draws k letter statistics. Conventional boxplots (Tukey 1977) are useful displays for conveying rough information about the central 50% of the data and the extent of the data.

Usage

LVboxplot(x, ...)

## S3 method for class 'formula'
LVboxplot(
  formula,
  alpha = 0.95,
  k = NULL,
  perc = NULL,
  horizontal = TRUE,
  xlab = NULL,
  ylab = NULL,
  col = "grey30",
  bg = "grey90",
  width = 0.9,
  width.method = "linear",
  median.col = "grey10",
  ...
)

## S3 method for class 'numeric'
LVboxplot(
  x,
  alpha = 0.95,
  k = NULL,
  perc = NULL,
  horizontal = TRUE,
  xlab = NULL,
  ylab = NULL,
  col = "grey30",
  bg = "grey90",
  width = 0.9,
  width.method = "linear",
  median.col = "grey10",
  ...
)

Arguments

x	numeric vector of data
...	passed onto plot
formula	a plotting formula of the form y ~ x, where x is a string or factor. The values of y will be split into groups according to their values on x and separate letter value box plots of y are drawn side by side in the same display.
alpha	if supplied, depth k is calculated such that (1-alpha)100 intervals of an LV statistic do not extend into neighboring LV statistics.
k	number of letter value statistics used
perc	if supplied, depth k is adjusted such that perc percent outliers are shown
horizontal	display horizontally (TRUE) or vertically (FALSE)
xlab	x axis label
ylab	y axis label
col	vector of colours to use
bg	background colour
width	maximum height/width of box
width.method	one of 'linear', 'height' or 'area'. Methods 'height' and 'area' ensure that these dimension are proportional to the number of observations within each box.
median.col	colour of the line for the median

Details

For moderate-sized data sets ($n < 1000$), detailed estimates of tail behavior beyond the quartiles may not be trustworthy, so the information provided by boxplots is appropriately somewhat vague beyond the quartiles, and the expected number of “outliers” and “far-out” values for a Gaussian sample of size $n$ is often less than 10 (Hoaglin, Iglewicz, and Tukey 1986). Large data sets ($n \approx 10,000-100,000$) afford more precise estimates of quantiles in the tails beyond the quartiles and also can be expected to present a large number of “outliers” (about $0.4 + 0.007 n$).

The letter-value box plot addresses both these shortcomings: it conveys more detailed information in the tails using letter values, only out to the depths where the letter values are reliable estimates of their corresponding quantiles (corresponding to tail areas of roughly $2^{-i}$); “outliers” are defined as a function of the most extreme letter value shown. All aspects shown on the letter-value boxplot are actual observations, thus remaining faithful to the principles that governed Tukey's original boxplot.

Examples

n <- 10
oldpar <- par()
par(mfrow=c(4,2), mar=c(3,3,3,3))
for (i in 1:4) {
  x <- rexp(10 ^ (i + 1))
  boxplot(x, col = "grey", horizontal = TRUE)
  title(paste("Exponential, n = ", length(x)))
  LVboxplot(x, col = "grey", xlab = "")
}
par(mfrow=oldpar$mfrow, mar=oldpar$mar)

with(ontime, LVboxplot(sqrt(TaxiIn + TaxiOut) ~ UniqueCarrier, horizontal=FALSE))

---

Compute table of k letter values for vector x

Description

Compute table of k letter values for vector x

Usage

lvtable(x, k, alpha = 0.95)

Arguments

x	input numeric vector
k	number of letter values to compute
alpha	alpha-threshold for confidence level

---

Ontime Flight Data

Description

Data set detailing on-time performance of national US flights in January 2015. This data is a subset of the data provided by the US Department of Transportation. The full data as well as archived or more recent data is available for download from http://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=236&DB_Short_Name=On-Time.

Usage

ontime

Format

A data frame consisting of the variables

FlightDate

a date variable of the day of the flight

UniqueCarrier

factor variable of the carrier (using the two letter abbreviation)

FlightNum

numeric variable of the flight number

CRSDepTime

scheduled departure time in hhmm format

DepTime

actual departure time in hhmm format

CRSArrTime

scheduled arrival time in hhmm format

ArrTime

actual arrival time in hhmm format

TaxiOut

numeric variable of the taxi out time in minutes

TaxiIn

numeric variable of the taxi in time in minutes

ArrDelay

Arrival delay, in Minutes

DepDelay

Departure delay, in Minutes

CarrierDelay

Carrier Delay, in Minutes

WeatherDelay

Weather Delay, in Minutes

NASDelay

National Air System Delay, in Minutes

SecurityDelay

Security Delay, in Minutes

LateAircraftDelay

Late Aircraft Delay, in Minutes

References

http://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=236&DB_Short_Name=On-Time

Examples

library(ggplot2)
ggplot(ontime, aes(UniqueCarrier, TaxiIn + TaxiOut)) +
  geom_lv(aes(fill = after_stat(LV))) +
  scale_fill_lv() +
  scale_y_sqrt() +
  theme_bw()

---