air_miss {miceFast} | R Documentation |
airquality dataset with additional variables
Description
airquality dataset with additional variables
Usage
air_miss
Format
A data frame and data table with 154 observations on 11 variables.
- Ozone
numeric Ozone (ppb) - Mean ozone in parts per billion from 1300 to 1500 hours at Roosevelt Island
- Solar.R
numeric Solar R (lang) - Solar radiation in Langleys in the frequency band 4000–7700 Angstroms from 0800 to 1200 hours at Central Park
- Wind
numeric Wind (mph) - Average wind speed in miles per hour at 0700 and 1000 hours at LaGuardia Airport
- Temp
numeric Temperature (degrees F) - Maximum daily temperature in degrees Fahrenheit at La Guardia Airport.
- Day
numeric Day of month (1–31)
- Intercept
numeric a constant
- index
numeric id
- weights
numeric positive values weights
- groups
factor Month (1–12)
- x_character
character discrete version of Solar.R (5-levels)
- Ozone_chac
character discrete version of Ozone (7-levels)
- Ozone_f
factor discrete version of Ozone (7-levels)
- Ozone_high
logical Ozone higher than its mean
Details
Daily readings of the following air quality values for May 1, 1973 (a Tuesday) to September 30, 1973.
Source
The data were obtained from the New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).
References
Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983) Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.
Examples
## Not run:
library(data.table)
data(airquality)
data <- cbind(as.matrix(airquality[, -5]),
Intercept = 1, index = 1:nrow(airquality),
# a numeric vector - positive values
weights = rnorm(nrow(airquality), 1, 0.01),
# months as groups
groups = airquality[, 5]
)
# data.table
air_miss <- data.table(data)
air_miss$groups <- factor(air_miss$groups)
# Distribution of Ozone - close to log-normal
# hist(air_miss$Ozone)
# Additional vars
# Make a character variable to show package capabilities
air_miss$x_character <- as.character(cut(air_miss$Solar.R, seq(0, 350, 70)))
# Discrete version of dependent variable
air_miss$Ozone_chac <- as.character(cut(air_miss$Ozone, seq(0, 160, 20)))
air_miss$Ozone_f <- cut(air_miss$Ozone, seq(0, 160, 20))
air_miss$Ozone_high <- air_miss$Ozone > mean(air_miss$Ozone, na.rm = T)
## End(Not run)