airquality dataset with additional variables

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

Source

The data were obtained from the New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).

Details

Daily readings of the following air quality values for May 1, 1973 (a Tuesday) to September 30, 1973.

References

Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983) Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

Examples

if (FALSE) {
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)
}