air_miss: airquality dataset with additional variables

In miceFast: Fast Imputations Using 'Rcpp' and 'Armadillo'

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)

miceFast documentation built on Nov. 18, 2022, 1:07 a.m.