Bin data, calculate mean and bootstrap confidence interval in the mean

binData() summarises data by intervals and calculates the mean and bootstrap confidence intervals (by default 95% CI) in the mean of a chosen variable in a data frame. Any other numeric variables are summarised by their mean intervals. This occurs via bootMeanDF(), which calculates the uncertainty intervals in the mean of a vector.

Usage

binData(
  mydata,
  bin = "nox",
  uncer = "no2",
  type = "default",
  n = 40,
  interval = NA,
  breaks = NA,
  conf.int = 0.95,
  B = 250,
  ...
)

bootMeanDF(x, conf.int = 0.95, B = 1000)

Arguments

mydata: Name of the data frame to process.
bin: The name of the column to divide into intervals.
uncer: The name of the column for which the mean, lower and upper uncertainties should be calculated for each interval of bin.
type: Used for splitting the data further. Passed to cutData(). Note that intervals are calculated on the whole dataset before the data is categorised, meaning intervals will be the same for the different groups.
n: The number of intervals to split bin into.
interval: The interval to be used for binning the data.
breaks: User specified breaks to use for binning.
conf.int: The confidence interval, defaulting to 0.95 (i.e., the 95% Confidence Interval).
B: The number of bootstrap simulations.
...: Other parameters that are passed on to cutData(), for use with type.
x: A vector from which the mean and bootstrap confidence intervals in the mean are to be calculated

Value

Returns a summarised data frame with new columns for the mean and upper / lower confidence intervals in the mean.

Details

There are three options for binning. The default is to bin bin into 40 intervals. Second, the user can choose an binning interval, e.g., interval = 5. Third, the user can supply their own breaks to use as binning intervals. Note that intervals are calculated on the whole dataset before the data is cut into categories using type.

Examples

# work with vectors
test <- rnorm(20, mean = 10)
bootMeanDF(test)
#>          mean      min      max  n
#> Mean 9.689424 9.321958 10.11362 20

# how does nox vary by intervals of wind speed?
results <- binData(mydata, bin = "ws", uncer = "nox")
if (FALSE) { # \dontrun{
library(ggplot2)
ggplot(results, aes(x = ws, y = mean, ymin = min, ymax = max)) +
  geom_pointrange()
} # }

# what about weekend vs weekday?
results2 <- binData(mydata, bin = "ws", uncer = "nox", type = "weekend")
if (FALSE) { # \dontrun{
ggplot(results2, aes(x = ws, y = mean, ymin = min, ymax = max)) +
  geom_pointrange() +
  facet_wrap(vars(weekend))
} # }