openair (version 0.3-13)

cutData: Function to split data in different ways for conditioning

Description

Utility function to split data frames up in various ways for conditioning plots. Users would generally not be expected to call this function directly. Widely used by many openair functions usually through the option type.

Usage

mydata<- cutData(x, type = "default")

Arguments

x

A data frame containing a field date.

type

A string giving the way in which the data frame should be split. Pre-defined values are: "default", "year", "hour", "month", "season", "weekday", "ws", "site", "weekend", "monthyear", "gmtbst" or "bstgmt". type can also be the nam

Value

• Returns a data frame with a column cond that is defined by type.

Details

This section give a brief description of each of the define levels of type. Note that all time dependent types require a column date. "default" does not split the data but will describe the levels as a date range in the format "day month year". "year" splits the data by each year. "month" splits the data by month of the year. "hour" splits the data by hour of the day. "monthyear" splits the data by year and month. It differs from month in that a level is defined for each month of the data set. This is useful sometimes to show an ordered sequence of months if the data set starts half way through a year; rather than starting in January. "weekend" splits the data by weekday and weekend. "weekday" splits the data by day of the week - ordered to start Monday. "gmtbst" or "bstgmt" will split the data by hours that are in GMT i.e. mostly winter months) and hours in British summertime. Each of the two periods will be in local time. The main purpose of this option is to test whether there is a shift in the diurnal profile when GMT and BST hours are compared. This option is particularly useful with the timeVariation function. For example, close to the source of road vehicle emissions, `rush-hour' will tend to occur at the same local time throughout the year e.g. 8 am and 5 pm. Therefore, comparing GMT hours with BST hours will tend to show similar diurnal patterns (at least in the timing of the peaks, if not magnitude) when expressed in local time. By contrast a variable such as wind speed or temperature should show a clear shift when expressed in local time for BST vs. GMT. In essence, this option when used with timeVariation may help determine whether the variation in a pollutant is driven by man-made emissions or natural processes. "wd" splits the data by 8 wind sectors and requires a column wd: "NE", "E", "SE", "S", "SW", "W", "NW", "N". "ws" splits the data by 8 quantiles of wind speed and requires a column ws. "site" splits the data by site and therefore requires a column site.

Examples

## split data by day of the week
mydata <- cutData(mydata, type = "weekday")