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Function to split data in different ways for conditioning

Source: R/cutData.R
cutData.Rd

Utility function to split data frames up in various ways for conditioning plots. Widely used by many openair functions usually through the option type.

Usage

cutData(
  x,
  type = "default",
  names = NULL,
  suffix = NULL,
  hemisphere = "northern",
  n.levels = 4,
  start.day = 1,
  is.axis = FALSE,
  local.tz = NULL,
  latitude = 51,
  longitude = -0.5,
  ...
)

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", "site", "weekend", "monthyear", "daylight", "dst" (daylight saving time).

type can also be the name of a numeric or factor. If a numeric column name is supplied cutData() will split the data into four quantiles. Factors levels will be used to split the data without any adjustment.

names

By default, the columns created by cutData() are named after their type option. Specifying names defines other names for the columns, which map onto the type options in the same order they are given. The length of names should therefore be equal to the length of type.

suffix

If name is not specified, suffix will be appended to any added columns that would otherwise overwrite existing columns. For example, cutData(mydata, "nox", suffix = "_cuts") would append a nox_cuts column rather than overwriting nox.

hemisphere

Can be "northern" or "southern", used to split data into seasons.

n.levels

Number of quantiles to split numeric data into.

start.day

What day of the week should the type = "weekday" start on? The user can change the start day by supplying an integer between 0 and 6. Sunday = 0, Monday = 1, ... For example to start the weekday plots on a Saturday, choose start.day = 6.

is.axis

A logical (TRUE/FALSE), used to request shortened cut labels for axes.

local.tz

Used for identifying whether a date has daylight savings time (DST) applied or not. Examples include local.tz = "Europe/London", local.tz = "America/New_York", i.e., time zones that assume DST. https://en.wikipedia.org/wiki/List_of_zoneinfo_time_zones shows time zones that should be valid for most systems. It is important that the original data are in GMT (UTC) or a fixed offset from GMT.

latitude, longitude

The decimal latitude and longitudes used when type = "daylight". Note that locations west of Greenwich have negative longitudes.

...

All additional parameters are passed on to next function(s).

Value

Returns the data frame, x, with columns appended as defined by type and name.

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.

  • "season" splits data up by season. In the northern hemisphere winter = December, January, February; spring = March, April, May etc. These definitions will change of hemisphere = "southern".

  • "seasonyear" (or "yearseason") will split the data into year-season intervals, keeping the months of a season together. For example, December 2010 is considered as part of winter 2011 (with January and February 2011). This makes it easier to consider contiguous seasons. In contrast, type = "season" will just split the data into four seasons regardless of the year.

  • "daylight" splits the data relative to estimated sunrise and sunset to give either daylight or nighttime. The cut is made by cutDaylight but more conveniently accessed via cutData, e.g. cutData(mydata, type = "daylight", latitude = my.latitude, longitude = my.longitude). The daylight estimation, which is valid for dates between 1901 and 2099, is made using the measurement location, date, time and astronomical algorithms to estimate the relative positions of the Sun and the measurement location on the Earth's surface, and is based on NOAA methods. Measurement location should be set using latitude (+ to North; - to South) and longitude (+ to East; - to West).

  • "dst" will split the data by hours that are in daylight saving time (DST) and hours that are not for appropriate time zones. The option also requires that the local time zone is given e.g. local.tz = "Europe/London", local.tz = "America/New_York". 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 DST and non-DST 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 non-DST hours with DST 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. 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".

Note that all the date-based types, e.g., "month"/"year" are derived from a column date. If a user already has a column with a name of one of the date-based types it will not be used.

Author

David Carslaw

Karl Ropkins ("daylight" option)

Examples

## split data by day of the week
mydata <- cutData(mydata, type = "weekday")
names(mydata)
#>  [1] "date"    "ws"      "wd"      "nox"     "no2"     "o3"      "pm10"   
#>  [8] "so2"     "co"      "pm25"    "weekday"
head(mydata)
#> # A tibble: 6 × 11
#>   date                   ws    wd   nox   no2    o3  pm10   so2    co  pm25
#>   <dttm>              <dbl> <int> <int> <int> <int> <int> <dbl> <dbl> <int>
#> 1 1998-01-01 00:00:00  0.6    280   285    39     1    29  4.72  3.37    NA
#> 2 1998-01-01 01:00:00  2.16   230    NA    NA    NA    37 NA    NA       NA
#> 3 1998-01-01 02:00:00  2.76   190    NA    NA     3    34  6.83  9.60    NA
#> 4 1998-01-01 03:00:00  2.16   170   493    52     3    35  7.66 10.2     NA
#> 5 1998-01-01 04:00:00  2.4    180   468    78     2    34  8.07  8.91    NA
#> 6 1998-01-01 05:00:00  3      190   264    42     0    16  5.50  3.05    NA
#> # ℹ 1 more variable: weekday <ord>