3. Overview of Ranadu functions

A more extensive discussion if Ranadu is contained in the Ranadu Manual, available herehere. Some of the central features are discussed in this chapter.

3.1 More about constructing data.frames

These Ranadu functions can be of use when constructing and modifying data.frames:

• getNetCDF ( ): loads a data.frame with requested variables
• standardVariables( ): defines a common set of variables for input to getNetCDF(). This list is the default if no variables are specified in the call to getNetCDF. The optional argument is a list of character names that specify additional variables to include.
• DataDirectory( ): This is system dependent and is intended to aid in portability by providing the usual location of the netCDF data files. It tries a set of likely names, but may need redefinition for some file systems.
• getProjectData( ): Constructs a data.frame that includes measurements from multiple flights in a project.
• getIndex( ): finds the index for a specified time
• setRange( ): sets a range of indices covering a specified time interval.
• selectTimes( ): produces a new data.frame with a limited time range.

In addition, some functions provided by the package “dplyr” can be very useful when working with data.frames. These actions can be implemented by normal R subsetting also (e.g., using “[[...]]” notation), but using these dplyr functions serves to clarify what the steps in the code are doing:

• dplyr::select( ): produces a new data.frame with only the variables listed as arguments in the call to the function.
• dplyr::filter( ): produces a subset data.frame where the argument is a logical test applied to the data.frame rows that must be met for the row to be included in the returned data.frame.
• dplyr::mutate( ): adds new variables to the data.frame. Ranadu::Rmutate( ) modifies this to preserve variable attributes.

The related function Ranadu::selectTime( ) can be used to restrict the times included in the data.frame, so it is often convenient to use this routine in combination with the above routines when constructing plots and for other data-analysis steps. See akso Ranadu::RSubset( ).

3.2 Getting information about the data

Many Ranadu functions provide information about the data set. A good starting point is DataFileInfo(), discussed in the previous chapter. In addition, these functions may be helpful:

1. Ranadu::getAttributes(): The data.frames produced by getNetCDF() have assigned attributes that match those in the original netCDF file, and these can be retrieved via getAttributes(), where the argument can be either the data.frame (for which the returned attributes are those associated with the data file) or a variable name (for which the attributes are those associated with the variable).^3.1 The result of the function call is a printed listing of attributes and creation of a list (here in Z) that contains the attributes, so it is usually preferable to assign the returned value to something to avoid the double listing that would be produced otherwise.
2. Ranadu::TellAbout() provides some information about the characteristics of a data.frame or a variable in the data.frame.
3. Ranadu::getStartEnd() returns the start and end times of a data frame.
4. Ranadu::ValueOf() and Ranadu::ValueOfAll() for a specified time return the value of a specified variable or of all variables, respectively.

The standard R routines “dim()”, “names()” and “str()” will also provide information about a data.frame or a variable. “str()” in particular can provide important information about all R variables.

3.3 Plotting routines

The routines plotWAC() and ggplotWAC() were introduced in the previous chapter. The following is a list of other Ranadu routines that produce plots. More detail about these routines is included in the next chapter.

1. Ranadu::plotTrack() plots a flight track on a background that shows geographic boundaries. In its simplest form it is called with a single argument of a data.frame that contains at least latitude and longitude in variables named LATC and LONC. This routine also has an option to plot the track in a reference frame that drifts with the wind. See ?plotTrack for more information on options.
2. Ranadu::VSpec() constructs a plot of the variance spectrum for a specified variable. See also Ranadu::CohPhase() for plots of the coherence and phase relationship between variables, and Ranadu::flux( ) for plotting the cospectrum and calculating various fluxes.
3. Ranadu::lineWAC() adds a line to a plot previously constructed by plotWAC(), with adjustment of the plotted time to correspond to the center of the interval averaged to obtain the measurements. Otherwise, like “lines()” in standard R.
4. Ranadu::plotSD() plots the size distribution measured by cloud or aerosol particle probes.
5. Ranadu::contourPlot() plots a scattergram-like display where the density of points is represented by colored areas. This can be a useful alternative to a scatterplot when the number of plotted points is large.
6. Ranadu::DemingFit() calculates a Deming fit to two variables (in which the distance from the fitted line to the two-dimensional locations of the measurements is minimized) and plots the resulting fit.
7. Ranadu::SkewTSounding() plots a skew-T thermodynamic diagram with measurements from a data.frame averaged and superimposed on the diagram. Options are available to include a hodograph and the denote values of the LCL and CAPE.

3.4 Computational Algorithms

A set of functions provide calculations like those used to process the original files and, in some cases, to extend those calculations. A full list of Ranadu functions can be viewed by using the R command “?Ranadu”, and clicking on the items in that list then will provide the standard “help” information for the functions. Some of these are listed below. For additional details, see the technical note titled “Processing Algorithms”.

1. AdiabaticTandLWC( ): Calculate the temperature and liquid water content produced by adiabatic ascent.
2. AirTemperature( ): The standard calculation of temperature from the measured recovery temperature.
3. BoltonEquivalentPotentialTemperature( ): Equivalent potential temperature as calculated using the Bolton formula. In standard processing, this has been replaced now by the Davies-Jones representation; see “EquivalentPotentialTemperature( )” below.
4. ButterworthFilter( ): Implementation of a Butterworth low-pass filter, used in wind processing; see “ComplementaryFilter( )”.
5. calcAttack( ): This function calculates the angle of attack from the pitch and rate of climb of the aircraft under the assumption that the vertical wind is zero. This algorithm may be useful when determining or checking sensitivity coefficients for the measured angle of attack.
6. ComplementaryFilter( ): The standard calculation of wind uses this complementary filter to combine the aircraft ground-speed components measured by the inertial system at high rate with the corresponding components measured by the global positioning system at lower rate.
7. CorrectHeading( ), CorrectPitch( ) and CorrectRoll( ): Algorithms that use the observed Schuler oscillation of the inertial system to improve the measurements of pitch and roll, and that calculates a related correction to the heading based on a comparison of the observed acceleration vector and the velocity derivatives from the global positioning system.
8. DPfromE( ): Calculation of the dewpoint temperature corresponding to a specified vapor pressure.
9. EquivalentPotentialTemperature( ): The pseudo-adiabatic equivalent potential temperature calculated using the Davies-Jones (2009) formula. See also BoltonEquivalentPotentialTemperature( ) and RossbyEquivalentPotentialTemperature( ) for alternate values.
10. GeoPotHeight( ): Calculates the geopotential height associated with a specified geometric height above sea level and location.
11. Gravity( ): The acceleration of gravity as represented by the Somigliana formula.
12. GV_AOAfromRadome( ), GV_YawFromRadome( ): Algorithms used to find the angles of attack and sideslip or yaw from pressure differences measured on the radome of the GV.
13. KingProbe( ): Calculation of the liquid water content from the power measured by the CSIRO/King probe.
14. LCL( ): Find the lifted condensation level from the observed pressure, temperature, and water-vapor mixing ratio.
15. MachNumber( ): Calculate the Mach number from the measured dynamic pressure, ambient pressure, and humidity.
16. memCoef( ), memEstimate( ): Routines used to implement the “maximum entropy” method of spectral estimation; see “VSpec()”.
17. MixingRatio( ): Calculates the water vapor mixing ratio.
18. MurphyKoop( ), MurphyKoopIce( ): Calculate the water vapor pressure from the Murphy-Koop formulas.
19. PCORfunction( ): The algorithm used to correct measured ambient and dynamic pressures for the static defect.
20. PotentialTemperature( ): Calculates the potential temperature.
21. PressureAltitude( ): Calculates the pressure altitude from the pressure.
22. RecoveryFactor( ): Returns the recovery factor used to process measurements of air temperature from probes exposed to the airstream.
23. RossbyEquivalentPotentialTemperature( ): The Rossby formula for the equivalent potential temperature.
24. SpecificHeats( ): At the value of the ratio of water vapor pressure to total pressure provided as an argument, returns the values of the specific heat at constant pressure, the specific heat at constant volume, and the gas constant.
25. Sqs( ): The quasi-steady supersaturation that will exist in a cloud with specified droplet size distribution and updraft.
26. StandardConstant( ): Provides standardized values of some constants used in the processing algorithms. See “StandardConstant(“?”) for a list of available constants.
27. TrueAirspeed( ): Calculate the airspeed from the Mach number and temperature.
28. VirtualTemperature( ) and VirtualPotentialTemperature( ): Functions to calculate these variables.
29. WetEquivalentPotentialTemperature( ): Wet-equivalent potential temperature.
30. WindProcessor( ): Calculates the wind vector from the basic measurements and adds new wind variables to the input data.frame.

3.5 Utility Functions

The following are some of the Ranadu functions that are provided to assist in various data-analysis tasks. The “getNetCDF()”, “DataDirectory()” and “standardVariables()” functions were already described. Others include:

1. binStats( ): A function to calculate average values and standard deviations in bins of a specified variable, as might to useful when constructing error-bar or box-and-whisker plots.
2. detrend( ): Simple removal of the mean and trend from a time-series variable. Used in the spectral-analysis routines.
3. df2tibble( ): Convert a data.frame to a “tibble”, a related data structure particularly suited to many data-analysis steps. tibble2df( ) transforms from a tibble back to a data.frame.
4. LagrangeInterpolate() implements standard Lagrange interpolation.
5. makeNetCDF( ): A utility that writes a new netCDF-format file from the measurements and attributes contained in a data.frame.
6. ncsubset( ): Produce a subset netCDF file from an existing netCDF file.
7. OpenInProgram( ): View the variables in a data.frame in another program like ncplot or Xanadu.
8. RAFdata: This is a sample data.frame as might be constructed by getNetCDF(), containing a short period of measurements from an NSF/NCAR GV flight in a project called IDEAS-4. The data.frame contains a set of measurements, one row per second, and a Time variable. This is provided for use in the Ranadu examples.
9. removeSpikes( ): Find “spikes” in a variable and replace them with linear-interpolated values. Useful in some studies of spectral variance where spikes distort the spectrum.
10. RSubset( ): Produce a subset of a data.frame while preserving attributes. This is useful because some standard R subsetting actions do not preserve attributes.
11. selectTimes( ): Produce a new data.frame with a subset of the time range included in the original data.frame. (Uses setRange.) This is of particular use in pipes.
12. setRange( ): Find the data.frame indices corresponding to a specified time range in a data.frame.
13. setVariableList( ): An interactive way of setting a list of variables.
14. ShiftInTime( ): Move a time-series variable forward or backward in time.
15. SmoothInterp( ): Fill missing values with linear interpolation and optionally smooth the resulting time series.
16. theme_WAC( ): An optional theme available for use with “ggplot2” plots.
17. XformLA( ): The transformation from the “local” Earth-relative reference frame to the aircraft or body reference frame, or the reverse. Used extensively in the Kalman processor and other GPS-updating functions.