| Title: | Easily Download and Visualise Climate Data from CliFlo |
|---|---|
| Description: | CliFlo is a web portal to the New Zealand National Climate Database and provides public access (via subscription) to around 6,500 various climate stations (see <https://cliflo.niwa.co.nz/> for more information). Collating and manipulating data from CliFlo (hence clifro) and importing into R for further analysis, exploration and visualisation is now straightforward and coherent. The user is required to have an internet connection, and a current CliFlo subscription (free) if data from stations, other than the public Reefton electronic weather station, is sought. |
| Authors: | Blake Seers [aut, cre] |
| Maintainer: | Blake Seers <[email protected]> |
| License: | GPL-2 |
| Version: | 3.2-5.9003 |
| Built: | 2024-08-29 22:59:12 UTC |
| Source: | https://github.com/ropensci/clifro |
Operators acting on cfDataList, cfDatatype, cfStation,
and dataFrame objects.
## S4 method for signature 'dataFrame' x[[i]] ## S4 method for signature 'dataFrame,ANY,ANY,ANY' x[i, j, drop] ## S4 method for signature 'dataFrame' x$name ## S4 method for signature 'cfStation,ANY,ANY,ANY' x[i, j, drop = TRUE] ## S4 method for signature 'cfDataList,ANY,ANY,ANY' x[i, j] ## S4 method for signature 'cfDataList' x[[i]] ## S4 method for signature 'cfDatatype,ANY,missing,missing' x[i, j, drop]## S4 method for signature 'dataFrame' x[[i]] ## S4 method for signature 'dataFrame,ANY,ANY,ANY' x[i, j, drop] ## S4 method for signature 'dataFrame' x$name ## S4 method for signature 'cfStation,ANY,ANY,ANY' x[i, j, drop = TRUE] ## S4 method for signature 'cfDataList,ANY,ANY,ANY' x[i, j] ## S4 method for signature 'cfDataList' x[[i]] ## S4 method for signature 'cfDatatype,ANY,missing,missing' x[i, j, drop]
x |
a clifro object |
i |
indices specifying elements to extract. Indices are
|
j |
indices specifying elements to extract. Indices are
|
drop |
if |
name |
a literal character string. This is partially matched to the names of the object. |
These are methods for the generic operators for classes within clifro.
They are intended to give the user the familiar functionality of subsetting
data.frame objects.
This operator allows you to add more datatypes or stations to
cfDatatype and cfStation objects respectively.
## S4 method for signature 'cfStation,cfStation' e1 + e2 ## S4 method for signature 'cfDatatype,cfDatatype' e1 + e2## S4 method for signature 'cfStation,cfStation' e1 + e2 ## S4 method for signature 'cfDatatype,cfDatatype' e1 + e2
e1 |
a |
e2 |
an object matching the class of e1 |
The cf_curl_opts function stores specific curl options that are used
for all the clifro queries.
cf_curl_opts(..., .opts = list())cf_curl_opts(..., .opts = list())
... |
a name-value pairs that are passed to |
.opts |
a named list or |
## Not run: # Specify options for use in all the curl handles created in clifro cf_curl_opts(.opts = list(proxy = "http://xxxxx.yyyy.govt.nz:8080", proxyusername = "uid", proxypassword = "pwd", ssl.verifypeer = FALSE)) # Or alternatively: cf_curl_opts(proxy = "http://xxxxx.yyyy.govt.nz:8080", proxyusername = "uid", proxypassword = "pwd", ssl.verifypeer = FALSE) ## End(Not run)## Not run: # Specify options for use in all the curl handles created in clifro cf_curl_opts(.opts = list(proxy = "http://xxxxx.yyyy.govt.nz:8080", proxyusername = "uid", proxypassword = "pwd", ssl.verifypeer = FALSE)) # Or alternatively: cf_curl_opts(proxy = "http://xxxxx.yyyy.govt.nz:8080", proxyusername = "uid", proxypassword = "pwd", ssl.verifypeer = FALSE) ## End(Not run)
Search for clifro stations based on name, region, location or network
number, and return a cfStation object.
cf_find_station( ..., search = c("name", "region", "network", "latlong"), datatype, combine = c("all", "any"), status = c("open", "closed", "all") )cf_find_station( ..., search = c("name", "region", "network", "latlong"), datatype, combine = c("all", "any"), status = c("open", "closed", "all") )
... |
arguments to pass into the search, these differ depending on
|
search |
one of |
datatype |
|
combine |
character string |
status |
character string indicating |
The cf_find_station function is a convenience function for finding
CliFlo stations in R. It uses the CliFlo
Find Stations
page to do the searching, and therefore means that the stations are not
stored within clifro.
If datatype is missing then the search is conducted
without any reference to datatypes. If it is supplied then the
search will only return stations that have any or all of the supplied
datatypes, depending on combine. The default behaviour is to search
for stations based on pattern matching the station name and return only the
open stations.
If the latlong search type is used the function expects named
arguments with names (partially) matching latitude,
longitude and radius. If the arguments are passed in without names they must
be in order of latitude, longitude and radius (see examples).
cfStation object
Since the searching is done by CliFlo there are obvious restrictions. Unfortunately the pattern matching for station name does not provide functionality for regular expressions, nor does it allow simultaneous searches although clifro does provide some extra functionality, see the 'OR query Search' example below.
cf_save_kml for saving the resulting stations as a KML
file, cf_station for creating cfStation objects
when the agent numbers are known, vignette("choose-station") for a
tutorial on finding clifro stations and vignette("cfStation")
for working with cfStation objects.
## Not run: # Station Name Search ------------------------------------------------------ # Return all open stations with 'island' in the name (pattern match search) # Note this example uses all the defaults island_st = cf_find_station("island") island_st # Region Search ------------------------------------------------------------ # Return all the closed stations from Queenstown (using partial matching) queenstown.st = cf_find_station("queen", search = "region", status = "closed") queenstown.st # Long/Lat Search ---------------------------------------------------------- # Return all open stations within a 10km radius of the Beehive in Wellington # From Wikipedia: latitude 41.2784 S, longitude 174.7767 E beehive.st = cf_find_station(lat = -41.2784, long = 174.7767, rad = 10, search = "latlong") beehive.st # Network ID Search -------------------------------------------------------- # Return all stations that share A42 in their network ID A42.st = cf_find_station("A42", search = "network", status = "all") A42.st # Using Datatypes in the Search -------------------------------------------- # Is the Reefton EWS station open and does it collect daily rain and/or wind # data? # First, create the daily rain and wind datatypes daily.dt = cf_datatype(c(2, 3), c(1, 1), list(4, 1), c(1, NA)) daily.dt # Then combine into the search. This will only return stations where at least # one datatype is available. cf_find_station("reefton EWS", datatype = daily.dt) # Yes # OR Query Search ---------------------------------------------------------- # Return all stations sharing A42 in their network ID *or* all the open # stations within 10km of the Beehive in Wellington (note this is not # currently available as a single query in CliFlo). cf_find_station("A42", search = "network", status = "all") + cf_find_station(lat = -41.2784, long = 174.7767, rad = 10, search = "latlong") # Note these are all ordered by open stations, then again by their end dates ## End(Not run)## Not run: # Station Name Search ------------------------------------------------------ # Return all open stations with 'island' in the name (pattern match search) # Note this example uses all the defaults island_st = cf_find_station("island") island_st # Region Search ------------------------------------------------------------ # Return all the closed stations from Queenstown (using partial matching) queenstown.st = cf_find_station("queen", search = "region", status = "closed") queenstown.st # Long/Lat Search ---------------------------------------------------------- # Return all open stations within a 10km radius of the Beehive in Wellington # From Wikipedia: latitude 41.2784 S, longitude 174.7767 E beehive.st = cf_find_station(lat = -41.2784, long = 174.7767, rad = 10, search = "latlong") beehive.st # Network ID Search -------------------------------------------------------- # Return all stations that share A42 in their network ID A42.st = cf_find_station("A42", search = "network", status = "all") A42.st # Using Datatypes in the Search -------------------------------------------- # Is the Reefton EWS station open and does it collect daily rain and/or wind # data? # First, create the daily rain and wind datatypes daily.dt = cf_datatype(c(2, 3), c(1, 1), list(4, 1), c(1, NA)) daily.dt # Then combine into the search. This will only return stations where at least # one datatype is available. cf_find_station("reefton EWS", datatype = daily.dt) # Yes # OR Query Search ---------------------------------------------------------- # Return all stations sharing A42 in their network ID *or* all the open # stations within 10km of the Beehive in Wellington (note this is not # currently available as a single query in CliFlo). cf_find_station("A42", search = "network", status = "all") + cf_find_station(lat = -41.2784, long = 174.7767, rad = 10, search = "latlong") # Note these are all ordered by open stations, then again by their end dates ## End(Not run)
Retrieve the last query submitted to CliFlo instead of querying the database again and losing subscription rows.
cf_last_query()cf_last_query()
This function is a back up for when the clifro query has been submitted and the data returned but has not been assigned, or inadvertently deleted. This saves the user resubmitting queries and using more rows from their subscription than needed.
Only the data from the last query is saved in clifro.
## Not run: # Query CliFlo for wind at Reefton Ews cf_query(cf_user(), cf_datatype(2, 1, 1, 1), cf_station(), "2012-01-01 00") # Oops! Forgot to assign it to a variable... reefton.wind = cf_last_query() reefton.wind ## End(Not run)## Not run: # Query CliFlo for wind at Reefton Ews cf_query(cf_user(), cf_datatype(2, 1, 1, 1), cf_station(), "2012-01-01 00") # Oops! Forgot to assign it to a variable... reefton.wind = cf_last_query() reefton.wind ## End(Not run)
Query the National Climate Database via CliFlo based on the clifro user and selected datatypes, stations and dates.
cf_query( user, datatype, station, start_date, end_date = now(tz), date_format = "ymd_h", tz = "Pacific/Auckland", output_tz = c("local", "NZST", "UTC"), quiet = FALSE )cf_query( user, datatype, station, start_date, end_date = now(tz), date_format = "ymd_h", tz = "Pacific/Auckland", output_tz = c("local", "NZST", "UTC"), quiet = FALSE )
user |
a |
datatype |
a |
station |
a |
start_date |
a character, Date or POSIXt object indicating the start
date. If a character string is supplied the date format
should be in the form |
end_date |
same as |
date_format |
a character string matching one of |
tz |
the timezone for which the start and end dates refer to. Conversion
to Pacific/Auckland time is done automatically through the
|
output_tz |
the timezone of the output. This can be one of either "local", "UTC", or "NZST". |
quiet |
logical. When |
The cf_query function is used to combine the clifro user
(cfUser), along with the desired datatypes
(cfDatatype) and stations (cfStation). The query
is 'built up' using these objects, along with the necessary dates. The
function then uses all these data to query the National Climate Database via
the CliFlo web portal and returns one of the many cfData
objects if one dataframe is returned, or a cfDataList object if
there is more than one dataframe returned from CliFlo. If a cfDataList
is returned, each element in the list is a subclass of the cfData
class, see the 'cfData Subclasses' section.
a cfData or cfDataList object.
There are 8 cfData subclasses that are returned from cf_query
depending on the datatype requested. Each of these subclasses have default
plot methods for usability and efficiency in exploring and plotting
clifro data.
The following table summarises these subclasses and how they are created, see also the examples on how to automatically create some of these subclasses.
| Subclass | CliFlo Datatype |
| cfWind | Any 'Wind' data |
| cfRain | Any 'Precipitation' data |
| cfScreen Obs | 'Temperature and Humidity' data measured in a standard screen |
| cfTemp | Maximum and minimum 'Temperature and Humidity' data |
| cfEarthTemp | 'Temperature and Humidity' data at a given depth |
| cfSunshine | Any 'Sunshine & Radiation' data |
| cfPressure | Any 'Pressure' data |
| cfOther | Any other CliFlo 'Daily and Hourly Observations' |
cf_user, cf_datatype and
cf_station for creating the objects needed for a query. See
plot,cfDataList,missing-method for general information on
default plotting of cfData and cfDataList objects, and the
links within.
## Not run: # Retrieve daily rain data from Reefton Ews daily.rain = cf_query(cf_user("public"), cf_datatype(3, 1, 1), cf_station(), "2012-01-01 00") daily.rain # returns a cfData object as there is only one datatype class(daily.rain) # 'cfRain' object - inherits 'cfData' # Look up the help page for cfRain plot methods ?plot.cfRain # Retrieve daily rain and wind data from Reefton Ews daily.dts = cf_query(cf_user("public"), cf_datatype(c(2, 3), c(1, 1), list(4, 1), c(1, NA)), cf_station(), "2012-01-01 00", "2013-01-01 00") daily.dts # returns a cfDataList object as there is more than one datatype. Each # element of the cfDataList is an object inheriting from the cfData class. class(daily.dts) # cfDataList class(daily.dts[1]) # cfRain class(daily.dts[2]) # cfWind # Create a cfSunshine object (inherits cfData) # Retrieve daily global radiation data at Reefton Ews rad.data = cf_query(cf_user(), cf_datatype(5,2,1), cf_station(), "2012-01-01 00") rad.data # The cf_query function automatically creates the appropriate cfData subclass class(rad.data) # The advantage of having these subclasses is that it makes plotting very easy plot(rad.data) plot(daily.rain) plot(daily.rain, include_runoff = FALSE) plot(daily.dts) plot(daily.dts, 2) ## End(Not run)## Not run: # Retrieve daily rain data from Reefton Ews daily.rain = cf_query(cf_user("public"), cf_datatype(3, 1, 1), cf_station(), "2012-01-01 00") daily.rain # returns a cfData object as there is only one datatype class(daily.rain) # 'cfRain' object - inherits 'cfData' # Look up the help page for cfRain plot methods ?plot.cfRain # Retrieve daily rain and wind data from Reefton Ews daily.dts = cf_query(cf_user("public"), cf_datatype(c(2, 3), c(1, 1), list(4, 1), c(1, NA)), cf_station(), "2012-01-01 00", "2013-01-01 00") daily.dts # returns a cfDataList object as there is more than one datatype. Each # element of the cfDataList is an object inheriting from the cfData class. class(daily.dts) # cfDataList class(daily.dts[1]) # cfRain class(daily.dts[2]) # cfWind # Create a cfSunshine object (inherits cfData) # Retrieve daily global radiation data at Reefton Ews rad.data = cf_query(cf_user(), cf_datatype(5,2,1), cf_station(), "2012-01-01 00") rad.data # The cf_query function automatically creates the appropriate cfData subclass class(rad.data) # The advantage of having these subclasses is that it makes plotting very easy plot(rad.data) plot(daily.rain) plot(daily.rain, include_runoff = FALSE) plot(daily.dts) plot(daily.dts, 2) ## End(Not run)
Save cfStation object information to a KML file.
cf_save_kml(station, file_name = "my_stations_", file_path = ".")cf_save_kml(station, file_name = "my_stations_", file_path = ".")
station |
|
file_name |
file name for the resulting KML file |
file_path |
file path for the resulting KML file |
The cf_save_kml function is for cfStation
objects to allow for the spatial visualisation of the selected stations. The
resulting KML file is saved and can then be opened by programs like Google
Earth (TM). The resultant KML file has the station names and locations shown
with green markers for open and red markers for closed stations. The agent
numbers, network ID's and date ranges are contained within the descriptions
for each station.
If no file name is specified, unique names are produced in the current R working directory.
The .kml suffix is appended automatically if it isn't already
present in the file_name argument.
cf_station and vignette("cfStation") for
working with stations when the agent numbers are known, otherwise
cf_find_station and codevignette("choose-station") for
creating cfStation objects when the agent numbers are unknown.
## Not run: # A selection of four Auckland region stations down the East Coast to the # upper Waitemata Harbour; Leigh 2 Ews, Warkworth Ews, Tiri Tiri Lighthouse # and Henderson my.stations = cf_station(17838, 1340, 1401, 12327) my.stations # Save these stations to a KML file cf_save_kml(my.stations) # Double click on the file to open with a default program (if available). All # the markers are green, indicating all these stations are open. # Where is the subscription-free Reefton Ews station? cf_save_kml(cf_station(), file_name = "reeftonEWS") # It's located in the sou'west quadrant of Reefton town, in the upper, western # part of the South Island, NZ. # Find all the open and closed Christchurch stations (using partial matching) all.chch.st = cf_find_station("christ", status = "all", search = "region") # How many stations in total? nrow(all.chch.st) # Save all the Christchurch stations cf_save_kml(all.chch.st, file_name = "all_Chch_stations") ## End(Not run)## Not run: # A selection of four Auckland region stations down the East Coast to the # upper Waitemata Harbour; Leigh 2 Ews, Warkworth Ews, Tiri Tiri Lighthouse # and Henderson my.stations = cf_station(17838, 1340, 1401, 12327) my.stations # Save these stations to a KML file cf_save_kml(my.stations) # Double click on the file to open with a default program (if available). All # the markers are green, indicating all these stations are open. # Where is the subscription-free Reefton Ews station? cf_save_kml(cf_station(), file_name = "reeftonEWS") # It's located in the sou'west quadrant of Reefton town, in the upper, western # part of the South Island, NZ. # Find all the open and closed Christchurch stations (using partial matching) all.chch.st = cf_find_station("christ", status = "all", search = "region") # How many stations in total? nrow(all.chch.st) # Save all the Christchurch stations cf_save_kml(all.chch.st, file_name = "all_Chch_stations") ## End(Not run)
Create a cfDatatype object by selecting one or more CliFlo datatypes
to build the clifro query.
cf_datatype( select_1 = NA, select_2 = NA, check_box = NA, combo_box = NA, graphics = FALSE )cf_datatype( select_1 = NA, select_2 = NA, check_box = NA, combo_box = NA, graphics = FALSE )
select_1 |
a numeric vector of first node selections |
select_2 |
a numeric vector of second node selections |
check_box |
a list containing the check box selections |
combo_box |
a numeric vector containing the combo box selection (if applicable) |
graphics |
a logical indicating whether a graphics menu should be used, if available |
An object inheriting from the cfDatatype class is created by
the constructor function cf_datatype. The function allows the
user to choose datatype(s) interactively (if no arguments are given), or to
create datatypes programmatically if the tree menu nodes are known a priori
(see examples). This function uses the same nodes, check box and combo box
options as CliFlo and can be viewed at the
datatype selection page.
cfDatatype object
For the 'public' user (see examples) only the Reefton Ews station data is available.
Currently clifro does not support datatypes from the special datasets (Ten minute, Tier2, Virtual Climate, Lysimeter) or upper air measurements from radiosondes and wind radar.
cf_user to create a clifro user,
cf_station to choose the CliFlo stations and
vignette("choose-datatype") for help choosing cfDatatypes.
## Not run: # Select the surface wind datatype manually (unknown tree nodes) hourly.wind.dt = cf_datatype() # 2 --> Datatype: Wind # 1 --> Datatype 2: Surface Wind # 2 --> Options: Hourly Wind # (2) --> Another option: No # 3 --> Units: Knots hourly.wind.dt # Or select the datatype programatically (using the selections seen above) hourly.wind.dt = cf_datatype(2, 1, 2, 3) hourly.wind.dt ## End(Not run)## Not run: # Select the surface wind datatype manually (unknown tree nodes) hourly.wind.dt = cf_datatype() # 2 --> Datatype: Wind # 1 --> Datatype 2: Surface Wind # 2 --> Options: Hourly Wind # (2) --> Another option: No # 3 --> Units: Knots hourly.wind.dt # Or select the datatype programatically (using the selections seen above) hourly.wind.dt = cf_datatype(2, 1, 2, 3) hourly.wind.dt ## End(Not run)
Create a cfStation object containing station information for one or
more CliFlo stations.
cf_station(...)cf_station(...)
... |
comma separated agent numbers |
A cfStation object is created by the constructor function
cf_station. The unique agent numbers of the stations are all that is
required to create a cfStation object using the cf_station
function. The rest of the station information including the name, network and
agent ID, start and end dates, coordinates, as well as other data is scraped
from CliFlo.
This function is used for when the agent numbers are already known. For help
creating cfStation objects when the agent numbers are unknown see the
cf_find_station function.
cfStation object
cf_find_station for creating cfStation objects
when the agent numbers are not known and vignette("cfStation")
for working with clifro stations including spatial plotting in R. For saving
cfStation objects as KML files refer to the vignette or
cf_save_kml.
## Not run: # Create a cfStation object for the Leigh 1 and 2 Ews stations leigh.st = cf_station(1339, 1340) leigh.st # Note, this can also be achieved using the '+' operator leigh.st = cf_station(1339) + cf_station(1340) leigh.st # Add another column showing how long the stations have been open for leigh.df = as(leigh.st, "data.frame") leigh.df$ndays = with(leigh.df, round(end - start)) leigh.df # Save the stations to the current working directory as a KML to visualise # the station locations cf_save_kml(leigh.st) ## End(Not run)## Not run: # Create a cfStation object for the Leigh 1 and 2 Ews stations leigh.st = cf_station(1339, 1340) leigh.st # Note, this can also be achieved using the '+' operator leigh.st = cf_station(1339) + cf_station(1340) leigh.st # Add another column showing how long the stations have been open for leigh.df = as(leigh.st, "data.frame") leigh.df$ndays = with(leigh.df, round(end - start)) leigh.df # Save the stations to the current working directory as a KML to visualise # the station locations cf_save_kml(leigh.st) ## End(Not run)
Create a cfUser object to allow the user to log into CliFlo from R
and build their query.
cf_user(username = "public", password = character())cf_user(username = "public", password = character())
username |
a character string to be used as the cliflo username |
password |
a character string to be used as the cliflo password |
An object inheriting from the cfUser class is created by the constructor
function cf_user. The user must have an active subscription to cliflo
in order to create a valid object, unless a 'public' user is sought.
Visit https://cliflo.niwa.co.nz/ for more information and to subscribe
to cliflo.
cfUser object
For the 'public' user (see examples) only the Reefton Ews station data is available.
valid_cfuser for details on the validation of
cfUser and summary,cfUser-method to summarise user
information.
## Not run: public.cfuser = cf_user(username = "public") public.cfuser ## End(Not run)## Not run: public.cfuser = cf_user(username = "public") public.cfuser ## End(Not run)
Import data from New Zealand's National Climate Database via CliFlo into R for exploring, analysis, plotting, exporting to KML, CSV, or other software.
The clifro package is intended to simplify the process of data
extraction, formatting and visualisation from the
CliFlo web portal. It
requires the user to build a query consisting of 3 main components; the user,
the datatype(s) and the station(s). These are
then combined using the cf_query function that sends the query
to the CliFlo database and returns the results that can easily be plotted
using generic plotting functions.
This package requires the user to already have a current subscription to the National Climate Database unless a public user is sought, where data is limited to Reefton Ews. Subscription is free and can obtained from https://cliflo.niwa.co.nz/pls/niwp/wsubform.intro.
cf_user, cf_datatype, and
cf_station for choosing the clifro user, datatypes and
stations, respectively.
## Not run: # Create a public user ---------------------------------------------------- public.user = cf_user() # Defaults to "public" public.user # Select datatypes -------------------------------------------------------- # 9am Surface wind (m/s) wind.dt = cf_datatype(2, 1, 4, 1) # Daily Rain rain.dt = cf_datatype(3, 1, 1) # Daily temperature extremes temp.dt = cf_datatype(4, 2, 2) # Combine them together all.dts = wind.dt + rain.dt + temp.dt all.dts # Select the Reefton Ews station ------------------------------------------ reefton.st = cf_station() reefton.st # Submit the query -------------------------------------------------------- # Retrieve all data from ~ six months ago at 9am reefton.data = cf_query(public.user, all.dts, reefton.st, paste(as.Date(Sys.time()) - 182, "9")) reefton.data # Plot the data ----------------------------------------------------------- # Plot the 9am surface wind data (first dataframe in the list) --- reefton.data[1] # all identical - although passed to different methods plot(reefton.data) #plot,cfDataList,missing-method plot(reefton.data, 1) #plot,cfDataList,numeric-method plot(reefton.data[1]) #plot,cfData,missing-method --> plot,cfWind,missing-method speed_plot(reefton.data) direction_plot(reefton.data) # Plot the daily rain data (second dataframe in the list) --- reefton.data[2] # With runoff and soil deficit plot(reefton.data, 2) # Just plot amount of rain (mm) plot(reefton.data, 2, include_runoff = FALSE) # Plot the hourly temperature data (third dataframe in the list) --- plot(reefton.data, 3) # Pass an argument to ggplot2::theme library(ggplot2) # for element_text() plot(reefton.data, 3, text = element_text(size = 18)) ## End(Not run)## Not run: # Create a public user ---------------------------------------------------- public.user = cf_user() # Defaults to "public" public.user # Select datatypes -------------------------------------------------------- # 9am Surface wind (m/s) wind.dt = cf_datatype(2, 1, 4, 1) # Daily Rain rain.dt = cf_datatype(3, 1, 1) # Daily temperature extremes temp.dt = cf_datatype(4, 2, 2) # Combine them together all.dts = wind.dt + rain.dt + temp.dt all.dts # Select the Reefton Ews station ------------------------------------------ reefton.st = cf_station() reefton.st # Submit the query -------------------------------------------------------- # Retrieve all data from ~ six months ago at 9am reefton.data = cf_query(public.user, all.dts, reefton.st, paste(as.Date(Sys.time()) - 182, "9")) reefton.data # Plot the data ----------------------------------------------------------- # Plot the 9am surface wind data (first dataframe in the list) --- reefton.data[1] # all identical - although passed to different methods plot(reefton.data) #plot,cfDataList,missing-method plot(reefton.data, 1) #plot,cfDataList,numeric-method plot(reefton.data[1]) #plot,cfData,missing-method --> plot,cfWind,missing-method speed_plot(reefton.data) direction_plot(reefton.data) # Plot the daily rain data (second dataframe in the list) --- reefton.data[2] # With runoff and soil deficit plot(reefton.data, 2) # Just plot amount of rain (mm) plot(reefton.data, 2, include_runoff = FALSE) # Plot the hourly temperature data (third dataframe in the list) --- plot(reefton.data, 3) # Pass an argument to ggplot2::theme library(ggplot2) # for element_text() plot(reefton.data, 3, text = element_text(size = 18)) ## End(Not run)
Retrieve the dimensions or dimension names of a dataFrame object.
## S4 method for signature 'dataFrame' dimnames(x) ## S4 method for signature 'dataFrame' dim(x)## S4 method for signature 'dataFrame' dimnames(x) ## S4 method for signature 'dataFrame' dim(x)
x |
a Specifically, a |
cf_query for creating cfData objects, and
cf_station for creating cfStation objects.
Plot the earth temperature for a given depth (degrees celsius) through time, for each chosen CliFlo station.
## S4 method for signature 'cfEarthTemp,missing' plot( x, y, ggtheme = c("grey", "gray", "bw", "linedraw", "light", "minimal", "classic"), scales = c("fixed", "free_x", "free_y", "free"), n_col = 1, ... )## S4 method for signature 'cfEarthTemp,missing' plot( x, y, ggtheme = c("grey", "gray", "bw", "linedraw", "light", "minimal", "classic"), scales = c("fixed", "free_x", "free_y", "free"), n_col = 1, ... )
x |
a cfEarthTemp object. |
y |
missing. |
ggtheme |
character string (partially) matching the
|
scales |
character string partially matching the |
n_col |
the number of columns of plots (default 1). |
... |
further arguments passed to |
plot,cfDataList,missing-method for general
information on default plotting of cfData and cfDataList
objects, and the links within. See cf_query for creating
cfEarthTemp objects.
Refer to theme for more possible arguments to pass
to these methods.
## Not run: # Retrieve public earth temperature data for the last 30 days at Reefton Ews # station, at a depth of 10cm # Subtract 30 days from today's date to get the start date last_month = paste(as.character(Sys.Date() - 30), 0) reefton_earth = cf_query(cf_user(), cf_datatype(4, 3, 2), cf_station(), start_date = last_month) class(reefton_earth) # cfTemp object # Plot the temperature data using the defaults plot(reefton_earth) # Enlarge the text and add the observations as points library(ggplot2) # for element_text() and geom_point() plot(reefton_earth, ggtheme = "bw", text = element_text(size = 16)) + geom_point(size = 3, shape = 1) # Save the plot as a png to the current working directory library(ggplot2) # for ggsave() ggsave("my_earthTemp_plot.png") ## End(Not run)## Not run: # Retrieve public earth temperature data for the last 30 days at Reefton Ews # station, at a depth of 10cm # Subtract 30 days from today's date to get the start date last_month = paste(as.character(Sys.Date() - 30), 0) reefton_earth = cf_query(cf_user(), cf_datatype(4, 3, 2), cf_station(), start_date = last_month) class(reefton_earth) # cfTemp object # Plot the temperature data using the defaults plot(reefton_earth) # Enlarge the text and add the observations as points library(ggplot2) # for element_text() and geom_point() plot(reefton_earth, ggtheme = "bw", text = element_text(size = 16)) + geom_point(size = 3, shape = 1) # Save the plot as a png to the current working directory library(ggplot2) # for ggsave() ggsave("my_earthTemp_plot.png") ## End(Not run)
Plot the MSL atmospheric pressure through time.
## S4 method for signature 'cfPressure,missing' plot( x, y, ggtheme = c("grey", "gray", "bw", "linedraw", "light", "minimal", "classic"), scales = c("fixed", "free_x", "free_y", "free"), n_col = 1, ... )## S4 method for signature 'cfPressure,missing' plot( x, y, ggtheme = c("grey", "gray", "bw", "linedraw", "light", "minimal", "classic"), scales = c("fixed", "free_x", "free_y", "free"), n_col = 1, ... )
x |
a cfPressure object. |
y |
missing. |
ggtheme |
character string (partially) matching the
|
scales |
character string partially matching the |
n_col |
the number of columns of plots (default 1). |
... |
further arguments passed to |
plot,cfDataList,missing-method for general
information on default plotting of cfData and cfDataList
objects, and the links within. See cf_query for creating
cfPressure objects.
Refer to theme for more possible arguments to pass
to these methods.
## Not run: # Retrieve public hourly atmospheric pressure data for the last 30 days at # Reefton Ews station # Subtract 30 days from today's date to get the start date last_month = paste(as.character(Sys.Date() - 30), 0) reefton_pressure = cf_query(cf_user(), cf_datatype(7, 1, 1), cf_station(), start_date = last_month) class(reefton_pressure) # cfPressure object # Plot the atmospheric pressure data using the defaults plot(reefton_pressure) # Enlarge the text and add the observations as points library(ggplot2) # for element_text() and geom_point() plot(reefton_pressure, ggtheme = "bw", text = element_text(size = 16)) + geom_point(size = 3, shape = 1) # Save the plot as a png to the current working directory library(ggplot2) # for ggsave() ggsave("my_pressure_plot.png") ## End(Not run)## Not run: # Retrieve public hourly atmospheric pressure data for the last 30 days at # Reefton Ews station # Subtract 30 days from today's date to get the start date last_month = paste(as.character(Sys.Date() - 30), 0) reefton_pressure = cf_query(cf_user(), cf_datatype(7, 1, 1), cf_station(), start_date = last_month) class(reefton_pressure) # cfPressure object # Plot the atmospheric pressure data using the defaults plot(reefton_pressure) # Enlarge the text and add the observations as points library(ggplot2) # for element_text() and geom_point() plot(reefton_pressure, ggtheme = "bw", text = element_text(size = 16)) + geom_point(size = 3, shape = 1) # Save the plot as a png to the current working directory library(ggplot2) # for ggsave() ggsave("my_pressure_plot.png") ## End(Not run)
Plot the amount of rainfall (mm) through time, with optional available soil water capacity and runoff amounts (if applicable).
## S4 method for signature 'cfRain,missing' plot( x, y, include_runoff = TRUE, ggtheme = c("grey", "gray", "bw", "linedraw", "light", "minimal", "classic"), scales = c("fixed", "free_x", "free_y", "free"), n_col = 1, ... )## S4 method for signature 'cfRain,missing' plot( x, y, include_runoff = TRUE, ggtheme = c("grey", "gray", "bw", "linedraw", "light", "minimal", "classic"), scales = c("fixed", "free_x", "free_y", "free"), n_col = 1, ... )
x |
a |
y |
missing. |
include_runoff |
a logical indicating whether to plot the soil moisture
deficit and runoff as well as the rainfall, if the data
is available (default |
ggtheme |
character string (partially) matching the
|
scales |
character string partially matching the |
n_col |
the number of columns of plots (default 1). |
... |
further arguments passed to |
When there is a rain event, the amount of runoff, if any, is dependent on how
much capacity the soil has available for more water. If there is no available
water capacity left in the soil then more rain will lead to a runoff event.
If include_runoff = TRUE, the available water capacity is plotted as
negative values and the runoff as positive values to signify this negative
relationship.
plot,cfDataList,missing-method for general
information on default plotting of cfData and cfDataList
objects, and the links within. See cf_query for creating
cfRain objects.
Refer to theme for more possible arguments to pass
to these methods.
## Not run: # Retrieve public rain data for a month from CliFlo (at Reefton Ews station) reefton_rain = cf_query(cf_user(), cf_datatype(3, 1, 1), cf_station(), start_date = "2012-08-01-00", end_date = "2012-09-01-00") class(reefton_rain) # cfRain object # Plot the rain data using the defaults plot(reefton_rain) # Change the ggtheme and enlarge the text library(ggplot2) # for element_text() plot(reefton_rain, ggtheme = "bw", text = element_text(size = 16)) # Save the plot as a png to the current working directory library(ggplot2) # for ggsave() ggsave("my_rain_plot.png") ## End(Not run)## Not run: # Retrieve public rain data for a month from CliFlo (at Reefton Ews station) reefton_rain = cf_query(cf_user(), cf_datatype(3, 1, 1), cf_station(), start_date = "2012-08-01-00", end_date = "2012-09-01-00") class(reefton_rain) # cfRain object # Plot the rain data using the defaults plot(reefton_rain) # Change the ggtheme and enlarge the text library(ggplot2) # for element_text() plot(reefton_rain, ggtheme = "bw", text = element_text(size = 16)) # Save the plot as a png to the current working directory library(ggplot2) # for ggsave() ggsave("my_rain_plot.png") ## End(Not run)
Plot temperature data from screen observations (degrees celsius) through time.
## S4 method for signature 'cfScreenObs,missing' plot( x, y, ggtheme = c("grey", "gray", "bw", "linedraw", "light", "minimal", "classic"), scales = c("fixed", "free_x", "free_y", "free"), n_col = 1, ... )## S4 method for signature 'cfScreenObs,missing' plot( x, y, ggtheme = c("grey", "gray", "bw", "linedraw", "light", "minimal", "classic"), scales = c("fixed", "free_x", "free_y", "free"), n_col = 1, ... )
x |
a cfScreenObs object. |
y |
missing. |
ggtheme |
character string (partially) matching the
|
scales |
character string partially matching the |
n_col |
the number of columns of plots (default 1). |
... |
further arguments passed to |
Temperature data from screen observations include the air, and wet bulb, temperature at the time the measurement was taken (dry bulb and wet bulb respectively), and the dew point. The dew point is the air temperature at which dew starts to form. That is the temperature to which a given air parcel must be cooled at constant pressure and constant water vapour content in order for saturation to occur.
The resulting figure plots the dry bulb, wet bulb and dew point temperatures on the same scale, for each station.
plot,cfDataList,missing-method for general
information on default plotting of cfData and cfDataList
objects, and the links within. See cf_query for creating
cfScreenObs objects.
Refer to theme for more possible arguments to pass
to these methods.
## Not run: # Retrieve public temperature data from screen observations for the last week # at Reefton Ews station # Subtract 7 days from today's date to get the start date last_week = paste(as.character(Sys.Date() - 7), 0) reefton_screenobs = cf_query(cf_user(), cf_datatype(4, 1, 1), cf_station(), start_date = last_week) class(reefton_screenobs) # cfScreenObs object # Plot the temperature data using the defaults plot(reefton_screenobs) # Enlarge the text and add the observations as points library(ggplot2) # for element_text() and geom_point() plot(reefton_screenobs, ggtheme = "bw", text = element_text(size = 16)) + geom_point(size = 3, shape = 1) # Save the plot as a png to the current working directory library(ggplot2) # for ggsave() ggsave("my_screenobs_plot.png") ## End(Not run)## Not run: # Retrieve public temperature data from screen observations for the last week # at Reefton Ews station # Subtract 7 days from today's date to get the start date last_week = paste(as.character(Sys.Date() - 7), 0) reefton_screenobs = cf_query(cf_user(), cf_datatype(4, 1, 1), cf_station(), start_date = last_week) class(reefton_screenobs) # cfScreenObs object # Plot the temperature data using the defaults plot(reefton_screenobs) # Enlarge the text and add the observations as points library(ggplot2) # for element_text() and geom_point() plot(reefton_screenobs, ggtheme = "bw", text = element_text(size = 16)) + geom_point(size = 3, shape = 1) # Save the plot as a png to the current working directory library(ggplot2) # for ggsave() ggsave("my_screenobs_plot.png") ## End(Not run)
Plot the duration of accumulated bright sunshine hours through time.
## S4 method for signature 'cfSunshine,missing' plot( x, y, ggtheme = c("grey", "gray", "bw", "linedraw", "light", "minimal", "classic"), scales = c("fixed", "free_x", "free_y", "free"), n_col = 1, ... )## S4 method for signature 'cfSunshine,missing' plot( x, y, ggtheme = c("grey", "gray", "bw", "linedraw", "light", "minimal", "classic"), scales = c("fixed", "free_x", "free_y", "free"), n_col = 1, ... )
x |
a cfSunshine object. |
y |
missing. |
ggtheme |
character string (partially) matching the
|
scales |
character string partially matching the |
n_col |
the number of columns of plots (default 1). |
... |
further arguments passed to |
plot,cfDataList,missing-method for general
information on default plotting of cfData and cfDataList
objects, and the links within. See cf_query for creating
cfSunshine objects.
Refer to theme for more possible arguments to pass
to these methods.
## Not run: # Retrieve public hourly sunshine data for the last 7 days at Reefton Ews # station # Subtract 7 days from today's date to get the start date last_week = paste(as.character(Sys.Date() - 7), 0) reefton_sun = cf_query(cf_user(), cf_datatype(5, 1, 2), cf_station(), start_date = last_week) class(reefton_sun) # cfSunshine object # Plot the temperature data using the defaults plot(reefton_sun) # Enlarge the text and add the observations as points library(ggplot2) # for element_text() and geom_point() plot(reefton_sun, ggtheme = "bw", text = element_text(size = 16)) + geom_point(size = 3, shape = 1) # Save the plot as a png to the current working directory library(ggplot2) # for ggsave() ggsave("my_sunshine_plot.png") ## End(Not run)## Not run: # Retrieve public hourly sunshine data for the last 7 days at Reefton Ews # station # Subtract 7 days from today's date to get the start date last_week = paste(as.character(Sys.Date() - 7), 0) reefton_sun = cf_query(cf_user(), cf_datatype(5, 1, 2), cf_station(), start_date = last_week) class(reefton_sun) # cfSunshine object # Plot the temperature data using the defaults plot(reefton_sun) # Enlarge the text and add the observations as points library(ggplot2) # for element_text() and geom_point() plot(reefton_sun, ggtheme = "bw", text = element_text(size = 16)) + geom_point(size = 3, shape = 1) # Save the plot as a png to the current working directory library(ggplot2) # for ggsave() ggsave("my_sunshine_plot.png") ## End(Not run)
Plot minimum and maximum temperature data for a given period (degrees celsius) through time, for each chosen CliFlo station.
## S4 method for signature 'cfTemp,missing' plot( x, y, ggtheme = c("grey", "gray", "bw", "linedraw", "light", "minimal", "classic"), scales = c("fixed", "free_x", "free_y", "free"), n_col = 1, ... )## S4 method for signature 'cfTemp,missing' plot( x, y, ggtheme = c("grey", "gray", "bw", "linedraw", "light", "minimal", "classic"), scales = c("fixed", "free_x", "free_y", "free"), n_col = 1, ... )
x |
a cfTemp object. |
y |
missing. |
ggtheme |
character string (partially) matching the
|
scales |
character string partially matching the |
n_col |
the number of columns of plots (default 1). |
... |
further arguments passed to |
This plotting method shows the temperature extremes as a grey region on the plot, with a black line indicating the average temperature (if available).
plot,cfDataList,missing-method for general
information on default plotting of cfData and cfDataList
objects, and the links within. See cf_query for creating
cfTemp objects.
Refer to theme for more possible arguments to pass
to these methods.
## Not run: # Retrieve public hourly minimum and maximum temperature data for the last week at Reefton Ews station # Subtract 7 days from today's date to get the start date last_week = paste(as.character(Sys.Date() - 7), 0) reefton_temp = cf_query(cf_user(), cf_datatype(4, 2, 2), cf_station(), start_date = last_week) class(reefton_temp) # cfTemp object # Plot the temperature data using the defaults plot(reefton_temp) # Enlarge the text and add the observations as points library(ggplot2) # for element_text() and geom_point() plot(reefton_temp, ggtheme = "bw", text = element_text(size = 16)) + geom_point(size = 3, shape = 1) # Save the plot as a png to the current working directory library(ggplot2) # for ggsave() ggsave("my_temperature_plot.png") ## End(Not run)## Not run: # Retrieve public hourly minimum and maximum temperature data for the last week at Reefton Ews station # Subtract 7 days from today's date to get the start date last_week = paste(as.character(Sys.Date() - 7), 0) reefton_temp = cf_query(cf_user(), cf_datatype(4, 2, 2), cf_station(), start_date = last_week) class(reefton_temp) # cfTemp object # Plot the temperature data using the defaults plot(reefton_temp) # Enlarge the text and add the observations as points library(ggplot2) # for element_text() and geom_point() plot(reefton_temp, ggtheme = "bw", text = element_text(size = 16)) + geom_point(size = 3, shape = 1) # Save the plot as a png to the current working directory library(ggplot2) # for ggsave() ggsave("my_temperature_plot.png") ## End(Not run)
Various plot methods for exploring wind speed and direction patterns for given CliFlo stations.
## S4 method for signature 'cfWind,missing' plot( x, y, n_directions = 12, n_speeds = 5, speed_cuts = NULL, col_pal = "GnBu", ggtheme = c("grey", "gray", "bw", "linedraw", "light", "minimal", "classic"), n_col = 1, ... ) ## S4 method for signature 'cfWind,missing' direction_plot( x, y, ggtheme = c("grey", "gray", "bw", "linedraw", "light", "minimal", "classic"), contours = 10, n_col = 1, ... ) ## S4 method for signature 'cfDataList,missing' direction_plot(x, y, ...) ## S4 method for signature 'cfDataList,numeric' direction_plot(x, y, ...) ## S4 method for signature 'cfWind,missing' speed_plot( x, y, ggtheme = c("grey", "gray", "bw", "linedraw", "light", "minimal", "classic"), scales = c("fixed", "free_x", "free_y", "free"), n_col = 1, ... ) ## S4 method for signature 'cfDataList,missing' speed_plot(x, y, ...) ## S4 method for signature 'cfDataList,numeric' speed_plot(x, y, ...)## S4 method for signature 'cfWind,missing' plot( x, y, n_directions = 12, n_speeds = 5, speed_cuts = NULL, col_pal = "GnBu", ggtheme = c("grey", "gray", "bw", "linedraw", "light", "minimal", "classic"), n_col = 1, ... ) ## S4 method for signature 'cfWind,missing' direction_plot( x, y, ggtheme = c("grey", "gray", "bw", "linedraw", "light", "minimal", "classic"), contours = 10, n_col = 1, ... ) ## S4 method for signature 'cfDataList,missing' direction_plot(x, y, ...) ## S4 method for signature 'cfDataList,numeric' direction_plot(x, y, ...) ## S4 method for signature 'cfWind,missing' speed_plot( x, y, ggtheme = c("grey", "gray", "bw", "linedraw", "light", "minimal", "classic"), scales = c("fixed", "free_x", "free_y", "free"), n_col = 1, ... ) ## S4 method for signature 'cfDataList,missing' speed_plot(x, y, ...) ## S4 method for signature 'cfDataList,numeric' speed_plot(x, y, ...)
x |
a |
y |
missing if |
n_directions |
the number of direction bins to plot (petals on the rose). The number of directions defaults to 12. |
n_speeds |
the number of equally spaced wind speed bins to plot. This is
used if |
speed_cuts |
numeric vector containing the cut points for the wind speed intervals, or NA (default). |
col_pal |
character string indicating the name of the
|
ggtheme |
character string (partially) matching the
|
n_col |
the number of columns of plots (default 1). |
... |
further arguments passed to |
contours |
the number of contour lines to draw (default 10). |
scales |
character string partially matching the |
If x is a cfDataList, by default the first datatype will be
plotted unless y is supplied.
For black and white windroses that may be preferred if plots are to be used
in journal articles for example, recommended ggthemes are 'bw',
'linedraw', 'minimal' or 'classic' and
the col_pal should be 'Greys'. Otherwise, any of the sequential
RColorBrewer colour palettes are recommended for
colour plots.
If x is a cfDataList object and y refers to a
clifro dataframe that is not a cfWind object then it will be
passed to another method, if available.
The default plot method plots a different windrose for each CliFlo
station. The direction_plot method plots wind direction contours
through time to visualise temporal patterns in wind directions. The
speed_plot method plots the time series of wind speeds with a +/-
standard deviation region (if applicable).
Given a value on the x-axis, the ends of the density function along the
y-axis are not constrained to be equal for any of the derivatives for the
direction_plot method. That is, the contours at direction = 0, do not
match the contours at direction = 360.
@seealso plot,cfDataList,missing-method for general
information on default plotting of cfData and cfDataList
objects, and the links within. See cf_query for creating
cfWind objects or windrose for plotting any wind data.
Refer to theme for more possible arguments to pass
to these methods. summary,cfWind-method for summarising wind
information at each CliFlo station.
## Not run: # Retrieve maximum wind gust data at the Reefton Ews station from CliFlo # (public data) reefton_wind = cf_query(cf_user(), cf_datatype(2, 2, 1, 1), cf_station(), start_date = "2012-01-01-00") class(reefton_wind) # Examples of the default plots -------------------------------------------- # Plot a windrose plot(reefton_wind) # Plot the wind direction contours direction_plot(reefton_wind) # Plot the wind speed time-series speed_plot(reefton_wind) # Examples of changing the defaults ---------------------------------------- # Plot black and white windroses plot(reefton_wind, ggtheme = "bw", col_pal = "Greys") plot(reefton_wind, ggtheme = "linedraw", col_pal = "Greys") plot(reefton_wind, ggtheme = "classic", col_pal = "Greys") plot(reefton_wind, ggtheme = "minimal", col_pal = "Greys") # Plot the wind directions using 20 contours and the ggtheme 'classic' direction_plot(reefton_wind, ggtheme = "classic", contours = 20) # Enlarge all the text to 18pt library(ggplot2) # for element_text() and geom_point() direction_plot(reefton_wind, ggtheme = "classic", contours = 20, text = element_text(size = 18)) # Include the actual observations in the plots direction_plot(reefton_wind) + geom_point(alpha = .2, size = 3) speed_plot(reefton_wind, ggtheme = "classic", text = element_text(size = 16)) + geom_point(shape = 1, size = 3) # or equivalently using base graphics: plot(reefton_wind$Date, reefton_wind$Speed, type = 'o', xlab = NA, ylab = "Daily max gust (m/s)", las = 1, main = "Reefton Ews") # Example of plotting a cfDataList ----------------------------------------- # Collect both surface wind run and hourly surface wind observations from # Reefton Ews reefton_list = cf_query(cf_user(), cf_datatype(2, 1, 1:2, 1), cf_station(), "2012-01-01 00", "2012-02-01 00") reefton_list class(reefton_list) #cfDataList # Plot the first (default) dataframe plot(reefton_list) # Error - no wind directions for wind run datatypes # Try speed_plot instead speed_plot(reefton_list) # Plot the second dataframe in the cfDataList plot(reefton_list, 2) # identical to plot(reefton_list[2]) speed_plot(reefton_list, 2) # identical to speed_plot(reefton_list[2]) direction_plot(reefton_list, 2) # identical to direction_plot(reefton_list[2]) # Save the ggplot externally ----------------------------------------------- # Save the plot as a png to the current working directory library(ggplot2) # for ggsave() ggsave("my_wind_plot.png") ## End(Not run)## Not run: # Retrieve maximum wind gust data at the Reefton Ews station from CliFlo # (public data) reefton_wind = cf_query(cf_user(), cf_datatype(2, 2, 1, 1), cf_station(), start_date = "2012-01-01-00") class(reefton_wind) # Examples of the default plots -------------------------------------------- # Plot a windrose plot(reefton_wind) # Plot the wind direction contours direction_plot(reefton_wind) # Plot the wind speed time-series speed_plot(reefton_wind) # Examples of changing the defaults ---------------------------------------- # Plot black and white windroses plot(reefton_wind, ggtheme = "bw", col_pal = "Greys") plot(reefton_wind, ggtheme = "linedraw", col_pal = "Greys") plot(reefton_wind, ggtheme = "classic", col_pal = "Greys") plot(reefton_wind, ggtheme = "minimal", col_pal = "Greys") # Plot the wind directions using 20 contours and the ggtheme 'classic' direction_plot(reefton_wind, ggtheme = "classic", contours = 20) # Enlarge all the text to 18pt library(ggplot2) # for element_text() and geom_point() direction_plot(reefton_wind, ggtheme = "classic", contours = 20, text = element_text(size = 18)) # Include the actual observations in the plots direction_plot(reefton_wind) + geom_point(alpha = .2, size = 3) speed_plot(reefton_wind, ggtheme = "classic", text = element_text(size = 16)) + geom_point(shape = 1, size = 3) # or equivalently using base graphics: plot(reefton_wind$Date, reefton_wind$Speed, type = 'o', xlab = NA, ylab = "Daily max gust (m/s)", las = 1, main = "Reefton Ews") # Example of plotting a cfDataList ----------------------------------------- # Collect both surface wind run and hourly surface wind observations from # Reefton Ews reefton_list = cf_query(cf_user(), cf_datatype(2, 1, 1:2, 1), cf_station(), "2012-01-01 00", "2012-02-01 00") reefton_list class(reefton_list) #cfDataList # Plot the first (default) dataframe plot(reefton_list) # Error - no wind directions for wind run datatypes # Try speed_plot instead speed_plot(reefton_list) # Plot the second dataframe in the cfDataList plot(reefton_list, 2) # identical to plot(reefton_list[2]) speed_plot(reefton_list, 2) # identical to speed_plot(reefton_list[2]) direction_plot(reefton_list, 2) # identical to direction_plot(reefton_list[2]) # Save the ggplot externally ----------------------------------------------- # Save the plot as a png to the current working directory library(ggplot2) # for ggsave() ggsave("my_wind_plot.png") ## End(Not run)
Plot clifro data based on the datatype.
## S4 method for signature 'cfDataList,numeric' plot(x, y, ...) ## S4 method for signature 'cfDataList,missing' plot(x, y, ...) ## S4 method for signature 'cfOther,missing' plot(x, y)## S4 method for signature 'cfDataList,numeric' plot(x, y, ...) ## S4 method for signature 'cfDataList,missing' plot(x, y, ...) ## S4 method for signature 'cfOther,missing' plot(x, y)
x |
a |
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y |
missing for |
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... |
arguments passed onto the different plotting methods. These methods are intended to simplify the data visualisation and exploration
of CliFlo data. The type of plot is determined by the type of the data output
from a clifro query. All of these methods plot individual plots for
each CliFlo station (if there is more than one in the query). If The following table links the datatypes to the corresponding plot methods:
|
cf_query to retrieve the CliFlo data and create
cfData objects.
Refer to theme for more possible arguments to pass
to these methods.
Show the subscription status for the clifro user
## S4 method for signature 'cfUser' summary(object)## S4 method for signature 'cfUser' summary(object)
object |
an object of class |
This is a summary method for cfWind objects.
## S4 method for signature 'cfWind' summary(object, calm_wind = 0)## S4 method for signature 'cfWind' summary(object, calm_wind = 0)
object |
a |
calm_wind |
a single number containing the wind speed that is considered calm. |
A dataframe is returned containing the percentage of calm days
(wind speed >= calm_days), percentage of variable days (wind speed =
990), and quantiles from the empirical cumulative distribution functions for
each CliFlo station at which there is wind data.
plot.cfWind for default plotting of
clifro wind data, and cf_query for creating cfWind
objects.
## Not run: # Retrieve maximum wind gust data at the Reefton Ews station from CliFlo # (public data) reefton_wind = cf_query(cf_user(), cf_datatype(2, 2, 1, 1), cf_station(), start_date = "2012-01-01-00") class(reefton_wind) # cfWind object # Summarise the information summary(reefton_wind) ## End(Not run)## Not run: # Retrieve maximum wind gust data at the Reefton Ews station from CliFlo # (public data) reefton_wind = cf_query(cf_user(), cf_datatype(2, 2, 1, 1), cf_station(), start_date = "2012-01-01-00") class(reefton_wind) # cfWind object # Summarise the information summary(reefton_wind) ## End(Not run)
Plot a wind rose showing the wind speed and direction for given facets using ggplot2.
windrose( speed, direction, facet, n_directions = 12, n_speeds = 5, speed_cuts = NA, col_pal = "GnBu", ggtheme = c("grey", "gray", "bw", "linedraw", "light", "minimal", "classic"), legend_title = "Wind Speed", calm_wind = 0, variable_wind = 990, n_col = 1, ... )windrose( speed, direction, facet, n_directions = 12, n_speeds = 5, speed_cuts = NA, col_pal = "GnBu", ggtheme = c("grey", "gray", "bw", "linedraw", "light", "minimal", "classic"), legend_title = "Wind Speed", calm_wind = 0, variable_wind = 990, n_col = 1, ... )
speed |
numeric vector of wind speeds. |
direction |
numeric vector of wind directions. |
facet |
character or factor vector of the facets used to plot the various wind roses. |
n_directions |
the number of direction bins to plot (petals on the rose). The number of directions defaults to 12. |
n_speeds |
the number of equally spaced wind speed bins to plot. This is
used if |
speed_cuts |
numeric vector containing the cut points for the wind speed
intervals, or |
col_pal |
character string indicating the name of the
|
ggtheme |
character string (partially) matching the
|
legend_title |
character string to be used for the legend title. |
calm_wind |
the direction of wind that is considered calm. Following convention of the National Weather Service, winds with a direction of 0 are considered calm by default. |
variable_wind |
numeric code for variable winds (if applicable). |
n_col |
The number of columns of plots (default 1). |
... |
further arguments passed to |
This is intended to be used as a stand-alone function for any wind dataset. A
different wind rose is plotted for each level of the faceting variable which
is coerced to a factor if necessary. The facets will generally be the station
where the data were collected, seasons or dates. Currently only one faceting
variable is allowed and is passed to facet_wrap with
the formula ~facet.
Note that calm winds are excluded from the wind rose.
a ggplot object.
For black and white wind roses that may be preferred if plots are to be used
in journal articles for example, recommended ggthemes are 'bw',
'linedraw', 'minimal' or 'classic' and
the col_pal should be 'Greys'. Otherwise, any of the sequential
RColorBrewer colour palettes are recommended for
colour plots.
theme for more possible arguments to pass to
windrose.
# Create some dummy wind data with predominant south to westerly winds, and # occasional yet higher wind speeds from the NE (not too dissimilar to # Auckland). wind_df = data.frame(wind_speeds = c(rweibull(80, 2, 4), rweibull(20, 3, 9)), wind_dirs = c(rnorm(80, 135, 55), rnorm(20, 315, 35)) %% 360, station = rep(rep(c("Station A", "Station B"), 2), rep(c(40, 10), each = 2))) # Plot a simple wind rose using all the defaults, ignoring any facet variable with(wind_df, windrose(wind_speeds, wind_dirs)) # Create custom speed bins, add a legend title, and change to a B&W theme with(wind_df, windrose(wind_speeds, wind_dirs, speed_cuts = c(3, 6, 9, 12), legend_title = "Wind Speed\n(m/s)", legend.title.align = .5, ggtheme = "bw", col_pal = "Greys")) # Note that underscore-separated arguments come from the windrose method, and # period-separated arguments come from ggplot2::theme(). # Include a facet variable with one level with(wind_df, windrose(wind_speeds, wind_dirs, "Artificial Auckland Wind")) # Plot a windrose for each level of the facet variable (each station) with(wind_df, windrose(wind_speeds, wind_dirs, station, n_col = 2)) ## Not run: # Save the plot as a png to the current working directory library(ggplot2) ggsave("my_windrose.png") ## End(Not run)# Create some dummy wind data with predominant south to westerly winds, and # occasional yet higher wind speeds from the NE (not too dissimilar to # Auckland). wind_df = data.frame(wind_speeds = c(rweibull(80, 2, 4), rweibull(20, 3, 9)), wind_dirs = c(rnorm(80, 135, 55), rnorm(20, 315, 35)) %% 360, station = rep(rep(c("Station A", "Station B"), 2), rep(c(40, 10), each = 2))) # Plot a simple wind rose using all the defaults, ignoring any facet variable with(wind_df, windrose(wind_speeds, wind_dirs)) # Create custom speed bins, add a legend title, and change to a B&W theme with(wind_df, windrose(wind_speeds, wind_dirs, speed_cuts = c(3, 6, 9, 12), legend_title = "Wind Speed\n(m/s)", legend.title.align = .5, ggtheme = "bw", col_pal = "Greys")) # Note that underscore-separated arguments come from the windrose method, and # period-separated arguments come from ggplot2::theme(). # Include a facet variable with one level with(wind_df, windrose(wind_speeds, wind_dirs, "Artificial Auckland Wind")) # Plot a windrose for each level of the facet variable (each station) with(wind_df, windrose(wind_speeds, wind_dirs, station, n_col = 2)) ## Not run: # Save the plot as a png to the current working directory library(ggplot2) ggsave("my_windrose.png") ## End(Not run)