From CliFlo to *clifro*: An Introduction
The National Climate Database holds climate data from around 6,500 climate stations around New Zealand including some offshore and Pacific Islands. Over 600 stations are currently active and are still receiving valuable climate data. CliFlo is a web interface to the database managed by NIWA, allowing users to submit queries and retrieve ten-minute, hourly, daily or summary data. The use of CliFlo is free given that the user has subscribed and accepted NIWA’s terms and conditions.
clifro package is designed to make CliFlo queries much simpler and provide extensions that are currently not offered by CliFlo. The intention is to simplify the data extraction, manipulation, exploration and visualisation processes and easily create publication-worthy graphics for some of the primary datatypes, especially for users with limited or non-existent previous R experience. Experienced useRs will also find this package helpful for maximising efficiency of climate data integration with R for further analysis, modelling or export.
This vignette provides an introduction to the
clifro package demonstrating the primary functionality by way of example. For more information on any of the functions in the
clifro package the user is referred to the help index for the
help(package = "clifro").
As stated above, if the intention is to extract data from any station other than Reefton Ews (subscription-free) and to maximise the potential of
clifro, a valid subscription is needed.
cf_user function is all that is required to create a valid
me = cf_user("username", "password")
password is substituted for the user’s CliFlo credentials.
Once the user has been authenticated, the next step is to choose the datatypes of interest, see the choose datatypes vignette for details on choosing datatypes. For this example we are interested in daily MSL atmospheric pressure, minimum and maximum temperature extremes (deg C), daily rainfall (mm) and daily surface wind. (m/s).
my.dts = cf_datatype(select_1 = c(7, 4, 3, 2), select_2 = c(1, 2, 1, 1), check_box = list(3, 1, 1, 4), combo_box = c(NA, NA, NA, 1)) my.dts
## dt.name dt.type dt.options dt.combo ## dt1 Pressure Pressure [9amMSL] ## dt2 Temperature and Humidity Max_min_temp [DailyMaxMin] ## dt3 Precipitation Rain (fixed periods) [Daily ] ## dt4 Wind Surface wind [9amWind] m/s
The third requisite for a valid
clifro query is the station where the data has been collected. If the agent numbers of the required CliFlo stations are known, the only function needed to create a clifro station
cfStation object is
cf_station. See the choose station vignette for help with choosing stations when the agent numbers are unknown, and the working with clifro stations vignette for further information and methods on
For this example we are interested in assessing how these datatypes differ in various parts of the country by taking a selection of stations from various regions. These include a station from Invercargill (5814), Nelson (4241), Hamilton (2112) and Auckland (1962)
my.stations = cf_station(5814, 4241, 2112, 1962) my.stations[, 1:5]
## name network agent start end ## 1 Invercargill Aero I68433 5814 1939-09-01 2020-08-18 02:00:00 ## 2 Nelson Aero G13222 4241 1940-07-01 2020-08-18 02:00:00 ## 3 Auckland Aero C74082 1962 1962-05-01 2020-08-18 02:00:00 ## 4 Hamilton Aws C75834 2112 1989-11-30 2020-08-18 02:00:00
Now that we have a valid
clifro user and the datatypes and stations of interest, a
clifro query can be conducted using the
cf_query function. We are interested in all available data from 2012 to 2014.
cf.datalist = cf_query(user = me, datatype = my.dts, station = my.stations, start_date = "2012-01-01 00", end_date = "2014-01-01 00") cf.datalist
## List containing clifro data frames: ## data type start end rows ## df 1) Pressure 9am only (2012-01-01 9:00) (2013-01-01 9:00) 1468 ## df 2) Max_min Daily (2012-01-01 9:00) (2013-12-31 9:00) 2923 ## df 3) Rain Daily (2012-01-01 9:00) (2013-12-31 9:00) 2923 ## df 4) Surface Wind 9am only (2012-01-01 9:00) (2013-01-01 9:00) 1468
We can see that the pressure and surface wind data only span one year.
There is now a list of 4 dataframes in R containing all the available data for each of the stations and datatypes chosen above. The plotting is simply done with a call to
plot, the type of plot and plotting options depends on the datatype. See
?'plot.cfDataList' for details on default
clifro plotting. The following are examples of some of the plots possible with
clifro, note how the optional
ggtheme argument changes the look of the plots.
This is the first dataframe in
cf.datalist. Since the first argument passed to
plot is a list of different datatypes (
cfDataList), the second argument (
y) tells the
plot method which of the four dataframes to plot.
We could therefore simply type
plot(cf.datalist, y = 1) and get a nice plot of the MSL atmospheric pressure, but it is usually nice to modify the defaults slightly. Since the plot method returns a
ggplot object, we can easily modify the plots using ggplot2.
# Load the ggplot2 library for element_text() and geom_smooth() functions library(ggplot2) # Increase the text size to 16pt and add a loess smoother with a span equal to a # quarter of the window plot(cf.datalist, ggtheme = "bw", text = element_text(size = 16)) + geom_smooth(method = "loess", span = 1/4)
This is the second dataframe in
y = 2. These are temperature data showing the air temperature extremes at each of the four stations, represented by a grey region in the plot. Note that if the average temperature were available, these would be plotted too.
# Try a different ggtheme plot(cf.datalist, 2, ggtheme = "linedraw")
This is the third dataframe in
y = 3. Currently there are two possible default plots available for rainfall; with or without soil deficit/runoff.
# Try yet another ggtheme plot(cf.datalist, 3, ggtheme = "light") # Or only plot the rainfall data # plot(cf.datalist, 3, ggtheme = "light", include_runoff = FALSE)
There are three types of plots available for wind data in
clifro. The default is to plot a windrose displaying wind speed and directions of the full time series at each station. The
windrose function in
clifro is also available for the user to plot their own directional data - see
?windrose. The other two optional plots for wind data in
clifro are the wind speed and wind direction plots. These plots display wind speed and direction patterns through time, adding valuable temporal information that is not portrayed in the windroses.
The wind datatype is the fourth dataframe in
y = 4.
# Defaults to windrose plot(cf.datalist, 4, n_col = 2)
The other two plotting methods for wind data are the
direction_plot functions to assess the temporal variability in wind (plots not shown).
# Export the data as separate CSV files to the current working directory for (i in seq_along(cf.datalist)) write.csv(cf.datalist[i], file = tempfile(paste0(cf.datalist[i]@dt_name, "_"), tmpdir = normalizePath("."), fileext = ".csv"), na = "", row.names = FALSE) # Each dataset is saved separately here: getwd()
The primary aim of this package is to make the substantial amount of climate data residing within the National Climate Database more accessible and easier to work with. The
clifro package has many advantages over using the CliFlo web portal including conducting searches much more efficiently, examining the spatial extent of the stations and enabling high quality plots to aid the data exploration and analysis stage.