Describe process to import data, clean the data and useful links to other tools

En paper de R menciona: Since the EC method in its traditional application (i.e. single-level and time-averaged measurements) cannot resolve the vertical and horizontal distribution of ecosystem flux sources and sinks, the above described quantities inevitably lack information on the vertical and horizontal structure of the ecosystem as well as on its components (e.g. soil and vegetation) when they are inferred directly from the measured fluxes. Approaches directed to circumvent this limitation are two-level sensor systems [28, 29], techniques resolving the spatio-temporal variability of the fluxes [30], or the inversion of more detailed models which separate e.g. sunlit from shaded canopy fractions [31, 32], soil from canopy components [33], or which represent the canopy as a multi-layered system [34].

Es un tema que debo de tocar en el draft. Este tema también lo tocan en el webinario de campbell scientific

Por defecto deja solo el día

In the carbon tower data we lack of the variable air_temperature but we have this one from the principe tower data. We can join this observation according to the closest date_time

In iterative function for plotting with map, get rid of numbers printed on HTML

Si no hay datos en una torre, con las otras torres se puede complementar los datos

En este post de stackoverflow menciona una manera sencilla de hacerlo:

The dat file has some lines of extra information before the actual data. Skip them with the skip argument:

read.table("http://www.nilu.no/projects/ccc/onlinedata/ozone/CZ03_2009.dat", 
           header=TRUE, skip=3)

An easy way to check this if you are unfamiliar with the dataset is to first use readLines to check a few lines, as below:

readLines("http://www.nilu.no/projects/ccc/onlinedata/ozone/CZ03_2009.dat", 
          n=10)
# [1] "Ozone data from CZ03 2009"   "Local time: GMT + 0"        
# [3] ""                            "Date        Hour      Value"
# [5] "01.01.2009 00:00       34.3" "01.01.2009 01:00       31.9"
# [7] "01.01.2009 02:00       29.9" "01.01.2009 03:00       28.5"
# [9] "01.01.2009 04:00       32.9" "01.01.2009 05:00       20.5"

Here, we can see that the actual data starts at [4], so we know to skip the first three lines.

https://github.com/donadigo/eddy
https://github.com/BotoKopo/eddypro-gui/tree/linux-compilation
https://www.licor.com/env/support/EddyPro/software.html

El paper The future of evapotranspiration: Global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources puede servir para resaltar la importancias de contar mediciones más precisas de la evapotranspiración

para tener las fechas correctas de las temporadas, utilizar el articulo de Ian (enviado por Arturo al correo)

There is no streams data for this tower

In order to check if we can use both towers, we need to be sure that measurements for the same dates are about the same. With a correlation we can check this