How to compare temperature data over a period of time

Question

My aim is to evaluate the effect of a treatment (on microclimate data) applied to a canopy compared to a control. Therefore I put three data logger in the canopy at 5 sites and each variant ("treatment applied" vs. "control"). Data is averaged every 5 minutes over a period of 217 days. The logged data looks like this:

Timepoint,Time,Celsius(°C),Humidity(%rh),dew point(°C)
1,27/03/2019 17:02:39,23.5,37.5,8.2
2,27/03/2019 17:07:39,23.5,36.5,7.8
3,27/03/2019 17:12:39,23.5,36.5,7.8
4,27/03/2019 17:17:39,24.0,37.5,8.6
5,27/03/2019 17:22:39,23.5,36.0,7.6
6,27/03/2019 17:27:39,23.0,37.0,7.5
7,27/03/2019 17:32:39,22.5,34.5,6.1
8,27/03/2019 17:37:39,22.5,34.5,6.1

Records are sumamrized daily to obtain mean/max/min temperature for each of the 217 days. Regardless of the site I want to determine the effect of the treatment applied and to expose the differences over time.

I was told that Time Series Analysis doesn't work here. I tried to apply linear regression (inspired from this paper: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0234436) on the data, but since the control does not affect the treatment I discarded this approach.

So my question is: which method would be the proper way to analyse this microclimatic data in R?

Answer 1

You can try running linear regression with Time as a function of humidity and Celsius for the control and the treatment separately, and then compare the slopes of both models for each site. Naturally if you get a higher slope on your treatment than on your control, this indicates a responsive result to the treatment - the higher the delta between the slopes, the better the response to treatment is. The model would go something like this(for a single site):

lm(Time~Celsius+Humidity, data = ControlData)
lm(Time~Celsius+Humidity, data = TreatmentData)

Then you can start playing with the coefficients and derive results from the differences, and the general slope of the regression line for each site. And after that, you can even combine the results by averaging the coefficients of the 5 control regression and compare them to the average of 5 treatment regressions (since the model is linear this should be statistically valid).