In lter/lterdatasampler: Educational Dataset Examples from the Long Term Ecological Research Program

Dataset sample used

• arc_weather

Introduction

The arc_weather data sample contains selected meteorological records from the Toolik Field Station at Toolik Lake, Alaska, from 1988 - 2018. This data set offers opportunities to explore and wrangle time series data, visualize patterns (e.g. seasonality), and apply different forecasting methods.

LTER research station Toolik Lake, LTER CC BY-SA 4.0

Data exploration

Attach required packages:

library(lterdatasampler)
library(tidyverse)
library(tsibble)
library(feasts)

Here, we highlight daily air temperature (the data sample also contains records for precipitation and wind speed) using functions from the tsibble and feasts R packages (both part of the fantastic tidyverts ecosystem of "tidy tools for time series").

# Calculate monthly average of daily mean air temperature and convert to tsibble: 
arc_weather_ts <- arc_weather %>% 
  mutate(yr_mo = yearmonth(date)) %>% # Make a column with just month and year from each date
  group_by(yr_mo) %>% # Group by year-month
  summarize(avg_mean_airtemp = mean(mean_airtemp, na.rm = TRUE)) %>% # Find monthly mean air temperature
  as_tsibble(index = yr_mo) # Convert to a tsibble (time series tibble)

# Check out the first 10 lines:
head(arc_weather_ts, 10)

Once the data are converted into a tsibble in the last line above, we can use helpful functions in feasts (like autoplot() and gg_season()) to explore the time series data a bit more.

# Create a line graph of monthly average of mean daily air temperatures:
arc_weather_ts %>% 
  autoplot(avg_mean_airtemp)

# Create  season plot to explore seasonality: 
arc_weather_ts %>% 
  gg_season(avg_mean_airtemp)

We might want to decompose the time series data to further explore components. See Chapter 3 Time series decomposition in Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos for more information on decomposing time series data.

arc_weather_ts %>% 
  model(STL(avg_mean_airtemp)) %>% 
  components() %>% 
  autoplot()

We can also explore autocorrelation:

arc_weather_ts %>% 
  ACF(avg_mean_airtemp) %>% 
  autoplot()

Then you can move on to time series forecasting and further analysis! Have fun with the arc_weather data sample from Arctic LTER.

Acknowledgements

Citation

Shaver, G. 2019. A multi-year DAILY weather file for the Toolik Field Station at Toolik Lake, AK starting 1988 to present. ver 4. Environmental Data Initiative. https://doi.org/10.6073/pasta/ce0f300cdf87ec002909012abefd9c5c (Accessed 2020-07-04).

How we processed the raw data

r knitr::spin_child(here::here("data-raw","arc_weather_data.R"))

lter/lterdatasampler documentation built on Oct. 12, 2023, 3:34 a.m.