1 Introduction

The dendroTools R package was developed in 2018 and is still being updated regularly and will continue to do so in the future. It provides dendroclimatological methods for analysing statistical relationships between tree rings and daily climate data. The most commonly used method is the Pearson correlation coefficient, but users can also use non-parametric correlations such as Kendall or Spearman correlations, and in the case of a multiproxy approach, linear regression can also be applied. Artificial neural networks (brnn) are also available for nonlinear analyses.

In this document I describe the basic principles behind the dendroTools R package and give some basic examples. All data included in the examples below is already included in the dendroTools R package. Please note that the examples presented here are less made computationally less intensive to accommodate the policy of CRAN. You are welcome to explore the full potential of my package by using the wide range of possible window widths.

2 Transformation and quick preview of daily data

One of the crucial step before using a dendroTools is to prepare climate data into a proper format. For daily_response() climate data must be in a format of 366 columns and n number of rows, which represent years, which are given as row names. A common format of daily data provided by many online sources is a table with two columns, where one column represents the date and the second is the value of the climate variable. To quickly transform such a format into a data frame with dimensions of 366 x n, dendroTools now offers the function data_transform(). The date can be in different formats, but it must be correctly specified with the argument date_format. For example, if the date is in the format “1988-01-30′′ (”year-month-day”), the argument date_format must be “ymd”.

When your data is in the proper format, the glimpse_daily_data() can be used for visual inspection of daily climate data. The main purpose here is to spot missing values and to evaluate the suitability of using specific daily data.

# Load the dendroTools and ggplot2 R packages
library(dendroTools)
library(ggplot2)

# 1 Load an example data (source: E-OBS)
data("swit272_daily_temperatures")
data("swit272_daily_precipitation")

# 2 Transform data into wide format
swit272_daily_temperatures <- data_transform(swit272_daily_temperatures, format = 'daily', date_format = 'ymd')
swit272_daily_precipitation <- data_transform(swit272_daily_precipitation, format = 'daily', date_format = 'ymd')

# 3 Glimpse daily data
glimpse_daily_data(env_data = swit272_daily_temperatures, na.color = "red") + 
  theme(legend.position = "bottom")
Figure 1: Glimpse of daily temperautres for swit272 site.

Figure 1: Glimpse of daily temperautres for swit272 site. 

glimpse_daily_data(env_data = swit272_daily_precipitation, na.color = "red") + 
  theme(legend.position = "bottom")