This package aims to make acquiring and working with MACA v2 climate data faster and easier and to provide a number of summary statistics that can be used to visualize different climate futures. Ultimately, having access to this data supports planning efforts that aim to incorporate climate change.
Until approval on CRAN, you can download the development version of
rcf
You can install the released version of rcf from CRAN with:
install.packages("rcf")
And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("nationalparkservice/rcf")
library(tidyverse)
#> -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
#> v ggplot2 3.3.5 v purrr 0.3.4
#> v tibble 3.1.3 v dplyr 1.0.7
#> v tidyr 1.1.3 v stringr 1.4.0
#> v readr 2.0.1 v forcats 0.5.1
#> -- Conflicts ------------------------------------------ tidyverse_conflicts() --
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
library(rcf)
Download data using the rcf_data()
function from the rcfdata
package
to start visualizing climate futures
# devtools::install_github("nationalparkservice/rcfdata")
# library(rcfdata)
# raw_data <- rcf_data(SiteID = "BAND",
# latitude = 35.75758546,
# longitude = -106.3054344,
# units = "imperial")
raw_data <- read_csv("https://irmadev.nps.gov/DataStore/DownloadFile/660685")
#> Rows: 2191480 Columns: 10
#> -- Column specification --------------------------------------------------------
#> Delimiter: ","
#> chr (2): gcm, units
#> dbl (7): yr, precip, tmin, tmax, tavg, rhmin, rhmax
#> dttm (1): date
#>
#> i Use `spec()` to retrieve the full column specification for this data.
#> i Specify the column types or set `show_col_types = FALSE` to quiet this message.
Calculate threshold values using calc_thresholds()
and summarize them
by month, season or year as well as by quadrant or the most extreme
model in each quadrant using cf_quadrant()
.
thresholds <- calc_thresholds("BAND", data = raw_data, units = "imperial")
#> Adding missing grouping variables: `gcm`
#> Warning in calc_thresholds("BAND", data = raw_data, units = "imperial"): Files
#> have been saved to temporary directory and will be deleted when this R session
#> is closed. To save locally, input a local directory in which to save files into
#> the `directory` argument.
#> Warning in calc_thresholds("BAND", data = raw_data, units = "imperial"):
#> thresholds.csv generated successfully. DO NOT edit this csv in excel. File is
#> too large and data will be lost, causing errors in future calculations.
quadrant_year <- cf_quadrant("BAND", data = thresholds, future_year = 2040, summarize_by = "year", method = "quadrant")
#> Warning in cf_quadrant("BAND", data = thresholds, future_year = 2040,
#> summarize_by = "year", : Files have been saved to temporary directory and
#> will be deleted when this R session is closed. To save locally, input a local
#> directory in which to save files into the `directory` argument.
From here we can use ggplot2
to visualize any variables and how they
compare between the 4 climate futures as well as between past and
future.
quadrant_year_future <- quadrant_year %>%
filter(time %in% c("Future"))
ggplot(data = quadrant_year_future, aes(x = cf, y = freeze_thaw)) +
geom_boxplot(alpha = 0.4,
aes(color = cf, fill = cf)) +
geom_jitter(alpha = 0.7,
aes(color = cf, fill = cf)) +
scale_fill_viridis_d() +
scale_color_viridis_d() +
labs(y = "Number of days per year",
title = "Days that have a freeze thaw cycle by climate future") +
theme(axis.title.x = element_blank()) +
theme_minimal()
For a more in-depth explanation of the rcf
package and different ways
to download the data, you can follow along with An Introduction to the
Reproducible Climate Futures
package.
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