Helpful packages for this tutorial:

#Packages to install (you only need to do this once)
#For data analysis:
install.packages("tidyverse") #includes dplyr, ggplot2

#For loading the ISRaD package below:

#Load these packages (you need to do this every time you restart R)

Working with the database in R

The ISRaD database includes:
1) ISRaD_data: The complete collection of data, reported from publications, compiled in one place
2) ISRaD_extra: An augmented dataset, with useful global variables and radiocarbon calculations completed for you
3) Tools: Options to compile your own dataset to compare to ISRaD, and functions to work with the data

Two options to download ISRaD data:

Manually Download & load into R:

  1. Download from following the blue “Download” link on this page.
  2. Unzip the files
  3. Load the .rda files into R. Specify the folder.
load("C:/Users/YourPathHere/ISRaD_data_v1-2019-08-13.rda") #Loads ISRaD_data
load("C:/Users/YourPathHere/ISRaD_data_v1-2019-08-13.rda") #Loads ISRaD_extra

Install the data through the ISRaD R package:

  1. Install and load the ‘devtools’ and ‘rcrossref’ packages
  2. Install the ISRaD package from Github
  3. Use the ‘get_data’ function to download the latest version of the database
# 'devtools' allows you to install the ISRaD package in its "beta"/development form
# 'rcrossref' is used by our QAQC tool, but sometimes ISRaD won't install without it
# 2) Install package 'ISRaD' from the development branch of our github repository:
devtools::install_github("International-Soil-Radiocarbon-Database/ISRaD", ref="master", force = T)
library(ISRaD) # load the package
# 3) Load data using the get_data function:
mydir <- "C:/Users/YourPathHere/"
ISRaD_extra <-ISRaD.getdata(directory = mydir, dataset="full", extra = T, force_download = T)

Some other useful packages for working with the data:

Tidyverse and dplyr (part of tidyverse) are useful packages for exploration of ISRaD data. This is a helpful cheatsheet.

We use packages dplyr for filtering data and ggplot for plotting. Both packages are included in the tidyverse library.


install.packages("ggplot2") #should be included in tidyverse, but you may want to install separately

Structure of ISRaD data

The ISRaD data are formated as a list in an R object called ISRaD_data. ISRaD_data is a list comprised of 8 data.frames (metadata, site, profile, flux, layer, interstitial, fraction, and incubation), which correspond to the template data tables. ‘ISRaD_extra’ follows the same structure. Here is a conceptual overview of the data structure.

#Check to make sure the package is loaded by looking for the database object "ISRaD_data"
#View a single table (e.g. Site, Profile, Layer, etc):
#This will show you all the sites compiled in the database

Creating Your Own “Flat” Data Frame of Interest

Ready to start your analysis? You may want to create your own “flat” data frame with the data of interest to you. To do this you can:

  1. Use the ISRaD built-in flatten function and specify the table of interest. The function flatten creates a data frame with all relevant layers of the hierarchy. You can create a flat data frame with data from the flux, layer, interstitial, fraction or incubation tables. You can flatten data from ISRaD_data, ISRaD_extra, or your own compiled data with a similar structure.
inc_data <- ISRaD.flatten(ISRaD_extra, 'incubation')
lyr_data <- ISRaD.flatten(ISRaD_extra, 'layer')
frc_data <- ISRaD.flatten(ISRaD_extra, 'fraction')
  1. Or flatten the data yourself by joining the data of interest with other levels of the hierarchy. This does the same thing!
#Flatten layer data:
lyr_data <- ISRaD_extra$layer %>% #Start with layer data
  left_join(ISRaD_extra$profile) %>% #Join to profile data
  left_join(ISRaD_extra$site) %>% #Join to site data
  left_join(ISRaD_extra$metadata) #Join to metadata
#or merge fraction data with other data up the hierarchy in an object called "frc_data"
frc_data <- ISRaD_extra$fraction %>% #Start with fraction data
  left_join(ISRaD_extra$layer) %>% #Join to layer data
  left_join(ISRaD_extra$profile) %>% #Join to profile data
  left_join(ISRaD_extra$site) %>% #Join to site data

#Take a look at it:

inc_data <- ISRaD_extra$incubation %>% #Start with incubation data
  left_join(ISRaD_extra$layer) %>% #Join to layer data
  left_join(ISRaD_extra$profile) %>% #Join to profile data
  left_join(ISRaD_extra$site) %>% #Join to site data

Or join with a more limited set of information:

#Merge flux data with site information only
flx_data <- ISRaD_data$flux %>% #Start with flux data
  left_join(ISRaD_data$site) #Join to site data

#Take a look at it:

Filtering the Data & Summary Statistics

How much incubation data do we have? How is it distributed? To answer this:

  1. Start with all inc data
  2. Filter by depths less than 50cm
  3. Group by land cover class
  4. Print the number of data points and the mean 14C for each class
inc_data %>%
  filter(lyr_bot < 50) %>% #filters depths above 50cm.
  filter( != TRUE) %>% #filters 14C data only
  group_by(pro_land_cover) %>% #groups by land cover class
  summarise(num_data_points = n(), #summarizes number of points
            mean_inc_14c = mean(inc_14c, na.rm=TRUE)) #Calculates mean 14C
## # A tibble: 8 x 3
##   pro_land_cover      num_data_points mean_inc_14c
##   <fct>                         <int>        <dbl>
## 1 bare                              8         49.1
## 2 cultivated                       56        119. 
## 3 forest                          623         93.7
## 4 rangeland/grassland              36         46.9
## 5 shrubland                        38         30.1
## 6 tundra                          223         16.6
## 7 wetland                          78         62.1
## 8 <NA>                            156        -40.4

Filter & Plot:

How do the density fractions in forests compare?

frc_data %>%
  filter(frc_scheme == "Density") %>%
  filter(frc_property != "NA") %>%
  geom_boxplot(aes(x=frc_property, y = frc_14c)) +
  theme_bw()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
  facet_wrap(~pro_parent_material) #Makes one plot for each parent material

Is there any temperature dependence in the free light fraction?

frc_data %>% 
  dplyr::filter(lyr_bot <= 30 & lyr_top >=0) %>%
  dplyr::filter(pro_treatment == 'control') %>%
  dplyr::filter(pro_land_cover == "forest") %>%
  dplyr::filter(frc_scheme == "Density") %>%
  dplyr::filter(frc_property == "free light")%>%
  dplyr::filter(lyr_obs_date_y >=1990 & lyr_obs_date_y <= 2010) %>%
  dplyr::filter( != TRUE) %>%
  #mutate(bin_temp2=cut(pro_MAT_wc, 4)) %>% #Add 4 temperature bins
  geom_point(aes(x = pro_MAT, y= frc_14c, color = pro_MAP))+
  xlab("Mean Annual Temperature (C)")+
  ylab(expression(Delta^14 *"C of Free Light Fraction")) +
  ggtitle("Free Light Fractions, 0-30cm, Forests, 1990-2010")+
  scale_color_gradient(low = "orange", high = "blue")+

Mapping ISRaD Data

Map all ISRaD layer data, and sites with 14C layer data

Install some helpful mapping packages:


First setup the basemap:

world_map <- map_data("world")

#Creat a base plot with gpplot2
p <- ggplot() + coord_fixed() +
  xlab("") + ylab("")

#Add map to base plot
#Pick color words here:
base_world_messy <- p + geom_polygon(data=world_map, aes(x=long, y=lat, group=group), 
                                     colour="cadetblue4", fill="cadetblue4")

cleanup <- theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), 
                 panel.background = element_rect(fill = 'white', colour = 'white'), 
                 axis.line = element_line(colour = "white"), legend.position="none",
                 axis.ticks=element_blank(), axis.text.x=element_blank(),

base_world <- base_world_messy + cleanup
#base_world #Plots map to screen

Plot locations of ISRaD profiles on the map. Yellow points have bulk soil (layer) 14C data. Red points have other data, but do not have bulk soil 14C data.

#Filter only data with 14C:
lyr_data_14c <- lyr_data %>%
  dplyr::filter( != TRUE) 

map_data <- 
  base_world +
  geom_point(data=lyr_data, #lyr_all, #put incubation data here to plot points
             aes(x=pro_long, y=pro_lat), colour="black", 
             shape = 21, size=4, alpha=I(0.7), fill = "red")+
  geom_point(data=lyr_data_14c, #lyr_all, #put incubation data here to plot points
           aes(x=pro_long, y=pro_lat), colour="black", 
           shape = 21, size=4, alpha=I(0.7), fill = "goldenrod1")#+

map_data # Plots map to screen

Compiling your own data offline:

Advanced users may wish to compile thier own data locally in order view it in the context of the larger database. (note: that this operation is not the same as submitting your data for ingest)

The compile function is used QA/QC and assemble additional datasets (that pass the QA/QC test) into an new list, which can later be merged with ISRaD_data.

In order to run compile on a set of user specified data entries, the user must create a local folder whose path is specified with dataset_directory. This folder must only contain the entries to be compiled in .xlsx format. If other files types exist in the directory, compile will fail. (note: entries cannot be open in Excel)

compiled<-compile(dataset_directory = "~/Directory/to/data/", write_report = T, write_out = T, return="list")

The paramter return determines format of the object that is returned and should be set to “list” unless the user prefers a flattend version of the database formatted as a single data.frame.

When set to “TRUE”, the parameter write_out will trigger the creation of several output files:

Description Location File name
Report files that identify issues with the files in the dataset_directory dataset_directory/QAQC QAQC_*.txt (* corresponds to dataset file names)
Flattened database file dataset_directory/database ISRaD_flat.csv
List structured database file in the same format as template dataset_directory/database ISRaD_list.xlsx
Log file generated by compile function. Most importatntly, tells you which files passed QAQC. dataset_directory/database ISRaD_log.txt
Summary statistics for datasets compiled into databaser dataset_directory/database ISRaD_summary.csv
QAQC check on compiled database. dataset_directory/database QAQC_ISRaD_list.txt

Merging a user compiled list with ISRaD_data

The function mapply can be used to merge the user compiled list with ISRaD_data as follows:

merged_data<-mapply(rbind, ISRaD_data, compiled, SIMPLIFY=FALSE)