AnAgeScrapeR lets you extract data for one or more species from the AnAge database: http://genomics.senescence.info/species/. The official citation is for the data is:
Tacutu, R., Thornton, D., Johnson, E., Budovsky, A., Barardo, D., Craig, T., Diana, E., Lehmann, G., Toren, D., Wang, J., Fraifeld, V. E., de Magalhaes, J. P. (2018) "Human Ageing Genomic Resources: new and updated databases." Nucleic Acids Research 46(D1):D1083-D1090.
You can install AnAgeScrapeR from GitHub. Dependencies are all from the tidyverse.
# install.packages("remotes")
remotes::install_github("mastoffel/AnAgeScrapeR", dependencies = TRUE)
So far, only the latin species names can be used (case insensitive). By default, there should be a space between Genus and Species name, but other delimiters can be used with the name_sep argument. The database is downloaded into your working directory (as it's a zip file which is difficult to directly read into R) when you extract data for the first time. Afterwards you can set download_data to FALSE, except for you want to download it again to be sure you have the newest version.
library(AnAgeScrapeR)
species <- c("felis catus", "Sterna paradisaea")
dat <- scrape_AnAge(latin_name = species,
vars = c("maximum_longevity_yrs","female_maturity_days"))
dat
#> # A tibble: 2 x 3
#> species_latin maximum_longevity_yrs female_maturity_days
#> <chr> <chr> <chr>
#> 1 sterna paradisaea 34 1095
#> 2 felis catus 30 289
There are quite a few more life history traits to extract, which can be found in AnAge_variables. The unit in which a variable is measured is simply the last bit of the variable name.
data(AnAge_variables)
AnAge_variables
#> [1] "female_maturity_days" "male_maturity_days"
#> [3] "gestation_incubation_days" "weaning_days"
#> [5] "litter_clutch_size" "litters_clutches_per year"
#> [7] "interlitter_interbirth_interval" "birth_weight_g"
#> [9] "weaning_weight_g" "adult_weight_g"
#> [11] "growth_rate_1_days" "maximum_longevity_yrs"
#> [13] "source" "specimen_origin"
#> [15] "sample_size" "data_quality"
#> [17] "imr_per_year" "mrdt_yrs"
#> [19] "metabolic_rate_w" "body_mass_g"
#> [21] "temperature_k" "references"
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