BETYdb: Data Access
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R dplyr Package
R dplyr interface
Connect to Database
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library(dplyr)
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library(data.table)
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## connection to database
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d <- list(host = 'localhost',
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dbname = 'bety',
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user = 'bety',
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password = 'bety')
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bety <- src_postgres(host = d$host, user = d$user, password = d$password, dbname = d$dbname)
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Query Miscanthus yield data
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species <- tbl(bety, 'species') %>%
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select(id, scientificname, genus) %>%
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filter(genus == "Miscanthus") %>%
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mutate(specie_id = id)
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yields <-tbl(bety, 'yields') %>%
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select(date, mean, site_id, specie_id)
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sites <- tbl(bety, 'sites') %>%
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select(id, sitename, city, country) %>%
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mutate(site_id = id)
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mxgdata <- inner_join(species, yields, by = 'specie_id') %>%
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left_join(sites, by = 'site_id') %>%
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select(-ends_with(".x"), -ends_with(".y")) %>% # drops duplicate rows
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collect()
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Yield data with experimental treatments
Here we query Miscanthus and Switchgrass yield data along with planting, irrigation, and fertilization rates in order to update teh meta-analysis originally performed by Heaton et al (2004).
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## query and join tables
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species <- tbl(bety, 'species') %>%
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select(id, scientificname, genus) %>%
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rename(specie_id = id)
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sites <- tbl(bety, sql(
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paste("select id as site_id, st_y(st_centroid(sites.geometry)) AS lat,",
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"st_x(st_centroid(sites.geometry)) AS lon,",
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" sitename, city, country from sites"))
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)
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citations <- tbl(bety, 'citations') %>%
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select(citation_id = id, author, year, title)
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yields <- tbl(bety, 'yields') %>%
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select(id, date, mean, n, statname, stat, site_id, specie_id, treatment_id, citation_id, cultivar_id) %>%
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left_join(species, by = 'specie_id') %>%
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left_join(sites, by = 'site_id') %>%
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left_join(citations, by = 'citation_id')
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managements_treatments <- tbl(bety, 'managements_treatments') %>%
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select(treatment_id, management_id)
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treatments <- tbl(bety, 'treatments') %>%
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dplyr::mutate(treatment_id = id) %>%
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dplyr::select(treatment_id, name, definition, control)
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managements <- tbl(bety, 'managements') %>%
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filter(mgmttype %in% c('fertilizer_N', 'fertilizer_N_rate', 'planting', 'irrigation')) %>%
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dplyr::mutate(management_id = id) %>%
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dplyr::select(management_id, date, mgmttype, level, units) %>%
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left_join(managements_treatments, by = 'management_id') %>%
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left_join(treatments, by = 'treatment_id')
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nitrogen <- managements %>%
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filter(mgmttype == "fertilizer_N_rate") %>%
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select(treatment_id, nrate = level)
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planting <- managements %>% filter(mgmttype == "planting") %>%
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select(treatment_id, planting_date = date)
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planting_rate <- managements %>% filter(mgmttype == "planting") %>%
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select(treatment_id, planting_date = date, planting_density = level)
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irrigation <- managements %>%
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filter(mgmttype == 'irrigation')
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irrigation_rate <- irrigation %>%
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filter(units == 'mm', !is.na(treatment_id)) %>%
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group_by(treatment_id, year = sql("extract(year from date)"), units) %>%
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summarise(irrig.mm = sum(level)) %>%
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group_by(treatment_id) %>%
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summarise(irrig.mm.y = mean(irrig.mm))
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irrigation_boolean <- irrigation %>%
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collect %>%
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group_by(treatment_id) %>%
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mutate(irrig = as.logical(mean(level))) %>%
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select(treatment_id, irrig = irrig)
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irrigation_all <- irrigation_boolean %>%
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full_join(irrigation_rate, copy = TRUE, by = 'treatment_id')
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grass_yields <- yields %>%
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filter(genus %in% c('Miscanthus', 'Panicum')) %>%
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left_join(nitrogen, by = 'treatment_id') %>%
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#left_join(planting, by = 'treatment_id') %>%
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left_join(planting_rate, by = 'treatment_id') %>%
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left_join(irrigation_all, by = 'treatment_id', copy = TRUE) %>%
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collect %>%
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mutate(age = year(date)- year(planting_date),
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nrate = ifelse(is.na(nrate), 0, nrate),
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SE = ifelse(statname == "SE", stat, ifelse(statname == 'SD', stat / sqrt(n), NA)),
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continent = ifelse(lon < -30, 'united_states', ifelse(lon < 75, 'europe', 'asia')))
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Last modified 3yr ago