> For the complete documentation index, see [llms.txt](https://pecan.gitbook.io/betydb-data-access/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://pecan.gitbook.io/betydb-data-access/r-dplyr-package.md).

# R dplyr Package

### R dplyr interface

Using dplyr requires having access to a PostgreSQL server running BETYdb or [installing your own](/betydb-data-access/installing-your-own-version-of-betydb.md).

Comprehensive documentation for the `dplyr` interface to databases is provided in the [dplyr vignette](https://cran.r-project.org/web/packages/dplyr/vignettes/databases.html)

## Connect to Database

```
library(dplyr)
library(data.table)
## connection to database
d <- list(host = 'localhost',
          dbname = 'bety',
          user = 'bety',
          password = 'bety')

bety <- src_postgres(host = d$host, user = d$user, password = d$password, dbname = d$dbname)
```

## Query Miscanthus yield data

```
species <- tbl(bety, 'species') %>% 
  select(id, scientificname, genus) %>% 
  filter(genus == "Miscanthus") %>% 
  mutate(specie_id = id) 

yields <-tbl(bety, 'yields') %>%
  select(date, mean, site_id, specie_id)

sites <- tbl(bety, 'sites') %>% 
  select(id, sitename, city, country) %>% 
  mutate(site_id = id)

mxgdata <- inner_join(species, yields, by = 'specie_id') %>%
  left_join(sites, by = 'site_id') %>% 
  select(-ends_with(".x"), -ends_with(".y")) %>% # drops duplicate rows
  collect()
```

## 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).

```r
## query and join tables
species <- tbl(bety, 'species') %>% 
  select(id, scientificname, genus) %>% 
  rename(specie_id = id)

sites <- tbl(bety, sql(
  paste("select id as site_id, st_y(st_centroid(sites.geometry)) AS lat,",
        "st_x(st_centroid(sites.geometry)) AS lon,",
        " sitename, city, country from sites"))
  )

citations <- tbl(bety, 'citations') %>%
  select(citation_id = id, author, year, title)

yields <- tbl(bety, 'yields') %>%
  select(id, date, mean, n, statname, stat, site_id, specie_id, treatment_id, citation_id, cultivar_id) %>% 
  left_join(species, by = 'specie_id') %>%
  left_join(sites, by = 'site_id') %>% 
  left_join(citations, by = 'citation_id')

managements_treatments <- tbl(bety, 'managements_treatments') %>%
  select(treatment_id, management_id)

treatments <- tbl(bety, 'treatments') %>% 
  dplyr::mutate(treatment_id = id) %>% 
  dplyr::select(treatment_id, name, definition, control)

managements <- tbl(bety, 'managements') %>%
  filter(mgmttype %in% c('fertilizer_N', 'fertilizer_N_rate', 'planting', 'irrigation')) %>%
  dplyr::mutate(management_id = id) %>%
  dplyr::select(management_id, date, mgmttype, level, units) %>%
  left_join(managements_treatments, by = 'management_id') %>%
  left_join(treatments, by = 'treatment_id') 

nitrogen <- managements %>% 
  filter(mgmttype == "fertilizer_N_rate") %>%
  select(treatment_id, nrate = level)

planting <- managements %>% filter(mgmttype == "planting") %>%
  select(treatment_id, planting_date = date)

planting_rate <- managements %>% filter(mgmttype == "planting") %>%
  select(treatment_id, planting_date = date, planting_density = level) 

irrigation <- managements %>% 
  filter(mgmttype == 'irrigation') 

irrigation_rate <- irrigation %>% 
  filter(units == 'mm', !is.na(treatment_id)) %>% 
  group_by(treatment_id, year = sql("extract(year from date)"), units) %>% 
  summarise(irrig.mm = sum(level)) %>% 
  group_by(treatment_id) %>% 
  summarise(irrig.mm.y = mean(irrig.mm))

irrigation_boolean <- irrigation %>%
  collect %>%   
  group_by(treatment_id) %>% 
  mutate(irrig = as.logical(mean(level))) %>% 
  select(treatment_id, irrig = irrig)

irrigation_all <- irrigation_boolean %>%
  full_join(irrigation_rate, copy = TRUE, by = 'treatment_id')

grass_yields <- yields %>% 
  filter(genus %in% c('Miscanthus', 'Panicum')) %>%
  left_join(nitrogen, by = 'treatment_id') %>% 
  #left_join(planting, by = 'treatment_id') %>% 
  left_join(planting_rate, by = 'treatment_id') %>% 
  left_join(irrigation_all, by = 'treatment_id', copy = TRUE) %>% 
  collect %>% 
  mutate(age = year(date)- year(planting_date),
         nrate = ifelse(is.na(nrate), 0, nrate),
         SE = ifelse(statname == "SE", stat, ifelse(statname == 'SD', stat / sqrt(n), NA)),
         continent = ifelse(lon < -30, 'united_states', ifelse(lon < 75, 'europe', 'asia')))
```


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://pecan.gitbook.io/betydb-data-access/r-dplyr-package.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.