bdc: Bavarian Data Cube

## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = T, comment = NA, warning=F, message=F, eval=F, echo=T, error=F, 
  comment = "#>", fig.width=8, fig.height=6, fig.path="../figures/"
)

## ----load_data----------------------------------------------------------------
#  # Load packages
#  library(dplyr); library(magrittr); library(scico)
#  library(lubridate); library(ggplot2)
#  
#  # Load Ornitho database
#  dat <- vroom::vroom("../rawdata/dda-observations_bayern_2021-03-17.csv")
#  #colnames(dat)
#  
#  # Only select needed columns
#  dat %<>% select(c("LATIN_SPECIES", "FAMILY_NAME", "DATE", "TIMING", "PLACE", "COORD_LAT", "COORD_LON",
#                    "COORD_F", "COORD_E", "COORD_N", "PRECISION", "TOTAL_COUNT", "ATLAS_CODE")); invisible(gc())

## -----------------------------------------------------------------------------
#  # Summarise number of records per year
#  dat$year <- year(dat$DATE)
#  dat %>% group_by(year) %>% summarise(n=n()) %>%
#    ggplot() + geom_histogram(aes(x=year, y=n), stat="identity") + theme_bw() +
#    scale_x_continuous(name="Year", expand=c(0,1)) +
#    scale_y_continuous(name="Number of records", limits=c(0,NA), expand=c(0,10000))

## -----------------------------------------------------------------------------
#  dat %>% group_by(year) %>% filter(year %in% c(1950:2010)) %>%
#    summarise(n=n()) %>%
#    ggplot() + geom_histogram(aes(x=year, y=n), stat="identity") + theme_bw() +
#    scale_x_continuous(name="Year (1950 - 2010)", expand=c(0,1)) +
#    scale_y_continuous(name="Number of records", #limits=c(0,7000),
#                       expand=c(0,0))

## -----------------------------------------------------------------------------
#  dat %>% group_by(year) %>% filter(year %in% c(2009:2021)) %>%
#    summarise(n=n()) %>%
#    ggplot() + geom_histogram(aes(x=year, y=n), stat="identity") + theme_bw() +
#    scale_x_continuous(name="Year (2009 - 2021)", expand=c(0,0)) +
#    scale_y_continuous(name="Number of records", limits=c(0,12e5), expand=c(0,0))

## -----------------------------------------------------------------------------
#  # Summarise number of records per month
#  dat$month <- month(dat$DATE)
#  dat %>% group_by(month) %>% summarise(n=n()) %>%
#    mutate(month = factor(month, levels=c(1:12), labels=month.abb)) %>%
#    ggplot() + geom_histogram(aes(x=month, y=n), stat="identity") +
#    theme_bw() +
#    labs(x="Month") + scale_y_continuous(name="Number of records", limits=c(0,NA), expand=c(0,5000))

## -----------------------------------------------------------------------------
#  dat %>% filter(year %in% c(2001:2021)) %>%
#    group_by(month, year) %>% summarise(n=n()) %>%
#    mutate(month = factor(month, levels=c(1:12), labels=month.abb)) %>%
#    mutate(year = factor(year)) %>%
#    ggplot() + geom_histogram(aes(x=month, y=n, fill=year), stat="identity", position="stack") +
#    theme_bw() +
#    labs(x="Month") + scale_y_continuous(name="Number of records", limits=c(0,NA), expand=c(0,5000))

## -----------------------------------------------------------------------------
#  # Summarise number of records per family
#  dat %>% group_by(FAMILY_NAME) %>% summarise(n=n()) %>%
#    ggplot() + geom_histogram(aes(x=FAMILY_NAME, y=n), stat="identity") +
#    theme_bw() + labs(x="Family") +
#    scale_y_continuous(name="Number of records", limits=c(0,NA)) +
#    theme(axis.text.x = element_text(angle=90))

## -----------------------------------------------------------------------------
#  # Summarise number of records per location
#  dat %>% ungroup() %>% mutate(COORD_LON = round(COORD_LON, 2),
#                 COORD_LAT = round(COORD_LAT, 2)) %>%
#    group_by(COORD_LON, COORD_LAT) %>% summarise(n=n()) %>%
#    ggplot() + geom_point(aes(x=COORD_LON, y=COORD_LAT, colour=log10(n)), size=1) +
#    scale_colour_scico(palette="roma") + coord_sf() + theme_bw()
#  rm(list=ls()); invisible(gc())