Loop plot for multiple species from userflora

userflora_export_plot.R

library(RODBC)
library(ggplot2)
library(nlme)
library(mgcv)
library(lme4)
library(MASS)
library(splines)

# additional library to install for parameterized queries
library(RODBCext)

# A function that can be reused for the data acquisition. It assumes that the
# QryTrend view contains all species to work with

#' Trend plot of species
#'
#' @param species_name name of the species to query the data
#' @param dbase name of the access database to connect with
#' @param rel_path path on the computer where to store the figures
#'
#' @return saves ggplot figure as jpg
#' @export
#'
#' @examples plot_trend_species("korenbloem", "UserFlorabank.mdb", "./images")
plot_trend_species <- function(species_name, dbase, rel_path = '.') {

    # dbase connection
    connection <- odbcConnectAccess(dbase)
    sqlCode <- "SELECT QryTrend.*  from QryTrend WHERE species = ?;"
    sqlPrepare(connection, sqlCode)
    trend_data <- sqlExecute(channel = connection, species_name,
                             fetch = TRUE)

    ggplot(trend_data, aes(x = BeginJaar, y = Percentage)) +
        geom_point() +
        geom_smooth() +
        ylab(paste0("Percentage van onderzochte kmhokken met ", species_name)) +
        xlab("jaar") +
        scale_y_continuous(limits = c(0, 100)) +
        theme(axis.text.x = element_text(size = 15),
              axis.title.x = element_text(size = 15, face = "bold"),
              axis.text.y = element_text(size = 10),
              axis.title.y = element_text(size = 15, face = "bold"))
    # save the figure
    ggsave(file.path(rel_path, paste0(species_name, ".jpg")))
}

# Load the individual species names (CSV-fiel, just a list, maybe with a query?)
species_list <- TODO #!
# apply the function to each of the species names:
lapply(species_list, plot_trend_species, "UserFlorabank.mdb", "./images")