building_a_module_examples.R

install.packages('zoon')

library(zoon)

vignette('Building_a_module')

#############
## Simples ##
#############

# I want to share my data via a ZOON module
# I uploaded it to figshare to make it accessible

# Here is the URL
URL <- "https://ndownloader.figshare.com/files/2519918"

# Here is the data
out <- read.csv(URL)

# Let's have a look
View(out)

# We don't need the date column
out <- out[, c("longitude", "latitude")]

# Our data is missing some of the required columns
out$value <- 1 # all our data are presences (0s would mean absence)
out$type <- 'presence'
out$fold <- 1 # we don't add any folds

# Now let's have a look
View(out)

# We can now write this as a function
Tom_Lorem_ipsum_UK <- function(){
  
  # Get data
  URL <- "https://ndownloader.figshare.com/files/2519918"
  out <- read.csv(URL)
  out <- out[, c("longitude", "latitude")]
  
  # Add in the columns we dont have
  out$value <- 1 # all our data are presences
  out$type <- 'presence'
  out$fold <- 1 # we wont add any folds
  
  return(out)
}

# We can test it in a workflow to make sure it works as 
# we would expect
library(zoon)

workl1 <- workflow(occurrence = Tom_Lorem_ipsum_UK,
                   covariate = UKBioclim,
                   process = OneHundredBackground,
                   model = LogisticRegression,
                   output = InteractiveMap)

# At the moment we have a function, to convert this into
# a module that we can share with others we use the 
# BuildModule function
BuildModule(object = Tom_Lorem_ipsum_UK,
            type = 'occurrence',
            title = 'A dataset of Lorem ipsum occurrences',
            description = paste0('The module retrieves a dataset of',
                                 'Lorem ipsum records from figshare. This dataset contains',
                                 'precence only data and was collected between 1990 and',
                                 '2000 by members of to Lorem ipsum appreciation society'),
            details = 'This dataset is fake, Lorem ipsum does not exist',
            author = 'A.B. Ceidi',
            email = 'ABCD@anemail.com',
            dataType = 'presence-only')

rm(list = 'Tom_Lorem_ipsum_UK')

# This is how you would use a module that a colleague has sent you
LoadModule(module = 'Tom_Lorem_ipsum_UK.R')

work2 <- workflow(occurrence = Tom_Lorem_ipsum_UK,
                  covariate = UKAir,
                  process = Background(n = 50),
                  model = LogisticRegression,
                  output = PrintMap)

## SLIDE ##

######################
## More complicated ##
######################

# I want to build a module that creates a random raster
# of a user defined extent and resolution

# Note parameters and defaults

NaiveRandomRaster <- function(extent = c(-10, 10, 45, 65), res = 0.5, seed = NULL){
  
  if(length(extent) != 4 | !inherits(extent, 'numeric')) stop("extent must be a numeric vector of length 4")
  
  if(extent[1] >= extent[2]) stop('in extent - min x must be smaller than max x')
  if(extent[3] >= extent[4]) stop('in extent - min y must be smaller than max y')
  
  nrow <- (extent[2] - extent[1])/res
  ncol <- (extent[4] - extent[3])/res
  
  if(!is.null(seed)) set.seed(seed)
  
  rasMat <- matrix(data = runif(n = nrow * ncol,
                                min = 0,
                                max = 100),
                   nrow = nrow,
                   ncol = ncol)
  
  ras <- raster::raster(x = rasMat,
                        xmn = extent[1],
                        xmx = extent[2],
                        ymn = extent[3],
                        ymx = extent[4],
                        crs = "+proj=longlat +datum=WGS84")
  
  return(ras)
}

# This time when we build the module we need to define the
# parameters

BuildModule(NaiveRandomRaster,
            type = 'covariate',
            title = 'Naive Random Raster', 
            description = 'Creates a random raster layer within the extent given',
            author = 'ZOON Developers',
            email = 'zoonproject@gmail.com',
            paras = list(extent = 'A numeric vector of length 4 giving the coordinates of the rectangular region within which to create the raaster. order: xmin, xmax, ymin, ymax. By default the extent of the UK',
                         res = 'Numeric giving the resolution of the raster to produce. 1 is one cell per degree, 0.5 means one cell per half degree.',
                         seed = 'Used with set.seed to set a seed. Default NULL, no seed is used'))

rm(list = 'NaiveRandomRaster')

LoadModule('NaiveRandomRaster.R')

# You can see the metadata above is used in the help file
ModuleHelp('NaiveRandomRaster')

# Here it is in action
work3 <- workflow(occurrence = UKAnophelesPlumbeus,
                  covariate = list(NaiveRandomRaster(extent = c(-10, 2, 49, 58),
                                                     res = 1),
                                   NaiveRandomRaster(extent = c(-10, 2, 49, 58),
                                                     res = 0.5),
                                   NaiveRandomRaster(extent = c(-10, 2, 49, 58),
                                                     res = 0.1)),
                  process = Background(n = 50),
                  model = LogisticRegression,
                  output = PrintMap)

par(mfrow = c(1,3))
for(i in Output(work3)) plot(i)

## SLIDE ##

##################
## Model module ##
##################

# Model modules havea  default argument .df which is a dataframe
# This looks similar to the occurrence dataset but with the
# covariate data added as extra columns

# Here is an example
.df <- Process(work3)[[1]]$df

View(.df)

# Lets write our function
GamGam <- function(.df){ # We need to add in the default arguments
  
  # Specify the packages we need using the function
  # GetPackage
  zoon::GetPackage("gam")
  
  # Create a data.frame of covariate data
  covs <- .df[colnames(.df) %in% attr(.df, 'covCols')]
  
  
  # do a bit of copy-pasting to define smooth terms for each covariate
  f <- sprintf('.df$value ~ s(%s)',
               paste(colnames(covs),
                     collapse = ') + s('))
  
  # Run our gam model
  m <- gam::gam(formula = formula(f),
                data = covs,
                family = binomial)
  
  # Create a ZoonModel object to return.
  # this includes our model, predict method
  # and the packages we need.
  ZoonModel(model = m,
            code = {
              
              # create empty vector of predictions
              p <- rep(NA, nrow(newdata))
              
              # omit NAs in new data
              newdata_clean <- na.omit(newdata)
              
              # get their indices
              na_idx <- attr(newdata_clean, 'na.action')
              
              # if there are no NAs then the index should 
              # include all rows, else it should name the 
              # rows to ignore
              if (is.null(na_idx)){
                idx <- 1:nrow(newdata)
              } else {
                idx <- -na_idx
              }
              
              # Use the predict function in gam to predict
              # our new values
              p[idx] <- gam::predict.gam(model,
                                         newdata_clean,
                                         type = 'response')
              return (p)
            },
            packages = 'gam')
  
}

##############
## Cite Me! ##
##############

ZoonCitation('NaiveRandomRaster')