exportSpatialization: export a spatialized object either to csv, json or geojson

In pokyah/agrometeoR: Spatial interpolation for Agromet project @ CRA-W

Description

you can both store the encoded string in a variable and in a file by specifying write = TRUE

Usage

exportSpatialization(spatialized, path = NULL, filename = NULL,
  format = "csv")

Arguments

spatialized	a dataframe containing the gridded predicted values
path	a character specifying the path where you want your export file to be stored. Default = NULL
filename	a character specifying the name you want to give to the file. If NULL the exportation is not printed into a file and the output is returned as a character. Default = NULL
format	a character specifying the type of export format. One of "csv", "json" or "geojson". Default = "csv"

Value

A 2 elements named list

• snitch : a boolean. Is TRUE if function has provided the expected result. Is FALSE is function throws an error

• output : a named list which elements are :

  • value : a character vector containing the data encoded into the desired exportation format

  • condition : a character specifying the condition encountered by the function : success, warning, or error.

  • message : a character specifying the message relative to the condition.

A 2 elements named list : snitch & output.
snitch is TRUE if function has provided the expected result.
output is a named list which contains :

• value is a character vector containing the data encoded into the desired exportation format.

• condition is a character specifying if the function has encountered success, warning, error.

• message is the message relative to the condition.

Author(s)

Thomas Goossens

Examples

# load magrittr for pipe use : %>%
library(magrittr)
## Not run: 
# create the dataset
myDataset = makeDataset(
  dfrom = "2017-03-04T15:00:00Z",
  dto = "2017-03-04T18:00:00Z",
  sensor = "tsa")

# extract the list of hourly sets of records
myDataset = myDataset$output$value

# create the tasks
myTasks = purrr::map(myDataset, makeTask, target = "tsa")

# extract the tasks from the outputs
myTasks = myTasks %>% purrr::modify_depth(1, ~.$"output"$"value"$"task")

# keep the first task
myTask = myTasks[[1]]

# create the model
myModel = makeModel(
  task = myTask,
  learner = agrometeorLearners$mulLR_lonLatAlt_NA)

# extract the relevant information
myModel = myModel$output$value

# spatialize using the trained model
mySpatialization = makeSpatialization(
model = myModel$trained,
pred.grid = grid.df) # grid.df comes precompiled with the package

# get the relevant information
mySpatialization = mySpatialization$output$value

# export the spatialized data a json as a character returned into myJson variable
myJson = exportSpatialization(spatialized = mySpatialization$spatialized, format = "json")

# show th json string
myJson$output$value

# show myJson
myJson$output$value

# export as a csv file
exportSpatialization(spatialized = mySpatialization$spatialized, filename = "test", path = getwd(), format = "csv")

## End(Not run)

pokyah/agrometeoR documentation built on May 26, 2019, 7 p.m.