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@ -3,8 +3,8 @@
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#' Get the counts of sensors for each observed phenomenon.
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#' Get the counts of sensors for each observed phenomenon.
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#'
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#'
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#' @param boxes A \code{sensebox data.frame} of boxes
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#' @param boxes A \code{sensebox data.frame} of boxes
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#' @return An \code{data.frame} containing the count of sensors observing a
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#' @return A named \code{list} containing the count of sensors observing a
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#' phenomenon per column.
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#' phenomenon per phenomenon
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#' @export
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#' @export
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osem_phenomena = function (boxes) UseMethod('osem_phenomena')
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osem_phenomena = function (boxes) UseMethod('osem_phenomena')
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@ -25,11 +25,10 @@ osem_phenomena = function (boxes) UseMethod('osem_phenomena')
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#'
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#'
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#' # get phenomena with at least 10 sensors on opensensemap
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#' # get phenomena with at least 10 sensors on opensensemap
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#' phenoms = osem_phenomena(osem_boxes())
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#' phenoms = osem_phenomena(osem_boxes())
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#' colnames(dplyr::select_if(phenoms, function(v) v > 9))
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#' names(phenoms[phenoms > 9])
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#'
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#'
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osem_phenomena.sensebox = function (boxes) {
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osem_phenomena.sensebox = function (boxes) {
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Reduce(`c`, boxes$phenomena) %>% # get all the row contents in a single vector
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Reduce(`c`, boxes$phenomena) %>% # get all the row contents in a single vector
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table() %>% # get count for each phenomenon
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table() %>% # get count for each phenomenon
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t() %>% # transform the table to a df
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as.list()
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as.data.frame.matrix()
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}
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}
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