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Bin observations

Source: R/bin.R
bin.Rd

Summarize observations within a given time window, typically with the idea of having a single observation per bin. The Minds for Mobile Agents model assumes that pedestrians take a walking decision every 0.5 seconds. This function ensures that the data reflects this assumption by binning all available data within that time-frame. Of course, this time window can be adjusted by the user to fit their purposes.

Usage

bin(data, span = 0.5, fx = mean, cols = NULL, .by = NULL)

Arguments

data

Dataframe that contains information on location (x- and y-coordinates) and the time at which the measurement was taken. By default, bin() will assume that this information is contained within the columns "x", "y", and "time" respectively. If this isn't the case, either change the column names in the data or specify the cols argument.

span

Numeric denoting the size of the bins. Will pertain to the values in the "time" variable. Defaults to 0.5.

fx

Function to execute on the data that falls within the bin. Will be executed on the "x" and "y" columns separately and should ouput only a single value. Defaults to the function mean().

cols

Named vector or named list containing the relevant column names in data if they didn't contain the prespecified column names "time", "x", and "y". The labels should conform to these prespecified column names and the values given to these locations should contain the corresponding column names in that dataset. Defaults to NULL, therefore assuming the structure explained in data.

.by

String denoting whether the moving window should be taken with respect to a given grouping variable. Defaults to NULL.

Value

Binned dataframe with a similar structure as data

Details

Note that if more variables than required are provided to this function, that these will get lost in translation. The reason is that it is difficult to implement meaningful aggregation across the different types of data that a user may supply.

Examples

# Generate data for illustration purposes
data <- data.frame(
  X = rnorm(100),
  Y = rnorm(100),
  seconds = rep(1:50, times = 2) / 10,
  tag = rep(1:2, each = 50)
)

# Bin the data together by taking the average for each second
bin(
  data,
  span = 1,
  fx = mean,
  cols = c(
    "time" = "seconds",
    "x" = "X",
    "y" = "Y"
  ),
  .by = "tag"
)
#>    seconds           X            Y tag
#> 1     0.60 -0.45157677 -0.506385797   1
#> 2     1.65 -0.07651055  0.321496390   1
#> 3     2.65 -0.07683792  0.125635920   1
#> 4     3.65  0.06563361 -0.121029117   1
#> 5     4.60  0.12115087  0.392878219   1
#> 6     0.60  0.21329556 -0.004112846   2
#> 7     1.65 -0.34352763 -0.232954779   2
#> 8     2.65  0.62033804  0.217737812   2
#> 9     3.65  0.08737841  0.699020375   2
#> 10    4.60  0.64943515  0.385132806   2