Get started with mantis

This vignette demonstrates how to use this package to generate different types of html reports that allow you to visually review multiple related time series quickly and easily.

Data format

The data frame containing the time series should be in long format, with the following columns (though the actual column names can be different):

one “timepoint” (date/posixt) column which will be used for the x-axes. Values should follow a regular pattern, e.g. daily or monthly, but do not have to be consecutive.
one or more “item” (character) columns containing categorical values identifying distinct time series.
one “value” (numeric) column containing the time series values which will be used for the y-axes.

Example:

The example_prescription_numbers dataset is provided with this package, and contains (synthetic) examples of aggregate numbers of antibiotic prescriptions given in a hospital over a period of a year. It contains 5 columns:

PrescriptionDate - The date the prescriptions were written
Antibiotic - The name of the antibiotic prescribed
Spectrum - The spectrum of activity of the antibiotic. This value is always the same for a particular antibiotic
NumberOfPrescriptions - The number of prescriptions written for this antibiotic on this day
Location - The hospital site where the prescription was written

# first, attach the package if you haven't already
library(mantis)

# this example data frame contains numbers of antibiotic prescriptions
# in long format
data("example_prescription_numbers")

head(example_prescription_numbers)
#> # A tibble: 6 × 5
#>   PrescriptionDate Antibiotic    Spectrum NumberOfPrescriptions Location
#>   <date>           <chr>         <chr>                    <dbl> <chr>   
#> 1 2022-01-01       Coamoxiclav   Broad                       45 SITE1   
#> 2 2022-01-01       Gentamicin    Broad                       34 SITE1   
#> 3 2022-01-01       Ceftriaxone   Broad                       36 SITE1   
#> 4 2022-01-01       Metronidazole Limited                     17 SITE1   
#> 5 2022-01-01       Meropenem     Broad                       10 SITE1   
#> 6 2022-01-01       Vancomycin    Limited                      0 SITE1

Specifying the data columns

You must specify which column(s) in the supplied data frame should be used to identify the individual time series (items), and which columns contain the timepoint (x-axis) and value (y-axis) for the time series. These are set using the item_cols, timepoint_col and value_col parameters of inputspec() respectively. Any other columns in the data frame are ignored.

Optionally, if there are multiple columns specified in item_cols, one of them can be used to group the time series into different tabs on the report, by using the tab_col parameter.

Members of item_cols should be specified in the order that you want them to appear in the output.

Example:

For the example_prescription_numbers dataset above, the combination of “Antibiotic” and “Location” columns uniquely identify a time series in the data frame, and so both columns must be included in item_cols.

The “Spectrum” column can also be added to item_cols as well if desired, in which case it will appear in the output as an additional column/label. Otherwise it will be ignored.

Here are some options for specifying the data columns, depending on how you want the report to look:


# create a flat report, and include the "Location" and "Antibiotic" fields
# in the content
inspec_flat <- inputspec(
  timepoint_col = "PrescriptionDate",
  item_cols = c("Location", "Antibiotic"),
  value_col = "NumberOfPrescriptions",
  timepoint_unit = "day"
)

# create a flat report, and include the "Location", "Spectrum", and "Antibiotic"
# fields in the content
inspec_flat2 <- inputspec(
  timepoint_col = "PrescriptionDate",
  item_cols = c("Location", "Spectrum", "Antibiotic"),
  value_col = "NumberOfPrescriptions",
  timepoint_unit = "day"
)

# create a tabbed report, with a separate tab for each unique value of
# "Location", and include just the "Antibiotic" field in the content of each tab
inspec_tabbed <- inputspec(
  timepoint_col = "PrescriptionDate",
  item_cols = c("Antibiotic", "Location"),
  value_col = "NumberOfPrescriptions",
  tab_col = "Location",
  timepoint_unit = "day"
)

# create a tabbed report, with a separate tab for each unique value of
# "Location", and include the "Antibiotic" and "Spectrum" fields in the content
# of each tab
inspec_tabbed2 <- inputspec(
  timepoint_col = "PrescriptionDate",
  item_cols = c("Antibiotic", "Spectrum", "Location"),
  value_col = "NumberOfPrescriptions",
  tab_col = "Location",
  timepoint_unit = "day"
)

# create a tabbed report, with a separate tab for each unique value of
# "Antibiotic", and include just the "Location" field in the content of each tab
inspec_tabbed3 <- inputspec(
  timepoint_col = "PrescriptionDate",
  item_cols = c("Antibiotic", "Location"),
  value_col = "NumberOfPrescriptions",
  tab_col = "Antibiotic",
  timepoint_unit = "day"
)

Specifying the timepoint unit

In this example dataset, the time series contain daily values, which is the default pattern of timepoints expected by the package. If the time series were to contain e.g. monthly or hourly values, this should be specified using the timepoint_unit parameter of inputspec().

Generating a report

The simplest way to create a report is to use the mantis_report() function.

We need to decide where to save the report to. The file name can only contain alphanumeric, - and _ characters, and should include the extension “.html”. If a path is supplied, the directory should already exist. We can also optionally specify a short description for the dataset, which will appear on the report.

There are 3 different options for visualising the time series. This is set using the outputspec parameter.

Interactive plots

This is the default visualisation, and produces a table with one time series in each row, with orderable and filterable columns showing the previously-specified item_cols for identifying the time series. Also in the table are the maximum value of each time series, and bar plots with adjustable axes and tooltips showing the individual dates and values.

There are some options for adjusting the output, such as changing column labels, plot type, and whether or not to use the same y-axis scale across the table. This can be done using the outputspec_interactive() function when supplying the outputspec parameter.

Example:

For the example_prescription_numbers dataset above, we will save a report in the working directory, with separate tabs for each hospital site.

mantis_report(
  df = example_prescription_numbers,
  file = "example_prescription_numbers_interactive.html",
  inputspec = inspec_tabbed2,
  outputspec = outputspec_interactive(),
  report_title = "mantis report",
  dataset_description = "Antibiotic prescriptions by site",
  show_progress = TRUE
)

Static plots

Alternatively, the report can output static plots. This can be useful if interactivity is not needed, or if file sizes need to be kept small. There are currently two types of static visualisations: heatmap or multipanel, and are selected by using the outputspec_static_heatmap() or outputspec_static_multipanel() function when supplying the outputspec parameter.

Heatmap

mantis_report(
  df = example_prescription_numbers,
  file = "example_prescription_numbers_heatmap.html",
  inputspec = inspec_tabbed,
  outputspec = outputspec_static_heatmap(),
  report_title = "mantis report",
  dataset_description = "Antibiotic prescriptions by site",
  show_progress = TRUE
)

Multipanel

mantis_report(
  df = example_prescription_numbers,
  file = "example_prescription_numbers_multipanel.html",
  inputspec = inspec_tabbed,
  outputspec = outputspec_static_multipanel(),
  report_title = "mantis report",
  dataset_description = "Antibiotic prescriptions by site",
  show_progress = TRUE
)