runcharter

Automated analysis and re-basing of run charts at scale.

Online documentation and vignettes : runcharter

Installation

You can install runcharter from github with:

# install.packages("devtools")
devtools::install_github("johnmackintosh/runcharter")

# to ensure the vignettes are built or ensure latest version is installed:

devtools::install_github("johnmackintosh/runcharter", 
                         build_vignettes = TRUE, force = TRUE)

Rationale

Run charts are easy to create and analyse on an individual basis, hence they are widely used in healthcare quality improvement.

A run chart is a regular line chart, with a central reference line.

This central line, calculated using the median of a number of values over a baseline period, allows the QI team to assess if any statistically significant improvement is taking place, as a result of their improvement initiatives.

These improvements are denoted by certain patterns, or signals, within the plotted data points, in relation to the median line. The main signal is a run of 9 or more consecutive values on the desired side of the median line.

If this signal occurs as a result of improvement activities, but performance is not yet at the target level, a new median line can be plotted.

This is calculated using the median of the points that contributed to the signal. The aim is to then continue to work on improvement, measure and plot data, and look for the next sustained signal, until the improvement initiative is operating at its target level.

While this 'rebasing' (calculating new medians) is manageable for a few charts, it quickly becomes labour intensive as QI initiatives expand or further QI programmes are launched.

While enterprise level database software can be used to store the raw data, their associated reporting systems are usually ill suited to the task of analysing QI data using run chart rules.

This package automatically creates rebased run charts, based on the run chart rule for sustained improvement commonly used in healthcare ( 9 consecutive points on the desired side of the median).

All sustained runs of improvement, in the desired direction, will be highlighted and the median re-phased, using the points that contributed to the run.

Non useful observations (points on the median) are ignored and are not highlighted.

The main motivation is to analyse many charts at once, but you can also create and analyse a single run chart, or iterate, plot and save many individual charts.

The runcharter function - input

The function requires a simple three column dataframe, with the following column names

grp : a character column indicating a grouping variable to identify each individual run chart and for faceted plots
date : a column of type 'date'.
y : the variable / value to plot.

runcharter function arguments

df : a three column dataframe with columns named 'grp', 'date' and 'y' as specified above
med_rows : How many rows / data points should the initial baseline median be calculated over?
runlength : How long a run of consecutive points do you want to find, before you rebase the median? The median will be rebased using all useful observations (points on the median are not useful, and are ignored).
chart_title : The main title for the chart
chart_subtitle : A subtitle for the chart
direction : "above" or "below" the median, or "both". Use "both" if you want to rebase the run chart any time a run of the desired length occurs, even if it is on the "wrong" side of the median line.
faceted : defaults to TRUE. Set to FALSE if you only need to plot a single run chart. This will ensure the chart plots correctly
facet_cols : the number of columns in a faceted plot - only required if faceted is set to TRUE, otherwise ignored
save_plot : Calls ggsave if TRUE, saving in the current working directory
plot_extension : one of "png", "pdf" or other valid extension for saving ggplot2 plots. Used in the call to ggsave.

example plot

library(runcharter)
library(dplyr)
#> Warning: package 'dplyr' was built under R version 3.5.2
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

signals %>%
dplyr::filter(grp == "WardX") %>%
runcharter(med_rows = 13,
runlength = 9,
chart_title = "Analysis of runs below median",
chart_subtitle = "Ward X",
direction = "below",
faceted = FALSE)
#> all sustained runs found, not enough rows remaining for analysis
#> $runchart
#> 
#> $sustained
#> # A tibble: 9 x 10
#>   grp       y date        flag rungroup cusum improve startdate  enddate   
#>   <chr> <int> <date>     <dbl>    <dbl> <dbl>   <int> <date>     <date>    
#> 1 WardX     7 2016-12-01    -1        1    -1       6 2016-12-01 2017-08-01
#> 2 WardX     5 2017-01-01    -1        1    -2       6 2016-12-01 2017-08-01
#> 3 WardX     4 2017-02-01    -1        1    -3       6 2016-12-01 2017-08-01
#> 4 WardX    10 2017-03-01    -1        1    -4       6 2016-12-01 2017-08-01
#> 5 WardX     4 2017-04-01    -1        1    -5       6 2016-12-01 2017-08-01
#> 6 WardX     9 2017-05-01    -1        1    -6       6 2016-12-01 2017-08-01
#> 7 WardX     4 2017-06-01    -1        1    -7       6 2016-12-01 2017-08-01
#> 8 WardX     8 2017-07-01    -1        1    -8       6 2016-12-01 2017-08-01
#> 9 WardX     6 2017-08-01    -1        1    -9       6 2016-12-01 2017-08-01
#> # ... with 1 more variable: lastdate <date>
#> 
#> $median_rows
#> # A tibble: 13 x 4
#>    grp       y date       baseline
#>    <chr> <int> <date>        <int>
#>  1 WardX     9 2014-01-01       11
#>  2 WardX    22 2014-02-01       11
#>  3 WardX    19 2014-03-01       11
#>  4 WardX    18 2014-04-01       11
#>  5 WardX     8 2014-05-01       11
#>  6 WardX     7 2014-06-01       11
#>  7 WardX    11 2014-07-01       11
#>  8 WardX    11 2014-08-01       11
#>  9 WardX    11 2014-09-01       11
#> 10 WardX    12 2014-10-01       11
#> 11 WardX     2 2014-11-01       11
#> 12 WardX     8 2014-12-01       11
#> 13 WardX     9 2015-01-01       11
#> 
#> $StartBaseline
#> [1] 11

Plot explanation

med_rows defines the initial baseline period. In the example below, the first 13 points are used to calculate the initial median. This is represented with a solid orange horizontal line. This median is then used as a reference for the remaining values, denoted by the extending orange dashed line
runlength specifies the length of run to be identified. Along with direction, which specifies which side of median represents improvement, the runlength is your target number of successive points on the desired side of the median (points on the median are ignored as they do not make or break a run). You can set the direction as either "above" or "below" the line, to evidence improvement in a specific direction. Searching for runs in "both" directions is also possible. This might be more applicable for long term monitoring, rather than improvement purposes.

If a run is identified, the points are highlighted (the purple coloured points), and a new median is calculated using them. The median is also plotted and extended into the future for further run chart rules analysis, with a new set of solid and dashed horizontal lines.

The analysis continues, rebasing any further runs, until no more runs are found or there are not enough data points remaining.

Example

By default the function returns a faceted plot, highlighting successive runs below the median:

library(runcharter)
runcharter(signals, 
           direction = "below",
           facet_cols = 2)
#> $runchart

#> 
#> $median_rows
#> # A tibble: 52 x 4
#>    grp       y date       baseline
#>    <chr> <int> <date>        <int>
#>  1 WardX     9 2014-01-01       11
#>  2 WardX    22 2014-02-01       11
#>  3 WardX    19 2014-03-01       11
#>  4 WardX    18 2014-04-01       11
#>  5 WardX     8 2014-05-01       11
#>  6 WardX     7 2014-06-01       11
#>  7 WardX    11 2014-07-01       11
#>  8 WardX    11 2014-08-01       11
#>  9 WardX    11 2014-09-01       11
#> 10 WardX    12 2014-10-01       11
#> # ... with 42 more rows
#> 
#> $sustained
#>      grp  y       date flag rungroup cusum improve  startdate    enddate
#> 1  WardX  7 2016-12-01   -1        1    -1       6 2016-12-01 2017-08-01
#> 2  WardX  5 2017-01-01   -1        1    -2       6 2016-12-01 2017-08-01
#> 3  WardX  4 2017-02-01   -1        1    -3       6 2016-12-01 2017-08-01
#> 4  WardX 10 2017-03-01   -1        1    -4       6 2016-12-01 2017-08-01
#> 5  WardX  4 2017-04-01   -1        1    -5       6 2016-12-01 2017-08-01
#> 6  WardX  9 2017-05-01   -1        1    -6       6 2016-12-01 2017-08-01
#> 7  WardX  4 2017-06-01   -1        1    -7       6 2016-12-01 2017-08-01
#> 8  WardX  8 2017-07-01   -1        1    -8       6 2016-12-01 2017-08-01
#> 9  WardX  6 2017-08-01   -1        1    -9       6 2016-12-01 2017-08-01
#> 10 WardY  7 2017-10-01   -1        1    -1       8 2017-10-01 2018-06-01
#> 11 WardY  3 2017-11-01   -1        1    -2       8 2017-10-01 2018-06-01
#> 12 WardY  8 2017-12-01   -1        1    -3       8 2017-10-01 2018-06-01
#> 13 WardY 11 2018-01-01   -1        1    -4       8 2017-10-01 2018-06-01
#> 14 WardY  7 2018-02-01   -1        1    -5       8 2017-10-01 2018-06-01
#> 15 WardY  9 2018-03-01   -1        1    -6       8 2017-10-01 2018-06-01
#> 16 WardY  8 2018-04-01   -1        1    -7       8 2017-10-01 2018-06-01
#> 17 WardY  8 2018-05-01   -1        1    -8       8 2017-10-01 2018-06-01
#> 18 WardY  7 2018-06-01   -1        1    -9       8 2017-10-01 2018-06-01
#>      lastdate
#> 1  2018-07-01
#> 2  2018-07-01
#> 3  2018-07-01
#> 4  2018-07-01
#> 5  2018-07-01
#> 6  2018-07-01
#> 7  2018-07-01
#> 8  2018-07-01
#> 9  2018-07-01
#> 10 2018-07-01
#> 11 2018-07-01
#> 12 2018-07-01
#> 13 2018-07-01
#> 14 2018-07-01
#> 15 2018-07-01
#> 16 2018-07-01
#> 17 2018-07-01
#> 18 2018-07-01
#> 
#> $StartBaseline
#> [1] 11 13  4  7

You can also look for any run, in any direction

library(runcharter)
runcharter(signals, 
           direction = "both",
           facet_cols = 2)
#> $runchart

#> 
#> $median_rows
#> # A tibble: 52 x 4
#>    grp       y date       baseline
#>    <chr> <int> <date>        <int>
#>  1 WardX     9 2014-01-01       11
#>  2 WardX    22 2014-02-01       11
#>  3 WardX    19 2014-03-01       11
#>  4 WardX    18 2014-04-01       11
#>  5 WardX     8 2014-05-01       11
#>  6 WardX     7 2014-06-01       11
#>  7 WardX    11 2014-07-01       11
#>  8 WardX    11 2014-08-01       11
#>  9 WardX    11 2014-09-01       11
#> 10 WardX    12 2014-10-01       11
#> # ... with 42 more rows
#> 
#> $sustained
#>      grp  y       date flag rungroup cusum improve  startdate    enddate
#> 1  WardX  7 2016-12-01   -1        1     1       6 2016-12-01 2017-08-01
#> 2  WardX  5 2017-01-01   -1        1     2       6 2016-12-01 2017-08-01
#> 3  WardX  4 2017-02-01   -1        1     3       6 2016-12-01 2017-08-01
#> 4  WardX 10 2017-03-01   -1        1     4       6 2016-12-01 2017-08-01
#> 5  WardX  4 2017-04-01   -1        1     5       6 2016-12-01 2017-08-01
#> 6  WardX  9 2017-05-01   -1        1     6       6 2016-12-01 2017-08-01
#> 7  WardX  4 2017-06-01   -1        1     7       6 2016-12-01 2017-08-01
#> 8  WardX  8 2017-07-01   -1        1     8       6 2016-12-01 2017-08-01
#> 9  WardX  6 2017-08-01   -1        1     9       6 2016-12-01 2017-08-01
#> 10 WardY  7 2017-10-01   -1        1     1       8 2017-10-01 2018-06-01
#> 11 WardY  3 2017-11-01   -1        1     2       8 2017-10-01 2018-06-01
#> 12 WardY  8 2017-12-01   -1        1     3       8 2017-10-01 2018-06-01
#> 13 WardY 11 2018-01-01   -1        1     4       8 2017-10-01 2018-06-01
#> 14 WardY  7 2018-02-01   -1        1     5       8 2017-10-01 2018-06-01
#> 15 WardY  9 2018-03-01   -1        1     6       8 2017-10-01 2018-06-01
#> 16 WardY  8 2018-04-01   -1        1     7       8 2017-10-01 2018-06-01
#> 17 WardY  8 2018-05-01   -1        1     8       8 2017-10-01 2018-06-01
#> 18 WardY  7 2018-06-01   -1        1     9       8 2017-10-01 2018-06-01
#> 19 WardZ  6 2017-06-01    1        1     1       9 2017-06-01 2018-03-01
#> 20 WardZ 10 2017-07-01    1        1     2       9 2017-06-01 2018-03-01
#> 21 WardZ  9 2017-08-01    1        1     3       9 2017-06-01 2018-03-01
#> 22 WardZ 12 2017-09-01    1        1     4       9 2017-06-01 2018-03-01
#> 23 WardZ  9 2017-10-01    1        1     5       9 2017-06-01 2018-03-01
#> 24 WardZ  7 2017-12-01    1        1     6       9 2017-06-01 2018-03-01
#> 25 WardZ  6 2018-01-01    1        1     7       9 2017-06-01 2018-03-01
#> 26 WardZ  5 2018-02-01    1        1     8       9 2017-06-01 2018-03-01
#> 27 WardZ  9 2018-03-01    1        1     9       9 2017-06-01 2018-03-01
#>      lastdate
#> 1  2018-07-01
#> 2  2018-07-01
#> 3  2018-07-01
#> 4  2018-07-01
#> 5  2018-07-01
#> 6  2018-07-01
#> 7  2018-07-01
#> 8  2018-07-01
#> 9  2018-07-01
#> 10 2018-07-01
#> 11 2018-07-01
#> 12 2018-07-01
#> 13 2018-07-01
#> 14 2018-07-01
#> 15 2018-07-01
#> 16 2018-07-01
#> 17 2018-07-01
#> 18 2018-07-01
#> 19 2018-07-01
#> 20 2018-07-01
#> 21 2018-07-01
#> 22 2018-07-01
#> 23 2018-07-01
#> 24 2018-07-01
#> 25 2018-07-01
#> 26 2018-07-01
#> 27 2018-07-01
#> 
#> $StartBaseline
#> [1] 11 13  4  7

Note how runs below the median are found for Wards X and Y, while a run above the median is highlighted for Ward Z.

The function will print the plot, and return a list, containing:

a ggplot2 object,
a dataframe / tibble containing the rows of data used to calculate the baseline median
if applicable, a dataframe / tibble showing the points in each sustained period of improvement.
the initial baseline median value

The latter 3 items can be retrieved from the list and used to create new plots (if, for example, you would like different plot themes or colours from the package defaults)

Don't try this at home - setting runlength of 6 and searching in both directions to confirm that successive runs are identified:

library(dplyr)
signals %>% 
  runcharter(med_rows = 6,
             runlength = 6, 
             direction = "both")
#> $runchart

#> 
#> $median_rows
#> # A tibble: 24 x 4
#>    grp       y date       baseline
#>    <chr> <int> <date>        <dbl>
#>  1 WardX     9 2014-01-01     13.5
#>  2 WardX    22 2014-02-01     13.5
#>  3 WardX    19 2014-03-01     13.5
#>  4 WardX    18 2014-04-01     13.5
#>  5 WardX     8 2014-05-01     13.5
#>  6 WardX     7 2014-06-01     13.5
#>  7 WardY    11 2014-01-01     13  
#>  8 WardY    18 2014-02-01     13  
#>  9 WardY     5 2014-03-01     13  
#> 10 WardY    14 2014-04-01     13  
#> # ... with 14 more rows
#> 
#> $sustained
#>      grp  y       date flag rungroup cusum improve  startdate    enddate
#> 1  WardX 11 2014-07-01   -1        1     1    11.0 2014-07-01 2014-12-01
#> 2  WardX 11 2014-08-01   -1        1     2    11.0 2014-07-01 2014-12-01
#> 3  WardX 11 2014-09-01   -1        1     3    11.0 2014-07-01 2014-12-01
#> 4  WardX 12 2014-10-01   -1        1     4    11.0 2014-07-01 2014-12-01
#> 5  WardX  2 2014-11-01   -1        1     5    11.0 2014-07-01 2014-12-01
#> 6  WardX  8 2014-12-01   -1        1     6    11.0 2014-07-01 2014-12-01
#> 7  WardX 21 2015-07-01    1        2     1    17.0 2015-07-01 2015-12-01
#> 8  WardX 12 2015-08-01    1        2     2    17.0 2015-07-01 2015-12-01
#> 9  WardX 21 2015-09-01    1        2     3    17.0 2015-07-01 2015-12-01
#> 10 WardX 12 2015-10-01    1        2     4    17.0 2015-07-01 2015-12-01
#> 11 WardX 21 2015-11-01    1        2     5    17.0 2015-07-01 2015-12-01
#> 12 WardX 13 2015-12-01    1        2     6    17.0 2015-07-01 2015-12-01
#> 13 WardX  5 2016-01-01   -1        3     1     6.5 2016-01-01 2016-06-01
#> 14 WardX  6 2016-02-01   -1        3     2     6.5 2016-01-01 2016-06-01
#> 15 WardX  4 2016-03-01   -1        3     3     6.5 2016-01-01 2016-06-01
#> 16 WardX 15 2016-04-01   -1        3     4     6.5 2016-01-01 2016-06-01
#> 17 WardX 12 2016-05-01   -1        3     5     6.5 2016-01-01 2016-06-01
#> 18 WardX  7 2016-06-01   -1        3     6     6.5 2016-01-01 2016-06-01
#> 19 WardX 14 2016-07-01    1        4     1    11.0 2016-07-01 2016-12-01
#> 20 WardX  7 2016-08-01    1        4     2    11.0 2016-07-01 2016-12-01
#> 21 WardX  9 2016-09-01    1        4     3    11.0 2016-07-01 2016-12-01
#> 22 WardX 13 2016-10-01    1        4     4    11.0 2016-07-01 2016-12-01
#> 23 WardX 13 2016-11-01    1        4     5    11.0 2016-07-01 2016-12-01
#> 24 WardX  7 2016-12-01    1        4     6    11.0 2016-07-01 2016-12-01
#> 25 WardX  5 2017-01-01   -1        5     1     4.5 2017-01-01 2017-06-01
#> 26 WardX  4 2017-02-01   -1        5     2     4.5 2017-01-01 2017-06-01
#> 27 WardX 10 2017-03-01   -1        5     3     4.5 2017-01-01 2017-06-01
#> 28 WardX  4 2017-04-01   -1        5     4     4.5 2017-01-01 2017-06-01
#> 29 WardX  9 2017-05-01   -1        5     5     4.5 2017-01-01 2017-06-01
#> 30 WardX  4 2017-06-01   -1        5     6     4.5 2017-01-01 2017-06-01
#> 31 WardY 16 2014-12-01    1        1     1    19.0 2014-12-01 2015-05-01
#> 32 WardY 19 2015-01-01    1        1     2    19.0 2014-12-01 2015-05-01
#> 33 WardY 18 2015-02-01    1        1     3    19.0 2014-12-01 2015-05-01
#> 34 WardY 20 2015-03-01    1        1     4    19.0 2014-12-01 2015-05-01
#> 35 WardY 19 2015-04-01    1        1     5    19.0 2014-12-01 2015-05-01
#> 36 WardY 19 2015-05-01    1        1     6    19.0 2014-12-01 2015-05-01
#> 37 WardY 18 2015-06-01   -1        2     1    13.0 2015-06-01 2015-11-01
#> 38 WardY 11 2015-07-01   -1        2     2    13.0 2015-06-01 2015-11-01
#> 39 WardY  8 2015-08-01   -1        2     3    13.0 2015-06-01 2015-11-01
#> 40 WardY 18 2015-09-01   -1        2     4    13.0 2015-06-01 2015-11-01
#> 41 WardY 15 2015-10-01   -1        2     5    13.0 2015-06-01 2015-11-01
#> 42 WardY  3 2015-11-01   -1        2     6    13.0 2015-06-01 2015-11-01
#> 43 WardY  7 2017-10-01   -1        3     1     7.5 2017-10-01 2018-03-01
#> 44 WardY  3 2017-11-01   -1        3     2     7.5 2017-10-01 2018-03-01
#> 45 WardY  8 2017-12-01   -1        3     3     7.5 2017-10-01 2018-03-01
#> 46 WardY 11 2018-01-01   -1        3     4     7.5 2017-10-01 2018-03-01
#> 47 WardY  7 2018-02-01   -1        3     5     7.5 2017-10-01 2018-03-01
#> 48 WardY  9 2018-03-01   -1        3     6     7.5 2017-10-01 2018-03-01
#> 49 WardZ  5 2015-01-01    1        1     1     8.0 2015-01-01 2015-06-01
#> 50 WardZ  6 2015-02-01    1        1     2     8.0 2015-01-01 2015-06-01
#> 51 WardZ  9 2015-03-01    1        1     3     8.0 2015-01-01 2015-06-01
#> 52 WardZ 11 2015-04-01    1        1     4     8.0 2015-01-01 2015-06-01
#> 53 WardZ  7 2015-05-01    1        1     5     8.0 2015-01-01 2015-06-01
#> 54 WardZ  9 2015-06-01    1        1     6     8.0 2015-01-01 2015-06-01
#> 55 WardZ  4 2015-12-01   -1        2     1     4.0 2015-12-01 2016-05-01
#> 56 WardZ  4 2016-01-01   -1        2     2     4.0 2015-12-01 2016-05-01
#> 57 WardZ  3 2016-02-01   -1        2     3     4.0 2015-12-01 2016-05-01
#> 58 WardZ  4 2016-03-01   -1        2     4     4.0 2015-12-01 2016-05-01
#> 59 WardZ  3 2016-04-01   -1        2     5     4.0 2015-12-01 2016-05-01
#> 60 WardZ  7 2016-05-01   -1        2     6     4.0 2015-12-01 2016-05-01
#> 61 WardZ  6 2017-06-01    1        3     1     9.0 2017-06-01 2017-12-01
#> 62 WardZ 10 2017-07-01    1        3     2     9.0 2017-06-01 2017-12-01
#> 63 WardZ  9 2017-08-01    1        3     3     9.0 2017-06-01 2017-12-01
#> 64 WardZ 12 2017-09-01    1        3     4     9.0 2017-06-01 2017-12-01
#> 65 WardZ  9 2017-10-01    1        3     5     9.0 2017-06-01 2017-12-01
#> 66 WardZ  7 2017-12-01    1        3     6     9.0 2017-06-01 2017-12-01
#> 67 WardV  6 2015-01-01    1        1     1    15.0 2015-01-01 2015-06-01
#> 68 WardV 13 2015-02-01    1        1     2    15.0 2015-01-01 2015-06-01
#> 69 WardV 17 2015-03-01    1        1     3    15.0 2015-01-01 2015-06-01
#> 70 WardV 15 2015-04-01    1        1     4    15.0 2015-01-01 2015-06-01
#> 71 WardV 15 2015-05-01    1        1     5    15.0 2015-01-01 2015-06-01
#> 72 WardV 15 2015-06-01    1        1     6    15.0 2015-01-01 2015-06-01
#> 73 WardV 11 2015-10-01   -1        2     1     9.0 2015-10-01 2016-03-01
#> 74 WardV 13 2015-11-01   -1        2     2     9.0 2015-10-01 2016-03-01
#> 75 WardV  2 2015-12-01   -1        2     3     9.0 2015-10-01 2016-03-01
#> 76 WardV 12 2016-01-01   -1        2     4     9.0 2015-10-01 2016-03-01
#> 77 WardV  2 2016-02-01   -1        2     5     9.0 2015-10-01 2016-03-01
#> 78 WardV  7 2016-03-01   -1        2     6     9.0 2015-10-01 2016-03-01
#>      lastdate
#> 1  2018-07-01
#> 2  2018-07-01
#> 3  2018-07-01
#> 4  2018-07-01
#> 5  2018-07-01
#> 6  2018-07-01
#> 7  2018-07-01
#> 8  2018-07-01
#> 9  2018-07-01
#> 10 2018-07-01
#> 11 2018-07-01
#> 12 2018-07-01
#> 13 2018-07-01
#> 14 2018-07-01
#> 15 2018-07-01
#> 16 2018-07-01
#> 17 2018-07-01
#> 18 2018-07-01
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#> 
#> $StartBaseline
#> [1] 13.5 13.0  4.5  4.0

Name		Name	Last commit message	Last commit date
Latest commit History 156 Commits
R		R
data-raw		data-raw
data		data
docs		docs
man		man
vignettes		vignettes
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
.travis.yml		.travis.yml
DESCRIPTION		DESCRIPTION
NAMESPACE		NAMESPACE
README.Rmd		README.Rmd
README.md		README.md
_pkgdown.yml		_pkgdown.yml
codecov.yml		codecov.yml

md0u80c9/runcharter

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runcharter

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The runcharter function - input

runcharter function arguments

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