Compute and visualise incidence (reworking of the original incidence package)

Overview

CRAN status Codecov test coverage R-CMD-check

incidence2

incidence2 is an R package that implements functions and classes to compute, handle and visualise incidence from linelist data. It refocusses the scope of the original incidence package. Unlike the original package, incidence2 concentrates only on the initial calculation, manipulation and plotting of the resultant incidence objects.

Installing the package

The development version, which this documentation refers to, can be installed from GitHub with:

if (!require(remotes)) {
  install.packages("remotes")
}
remotes::install_github("reconverse/incidence2", build_vignettes = TRUE)

You can install the current version of the package from the releases page or directly from CRAN with:

install.packages("incidence2")

Resources

Vignettes

A short overview of incidence2 is provided below in the worked example below. More detailed tutorials are distributed as vignettes with the package:

  • vignette("Introduction", package = "incidence2")
  • vignette("handling_incidence_objects", package = "incidence2")
  • vignette("customizing_incidence_plots", package = "incidence2")
  • vignette("alternative_date_groupings", package = "incidence2")

Getting help online

A quick overview

This short example uses the simulated Ebola Virus Disease (EVD) outbreak from the package outbreaks. It shows how to compute incidence for various time steps and plot the resulting output.

First, we load the data:

$ generation : int 0 1 1 2 2 0 3 3 2 3 ... #> $ date_of_infection : Date, format: NA "2014-04-09" ... #> $ date_of_onset : Date, format: "2014-04-07" "2014-04-15" ... #> $ date_of_hospitalisation: Date, format: "2014-04-17" "2014-04-20" ... #> $ date_of_outcome : Date, format: "2014-04-19" NA ... #> $ outcome : Factor w/ 2 levels "Death","Recover": NA NA 2 1 2 NA 2 1 2 1 ... #> $ gender : Factor w/ 2 levels "f","m": 1 2 1 1 1 1 1 1 2 2 ... #> $ hospital : Factor w/ 5 levels "Connaught Hospital",..: 2 1 3 NA 3 NA 1 4 3 5 ... #> $ lon : num -13.2 -13.2 -13.2 -13.2 -13.2 ... #> $ lat : num 8.47 8.46 8.48 8.46 8.45 ... ">
library(outbreaks)
library(incidence2)

dat <- ebola_sim_clean$linelist
str(dat)
#> 'data.frame':    5829 obs. of  11 variables:
#>  $ case_id                : chr  "d1fafd" "53371b" "f5c3d8" "6c286a" ...
#>  $ generation             : int  0 1 1 2 2 0 3 3 2 3 ...
#>  $ date_of_infection      : Date, format: NA "2014-04-09" ...
#>  $ date_of_onset          : Date, format: "2014-04-07" "2014-04-15" ...
#>  $ date_of_hospitalisation: Date, format: "2014-04-17" "2014-04-20" ...
#>  $ date_of_outcome        : Date, format: "2014-04-19" NA ...
#>  $ outcome                : Factor w/ 2 levels "Death","Recover": NA NA 2 1 2 NA 2 1 2 1 ...
#>  $ gender                 : Factor w/ 2 levels "f","m": 1 2 1 1 1 1 1 1 2 2 ...
#>  $ hospital               : Factor w/ 5 levels "Connaught Hospital",..: 2 1 3 NA 3 NA 1 4 3 5 ...
#>  $ lon                    : num  -13.2 -13.2 -13.2 -13.2 -13.2 ...
#>  $ lat                    : num  8.47 8.46 8.48 8.46 8.45 ...

Computing and plotting incidence

We compute the weekly incidence:

i_7 <- incidence(dat, date_index = date_of_onset, interval = 7)
i_7
#> An incidence object: 56 x 2
#> date range: [2014-04-07 to 2014-04-13] to [2015-04-27 to 2015-05-03]
#> cases: 5829
#> interval: 7 days
#> cumulative: FALSE
#> 
#>                  date_index count
#>                    
    
    
   
#>  1 2014-04-07 to 2014-04-13     1
#>  2 2014-04-14 to 2014-04-20     1
#>  3 2014-04-21 to 2014-04-27     5
#>  4 2014-04-28 to 2014-05-04     4
#>  5 2014-05-05 to 2014-05-11    12
#>  6 2014-05-12 to 2014-05-18    17
#>  7 2014-05-19 to 2014-05-25    15
#>  8 2014-05-26 to 2014-06-01    19
#>  9 2014-06-02 to 2014-06-08    23
#> 10 2014-06-09 to 2014-06-15    21
#> # … with 46 more rows
summary(i_7)
#> date range: [2014-04-07 to 2014-04-13] to [2015-04-27 to 2015-05-03]
#> cases: 5829
#> interval: 7 days
#> cumulative: FALSE
#> timespan: 392 days
plot(i_7, color = "white")

Notice how specifying the interval as 7 creates weekly intervals with the coverage displayed by date. incidence() also allows us to create year-weekly groupings with the default being weeks starting on a Monday (following the ISO 8601 date and time standard). incidence() can also compute incidence by specified groups using the groups argument. As an example, below we can compute the weekly incidence by gender and plot in a single, stacked chart:

An incidence object: 109 x 3 #> date range: [2014-W15] to [2015-W18] #> cases: 5829 #> interval: 1 (Monday) week #> cumulative: FALSE #> #> date_index gender count #> #> 1 2014-W15 f 1 #> 2 2014-W16 m 1 #> 3 2014-W17 f 4 #> 4 2014-W17 m 1 #> 5 2014-W18 f 4 #> 6 2014-W19 f 9 #> 7 2014-W19 m 3 #> 8 2014-W20 f 7 #> 9 2014-W20 m 10 #> 10 2014-W21 f 8 #> # … with 99 more rows summary(iw) #> date range: [2014-W15] to [2015-W18] #> cases: 5829 #> interval: 1 (Monday) week #> cumulative: FALSE #> timespan: 392 days #> #> 1 grouped variable #> #> gender count #> #> 1 f 2934 #> 2 m 2895 plot(iw, fill = "gender", color = "white") ">
iw <- incidence(dat, interval = "week", date_index = date_of_onset, groups = gender)
iw
#> An incidence object: 109 x 3
#> date range: [2014-W15] to [2015-W18]
#> cases: 5829
#> interval: 1 (Monday) week 
#> cumulative: FALSE
#> 
#>    date_index gender count
#>        
         
           
          
         
        
#>  1   2014-W15 f          1
#>  2   2014-W16 m          1
#>  3   2014-W17 f          4
#>  4   2014-W17 m          1
#>  5   2014-W18 f          4
#>  6   2014-W19 f          9
#>  7   2014-W19 m          3
#>  8   2014-W20 f          7
#>  9   2014-W20 m         10
#> 10   2014-W21 f          8
#> # … with 99 more rows
summary(iw)
#> date range: [2014-W15] to [2015-W18]
#> cases: 5829
#> interval: 1 (Monday) week 
#> cumulative: FALSE
#> timespan: 392 days
#> 
#> 1 grouped variable
#> 
#>   gender count
#>   
          
         
        
#> 1 f       2934
#> 2 m       2895
plot(iw, fill = "gender", color = "white")

we can also facet our plot (grouping detected automatically):

facet_plot(iw, n_breaks = 3, color = "white")

It is also possible to group by multiple variables specifying different facets and fills:

An incidence object: 601 x 4 #> date range: [2014-W15] to [2015-W18] #> cases: 5829 #> interval: 1 (Monday) week #> cumulative: FALSE #> #> date_index gender hospital count #> #> 1 2014-W15 f Military Hospital 1 #> 2 2014-W16 m Connaught Hospital 1 #> 3 2014-W17 f 2 #> 4 2014-W17 f other 2 #> 5 2014-W17 m other 1 #> 6 2014-W18 f 1 #> 7 2014-W18 f Connaught Hospital 1 #> 8 2014-W18 f Princess Christian Maternity Hospital (PCMH) 1 #> 9 2014-W18 f Rokupa Hospital 1 #> 10 2014-W19 f 1 #> # … with 591 more rows summary(iw2) #> date range: [2014-W15] to [2015-W18] #> cases: 5829 #> interval: 1 (Monday) week #> cumulative: FALSE #> timespan: 392 days #> #> 2 grouped variables #> #> gender count #> #> 1 f 2934 #> 2 m 2895 #> #> #> hospital count #> #> 1 Military Hospital 889 #> 2 Connaught Hospital 1737 #> 3 1456 #> 4 other 876 #> 5 Princess Christian Maternity Hospital (PCMH) 420 #> 6 Rokupa Hospital 451 facet_plot(iw2, facets = gender, fill = hospital, n_breaks = 3) ">
iw2 <- incidence(dat, date_of_onset, interval = "week",  groups = c(gender, hospital))
iw2
#> An incidence object: 601 x 4
#> date range: [2014-W15] to [2015-W18]
#> cases: 5829
#> interval: 1 (Monday) week 
#> cumulative: FALSE
#> 
#>    date_index gender hospital                                     count
#>        
                
                  
                                                         
                  
                 
                
               
#>  1   2014-W15 f      Military Hospital                                1
#>  2   2014-W16 m      Connaught Hospital                               1
#>  3   2014-W17 f      
               
                                                             2
               
#>  4   2014-W17 f      other                                            2
#>  5   2014-W17 m      other                                            1
#>  6   2014-W18 f      
               
                                                             1
               
#>  7   2014-W18 f      Connaught Hospital                               1
#>  8   2014-W18 f      Princess Christian Maternity Hospital (PCMH)     1
#>  9   2014-W18 f      Rokupa Hospital                                  1
#> 10   2014-W19 f      
               
                                                             1
               
#> # … with 591 more rows
summary(iw2)
#> date range: [2014-W15] to [2015-W18]
#> cases: 5829
#> interval: 1 (Monday) week 
#> cumulative: FALSE
#> timespan: 392 days
#> 
#> 2 grouped variables
#> 
#>   gender count
#>   
                 
                
               
#> 1 f       2934
#> 2 m       2895
#> 
#> 
#>   hospital                                     count
#>   
                                                       
                
               
#> 1 Military Hospital                              889
#> 2 Connaught Hospital                            1737
#> 3 
               
                                                          1456
               
#> 4 other                                          876
#> 5 Princess Christian Maternity Hospital (PCMH)   420
#> 6 Rokupa Hospital                                451
facet_plot(iw2, facets = gender, fill = hospital, n_breaks = 3)

Using an alternative function

The incidence() function wraps the date grouping functionality of the grates package, providing an easy to use interface for constructing incidence objects. Sometimes, however, you may want greater flexibility in choosing how you would like to transform your “date” inputs. Using the function build_incidence(),you can specify the function you wish to apply. We illustrate this below with the excellent clock package:

An incidence object: 601 x 4 #> date range: [2014-W15] to [2015-W18] #> cases: 5829 #> #> date_index gender hospital count #> > #> 1 2014-W15 f Military Hospital 1 #> 2 2014-W16 m Connaught Hospital 1 #> 3 2014-W17 f other 2 #> 4 2014-W17 f 2 #> 5 2014-W17 m other 1 #> 6 2014-W18 f Connaught Hospital 1 #> 7 2014-W18 f Princess Christian Maternity Hospital (PCMH) 1 #> 8 2014-W18 f Rokupa Hospital 1 #> 9 2014-W18 f 1 #> 10 2014-W19 f Connaught Hospital 2 #> # … with 591 more rows ">
library(clock)

# create a week function comparable to above approach
isoweek <- function(x) calendar_narrow(as_iso_year_week_day(x), "week")

clock_week_inci <- 
  build_incidence(
    dat,
    date_index = date_of_onset,
    groups = c(gender, hospital),
    FUN = isoweek
  )

clock_week_inci
#> An incidence object: 601 x 4
#> date range: [2014-W15] to [2015-W18]
#> cases: 5829
#> 
#>    date_index      gender hospital                                     count
#>    
          
           
            > 
              
                                                     
              
             
            
           
          
#>  1 2014-W15        f      Military Hospital                                1
#>  2 2014-W16        m      Connaught Hospital                               1
#>  3 2014-W17        f      other                                            2
#>  4 2014-W17        f      
          
                                                        2
          
#>  5 2014-W17        m      other                                            1
#>  6 2014-W18        f      Connaught Hospital                               1
#>  7 2014-W18        f      Princess Christian Maternity Hospital (PCMH)     1
#>  8 2014-W18        f      Rokupa Hospital                                  1
#>  9 2014-W18        f      
          
                                                        1
          
#> 10 2014-W19        f      Connaught Hospital                               2
#> # … with 591 more rows
Comments
  • Default color palette

    Default color palette

    The 3 requirements of the new color palette would be:

    • [x] look nice to as many humans as possible
    • [ ] be colorblind friendly
    • [ ] correspond to categorical variables

    Quite a bit of thinking on these has been done by the viridis package: https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html

    As well, these very good resources:

    • https://personal.sron.nl/~pault/#sec:qualitative
    • https://www.osapublishing.org/oe/abstract.cfm?uri=oe-21-8-9862

    I suggest we use this issue to propose palettes. Ideally put them to a vote at some point.

    enhancement help wanted discussion 
    opened by thibautjombart 10
  • Not importing incidence 1?

    Not importing incidence 1?

    at I'm mostly just curious, but what is the reasoning behind the design decision to copy over the code from {incidence} initially instead of importing? From my perspective, if there's a bug in the future, then there are two places where it needs to be fixed.

    opened by zkamvar 9
  • Adding moving average as geom_line

    Adding moving average as geom_line

    Hey - not a must have but might be nice to have option to add a moving average line. This pretty commonly used on messy epi data. Should be quite easy to implement now that {slider} has been released. See r4epi discussion

    Maybe should just be left for users to so separately (i.e. add to plot themselves after)?

    enhancement 
    opened by aspina7 8
  • Width argument to specify no gap between bars

    Width argument to specify no gap between bars

    Hello! Great improvements on the original package - thank you very much! I really like the ability to facet and to use count data.

    I would like to ask if the plotting functions can allow a width argument or otherwise an option for the there to be no gap between bars. At the US CDC and it seems in Europe as well (see ref below) there is a traditional guideline that epidemic curves (when large enough that cases are not shown as boxes) should be histograms and not bar charts - or at least that there be no spaces between the bars. If this option can be offered I think it would also offer a solution to the varying width and frequency of "white lines"/gaps between bars, which appear for example in the github readme (below).

    From the vignette - "white lines"/bar gaps appearing at different frequencies across the plot image

    From the vignette - "white lines"/bar gaps of varying thickness across the plot image

    I tried to include a width argument in plot() but it was not accepted. When I tried to add a geom_col() to plot() and specify width that also did not work. While experimenting, I tried to use geom_col alone directly on a weekly incidence2 object. When I specified width = 7 I was able to achieve non-overlapping bars without any gaps. This makes sense given that it was a weekly incidence object and according to this ggplot2 issue discussion which says that geom_col width is interpreted in absolute units (days in this case).

    Here is that example - the outbreaks ebola_sim_clean linelist

    pacman::p_load(incidence2, tidyverse, outbreaks)
    b <- incidence2::incidence(outbreaks::ebola_sim_clean$linelist, date_index = date_of_onset, groups = gender, interval = "week")
    plot(b, fill = gender) # weird varying white "gaps" between bars
    ggplot(data = b)+geom_col(aes(x = bin_date, y = count, fill = gender), width = 7) # no gaps
    

    I just wanted to chime in and see if this was something that is possible. Perhaps at the least the width argument could be allowed to pass to the underlying geom_col? Then the user could tinker and find the correct width?

    Thanks very much for considering!

    ECDC guidelines for presentation of surveillance data

    opened by nsbatra 7
  • Where to put labels on the x-axis?

    Where to put labels on the x-axis?

    There has been debates in the past on where dates should appear on the x-axis. I will try to sum up views / things to take into account below, and maybe some will add thoughts to it.

    Original incidence package

    • epicurves were treated as histograms; bars represent case counts between 2 time points, so that e.g. for monthly incidence, a date on the x-axis marks the left hand-side of the bin (label to the left)
    • for fitting, a single date needs to be associated to a case count; thus we were using the middle of the time interval (label in the middle)
    • we did not have options for plotting epicurves as points / lines
    • several users complained that label in the middle was more intuitive

    Current considerations

    • I suspect most epis do not read epicurves as histograms, so label in the middle would make sense
    • if we add geom_point and geom_line as options for plot and facet_plot, it is preferrable to have a consistent label positioning, which works the same for all geoms; label in the middle seems better for this: it still makes sense with geom_bar
    • model predictions will probably work better with label in the middle
    • devel-wise, it is safer to go with the least-amount of fiddling with ggplot2 handing of the x-axis
    discussion 
    opened by thibautjombart 7
  • consider using {tsibble}

    consider using {tsibble}

    hi - like the idea of moving this to cleaner syntax!

    Just thought I would suggest using tsibble to do some of the underlying legwork. There are yearweek and year* functions and all works pretty clean.

    The advantage over aweek is that is recognised as a date automatically. The disadvantage over aweek is that cannot (as of yet) set a different start day for a week.

    Actually just posted issues this morning on {aweek} and {tsibble} about this.

    As a sidenote - while in the process of redoing everything might be worth considering renaming just to make certain epis happy and avoid semantic discussions around incidence vs incidence rate vs prevalence (see)

    discussion 
    opened by aspina7 6
  • Allowing adding non-integer numbers to grate objects

    Allowing adding non-integer numbers to grate objects

    A maybe not very frequent use case: define limits between two time intervals defined by grate, e.g. to visually delineate epochs in a graph using a vertical line.

    Currently the following will error on purpose:

    > as_yrwk("2021-W03") + 1
    [1] "2021-W04"
    
    > as_yrwk("2021-W03") + 1.5
    Error: Can only add whole numbers to <yrwk> objects
    

    But unsure if we want to change this or not.

    opened by thibautjombart 4
  • User request: allow date adjustment using % strptime abbreviations syntax

    User request: allow date adjustment using % strptime abbreviations syntax

    @nsbatra - the following requires the dev (GitHub main/master branches) of incidence2 and grates but hopefully works as you were hoping. Let me know what you think:

    library(outbreaks)
    library(incidence2)
    
    dat <- ebola_sim_clean$linelist
    x <- incidence(dat, date_of_onset, interval = "month")
    x
    #> An incidence2 object: 13 x 2
    #> 5829 cases from 2014-Apr to 2015-Apr
    #> interval: 1 month
    #> cumulative: FALSE
    #> 
    #>    date_index count
    #>    <month>    <int>
    #>  1 2014-Apr       7
    #>  2 2014-May      67
    #>  3 2014-Jun     102
    #>  4 2014-Jul     228
    #>  5 2014-Aug     540
    #>  6 2014-Sep    1144
    #>  7 2014-Oct    1199
    #>  8 2014-Nov     779
    #>  9 2014-Dec     567
    #> 10 2015-Jan     427
    #> 11 2015-Feb     307
    #> 12 2015-Mar     277
    #> 13 2015-Apr     185
     
    # centred dates (default for yearweek, single months, quarters and years)
    plot(x, color = "white")
    

    
    # histogram-esque dates on the breaks (defaults to "%Y-%m-%d")
    plot(x, color = "white", centre_dates = FALSE)
    

    
    # can specify a different format
    plot(x, color = "white", centre_dates = FALSE, date_format = "%d-%m-%Y")
    

    Created on 2021-05-19 by the reprex package (v2.0.0)

    opened by TimTaylor 3
  • Strategy for renaming functions

    Strategy for renaming functions

    For instance, pool may be better named regroup, and I guess there could be more cases like this. Generally speaking, renaming things from the original incidence poses some trade-offs. There are several strategies we may consider:

    Stick to the old

    We keep old names as much as possible, and only use new names for new features.

    Scrap the old

    As this is a reboot, we can do away with old names, and rely on documentation for people to find out correspondence. A softer version would be to have incidence2::pool merely return NULL (or an error) and throw a message saying that this feature is now called regroup in incidence2.

    Aliases

    We could have incidence2::regroup <- incidence2::pool. If so, do we want to:

    • keep aliases going forward (I think not)
    • mark old names as deprecated and eventually remove them? It might make sense in terms of transition, but it is weird to develop a new package with already deprecated functions, with a schedule that explicitely plans breaking backward compatibility fruther down the line.
    discussion 
    opened by thibautjombart 3
  • Possible new features: subsetting time windows

    Possible new features: subsetting time windows

    Subsetting objects by given time windows may be one of the only things made slightly easier in the original incidence package. For instance, x[1:5] would get you the first 5 time steps (days / weeks / months) of the object, which is a little trickier to do now. It would be useful to have some functions helping with this - see some proposed example uses below.

    Filter first / last days / weeks / months etc.

    Filter the data to retain the first or last data points, predicated on a duration. There is a question here, as to how duration can be specified:

    1. simplest form: duration is provided as integer days
    2. other simple form: duration is provided as integer time intervals (as specified by the bins of the object)
    3. interpreted like the 'interval' argument of incidence2::incidence, so we could do things like "3 months" to have the first 3 months of data (possibly months 1 and 3 not being complete)

    Examples would be (depending on the option above we retain):

    • filter_first(x, 30): retain the first 30 days of data, or all of it if there are less than 30 days
    • filter_first(x, "1 month"): retain the first month of data; may not be a full month, only data from the first reported month
    • filter_last(x, "4 weeks"): retain the last 4 weeks of data; the last week may not be complete e.g. if the last date is a Thursday, so this may not be 28 days of data
    • filter_last(x, 28): filters the first 28 days data (irrespective of week definition)

    Subset

    We could re-implement the features of incidence::subset(), but possibly renaming the function. It would merely be a wrapper for filter on dates.

    enhancement 
    opened by thibautjombart 2
  • On the behaviour of `facet_plot`

    On the behaviour of `facet_plot`

    Some thoughts on how facetting may work, esp with regards to using groups for facetting and/or color-filling. Nothing hard-set, more for discussion purpose. It would be useful to have a facet argument handling which grouping variables are used for facetting. Together with fill, this should give more flexibility to the user for designing plots with different grouping variables displayed.

    Here are some proposed behaviours:

    • facet_plot(x): plot the incidence object using all grouping variables for facetting
    • facet_plot(x, facet = "foo"): same, using only variable foo for facetting
    • facet_plot(x, facet = c(foo, bar)): same, using variables foo and bar
    • facet_plot(x, facet = "foo", fill = bar): use foo for facetting and bar for filling
    • facet_plot(x, facet = c("foo", "bar"), fill = bar): use foo and bar for facetting, and bar for filling; redundant, but that's okay, the user asked for it

    What do you think?

    opened by thibautjombart 2
  • version2 - dplyr for vctrs_rcrd and POSIXlt support

    version2 - dplyr for vctrs_rcrd and POSIXlt support

    Currently using only data.table for aggregation which means we cannot support non-atomic columns (see https://github.com/Rdatatable/data.table/issues?q=is%3Aopen+is%3Aissue+label%3A%22non-atomic+column%22 for further discussion). Previously I switched behaviour based on the input and dispatched to either data.table or dplyr accordingly. Need to think about whether I do the same thing going forward or error on known problematic inputs.

    I want to avoid changing user inputs so if I do not go with the dual use I'd like to error on POSIXlt input .

    version2 development 
    opened by TimTaylor 1
  • incidence() works with POSIXt columns when interval =

    incidence() works with POSIXt columns when interval = "week" but interval = "day"

    Please place an "x" in all the boxes that apply

    • [x] I have the most recent version of incidence2 and R
    • [x] I have found a bug
    • [x] I have a reproducible example

    If a column is stored as POSIXt / datetime rather than a plain Date, incidence() will have an inconsistent behaviour and work when interval = "week" but interval = "day".

    library(dplyr)
    #> 
    #> 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
    library(incidence2)
    
    # Create a dataset with Dates stored as datetimes / POSIXt
    data("covid19_england_nhscalls_2020", package = "outbreaks")
    
    d <- covid19_england_nhscalls_2020 %>% 
      mutate(across(where(lubridate::is.Date), as.POSIXct, tz = "UTC"))
    
    # Convert this dataset to incidence2
    
    # WORKS
    d %>%
      incidence("date",
                interval = "week",
                counts = "count"
      )
    #> An incidence object: 27 x 2
    #> date range: [2020-W12] to [2020-W38]
    #> cases: 4101446
    #> interval: 1 (Monday) week 
    #> cumulative: FALSE
    #> 
    #>    date_index  count
    #>        <yrwk>  <int>
    #>  1   2020-W12 677547
    #>  2   2020-W13 866757
    #>  3   2020-W14 522298
    #>  4   2020-W15 331617
    #>  5   2020-W16 212969
    #>  6   2020-W17 156984
    #>  7   2020-W18 154765
    #>  8   2020-W19 117314
    #>  9   2020-W20 107629
    #> 10   2020-W21  88949
    #> # … with 17 more rows
    
    # DOESN'T WORK
    d %>%
      incidence("date",
                interval = "day",
                counts = "count"
      )
    #> Error in `create_interval_string()`:
    #> ! Not implemented for class POSIXct, POSIXt
    
    #> Backtrace:
    #>     ▆
    #>  1. ├─d %>% incidence("date", interval = "day", counts = "count")
    #>  2. └─incidence2::incidence(., "date", interval = "day", counts = "count")
    #>  3.   ├─incidence2:::create_interval_string(dat$date_index)
    #>  4.   └─incidence2:::create_interval_string.default(dat$date_index)
    #>  5.     └─rlang::abort(...)
    

    Created on 2022-11-21 with reprex v2.0.2.9000

    bug released 
    opened by Bisaloo 1
  • Warning about change in tidyselect

    Warning about change in tidyselect

    Please place an "x" in all the boxes that apply

    • [x] I have the most recent version of incidence2 and R
    • [x] I have found a bug
    • [x] I have a reproducible example

    Please include a brief description of the problem with a code example:

    library(incidence2)
    
    data(ebola_sim_clean, package = "outbreaks")
    dat <- ebola_sim_clean$linelist
    
    inci <- incidence(dat,
                      date_index = date_of_onset,
                      interval = 7,
                      groups = hospital)
    
    green_grey <- "#5E7E80"
    
    facet_plot(inci, fill = green_grey)
    #> Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
    #> ℹ Please use `all_of()` or `any_of()` instead.
    #>   # Was:
    #>   data %>% select(green_grey)
    #> 
    #>   # Now:
    #>   data %>% select(all_of(green_grey))
    #> 
    #> See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
    

    Created on 2022-10-26 with reprex v2.0.2.9000

    Looking at the full error stack, this is caused by

    https://github.com/reconverse/incidence2/blob/6d527d1f2d5e0de682a435643d2f6d193f3f734e/R/plot.R#L212-L218


    Related: #78

    bug released 
    opened by Bisaloo 1
  • Improve build_incidence() documentation

    Improve build_incidence() documentation

    Currently build_incidence() does not mention that it cannot be used with the built in plotting functionality. This should be documented, and potentially we should create a plot methods that warns when called directly.

    opened by TimTaylor 0
Releases(v1.2.2)
  • v1.2.2(Aug 23, 2021)

  • v1.2.1(Jul 15, 2021)

    Bug fixes

    • Fixes bug in incidence() when more than one column was given for the date_index.
    • Fixes incorrect test that did not take in to account changing time zones.
    Source code(tar.gz)
    Source code(zip)
  • v1.2.0(Jul 7, 2021)

    New functions

    • new_incidence(): A minimal incidence constructor.
    • validate_incidence(): Check for internal consistency of incidence-like object.
    • build_incidence(): Allows you to construct an incidence object whilst specifying your own date grouping function.
    • format.incidence()

    Deprecated functions

    • cumulate() will now give a deprecation error. We have removed the function to avoid users erroneously regressing against a cumulative count.

    Bug fixes

    • Fixes bug in incidence() when dates were a character vector and the the default, daily, interval was specified.

    Other updates

    • Now uses dplyr to handle list based columns (e.g. record-type objects from vctrs). For data.frames with only atomic columns, data.table is still used.
    • Printing and summaries of incidence objects have been improved to remove duplication in the overview section.
    Source code(tar.gz)
    Source code(zip)
  • v1.1(May 29, 2021)

    • New function complete_counts().
    • plot() and facet_plot() now have a centre_dates argument which can be set to FALSE to get histogram-esque date labels for single month, quarter and yearweek groupings.
    • Internal refactoring due to breakages changes in the upstream grates package.
    Source code(tar.gz)
    Source code(zip)
  • v1.0.0(Mar 30, 2021)

    Due to multiple changes in the underlying representation of incidence2 objects this release may possibly break old workflows particularly those relying on the old implementations of date grouping:

    • Now uses the package grates for date grouping. This introduces the s3 classes yrwk, yrmon, yrqtr, yr, period and int_period as well as associated constructors which incidence now builds upon. As a result of this the aweek dependency has been dropped.
    • Add's keep_first and keep_last functions.
    • Construction of incidence objects now faster due to underlying use of data.table.
    Source code(tar.gz)
    Source code(zip)
  • v0.2.2(Nov 12, 2020)

    • Fixes bug in get_interval.
    • Removes message that was displayed when incidence class dropped.
    • Refactoring of internal code to improve maintainability.
    • Tests now use the 3rd edition of testthat.
    Source code(tar.gz)
    Source code(zip)
  • v0.2.1(Oct 16, 2020)

  • v0.2.0(Sep 22, 2020)

    • Fixes issue with monthly incidence objects when show_cases = TRUE (see #42).
    • Additional checks added for assessing whether a manipulated incidence object maintains its class.
    • Improved implementation speed.
    • NA's now ignored in the count variable of a pre-aggregated input to incidence function.
    • Fixes axis labelling and spacing.
    Source code(tar.gz)
    Source code(zip)
  • v0.1.0(Sep 10, 2020)

A python script to visualise explain plans as a graph using graphviz

README Needs to be improved Prerequisites Need to have graphiz installed on the machine. Refer to https://graphviz.readthedocs.io/en/stable/manual.htm

Edward Mallia 1 Sep 28, 2021
Visualise top-rated GitHub repositories in a barchart by keyword

This python script was written for simple purpose -- to visualise top-rated GitHub repositories in a barchart by keyword. Script generates html-page with barchart and information about repository owner, description, create date and update date.

Cur1iosity 2 Feb 7, 2022
This package creates clean and beautiful matplotlib plots that work on light and dark backgrounds

This package creates clean and beautiful matplotlib plots that work on light and dark backgrounds. Inspired by the work of Edward Tufte.

Nico Schlömer 205 Jan 7, 2023
eoplatform is a Python package that aims to simplify Remote Sensing Earth Observation by providing actionable information on a wide swath of RS platforms and provide a simple API for downloading and visualizing RS imagery

An Earth Observation Platform Earth Observation made easy. Report Bug | Request Feature About eoplatform is a Python package that aims to simplify Rem

Matthew Tralka 4 Aug 11, 2022
Python package for hypergraph analysis and visualization.

The HyperNetX library provides classes and methods for the analysis and visualization of complex network data. HyperNetX uses data structures designed to represent set systems containing nested data and/or multi-way relationships. The library generalizes traditional graph metrics to hypergraphs.

Pacific Northwest National Laboratory 304 Dec 27, 2022
A Python package that provides evaluation and visualization tools for the DexYCB dataset

DexYCB Toolkit DexYCB Toolkit is a Python package that provides evaluation and visualization tools for the DexYCB dataset. The dataset and results wer

NVIDIA Research Projects 107 Dec 26, 2022
Attractors is a package for simulation and visualization of strange attractors.

attractors Attractors is a package for simulation and visualization of strange attractors. Installation The simplest way to install the module is via

Vignesh M 45 Jul 31, 2022
Python package to Create, Read, Write, Edit, and Visualize GSFLOW models

pygsflow pyGSFLOW is a python package to Create, Read, Write, Edit, and Visualize GSFLOW models API Documentation pyGSFLOW API documentation can be fo

pyGSFLOW 21 Dec 14, 2022
A Python package for caclulations and visualizations in geological sciences.

geo_calcs A Python package for caclulations and visualizations in geological sciences. Free software: MIT license Documentation: https://geo-calcs.rea

Drew Heasman 1 Jul 12, 2022
Python package for the analysis and visualisation of finite-difference fields.

discretisedfield Marijan Beg1,2, Martin Lang2, Samuel Holt3, Ryan A. Pepper4, Hans Fangohr2,5,6 1 Department of Earth Science and Engineering, Imperia

ubermag 12 Dec 14, 2022
Pydrawer: The Python package for visualizing curves and linear transformations in a super simple way

pydrawer ?? The Python package for visualizing curves and linear transformations in a super simple way. ✏️ Installation Install pydrawer package with

Dylan Tintenfich 56 Dec 30, 2022
Curvipy - The Python package for visualizing curves and linear transformations in a super simple way

Curvipy - The Python package for visualizing curves and linear transformations in a super simple way

Dylan Tintenfich 55 Dec 28, 2022
A python package for animating plots build on matplotlib.

animatplot A python package for making interactive as well as animated plots with matplotlib. Requires Python >= 3.5 Matplotlib >= 2.2 (because slider

Tyler Makaro 394 Dec 18, 2022
nptsne is a numpy compatible python binary package that offers a number of APIs for fast tSNE calculation.

nptsne nptsne is a numpy compatible python binary package that offers a number of APIs for fast tSNE calculation and HSNE modelling. For more detail s

Biomedical Visual Analytics Unit LUMC - TU Delft 29 Jul 5, 2022
A python package for animating plots build on matplotlib.

animatplot A python package for making interactive as well as animated plots with matplotlib. Requires Python >= 3.5 Matplotlib >= 2.2 (because slider

Tyler Makaro 356 Feb 16, 2021
nptsne is a numpy compatible python binary package that offers a number of APIs for fast tSNE calculation.

nptsne nptsne is a numpy compatible python binary package that offers a number of APIs for fast tSNE calculation and HSNE modelling. For more detail s

Biomedical Visual Analytics Unit LUMC - TU Delft 24 Feb 3, 2021
Python package that generates hardware pinout diagrams as SVG images

PinOut A Python package that generates hardware pinout diagrams as SVG images. The package is designed to be quite flexible and works well for general

null 336 Dec 20, 2022
A minimal Python package that produces slice plots through h5m DAGMC geometry files

A minimal Python package that produces slice plots through h5m DAGMC geometry files Installation pip install dagmc_geometry_slice_plotter Python API U

Fusion Energy 4 Dec 2, 2022
Python Package for CanvasXpress JS Visualization Tools

CanvasXpress Python Library About CanvasXpress for Python CanvasXpress was developed as the core visualization component for bioinformatics and system

Dr. Todd C. Brett 5 Nov 7, 2022