MadsR - let's get to the fun part.
MadsR is a package designed for my own uses with data from the danish microbiological system MADS.
devtools::install_github("marcmtk/MadsR")
This package provides 6 functions to assist with epidemiological analyses of MADS data and 2 functions to generate MADS like data for testing purposes.
read_mads- reads a csv file, applies appropriate types to columns, adds time columns and splits name of sender into useful categories.filter_cases- easy filtering of first episode in a timewindow.since_last- calculates the time since a new positive sample was last seen.tally_by_department- calculates positive cases by department, provides functions to exclude observations based on different windows, see examplesslplot- a "Since last" plottally_map- a heatmap of tally datagenerate_MADS_like_data- as the name impliesgenerate_res- generates resistance patterns
Consider the dataset provided in analyser-like.csv with read_csv vs read_mads
df <- read.csv("./extdata/analyser-like.csv")
str(df)
## 'data.frame': 1000 obs. of 7 variables:
## $ cprnr. : int 168 6 339 190 325 333 212 157 107 72 ...
## $ navn : logi NA NA NA NA NA NA ...
## $ afsendt : int 29052013 16102012 12042012 25112012 8032013 2042012 3102012 15102013 1082012 21112012 ...
## $ modtaget: logi NA NA NA NA NA NA ...
## $ besvaret: logi NA NA NA NA NA NA ...
## $ afsender: Factor w/ 352 levels "00071","00389",..: 278 344 343 340 340 282 306 314 340 231 ...
## $ result : Factor w/ 2 levels "Negativ","Positiv": 1 1 1 1 1 1 1 1 1 1 ...
head(df)
## cprnr. navn afsendt modtaget besvaret afsender result
## 1 168 NA 29052013 NA NA 47454 Negativ
## 2 6 NA 16102012 NA NA S R Negativ
## 3 339 NA 12042012 NA NA S Q Negativ
## 4 190 NA 25112012 NA NA S N Negativ
## 5 325 NA 8032013 NA NA S N Negativ
## 6 333 NA 2042012 NA NA 47535 Negativ
df <- read_mads("./extdata/analyser-like.csv", "analyser")
str(df)
## 'data.frame': 1000 obs. of 16 variables:
## $ cprnr. : int 168 6 339 190 325 333 212 157 107 72 ...
## $ navn : logi NA NA NA NA NA NA ...
## $ afsendt : Date, format: "2013-05-29" "2012-10-16" ...
## $ modtaget: Date, format: NA NA ...
## $ besvaret: Date, format: NA NA ...
## $ afsender: chr "47454" "S R" "S Q" "S N" ...
## $ hospital: chr "AP" "S" "S" "S" ...
## $ afdeling: chr "" "R" "Q" "N" ...
## $ afsnit : chr NA NA NA NA ...
## $ result : chr "Negativ" "Negativ" "Negativ" "Negativ" ...
## $ year : num 2013 2012 2012 2012 2013 ...
## $ quarter : num 2 4 2 4 1 2 4 4 3 4 ...
## $ month : Ord.factor w/ 12 levels "January"<"February"<..: 5 10 4 11 3 4 10 10 8 11 ...
## $ week : num 22 42 15 48 10 14 40 42 31 47 ...
## $ weekday : Ord.factor w/ 7 levels "Sunday"<"Monday"<..: 4 3 5 1 6 2 4 3 4 4 ...
## $ hosp_afd: chr "AP " "S R" "S Q" "S N" ...
head(df)
## cprnr. navn afsendt modtaget besvaret afsender hospital afdeling
## 1 168 NA 2013-05-29 <NA> <NA> 47454 AP
## 2 6 NA 2012-10-16 <NA> <NA> S R S R
## 3 339 NA 2012-04-12 <NA> <NA> S Q S Q
## 4 190 NA 2012-11-25 <NA> <NA> S N S N
## 5 325 NA 2013-03-08 <NA> <NA> S N S N
## 6 333 NA 2012-04-02 <NA> <NA> 47535 AP
## afsnit result year quarter month week weekday hosp_afd
## 1 <NA> Negativ 2013 2 May 22 Wednesday AP
## 2 <NA> Negativ 2012 4 October 42 Tuesday S R
## 3 <NA> Negativ 2012 2 April 15 Thursday S Q
## 4 <NA> Negativ 2012 4 November 48 Sunday S N
## 5 <NA> Negativ 2013 1 March 10 Friday S N
## 6 <NA> Negativ 2012 2 April 14 Monday AP
Let's filter some cases and look at the results
cases <- filter_cases(df, result=="Positiv", min.days.to.new.episode=14)
table(cases$hosp_afd, cases$episode)
##
## 1 2 3
## AP 28 5 1
## O A 2 0 0
## O B 1 0 0
## O C 2 0 0
## O D 1 1 0
## O E 1 0 0
## O F 3 0 0
## O H 3 0 0
## O I 1 0 0
## O K 3 0 0
## O L 3 0 0
## O M 3 1 0
## O N 1 0 0
## O O 1 0 0
## O P 1 2 0
## O Q 1 1 0
## O S 1 0 0
## O T 1 0 0
## O U 0 1 0
## O V 1 0 0
## O W 1 0 0
## O Y 2 0 0
## O Z 2 0 0
## S A 1 0 0
## S C 2 0 0
## S F 2 0 0
## S I 4 0 0
## S J 2 0 0
## S K 2 0 0
## S M 1 0 0
## S N 0 1 0
## S O 2 0 0
## S P 1 0 0
## S T 3 1 0
## S U 2 0 0
## S X 2 1 0
## S Y 1 0 0
## S Z 3 0 0
table(cases$sl)
##
## 15 17 53 95 124 144 306 315 343 369 437 470 530 534 598
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Now let's look at a since last plot, after all there may be an epidemic out there!
since_last(df) %>% slplot()
since_last(df) %>% filter(hosp_afd=="S V") %>% slplot()
filter(df, hosp_afd == "S V") %>% since_last() %>% slplot()
Note the difference between plot 2 and plot 3. It is very important that time since last positive case is computed after relevant filtering.
Last useful functions, tallying by department and heatmapping the results:1
tbd <- tally_by_department(df, "patient", result == "Positiv")
## Joining by: c("hosp_afd", "year", "month")
## Joining by: c("hosp_afd", "year", "month")
tally_map(tbd)
filter(tbd, hosp_afd != "AP ") %>% tally_map
sessionInfo()
## R version 3.2.3 (2015-12-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 10240)
##
## locale:
## [1] LC_COLLATE=Danish_Denmark.1252 LC_CTYPE=Danish_Denmark.1252
## [3] LC_MONETARY=Danish_Denmark.1252 LC_NUMERIC=C
## [5] LC_TIME=Danish_Denmark.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggplot2_2.1.0 dplyr_0.4.3 MadsR_0.1.2
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.4 knitr_1.12.3 magrittr_1.5 munsell_0.4.3
## [5] lattice_0.20-33 colorspace_1.2-6 R6_2.1.2 stringr_1.0.0
## [9] plyr_1.8.3 tools_3.2.3 parallel_3.2.3 grid_3.2.3
## [13] nlme_3.1-127 gtable_0.2.0 mgcv_1.8-12 DBI_0.3.1
## [17] htmltools_0.3.5 yaml_2.1.13 lazyeval_0.1.10 assertthat_0.1
## [21] digest_0.6.9 Matrix_1.2-5 gridExtra_2.2.1 formatR_1.3
## [25] tidyr_0.4.1 viridis_0.3.4 evaluate_0.8.3 rmarkdown_0.9.5
## [29] labeling_0.3 stringi_1.0-1 scales_0.4.0 lubridate_1.5.6