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README.md
In repijson: Tools for Handling EpiJSON (Epidemiology Data) Files
EpiJSON is a generic JSON format for storing epidemiological data.
repijson is an R package that allows conversion between EpiJSON files and R data formats.
This vignette is a demonstration of the package repijson.
Epidemiological data is often stored and transferred as spread-sheets, databases, and text files with little standardisation in row, column and field names. A universal format enabling the coherent storage and transfer of these data is lacking. In many cases where transfer does occur, there is room for misinterpretation and preventable errors may be introduced into reports and analyses.
EpiJSON provides a potential solution for the unambiguous storage and transfer of epidemiological data. repijson facilitates the use of EpiJSON within R.
Installing repijson
To install the development version from github:
library(devtools)
install_github("hackout2/repijson")
Then, to load the package, use:
library("repijson")
The EpiJSON format
This is a simplified representation of the EpiJSON format.
The repijson objects used to store EpiJSON are represented in the following diagram.
First simple example creating an repijson object from a dataframe
toyll is a small example dataframe within the repijson package. It follows the structure of a disease outbreak line list, with individuals in rows and data stored in columns. This is what the first 3 rows and 8 columns look like :
toyll[1:3,1:8]
## id name dob gender date.of.onset date.of.admission
## 1 A Tom 1981-01-12 male 2014-12-28 <NA>
## 2 B Andy 1980-11-11 male 2014-12-29 2015-01-05
## 3 3D Ellie 1982-02-10 female 2015-01-03 2015-01-12
## date.of.discharge hospital
## 1 <NA> <NA>
## 2 <NA> St Marys
## 3 2015-01-17 Whittington
The example below creates an ejObject from the first 3 rows. It assigns the columns "name" and "gender" as record attributes. It defines two events, "admission" and "discharge". The "hospital" column is assigned as an attribute of the first event.
#converting dates to date format
toyll$date.of.admission <- as.POSIXct(toyll$date.of.admission)
toyll$date.of.discharge <- as.POSIXct(toyll$date.of.discharge)
#create ejObject
ejOb <- as.ejObject(toyll[1:3,],
recordAttributes=c("name","gender"),
eventDefinitions=list(
define_ejEvent(name="admission", date="date.of.admission",attributes="hospital"),
define_ejEvent(name="discharge", date="date.of.discharge")
))
#display ejObject
ejOb
## {
## "metadata": [
##
## ],
## "records": [
## {
## "id": 1,
## "attributes": [
## {
## "name": "name",
## "type": "string",
## "value": "Tom"
## },
## {
## "name": "gender",
## "type": "string",
## "value": "male"
## }
## ],
## "events": [
##
## ]
## },
## {
## "id": 2,
## "attributes": [
## {
## "name": "name",
## "type": "string",
## "value": "Andy"
## },
## {
## "name": "gender",
## "type": "string",
## "value": "male"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "admission",
## "date": "2015-01-05T00:00:00Z",
## "attributes": [
## {
## "name": "hospital",
## "type": "string",
## "value": "St Marys"
## }
## ]
## }
## ]
## },
## {
## "id": 3,
## "attributes": [
## {
## "name": "name",
## "type": "string",
## "value": "Ellie"
## },
## {
## "name": "gender",
## "type": "string",
## "value": "female"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "admission",
## "date": "2015-01-12T00:00:00Z",
## "attributes": [
## {
## "name": "hospital",
## "type": "string",
## "value": "Whittington"
## }
## ]
## },
## {
## "id": 2,
## "name": "discharge",
## "date": "2015-01-17T00:00:00Z",
## "attributes": [
##
## ]
## }
## ]
## }
## ]
## }
##
Load the required packages for further examples.
library(OutbreakTools)
library(sp)
library(HistData)
Creating example dataframe 1.
data(Snow.deaths)
Adding some dates, pumps, some genders
simulated <- Snow.deaths
simulated$gender <- c("male","female")[(runif(nrow(simulated))>0.5) +1]
simulated$date <- as.POSIXct("1854-04-05") + rnorm(nrow(simulated), 10) * 86400
simulated$pump <- ceiling(runif(nrow(simulated)) * 5)
exampledata1<-head(simulated)
exampledata1
## case x y gender date pump
## 1 1 13.588010 11.095600 male 1854-04-15 10:54:43 2
## 2 2 9.878124 12.559180 male 1854-04-14 20:25:50 5
## 3 3 14.653980 10.180440 male 1854-04-16 02:20:53 5
## 4 4 15.220570 9.993003 female 1854-04-17 01:12:46 4
## 5 5 13.162650 12.963190 female 1854-04-15 02:34:48 5
## 6 6 13.806170 8.889046 female 1854-04-16 15:33:10 3
Creating example dataframe 2.
exampledata2<- data.frame(id=c(1,2,3,4,5),
name=c("Tom","Andy","Ellie","Ana","Tibo"),
dob=c("1981-01-12","1980-11-11","1982-02-10","1981-12-09","1983-03-08"),
gender=c("male","male","female","female","male"),
date.of.onset=c("2014-12-28","2014-12-29","2015-01-03","2015-01-08","2015-01-04"),
date.of.admission=c(NA,"2015-01-05","2015-01-12",NA,"2015-01-14"),
date.of.discharge=c(NA,NA,"2015-01-17",NA,"2015-01-17"),
hospital=c(NA,"St Marys","Whittington",NA,"Whittington"),
fever=c("yes","yes","no","no","yes"),
sleepy=c("no","yes","yes","no","yes"),
contact1.id=c("B","A","5",NA,"3D"),
contact1.date=c("2014-12-26","2014-12-26","2014-12-28",NA,"2014-12-28"),
contact2.id=c("3D","3D","5",NA,"A"),
contact2.date=c("2014-12-25","2014-12-26","2015-01-14",NA,"2014-12-25"),
contact3.id=c("B",NA,NA,NA,NA),
contact3.date=c("2015-01-08",NA,NA,NA,NA)
)
exampledata2
## id name dob gender date.of.onset date.of.admission
## 1 1 Tom 1981-01-12 male 2014-12-28 <NA>
## 2 2 Andy 1980-11-11 male 2014-12-29 2015-01-05
## 3 3 Ellie 1982-02-10 female 2015-01-03 2015-01-12
## 4 4 Ana 1981-12-09 female 2015-01-08 <NA>
## 5 5 Tibo 1983-03-08 male 2015-01-04 2015-01-14
## date.of.discharge hospital fever sleepy contact1.id contact1.date
## 1 <NA> <NA> yes no B 2014-12-26
## 2 <NA> St Marys yes yes A 2014-12-26
## 3 2015-01-17 Whittington no yes 5 2014-12-28
## 4 <NA> <NA> no no <NA> <NA>
## 5 2015-01-17 Whittington yes yes 3D 2014-12-28
## contact2.id contact2.date contact3.id contact3.date
## 1 3D 2014-12-25 B 2015-01-08
## 2 3D 2014-12-26 <NA> <NA>
## 3 5 2015-01-14 <NA> <NA>
## 4 <NA> <NA> <NA> <NA>
## 5 A 2014-12-25 <NA> <NA>
Transition 1: data.frame to EpiJSON format
Use the repijson package to convert a data.frame object into a EpiJSON object within R:
eg1 <- as.ejObject(exampledata1,
recordAttributes = "gender",
eventDefinitions = list(define_ejEvent(date="date", name="Death", location=list(x="x", y="y", proj4string=""), attributes="pump")),
metadata=list())
eg1
## {
## "metadata": [
##
## ],
## "records": [
## {
## "id": 1,
## "attributes": [
## {
## "name": "gender",
## "type": "string",
## "value": "male"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "Death",
## "date": "1854-04-15T10:54:43Z",
## "location": {
## "type": "FeatureCollection",
## "features": [
## {
## "type": "Feature",
## "id": 1,
## "properties": {
## "dat": 1
## },
## "geometry": {
## "type": "Point",
## "coordinates": [
## 13.588,
## 11.0956
## ]
## }
## }
## ]
## },
## "attributes": [
## {
## "name": "pump",
## "type": "number",
## "value": 2
## }
## ]
## }
## ]
## },
## {
## "id": 2,
## "attributes": [
## {
## "name": "gender",
## "type": "string",
## "value": "male"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "Death",
## "date": "1854-04-14T20:25:50Z",
## "location": {
## "type": "FeatureCollection",
## "features": [
## {
## "type": "Feature",
## "id": 2,
## "properties": {
## "dat": 1
## },
## "geometry": {
## "type": "Point",
## "coordinates": [
## 9.8781,
## 12.5592
## ]
## }
## }
## ]
## },
## "attributes": [
## {
## "name": "pump",
## "type": "number",
## "value": 5
## }
## ]
## }
## ]
## },
## {
## "id": 3,
## "attributes": [
## {
## "name": "gender",
## "type": "string",
## "value": "male"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "Death",
## "date": "1854-04-16T02:20:53Z",
## "location": {
## "type": "FeatureCollection",
## "features": [
## {
## "type": "Feature",
## "id": 3,
## "properties": {
## "dat": 1
## },
## "geometry": {
## "type": "Point",
## "coordinates": [
## 14.654,
## 10.1804
## ]
## }
## }
## ]
## },
## "attributes": [
## {
## "name": "pump",
## "type": "number",
## "value": 5
## }
## ]
## }
## ]
## },
## {
## "id": 4,
## "attributes": [
## {
## "name": "gender",
## "type": "string",
## "value": "female"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "Death",
## "date": "1854-04-17T01:12:46Z",
## "location": {
## "type": "FeatureCollection",
## "features": [
## {
## "type": "Feature",
## "id": 4,
## "properties": {
## "dat": 1
## },
## "geometry": {
## "type": "Point",
## "coordinates": [
## 15.2206,
## 9.993
## ]
## }
## }
## ]
## },
## "attributes": [
## {
## "name": "pump",
## "type": "number",
## "value": 4
## }
## ]
## }
## ]
## },
## {
## "id": 5,
## "attributes": [
## {
## "name": "gender",
## "type": "string",
## "value": "female"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "Death",
## "date": "1854-04-15T02:34:48Z",
## "location": {
## "type": "FeatureCollection",
## "features": [
## {
## "type": "Feature",
## "id": 5,
## "properties": {
## "dat": 1
## },
## "geometry": {
## "type": "Point",
## "coordinates": [
## 13.1626,
## 12.9632
## ]
## }
## }
## ]
## },
## "attributes": [
## {
## "name": "pump",
## "type": "number",
## "value": 5
## }
## ]
## }
## ]
## },
## {
## "id": 6,
## "attributes": [
## {
## "name": "gender",
## "type": "string",
## "value": "female"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "Death",
## "date": "1854-04-16T15:33:10Z",
## "location": {
## "type": "FeatureCollection",
## "features": [
## {
## "type": "Feature",
## "id": 6,
## "properties": {
## "dat": 1
## },
## "geometry": {
## "type": "Point",
## "coordinates": [
## 13.8062,
## 8.889
## ]
## }
## }
## ]
## },
## "attributes": [
## {
## "name": "pump",
## "type": "number",
## "value": 3
## }
## ]
## }
## ]
## }
## ]
## }
##
The repijson package does not convert dates represented as strings for you. This is because the process
of conversion from character to date-time is fraught with difficulty and the hidden corruption of dates is much
worse than being told by R to provide date objects. Here we convert the dates in the example two data to real
dates. We use POSIXct as this is more firendly to data.frames.
exampledata2$date.of.onset <- as.POSIXct(exampledata2$date.of.onset)
exampledata2$date.of.admission <- as.POSIXct(exampledata2$date.of.admission)
exampledata2$date.of.discharge <- as.POSIXct(exampledata2$date.of.discharge)
exampledata2$contact1.date <- as.POSIXct(exampledata2$contact1.date)
exampledata2$contact2.date <- as.POSIXct(exampledata2$contact2.date)
exampledata2$contact3.date <- as.POSIXct(exampledata2$contact3.date)
We are now set to convert the exampledata2 dataframe to an EpiJSON object.
eg2 <- as.ejObject(exampledata2, recordAttributes = c("id","name","dob","gender"),
eventDefinitions = list(define_ejEvent(name="Date Of Onset", date="date.of.onset",
attributes=list()),
define_ejEvent(name="Hospital admission", date="date.of.admission",
attributes=list("hospital", "fever", "sleepy")),
define_ejEvent(name="Hospital discharge", date="date.of.discharge"),
define_ejEvent(name="Contact1", date="contact1.date", attributes=list("contact1.id")),
define_ejEvent(name="Contact2", date="contact2.date", attributes=list("contact2.id")),
define_ejEvent(name="Contact3", date="contact3.date", attributes=list("contact3.id"))
),
metadata=list())
eg2
## {
## "metadata": [
##
## ],
## "records": [
## {
## "id": 1,
## "attributes": [
## {
## "name": "id",
## "type": "number",
## "value": 1
## },
## {
## "name": "name",
## "type": "string",
## "value": "Tom"
## },
## {
## "name": "dob",
## "type": "string",
## "value": "1981-01-12"
## },
## {
## "name": "gender",
## "type": "string",
## "value": "male"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "Date Of Onset",
## "date": "2014-12-28T00:00:00Z",
## "attributes": [
##
## ]
## },
## {
## "id": 4,
## "name": "Contact1",
## "date": "2014-12-26T00:00:00Z",
## "attributes": [
## {
## "name": "contact1.id",
## "type": "string",
## "value": "B"
## }
## ]
## },
## {
## "id": 5,
## "name": "Contact2",
## "date": "2014-12-25T00:00:00Z",
## "attributes": [
## {
## "name": "contact2.id",
## "type": "string",
## "value": "3D"
## }
## ]
## },
## {
## "id": 6,
## "name": "Contact3",
## "date": "2015-01-08T00:00:00Z",
## "attributes": [
## {
## "name": "contact3.id",
## "type": "string",
## "value": "B"
## }
## ]
## }
## ]
## },
## {
## "id": 2,
## "attributes": [
## {
## "name": "id",
## "type": "number",
## "value": 2
## },
## {
## "name": "name",
## "type": "string",
## "value": "Andy"
## },
## {
## "name": "dob",
## "type": "string",
## "value": "1980-11-11"
## },
## {
## "name": "gender",
## "type": "string",
## "value": "male"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "Date Of Onset",
## "date": "2014-12-29T00:00:00Z",
## "attributes": [
##
## ]
## },
## {
## "id": 2,
## "name": "Hospital admission",
## "date": "2015-01-05T00:00:00Z",
## "attributes": [
## {
## "name": "hospital",
## "type": "string",
## "value": "St Marys"
## },
## {
## "name": "fever",
## "type": "string",
## "value": "yes"
## },
## {
## "name": "sleepy",
## "type": "string",
## "value": "yes"
## }
## ]
## },
## {
## "id": 4,
## "name": "Contact1",
## "date": "2014-12-26T00:00:00Z",
## "attributes": [
## {
## "name": "contact1.id",
## "type": "string",
## "value": "A"
## }
## ]
## },
## {
## "id": 5,
## "name": "Contact2",
## "date": "2014-12-26T00:00:00Z",
## "attributes": [
## {
## "name": "contact2.id",
## "type": "string",
## "value": "3D"
## }
## ]
## }
## ]
## },
## {
## "id": 3,
## "attributes": [
## {
## "name": "id",
## "type": "number",
## "value": 3
## },
## {
## "name": "name",
## "type": "string",
## "value": "Ellie"
## },
## {
## "name": "dob",
## "type": "string",
## "value": "1982-02-10"
## },
## {
## "name": "gender",
## "type": "string",
## "value": "female"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "Date Of Onset",
## "date": "2015-01-03T00:00:00Z",
## "attributes": [
##
## ]
## },
## {
## "id": 2,
## "name": "Hospital admission",
## "date": "2015-01-12T00:00:00Z",
## "attributes": [
## {
## "name": "hospital",
## "type": "string",
## "value": "Whittington"
## },
## {
## "name": "fever",
## "type": "string",
## "value": "no"
## },
## {
## "name": "sleepy",
## "type": "string",
## "value": "yes"
## }
## ]
## },
## {
## "id": 3,
## "name": "Hospital discharge",
## "date": "2015-01-17T00:00:00Z",
## "attributes": [
##
## ]
## },
## {
## "id": 4,
## "name": "Contact1",
## "date": "2014-12-28T00:00:00Z",
## "attributes": [
## {
## "name": "contact1.id",
## "type": "string",
## "value": "5"
## }
## ]
## },
## {
## "id": 5,
## "name": "Contact2",
## "date": "2015-01-14T00:00:00Z",
## "attributes": [
## {
## "name": "contact2.id",
## "type": "string",
## "value": "5"
## }
## ]
## }
## ]
## },
## {
## "id": 4,
## "attributes": [
## {
## "name": "id",
## "type": "number",
## "value": 4
## },
## {
## "name": "name",
## "type": "string",
## "value": "Ana"
## },
## {
## "name": "dob",
## "type": "string",
## "value": "1981-12-09"
## },
## {
## "name": "gender",
## "type": "string",
## "value": "female"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "Date Of Onset",
## "date": "2015-01-08T00:00:00Z",
## "attributes": [
##
## ]
## }
## ]
## },
## {
## "id": 5,
## "attributes": [
## {
## "name": "id",
## "type": "number",
## "value": 5
## },
## {
## "name": "name",
## "type": "string",
## "value": "Tibo"
## },
## {
## "name": "dob",
## "type": "string",
## "value": "1983-03-08"
## },
## {
## "name": "gender",
## "type": "string",
## "value": "male"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "Date Of Onset",
## "date": "2015-01-04T00:00:00Z",
## "attributes": [
##
## ]
## },
## {
## "id": 2,
## "name": "Hospital admission",
## "date": "2015-01-14T00:00:00Z",
## "attributes": [
## {
## "name": "hospital",
## "type": "string",
## "value": "Whittington"
## },
## {
## "name": "fever",
## "type": "string",
## "value": "yes"
## },
## {
## "name": "sleepy",
## "type": "string",
## "value": "yes"
## }
## ]
## },
## {
## "id": 3,
## "name": "Hospital discharge",
## "date": "2015-01-17T00:00:00Z",
## "attributes": [
##
## ]
## },
## {
## "id": 4,
## "name": "Contact1",
## "date": "2014-12-28T00:00:00Z",
## "attributes": [
## {
## "name": "contact1.id",
## "type": "string",
## "value": "3D"
## }
## ]
## },
## {
## "id": 5,
## "name": "Contact2",
## "date": "2014-12-25T00:00:00Z",
## "attributes": [
## {
## "name": "contact2.id",
## "type": "string",
## "value": "A"
## }
## ]
## }
## ]
## }
## ]
## }
##
Transition 2: EpiJSON object to data.frame format
Use the repijson package to convert a JSON object into a data.frame object:
as.data.frame(eg1)
## id gender Death_date Death_locationX Death_locationY
## 1 1 male 1854-04-15 10:54:43 13.588010 11.095600
## 2 2 male 1854-04-14 20:25:50 9.878124 12.559180
## 3 3 male 1854-04-16 02:20:53 14.653980 10.180440
## 4 4 female 1854-04-17 01:12:46 15.220570 9.993003
## 5 5 female 1854-04-15 02:34:48 13.162650 12.963190
## 6 6 female 1854-04-16 15:33:10 13.806170 8.889046
## Death_locationCRS pump
## 1 <NA> 2
## 2 <NA> 5
## 3 <NA> 5
## 4 <NA> 4
## 5 <NA> 5
## 6 <NA> 3
as.data.frame(eg2)
## id id.1 name dob gender Date.Of.Onset_date Contact1_date
## 1 1 1 Tom 1981-01-12 male 2014-12-28 2014-12-26
## 2 2 2 Andy 1980-11-11 male 2014-12-29 2014-12-26
## 3 3 3 Ellie 1982-02-10 female 2015-01-03 2014-12-28
## 4 4 4 Ana 1981-12-09 female 2015-01-08 <NA>
## 5 5 5 Tibo 1983-03-08 male 2015-01-04 2014-12-28
## contact1.id Contact2_date contact2.id Contact3_date contact3.id
## 1 B 2014-12-25 3D 2015-01-08 B
## 2 A 2014-12-26 3D <NA> <NA>
## 3 5 2015-01-14 5 <NA> <NA>
## 4 <NA> <NA> <NA> <NA> <NA>
## 5 3D 2014-12-25 A <NA> <NA>
## Hospital.admission_date hospital fever sleepy Hospital.discharge_date
## 1 <NA> <NA> <NA> <NA> <NA>
## 2 2015-01-05 St Marys yes yes <NA>
## 3 2015-01-12 Whittington no yes 2015-01-17
## 4 <NA> <NA> <NA> <NA> <NA>
## 5 2015-01-14 Whittington yes yes 2015-01-17
Transition 3: From obkData to an EpiJSON object
These are example data in obkData format
data(ToyOutbreak)
Use the repijson package to convert an obkData object to JSON object into :
eg3 <- as.ejObject(ToyOutbreak)
Transition 4: From an EpiJSON object to obkData
Next function to produce
Transition 5: From an EpiJSON object to spatial
Use the repijson package to convert from an EpiJSON object to spatial (sp).
Here we get the location of all the events as a SpatialPointsDataFrame
sp_eg1 <- as.SpatialPointsDataFrame.ejObject(eg1)
plot(sp_eg1,pch=20,col="green")
text(10,17,"Example from Snow Deaths data")
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EpiJSON is a generic JSON format for storing epidemiological data.
repijson is an R package that allows conversion between EpiJSON files and R data formats.
This vignette is a demonstration of the package repijson.
Epidemiological data is often stored and transferred as spread-sheets, databases, and text files with little standardisation in row, column and field names. A universal format enabling the coherent storage and transfer of these data is lacking. In many cases where transfer does occur, there is room for misinterpretation and preventable errors may be introduced into reports and analyses.
EpiJSON provides a potential solution for the unambiguous storage and transfer of epidemiological data. repijson facilitates the use of EpiJSON within R.
Installing repijson
To install the development version from github:
library(devtools)
install_github("hackout2/repijson")
Then, to load the package, use:
library("repijson")
The EpiJSON format
This is a simplified representation of the EpiJSON format.
The repijson objects used to store EpiJSON are represented in the following diagram.
First simple example creating an repijson object from a dataframe
toyll is a small example dataframe within the repijson package. It follows the structure of a disease outbreak line list, with individuals in rows and data stored in columns. This is what the first 3 rows and 8 columns look like :
toyll[1:3,1:8]
## id name dob gender date.of.onset date.of.admission
## 1 A Tom 1981-01-12 male 2014-12-28 <NA>
## 2 B Andy 1980-11-11 male 2014-12-29 2015-01-05
## 3 3D Ellie 1982-02-10 female 2015-01-03 2015-01-12
## date.of.discharge hospital
## 1 <NA> <NA>
## 2 <NA> St Marys
## 3 2015-01-17 Whittington
The example below creates an ejObject from the first 3 rows. It assigns the columns "name" and "gender" as record attributes. It defines two events, "admission" and "discharge". The "hospital" column is assigned as an attribute of the first event.
#converting dates to date format
toyll$date.of.admission <- as.POSIXct(toyll$date.of.admission)
toyll$date.of.discharge <- as.POSIXct(toyll$date.of.discharge)
#create ejObject
ejOb <- as.ejObject(toyll[1:3,],
recordAttributes=c("name","gender"),
eventDefinitions=list(
define_ejEvent(name="admission", date="date.of.admission",attributes="hospital"),
define_ejEvent(name="discharge", date="date.of.discharge")
))
#display ejObject
ejOb
## {
## "metadata": [
##
## ],
## "records": [
## {
## "id": 1,
## "attributes": [
## {
## "name": "name",
## "type": "string",
## "value": "Tom"
## },
## {
## "name": "gender",
## "type": "string",
## "value": "male"
## }
## ],
## "events": [
##
## ]
## },
## {
## "id": 2,
## "attributes": [
## {
## "name": "name",
## "type": "string",
## "value": "Andy"
## },
## {
## "name": "gender",
## "type": "string",
## "value": "male"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "admission",
## "date": "2015-01-05T00:00:00Z",
## "attributes": [
## {
## "name": "hospital",
## "type": "string",
## "value": "St Marys"
## }
## ]
## }
## ]
## },
## {
## "id": 3,
## "attributes": [
## {
## "name": "name",
## "type": "string",
## "value": "Ellie"
## },
## {
## "name": "gender",
## "type": "string",
## "value": "female"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "admission",
## "date": "2015-01-12T00:00:00Z",
## "attributes": [
## {
## "name": "hospital",
## "type": "string",
## "value": "Whittington"
## }
## ]
## },
## {
## "id": 2,
## "name": "discharge",
## "date": "2015-01-17T00:00:00Z",
## "attributes": [
##
## ]
## }
## ]
## }
## ]
## }
##
Load the required packages for further examples.
library(OutbreakTools)
library(sp)
library(HistData)
Creating example dataframe 1.
data(Snow.deaths)
Adding some dates, pumps, some genders
simulated <- Snow.deaths
simulated$gender <- c("male","female")[(runif(nrow(simulated))>0.5) +1]
simulated$date <- as.POSIXct("1854-04-05") + rnorm(nrow(simulated), 10) * 86400
simulated$pump <- ceiling(runif(nrow(simulated)) * 5)
exampledata1<-head(simulated)
exampledata1
## case x y gender date pump
## 1 1 13.588010 11.095600 male 1854-04-15 10:54:43 2
## 2 2 9.878124 12.559180 male 1854-04-14 20:25:50 5
## 3 3 14.653980 10.180440 male 1854-04-16 02:20:53 5
## 4 4 15.220570 9.993003 female 1854-04-17 01:12:46 4
## 5 5 13.162650 12.963190 female 1854-04-15 02:34:48 5
## 6 6 13.806170 8.889046 female 1854-04-16 15:33:10 3
Creating example dataframe 2.
exampledata2<- data.frame(id=c(1,2,3,4,5),
name=c("Tom","Andy","Ellie","Ana","Tibo"),
dob=c("1981-01-12","1980-11-11","1982-02-10","1981-12-09","1983-03-08"),
gender=c("male","male","female","female","male"),
date.of.onset=c("2014-12-28","2014-12-29","2015-01-03","2015-01-08","2015-01-04"),
date.of.admission=c(NA,"2015-01-05","2015-01-12",NA,"2015-01-14"),
date.of.discharge=c(NA,NA,"2015-01-17",NA,"2015-01-17"),
hospital=c(NA,"St Marys","Whittington",NA,"Whittington"),
fever=c("yes","yes","no","no","yes"),
sleepy=c("no","yes","yes","no","yes"),
contact1.id=c("B","A","5",NA,"3D"),
contact1.date=c("2014-12-26","2014-12-26","2014-12-28",NA,"2014-12-28"),
contact2.id=c("3D","3D","5",NA,"A"),
contact2.date=c("2014-12-25","2014-12-26","2015-01-14",NA,"2014-12-25"),
contact3.id=c("B",NA,NA,NA,NA),
contact3.date=c("2015-01-08",NA,NA,NA,NA)
)
exampledata2
## id name dob gender date.of.onset date.of.admission
## 1 1 Tom 1981-01-12 male 2014-12-28 <NA>
## 2 2 Andy 1980-11-11 male 2014-12-29 2015-01-05
## 3 3 Ellie 1982-02-10 female 2015-01-03 2015-01-12
## 4 4 Ana 1981-12-09 female 2015-01-08 <NA>
## 5 5 Tibo 1983-03-08 male 2015-01-04 2015-01-14
## date.of.discharge hospital fever sleepy contact1.id contact1.date
## 1 <NA> <NA> yes no B 2014-12-26
## 2 <NA> St Marys yes yes A 2014-12-26
## 3 2015-01-17 Whittington no yes 5 2014-12-28
## 4 <NA> <NA> no no <NA> <NA>
## 5 2015-01-17 Whittington yes yes 3D 2014-12-28
## contact2.id contact2.date contact3.id contact3.date
## 1 3D 2014-12-25 B 2015-01-08
## 2 3D 2014-12-26 <NA> <NA>
## 3 5 2015-01-14 <NA> <NA>
## 4 <NA> <NA> <NA> <NA>
## 5 A 2014-12-25 <NA> <NA>
Transition 1: data.frame to EpiJSON format
Use the repijson package to convert a data.frame object into a EpiJSON object within R:
eg1 <- as.ejObject(exampledata1,
recordAttributes = "gender",
eventDefinitions = list(define_ejEvent(date="date", name="Death", location=list(x="x", y="y", proj4string=""), attributes="pump")),
metadata=list())
eg1
## {
## "metadata": [
##
## ],
## "records": [
## {
## "id": 1,
## "attributes": [
## {
## "name": "gender",
## "type": "string",
## "value": "male"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "Death",
## "date": "1854-04-15T10:54:43Z",
## "location": {
## "type": "FeatureCollection",
## "features": [
## {
## "type": "Feature",
## "id": 1,
## "properties": {
## "dat": 1
## },
## "geometry": {
## "type": "Point",
## "coordinates": [
## 13.588,
## 11.0956
## ]
## }
## }
## ]
## },
## "attributes": [
## {
## "name": "pump",
## "type": "number",
## "value": 2
## }
## ]
## }
## ]
## },
## {
## "id": 2,
## "attributes": [
## {
## "name": "gender",
## "type": "string",
## "value": "male"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "Death",
## "date": "1854-04-14T20:25:50Z",
## "location": {
## "type": "FeatureCollection",
## "features": [
## {
## "type": "Feature",
## "id": 2,
## "properties": {
## "dat": 1
## },
## "geometry": {
## "type": "Point",
## "coordinates": [
## 9.8781,
## 12.5592
## ]
## }
## }
## ]
## },
## "attributes": [
## {
## "name": "pump",
## "type": "number",
## "value": 5
## }
## ]
## }
## ]
## },
## {
## "id": 3,
## "attributes": [
## {
## "name": "gender",
## "type": "string",
## "value": "male"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "Death",
## "date": "1854-04-16T02:20:53Z",
## "location": {
## "type": "FeatureCollection",
## "features": [
## {
## "type": "Feature",
## "id": 3,
## "properties": {
## "dat": 1
## },
## "geometry": {
## "type": "Point",
## "coordinates": [
## 14.654,
## 10.1804
## ]
## }
## }
## ]
## },
## "attributes": [
## {
## "name": "pump",
## "type": "number",
## "value": 5
## }
## ]
## }
## ]
## },
## {
## "id": 4,
## "attributes": [
## {
## "name": "gender",
## "type": "string",
## "value": "female"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "Death",
## "date": "1854-04-17T01:12:46Z",
## "location": {
## "type": "FeatureCollection",
## "features": [
## {
## "type": "Feature",
## "id": 4,
## "properties": {
## "dat": 1
## },
## "geometry": {
## "type": "Point",
## "coordinates": [
## 15.2206,
## 9.993
## ]
## }
## }
## ]
## },
## "attributes": [
## {
## "name": "pump",
## "type": "number",
## "value": 4
## }
## ]
## }
## ]
## },
## {
## "id": 5,
## "attributes": [
## {
## "name": "gender",
## "type": "string",
## "value": "female"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "Death",
## "date": "1854-04-15T02:34:48Z",
## "location": {
## "type": "FeatureCollection",
## "features": [
## {
## "type": "Feature",
## "id": 5,
## "properties": {
## "dat": 1
## },
## "geometry": {
## "type": "Point",
## "coordinates": [
## 13.1626,
## 12.9632
## ]
## }
## }
## ]
## },
## "attributes": [
## {
## "name": "pump",
## "type": "number",
## "value": 5
## }
## ]
## }
## ]
## },
## {
## "id": 6,
## "attributes": [
## {
## "name": "gender",
## "type": "string",
## "value": "female"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "Death",
## "date": "1854-04-16T15:33:10Z",
## "location": {
## "type": "FeatureCollection",
## "features": [
## {
## "type": "Feature",
## "id": 6,
## "properties": {
## "dat": 1
## },
## "geometry": {
## "type": "Point",
## "coordinates": [
## 13.8062,
## 8.889
## ]
## }
## }
## ]
## },
## "attributes": [
## {
## "name": "pump",
## "type": "number",
## "value": 3
## }
## ]
## }
## ]
## }
## ]
## }
##
The repijson package does not convert dates represented as strings for you. This is because the process of conversion from character to date-time is fraught with difficulty and the hidden corruption of dates is much worse than being told by R to provide date objects. Here we convert the dates in the example two data to real dates. We use POSIXct as this is more firendly to data.frames.
exampledata2$date.of.onset <- as.POSIXct(exampledata2$date.of.onset)
exampledata2$date.of.admission <- as.POSIXct(exampledata2$date.of.admission)
exampledata2$date.of.discharge <- as.POSIXct(exampledata2$date.of.discharge)
exampledata2$contact1.date <- as.POSIXct(exampledata2$contact1.date)
exampledata2$contact2.date <- as.POSIXct(exampledata2$contact2.date)
exampledata2$contact3.date <- as.POSIXct(exampledata2$contact3.date)
We are now set to convert the exampledata2 dataframe to an EpiJSON object.
eg2 <- as.ejObject(exampledata2, recordAttributes = c("id","name","dob","gender"),
eventDefinitions = list(define_ejEvent(name="Date Of Onset", date="date.of.onset",
attributes=list()),
define_ejEvent(name="Hospital admission", date="date.of.admission",
attributes=list("hospital", "fever", "sleepy")),
define_ejEvent(name="Hospital discharge", date="date.of.discharge"),
define_ejEvent(name="Contact1", date="contact1.date", attributes=list("contact1.id")),
define_ejEvent(name="Contact2", date="contact2.date", attributes=list("contact2.id")),
define_ejEvent(name="Contact3", date="contact3.date", attributes=list("contact3.id"))
),
metadata=list())
eg2
## {
## "metadata": [
##
## ],
## "records": [
## {
## "id": 1,
## "attributes": [
## {
## "name": "id",
## "type": "number",
## "value": 1
## },
## {
## "name": "name",
## "type": "string",
## "value": "Tom"
## },
## {
## "name": "dob",
## "type": "string",
## "value": "1981-01-12"
## },
## {
## "name": "gender",
## "type": "string",
## "value": "male"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "Date Of Onset",
## "date": "2014-12-28T00:00:00Z",
## "attributes": [
##
## ]
## },
## {
## "id": 4,
## "name": "Contact1",
## "date": "2014-12-26T00:00:00Z",
## "attributes": [
## {
## "name": "contact1.id",
## "type": "string",
## "value": "B"
## }
## ]
## },
## {
## "id": 5,
## "name": "Contact2",
## "date": "2014-12-25T00:00:00Z",
## "attributes": [
## {
## "name": "contact2.id",
## "type": "string",
## "value": "3D"
## }
## ]
## },
## {
## "id": 6,
## "name": "Contact3",
## "date": "2015-01-08T00:00:00Z",
## "attributes": [
## {
## "name": "contact3.id",
## "type": "string",
## "value": "B"
## }
## ]
## }
## ]
## },
## {
## "id": 2,
## "attributes": [
## {
## "name": "id",
## "type": "number",
## "value": 2
## },
## {
## "name": "name",
## "type": "string",
## "value": "Andy"
## },
## {
## "name": "dob",
## "type": "string",
## "value": "1980-11-11"
## },
## {
## "name": "gender",
## "type": "string",
## "value": "male"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "Date Of Onset",
## "date": "2014-12-29T00:00:00Z",
## "attributes": [
##
## ]
## },
## {
## "id": 2,
## "name": "Hospital admission",
## "date": "2015-01-05T00:00:00Z",
## "attributes": [
## {
## "name": "hospital",
## "type": "string",
## "value": "St Marys"
## },
## {
## "name": "fever",
## "type": "string",
## "value": "yes"
## },
## {
## "name": "sleepy",
## "type": "string",
## "value": "yes"
## }
## ]
## },
## {
## "id": 4,
## "name": "Contact1",
## "date": "2014-12-26T00:00:00Z",
## "attributes": [
## {
## "name": "contact1.id",
## "type": "string",
## "value": "A"
## }
## ]
## },
## {
## "id": 5,
## "name": "Contact2",
## "date": "2014-12-26T00:00:00Z",
## "attributes": [
## {
## "name": "contact2.id",
## "type": "string",
## "value": "3D"
## }
## ]
## }
## ]
## },
## {
## "id": 3,
## "attributes": [
## {
## "name": "id",
## "type": "number",
## "value": 3
## },
## {
## "name": "name",
## "type": "string",
## "value": "Ellie"
## },
## {
## "name": "dob",
## "type": "string",
## "value": "1982-02-10"
## },
## {
## "name": "gender",
## "type": "string",
## "value": "female"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "Date Of Onset",
## "date": "2015-01-03T00:00:00Z",
## "attributes": [
##
## ]
## },
## {
## "id": 2,
## "name": "Hospital admission",
## "date": "2015-01-12T00:00:00Z",
## "attributes": [
## {
## "name": "hospital",
## "type": "string",
## "value": "Whittington"
## },
## {
## "name": "fever",
## "type": "string",
## "value": "no"
## },
## {
## "name": "sleepy",
## "type": "string",
## "value": "yes"
## }
## ]
## },
## {
## "id": 3,
## "name": "Hospital discharge",
## "date": "2015-01-17T00:00:00Z",
## "attributes": [
##
## ]
## },
## {
## "id": 4,
## "name": "Contact1",
## "date": "2014-12-28T00:00:00Z",
## "attributes": [
## {
## "name": "contact1.id",
## "type": "string",
## "value": "5"
## }
## ]
## },
## {
## "id": 5,
## "name": "Contact2",
## "date": "2015-01-14T00:00:00Z",
## "attributes": [
## {
## "name": "contact2.id",
## "type": "string",
## "value": "5"
## }
## ]
## }
## ]
## },
## {
## "id": 4,
## "attributes": [
## {
## "name": "id",
## "type": "number",
## "value": 4
## },
## {
## "name": "name",
## "type": "string",
## "value": "Ana"
## },
## {
## "name": "dob",
## "type": "string",
## "value": "1981-12-09"
## },
## {
## "name": "gender",
## "type": "string",
## "value": "female"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "Date Of Onset",
## "date": "2015-01-08T00:00:00Z",
## "attributes": [
##
## ]
## }
## ]
## },
## {
## "id": 5,
## "attributes": [
## {
## "name": "id",
## "type": "number",
## "value": 5
## },
## {
## "name": "name",
## "type": "string",
## "value": "Tibo"
## },
## {
## "name": "dob",
## "type": "string",
## "value": "1983-03-08"
## },
## {
## "name": "gender",
## "type": "string",
## "value": "male"
## }
## ],
## "events": [
## {
## "id": 1,
## "name": "Date Of Onset",
## "date": "2015-01-04T00:00:00Z",
## "attributes": [
##
## ]
## },
## {
## "id": 2,
## "name": "Hospital admission",
## "date": "2015-01-14T00:00:00Z",
## "attributes": [
## {
## "name": "hospital",
## "type": "string",
## "value": "Whittington"
## },
## {
## "name": "fever",
## "type": "string",
## "value": "yes"
## },
## {
## "name": "sleepy",
## "type": "string",
## "value": "yes"
## }
## ]
## },
## {
## "id": 3,
## "name": "Hospital discharge",
## "date": "2015-01-17T00:00:00Z",
## "attributes": [
##
## ]
## },
## {
## "id": 4,
## "name": "Contact1",
## "date": "2014-12-28T00:00:00Z",
## "attributes": [
## {
## "name": "contact1.id",
## "type": "string",
## "value": "3D"
## }
## ]
## },
## {
## "id": 5,
## "name": "Contact2",
## "date": "2014-12-25T00:00:00Z",
## "attributes": [
## {
## "name": "contact2.id",
## "type": "string",
## "value": "A"
## }
## ]
## }
## ]
## }
## ]
## }
##
Transition 2: EpiJSON object to data.frame format
Use the repijson package to convert a JSON object into a data.frame object:
as.data.frame(eg1)
## id gender Death_date Death_locationX Death_locationY
## 1 1 male 1854-04-15 10:54:43 13.588010 11.095600
## 2 2 male 1854-04-14 20:25:50 9.878124 12.559180
## 3 3 male 1854-04-16 02:20:53 14.653980 10.180440
## 4 4 female 1854-04-17 01:12:46 15.220570 9.993003
## 5 5 female 1854-04-15 02:34:48 13.162650 12.963190
## 6 6 female 1854-04-16 15:33:10 13.806170 8.889046
## Death_locationCRS pump
## 1 <NA> 2
## 2 <NA> 5
## 3 <NA> 5
## 4 <NA> 4
## 5 <NA> 5
## 6 <NA> 3
as.data.frame(eg2)
## id id.1 name dob gender Date.Of.Onset_date Contact1_date
## 1 1 1 Tom 1981-01-12 male 2014-12-28 2014-12-26
## 2 2 2 Andy 1980-11-11 male 2014-12-29 2014-12-26
## 3 3 3 Ellie 1982-02-10 female 2015-01-03 2014-12-28
## 4 4 4 Ana 1981-12-09 female 2015-01-08 <NA>
## 5 5 5 Tibo 1983-03-08 male 2015-01-04 2014-12-28
## contact1.id Contact2_date contact2.id Contact3_date contact3.id
## 1 B 2014-12-25 3D 2015-01-08 B
## 2 A 2014-12-26 3D <NA> <NA>
## 3 5 2015-01-14 5 <NA> <NA>
## 4 <NA> <NA> <NA> <NA> <NA>
## 5 3D 2014-12-25 A <NA> <NA>
## Hospital.admission_date hospital fever sleepy Hospital.discharge_date
## 1 <NA> <NA> <NA> <NA> <NA>
## 2 2015-01-05 St Marys yes yes <NA>
## 3 2015-01-12 Whittington no yes 2015-01-17
## 4 <NA> <NA> <NA> <NA> <NA>
## 5 2015-01-14 Whittington yes yes 2015-01-17
Transition 3: From obkData to an EpiJSON object
These are example data in obkData format
data(ToyOutbreak)
Use the repijson package to convert an obkData object to JSON object into :
eg3 <- as.ejObject(ToyOutbreak)
Transition 4: From an EpiJSON object to obkData
Next function to produce
Transition 5: From an EpiJSON object to spatial
Use the repijson package to convert from an EpiJSON object to spatial (sp). Here we get the location of all the events as a SpatialPointsDataFrame
sp_eg1 <- as.SpatialPointsDataFrame.ejObject(eg1)
plot(sp_eg1,pch=20,col="green")
text(10,17,"Example from Snow Deaths data")
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