R package to read, parse and do basic manipulation of INE Morbidity microdata Morbilidad Hospitalaria Microdatos INE. The metadata of the microdata is documented here.
This packages uses international classification of diseases documented here
library(devtools)
install_github("rOpenSpain/MorbiditySpainR")
The function GetMorbiData recives the years to read morbidity data, downloads the files from INE's ftp server and parses them.
data <- GetMorbiData(y1=2010,y2=2011)
head(data)
The function FilterProvincia recives the id of the provincia (regional administration) to filter data.
data <- data <- data_ejemplo %>% FilterProvincia(28)
head(data)
The function FilterEmergency recives a boolean (defect TRUE) to filter data by wether or not is an ER item.
data <- data_ejemplo %>% FilterEmergency()
head(data)
The function FilterDiagnosis recives a integer (id of diagnosis) to filter data by principal diagnosis.
data <- data_ejemplo %>% FilterDiagnosis1(2)
head(data)
The function FilterDiagnosis2 recives a integer (id of diagnosis) to filter data by secondary diagnosis.
data <- data_ejemplo %>% FilterDiagnosis2(20)
head(data)
The function AddDiagnosis1 add column daig1 with principal diagnosis.
data <- data_ejemplo %>% AddDiagnosis1()
head(data)
The function AddDiagnosis2 add column daig2 with secondary diagnosis.
data <- data_ejemplo %>% AddDiagnosis2()
head(data)
The function AddDiagnosis3 add column daig3 with specific diagnosis.
data <- data_ejemplo %>% AddDiagnosis3()
head(data)
The function ReduceData does different grouping manipulations by provincia, date, diagnosis or sex.
data <- data_ejemplo %>% ReduceData(provincia = TRUE,date = "day")
head(data)
The function SetPrevalence gets relative values from grouped values and population (total or by sex) of provinces.
data <- data_ejemplo %>% ReduceData(provincia = TRUE,date="year") %>% SetPrevalence()
head(data)
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