Counting RMS Incident Types by Category

Load quarterly RMS files into memory

datq1 <- read.csv("smcf_rms/01012014_03312014.csv",header=TRUE)
datq2 <- read.csv("smcf_rms/04012014_06302014.csv",header=TRUE)
datq3 <- read.csv("smcf_rms/07012014_09302014.csv",header=TRUE)
datq4 <- read.csv("smcf_rms/10012014_12312014.csv",header=TRUE)

Reduce to essential fields, and merge into 1 dataset

keep_cols <- c("Incidentnumber","Incidenttype")
datq1 <- datq1[keep_cols]
datq2 <- datq2[keep_cols]
datq3 <- datq3[keep_cols]
datq4 <- datq4[keep_cols]

dat2014 <- rbind(datq1,datq2)
dat2014 <- rbind(dat2014,datq3)
dat2014 <- rbind(dat2014,datq4)

De-duplicate records, the result is 1 instance of each incident number Rename the columns

dat2014.unq <- unique(dat2014)
colnames(dat2014.unq) <- c("Incidentnumber","Type")

Load the look-up table of incident codes & categories Rename the columns

tbl.lookup <- read.csv("ref/r_incident_lookup.csv",header=TRUE)
colnames(tbl.lookup) <- c("Type","Category","Detail")

Join the tables by the Type field

tbl.join <- merge(tbl.lookup,dat2014.unq,by="Type")
attach(tbl.join)
category.counts <- data.frame(table(Category))
category.counts

##              Category Freq
## 1           Cancelled  200
## 2  Duplicate Location    1
## 3    Emergency Assist  133
## 4    Emergency Rescue   47
## 5         False Alarm 1780
## 6                Fire  280
## 7              Hazmat   45
## 8             Medical 6139
## 9         Other (NEC)    6
## 10            Service  669
## 11            Unknown    0
## 12   Vehicle Accident  383