github repo for rest of specialization: Data Science Coursera
The zip file containing the data can be downloaded here: Assignment 3 Data
Part 1 Plot the 30-day mortality rates for heart attack (outcome.R)
# install.packages("data.table")
library("data.table")
# Reading in data
outcome <- data.table::fread('outcome-of-care-measures.csv')
outcome[, (11) := lapply(.SD, as.numeric), .SDcols = (11)]
outcome[, lapply(.SD
, hist
, xlab= "Deaths"
, main = "Hospital 30-Day Death (Mortality) Rates from Heart Attack"
, col="lightblue")
, .SDcols = (11)]
Part 2 Finding the best hospital in a state (best.R)
best <- function(state, outcome) {
# Read outcome data
out_dt <- data.table::fread('outcome-of-care-measures.csv')
outcome <- tolower(outcome)
# Column name is same as variable so changing it
chosen_state <- state
# Check that state and outcome are valid
if (!chosen_state %in% unique(out_dt[["State"]])) {
stop('invalid state')
}
if (!outcome %in% c("heart attack", "heart failure", "pneumonia")) {
stop('invalid outcome')
}
# Renaming Columns to be less verbose and lowercase
setnames(out_dt
, tolower(sapply(colnames(out_dt), gsub, pattern = "^Hospital 30-Day Death \\(Mortality\\) Rates from ", replacement = "" ))
)
#Filter by state
out_dt <- out_dt[state == chosen_state]
# Columns indices to keep
col_indices <- grep(paste0("hospital name|state|^",outcome), colnames(out_dt))
# Filtering out unnessecary data
out_dt <- out_dt[, .SD ,.SDcols = col_indices]
# Find out what class each column is
# sapply(out_dt,class)
out_dt[, outcome] <- out_dt[, as.numeric(get(outcome))]
# Removing Missing Values for numerical datatype (outcome column)
out_dt <- out_dt[complete.cases(out_dt),]
# Order Column to Top
out_dt <- out_dt[order(get(outcome), `hospital name`)]
return(out_dt[, "hospital name"][1])
}Part 3 Ranking hospitals by outcome in a state (rankhospital.R)
rankhospital <- function(state, outcome, num = "best") {
# Read outcome data
out_dt <- data.table::fread('outcome-of-care-measures.csv')
outcome <- tolower(outcome)
# Column name is same as variable so changing it
chosen_state <- state
# Check that state and outcome are valid
if (!chosen_state %in% unique(out_dt[["State"]])) {
stop('invalid state')
}
if (!outcome %in% c("heart attack", "heart failure", "pneumonia")) {
stop('invalid outcome')
}
# Renaming Columns to be less verbose and lowercase
setnames(out_dt
, tolower(sapply(colnames(out_dt), gsub, pattern = "^Hospital 30-Day Death \\(Mortality\\) Rates from ", replacement = "" ))
)
#Filter by state
out_dt <- out_dt[state == chosen_state]
# Columns indices to keep
col_indices <- grep(paste0("hospital name|state|^",outcome), colnames(out_dt))
# Filtering out unnessecary data
out_dt <- out_dt[, .SD ,.SDcols = col_indices]
# Find out what class each column is
# sapply(out_dt,class)
out_dt[, outcome] <- out_dt[, as.numeric(get(outcome))]
# Removing Missing Values for numerical datatype (outcome column)
out_dt <- out_dt[complete.cases(out_dt),]
# Order Column to Top
out_dt <- out_dt[order(get(outcome), `hospital name`)]
out_dt <- out_dt[, .(`hospital name` = `hospital name`, state = state, rate = get(outcome), Rank = .I)]
if (num == "best"){
return(out_dt[1,`hospital name`])
}
if (num == "worst"){
return(out_dt[.N,`hospital name`])
}
return(out_dt[num,`hospital name`])
}Part 4 Ranking hospitals in all states (rankall.R)
rankall <- function(outcome, num = "best") {
# Read outcome data
out_dt <- data.table::fread('outcome-of-care-measures.csv')
outcome <- tolower(outcome)
if (!outcome %in% c("heart attack", "heart failure", "pneumonia")) {
stop('invalid outcome')
}
# Renaming Columns to be less verbose and lowercase
setnames(out_dt
, tolower(sapply(colnames(out_dt), gsub, pattern = "^Hospital 30-Day Death \\(Mortality\\) Rates from ", replacement = "" ))
)
# Columns indices to keep
col_indices <- grep(paste0("hospital name|state|^",outcome), colnames(out_dt))
# Filtering out unnessecary data
out_dt <- out_dt[, .SD ,.SDcols = col_indices]
# Find out what class each column is
# sapply(out_dt,class)
# Change outcome column class
out_dt[, outcome] <- out_dt[, as.numeric(get(outcome))]
if (num == "best"){
return(out_dt[order(state, get(outcome), `hospital name`)
, .(hospital = head(`hospital name`, 1))
, by = state])
}
if (num == "worst"){
return(out_dt[order(get(outcome), `hospital name`)
, .(hospital = tail(`hospital name`, 1))
, by = state])
}
return(out_dt[order(state, get(outcome), `hospital name`)
, head(.SD,num)
, by = state, .SDcols = c("hospital name") ])
}
Many thanks for sharing!! I also thought I was the only one struggling with the course but I'm "glad" to see is the course itself.
Now I am able to analyze and compare what I was doing. Thanks a lot!