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") ])
}
Thank you for your solution.
I am glad that there are so many people feeling the same.
As I don't have any knowledge regarding coding in general and I wanted to take this course to learn something new such as Data Science. This course could be great if designed properly but the course is not that learner friendly, in some cases even people with experience with coding may not able to cope it and this has led me thoughts to quit.
This course are expecting learners to do their own coding without giving guidance to learners. WHY! There are so little guidance given to readers until I had to Google Search for answers. I need to take sometime to study it after I have passed this.