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") ])
}
This course is just designed to make me feel bad. I was in the honor's college while I was a senior, now I am getting my master in sociology.
Throughout my academic career so far I've never Googled anyone else's assignment. And this course makes me do this for every assignment!!!!!! By the way, I have a decent knowledge of programming where I gain from learning Python. I thought this class would be easy for me (after quickly going through the lecture videos), yet from the first assignment I began to scratch my head for an answer.
Guess what? the following courses for this specialization are just no better. At first sight I thought the instructors may have problems with their pedagogy. After going through several courses of JHU's Data Science Specialization, I highly doubt it's not just pedagogy, it is their attitude. There is no way the three instructors who should be incredibly smart people cannot find the embarrassingly obvious large gap between the course material and the assignments/quizzes in every one of their courses. And they rush through every course in this specialization. I paid for those courses though! I really want to file a complaint on Coursera.