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") ])
}
I'm glad many are learning this specialization even today, I was hoping if anyone can direct me to any discord servers or like reddit groups to talk about the assignments, for discussions and to learn from each other. Thanks! And yea the assignments are crazy hard, but to be fair the coursera courses can't fit everything into their videos, so i believe it's encouraged to find ways to create a solution for yourself with the abundance of resources online.
is this part wrong?
return(dt[order(state, get(outcome),hospital name) , head(.SD, num) , by = state , .SDcols = c("hospital name")])I can't seem to get the right answers with this part. Anyone has a revision of this part of the code?