library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.0     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(dplyr)
library(knitr)
dataset<-read_csv("dataset_final.csv")
## New names:
## Rows: 51 Columns: 7
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (1): State dbl (6): ...1, Spending_per_enrollee, 65-74 Population, 75-84
## Population, 85...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
dataset<-dataset[,-1]

str(dataset)
## tibble [51 × 6] (S3: tbl_df/tbl/data.frame)
##  $ State                : chr [1:51] "ALABAMA" "ALASKA" "ARIZONA" "ARKANSAS" ...
##  $ Spending_per_enrollee: num [1:51] 10688 9939 10092 9919 12586 ...
##  $ 65-74 Population     : num [1:51] 541944 66426 783000 315422 3558013 ...
##  $ 75-84 Population     : num [1:51] 261671 24499 416355 158588 1721688 ...
##  $ 85+ Population       : num [1:51] 85202 6738 133691 54091 677391 ...
##  $ Total_Deaths         : num [1:51] 3792 254 5295 2427 23452 ...
dataset_graph<-ggplot(dataset, aes(x =`Spending_per_enrollee`, y =`Total_Deaths`)) +
  geom_point(alpha = 0.5)+
  scale_x_continuous(breaks = pretty(dataset$Total_Deaths, n=10))+
  labs(title = "Mortality Rates by Healthcare Spending", x= "Medicare Funding Spent", y= "Total Deaths") +
  theme_minimal()

dataset_graph

dataset_mortality<- dataset %>% 
  mutate(Population_over_65=dataset$`65-74 Population`+ dataset$`75-84 Population`+ dataset$`85+ Population`) %>%
  mutate(Mortality_rate=(Total_Deaths/Population_over_65)*100)
dataset_table<- kable(dataset_mortality, digits = c(0,2,0,0,0,0,0,2),
                            col.names = c("State", "Medicare Spending per Enrollee",
                                          "65-74 Population", "75-84 Population",
                                          "85+ Population","Total Deaths", 
                                          "Population over 65", "Mortality Rate"),
                            caption = "Mortality for Selected Causes and Medicare
                            Spending by State in 2018")

dataset_table
Mortality for Selected Causes and Medicare Spending by State in 2018
State Medicare Spending per Enrollee 65-74 Population 75-84 Population 85+ Population Total Deaths Population over 65 Mortality Rate
ALABAMA 10687.68 541944 261671 85202 3792 888817 0.43
ALASKA 9938.61 66426 24499 6738 254 97663 0.26
ARIZONA 10092.20 783000 416355 133691 5295 1333046 0.40
ARKANSAS 9918.67 315422 158588 54091 2427 528101 0.46
CALIFORNIA 12586.06 3558013 1721688 677391 23452 5957092 0.39
COLORADO 9452.91 558844 239521 81288 2237 879653 0.25
CONNECTICUT 12200.54 378532 190277 80426 1680 649235 0.26
DELAWARE 11418.66 124397 58586 18663 568 201646 0.28
DISTRICT OF COLUMBIA 11308.70 50717 24518 10603 397 85838 0.46
FLORIDA 12170.40 2591258 1470807 536321 15009 4598386 0.33
GEORGIA 10686.99 987987 455027 141057 6459 1584071 0.41
HAWAII 7472.03 162622 80866 38816 800 282304 0.28
IDAHO 8929.01 197346 90494 27616 1003 315456 0.32
ILLINOIS 11283.33 1259846 601395 240221 6824 2101462 0.32
INDIANA 10757.30 681528 316728 116432 4258 1114688 0.38
IOWA 9853.68 336104 159823 69346 2110 565273 0.37
KANSAS 10533.20 294178 137320 58140 1954 489638 0.40
KENTUCKY 10120.59 476140 220507 73613 2927 770260 0.38
LOUISIANA 11650.01 471409 216146 74255 3102 761810 0.41
MAINE 9158.84 183139 84057 29969 1113 297165 0.37
MARYLAND 11898.85 604486 289158 109513 3185 1003157 0.32
MASSACHUSETTS 11661.35 726907 346225 141561 2993 1214693 0.25
MICHIGAN 10879.44 1118078 517196 187508 7179 1822782 0.39
MINNESOTA 11489.31 577187 268593 109903 2873 955683 0.30
MISSISSIPPI 11428.40 303969 142839 47436 3155 494244 0.64
MISSOURI 10477.43 649461 315255 119051 3446 1083767 0.32
MONTANA 8648.68 135439 60197 20787 515 216423 0.24
NEBRASKA 10669.59 193977 89882 38031 1254 321890 0.39
NEVADA 11120.74 321908 154041 42518 2216 518467 0.43
NEW HAMPSHIRE 9369.38 166691 73406 27424 643 267521 0.24
NEW JERSEY 12394.35 920871 458662 186384 4235 1565917 0.27
NEW MEXICO 8665.11 238520 114957 38469 1235 391946 0.32
NEW YORK 13138.75 2037717 1011298 428706 13206 3477721 0.38
NORTH CAROLINA 10106.07 1101147 520528 171639 6170 1793314 0.34
NORTH DAKOTA 11304.07 73642 34310 16689 436 124641 0.35
OHIO 10629.95 1278292 593832 226875 8008 2098999 0.38
OKLAHOMA 11332.89 388432 189996 66283 5433 644711 0.84
OREGON 8609.80 489637 224253 74489 2481 788379 0.31
PENNSYLVANIA 10590.53 1465176 706858 292420 7656 2464454 0.31
RHODE ISLAND 9737.28 118373 56012 24406 577 198791 0.29
SOUTH CAROLINA 10257.25 600896 281831 83672 3444 966399 0.36
SOUTH DAKOTA 11399.19 97689 40563 18166 565 156418 0.36
TENNESSEE 9889.50 728268 345555 111449 5860 1185272 0.49
TEXAS 11963.65 2408573 1097502 369909 14633 3875984 0.38
UTAH 9774.65 242367 111129 35652 1281 389148 0.33
VERMONT 9206.11 82540 37555 13163 406 133258 0.30
VIRGINIA 9474.86 853298 412369 140985 4376 1406652 0.31
WASHINGTON 8736.53 782531 352000 120647 4336 1255178 0.35
WEST VIRGINIA 10508.27 226067 107444 35909 1782 369420 0.48
WISCONSIN 10212.38 649145 295589 112509 3158 1057243 0.30
WYOMING 9975.40 65986 28167 9724 321 103877 0.31