Exoloring The Leading Cause of Deaths Amongst Maryland’s Infants
Imani Pillow
2022-11-07
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.2.2## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──## ✔ tibble 3.1.8 ✔ purrr 0.3.5
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.3 ✔ forcats 0.5.2## Warning: package 'forcats' was built under R version 4.2.2## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(leaflet)## Warning: package 'leaflet' was built under R version 4.2.2library(treemap)## Warning: package 'treemap' was built under R version 4.2.2library(readr)
chart_data <- read_csv("chart_data.csv")## Rows: 6 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Race
## dbl (1): Infant mortality rate per 1000 live births
##
## 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.View(chart_data)Introduce National Data
IMUSA <- chart_data %>%
select(Race, `Infant mortality rate per 1000 live births`)
IMUSA## # A tibble: 6 × 2
## Race Infant mortality rate…¹
## <chr> <dbl>
## 1 Non-Hispanic Black 10.6
## 2 Non-Hispanic Native Hawaiian or other Pacific Islander 8.2
## 3 Non-Hispanic American Indian or Alaska Native 7.9
## 4 Hispanic 5
## 5 Non-Hispanic White 4.5
## 6 Non-Hispanic Asian 3.4
## # … with abbreviated variable name
## # ¹`Infant mortality rate per 1000 live births`!!! CREATE A CHART !!!!
library(readr)
states<- read_csv("statesdata.csv")## Rows: 400 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): STATE, RATE, URL
## dbl (2): YEAR, DEATHS
##
## 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.states## # A tibble: 400 × 5
## YEAR STATE RATE DEATHS URL
## <dbl> <chr> <chr> <dbl> <chr>
## 1 2020 AL 6.99 403 /nchs/pressroom/states/alabama/al.htm
## 2 2020 AK 5.07 48 /nchs/pressroom/states/alaska/ak.htm
## 3 2020 AZ 5.19 399 /nchs/pressroom/states/arizona/az.htm
## 4 2020 AR 7.38 260 /nchs/pressroom/states/arkansas/ar.htm
## 5 2020 CA 3.92 1648 /nchs/pressroom/states/california/ca.htm
## 6 2020 CO 4.8 295 /nchs/pressroom/states/colorado/co.htm
## 7 2020 CT 4.33 145 /nchs/pressroom/states/connecticut/ct.htm
## 8 2020 DE 5.1 53 /nchs/pressroom/states/delaware/de.htm
## 9 2020 FL 5.8 1217 /nchs/pressroom/states/florida/fl.htm
## 10 2020 GA 6.28 769 /nchs/pressroom/states/georgia/ga.htm
## # … with 390 more rowsPULL OUT THE REGION & CREATE A REGIONAL COMPARISION
DC 4.5 (infant deaths per 1,000 live births) - not included in CDC CVS MD, VA, DE, WV
State_side <- states %>%
select(STATE, RATE, YEAR, DEATHS) %>%
filter(STATE %in% c("MD", "DE", "VA", "WV")) %>%
filter(YEAR > 2019) %>%
group_by(STATE)
State_side## # A tibble: 4 × 4
## # Groups: STATE [4]
## STATE RATE YEAR DEATHS
## <chr> <chr> <dbl> <dbl>
## 1 DE 5.1 2020 53
## 2 MD 5.73 2020 393
## 3 VA 5.76 2020 546
## 4 WV 7.33 2020 127State_sidemap <- treemap(State_side,
index=c("STATE","RATE"),
vSize = "DEATHS",
type="index",
palette = "PuRd",
title="Regional Comparison: Maryland, Virginia, Delaware, West Virginia",
fontsize.title = 7,
border.col= "Gold"
)
State_sidemap## $tm
## STATE RATE vSize vColor stdErr vColorValue level x0 y0
## 1 DE 5.1 53 1 53 NA 2 0.8492255 0.0000000
## 2 DE <NA> 53 1 53 NA 1 0.8492255 0.0000000
## 3 MD 5.73 393 1 393 NA 2 0.4879357 0.3141361
## 4 MD <NA> 393 1 393 NA 1 0.4879357 0.3141361
## 5 VA 5.76 546 1 546 NA 2 0.0000000 0.0000000
## 6 VA <NA> 546 1 546 NA 1 0.0000000 0.0000000
## 7 WV 7.33 127 1 127 NA 2 0.4879357 0.0000000
## 8 WV <NA> 127 1 127 NA 1 0.4879357 0.0000000
## w h color
## 1 0.1507745 0.3141361 #F7F4F9
## 2 0.1507745 0.3141361 #F7F4F9
## 3 0.5120643 0.6858639 #E7E1EF
## 4 0.5120643 0.6858639 #E7E1EF
## 5 0.4879357 1.0000000 #D4B9DA
## 6 0.4879357 1.0000000 #D4B9DA
## 7 0.3612898 0.3141361 #C994C7
## 8 0.3612898 0.3141361 #C994C7
##
## $type
## [1] "index"
##
## $vSize
## [1] "DEATHS"
##
## $vColor
## [1] NA
##
## $stdErr
## [1] "DEATHS"
##
## $algorithm
## [1] "pivotSize"
##
## $vpCoorX
## [1] 0.02812148 0.97187852
##
## $vpCoorY
## [1] 0.01968504 0.94531496
##
## $aspRatio
## [1] 1.427417
##
## $range
## [1] NA
##
## $mapping
## [1] NA NA NA
##
## $draw
## [1] TRUECreate a CHART !!!!!
MARYLAND !!!
Maryland <- states %>%
select(STATE, RATE, YEAR, DEATHS) %>%
filter(STATE %in% c("MD")) %>%
filter(YEAR > 2015) %>%
group_by(YEAR)
Maryland## # A tibble: 5 × 4
## # Groups: YEAR [5]
## STATE RATE YEAR DEATHS
## <chr> <chr> <dbl> <dbl>
## 1 MD 5.73 2020 393
## 2 MD 5.84 2019 410
## 3 MD 6.02 2018 428
## 4 MD 6.43 2017 461
## 5 MD 6.54 2016 478MarylandMap <- ggplot(Maryland, aes(x=YEAR, y=RATE, group= STATE, color= DEATHS)) +
labs(title= "Infant Mortality Rate Amongst Maryland Infants") +
xlab("Years 2016 - 2020")+
ylab("Infant Mortality Rate") +
theme_minimal(base_size = 12) +
geom_line()
MarylandMap
Where is Montgomery County Rates? [Insert Public Health Report Infomration on Montgomery County about Infant Mortality Rates] [Begin to Explore the Leading Causes of Death for Infants in Montgomery County for Each Race variable]
[Introduce Country Hospital, Resource Centers, and Maps]
library(readr)
HospitalsMC<- read_csv("Hospitals_Map.csv")## Rows: 15 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): NAME, ADDRESS, CITY, PHONE, URL, IN COUNTY, LOCATION
## dbl (3): ZIP CODE, LONGITUDE, LATITUDE
##
## 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.View(HospitalsMC)HospitalsMC## # A tibble: 15 × 10
## NAME ADDRESS CITY ZIP C…¹ PHONE URL IN CO…² LONGI…³ LATIT…⁴ LOCAT…⁵
## <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <dbl> <dbl> <chr>
## 1 Washington… 7600 C… Tako… 20912 301-… http… In-Cou… -77.0 39.0 POINT …
## 2 Suburban H… 8600 O… Beth… 20814 301-… http… In-Cou… -77.1 39.0 POINT …
## 3 National I… 9000 R… Beth… 20892 301-… http… In-Cou… -77.1 39.0 POINT …
## 4 Walter Ree… 8901 R… Beth… 20889 301-… http… In-Cou… -77.1 39.0 POINT …
## 5 Holy Cross… 1500 F… Silv… 20910 301-… http… In-Cou… -77.0 39.0 POINT …
## 6 Laurel Reg… 7300 V… Laur… 20707 301-… http… Out-of… -76.9 39.1 POINT …
## 7 Shady Grov… 9901 M… Rock… 20850 240-… http… In-Cou… -77.2 39.1 POINT …
## 8 Montgomery… 18101 … Olney 20832 301-… http… In-Cou… -77.1 39.2 POINT …
## 9 Germantown… 19731 … Germ… 20874 301-… http… In-Cou… -77.3 39.2 POINT …
## 10 Frederick … 400 W … Fred… 21701 240-… http… Out-of… -77.4 39.4 POINT …
## 11 Howard Cou… 5755 C… Colu… 21044 410-… http… Out-of… -76.9 39.2 POINT …
## 12 Sibley Mem… 5255 L… Wash… 20016 202-… http… Out-of… -77.1 38.9 POINT …
## 13 Washington… 110 Ir… Wash… 20010 202-… http… Out-of… -77.0 38.9 POINT …
## 14 Inova Fair… 3300 G… Fall… 22042 703-… http… Out-of… -77.2 38.9 POINT …
## 15 Childrens … 111 Mi… Wash… 20010 202-… http… Out-of… -77.0 38.9 POINT …
## # … with abbreviated variable names ¹`ZIP CODE`, ²`IN COUNTY`, ³LONGITUDE,
## # ⁴LATITUDE, ⁵LOCATIONHMC1 <- HospitalsMC %>%
select()CREATE MAP !!!!
HMCMap <- leaflet() %>%
addTiles() %>%
addMarkers(data = HospitalsMC, lng= HospitalsMC$LONGITUDE, lat= HospitalsMC$LATITUDE, popup = HospitalsMC$NAME)
HMCMap[Resource Center]
library(readr)
HHSF <- read_csv("Health_and_Human_Services_Facilities_List.csv")## Rows: 135 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Building Name, Program Name, Phone Number, 24 Hour Emergency Phone ...
## dbl (2): Street Number, Zip
##
## 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.(HHSF)## # A tibble: 135 × 11
## Buildin…¹ Progr…² Phone…³ 24 Ho…⁴ Days …⁵ Hours…⁶ Stree…⁷ Stree…⁸ Zip Link
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr>
## 1 Communit… Mental… 240-77… <NA> Monday… 8:30 A… NA <NA> NA http…
## 2 Delivere… LOCATE… 877-26… <NA> Monday… 8:30 A… NA <NA> NA http…
## 3 MidCount… Rental… 240-77… <NA> Monday… 8:30 A… 1301 Piccar… 20850 http…
## 4 DHHS Off… ChildL… 240-77… <NA> Monday… 8:00 A… 1401 Rockvi… 20852 http…
## 5 DHHS Col… Africa… 240-77… <NA> Monday… 9:00 A… 14015 New Ha… 20904 http…
## 6 MidCount… Emerge… 240-77… <NA> Monday… 8:30 A… 1301 Piccar… 20850 http…
## 7 UpCounty… Dental… 240-77… <NA> Monday… 7:45 A… 12900 Middle… 20874 http…
## 8 DHHS Adm… Senior… 240-77… <NA> Monday… 8:30 A… 401 Hunger… 20850 http…
## 9 MidCount… Mobile… 240-77… <NA> Monday… 12:00 … 1301 Piccar… 20850 http…
## 10 Based in… Linkag… 240-77… <NA> Monday… 8:00 A… 1401 Rockvi… 20852 http…
## # … with 125 more rows, 1 more variable: `New Georeferenced Column` <chr>, and
## # abbreviated variable names ¹`Building Name`, ²`Program Name`,
## # ³`Phone Number`, ⁴`24 Hour Emergency Phone Number`, ⁵`Days of Operation`,
## # ⁶`Hours of Operation`, ⁷`Street Number`, ⁸`Street Name`[Introduce MARIA]
[EXTRA] Create Infant Mortality Chart + Map for Baltimore City [This is what I would have wanted to do for Montgomery County if I was given access to the raw dataset similar to the Baltimore City dataset]
library(readr)
Baltimore <- read_csv("Infant_Mortality_Rate.csv")## Rows: 55 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): CSA2010
## dbl (11): OBJECTID, mort1_11, mort1_12, mort1_13, mort1_14, mort1_15, mort1_...
##
## 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.View(Baltimore)