Project3B
Nia Pillow
2022-04-15
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)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v tibble 3.1.6 v purrr 0.3.4
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Read in Maternal Mortality dataset
library(readr)
mm_df <- read_csv("maternalmortality.csv")## Rows: 51 Columns: 7
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): State
## dbl (6): MMR, Prenatal, Csection, Underserved, Uninsured, Population_18
##
## 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(mm_df)I used the Maternal Mortality Rates dataset created by Professor Rachel Saidi. I retrieved this dataset from the course resources tab through a shared one drive link provided to students by Professor Saidi. The variables within this dataset include state, MMR (maternal mortality rate), prenatal, C-section, underserved, population, and year. I really enjoyed working with this dataset since I am building a career in healthcare. All the variables were quantitative except for the state. The state was my only categorical variable.
4.) Summary Statistics
summary(mm_df)## State MMR Prenatal Csection
## Length:51 Min. : 1.2 Min. : 9.20 Min. :22.20
## Class :character 1st Qu.: 7.2 1st Qu.:13.75 1st Qu.:28.30
## Mode :character Median :10.4 Median :15.70 Median :30.70
## Mean :10.2 Mean :16.35 Mean :30.62
## 3rd Qu.:12.0 3rd Qu.:18.90 3rd Qu.:33.50
## Max. :34.9 Max. :30.90 Max. :38.30
## Underserved Uninsured Population_18
## Min. :22.00 Min. : 8.70 Min. : 577737
## 1st Qu.:39.00 1st Qu.:12.35 1st Qu.: 1780020
## Median :45.00 Median :15.70 Median : 4468402
## Mean :43.51 Mean :16.45 Mean : 6417545
## 3rd Qu.:50.00 3rd Qu.:19.95 3rd Qu.: 7353618
## Max. :61.00 Max. :27.80 Max. :39557045EDA
I used the dplyr function to filter out three states in total: Georgia, Mississippi, Maryland, and the District of Columbia.
mm_dfUN <- mm_df %>%
select(State, MMR, Uninsured) %>%
group_by(State) %>%
filter(State %in% c("DC", "MD", "MS", "GA"))summary(mm_dfUN)## State MMR Uninsured
## Length:4 Min. :15.20 Min. :11.5
## Class :character 1st Qu.:16.18 1st Qu.:14.2
## Mode :character Median :18.50 Median :17.4
## Mean :21.77 Mean :16.8
## 3rd Qu.:24.10 3rd Qu.:20.0
## Max. :34.90 Max. :20.9- I wanted to see if there was a correlation between MMR and uninsured mothers in the following states.
mm_dfUNp1 <- mm_dfUN %>%
filter(State %in% c("DC", "MD", "GA", "MS")) %>%
ggplot(aes(x= Uninsured, y= MMR)) +
labs(title= "Correlation Between MMR and Uninsured Mother's in Four States" ) +
xlab("Uninsured Mother's [in pecernt]")+
ylab("Mortality Rate") +
theme_minimal(base_size = 7) +
geom_point(aes(color= State), size= 5)+
labs(color= "State") +
scale_color_brewer(palette = "Set1")- I wanted to look at the MMR for the percentages of women who DID receive prenatal care in underserved communities.
mm_dfPre <- mm_df %>%
select(State, MMR, Prenatal, Underserved) %>%
group_by(Underserved) %>%
filter(State %in% c("DC", "MD", "MS", "GA"))
mm_dfPre## # A tibble: 4 x 4
## # Groups: Underserved [4]
## State MMR Prenatal Underserved
## <chr> <dbl> <dbl> <dbl>
## 1 GA 20.5 15.8 41
## 2 MD 16.5 16.6 40
## 3 MS 15.2 15.6 46
## 4 DC 34.9 23.2 50summary(mm_dfPre)## State MMR Prenatal Underserved
## Length:4 Min. :15.20 Min. :15.60 Min. :40.00
## Class :character 1st Qu.:16.18 1st Qu.:15.75 1st Qu.:40.75
## Mode :character Median :18.50 Median :16.20 Median :43.50
## Mean :21.77 Mean :17.80 Mean :44.25
## 3rd Qu.:24.10 3rd Qu.:18.25 3rd Qu.:47.00
## Max. :34.90 Max. :23.20 Max. :50.00mm_dfPrep2 <- mm_dfPre %>%
ggplot(aes(x=State, y=Prenatal, fill= MMR)) +
geom_bar(colour="white", width=.8, stat="identity") +
xlab("State") + ylab("Prenatal Care") +
ggtitle("Prenatal Care and Maternal Mortality Rates by State")
mm_dfPrep2
3. I took it just one more step further by adding an additional variable
to my EDA…C-section rates.
mm_dfCS <- mm_df %>%
select(State, MMR, Csection, Underserved, Uninsured) %>%
group_by(Uninsured) %>%
filter(State %in% c("DC", "MD", "MS", "GA"))
mm_dfCS## # A tibble: 4 x 5
## # Groups: Uninsured [4]
## State MMR Csection Underserved Uninsured
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 GA 20.5 32 41 19.7
## 2 MD 16.5 33.1 40 15.1
## 3 MS 15.2 36.2 46 20.9
## 4 DC 34.9 32.6 50 11.5summary(mm_dfCS)## State MMR Csection Underserved
## Length:4 Min. :15.20 Min. :32.00 Min. :40.00
## Class :character 1st Qu.:16.18 1st Qu.:32.45 1st Qu.:40.75
## Mode :character Median :18.50 Median :32.85 Median :43.50
## Mean :21.77 Mean :33.48 Mean :44.25
## 3rd Qu.:24.10 3rd Qu.:33.88 3rd Qu.:47.00
## Max. :34.90 Max. :36.20 Max. :50.00
## Uninsured
## Min. :11.5
## 1st Qu.:14.2
## Median :17.4
## Mean :16.8
## 3rd Qu.:20.0
## Max. :20.9mm_dfCS1 <- mm_dfCS %>%
ggplot(aes(x=MMR, y=Csection, fill= State)) +
geom_bar(colour="white", width=.8, stat="identity") +
xlab("Maternal Mortality Rates") + ylab("C- Sections") +
ggtitle("C-Section and Maternal Mortality Rates by State")What did I learn?
The District of Columbia had the highest maternal mortality rates (34.9%), underserving half (50%) of the population of mothers in the dataset and had the lowest number of uninsured mothers (11.5%) in 2018. Georgia had the second-highest maternal mortality rates (20.5%), the third-highest underserved population (41%), and the second-highest number of uninsured mothers (19.7%) in 2018. Maryland had the third-highest maternal mortality rate (16.5%), an underserved population of about (40%), and had the second-lowest number of uninsured mothers (15.1%) in 2018. Mississippi had the highest number of uninsured mothers (20.9%) with an underserved population of 46%. Mississippi had the lowest MMR (15.2%) C-section rates for these four states stayed around 32-33.6% for all three states and The District of Columbia.