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-US Heart Disease Mortality Rates - 10 Year -Analysis
-Clint Barnard-El
-March 12, 2024
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-Objective
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- Determine the groups and regions of the US that has been most/least -impacted by heart disease mortality rates during the period between -2010-2019 -
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-Data Sources
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- Centers for Disease Control & Prevention (CDC) -
- National -Vital Statistics System (NVSS) -
- Period: 2010-2019 -
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-R Markdown
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-
-Load Libraries
-library(gridExtra)
-library(maps)
-library(geosphere)
-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::combine() masks gridExtra::combine()
-## ✖ dplyr::filter() masks stats::filter()
-## ✖ dplyr::lag() masks stats::lag()
-## ✖ purrr::map() masks maps::map()
-## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(dtplyr)
-library(readxl)
-library(tidyselect)
-library(janitor)##
-## Attaching package: 'janitor'
-##
-## The following objects are masked from 'package:stats':
-##
-## chisq.test, fisher.testlibrary(skimr)
-library(tools)
-
-Import, clean dataset
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- Import table from NVSS, clean columns -
df_raw <- read_csv("C:/Users/Clint/OneDrive/Documents/Data/cdc_heart_disease/nvss_hd_2010_2020_raw.csv")## Rows: 91520 Columns: 16
-## ── Column specification ────────────────────────────────────────────────────────
-## Delimiter: ","
-## chr (12): RowId, LocationAbbr, Topic, Data_Value_Type, Data_Value_Unit, Brea...
-## dbl (4): YearStart, Data_Value, Data_Value_Alt, LocationId
-##
-## 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.clean_df <- clean_names(df_raw) #lower case all col, no spaces
-print(clean_df)## # A tibble: 91,520 × 16
-## row_id year_start location_abbr topic data_value_type data_value_unit
-## <chr> <dbl> <chr> <chr> <chr> <chr>
-## 1 NVSS~2010~1~N… 2010 AL Majo… Age-Standardiz… Rate per 100,0…
-## 2 NVSS~2010~1~N… 2010 AL Majo… Crude Rate per 100,0…
-## 3 NVSS~2010~1~N… 2010 AL Majo… Crude Rate per 100,0…
-## 4 NVSS~2010~1~N… 2010 AL Majo… Age-Standardiz… Rate per 100,0…
-## 5 NVSS~2010~1~N… 2010 AL Majo… Age-Standardiz… Rate per 100,0…
-## 6 NVSS~2010~1~N… 2010 AL Majo… Crude Rate per 100,0…
-## 7 NVSS~2010~1~N… 2010 AL Majo… Crude Rate per 100,0…
-## 8 NVSS~2010~1~N… 2010 AL Majo… Crude Rate per 100,0…
-## 9 NVSS~2010~1~N… 2010 AL Majo… Crude Rate per 100,0…
-## 10 NVSS~2010~1~N… 2010 AL Majo… Crude Rate per 100,0…
-## # ℹ 91,510 more rows
-## # ℹ 10 more variables: data_value <dbl>, data_value_alt <dbl>,
-## # break_out_category <chr>, break_out <chr>, class_id <chr>, topic_id <chr>,
-## # data_value_type_id <chr>, break_out_category_id <chr>, break_out_id <chr>,
-## # location_id <dbl>str(clean_df) #review col structure## spc_tbl_ [91,520 × 16] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
-## $ row_id : chr [1:91520] "NVSS~2010~1~NV001~OVR01~Age-Standardized" "NVSS~2010~1~NV001~OVR01~Crude" "NVSS~2010~1~NV001~GEN01~Crude" "NVSS~2010~1~NV001~GEN01~Age-Standardized" ...
-## $ year_start : num [1:91520] 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 ...
-## $ location_abbr : chr [1:91520] "AL" "AL" "AL" "AL" ...
-## $ topic : chr [1:91520] "Major Cardiovascular Disease" "Major Cardiovascular Disease" "Major Cardiovascular Disease" "Major Cardiovascular Disease" ...
-## $ data_value_type : chr [1:91520] "Age-Standardized" "Crude" "Crude" "Age-Standardized" ...
-## $ data_value_unit : chr [1:91520] "Rate per 100,000" "Rate per 100,000" "Rate per 100,000" "Rate per 100,000" ...
-## $ data_value : num [1:91520] 382 430 438 416 352 ...
-## $ data_value_alt : num [1:91520] 382 430 438 416 352 ...
-## $ break_out_category : chr [1:91520] "Overall" "Overall" "Gender" "Gender" ...
-## $ break_out : chr [1:91520] "Overall" "Overall" "Male" "Male" ...
-## $ class_id : chr [1:91520] "C1" "C1" "C1" "C1" ...
-## $ topic_id : chr [1:91520] "T1" "T1" "T1" "T1" ...
-## $ data_value_type_id : chr [1:91520] "AgeStdz" "Crude" "Crude" "AgeStdz" ...
-## $ break_out_category_id: chr [1:91520] "BOC01" "BOC01" "BOC02" "BOC02" ...
-## $ break_out_id : chr [1:91520] "OVR01" "OVR01" "GEN01" "GEN01" ...
-## $ location_id : num [1:91520] 1 1 1 1 1 1 1 1 1 1 ...
-## - attr(*, "spec")=
-## .. cols(
-## .. RowId = col_character(),
-## .. YearStart = col_double(),
-## .. LocationAbbr = col_character(),
-## .. Topic = col_character(),
-## .. Data_Value_Type = col_character(),
-## .. Data_Value_Unit = col_character(),
-## .. Data_Value = col_double(),
-## .. Data_Value_Alt = col_double(),
-## .. Break_Out_Category = col_character(),
-## .. Break_Out = col_character(),
-## .. ClassId = col_character(),
-## .. TopicId = col_character(),
-## .. Data_Value_TypeID = col_character(),
-## .. BreakOutCategoryId = col_character(),
-## .. BreakOutId = col_character(),
-## .. LocationId = col_double()
-## .. )
-## - attr(*, "problems")=<externalptr>#remove NA from table
-clean_df1 <- clean_df[!is.na(clean_df$data_value), ]
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-Filter Relevant Data
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- Include data during 2010-2019 (10 years) -
- Target specific (break_out_category) data by omitting nationwide -“overall” data -
- Include “age-standardized” data since the objective is to compare -groups, exclude “crude” type -
- Include relevant data only (i.e. year, HD type, state, mortality -rate, etc.) -
df_age1.1 <- clean_df1 %>% #filter 2020, omit 'overall" from break_out, exclude negative #'s, type id "age" = break out category is gender/race only
- filter(data_value_type_id == "AgeStdz") %>%
- filter(year_start != "2020") %>%
- filter(break_out_category != "Overall") %>%
- filter(data_value_alt > 0)
-print(df_age1.1)## # A tibble: 20,468 × 16
-## row_id year_start location_abbr topic data_value_type data_value_unit
-## <chr> <dbl> <chr> <chr> <chr> <chr>
-## 1 NVSS~2010~1~N… 2010 AL Majo… Age-Standardiz… Rate per 100,0…
-## 2 NVSS~2010~1~N… 2010 AL Majo… Age-Standardiz… Rate per 100,0…
-## 3 NVSS~2010~1~N… 2010 AL Majo… Age-Standardiz… Rate per 100,0…
-## 4 NVSS~2010~1~N… 2010 AL Majo… Age-Standardiz… Rate per 100,0…
-## 5 NVSS~2010~1~N… 2010 AL Majo… Age-Standardiz… Rate per 100,0…
-## 6 NVSS~2010~1~N… 2010 AL Majo… Age-Standardiz… Rate per 100,0…
-## 7 NVSS~2010~2~N… 2010 AK Majo… Age-Standardiz… Rate per 100,0…
-## 8 NVSS~2010~2~N… 2010 AK Majo… Age-Standardiz… Rate per 100,0…
-## 9 NVSS~2010~2~N… 2010 AK Majo… Age-Standardiz… Rate per 100,0…
-## 10 NVSS~2010~2~N… 2010 AK Majo… Age-Standardiz… Rate per 100,0…
-## # ℹ 20,458 more rows
-## # ℹ 10 more variables: data_value <dbl>, data_value_alt <dbl>,
-## # break_out_category <chr>, break_out <chr>, class_id <chr>, topic_id <chr>,
-## # data_value_type_id <chr>, break_out_category_id <chr>, break_out_id <chr>,
-## # location_id <dbl>#remove columns outside objective
-df_age1 <- df_age1.1[, c(1:4, 7, 9:10)]
-print(df_age1)## # A tibble: 20,468 × 7
-## row_id year_start location_abbr topic data_value break_out_category break_out
-## <chr> <dbl> <chr> <chr> <dbl> <chr> <chr>
-## 1 NVSS~… 2010 AL Majo… 416. Gender Male
-## 2 NVSS~… 2010 AL Majo… 352. Gender Female
-## 3 NVSS~… 2010 AL Majo… 370. Race Non-Hisp…
-## 4 NVSS~… 2010 AL Majo… 453. Race Non-Hisp…
-## 5 NVSS~… 2010 AL Majo… 102. Race Hispanic
-## 6 NVSS~… 2010 AL Majo… 133. Race Other
-## 7 NVSS~… 2010 AK Majo… 236. Gender Male
-## 8 NVSS~… 2010 AK Majo… 177 Gender Female
-## 9 NVSS~… 2010 AK Majo… 196. Race Non-Hisp…
-## 10 NVSS~… 2010 AK Majo… 268. Race Other
-## # ℹ 20,458 more rows
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-Clean Filtered Data
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- Rename columns, race groups to ease understanding of values (simple -terms) -
- Group HD types that did not include a specification -(i.e. “Stroke”) -
#rename columns
-new_names <- c("year", "state", "type", "rate", "group", "sub_group")
-colnames(df_age1)[c(2:7)] <- new_names
-print(df_age1)## # A tibble: 20,468 × 7
-## row_id year state type rate group sub_group
-## <chr> <dbl> <chr> <chr> <dbl> <chr> <chr>
-## 1 NVSS~2010~1~NV001~GEN01~Age-Standard… 2010 AL Majo… 416. Gend… Male
-## 2 NVSS~2010~1~NV001~GEN02~Age-Standard… 2010 AL Majo… 352. Gend… Female
-## 3 NVSS~2010~1~NV001~RAC01~Age-Standard… 2010 AL Majo… 370. Race Non-Hisp…
-## 4 NVSS~2010~1~NV001~RAC02~Age-Standard… 2010 AL Majo… 453. Race Non-Hisp…
-## 5 NVSS~2010~1~NV001~RAC04~Age-Standard… 2010 AL Majo… 102. Race Hispanic
-## 6 NVSS~2010~1~NV001~RAC07~Age-Standard… 2010 AL Majo… 133. Race Other
-## 7 NVSS~2010~2~NV001~GEN01~Age-Standard… 2010 AK Majo… 236. Gend… Male
-## 8 NVSS~2010~2~NV001~GEN02~Age-Standard… 2010 AK Majo… 177 Gend… Female
-## 9 NVSS~2010~2~NV001~RAC01~Age-Standard… 2010 AK Majo… 196. Race Non-Hisp…
-## 10 NVSS~2010~2~NV001~RAC07~Age-Standard… 2010 AK Majo… 268. Race Other
-## # ℹ 20,458 more rows#update type values, verify
-df_age1$type[grepl("^Major |^Diseases of", df_age1$type)] <- "Type Unspecified"
-df_age1$type[grepl("^Acute", df_age1$type)] <- "Heart Attack"
-unique(df_age1$type)## [1] "Type Unspecified" "Heart Attack" "Coronary Heart Disease"
-## [4] "Heart Failure" "Stroke"#rename sub groups, verify
-df_age1$sub_group[grepl("^Non-Hispanic Wh", df_age1$sub_group)] <- "White"
-df_age1$sub_group[grepl("^Non-Hispanic Bl", df_age1$sub_group)] <- "Black"
-df_age1$sub_group[grepl("^Oth", df_age1$sub_group)] <- "Unknown/Other"
-unique(df_age1$sub_group)## [1] "Male" "Female" "White" "Black"
-## [5] "Hispanic" "Unknown/Other"
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-Summarize Data into 5 distinct tables
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-Summary stats of mortality rates grouped by (1) Gender, (2) Race, -(3) Type/Gender, (4) Type/All Groups, (5) Top/Bottom 5 states
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- Plot 1 - Gender -
- Includes mean/min/max rates of both genders -
#summarize gender sub_group
-plot1.1 <- df_age1 %>% #------------------------------------- By Gender
- filter(group == 'Gender') %>%
- group_by(year, sub_group) %>%
- summarize(rate_sum = sum(rate),
- avg_rate = mean(rate),
- min_rate = min(rate),
- max_rate = max(rate))## `summarise()` has grouped output by 'year'. You can override using the
-## `.groups` argument.plot1 <- plot1.1 %>%
- mutate(avg_rate = round(avg_rate, 1))
-print(plot1)## # A tibble: 20 × 6
-## # Groups: year [10]
-## year sub_group rate_sum avg_rate min_rate max_rate
-## <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
-## 1 2010 Female 39430. 94.8 8.5 375.
-## 2 2010 Male 48248. 116. 6.3 444.
-## 3 2011 Female 37971. 91.5 7.5 358.
-## 4 2011 Male 47104. 114. 6.5 425.
-## 5 2012 Female 37085. 89.1 8.1 345.
-## 6 2012 Male 46328 112. 7.1 421.
-## 7 2013 Female 36189. 87 7.3 343.
-## 8 2013 Male 46144. 111. 5.7 441.
-## 9 2014 Female 35441. 85.2 6.1 332.
-## 10 2014 Male 45709. 110. 7.7 433.
-## 11 2015 Female 35491. 85.3 6.5 347.
-## 12 2015 Male 45637. 110 5.2 448
-## 13 2016 Female 34480. 82.9 7.9 332.
-## 14 2016 Male 45285. 109. 7.2 442
-## 15 2017 Female 33973. 81.7 6.2 338.
-## 16 2017 Male 45039. 109. 6.3 444.
-## 17 2018 Female 33188. 79.8 5.9 321.
-## 18 2018 Male 44740. 108. 6.7 433.
-## 19 2019 Female 32510. 78.1 6.6 318.
-## 20 2019 Male 44228. 106. 5.5 444.-
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- Plot 2 - Race -
- Includes mean/min/max rates of all 4 race groups -
#summarize race sub_group
-plot2.1 <- df_age1 %>% #------------------------------------- By Race
- filter(group == 'Race') %>%
- group_by(year, sub_group) %>%
- summarize(rate_sum = sum(rate),
- avg_rate = mean(rate),
- min_rate = min(rate),
- max_rate = max(rate))## `summarise()` has grouped output by 'year'. You can override using the
-## `.groups` argument. plot2 <- plot2.1 %>%
- mutate(avg_rate = round(avg_rate, 1))
- print(plot2)## # A tibble: 40 × 6
-## # Groups: year [10]
-## year sub_group rate_sum avg_rate min_rate max_rate
-## <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
-## 1 2010 Black 38890 131. 8.9 491.
-## 2 2010 Hispanic 15554. 72 4.8 274.
-## 3 2010 Unknown/Other 18408. 77.7 2.7 386.
-## 4 2010 White 43930. 106. 8 391.
-## 5 2011 Black 37619. 128 8.3 443.
-## 6 2011 Hispanic 15636. 71.1 4.9 270.
-## 7 2011 Unknown/Other 18693 76.6 2.7 309.
-## 8 2011 White 42830. 104. 5.6 384.
-## 9 2012 Black 37086. 127. 9.5 468.
-## 10 2012 Hispanic 15820. 68.2 5.2 284.
-## # ℹ 30 more rows-
-
- Plot 3 - Type (All Genders) -
- Includes mean rates of all genders -
plot3.1 <- df_age1 %>% #------------------------------------- By Type, All Genders
- filter(group == 'Gender', type != 'Type Unspecified') %>% #excludes generic types
- group_by(year, type, sub_group) %>%
- summarize(avg_rate = mean(rate))## `summarise()` has grouped output by 'year', 'type'. You can override using the
-## `.groups` argument. plot3 <- plot3.1 %>%
- mutate(avg_rate = round(avg_rate, 1))
- print(plot3)## # A tibble: 80 × 4
-## # Groups: year, type [40]
-## year type sub_group avg_rate
-## <dbl> <chr> <chr> <dbl>
-## 1 2010 Coronary Heart Disease Female 115.
-## 2 2010 Coronary Heart Disease Male 174.
-## 3 2010 Heart Attack Female 37.8
-## 4 2010 Heart Attack Male 59.1
-## 5 2010 Heart Failure Female 24.6
-## 6 2010 Heart Failure Male 22.3
-## 7 2010 Stroke Female 34.1
-## 8 2010 Stroke Male 28.1
-## 9 2011 Coronary Heart Disease Female 109.
-## 10 2011 Coronary Heart Disease Male 170.
-## # ℹ 70 more rows-
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- Plot 4 - Type (All Groups/Sub-Groups) -
- Includes mean rates of population whole -
plot4 <- df_age1 %>% #------------------------------------- By Type, Overall
- filter(type != 'Type Unspecified') %>% #excludes missing type
- group_by(year, type) %>%
- summarize(avg_rate = mean(rate))## `summarise()` has grouped output by 'year'. You can override using the
-## `.groups` argument.print(plot4)## # A tibble: 40 × 3
-## # Groups: year [10]
-## year type avg_rate
-## <dbl> <chr> <dbl>
-## 1 2010 Coronary Heart Disease 127.
-## 2 2010 Heart Attack 44.7
-## 3 2010 Heart Failure 22.6
-## 4 2010 Stroke 31.2
-## 5 2011 Coronary Heart Disease 121.
-## 6 2011 Heart Attack 42.9
-## 7 2011 Heart Failure 22.0
-## 8 2011 Stroke 30.1
-## 9 2012 Coronary Heart Disease 119.
-## 10 2012 Heart Attack 41.3
-## # ℹ 30 more rows-
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- Plot 5 - Top/Bottom 5 States) -
- Includes mean rates of 5 states with lowest and highest avg. -mortality rates during total period - Sort table based on avg_rate in -ascending order - create table that includes only the top/bottom 5 -states (exclude) -
plot5.1 <- df_age1 %>% #------------------------------------- By Top/Bottom 5 States
- filter(state != "DC") %>%
- group_by(state) %>%
- summarize(avg_rate = mean(rate))
-print(plot5.1)## # A tibble: 51 × 2
-## state avg_rate
-## <chr> <dbl>
-## 1 AK 79.7
-## 2 AL 118.
-## 3 AR 130.
-## 4 AZ 79.0
-## 5 CA 88.5
-## 6 CO 71.8
-## 7 CT 79.8
-## 8 DE 97.0
-## 9 FL 83.6
-## 10 GA 79.1
-## # ℹ 41 more rowsplot5.2 <- plot5.1[order(-plot5.1$avg_rate), ] #sort table
-
-top_bottom <- plot5.2 %>% #create new table, top/bottom 5
- arrange(avg_rate) %>%
- filter(row_number() <= 5 | row_number() > n() - 5)
-
-plot5 <- top_bottom
-print(plot5)## # A tibble: 10 × 2
-## state avg_rate
-## <chr> <dbl>
-## 1 MA 68.1
-## 2 CO 71.8
-## 3 MN 73.7
-## 4 WA 74.3
-## 5 VA 75.7
-## 6 SD 119.
-## 7 OK 121.
-## 8 WV 124.
-## 9 AR 130.
-## 10 MS 135.
-
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-Create Plots 1-5
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-Plot 1: Heart Disease Mortality Rate - Gender
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- Visualize avg. mortality rate change during period (all -genders) -
ggplot(plot1, aes(x = year, y = avg_rate, color = sub_group)) +
- geom_line() +
- geom_point() +
- scale_x_continuous(labels = scales::number_format(accuracy = 1)) +
- scale_y_continuous(labels = scales::number_format(accuracy = 1)) +
- labs(title = "Heart Disease Mortality Rate - Gender",
- subtitle = "(2010-2019)",
- x = "Year",
- y = "Avg Rate per 100K",
- color = "Gender") +
- theme_bw()
-
-Plot 2: Heart Disease Mortality Rate - Race
--
-
- Visualize avg. mortality rate change during period (all race -groups) -
ggplot(plot2, aes(x = year, y = avg_rate, color = sub_group)) +
- geom_line() +
- geom_point() +
- scale_x_continuous(labels = scales::number_format(accuracy = 1)) +
- scale_y_continuous(labels = scales::number_format(accuracy = 1)) +
- labs(title = "Heart Disease Mortality Rate - Race",
- subtitle = "(2010-2019)",
- x = "Year",
- y = "Avg Rate per 100K",
- color = "Race") +
- theme_bw()
-
-Plot 3: Heart Disease Mortality Rate - -Gender+Type
--
-
- Visualize which types of heart disease had the most impact, divide
-by genders
-
-
ggplot(plot3, aes(x=type, y=avg_rate, fill=type)) +
- geom_bar(stat = "identity", position = "dodge") +
- facet_wrap(~ sub_group) +
- labs(title = "Heart Disease Mortality Rate - Gender+Type",
- subtitle = "(2010-2019)",
- x = "Type",
- y = "Avg Rate per 100K",
- color = "Gender") +
- theme(axis.text.x = element_text(angle = 45, hjust = 1))
-
-Plot 4: Heart Disease Mortality Rate - Type -(Overall)
--
-
- Visualize mortality rate changes during this period for each heart
-disease type
-
-
ggplot(plot4, aes(x = year, y = avg_rate, color = type)) +
- geom_line() +
- geom_point() +
- scale_x_continuous(labels = scales::number_format(accuracy = 1)) +
- scale_y_continuous(labels = scales::number_format(accuracy = 1)) +
- labs(title = "Heart Disease Mortality Rate - Type",
- subtitle = "(2010-2019)",
- x = "Year",
- y = "Avg Rate per 100K",
- color = "Type") +
- theme_bw()
-
-Plot 5: Heart Disease Mortality Rate - Top/Bottom 5 -States
--
-
- Visualize the top and bottom 5 states that had the highest/lowest
-mortality rates during this period
-
-
ggplot(plot5, aes(x = reorder(state, -avg_rate), y = avg_rate, fill=avg_rate)) +
- geom_bar(stat = "identity") +
- #facet_wrap(~ sub_group) +
- labs(title = "Heart Disease Mortality Rates - Top/Bottom 5 States",
- subtitle = "(2010-2019)",
- x = "State",
- y = "Avg Rate per 100K",
- color = "Rate")
theme_minimal()## List of 136
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