+
+
+
+
+
+
+US Heart Disease Mortality Rates - 10 Year +Analysis
+Clint Barnard-El
+March 12, 2024
+ +
+
+Objective
+-
+
- 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 +
+
+Data Sources
+-
+
- Centers for Disease Control & Prevention (CDC) +
- National +Vital Statistics System (NVSS) +
- Period: 2010-2019 +
+
+R Markdown
+
+
+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
+-
+
- 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), ]
+
+Filter Relevant Data
+-
+
- 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
+
+Clean Filtered Data
+-
+
- 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"
+
+Summarize Data into 5 distinct tables
+
+
+Summary stats of mortality rates grouped by (1) Gender, (2) Race, +(3) Type/Gender, (4) Type/All Groups, (5) Top/Bottom 5 states
+-
+
- 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.-
+
- 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-
+
- 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-
+
- 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.
+
+
+
+
+
+Create Plots 1-5
+
+
+Plot 1: Heart Disease Mortality Rate - Gender
+-
+
- 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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+## ..$ margin : 'margin' num [1:4] 0points 2.75points 0points 0points
+## .. ..- attr(*, "unit")= int 8
+## ..$ debug : NULL
+## ..$ inherit.blank: logi TRUE
+## ..- attr(*, "class")= chr [1:2] "element_text" "element"
+## $ axis.title.y.left : NULL
+## $ axis.title.y.right :List of 11
+## ..$ family : NULL
+## ..$ face : NULL
+## ..$ colour : NULL
+## ..$ size : NULL
+## ..$ hjust : NULL
+## ..$ vjust : num 1
+## ..$ angle : num -90
+## ..$ lineheight : NULL
+## ..$ margin : 'margin' num [1:4] 0points 0points 0points 2.75points
+## .. ..- attr(*, "unit")= int 8
+## ..$ debug : NULL
+## ..$ inherit.blank: logi TRUE
+## ..- attr(*, "class")= chr [1:2] "element_text" "element"
+## $ axis.text :List of 11
+## ..$ family : NULL
+## ..$ face : NULL
+## ..$ colour : chr "grey30"
+## ..$ size : 'rel' num 0.8
+## ..$ hjust : NULL
+## ..$ vjust : NULL
+## ..$ angle : NULL
+## ..$ lineheight : NULL
+## ..$ margin : NULL
+## ..$ debug : NULL
+## ..$ inherit.blank: logi TRUE
+## ..- attr(*, "class")= chr [1:2] "element_text" "element"
+## $ axis.text.x :List of 11
+## ..$ family : NULL
+## ..$ face : NULL
+## ..$ colour : NULL
+## ..$ size : NULL
+## ..$ hjust : NULL
+## ..$ vjust : num 1
+## ..$ angle : NULL
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+## .. ..- attr(*, "unit")= int 8
+## ..$ debug : NULL
+## ..$ inherit.blank: logi TRUE
+## ..- attr(*, "class")= chr [1:2] "element_text" "element"
+## $ axis.text.x.top :List of 11
+## ..$ family : NULL
+## ..$ face : NULL
+## ..$ colour : NULL
+## ..$ size : NULL
+## ..$ hjust : NULL
+## ..$ vjust : num 0
+## ..$ angle : NULL
+## ..$ lineheight : NULL
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+## .. ..- attr(*, "unit")= int 8
+## ..$ debug : NULL
+## ..$ inherit.blank: logi TRUE
+## ..- attr(*, "class")= chr [1:2] "element_text" "element"
+## $ axis.text.x.bottom : NULL
+## $ axis.text.y :List of 11
+## ..$ family : NULL
+## ..$ face : NULL
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+## ..$ hjust : num 1
+## ..$ vjust : NULL
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+## ..$ lineheight : NULL
+## ..$ margin : 'margin' num [1:4] 0points 2.2points 0points 0points
+## .. ..- attr(*, "unit")= int 8
+## ..$ debug : NULL
+## ..$ inherit.blank: logi TRUE
+## ..- attr(*, "class")= chr [1:2] "element_text" "element"
+## $ axis.text.y.left : NULL
+## $ axis.text.y.right :List of 11
+## ..$ family : NULL
+## ..$ face : NULL
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+## ..$ hjust : num 0
+## ..$ vjust : NULL
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+## .. ..- attr(*, "unit")= int 8
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+## ..$ inherit.blank: logi TRUE
+## ..- attr(*, "class")= chr [1:2] "element_text" "element"
+## $ axis.text.theta : NULL
+## $ axis.text.r :List of 11
+## ..$ family : NULL
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+## ..$ vjust : NULL
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+## .. ..- attr(*, "unit")= int 8
+## ..$ debug : NULL
+## ..$ inherit.blank: logi TRUE
+## ..- attr(*, "class")= chr [1:2] "element_text" "element"
+## $ axis.ticks : list()
+## ..- attr(*, "class")= chr [1:2] "element_blank" "element"
+## $ axis.ticks.x : NULL
+## $ axis.ticks.x.top : NULL
+## $ axis.ticks.x.bottom : NULL
+## $ axis.ticks.y : NULL
+## $ axis.ticks.y.left : NULL
+## $ axis.ticks.y.right : NULL
+## $ axis.ticks.theta : NULL
+## $ axis.ticks.r : NULL
+## $ axis.minor.ticks.x.top : NULL
+## $ axis.minor.ticks.x.bottom : NULL
+## $ axis.minor.ticks.y.left : NULL
+## $ axis.minor.ticks.y.right : NULL
+## $ axis.minor.ticks.theta : NULL
+## $ axis.minor.ticks.r : NULL
+## $ axis.ticks.length : 'simpleUnit' num 2.75points
+## ..- attr(*, "unit")= int 8
+## $ axis.ticks.length.x : NULL
+## $ axis.ticks.length.x.top : NULL
+## $ axis.ticks.length.x.bottom : NULL
+## $ axis.ticks.length.y : NULL
+## $ axis.ticks.length.y.left : NULL
+## $ axis.ticks.length.y.right : NULL
+## $ axis.ticks.length.theta : NULL
+## $ axis.ticks.length.r : NULL
+## $ axis.minor.ticks.length : 'rel' num 0.75
+## $ axis.minor.ticks.length.x : NULL
+## $ axis.minor.ticks.length.x.top : NULL
+## $ axis.minor.ticks.length.x.bottom: NULL
+## $ axis.minor.ticks.length.y : NULL
+## $ axis.minor.ticks.length.y.left : NULL
+## $ axis.minor.ticks.length.y.right : NULL
+## $ axis.minor.ticks.length.theta : NULL
+## $ axis.minor.ticks.length.r : NULL
+## $ axis.line : list()
+## ..- attr(*, "class")= chr [1:2] "element_blank" "element"
+## $ axis.line.x : NULL
+## $ axis.line.x.top : NULL
+## $ axis.line.x.bottom : NULL
+## $ axis.line.y : NULL
+## $ axis.line.y.left : NULL
+## $ axis.line.y.right : NULL
+## $ axis.line.theta : NULL
+## $ axis.line.r : NULL
+## $ legend.background : list()
+## ..- attr(*, "class")= chr [1:2] "element_blank" "element"
+## $ legend.margin : 'margin' num [1:4] 5.5points 5.5points 5.5points 5.5points
+## ..- attr(*, "unit")= int 8
+## $ legend.spacing : 'simpleUnit' num 11points
+## ..- attr(*, "unit")= int 8
+## $ legend.spacing.x : NULL
+## $ legend.spacing.y : NULL
+## $ legend.key : list()
+## ..- attr(*, "class")= chr [1:2] "element_blank" "element"
+## $ legend.key.size : 'simpleUnit' num 1.2lines
+## ..- attr(*, "unit")= int 3
+## $ legend.key.height : NULL
+## $ legend.key.width : NULL
+## $ legend.key.spacing : 'simpleUnit' num 5.5points
+## ..- attr(*, "unit")= int 8
+## $ legend.key.spacing.x : NULL
+## $ legend.key.spacing.y : NULL
+## $ legend.frame : NULL
+## $ legend.ticks : NULL
+## $ legend.ticks.length : 'rel' num 0.2
+## $ legend.axis.line : NULL
+## $ legend.text :List of 11
+## ..$ family : NULL
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+## ..$ hjust : NULL
+## ..$ vjust : NULL
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+## ..$ lineheight : NULL
+## ..$ margin : NULL
+## ..$ debug : NULL
+## ..$ inherit.blank: logi TRUE
+## ..- attr(*, "class")= chr [1:2] "element_text" "element"
+## $ legend.text.position : NULL
+## $ legend.title :List of 11
+## ..$ family : NULL
+## ..$ face : NULL
+## ..$ colour : NULL
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+## ..$ vjust : NULL
+## ..$ angle : NULL
+## ..$ lineheight : NULL
+## ..$ margin : NULL
+## ..$ debug : NULL
+## ..$ inherit.blank: logi TRUE
+## ..- attr(*, "class")= chr [1:2] "element_text" "element"
+## $ legend.title.position : NULL
+## $ legend.position : chr "right"
+## $ legend.position.inside : NULL
+## $ legend.direction : NULL
+## $ legend.byrow : NULL
+## $ legend.justification : chr "center"
+## $ legend.justification.top : NULL
+## $ legend.justification.bottom : NULL
+## $ legend.justification.left : NULL
+## $ legend.justification.right : NULL
+## $ legend.justification.inside : NULL
+## $ legend.location : NULL
+## $ legend.box : NULL
+## $ legend.box.just : NULL
+## $ legend.box.margin : 'margin' num [1:4] 0cm 0cm 0cm 0cm
+## ..- attr(*, "unit")= int 1
+## $ legend.box.background : list()
+## ..- attr(*, "class")= chr [1:2] "element_blank" "element"
+## $ legend.box.spacing : 'simpleUnit' num 11points
+## ..- attr(*, "unit")= int 8
+## [list output truncated]
+## - attr(*, "class")= chr [1:2] "theme" "gg"
+## - attr(*, "complete")= logi TRUE
+## - attr(*, "validate")= logi TRUE