---
title: "Pima County, AZ: Dashboard Describing the Cancer Burden"
output: 
  flexdashboard::flex_dashboard:
    storyboard: true
    social: menu
    theme: journal
    source: embed
---


```{r setup, include=FALSE}
# load packages
library(flexdashboard)
library(here)
library(tidyverse)
library(knitr)
library(ggthemes)
library(plotly)

# incidence by cancer and sex
# read data
pima_incidence_by_cancer <- read_rds("data/tidy/azdhs_pima_2013-2017_incidence_by_cancer.rds")

# plot
plot_pima_incidence_by_cancer <- pima_incidence_by_cancer %>%
  group_by(Sex) %>%
  filter(Cancer != "All") %>%
  arrange(desc(Age_Adj_Rate)) %>%
  slice(1:5) %>%
  ggplot(mapping = aes(y = reorder(Cancer, Age_Adj_Rate), x = Age_Adj_Rate)) +
  geom_col(position = "dodge", alpha = 0.8) +
  facet_wrap("Sex") +
  theme_solarized() +
  theme(legend.position = "bottom") +
  labs(title = "Top 5 Incident Cancer for Pima County, AZ",
       subtitle = "2013-2017; All races; Grouped by sex",
       x = "Age Adjusted Rate per 100,000",
       y = "",
       caption = "Source: Arizona Cancer Registry Query Module")

# incidence by race and sex
# read data
pima_incidence_by_race <- read_rds("data/tidy/azdhs_pima_2013-2017_incidence_by_race.rds")

# plot
plot_pima_incidence_by_race <-  pima_incidence_by_race %>%
  group_by(Race, Sex) %>%
  arrange(desc(Age_Adj_Rate)) %>%
  slice(1:5) %>%
  ggplot(mapping = aes(y = reorder(Sex, Age_Adj_Rate), x = Age_Adj_Rate, fill = Sex)) +
  geom_col(position = "dodge", alpha = 0.8) +
  facet_wrap("Race") +
  theme_solarized() +
  theme(legend.position = "bottom") +
  labs(title = "Comparison of Rates of New Cancer for each Race and Sex",
       subtitle = "2013-2017",
       x = "Age Adjusted Incidence Rate per 100,000",
       y = "",
       caption = "Source: Arizona Cancer Registry Query Module")

# incidence by age and sex
# read data
pima_incidence_by_age <- read_rds("data/tidy/azdhs_pima_2013-2017_incidence_by_age.rds")

# show top five as plot 
plot_pima_incidence_by_age <- pima_incidence_by_age %>%
  filter(Age_Group != "All") %>%
  group_by(Sex) %>%
  arrange(desc(Case_Count)) %>%
  slice(1:5) %>%
  ggplot(mapping = aes(y = reorder(Age_Group, Case_Count), x = Case_Count)) +
  geom_col(position = "dodge", alpha = 0.8) +
  facet_wrap("Sex") +
  theme_solarized() +
  theme(legend.position = "bottom") +
  labs(title = "Age Groups with Highest Number of New Cancer Cases",
       subtitle = "2013-2017; All races; Grouped by sex",
       x = "New Cases",
       y = "",
       caption = "Source: Arizona Cancer Registry Query Module")

# incidence by sex over time
# read data
# pima incidence over time by year ----
# read data
pima_incidence_by_year <- read_rds("data/tidy/azdhs_pima_1995-2017_incidence_by_year.rds")

# show plot 
plot_pima_incidence_by_year <- pima_incidence_by_year %>%
  ggplot(mapping = aes(y = Age_Adj_Rate, x = Year, group = Sex, color = Sex)) +
  geom_line() +
  theme_solarized() +
  ylim(c(200, 600)) +
  theme(legend.position = "bottom") +
  labs(title = "Age Adjusted Incidence Rate of New Cancers in Pima County, AZ",
       subtitle = "1995-2017; All races combined; Grouped by sex",
       x = "Year",
       y = "Age Adjusted Incidence Rate per 100,000",
       caption = "Source: Arizona Cancer Registry Query Module")


# mortality by cancer and sex
# read data
pima_mortality_by_cancer <- read_rds("data/tidy/seer_stat_pima_mortality_2013-2017.rds")

# determine top five cancers for each sex grouping 
# filter out "system"
# and save to value for use later
cancer_to_exclude <- str_subset(pima_mortality_by_cancer$cancer, " System")

# show plot
plot_pima_mortality_by_cancer <- pima_mortality_by_cancer %>% 
  group_by(sex) %>%
  filter(cancer != "Miscellaneous Malignant Cancer" &
           cancer != "Colon excluding Rectum" &
           cancer != "All Causes of Death" & 
           cancer != "All Malignant Cancers" & 
           !(cancer %in% cancer_to_exclude)) %>%
  select(sex, cancer, age_adjusted_rate) %>%
  arrange(desc(age_adjusted_rate)) %>%
  slice(1:5) %>%
  ggplot(mapping = aes(x = age_adjusted_rate, y = reorder(cancer, age_adjusted_rate))) +
  geom_col(position = "dodge", alpha = 0.8) +
  labs(title = "Top 5 Cancer Deaths in Pima County, AZ",
       subtitle = "Five year average 2013-2017; All races",
       x = "Age Adjusted Mortality Rate per 100,000",
       y = "",
       caption = "Source: Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.cancer.gov)") +
  facet_wrap("sex") +
  theme_solarized()

# mortality by race and sex
# read data
pima_mortality_by_race <- read_rds("data/tidy/seer_stat_pima_mortality_2013-2017_by_race.rds")

# show plot
plot_pima_mortality_by_race <- pima_mortality_by_race %>% 
  group_by(race, sex) %>%
  ggplot(mapping = aes(x = age_adjusted_rate, y = reorder(sex, age_adjusted_rate), fill = sex)) +
  geom_col(position = "dodge", alpha = 0.8) +
  labs(title = "Cancer Deaths by Race and Sex",
       subtitle = "Five year average 2013-2017; All races",
       x = "Age Adjusted Mortality Rate per 100,000",
       y = "",
       caption = "Source: Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.cancer.gov)") +
  facet_wrap("race") +
  theme_solarized() +
  theme(legend.position = "bottom")

# mortality by age and sex
# read data
pima_mortality_by_age <- read_rds("data/tidy/seer_stat_pima_mortality_2013-2017_by_age.rds")

# show plot
plot_pima_mortality_by_age <- pima_mortality_by_age %>%
  group_by(sex) %>%
  arrange(desc(age_adjusted_rate)) %>%
  slice(1:5) %>%
  ggplot(mapping = aes(x = age_adjusted_rate, y = reorder(age_group, age_adjusted_rate))) +
  geom_col(position = "dodge") +
  facet_wrap("sex") +
  labs(title = "Age Groups with Most Cancer Deaths in Pima County, AZ",
       subtitle = "Five year average 2013-2017; All races combined",
       x = "Age Adjusted Mortality Rate per 100,000",
       y = "",
       caption = "Source: Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.cancer.gov)") +
  theme_solarized() +
  theme(legend.position = "bottom")

# mortality by sex over time
# read data

# show plot
```


### Top 5 New Cancers for Pima County, AZ grouped by sex, for all races 


```{r incidence_by_cancer}
# show top five as plot 
ggplotly(plot_pima_incidence_by_cancer)
```


***


The chart shows the top five incident cancers for:


- male and female combined
- male only
- female only


Data shown are the 5 most common new cancers for each sex, shown as a five year average age adjusted incident rate for years 2013-2017, for all races combined.


Source: 


The Arizona Department of Health Services (ADHS) Indicator Based Information System for Public Health (IBIS-PH) system allows the public to query cancer rates, mortality rates and population estimates for Arizona. These public health data sets are intended to support evidenced-based decision making in Arizona to plan and improve service delivery, evaluate health care systems, and inform policy decisions. Other uses are not permissible. Available at [http://healthdata.az.gov/](http://healthdata.az.gov/query/module_selection/azcr/AzCRSelection.html)


### Comparing new cancers for each race and sex in Pima County, AZ


```{r incidence_by_race}
ggplotly(plot_pima_incidence_by_race)
```


*** 


The chart shows the five year average (2013-2017) of incident cancer for each race in Pima County, AZ grouped by sex. Differences in rates between racial groups show possible disparities in screening or access to care.


Data source: 


The Arizona Department of Health Services (ADHS) Indicator Based Information System for Public Health (IBIS-PH) system allows the public to query cancer rates, mortality rates and population estimates for Arizona. These public health data sets are intended to support evidenced-based decision making in Arizona to plan and improve service delivery, evaluate health care systems, and inform policy decisions. Other uses are not permissible. Available at [http://healthdata.az.gov/](http://healthdata.az.gov/query/module_selection/azcr/AzCRSelection.html)


### Age groups with highest number of new cancer cases in Pima County, AZ


```{r incidence_by_age}
ggplotly(plot_pima_incidence_by_age)
```


*** 


This panel shows a graph of the age groups with the highest number of new cases for the five year period from 2013 - 2017; for Pima County, AZ. 


Data source: 


The Arizona Department of Health Services (ADHS) Indicator Based Information System for Public Health (IBIS-PH) system allows the public to query cancer rates, mortality rates and population estimates for Arizona. These public health data sets are intended to support evidenced-based decision making in Arizona to plan and improve service delivery, evaluate health care systems, and inform policy decisions. Other uses are not permissible. Available at [http://healthdata.az.gov/](http://healthdata.az.gov/query/module_selection/azcr/AzCRSelection.html)


### Changes in rates of new cancer over time for Pima County, AZ


```{r incidence_by_year}
ggplotly(plot_pima_incidence_by_year)
```


*** 


This panel shows a graph of the age adjusted incidence rates by for Pima County, AZ over time. Starting from year 1995 through 2017 data provided by ADHS indicates a downward trend in new cancer diagnoses. This may be indicative of:


- changes in demographics
- changes in health behavior
- changes in cancer screening behavior


Data source: 


The Arizona Department of Health Services (ADHS) Indicator Based Information System for Public Health (IBIS-PH) system allows the public to query cancer rates, mortality rates and population estimates for Arizona. These public health data sets are intended to support evidenced-based decision making in Arizona to plan and improve service delivery, evaluate health care systems, and inform policy decisions. Other uses are not permissible. Available at [http://healthdata.az.gov/](http://healthdata.az.gov/query/module_selection/azcr/AzCRSelection.html)



### Top 5 Cancer Deaths for Pima County, AZ grouped by sex


```{r mortality_by_cancer}
ggplotly(plot_pima_mortality_by_cancer)
```


*** 


This panel shows a graph of the top 5 cancer deaths for Pima County, AZ for each sex. 


Data source: 


Surveillance, Epidemiology, and End Results (SEER) Program ([www.seer.cancer.gov](www.seer.cancer.gov)) SEER*Stat Database: Mortality - All COD, Aggregated With County, Total U.S. (1990-2017)  - Linked To County Attributes - Total U.S., 1969-2018 Counties, National Cancer Institute, DCCPS, Surveillance Research Program, released December 2019.  Underlying mortality data provided by NCHS ([www.cdc.gov/nchs](www.cdc.gov/nchs)).


### Cancer Deaths for Pima County, AZ grouped by race and sex


```{r mortality_by_race}
ggplotly(plot_pima_mortality_by_race)
```


*** 


This panel shows a graph of the cancer deaths for Pima County, AZ for each race and sex but does not distinguish between Hispanic or non-Hispanic ethnicity, i.e. these rates include both Hispanic and non-Hispanic. 


Data source: 


Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.cancer.gov) SEER*Stat Database: Mortality - All COD, Aggregated With County, Total U.S. (1990-2017)  - Linked To County Attributes - Total U.S., 1969-2018 Counties, National Cancer Institute, DCCPS, Surveillance Research Program, released December 2019.  Underlying mortality data provided by NCHS (www.cdc.gov/nchs).


### Age Groups with highest number of Cancer Deaths for Pima County, AZ 


```{r mortality_by_age}
ggplotly(plot_pima_mortality_by_age)
```


***


The age groups with the highest number of cancer deaths in Pima County, AZ; presented as an age adjusted rate.


Data source: 


Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.cancer.gov) SEER*Stat Database: Mortality - All COD, Aggregated With County, Total U.S. (1990-2017)  - Linked To County Attributes - Total U.S., 1969-2018 Counties, National Cancer Institute, DCCPS, Surveillance Research Program, released December 2019.  Underlying mortality data provided by NCHS (www.cdc.gov/nchs).


### Cancer Deaths Over Time in Pima County, AZ


Cancer mortality data is not available for each year.


***


No data available.


### Population Demographics: Total population in Pima County 





***


The panel shows total population for Pima County for each racial and ethnic category; all sexes combined. 


Data is aggregated by Data Planet and sourced from the US Census Bureau.


Source: United States Census Bureau (2020). American Community Survey Summary File: 5-Year Estimates, 2014-2018: B01001. Sex by Age by Race and Ethnicity | State: Arizona | Demographic Indicator: Total: | Race/Ethnicity*: White Alone, Black or African American Alone, American Indian or Alaskan Native Alone, Asian Alone, Native Hawaiian or Other Pacific Islander Alone, White Alone, Not Hispanic or Latino, Hispanic or Latino, 2018. Data Planet™ Statistical Datasets: A SAGE Publishing Resource. (Dataset). Dataset-ID:  001-068-551. https://doi-org.ezproxy2.library.arizona.edu/10.6068/DP173530C65C915


### Population Demographics: Sex 





***


Displays the total population for Pima County, AZ grouped into either male or female sex.


Data is aggregated by Data Planet and sourced from the US Census Bureau.


Source: United States Census Bureau (2020). American Community Survey Summary File: 5-Year Estimates, 2014-2018: B01001. Sex by Age | State: Arizona | Demographic Indicator: Male:, Female:, 2018. Data Planet™ Statistical Datasets: A SAGE Publishing Resource. (Dataset). Dataset-ID:  001-068-003. https://doi-org.ezproxy2.library.arizona.edu/10.6068/DP1735310E93D16


### Population Demographics: Sex and Race 


... in progress ...


***


Describe the visualization


### Population Demographics: Educational Attainment





***


Chart shows the number of people in Pima County, AZ with a high school diploma equivalent or greater.


Data is aggregated by Data Planet and sourced from the US Census Bureau.


Source: United States Census Bureau (2020). American Community Survey Summary File: 5-Year Estimates, 2014-2018: B01001. Sex by Age | State: Arizona | Demographic Indicator: Male:, Female:, 2018. Data Planet™ Statistical Datasets: A SAGE Publishing Resource. (Dataset). Dataset-ID:  001-068-003. https://doi-org.ezproxy2.library.arizona.edu/10.6068/DP1735310E93D16


### Population Demographics: Income 





***


This shows mean household income distributed into five classes. The United States Census Bureau divides households into five equal parts (quintiles) based on household income. Reported here is the mean household income of each quintile and of the top 5% of households. This measure represents an arithmetic average of household income in each quintile. Household income includes the income of the householder and all other individuals 15 years old and over in the household, whether they are related to the householder or not.


United States Census Bureau (2020). American Community Survey Summary File: 5-Year Estimates, 2014-2018: B19081. Mean Household Income of Quintiles | State: Arizona | Demographic Indicator: Lowest Quintile, Second Quintile, Third Quintile, Fourth Quintile, Highest Quintile, 2018. Data Planet™ Statistical Datasets: A SAGE Publishing Resource. (Dataset). Dataset-ID:  001-068-288. https://doi-org.ezproxy2.library.arizona.edu/10.6068/DP173531BA11219


### Screening:


... in progress ...


***


...


### Health Behavior


... in progress ...


***


...