New Cancer Rate

Rate of New Cancers in the United States All Types of Cancer, All Ages, All Races/Ethnicities, Male and Female Rate per 100,000 people

USCS_OverviewMap <- read.csv("C:/Users/Joe/Downloads/USCS_OverviewMap.csv")

USCS_OverviewMap$Area <-gsub("[^a-zA-Z]", "", USCS_OverviewMap$Area)
USCS_OverviewMap$CancerType <-gsub("[^a-zA-Z]", "", USCS_OverviewMap$CancerType)
USCS_OverviewMap$Year <- as.numeric(stringr::str_extract(USCS_OverviewMap$Year, "\\d+"))
USCS_OverviewMap$Rate <- as.numeric(stringr::str_extract(USCS_OverviewMap$AgeAdjustedRate, "\\d+"))
USCS_OverviewMap <- arrange(USCS_OverviewMap, Area)

 USCS_TopTen <- read.csv("C:/Users/Joe/Downloads/USCS_TopTen.csv")
 USCS_TopTen$CancerType <-gsub("[^a-zA-Z]", "", USCS_TopTen$CancerType)
USCS_TopTen$Rate <- as.numeric(stringr::str_extract(USCS_TopTen$AgeAdjustedRate, "\\d+"))
USCS_TopTen <- arrange(USCS_TopTen, desc(Rate))

Choropleth Mapping

Top States

top_state <- top_n(df_pop_state,10,value)
top_state
##           region value
## 1       delaware   487
## 2           iowa   473
## 3       kentucky   509
## 4      louisiana   473
## 5          maine   473
## 6  new hampshire   480
## 7     new jersey   474
## 8       new york   474
## 9   pennsylvania   482
## 10 west virginia   472
ggplot(data=top_state, aes(x=reorder(region,value), y=value,fill=region)) +
  geom_bar(stat="identity")+
  theme_minimal()+
  coord_flip()

bottom_state <- top_n(df_pop_state,10,-value)
bottom_state
##                  region value
## 1               arizona   376
## 2            california   385
## 3              colorado   388
## 4  district of columbia   378
## 5         massachusetts   404
## 6                nevada   385
## 7            new mexico   359
## 8                 texas   391
## 9                  utah   390
## 10              wyoming   402
ggplot(data=bottom_state, aes(x=reorder(region,value), y=value,fill=region)) +
  geom_bar(stat="identity")+
  theme_minimal()+
  coord_flip()

Types of Cancer

ggplot(data=USCS_TopTen, aes(x=reorder(CancerType,Rate), y=Rate,fill=CancerType)) +
  geom_bar(stat="identity")+
  theme_minimal()+
  labs(title = "Top New Cancer Types in 2016", x="Cancer Type",y="Rate per 100,000 population")+
  coord_flip()