Food Safety
#Boston, MA
env_u6ld_bos <- env_u6ld %>% filter(Value < 100, Place =="Boston, MA")
env_u6ld_bos## # A tibble: 15 x 7
## `Indicator Catego… Indicator Year Sex Race Value Place
## <chr> <chr> <fct> <chr> <chr> <dbl> <chr>
## 1 Environment Percent of Children (Teste… 2011 Both All 3.5 Bosto…
## 2 Environment Percent of Children (Teste… 2011 Fema… All 3 Bosto…
## 3 Environment Percent of Children (Teste… 2011 Male All 3.9 Bosto…
## 4 Environment Percent of Children (Teste… 2012 Both All 3.3 Bosto…
## 5 Environment Percent of Children (Teste… 2012 Fema… All 3.1 Bosto…
## 6 Environment Percent of Children (Teste… 2012 Male All 3.6 Bosto…
## 7 Environment Percent of Children (Teste… 2013 Both All 2.8 Bosto…
## 8 Environment Percent of Children (Teste… 2013 Fema… All 2.5 Bosto…
## 9 Environment Percent of Children (Teste… 2013 Male All 3.2 Bosto…
## 10 Environment Percent of Children (Teste… 2014 Both All 2.4 Bosto…
## 11 Environment Percent of Children (Teste… 2014 Fema… All 2.3 Bosto…
## 12 Environment Percent of Children (Teste… 2014 Male All 2.6 Bosto…
## 13 Environment Percent of Children (Teste… 2015 Both All 2.3 Bosto…
## 14 Environment Percent of Children (Teste… 2015 Fema… All 2.2 Bosto…
## 15 Environment Percent of Children (Teste… 2015 Male All 2.4 Bosto…ggplot(data = env_u6ld_bos) +
geom_point (mapping = aes(x= Year, y = Value, color = Sex, shape = Year)) +
facet_wrap(facets = vars(Sex)) +
labs(title = "Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels - Boston, MA", x = "Year", y = "Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels", caption = "Data includes 2011-2015") +
theme_light()
:
#Cleveland, OH - Cannot be used
env_u6ld_cle <- env_u6ld %>% filter(Value < 100, Place =="Cleveland, OH")
env_u6ld_cle## # A tibble: 16 x 7
## `Indicator Catego… Indicator Year Sex Race Value Place
## <chr> <chr> <fct> <chr> <chr> <dbl> <chr>
## 1 Environment Percent of Children (Test… 2012 Both All 15.6 Clevel…
## 2 Environment Percent of Children (Test… 2013 Both All 13.9 Clevel…
## 3 Environment Percent of Children (Test… 2013 Both Asia… 0 Clevel…
## 4 Environment Percent of Children (Test… 2013 Both Black 6.8 Clevel…
## 5 Environment Percent of Children (Test… 2013 Both Hisp… 0.9 Clevel…
## 6 Environment Percent of Children (Test… 2013 Both Other 0.2 Clevel…
## 7 Environment Percent of Children (Test… 2013 Both White 1.5 Clevel…
## 8 Environment Percent of Children (Test… 2013 Fema… All 6.4 Clevel…
## 9 Environment Percent of Children (Test… 2013 Male All 7.5 Clevel…
## 10 Environment Percent of Children (Test… 2014 Both All 14.2 Clevel…
## 11 Environment Percent of Children (Test… 2014 Both Asia… 0 Clevel…
## 12 Environment Percent of Children (Test… 2014 Both Black 7.8 Clevel…
## 13 Environment Percent of Children (Test… 2014 Both Hisp… 0.5 Clevel…
## 14 Environment Percent of Children (Test… 2014 Both White 0.9 Clevel…
## 15 Environment Percent of Children (Test… 2014 Fema… All 6.4 Clevel…
## 16 Environment Percent of Children (Test… 2014 Male All 7.7 Clevel…ggplot(data = env_u6ld_cle) +
geom_point (mapping = aes(x= Year, y = Value, color = Sex, shape = Sex)) +
facet_wrap(facets = vars(Race)) +
labs(title = "Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels - Cleveland, OH", x = "Year", y = "Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels", caption = "Data includes 2012-2014") +
theme_light()
see_unem_15 <- see_unem %>% filter(Value > 20)
see_unem_15_1 <- ggplot (see_unem_15) +
geom_point(mapping = aes(x= Year, y= Value, color = Place)) +
facet_grid(rows = vars(Sex)) +
theme_light()
see_unem_15a <- ggplotly (see_unem_15_1)
see_unem_15aenv_u6ld_cle <- env_u6ld %>% filter(Value < 100, Place =="Cleveland, OH")
env_u6ld_cle## # A tibble: 16 x 7
## `Indicator Catego… Indicator Year Sex Race Value Place
## <chr> <chr> <fct> <chr> <chr> <dbl> <chr>
## 1 Environment Percent of Children (Test… 2012 Both All 15.6 Clevel…
## 2 Environment Percent of Children (Test… 2013 Both All 13.9 Clevel…
## 3 Environment Percent of Children (Test… 2013 Both Asia… 0 Clevel…
## 4 Environment Percent of Children (Test… 2013 Both Black 6.8 Clevel…
## 5 Environment Percent of Children (Test… 2013 Both Hisp… 0.9 Clevel…
## 6 Environment Percent of Children (Test… 2013 Both Other 0.2 Clevel…
## 7 Environment Percent of Children (Test… 2013 Both White 1.5 Clevel…
## 8 Environment Percent of Children (Test… 2013 Fema… All 6.4 Clevel…
## 9 Environment Percent of Children (Test… 2013 Male All 7.5 Clevel…
## 10 Environment Percent of Children (Test… 2014 Both All 14.2 Clevel…
## 11 Environment Percent of Children (Test… 2014 Both Asia… 0 Clevel…
## 12 Environment Percent of Children (Test… 2014 Both Black 7.8 Clevel…
## 13 Environment Percent of Children (Test… 2014 Both Hisp… 0.5 Clevel…
## 14 Environment Percent of Children (Test… 2014 Both White 0.9 Clevel…
## 15 Environment Percent of Children (Test… 2014 Fema… All 6.4 Clevel…
## 16 Environment Percent of Children (Test… 2014 Male All 7.7 Clevel…ggplot(data = env_u6ld_cle) +
geom_point (mapping = aes(x= Year, y = Value, color = Sex, shape = Sex)) +
facet_wrap(facets = vars(Race)) +
labs(title = "Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels - Cleveland, OH", x = "Year", y = "Percent of Children (Tested) Under Age 6 with Elevated Blood Lead Levels", caption = "Data includes 2012-2014") +
theme_light()
##Food Safety - E Coli -
fs_ecoli <- read_xlsx("/Users/na/Desktop/Shri R Projects/Grad695 Prject/Data Feb2020/Food Safety.xlsx", sheet = 2)
fs_ecoli$Year <- factor(fs_ecoli$Year)
#Cleveland, OH
fs_ecoli_cle <- fs_ecoli %>% filter(Place =="Cleveland, OH")
ggplot(data = fs_ecoli_cle) +
geom_point (mapping = aes(x= Year, y = Value, color = Race, shape = Sex)) +
facet_wrap(facets = vars(Race)) +
labs(title = "Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people) - Cleveland, OH", x = "Year", y = "Percent of E Coli Infection", caption = "Data includes 2010-2014") +
theme_light()
#Dallas, TX
fs_ecoli_dal <- fs_ecoli %>% filter(Place =="Dallas, TX")
ggplot(data = fs_ecoli_dal) +
geom_point (mapping = aes(x= Year, y = Value, color = Race, shape = Sex)) +
facet_wrap(facets = vars(Race)) +
labs(title = "Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people) - Dallas, TX", x = "Year", y = "Percent of E Coli Infection", caption = "Data includes 2010-2016") +
theme_light()
#Denver, CO
fs_ecoli_den <- fs_ecoli %>% filter(Value < 30, Place =="Denver, CO")
ggplot(data = fs_ecoli_den) +
geom_point (mapping = aes(x= Year, y = Value, color = Race, shape = Sex)) +
facet_wrap(facets = vars(Race)) +
labs(title = "Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people) - Denver, CO", x = "Year", y = "Percent of E Coli Infection", caption = "Data includes 2010-2016") +
theme_light()
#Fort Worth (Tarrant County), TX
fs_ecoli_ftw <- fs_ecoli %>% filter(Value > 0, Place =="Fort Worth (Tarrant County), TX")
ggplot(data = fs_ecoli_ftw) +
geom_point (mapping = aes(x= Year, y = Value, color = Race, shape = Sex)) +
facet_wrap(facets = vars(Race)) +
labs(title = "Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people) - Fort Worth (Tarrant County), TX", x = "Year", y = "Percent of E Coli Infection", caption = "Data includes 2010-2016") +
theme_light()
#Philadelphia, PA
fs_ecoli_phi <- fs_ecoli %>% filter(Value > 0, Place =="Philadelphia, PA")
ggplot(data = fs_ecoli_phi) +
geom_point (mapping = aes(x= Year, y = Value, color = Race, shape = Sex)) +
facet_wrap(facets = vars(Race)) +
labs(title = "Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people) - Philadelphia, PA", x = "Year", y = "Percent of E Coli Infection", caption = "Data includes 2010-2016") +
theme_light()
#Phoenix, AZ
fs_ecoli_pho <- fs_ecoli %>% filter(Value > 0, Place =="Phoenix, AZ")
ggplot(data = fs_ecoli_pho) +
geom_point (mapping = aes(x= Year, y = Value, color = Race, shape = Sex)) +
facet_wrap(facets = vars(Race)) +
labs(title = "Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people) - Phoenix, AZ", x = "Year", y = "Percent of E Coli Infection", caption = "Data includes 2012-2014") +
theme_light()
#San Jose, CA
fs_ecoli_sj <- fs_ecoli %>% filter(Value > 0, Place =="San Jose, CA")
ggplot(data = fs_ecoli_sj) +
geom_point (mapping = aes(x= Year, y = Value, color = Race, shape = Sex)) +
facet_wrap(facets = vars(Race)) +
labs(title = "Rate of Laboratory Confirmed Infections Caused by Shiga Toxin-Producing E-Coli (Per 100,000 people) - San Jose, CA", x = "Year", y = "Percent of E Coli Infection", caption = "Data includes 2012-2014") +
theme_light()
##Smokers
cd_smo <- read_xlsx("/Users/na/Desktop/Shri R Projects/Grad695 Prject/Data Feb2020/Chronic Disease/Chronic Disease Smokers.xlsx", sheet = 2)## New names:
## * `` -> ...8
## * `` -> ...9
## * `` -> ...10
## * `` -> ...11
## * `` -> ...12
## * … and 1 more problemcd_smo$Year <- factor(cd_smo$Year)
#Denver, CO
cd_smo_den <- cd_smo %>% filter(Place =="Denver, CO")
ggplot(data = cd_smo_den) +
geom_point (mapping = aes(x= Year, y = Value, color = Race, shape = Sex)) +
facet_wrap(facets = vars(Race)) +
labs(title = "Chronic Disease - Denver, CO - Percent of Adults Who Currently Smoke", x = "Year", y = "Percent of Adults Who Currently Smoke", caption = "Data includes 2012-2016") +
theme_light()## Warning: Removed 4 rows containing missing values (geom_point).
#Boston, MA
cd_smo_bos <- cd_smo %>% filter(Place =="Boston, MA")
ggplot(data = cd_smo_bos) +
geom_point (mapping = aes(x= Year, y = Value, color = Race, shape = Sex)) +
facet_wrap(facets = vars(Race)) +
labs(title = "Chronic Disease - Boston, MA - Percent of Adults Who Currently Smoke", x = "Year", y = "Percent of Adults Who Currently Smoke", caption = "Data includes 2012-2016") +
theme_light()
#Charlotte, NC
cd_smo_cha <- cd_smo %>% filter(Place =="Charlotte, NC")
ggplot(data = cd_smo_cha) +
geom_point (mapping = aes(x= Year, y = Value, color = Race, shape = Sex)) +
facet_wrap(facets = vars(Race)) +
labs(title = "Chronic Disease - Charlotte, NC - Percent of Adults Who Currently Smoke", x = "Year", y = "Percent of Adults Who Currently Smoke", caption = "Data includes 2012-2015") +
theme_light()
#Columbus, OH
cd_smo_col <- cd_smo %>% filter(Place =="Columbus, OH")
ggplot(data = cd_smo_col) +
geom_point (mapping = aes(x= Year, y = Value, color = Race, shape = Sex)) +
facet_wrap(facets = vars(Race)) +
labs(title = "Chronic Disease - Columbus, OH - Percent of Adults Who Currently Smoke", x = "Year", y = "Percent of Adults Who Currently Smoke", caption = "Data includes 2012-2016") +
theme_light()## Warning: Removed 18 rows containing missing values (geom_point).
#Detroit, MI
cd_smo_det <- cd_smo %>% filter(Place =="Detroit, MI")
ggplot(data = cd_smo_det) +
geom_point (mapping = aes(x= Year, y = Value, color = Race, shape = Sex)) +
facet_wrap(facets = vars(Race)) +
labs(title = "Chronic Disease - Detroit, MI - Percent of Adults Who Currently Smoke", x = "Year", y = "Percent of Adults Who Currently Smoke", caption = "Data includes 2012-2014") +
theme_light()
#Las Vegas (Clark County), NV
cd_smo_lv <- cd_smo %>% filter(Place =="Las Vegas (Clark County), NV")
ggplot(data = cd_smo_lv) +
geom_point (mapping = aes(x= Year, y = Value, color = Race, shape = Sex)) +
facet_wrap(facets = vars(Race)) +
labs(title = "Chronic Disease - Las Vegas (Clark County), NV - Percent of Adults Who Currently Smoke", x = "Year", y = "Percent of Adults Who Currently Smoke", caption = "Data includes 2011-2016") +
theme_light()
#New York City, NY
cd_smo_ny <- cd_smo %>% filter(Place =="New York City, NY")
ggplot(data = cd_smo_ny) +
geom_point (mapping = aes(x= Year, y = Value, color = Race, shape = Sex)) +
facet_wrap(facets = vars(Race)) +
labs(title = "Chronic Disease - New York City, NY - Percent of Adults Who Currently Smoke", x = "Year", y = "Percent of Adults Who Currently Smoke", caption = "Data includes 2011-2015") +
theme_light()
#Philadelphia, PA
cd_smo_phi <- cd_smo %>% filter(Place =="Philadelphia, PA")
ggplot(data = cd_smo_phi) +
geom_point (mapping = aes(x= Year, y = Value, color = Race, shape = Sex)) +
facet_wrap(facets = vars(Race)) +
labs(title = "Chronic Disease - Philadelphia, PA - Percent of Adults Who Currently Smoke", x = "Year", y = "Percent of Adults Who Currently Smoke", caption = "Data includes 2010-2016") +
theme_light()
#Phoenix, AZ
cd_smo_pho <- cd_smo %>% filter(Place =="Phoenix, AZ")
ggplot(data = cd_smo_pho) +
geom_point (mapping = aes(x= Year, y = Value, color = Race, shape = Sex)) +
facet_wrap(facets = vars(Race)) +
labs(title = "Chronic Disease - Phoenix, AZ - Percent of Adults Who Currently Smoke", x = "Year", y = "Percent of Adults Who Currently Smoke", caption = "Data includes 2012-2014") +
theme_light()
#San Antonio, TX
cd_smo_sanant <- cd_smo %>% filter(Place =="San Antonio, TX")
ggplot(data = cd_smo_sanant) +
geom_point (mapping = aes(x= Year, y = Value, color = Race, shape = Sex)) +
facet_wrap(facets = vars(Race)) +
labs(title = "Chronic Disease - San Antonio, TX - Percent of Adults Who Currently Smoke", x = "Year", y = "Percent of Adults Who Currently Smoke", caption = "Data includes 2012-2014") +
theme_light()
#Seattle, WA
cd_smo_sea <- cd_smo %>% filter(Place =="Seattle, WA")
ggplot(data = cd_smo_sea) +
geom_point (mapping = aes(x= Year, y = Value, color = Race, shape = Sex)) +
facet_wrap(facets = vars(Race)) +
labs(title = "Chronic Disease - Seattle, WA - Percent of Adults Who Currently Smoke", x = "Year", y = "Percent of Adults Who Currently Smoke", caption = "Data includes 2010-2015") +
theme_light()## Warning: Removed 5 rows containing missing values (geom_point).
#U.S. Total, U.S. Total
cd_smo_us <- cd_smo %>% filter(Place =="U.S. Total, U.S. Total")
ggplot(data = cd_smo_us) +
geom_point (mapping = aes(x= Year, y = Value, color = Race, shape = Sex)) +
facet_wrap(facets = vars(Race)) +
labs(title = "Chronic Disease - U.S. Total, U.S. Total - Percent of Adults Who Currently Smoke", x = "Year", y = "Percent of Adults Who Currently Smoke", caption = "Data includes 2010-2015") +
theme_light()