R/Web_Prep2.R
In ctclarks99/HealthEVV: EVPL Health Project
Defines functions
prediction_map
Documented in
prediction_map
#' Final Data Frame
#' @export
#' @import dplyr
df_final <- read.csv(file = "data_final.csv") %>% mutate(white_poverty = white*poverty,
black_older = black*older,
poverty_older = poverty*older,
poverty_young = poverty*young,
hispanic_older = hispanic*older,
male_upper_class = male*upper_class)
#' Import census data to save time
#' @export
Census_data <- read.csv(file = "Census_Data.csv") %>% mutate(GEOID = as.character(GEOID))
#' Best Smoking
#' @export
Best_Smoking <- glm(smoke ~ male + white + black + asian + alaska_indian + hispanic +
poverty + upper_class + no_GED + college + young + older +
white_poverty + black_older + hispanic_older,
data = df_final,
family = binomial(link = "logit"))
#' Best Asthma
#' @export
Best_Asthma <- glm(asthma ~ male + white + black + asian + alaska_indian + hispanic +
poverty + upper_class + no_GED + college + young + older +
black_older + hispanic_older + poverty_older,
data = df_final,
family = binomial(link = "logit"))
#' Best Overweight
#' @export
Best_Overweight <- glm(overweight ~ male + white + black + asian + alaska_indian + hispanic +
poverty + upper_class + no_GED + college + young + older +
male_upper_class + poverty_older,
data = df_final,
family = binomial(link = "logit"))
#' Binge Drinking
#' @export
Best_Binge_Drinker <- glm(binge_drinker ~ male + white + black + asian + alaska_indian +
hispanic + poverty + upper_class + no_GED + college + young +
older + black_older + hispanic_older + poverty_young,
data = df_final,
family = binomial(link = "logit"))
#' Arthritis
#' @export
Best_Arthritis <- glm(arthritis ~ male + white + black + asian + alaska_indian + hispanic +
poverty + upper_class + no_GED + college + young + older + male_upper_class +
white_poverty + black_older + hispanic_older + poverty_young + poverty_older,
data = df_final,
family = binomial(link = "logit"))
#' Depression
#' @export
Best_Depression <- glm(depression ~ male + white + black + asian + hispanic + poverty +
upper_class + no_GED + college + young + older + white_poverty +
black_older + hispanic_older + poverty_young,
data = df_final,
family = binomial(link = "logit"))
#' High Cholestorol
#' @export
Best_High_Cholesterol <- glm(high_cholesterol ~ male + white + black + asian + alaska_indian +
poverty + upper_class + no_GED + college + young + older +
male_upper_class + white_poverty + black_older + poverty_older,
data = df_final,
family = binomial(link = "logit"))
#' Best High Blood Pressure
#' @export
Best_High_Blood_Pressure <- glm(high_blood_pressure ~ male + white + black + asian + alaska_indian +
hispanic + poverty + upper_class + no_GED + college + young +
older + male_upper_class + white_poverty,
data = df_final,
family = binomial(link = "logit"))
#' Diabetes
#' @export
Best_Diabetes <- glm(diabetic ~ male + white + black + other_race + poverty + upper_class +
no_GED + college + young + older + male_upper_class + white_poverty + poverty_young + poverty_older,
data = df_final,
family = binomial(link = "logit"))
#' Angina or Coronary
#' @export
Best_Angina_Coronary <- glm(angina ~ male + white + black + other_race + poverty +
upper_class + no_GED + college + young + older + black_older + poverty_older,
data = df_final,
family = binomial(link = "logit"))
#' Evansville Map
evansville <- c(left = -87.70264,
bottom = 37.83241,
right = -87.45628,
top = 38.20472)
#' Get Map
#' @import maps
evv_map <- get_map(evansville, maptype = "roadmap")
#' Reading
#' @import sf
evv <- st_read("Census_Tracts.shp")
#' Transform
evv <- st_transform(evv, crs = "+proj=longlat +datumWGS84 +no_defs +ellps=WGS84")
#' Frame
evv <- data.frame(evv) %>% transmute(GEOID = as.character(GEOID10), geometry)
#' Complete Data Frame
eville <- evv %>% left_join(Census_data, by = "GEOID")
#' Convert Census Data
eville <- eville %>% mutate(male = males / Pop_18_and,
white = white / Pop_18_and,
black = black / Pop_18_and,
asian = asian / Pop_18_and,
alaska_indian = alaska_indian / Pop_18_and,
hispanic = hispanic / Pop_18_and,
other_race = (asian + alaska_indian + hispanic),
male = males / Pop_18_and,
poverty = poverty / Pop_18_and,
upper_class = upper_class / Pop_18_and,
no_GED = no_GED / Pop_18_and,
college = college / Pop_18_and,
young = young / Pop_18_and,
older = older / Pop_18_and,
white_poverty = white_poverty / Pop_18_and,
black_older = black_older / Pop_18_and,
poverty_older = poverty_older / Pop_18_and,
hispanic_older = hispanic_older / Pop_18_and,
poverty_young = poverty_young / Pop_18_and,
male_upper_class = male_upper_class / Pop_18_and
)
#' Prediction Values
#' @export
eville <- eville %>% mutate(Proportion_Smokers = predict.glm(Best_Smoking, newdata = eville, type = "response"),
Predicted_Number_Smokers = predict.glm(Best_Smoking, newdata = eville, type = "response") * Pop_18_and,
Difference_From_Average_Smokers = (predict.glm(Best_Smoking, newdata = eville, type = "response") - mean(df_final$smoke)),
Proportion_Overweight = predict.glm(Best_Overweight, newdata = eville, type = "response"),
Predicted_Number_Overweight = predict.glm(Best_Overweight, newdata = eville, type = "response") * Pop_18_and,
Difference_From_Average_Overweight = (predict.glm(Best_Overweight, newdata = eville, type = "response") - mean(df_final$overweight)),
Proportion_Asthma = predict.glm(Best_Asthma, newdata = eville, type = "response"),
Predicted_Number_Asthma = predict.glm(Best_Asthma, newdata = eville, type = "response") * Pop_18_and,
Difference_From_Average_Asthma = (predict.glm(Best_Asthma, newdata = eville, type = "response") - mean(df_final$asthma)),
Proportion_Binge = predict.glm(Best_Binge_Drinker, newdata = eville, type = "response"),
Predicted_Number_Binge = predict.glm(Best_Binge_Drinker, newdata = eville, type = "response") * Pop_18_and,
Difference_From_Average_Binge = (predict.glm(Best_Binge_Drinker, newdata = eville, type = "response") - mean(df_final$binge_drinker)),
Proportion_Arthritis = predict.glm(Best_Arthritis, newdata = eville, type = "response"),
Predicted_Number_Arthritis = predict.glm(Best_Arthritis, newdata = eville, type = "response") * Pop_18_and,
Difference_From_Average_Arthritis = (predict.glm(Best_Arthritis, newdata = eville, type = "response") - mean(df_final$arthritis)),
Proportion_High_BP = predict.glm(Best_High_Blood_Pressure, newdata = eville, type = "response"),
Predicted_Number_High_BP = predict.glm(Best_High_Blood_Pressure, newdata = eville, type = "response") * Pop_18_and,
Difference_From_Average_High_BP = (predict.glm(Best_High_Blood_Pressure, newdata = eville, type = "response") - mean(df_final$high_blood_pressure)),
Proportion_Angina = predict.glm(Best_Angina_Coronary, newdata = eville, type = "response"),
Predicted_Number_Angina = predict.glm(Best_Angina_Coronary, newdata = eville, type = "response") * Pop_18_and,
Difference_From_Average_Angina = (predict.glm(Best_Angina_Coronary, newdata = eville, type = "response") - mean(df_final$angina)),
Proportion_Depression = predict.glm(Best_Depression, newdata = eville, type = "response"),
Predicted_Number_Depression = predict.glm(Best_Depression, newdata = eville, type = "response") * Pop_18_and,
Difference_From_Average_Depression = (predict.glm(Best_Depression, newdata = eville, type = "response") - mean(df_final$depression)),
Proportion_Diabetes = predict.glm(Best_Diabetes, newdata = eville, type = "response"),
Predicted_Number_Diabetes = predict.glm(Best_Diabetes, newdata = eville, type = "response") * Pop_18_and,
Difference_From_Average_Diabetes = (predict.glm(Best_Diabetes, newdata = eville, type = "response") - mean(df_final$diabetic)),
Proportion_High_Cholesterol = predict.glm(Best_High_Cholesterol, newdata = eville, type = "response"),
Predicted_Number_High_Cholesterol = predict.glm(Best_High_Cholesterol, newdata = eville, type = "response") * Pop_18_and,
Difference_From_Average_High_Cholesterol = (predict.glm(Best_High_Cholesterol, newdata = eville, type = "response") - mean(df_final$high_cholesterol)))
#' Find correct ggplot to return
#'
#' Returns ggplot
#' @import ggplot2
#' @import ggmap
#' @param input1 Selected response from shiny UI
#' @param input2 Selected plot type from shiny UI
#' @return correct ggplot
#' @export
prediction_map <- function(input1,input2) {
if(input1 == "Smoking" & input2 == "Predicted Proportion") { return(ggmap(evv_map) +
geom_sf(aes(fill = Proportion_Smokers, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Proportion of Smokers") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Smoking" & input2 == "Predicted Number of People") { return(ggmap(evv_map) +
geom_sf(aes(fill = Predicted_Number_Smokers, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Number of Smokers") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Smoking" & input2 == "Predicted Difference From Indiana Average") { return(ggmap(evv_map) +
geom_sf(aes(fill = Difference_From_Average_Smokers, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Difference from Average for Smoking") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Overweight" & input2 == "Predicted Proportion") { return(ggmap(evv_map) +
geom_sf(aes(fill = Proportion_Overweight, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Proportion of Overweight Citizens") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Overweight" & input2 == "Predicted Number of People") { return(ggmap(evv_map) +
geom_sf(aes(fill = Predicted_Number_Overweight, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Number of Overweight Citizens") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Overweight" & input2 == "Predicted Difference From Indiana Average") { return(ggmap(evv_map) +
geom_sf(aes(fill = Difference_From_Average_Overweight, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Difference from Average for Overweight") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Asthma" & input2 == "Predicted Proportion") { return(ggmap(evv_map) +
geom_sf(aes(fill = Proportion_Asthma, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Proportion of Asthmatics") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Asthma" & input2 == "Predicted Number of People") { return(ggmap(evv_map) +
geom_sf(aes(fill = Predicted_Number_Asthma, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Number of Asthmatic Citizens") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Asthma" & input2 == "Predicted Difference From Indiana Average") { return(ggmap(evv_map) +
geom_sf(aes(fill = Difference_From_Average_Asthma,
geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Difference from Average for Asthmatics") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Binge Drinking" & input2 == "Predicted Proportion") { return(ggmap(evv_map) +
geom_sf(aes(fill = Proportion_Binge, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Proportion of Binge Drinkers") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Binge Drinking" & input2 == "Predicted Number of People") { return(ggmap(evv_map) +
geom_sf(aes(fill = Predicted_Number_Binge, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Number of Binge Drinkers") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Binge Drinking" & input2 == "Predicted Difference From Indiana Average") { return(ggmap(evv_map) +
geom_sf(aes(fill = Difference_From_Average_Binge, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Difference from Average for Binge Drinkers") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Arthritis" & input2 == "Predicted Proportion") { return(ggmap(evv_map) +
geom_sf(aes(fill = Proportion_Arthritis, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Proportion of Citizens with Arthritis") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Arthritis" & input2 == "Predicted Number of People") { return(ggmap(evv_map) +
geom_sf(aes(fill = Predicted_Number_Arthritis, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Number of Citizens with Arthritis") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Arthritis" & input2 == "Predicted Difference From Indiana Average") { return(ggmap(evv_map) +
geom_sf(aes(fill = Difference_From_Average_Arthritis, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Difference from Average for Arthritis") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "High Blood Pressure" & input2 == "Predicted Proportion") { return(ggmap(evv_map) +
geom_sf(aes(fill = Proportion_High_BP, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Proportion of Citizens with High Blood Pressure") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "High Blood Pressure" & input2 == "Predicted Number of People") { return(ggmap(evv_map) +
geom_sf(aes(fill = Predicted_Number_High_BP, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Number of Citizens with High Blood Pressure") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "High Blood Pressure" & input2 == "Predicted Difference From Indiana Average") { return(ggmap(evv_map) +
geom_sf(aes(fill = Difference_From_Average_High_BP, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Difference from Average for Citizens with High Blood Pressure") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Angina or Coronary Heart Disease" & input2 == "Predicted Proportion") { return(ggmap(evv_map) +
geom_sf(aes(fill = Proportion_Angina, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Proportion of Citizens with Angina or Coronary Heart Disease") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Angina or Coronary Heart Disease" & input2 == "Predicted Number of People") { return(ggmap(evv_map) +
geom_sf(aes(fill = Predicted_Number_Angina, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Number of Citizens with Angina or Coronary Heart Disease") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Angina or Coronary Heart Disease" & input2 == "Predicted Difference From Indiana Average") { return(ggmap(evv_map) +
geom_sf(aes(fill = Difference_From_Average_Angina, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Difference from Average for Citizens with Angina or Coronary Heart Disease") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Depression" & input2 == "Predicted Proportion") { return(ggmap(evv_map) +
geom_sf(aes(fill = Proportion_Depression, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Proportion of Citizens with Depression") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Depression" & input2 == "Predicted Number of People") { return(ggmap(evv_map) +
geom_sf(aes(fill = Predicted_Number_Depression, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Number of Citizens with Depression") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Depression" & input2 == "Predicted Difference From Indiana Average") { return(ggmap(evv_map) +
geom_sf(aes(fill = Difference_From_Average_Depression, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Difference from Average for Citizens with Depression") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "High Cholesterol" & input2 == "Predicted Proportion") { return(ggmap(evv_map) +
geom_sf(aes(fill = Proportion_High_Cholesterol, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Proportion of Citizens with High Cholesterol") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "High Cholesterol" & input2 == "Predicted Number of People") { return(ggmap(evv_map) +
geom_sf(aes(fill = Predicted_Number_High_Cholesterol, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Number of Citizens with High Cholesterol") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "High Cholesterol" & input2 == "Predicted Difference From Indiana Average") { return(ggmap(evv_map) +
geom_sf(aes(fill = Difference_From_Average_High_Cholesterol, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Difference from Average for Citizens with High Cholesterol") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Diabetes" & input2 == "Predicted Proportion") { return(ggmap(evv_map) +
geom_sf(aes(fill = Proportion_Diabetes, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Proportion of Citizens with Diabetes") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Diabetes" & input2 == "Predicted Number of People") { return(ggmap(evv_map) +
geom_sf(aes(fill = Predicted_Number_Diabetes, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Number of Citizens with Diabetes") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Diabetes" & input2 == "Predicted Difference From Indiana Average") { return(ggmap(evv_map) +
geom_sf(aes(fill = Difference_From_Average_Diabetes, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Difference from Average for Citizens with Diabetes") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
}
ctclarks99/HealthEVV documentation built on May 26, 2019, 10:34 a.m.
R Package Documentation
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Defines functions prediction_map
Documented in prediction_map
#' Final Data Frame
#' @export
#' @import dplyr
df_final <- read.csv(file = "data_final.csv") %>% mutate(white_poverty = white*poverty,
black_older = black*older,
poverty_older = poverty*older,
poverty_young = poverty*young,
hispanic_older = hispanic*older,
male_upper_class = male*upper_class)
#' Import census data to save time
#' @export
Census_data <- read.csv(file = "Census_Data.csv") %>% mutate(GEOID = as.character(GEOID))
#' Best Smoking
#' @export
Best_Smoking <- glm(smoke ~ male + white + black + asian + alaska_indian + hispanic +
poverty + upper_class + no_GED + college + young + older +
white_poverty + black_older + hispanic_older,
data = df_final,
family = binomial(link = "logit"))
#' Best Asthma
#' @export
Best_Asthma <- glm(asthma ~ male + white + black + asian + alaska_indian + hispanic +
poverty + upper_class + no_GED + college + young + older +
black_older + hispanic_older + poverty_older,
data = df_final,
family = binomial(link = "logit"))
#' Best Overweight
#' @export
Best_Overweight <- glm(overweight ~ male + white + black + asian + alaska_indian + hispanic +
poverty + upper_class + no_GED + college + young + older +
male_upper_class + poverty_older,
data = df_final,
family = binomial(link = "logit"))
#' Binge Drinking
#' @export
Best_Binge_Drinker <- glm(binge_drinker ~ male + white + black + asian + alaska_indian +
hispanic + poverty + upper_class + no_GED + college + young +
older + black_older + hispanic_older + poverty_young,
data = df_final,
family = binomial(link = "logit"))
#' Arthritis
#' @export
Best_Arthritis <- glm(arthritis ~ male + white + black + asian + alaska_indian + hispanic +
poverty + upper_class + no_GED + college + young + older + male_upper_class +
white_poverty + black_older + hispanic_older + poverty_young + poverty_older,
data = df_final,
family = binomial(link = "logit"))
#' Depression
#' @export
Best_Depression <- glm(depression ~ male + white + black + asian + hispanic + poverty +
upper_class + no_GED + college + young + older + white_poverty +
black_older + hispanic_older + poverty_young,
data = df_final,
family = binomial(link = "logit"))
#' High Cholestorol
#' @export
Best_High_Cholesterol <- glm(high_cholesterol ~ male + white + black + asian + alaska_indian +
poverty + upper_class + no_GED + college + young + older +
male_upper_class + white_poverty + black_older + poverty_older,
data = df_final,
family = binomial(link = "logit"))
#' Best High Blood Pressure
#' @export
Best_High_Blood_Pressure <- glm(high_blood_pressure ~ male + white + black + asian + alaska_indian +
hispanic + poverty + upper_class + no_GED + college + young +
older + male_upper_class + white_poverty,
data = df_final,
family = binomial(link = "logit"))
#' Diabetes
#' @export
Best_Diabetes <- glm(diabetic ~ male + white + black + other_race + poverty + upper_class +
no_GED + college + young + older + male_upper_class + white_poverty + poverty_young + poverty_older,
data = df_final,
family = binomial(link = "logit"))
#' Angina or Coronary
#' @export
Best_Angina_Coronary <- glm(angina ~ male + white + black + other_race + poverty +
upper_class + no_GED + college + young + older + black_older + poverty_older,
data = df_final,
family = binomial(link = "logit"))
#' Evansville Map
evansville <- c(left = -87.70264,
bottom = 37.83241,
right = -87.45628,
top = 38.20472)
#' Get Map
#' @import maps
evv_map <- get_map(evansville, maptype = "roadmap")
#' Reading
#' @import sf
evv <- st_read("Census_Tracts.shp")
#' Transform
evv <- st_transform(evv, crs = "+proj=longlat +datumWGS84 +no_defs +ellps=WGS84")
#' Frame
evv <- data.frame(evv) %>% transmute(GEOID = as.character(GEOID10), geometry)
#' Complete Data Frame
eville <- evv %>% left_join(Census_data, by = "GEOID")
#' Convert Census Data
eville <- eville %>% mutate(male = males / Pop_18_and,
white = white / Pop_18_and,
black = black / Pop_18_and,
asian = asian / Pop_18_and,
alaska_indian = alaska_indian / Pop_18_and,
hispanic = hispanic / Pop_18_and,
other_race = (asian + alaska_indian + hispanic),
male = males / Pop_18_and,
poverty = poverty / Pop_18_and,
upper_class = upper_class / Pop_18_and,
no_GED = no_GED / Pop_18_and,
college = college / Pop_18_and,
young = young / Pop_18_and,
older = older / Pop_18_and,
white_poverty = white_poverty / Pop_18_and,
black_older = black_older / Pop_18_and,
poverty_older = poverty_older / Pop_18_and,
hispanic_older = hispanic_older / Pop_18_and,
poverty_young = poverty_young / Pop_18_and,
male_upper_class = male_upper_class / Pop_18_and
)
#' Prediction Values
#' @export
eville <- eville %>% mutate(Proportion_Smokers = predict.glm(Best_Smoking, newdata = eville, type = "response"),
Predicted_Number_Smokers = predict.glm(Best_Smoking, newdata = eville, type = "response") * Pop_18_and,
Difference_From_Average_Smokers = (predict.glm(Best_Smoking, newdata = eville, type = "response") - mean(df_final$smoke)),
Proportion_Overweight = predict.glm(Best_Overweight, newdata = eville, type = "response"),
Predicted_Number_Overweight = predict.glm(Best_Overweight, newdata = eville, type = "response") * Pop_18_and,
Difference_From_Average_Overweight = (predict.glm(Best_Overweight, newdata = eville, type = "response") - mean(df_final$overweight)),
Proportion_Asthma = predict.glm(Best_Asthma, newdata = eville, type = "response"),
Predicted_Number_Asthma = predict.glm(Best_Asthma, newdata = eville, type = "response") * Pop_18_and,
Difference_From_Average_Asthma = (predict.glm(Best_Asthma, newdata = eville, type = "response") - mean(df_final$asthma)),
Proportion_Binge = predict.glm(Best_Binge_Drinker, newdata = eville, type = "response"),
Predicted_Number_Binge = predict.glm(Best_Binge_Drinker, newdata = eville, type = "response") * Pop_18_and,
Difference_From_Average_Binge = (predict.glm(Best_Binge_Drinker, newdata = eville, type = "response") - mean(df_final$binge_drinker)),
Proportion_Arthritis = predict.glm(Best_Arthritis, newdata = eville, type = "response"),
Predicted_Number_Arthritis = predict.glm(Best_Arthritis, newdata = eville, type = "response") * Pop_18_and,
Difference_From_Average_Arthritis = (predict.glm(Best_Arthritis, newdata = eville, type = "response") - mean(df_final$arthritis)),
Proportion_High_BP = predict.glm(Best_High_Blood_Pressure, newdata = eville, type = "response"),
Predicted_Number_High_BP = predict.glm(Best_High_Blood_Pressure, newdata = eville, type = "response") * Pop_18_and,
Difference_From_Average_High_BP = (predict.glm(Best_High_Blood_Pressure, newdata = eville, type = "response") - mean(df_final$high_blood_pressure)),
Proportion_Angina = predict.glm(Best_Angina_Coronary, newdata = eville, type = "response"),
Predicted_Number_Angina = predict.glm(Best_Angina_Coronary, newdata = eville, type = "response") * Pop_18_and,
Difference_From_Average_Angina = (predict.glm(Best_Angina_Coronary, newdata = eville, type = "response") - mean(df_final$angina)),
Proportion_Depression = predict.glm(Best_Depression, newdata = eville, type = "response"),
Predicted_Number_Depression = predict.glm(Best_Depression, newdata = eville, type = "response") * Pop_18_and,
Difference_From_Average_Depression = (predict.glm(Best_Depression, newdata = eville, type = "response") - mean(df_final$depression)),
Proportion_Diabetes = predict.glm(Best_Diabetes, newdata = eville, type = "response"),
Predicted_Number_Diabetes = predict.glm(Best_Diabetes, newdata = eville, type = "response") * Pop_18_and,
Difference_From_Average_Diabetes = (predict.glm(Best_Diabetes, newdata = eville, type = "response") - mean(df_final$diabetic)),
Proportion_High_Cholesterol = predict.glm(Best_High_Cholesterol, newdata = eville, type = "response"),
Predicted_Number_High_Cholesterol = predict.glm(Best_High_Cholesterol, newdata = eville, type = "response") * Pop_18_and,
Difference_From_Average_High_Cholesterol = (predict.glm(Best_High_Cholesterol, newdata = eville, type = "response") - mean(df_final$high_cholesterol)))
#' Find correct ggplot to return
#'
#' Returns ggplot
#' @import ggplot2
#' @import ggmap
#' @param input1 Selected response from shiny UI
#' @param input2 Selected plot type from shiny UI
#' @return correct ggplot
#' @export
prediction_map <- function(input1,input2) {
if(input1 == "Smoking" & input2 == "Predicted Proportion") { return(ggmap(evv_map) +
geom_sf(aes(fill = Proportion_Smokers, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Proportion of Smokers") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Smoking" & input2 == "Predicted Number of People") { return(ggmap(evv_map) +
geom_sf(aes(fill = Predicted_Number_Smokers, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Number of Smokers") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Smoking" & input2 == "Predicted Difference From Indiana Average") { return(ggmap(evv_map) +
geom_sf(aes(fill = Difference_From_Average_Smokers, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Difference from Average for Smoking") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Overweight" & input2 == "Predicted Proportion") { return(ggmap(evv_map) +
geom_sf(aes(fill = Proportion_Overweight, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Proportion of Overweight Citizens") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Overweight" & input2 == "Predicted Number of People") { return(ggmap(evv_map) +
geom_sf(aes(fill = Predicted_Number_Overweight, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Number of Overweight Citizens") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Overweight" & input2 == "Predicted Difference From Indiana Average") { return(ggmap(evv_map) +
geom_sf(aes(fill = Difference_From_Average_Overweight, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Difference from Average for Overweight") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Asthma" & input2 == "Predicted Proportion") { return(ggmap(evv_map) +
geom_sf(aes(fill = Proportion_Asthma, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Proportion of Asthmatics") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Asthma" & input2 == "Predicted Number of People") { return(ggmap(evv_map) +
geom_sf(aes(fill = Predicted_Number_Asthma, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Number of Asthmatic Citizens") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Asthma" & input2 == "Predicted Difference From Indiana Average") { return(ggmap(evv_map) +
geom_sf(aes(fill = Difference_From_Average_Asthma,
geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Difference from Average for Asthmatics") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Binge Drinking" & input2 == "Predicted Proportion") { return(ggmap(evv_map) +
geom_sf(aes(fill = Proportion_Binge, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Proportion of Binge Drinkers") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Binge Drinking" & input2 == "Predicted Number of People") { return(ggmap(evv_map) +
geom_sf(aes(fill = Predicted_Number_Binge, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Number of Binge Drinkers") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Binge Drinking" & input2 == "Predicted Difference From Indiana Average") { return(ggmap(evv_map) +
geom_sf(aes(fill = Difference_From_Average_Binge, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Difference from Average for Binge Drinkers") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Arthritis" & input2 == "Predicted Proportion") { return(ggmap(evv_map) +
geom_sf(aes(fill = Proportion_Arthritis, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Proportion of Citizens with Arthritis") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Arthritis" & input2 == "Predicted Number of People") { return(ggmap(evv_map) +
geom_sf(aes(fill = Predicted_Number_Arthritis, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Number of Citizens with Arthritis") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Arthritis" & input2 == "Predicted Difference From Indiana Average") { return(ggmap(evv_map) +
geom_sf(aes(fill = Difference_From_Average_Arthritis, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Difference from Average for Arthritis") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "High Blood Pressure" & input2 == "Predicted Proportion") { return(ggmap(evv_map) +
geom_sf(aes(fill = Proportion_High_BP, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Proportion of Citizens with High Blood Pressure") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "High Blood Pressure" & input2 == "Predicted Number of People") { return(ggmap(evv_map) +
geom_sf(aes(fill = Predicted_Number_High_BP, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Number of Citizens with High Blood Pressure") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "High Blood Pressure" & input2 == "Predicted Difference From Indiana Average") { return(ggmap(evv_map) +
geom_sf(aes(fill = Difference_From_Average_High_BP, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Difference from Average for Citizens with High Blood Pressure") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Angina or Coronary Heart Disease" & input2 == "Predicted Proportion") { return(ggmap(evv_map) +
geom_sf(aes(fill = Proportion_Angina, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Proportion of Citizens with Angina or Coronary Heart Disease") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Angina or Coronary Heart Disease" & input2 == "Predicted Number of People") { return(ggmap(evv_map) +
geom_sf(aes(fill = Predicted_Number_Angina, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Number of Citizens with Angina or Coronary Heart Disease") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Angina or Coronary Heart Disease" & input2 == "Predicted Difference From Indiana Average") { return(ggmap(evv_map) +
geom_sf(aes(fill = Difference_From_Average_Angina, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Difference from Average for Citizens with Angina or Coronary Heart Disease") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Depression" & input2 == "Predicted Proportion") { return(ggmap(evv_map) +
geom_sf(aes(fill = Proportion_Depression, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Proportion of Citizens with Depression") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Depression" & input2 == "Predicted Number of People") { return(ggmap(evv_map) +
geom_sf(aes(fill = Predicted_Number_Depression, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Number of Citizens with Depression") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Depression" & input2 == "Predicted Difference From Indiana Average") { return(ggmap(evv_map) +
geom_sf(aes(fill = Difference_From_Average_Depression, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Difference from Average for Citizens with Depression") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "High Cholesterol" & input2 == "Predicted Proportion") { return(ggmap(evv_map) +
geom_sf(aes(fill = Proportion_High_Cholesterol, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Proportion of Citizens with High Cholesterol") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "High Cholesterol" & input2 == "Predicted Number of People") { return(ggmap(evv_map) +
geom_sf(aes(fill = Predicted_Number_High_Cholesterol, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Number of Citizens with High Cholesterol") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "High Cholesterol" & input2 == "Predicted Difference From Indiana Average") { return(ggmap(evv_map) +
geom_sf(aes(fill = Difference_From_Average_High_Cholesterol, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Difference from Average for Citizens with High Cholesterol") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Diabetes" & input2 == "Predicted Proportion") { return(ggmap(evv_map) +
geom_sf(aes(fill = Proportion_Diabetes, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Proportion of Citizens with Diabetes") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Diabetes" & input2 == "Predicted Number of People") { return(ggmap(evv_map) +
geom_sf(aes(fill = Predicted_Number_Diabetes, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Predicted Number of Citizens with Diabetes") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
if(input1 == "Diabetes" & input2 == "Predicted Difference From Indiana Average") { return(ggmap(evv_map) +
geom_sf(aes(fill = Difference_From_Average_Diabetes, geometry = geometry),
inherit.aes=FALSE,
alpha = .75,
data = eville) +
scale_fill_viridis_c(option = "B") +
ggtitle("Difference from Average for Citizens with Diabetes") +
theme(plot.title = element_text(size = rel(1.3)),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
)
}
}
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