PrintingNotebook.Rmd

---
title: "Ventura QRIS Maps"
output:
  html_document: default
  html_notebook: default
  pdf_document: default
date: "Updated April 2018"
---



```{r echo = FALSE, message = FALSE, warning = FALSE, include=FALSE}
library(tidyverse)
library(plyr)
library(readxl)
library(tigris)
library(acs)
library(stringr)
library(leaflet)
library(ggplot2)
library(tidyr)
library(ggmap)
library(sp)
library(data.table)
library(tidyverse)


## Extract all data from excel sheets into one list of df
path <- "~/Google Drive/SI/DataScience/Side projects/Ventura Preschool /raw_data/CDF_data.xlsx"

sheet_names <- excel_sheets(path)
length(sheet_names)

sheet_list <- list()
sheet_temp_list <- list()
tracker <- 1
# For this df, each 3 sheets is one set of data 
for(counter in seq(from=1, to=length(sheet_names), by=3)){# process through each set of excel files
  for(x in 1:3) {
    sheet_temp_list[[x]] <- read_excel("~/Google Drive/SI/DataScience/Side projects/Ventura Preschool /raw_data/CDF_data.xlsx", sheet=sheet_names[x+counter-1]) #read sheet into a temp sheet
    if((x %% 3) == 1){ # if it is the first sheet of a new set
      col_names <- colnames(sheet_temp_list[[x]]) # get the column names 
    } else {
      colnames(sheet_temp_list[[x]]) <-  col_names # apply the column names from the first sheet in a set to all other sheets in that set
    }
    if((x %% 3) == 0){
      sheet_list[[tracker]] <- rbind.fill(sheet_temp_list) # if at the last sheet of a set, combine into one sheet and store in list
    }
  }
  tracker <- tracker + 1 # increase number of sets stored in sheet_list by 1
}

## Merge dfs from list into single df
merge.all <- function(x, y) {
  merge(x, y, all=TRUE)
}

merged_df <- Reduce(merge.all, sheet_list)
merged_df <- merged_df[-116,] # delete extra goal_dq from sheet 39
merged_df <- merged_df[-72,]
merged_df <- merged_df[-71,]

qris_all  <- merged_df %>% # mutate to make site names match the names in qrisaddresses
  mutate(site_name = gsub("CDR - ", "", site_name)) %>% 
  mutate(site_name = gsub("Cal-", "", site_name)) %>% 
  mutate(site_name = gsub("CDI - ", "", site_name)) %>% 
  mutate(site_name = gsub("CAPSLO - ", "", site_name)) %>% 
  mutate(site_name = gsub("SAFE ", "", site_name)) %>% 
  mutate(site_name = gsub("CDR/", "", site_name)) %>% 
  mutate(site_name = gsub("CVUSD ‐ ", "", site_name)) %>%
  mutate(site_name = gsub("CVUSD - ", "", site_name)) %>% 
  mutate(site_name = gsub("‐", "-", site_name)) %>%
  mutate(site_name = gsub("VUSD - ", "", site_name)) %>% 
  mutate(site_name = gsub("FCC", "Family Child Care", site_name)) %>% 
  mutate(site_name = gsub("HS", "High School", site_name)) %>% 
  mutate(site_name = ifelse(`IMPACT SIT_`==86 & site_name=="Lopez Family Child Care", "86 Lopez Family Child Care", site_name))

# 

addresses <- read.csv("~/Google Drive/SI/DataScience/Side projects/Ventura Preschool /raw_data/qrisaddresses.csv", header=TRUE, sep=",")

df <- merge(qris_all, addresses, by = "site_name")


#compare 
final_df <- rbind(qris_all["site_name"], addresses["site_name"])
(not_shared <- distinct(final_df["site_name"],.keep_all = FALSE))

qris_exp <- qris_all %>% 
  mutate(`IMPACT SIT`= as.numeric(`IMPACT SIT`)) %>% 
  slice(1:113)

addresses_new <- addresses %>% 
  dplyr::rename('IMPACT SIT' = 'X') %>% 
  mutate(`IMPACT SIT`= as.numeric(`IMPACT SIT`))



qris_mg <- qris_exp %>% 
  merge(addresses_new, by = 'IMPACT SIT')


final_df <- qris_mg


#geocodes <- geocode(as.character(qris_mg$address))
#row.names(geocodes) <- row.names(final_df)
#QRSADRS <- merge(select(final_df,site_name.x),geocodes, by="row.names",all.x=TRUE)
#not_geocoded <- subset(QRSADRS, is.na(lat))
######
#write.xlsx(geocodes, "C:\\Users\\Dylan Wootton\\Desktop\\College\\Fall 2017\\Sorenson\\Data Science\\Ventura Preshool\\Preschool Project\\finalgeocodes.xlsx")
```

```{r echo = FALSE, message=FALSE, warning=FALSE, include=FALSE, results='hide'}
#### Start the geocode analysis
geocodes <- read_excel('~/Google Drive/SI/DataScience/Side projects/Ventura Preschool /raw_data/finalgeocodes1 .xlsx')

QRIS1AND2 <- subset(final_df, overallqris_trating == 1 | overallqris_trating == 2)
#QRIS2 <- subset(final_df, overallqris_trating == 2)
QRIS3 <- subset(final_df, overallqris_trating == 3)
QRIS4AND5 <- subset(final_df, overallqris_trating == 4 | overallqris_trating == 5)
#QRIS5 <- subset(final_df, overallqris_trating == 5)
colnames(geocodes)[colnames(geocodes) == 'lon'] <- 'lng'



# geocodes1 <- geocode(as.character(QRIS1$address))
# colnames(geocodes1)[colnames(geocodes1) == 'lon'] <- 'lng'
# geocodes2 <- geocode(as.character(QRIS2$address)) 
# colnames(geocodes2)[colnames(geocodes2) == 'lon'] <- 'lng'
# geocodes3 <- geocode(as.character(QRIS3$address)) 
# colnames(geocodes3)[colnames(geocodes3) == 'lon'] <- 'lng'
# geocodes4 <- geocode(as.character(QRIS4$address)) 
# colnames(geocodes4)[colnames(geocodes4) == 'lon'] <- 'lng'
# geocodes5 <- geocode(as.character(QRIS5$address))
# colnames(geocodes5)[colnames(geocodes5) == 'lon'] <- 'lng'

QRSADRS1 <- merge(select(QRIS1AND2,site_name.x),geocodes, by="row.names",all.x=TRUE)
#QRSADRS2 <- merge(select(QRIS2,site_name.x),geocodes, by="row.names",all.x=TRUE)
QRSADRS3 <- merge(select(QRIS3,site_name.x),geocodes, by="row.names",all.x=TRUE)
QRSADRS4 <- merge(select(QRIS4AND5,site_name.x),geocodes, by="row.names",all.x=TRUE)
#QRSADRS5 <- merge(select(QRIS5,site_name.x),geocodes, by="row.names",all.x=TRUE)

# NA_df <- rbind(subset(QRSADRS2, is.na(lat)),subset(QRSADRS3, is.na(lat)),subset(QRSADRS4, is.na(lat)),subset(QRSADRS5, is.na(lat))) # when empty, all values correctly inputted

NA_df <- rbind(subset(QRSADRS1, is.na(lat)),subset(QRSADRS3, is.na(lat)),subset(QRSADRS4, is.na(lat))) # when empty, all values correctly inputted



# noter: families under 200% the federal poverty lelve will be eligible for subsidized preschool

```

# SNAP/Food Stamps 
The following map displays ACS SNAP data and QRIS rated sites. 

The legend of QRIS ratings is as follows: 

* Light Blue - QRIS rating of 1 or 2;

* Dark Blue - QRIS rating of 3;

* Dark Red - QRIS rating of 4 or 5.




```{r echo = FALSE, message=FALSE, warning=FALSE, results='hide'}
# public assistance income could be an option. 
# B19067


counties <- c(111) # FIPS for Ventura
tracts <- tracts(state = 'CA', county = c(111), cb=TRUE)

api.key.install(key="f5076b03b081b910cd6d0cebe26ad28d2ad49454")

geo <- geo.make(state=c("CA"), 
                county=c(111), tract="*") 

# # Fetch SNAP/Food Stamps Data
# poverty <- acs.fetch(endyear = 2015, span = 5, geography = geo, 
#                      table.number = "B22002", col.names = "pretty")
# 
# poverty_df <- data.frame(paste0(str_pad(poverty@geography$state, 2, "left", pad="0"), 
#                                 str_pad(poverty@geography$county, 3, "left", pad = "0"), 
#                                 str_pad(poverty@geography$tract, 6, "left", pad = "0")), 
#                          poverty@estimate[,c("Receipt of Food Stamps/SNAP by Presence of Children Under 18 Years by Household Type for Households: Household did not receive Food Stamps/SNAP in the past 12 months:", "Receipt of Food Stamps/SNAP by Presence of Children Under 18 Years by Household Type for Households: Household received Food Stamps/SNAP in the past 12 months:", "Receipt of Food Stamps/SNAP by Presence of Children Under 18 Years by Household Type for Households: Household received Food Stamps/SNAP in the past 12 months: With children under 18 years:", "Receipt of Food Stamps/SNAP by Presence of Children Under 18 Years by Household Type for Households: Household received Food Stamps/SNAP in the past 12 months: With children under 18 years: Married-couple family", "Receipt of Food Stamps/SNAP by Presence of Children Under 18 Years by Household Type for Households: Household received Food Stamps/SNAP in the past 12 months: With children under 18 years: Other family:","Receipt of Food Stamps/SNAP by Presence of Children Under 18 Years by Household Type for Households: Household received Food Stamps/SNAP in the past 12 months: With children under 18 years: Other family: Male householder, no wife present", "Receipt of Food Stamps/SNAP by Presence of Children Under 18 Years by Household Type for Households: Household received Food Stamps/SNAP in the past 12 months: With children under 18 years: Other family: Female householder, no husband present", "Receipt of Food Stamps/SNAP by Presence of Children Under 18 Years by Household Type for Households: Household received Food Stamps/SNAP in the past 12 months: With children under 18 years: Nonfamily households")], 
#                          stringsAsFactors = FALSE)
# 
# # Selects df
# poverty_df <- select(poverty_df, 1:9)
# rownames(poverty_df)<-1:nrow(poverty_df)
# names(poverty_df)<-c("GEOID", "didnottotal", "didtotal", "wkids", "married", "otherfam", "maleonly", "femaleonly", "nonfam")
# poverty_df$percent <- 100*(poverty_df$wkids/(poverty_df$didnottotal+poverty_df$didtotal))
# 
# write_rds(poverty_df, "~/Google Drive/SI/DataScience/Side projects/Ventura Preschool /raw_data/poverty_df.rds")

poverty_df <- read_rds("~/Google Drive/SI/DataScience/Side projects/Ventura Preschool /raw_data/poverty_df.rds")
```

```{r, echo=FALSE, message=FALSE, warning=FALSE}
#look into toggles 
# Merge 
poverty_merged<- geo_join(tracts, poverty_df, "GEOID", "GEOID")

# Exclude no land tracts
poverty_merged <- poverty_merged[poverty_merged$ALAND>0,]

schooldistrictunified <- school_districts(state = 'CA', type = "elementary")

geodistrict <- geo.make(state=c("CA"), 
                school.district.elementary = "*") 

schooldistrict <- schooldistrictunified[schooldistrictunified$GEOID %in% c("0629220"),]

# Exclude no land tracts
schooldistrict <- schooldistrict[schooldistrict$ALAND>0,]


# make the pop up
popup <- paste0("GEOID: ", poverty_merged$GEOID, "<br>", "Percent of Households w/ Children using SNAP/Food Stamps ", poverty_merged$percent)
pal <- colorNumeric(
  palette = "YlGnBu",
  domain = poverty_merged$percent
)


map<-leaflet() %>%
  addProviderTiles("CartoDB.Positron") %>%
  addPolygons(data = poverty_merged, 
              group = "SNAP",
              fillColor = ~pal(percent), 
              color = "#b2aeae", # you need to use hex colors
              fillOpacity = 0.7, 
              weight = 1, 
              smoothFactor = 0.2,
              popup = popup) %>%
    addPolygons(data = schooldistrict, 
              group = "SNAP",
              color = "#ff0000", # you need to use hex colors
              fillOpacity = 0, 
              weight = 2, 
              smoothFactor = 0.2,
              popup = popup) %>%
  addLegend(pal = pal, 
            values = poverty_merged$percent, 
            position = "bottomright", 
            title = "Percent of Households<br>w/ Children using SNAP/Food Stamps",
            labFormat = labelFormat(suffix = "%")) %>%

  # addAwesomeMarkers(QRSADRS1$lng, QRSADRS1$lat, popup = QRSADRS1$site_name.x, icon = awesomeIcons(icon = "ion-university", library = 'fa', markerColor = 'darkred')) %>%
 ### addAwesomeMarkers(QRSADRS1$lng, QRSADRS1$lat, popup = QRSADRS1$site_name.x, icon = awesomeIcons(icon = "ion-university", library = 'fa', markerColor = 'lightblue'))%>%
 ### addAwesomeMarkers(QRSADRS3$lng, QRSADRS3$lat, popup = QRSADRS3$site_name.x, icon = awesomeIcons(icon = "ion-university", library = 'fa', markerColor = 'darkblue'))%>%
 ### addAwesomeMarkers(QRSADRS4$lng, QRSADRS4$lat, popup = QRSADRS4$site_name.x, icon = awesomeIcons(icon = "ion-university", library = 'fa', markerColor = 'darkred'))%>%
  # addAwesomeMarkers(QRSADRS5$lng, QRSADRS5$lat, popup = QRSADRS5$site_name.x,popupOptions = popupOptions(style = list("color" = "black")), icon = awesomeIcons(icon = "ion-university", library = 'fa', markerColor = 'lightblue'))%>%
  addLayersControl(
                   overlayGroups = c("SNAP"), 
                   options = layersControlOptions(collapsed = FALSE))
  #addMarkers(map_df$lng, map_df$lat, popup= map_df$site_name.x)
  # dark red = 1, red = 2, darkblue = 3, blue = 4, light blue = 5
  

map

```

# % of Federal Poverty Line
The following map displays ACS % of Federal Poverty Line data and QRIS rated sites. 

The legend of QRIS ratings is as follows: 

* Light Blue - QRIS rating of 1 or 2;

* Dark Blue - QRIS rating of 3;

* Dark Red - QRIS rating of 4 or 5.

```{r echo = FALSE, message=FALSE, warning=FALSE, include=FALSE}
##### For Poverty 200% https://www.cde.ca.gov/sp/cd/ce/ltrgvnrapr2016.asp
# poverty <- acs.fetch(endyear = 2015, span = 5, geography = geo, 
#                      table.number = "C17002", col.names = "pretty")
# 
# attr(poverty, "acs.colnames")
# 
# # Gets columns from df
# poverty_df <- data.frame(paste0(str_pad(poverty@geography$state, 2, "left", pad="0"), 
#                                 str_pad(poverty@geography$county, 3, "left", pad = "0"), 
#                                 str_pad(poverty@geography$tract, 6, "left", pad = "0")), 
#                          poverty@estimate[,c( "Ratio of Income to Poverty Level in the Past 12 Months: Total:", "Ratio of Income to Poverty Level in the Past 12 Months: Under .50","Ratio of Income to Poverty Level in the Past 12 Months: .50 to .99","Ratio of Income to Poverty Level in the Past 12 Months: 1.00 to 1.24","Ratio of Income to Poverty Level in the Past 12 Months: 1.25 to 1.49","Ratio of Income to Poverty Level in the Past 12 Months: 1.50 to 1.84","Ratio of Income to Poverty Level in the Past 12 Months: 1.85 to 1.99" )], 
#                          stringsAsFactors = FALSE)
# 
# # Changes names of poverty_df
# rownames(poverty_df)<-1:nrow(poverty_df)
# names(poverty_df)<-c("GEOID", "Total", "50%", "100% ", "125%", "150%","185","200")
# 
# poverty_df$percent <- 0
# # Calculates % below 200% of poverty rate
# 
# poverty_df$percent <- 100*(((poverty_df$"50%"+poverty_df$"100%"+poverty_df$"125%"+poverty_df$"150%")/(poverty_df$"Total")))

# write_rds(poverty_df, "~/Google Drive/SI/DataScience/Side projects/Ventura Preschool /raw_data/poverty_df2.rds")

poverty_df <- read_rds("~/Google Drive/SI/DataScience/Side projects/Ventura Preschool /raw_data/poverty_df2.rds")

# Merge Poverty
poverty_merged<- geo_join(tracts, poverty_df, "GEOID", "GEOID")

# Exclude useless tracts
poverty_merged <- poverty_merged[poverty_merged$ALAND>0,]

# Make popup
popup <- paste0("GEOID: ", poverty_merged$GEOID, "<br>", "Percent of Individuals Below Poverty Line", round(poverty_merged$percent,2))
pal <- colorNumeric(
  palette = "YlGnBu",
  domain = poverty_merged$percent
)



```

```{r echo = FALSE, message=FALSE,warning=FALSE,dpi = 92}
# Map in leaflet
map<-leaflet() %>%
  addProviderTiles("CartoDB.Positron") %>%
  addPolygons(data = poverty_merged, 
              fillColor = ~pal(percent), 
              color = "#b2aeae", # you need to use hex colors
              fillOpacity = 0.7, 
              weight = 1, 
              smoothFactor = 0.2,
              popup = popup,
              group = "% of Poverty Line") %>%
  addLegend(pal = pal, 
            values = poverty_merged$percent, 
            position = "bottomright", 
            title = "Percent of Households<br> Below 150% of the Poverty Line",
            labFormat = labelFormat(suffix = "%")) %>%
  addAwesomeMarkers(QRSADRS1$lng, QRSADRS1$lat, popup = QRSADRS1$site_name.x, icon = awesomeIcons(icon = "ion-university", library = 'fa', markerColor = 'lightblue'))%>%
  addAwesomeMarkers(QRSADRS3$lng, QRSADRS3$lat, popup = QRSADRS3$site_name.x, icon = awesomeIcons(icon = "ion-university", library = 'fa', markerColor = 'darkblue'))%>%
  addAwesomeMarkers(QRSADRS4$lng, QRSADRS4$lat, popup = QRSADRS4$site_name.x, icon = awesomeIcons(icon = "ion-university", library = 'fa', markerColor = 'darkred'))%>%
  addLayersControl(
                   overlayGroups = c("% of Poverty Line"), 
                   options = layersControlOptions(collapsed = FALSE))
map

```

# Map of Preschool Need in Ventura County As Calculated by Professor Damooie

```{r map, echo=FALSE, message=FALSE, warning=FALSE}

data <- read.csv("~/Google Drive/SI/DataScience/Side projects/Ventura Preschool /raw_data/ConsultantDataFixed.csv")
data <- data[1:84,]
data$address <- paste(data$"Address..1.", data$"City", data$"State", data$"Zip", sep=" ")
# Consultant_geocodes <- geocode(as.character(data$address))
# 
# ## weirdness
# Consultant_geocodes <-  Consultant_geocodes %>%
#   slice(1:76) %>%
#   slice(-7:-8)

# write.csv(Consultant_geocodes, "~/Google Drive/SI/DataScience/Side projects/Ventura Preschool /raw_data/consultant_geocodes.csv")

Consultant_geocodes <- read_csv("~/Google Drive/SI/DataScience/Side projects/Ventura Preschool /raw_data/consultant_geocodes.csv")

Consultant_geocodes <- Consultant_geocodes %>% 
  select(lat, lon)

Consultant_Supply <- merge(select(data,c(Site.Name,Total.supply..spaces.)),Consultant_geocodes, by="row.names",all.x=TRUE)
Consultant_Need <- merge(select(data,c(Site.Name,Total.need)),Consultant_geocodes, by="row.names",all.x=TRUE)
Consultant_Gap <- merge(select(data,c(Site.Name,Accumulated.Gap)),Consultant_geocodes, by="row.names",all.x=TRUE)
## Run from here

#write.csv(Consultant_Supply, "C:\\Users\\Dylan Wootton\\Desktop\\College\\Fall 2017\\Sorenson\\Data Science\\Ventura Preshool\\Preschool Project\\Updated_ConsultantData.csv")


#map <- get_map(location = "Ventura", zoom = 12)
# Consultant_Need <- setDT(Consultant_Need)
# Consultant_Gap <- setDT(Consultant_Gap)
# 
# Consultant_Supply <- setDT(read.csv("~/Google Drive/SI/DataScience/Side projects/Ventura Preschool /raw_data/Updated_ConsultantData.csv", stringsAsFactors = FALSE))
Consultant_Supply <- Consultant_Supply[1:74,]
Consultant_Need <- Consultant_Need[1:74,]
Consultant_Gap <- Consultant_Gap[1:74,]


# new_CS <- Consultant_Supply
# Consultant_Supply <-  new_CS
# 
# new_CS <- Consultant_Need
# Consultant_Need <-  new_CS
# 
# new_CS <- Consultant_Gap
# Consultant_Gap <-  new_CS

##read in zipcode data because we need to add zip back in
 zips <- read.csv("~/Google Drive/SI/DataScience/Side projects/Ventura Preschool /raw_data/names and zipcodes.csv")

 zips <- zips %>% 
  slice(-7:-8)

#put zipcode data back into each
Consultant_Need <- Consultant_Need %>% 
  mutate(Row.names = as.numeric(Row.names)) %>% 
  arrange(Row.names)

 Consultant_Need$zip <- zips$Zip

Consultant_Supply <- Consultant_Supply  %>% 
  mutate(Row.names = as.numeric(Row.names)) %>% 
  arrange(Row.names)

 Consultant_Supply$zip <- zips$Zip
 
Consultant_Gap <- Consultant_Gap  %>% 
  mutate(Row.names = as.numeric(Row.names)) %>% 
  arrange(Row.names) 

 Consultant_Gap$zip <- zips$Zip

## make the zip dataframe into a vector
zipdf <- zips %>%
  select(Zip) %>%
  distinct(Zip)

zipvec <- as.vector(zipdf)

###download the tract 
#counties <- c(111) # FIPS for Ventura
options(tigris_use_cache = TRUE)
zipcodes <- zctas(cb = TRUE, starts_with = c("91", "93"))
# zipcodes <- zctas(starts_with = c("91", "93"))
# counties <- c(111) # FIPS for Ventura
# tracts <- tracts(state = 'CA', county = c(111), cb=TRUE)


Consultant_Need <- Consultant_Need %>% 
  mutate(ZCTA5CE10 = zip)

consult_merged<- geo_join(zipcodes, Consultant_Need, "ZCTA5CE10", "ZCTA5CE10", how="inner")
# we need to only include the salt lake county ids

##pop up makes the box when you click on a specific region, so you can label it with a name or geoid, or something else in your data
popup <- paste0("<b>", consult_merged$ZCTA5CE10, "</b><br>", "Total Need by Zipcode: ", prettyNum(round(consult_merged$Total.need,2), big.mark=","))
pal <- colorNumeric(
  palette = "YlGnBu",
  domain = consult_merged$Total.need
)

map33<-leaflet() %>%
  addProviderTiles("CartoDB.Positron") %>%
  addPolygons(data = consult_merged, 
              fillColor = ~pal(Total.need), 
              color = "#b2aeae", # you need to use hex colors
              fillOpacity = 0.7, 
              weight = 1, 
              smoothFactor = 0.2,
              popup = popup) %>%
  addLegend(pal = pal, 
            values = consult_merged$Total.need, 
            position = "bottomright", 
            title = "Total Need by Zipcode"#,
            #labFormat = labelFormat(prefix = " ")
           ) 
map33




```

# Map of Preschool Supply in Ventura County As Calculated by Professor Damooie


```{r supply, echo=FALSE, message=FALSE}


Consultant_Supply <- Consultant_Supply %>% 
  mutate(ZCTA5CE10 = zip)

consult_merged2<- geo_join(zipcodes, Consultant_Supply, "ZCTA5CE10", "ZCTA5CE10", how="inner")
# we need to only include the salt lake county ids

##pop up makes the box when you click on a specific region, so you can label it with a name or geoid, or something else in your data
popup <- paste0("<b>", consult_merged2$ZCTA5CE10, "</b><br>", "Total Supply by Zipcode: ", prettyNum(round(consult_merged2$Total.supply..spaces.,2), big.mark=","))
pal <- colorNumeric(
  palette = "YlGnBu",
  domain = consult_merged2$Total.supply..spaces.
)

map34<-leaflet() %>%
  addProviderTiles("CartoDB.Positron") %>%
  addPolygons(data = consult_merged2, 
              fillColor = ~pal(Total.supply..spaces.), 
              color = "#b2aeae", # you need to use hex colors
              fillOpacity = 0.7, 
              weight = 1, 
              smoothFactor = 0.2,
              popup = popup) %>%
  addLegend(pal = pal, 
            values = consult_merged2$Total.supply..spaces., 
            position = "bottomright", 
            title = "Total Supply by Zipcode"#,
            #labFormat = labelFormat(prefix = " ")
           ) 
map34

```

# Map of Preschool Gap in Ventura County As Calculated by Professor Damooie

```{r gap, echo=FALSE, message=FALSE}

Consultant_Gap <- Consultant_Gap %>% 
  mutate(ZCTA5CE10 = zip)

Consultant_Gap <- Consultant_Gap %>% 
  mutate(gap = as.numeric(Accumulated.Gap))

consult_merged3<- geo_join(zipcodes, Consultant_Gap, "ZCTA5CE10", "ZCTA5CE10", how="inner")
# we need to only include the salt lake county ids

##pop up makes the box when you click on a specific region, so you can label it with a name or geoid, or something else in your data
popup <- paste0("<b>", consult_merged3$ZCTA5CE10, "</b><br>", "Total Gap by Zipcode: ", prettyNum(consult_merged3$gap), big.mark=",")
pal <- colorNumeric(
  palette = "YlGnBu",
  reverse = TRUE,
  domain = consult_merged3$gap
)

map35<-leaflet() %>%
  addProviderTiles("CartoDB.Positron") %>%
  addPolygons(data = consult_merged3, 
              fillColor = ~pal(gap), 
              color = "#b2aeae", # you need to use hex colors
              fillOpacity = 0.7, 
              weight = 1, 
              smoothFactor = 0.2,
              popup = popup) %>%
  addLegend(pal = pal, 
            values = consult_merged3$gap, 
            position = "bottomright", 
            title = "Total Gap by Zipcode"#,
            #labFormat = labelFormat(prefix = " ")
           ) 
map35

```