# Import any packages you want to use here
library(leaflet)

#Chapter 2 trying out notes first time
# Add your code snippets here
clearBounds()
clearMarkers()
glimpse(ipeds)
ipeds %>% group_by(sector_label) %>% count %>% arrange(desc(n))

# Create a list of US States in descending order by the number of colleges in each state
ipeds  %>% 
    group_by(state)  %>% 
    count()  %>% 
    arrange(desc(n))

# Create a dataframe called `ca` with data on only colleges in California
ca <- ipeds %>%
        filter(state == "CA")

# Use `addMarkers` to plot all of the colleges in `ca` on the `m` leaflet map
map %>%
    addMarkers(lng = ca$lng, lat = ca$lat)

# Set the zoom level to 8 and store in the m object
map_zoom <-
    map %>%
    addMarkers(data = ca) %>%
     setView(lat = la_coords$lat, lng = la_coords$lon, zoom = 8)

map_zoom

# Change the radius of each circle to be 2 pixels and the color to red
map2 %>% 
    addCircleMarkers(lng = ca$lng, lat = ca$lat,
                     radius = 2, color = "red")

# Add circle markers with popups for college names
map %>%
    addCircleMarkers(data = ca, radius = 2, popup = ~name)

# Change circle color to #2cb42c and store map in map_color object
map_color <- map %>% 
    addCircleMarkers(data = ca, radius = 2, color = "#2cb42c", popup = ~name)

# Print map_color
map_color

# Customize the legend
m %>% 
    addLegend(pal = pal, 
              values = c("Public", "Private", "For-Profit"),
              # opacity of .5, title of Sector, and position of topright
              opacity = 0.5, title = "Sector", position = "topright")

Chapter 3 
library(leaflet.extras)

leaflet() %>%
  addTiles() %>% 
  addSearchOSM() %>% 
  addReverseSearchOSM() 

m2 <-
    ipeds %>% 
        leaflet() %>% 
            # use the CartoDB provider tile
            addProviderTiles("CartoDB") %>% 
            # center on the middle of the US with zoom of 3
            setView(lat = 39.8282, lng = -98.5795, zoom = 3)

# Map all American colleges 
m2 %>% 
    addCircleMarkers() 

# Load the htmltools package
library(htmltools)

# Create data frame called public with only public colleges
public <- filter(ipeds, sector_label == "Public")  

# Create a leaflet map of public colleges called m3 
m3 <- leaflet() %>% 
        addProviderTiles("CartoDB") %>% 
        addCircleMarkers(data = public, radius = 2, label = ~htmlEscape(name),
                         color = ~pal(sector_label), group = "Public")

m3

# Create data frame called private with only private colleges
private <- filter(ipeds, sector_label == "Private")  

# Add private colleges to `m3` as a new layer
m3 <- m3 %>% 
        addCircleMarkers(data = private, radius = 2, label = ~htmlEscape(name),
                         color = ~pal(sector_label), group = "Private") %>% 
        addLayersControl(overlayGroups = c("Public", "Private"))

m3

# Map with choice of baselayers and groups of colleges in their own layer
m4 <- leaflet() %>% 
        addTiles(group = "OSM") %>% 
        addProviderTiles("CartoDB", group = "Carto") %>% 
        addProviderTiles("Esri", group = "Esri") %>% 
        addCircleMarkers(data = public, radius = 2, label = ~htmlEscape(name),
                         color = ~pal(sector_label),  group = "Public") %>% 
        addCircleMarkers(data = private, radius = 2, label = ~htmlEscape(name),
                           color = ~pal(sector_label), group = "Private")  %>% 
        addCircleMarkers(data = profit, radius = 2, label = ~htmlEscape(name),
                         color = ~pal(sector_label), group = "For-Profit")  %>% 
        addLayersControl(baseGroups = c("OSM", "Carto", "Esri"), 
                         overlayGroups = c("Public", "Private", "For-Profit")) %>% 
        setView(lat = 39.8282, lng = -98.5795, zoom = 4) 

m4

ipeds %>% 
    leaflet() %>% 
        addTiles() %>% 
        # Sanitize any html in our labels
        addCircleMarkers(radius = 2, label = ~htmlEscape(name),
                         # Color code colleges by sector using the `pal` color palette
                         color = ~pal(sector_label),
                         # Cluster all colleges using `clusterOptions`
                         clusterOptions = markerClusterOptions())

#Chapter 4
    script.R

1
2

    R Console
    Slides
    Notes

# Print a summary of the `shp` data
summary(shp)
Object of class SpatialPolygonsDataFrame
Coordinates:
        min       max
x -84.32187 -75.46089
y  33.84232  36.58812
Is projected: FALSE 
proj4string :
[+init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84
+towgs84=0,0,0]
Data attributes:
    GEOID10         ALAND10   
 27006  :  1   100240769:  1  
 27007  :  1   100252722:  1  
 27009  :  1   1003885  :  1  
 27011  :  1   100620829:  1  
 27012  :  1   100707703:  1  
 27013  :  1   101001856:  1  
 (Other):802   (Other)  :802  
# Print the class of `shp`
class(shp)
[1] "SpatialPolygonsDataFrame"
attr(,"package")
[1] "sp"
# Print the class of `shp`
class(shp)
[1] "SpatialPolygonsDataFrame"
attr(,"package")
[1] "sp"

# Print the class of the data slot of shp
class(shp@data)
[1] "data.frame"
# Print the class of the data slot of shp
class(shp@data)
[1] "data.frame"
# Print GEOID10
shp@data$GEOID10
  [1] 27925 28754 28092 27217 28711 28666 28602 27841 27831 28785 27504 27330
 [13] 28768 28658 28716 28139 27565 28394 27982 28025 28159 28382 28312 28342
 [25] 27839 27852 28723 28077 28039 28452 27306 28375 28713 28743 28717 28150
 [37] 28447 27205 27379 28425 27827 27540 28114 28451 27892 27249 28628 27873
 [49] 28781 27916 28705 28714 28101 28102 28445 28448 28458 28719 28478 28479
 [61] 28501 28748 28752 28207 28753 28757 28209 28212 28560 28504 27983 27985
 [73] 28018 28019 28562 28906 28530 28771 28779 28782 28376 28581 28152 28169
 [85] 28170 28657 28021 28204 28533 28540 28543 28551 28262 28280 28575 28790
 [97] 28792 28667 28672 28108 28462 28681 28465 28734 28739 28694 28697 28702
[109] 28745 28127 28420 28422 28424 28428 28435 28088 28089 28090 27562 28334
[121] 28787 28433 27360 27534 28043 27370 28444 27531 28675 28712 28449 27053
[133] 27944 28367 28326 28740 28659 28282 27244 27597 27017 28761 28457 28441
[145] 27956 27889 28652 28146 28513 28777 28786 27596 27530 28369 28327 27340
[157] 27028 27823 27879 28244 27810 27886 28306 27025 27239 27967 27824 27826
[169] 27834 27030 28358 28365 27520 27524 27525 27526 27292 27874 27882 27883
[181] 27885 27253 27576 27577 27582 27295 27298 27332 27910 27052 27055 27344
[193] 27516 27850 27856 27265 27603 27605 27537 27539 27541 28601 28604 27809
[205] 27278 27284 27371 27201 27312 28320 28325 27207 28330 28607 28611 28612
[217] 27549 27555 27317 27320 27703 27709 28350 28643 28337 28621 27569 28645
[229] 28651 27948 28630 27923 27929 27936 27943 28721 28512 27546 27891 28379
[241] 27822 27909 28655 28662 27587 27589 28625 28742 28553 27941 28134 27043
[253] 27893 28328 28135 28007 28338 27110 28472 28756 28110 28519 27861 27407
[265] 28374 28211 28668 27214 27965 27949 27806 28340 27917 27288 27563 28669
[277] 27229 27283 27109 27843 27047 28303 28585 28676 28689 28305 28635 28640
[289] 27016 27863 27968 28528 27915 27981 28411 28577 27326 27954 28556 27105
[301] 27545 27813 27974 27301 28168 28670 28801 27050 28610 28665 28125 28538
[313] 27849 28036 28586 27801 27807 28904 27875 28557 27958 28468 27536 28213
[325] 28341 28747 28707 27262 28006 28360 28031 27845 28166 28616 27572 27014
[337] 27503 27011 28572 28386 27291 28432 27804 27343 28073 28467 28173 28539
[349] 28352 27828 28515 28555 27855 27583 28310 28396 28348 28138 28642 27542
[361] 27408 28215 27821 28105 28270 28206 28301 27876 28627 27019 28574 28647
[373] 28806 27349 28091 28660 28726 28508 27840 28803 28511 27964 27978 28086
[385] 27927 28774 28383 27559 28523 28332 28749 27962 27455 28056 27501 28027
[397] 27527 27282 27837 28682 27310 28356 27233 27231 27006 28144 27857 27042
[409] 28314 27612 28525 27281 28147 28366 28629 27523 27937 28119 28012 27048
[421] 27880 27350 27027 27606 27938 28638 28720 28580 27103 27986 28001 28034
[433] 28393 28032 28040 28677 28395 28391 28678 28399 28455 28098 28401 28103
[445] 28684 28685 28409 28071 28683 28083 28708 28097 28450 28431 28453 28454
[457] 28709 28439 28377 28715 28443 28436 28438 28751 28129 28133 28763 28109
[469] 28120 28466 28746 28137 28480 28759 28731 28762 28405 28054 28698 28081
[481] 28403 28052 28701 28690 28412 28704 28078 28421 28693 28544 28516 28773
[493] 28775 28905 28174 28203 28570 28208 28210 28202 28804 28805 28791 28901
[505] 28547 28107 28722 28729 28461 28730 28463 28552 28554 28115 28732 28112
[517] 28214 28733 28308 28304 28571 28584 28582 28583 28273 28587 28278 28578
[529] 28579 28323 28164 28605 28518 28520 28526 28783 28529 28167 28521 28531
[541] 28311 28163 28537 28772 28626 27942 27928 28634 28649 28339 28357 27935
[553] 28623 28618 28654 28624 28619 27922 28307 28226 27946 27947 28347 28349
[565] 28227 28637 27926 27920 28646 28573 27921 28351 28269 28590 27341 28364
[577] 27604 27976 28615 27357 28344 28613 28609 28343 27409 27376 27377 27701
[589] 27610 27979 27405 27704 27705 27959 27960 27403 27966 27953 27970 27972
[601] 27973 27707 27957 27401 27517 27502 27507 27508 27509 27510 27518 27505
[613] 27020 27613 27024 27514 27519 27713 27614 27803 27616 27617 27513 27511
[625] 27023 27046 27844 27869 27853 27051 27041 27521 27871 27872 27842 27106
[637] 27830 27846 27013 27862 27104 27832 27847 27858 27865 27851 27825 27829
[649] 27012 27816 27817 27557 27808 27209 27208 27820 27888 27814 27551 27556
[661] 27045 27235 27560 27215 27054 27248 27242 27260 27243 27258 27581 27812
[673] 27601 27592 27591 27544 27316 27313 27325 27314 27311 27896 27007 28650
[685] 28606 27009 28735 28673 28725 28033 27870 27864 28429 28384 28663 27022
[697] 28333 27574 28524 28527 28277 27263 28023 27573 27615 28020 28464 28128
[709] 28009 28205 28104 27299 27884 28076 28080 28160 28532 27302 28124 27932
[721] 27924 28037 27819 27608 28789 28079 28398 27553 27878 27018 27040 28392
[733] 27315 28594 27950 28442 27410 27805 28371 27305 28778 28692 28072 28456
[745] 28589 28363 27355 27358 28385 28736 27890 27522 28617 28671 28387 28390
[757] 27212 27609 27568 28679 27881 27101 28622 28644 28631 28636 28373 28345
[769] 27712 28117 27866 27021 27406 28741 28372 27897 28430 27980 28017 27203
[781] 28909 27127 27607 27939 28217 28216 27252 28423 28718 27919 28510 28460
[793] 28434 28470 28766 28546 27818 27529 28469 28016 28075 28318 27107 27356
[805] 28315 27571 27860 28902

# Glimpse the nc_income data
glimpse(nc_income)

# Summarize the nc_income data
summary(nc_income)

# Left join nc_income onto shp@data and store in shp_nc_income
shp_nc_income <- shp@data %>% 
                left_join(nc_income, by = c("GEOID10" = "zipcode"))

# Print the number of missing values of each variable in shp_nc_income
shp_nc_income  %>%
  summarize(across(everything(), ~sum(is.na(.x))))

# summarize the mean income variable
summary(shp$mean_income)

# subset shp to include only zip codes in the top quartile of mean income
high_inc <- shp[!is.na(shp$mean_income) & shp$mean_income > 55917,]

# map the boundaries of the zip codes in the top quartile of mean income
high_inc %>%
  leaflet() %>%
  addTiles() %>%
  addPolygons()

# create color palette with colorNumeric()
nc_pal <- colorNumeric("YlGn", domain = high_inc@data$mean_income)

high_inc %>%
  leaflet() %>%
  addTiles() %>%
  # set boundary thickness to 1 and color polygons
  addPolygons(weight = 1, color = ~nc_pal(mean_income),
              # add labels that display mean income
              label = ~paste0("Mean Income: ", dollar(mean_income)),
              # highlight polygons on hover
              highlight = highlightOptions(weight = 5, color = "white",
              bringToFront = TRUE))

# Use the log function to create a new version of nc_pal
nc_pal <- colorNumeric("YlGn", domain = log(high_inc@data$mean_income))

# comment out the map tile
high_inc %>%
  leaflet() %>%
  #addProviderTiles("CartoDB") %>%
  # apply the new nc_pal to the map
  addPolygons(weight = 1, color = ~nc_pal(log(mean_income)), fillOpacity = 1,
              label = ~paste0("Mean Income: ", dollar(mean_income)),
              highlightOptions = highlightOptions(weight = 5, color = "white", bringToFront = TRUE))

# Print a summary of the `mean_income` variable
summary(wealthy_zips@data$mean_income)

# plot zip codes with mean incomes >= $200k
wealthy_zips %>% 
  leaflet() %>% 
  addProviderTiles("CartoDB") %>% 
  # set color to green and create Wealth Zipcodes group
  addPolygons(weight = 1, fillOpacity = .7, color = "green",  group = "Wealthy Zipcodes", 
              label = ~paste0("Mean Income: ", dollar(mean_income)),
              highlightOptions = highlightOptions(weight = 5, color = "white", bringToFront = TRUE))

# Add polygons using wealthy_zips
final_map <- m4 %>% 
   addPolygons(data = wealthy_zips, weight = 1, fillOpacity = .5, color = "Grey",  group = "Wealthy Zip Codes", 
              label = ~paste0("Mean Income: ", dollar(mean_income)),
              highlightOptions = highlightOptions(weight = 5, color = "white", bringToFront = TRUE)) %>% 
    # Update layer controls including "Wealthy Zip Codes"
    addLayersControl(baseGroups = c("OSM", "Carto", "Esri"), 
                         overlayGroups = c("Public", "Private", "For-Profit", "Wealthy Zip Codes"))     

# Print and explore your very last map of the course!
final_map