All the contents are from DataCamp

In chapter 2 students will build on the leaflet map they created in chapter 1 to create an interactive web map of every four year college in California. After plotting hundreds of points on an interactive leaflet map, students will learn to customize the markers on their leaflet map. This chapter will also how to color code markers based on a factor variable.

Chapter 1. Cleaning up the Base Map¶

If you are storing leaflet maps in objects, there will come a time when you need to remove markers or reset the view. You can accomplish these tasks with the following functions.

clearMarkers()- Remove one or more features from a map clearBounds()- Clear bounds and automatically determine bounds based on map elements

To remove the markers and to reset the bounds of our m map we would:

{r}
m <- m  %>% 
        addMarkers(lng = dc_hq$lon, lat = dc_hq$lat) %>% 
        setView(lat = 50.9, lng = 4.7, zoom = 5)

m  %>% 
    clearMarkers() %>% 
    clearBounds()

The leaflet map of DataCamp's headquarters has been printed for you.

```{r}

Store leaflet hq map in an object called map¶

Plot DataCamp's NYC HQ¶

pkgs <- c("tidyverse", "leaflet", "htmlwidgets", "webshot") sapply(pkgs, require, character.only = TRUE)

dc_hq <- data.frame(hq = c("DataCamp - NYC", "DataCamp - Belgium"), lon = c(-74.0, 4.72), lat = c(40.7, 50.9))

map <- leaflet() %>% addProviderTiles("CartoDB") %>%

      # Use dc_hq to add the hq column as popups
      addMarkers(lng = dc_hq$lon, lat = dc_hq$lat,
                 popup = dc_hq$hq)

Center the view of map on the Belgium HQ with a zoom of 5¶

map_zoom <- map %>% setView(lat = 50.881363, lng = 4.717863, zoom = 5) ```{r}

{r}
# Remove markers, reset bounds, and store the updated map in the m object
map_clear <- map %>%
        clearMarkers() %>% 
        clearBounds()

# Print the cleared map
map_clear

Chapter 2. Exploring the IPEDS Data¶

In Chapters 2 and 3, we will be using a subset of the IPEDS data that focuses on public, private, and for-profit four-year institutions. The United States also has many institutions that are classified as two-year colleges or vocational institutions, which are not included this course. Our subset has five variables on 3,146 colleges.

The sector_label column in the ipeds data frame indicates whether a college is public, private, or for-profit. In the console, use the group_by() and the count() functions from the dplyr package to determine which sector of college is most common.

The tidyverse package, which includes dplyr, has been loaded for you. In your workspace, you also have access to the ipeds dataframe.

Which sector of college is most common in the IPEDS data?

Data comes from tableu public data You can directly download data here.

# data cleansing function
# this code I built
data_cleansing <- function(data = data) {
  library(dplyr)
  
  data <- data %>% select(Name, 'Longitude location of institution', 'Latitude location of institution', 'State abbreviation', 'Sector of institution')
  
  names(data) <- c("name", "lng", "lat", "state", "sector_label")
  
  data$sector_label[grepl('Private', data$sector_label)] <- 'private'
  data$sector_label[grepl('Public', data$sector_label)] <- 'public'
  
  return(data)
}

library(rio)
# step 1. data import
data <- import("data/IPEDS_data.xlsx")

# step 2. data cleansing
ipeds <- data_cleansing(data = data)
glimpse(ipeds)

Observations: 1,534
Variables: 5
$ name         <chr> "Alabama A & M University", "University of Alabama at Bi…
$ lng          <dbl> -86.56850, -86.80917, -86.17401, -86.63842, -86.29568, -…
$ lat          <dbl> 34.78337, 33.50223, 32.36261, 34.72282, 32.36432, 33.214…
$ state        <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "…
$ sector_label <chr> "public", "public", "private", "public", "public", "publ…

Chapter 3. Exploring the IPEDS Data II¶

Most analyses require data wrangling. Luckily, there are many functions in the tidyverse that facilitate data frame cleaning. For example, the drop_na() function will remove observations with missing values. By default, drop_na() will check all columns for missing values and will remove all observations with one or more missing values.

{r}
miss_ex <- tibble(
             animal = c("dog", "cat", "rat", NA),
             name   = c("Woodruf", "Stryker", NA, "Morris"),
             age    = c(1:4))
miss_ex

miss_ex %>% 
     drop_na() %>% 
     arrange(desc(age))

# A tibble: 2 x 3
  animal    name   age
   <chr>   <chr> <dbl>
1    cat Stryker     2
2    dog Woodruf     1

# Remove colleges with missing sector information
library(tidyverse)
ipeds2 <- ipeds %>% drop_na()
glimpse(ipeds2)

Observations: 1,534
Variables: 5
$ name         <chr> "Alabama A & M University", "University of Alabama at Bi…
$ lng          <dbl> -86.56850, -86.80917, -86.17401, -86.63842, -86.29568, -…
$ lat          <dbl> 34.78337, 33.50223, 32.36261, 34.72282, 32.36432, 33.214…
$ state        <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "…
$ sector_label <chr> "public", "public", "private", "public", "public", "publ…

# Count the number of four-year colleges in each state
ipeds2 %>% group_by(state) %>% count() %>% head(6)

# Create a list of US States in descending order by the number of colleges in each state
ipeds2 %>% 
    group_by(state) %>% 
    count() %>% 
    arrange(desc(n)) %>% 
    head(6)

4. California Colleges¶

Now it is your turn to map all of the colleges in a state. In this exercise, we'll apply our example of mapping Maine's colleges to California's colleges. The first step is to set up your data by filtering the ipeds data frame to include only colleges in California. For reference, you will find how we accomplished this with the colleges in Maine below.

{r}
maine_colleges <- 
    ipeds %>% 
        filter(state == "ME")

maine_colleges

# A tibble: 21 x 5
                     name       lng      lat state sector_label
                    <chr>     <dbl>    <dbl> <chr>        <chr>
1           Bates College -70.20333 44.10530    ME      Private
2         Bowdoin College -69.96524 43.90690    ME      Private

## Create Dataframe called 'ca' with data on only colleges in California
ca <- ipeds2 %>% filter(state == "California")
glimpse(ca)

Observations: 93
Variables: 5
$ name         <chr> "Azusa Pacific University", "Biola University", "Califor…
$ lng          <dbl> -117.8880, -118.0173, -122.4165, -117.4259, -118.1257, -…
$ lat          <dbl> 34.13087, 33.90482, 37.77477, 33.92857, 34.13927, 34.225…
$ state        <chr> "California", "California", "California", "California", …
$ sector_label <chr> "private", "private", "private", "private", "private", "…

# Use `addMarkers` to plot all of the colleges in `ca` on the `m` leaflet map
library(leaflet)

{r}
map <- leaflet() %>% addProviderTiles("CartoDB")
map %>% 
    addMarkers(lng = ca$lng, lat = ca$lat)

Chapter 5. The City of Colleges¶

Based on our map of California colleges it appears that there is a cluster of colleges in and around the City of Angels (e.g., Los Angeles). Let's take a closer look at these institutions on our leaflet map.

The coordinates for the center of LA are provided for you in the la_coords data frame.

{r}
la_coords <- data.frame(lat = 34.05223, lon = -118.2437)

Once you create a map focused on LA, try panning and zooming the map. Can you find the cluster of colleges East of LA known as the Claremont Colleges?

When there are hundreds of markers, do you find the pin markers helpful or do they get in your way?

The coordinates of LA have been provided in the la_coords data frame and the ca data frame of California colleges and the map have been loaded for you.

{r}
la_coords <- data.frame(lat = 34.05223, lon = -118.2437) 

# Center the map on LA 
map %>% 
   addMarkers(data = ca) %>% 
   setView(lat = la_coords$lat, lng = la_coords$lon, zoom = 12)

{r}
# 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

Chapter 6. Circle Markers¶

Circle markers are notably different from pin markers:

We can control their size They do not "stand-up" on the map We can more easily change their color There are many ways to customize circle markers and the design of your leaflet map. To get started we will focus on the following arguments.

{r}
addCircleMarkers(map, lng = NULL, lat = NULL, 
                 radius = 10, color = "#03F", popup = NULL)

The first argument map takes a leaflet object, which we will pipe directly into addCircleMarkers(). lng and lat are the coordinates we are mapping. The other arguments can customize the appearance and information presented by each marker.

The ca data frame and the leaflet object map have been loaded for you.

{r}
# Clear the markers from the map 
map2 <- map %>% 
            clearMarkers()

{r}
# Use addCircleMarkers() to plot each college as a circle
map2 %>%
    addCircleMarkers(lng = ca$lng, lat = ca$lat)

{r}
# 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")

7. Making our Map Pop¶

Similar to building a plot with ggplot2 or manipulating data with dplyr, your map needs to be stored in an object if you reference it later in your code.

Speaking of dplyr, the %>% operator can pipe data into the function chain that creates a leaflet map.

{r}
ipeds %>% 
    leaflet()  %>% 
        addTiles() %>% 
        addCircleMarkers(popup = ~name, color = "#FF0000")

Piping makes our code more readable and allows us to refer to variables using the ~ operator rather than repeatedly specifying the data frame.

The color argument in addCircleMarkers() takes the name of a color or a hex code. For example, red or #FF0000.

map has been printed for you. Notice the circle markers are gone!

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

{r}
# 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

Chapter 8. Building a Better Pop-up¶

With the paste0() function and a few html tags, we can customize our popups. paste0() converts its arguments to characters and combines them into a single string without separating the arguments.

{r}
addCircleMarkers(popup = ~paste0(name,
                                 "<br/>",
                                 sector_label))

We can use the
tag to create a line break to have each element appear on a separate line.

To distinguish different data elements, we can make the name of each college italics by wrapping the name variable in

{r}
addCircleMarkers(popup = ~paste0("<i>",
                                 name,
                                 "</i>", 
                                 "<br/>", 
                                 sector_label))

# Clear the bounds and markers on the map object and store in map2
map2 <- map %>% 
        clearMarkers() %>% 
        clearBounds()

{r}
# Add circle markers with popups that display both the institution name and sector
map2 %>% 
    addCircleMarkers(data = ca, radius = 2, 
                     popup = ~paste0(name, "<br/>", sector_label))

{r}
# Make the institution name in each popup bold
map2 %>% 
    addCircleMarkers(data = ca, radius = 2, 
                     popup = ~paste0("<b>", name, "</b>", "<br/>", sector_label))

9. Swapping Popups for Labels¶

Popups are great, but they require a little extra effort. That is when labels come to our the aid. Using the label argument in the addCircleMarkers() function we can get more information about one of our markers with a simple hover!

{r}
ipeds %>% 
    leaflet()  %>% 
    addProviderTiles("CartoDB")  %>% 
    addCircleMarkers(label = ~name, radius = 2)

Labels are especially helpful when mapping more than a few locations as they provide quick access to detail about what each marker represents.

{r}
# Add circle markers with labels identifying the name of each college
map %>% 
    addCircleMarkers(data = ca, radius = 2, label = ~name)

# Use paste0 to add sector information to the label inside parentheses 
map %>% 
    addCircleMarkers(data = ca, radius = 2, label = ~paste0(name, " (", sector_label, ")"))

Assuming "lng" and "lat" are longitude and latitude, respectively

Chapter 10. Creating a Color Palette using colorFactor¶

So far we have only used color to customize the style of our map. With colorFactor() we can create a color palette that maps colors the levels of a factor variable.

{r}
pal <- 
   colorFactor(palette = c("blue", "red", "green"), 
               levels = c("Public", "Private", "For-Profit"))

m %>% 
    addCircleMarkers(color = ~pal(sector_label))

Why might we not want to use this particular color palette?

If you are interested in using a continuous variable to color a map see colorNumeric().

{r}
pal <- colorNumeric(palette = "RdBu", domain = c(25:50))

ipeds %>% 
    leaflet() %>% 
        addProviderTiles("CartoDB")  %>% 
        addCircleMarkers(radius = 2, color = ~pal(lat))

{r}

# Make a color palette called pal for the values of `sector_label` using `colorFactor()`  
# Colors should be: "red", "blue", and "#9b4a11" for "Public", "Private", and "For-Profit" colleges, respectively
pal <- colorFactor(palette = c("red", "blue"), 
                   levels = c("public", "private"))

# Add circle markers that color colleges using pal() and the values of sector_label
map2 <- 
    map %>% 
        addCircleMarkers(data = ca, radius = 2, 
                         color = ~pal(sector_label), 
                         label = ~paste0(name, " (", sector_label, ")"))

# Print map2
map2

Chapter 11. A Legendary Map¶

Adding information to our map using color is great, but it is only helpful if we remember what the colors represent. With addLegend() we can add a legend to remind us.

There are several arguments we can use to custom the legend to our liking, including opacity, title, and position. To create a legend for our colorNumeric() example, we would do the following.

{r}
pal <- colorNumeric(palette = "RdBu", domain = c(25:50))

ipeds %>% 
    leaflet() %>% 
        addProviderTiles("CartoDB")  %>% 
        addCircleMarkers(radius = 2,
                         color = ~pal(lat)) %>% 
         addLegend(pal = pal,
                   values = c(25:50),
                   opacity = 0.75,
                   title = "Latitude",
                   position = "topleft")

{r}
# Make a color palette called pal for the values of `sector_label` using `colorFactor()`  
# Colors should be: "red", "blue", and "#9b4a11" for "Public", "Private", and "For-Profit" colleges, respectively
pal <- colorFactor(palette = c("red", "blue"), 
                   levels = c("public", "private"))

# Customize the legend
map2 %>% 
    addLegend(pal = pal, 
              values = c("public", "private"),
              # opacity of .5, title of Sector, and position of topright
              opacity = 0.5, title = "Sector", position = "topright")

The Final Output code is followed.

# Store leaflet hq map in an object called map
# Plot DataCamp's NYC HQ
pkgs <- c("tidyverse", "leaflet", "htmlwidgets", "webshot", "rio")
sapply(pkgs, require, character.only = TRUE)

# step 1. data import
data <- import("data/IPEDS_data.xlsx")

# step 2. data cleansing
ipeds <- data_cleansing(data = data)

# step 3. visualization
pal <- colorFactor(palette = c("red", "blue"), 
                   levels = c("public", "private"))
map_circle <- ipeds %>% 
    leaflet() %>% 
    addProviderTiles("CartoDB") %>% 
    addCircleMarkers(radius = 2, 
                     color = ~pal(sector_label), 
                     label = ~paste0(name, " (", sector_label, ")")) %>% 
    addLegend(pal = pal, 
              values = c("public", "private"),
              # opacity of .5, title of Sector, and position of topright
              opacity = 0.5, title = "Sector", position = "topright")

# saving leaflet
## create .html and .png
## save html to png
saveWidget(map_circle, "chapter2_map_circle.html", selfcontained = FALSE)
webshot("chapter2_map_circle.html", file = "chapter2_map_circle.png",
        cliprect = "viewport")

Assuming "lng" and "lat" are longitude and latitude, respectively

Chapter 1 will introduce students to the htmlwidgets package and the leaflet package. Following this introduction, students will build their first interactive web map using leaflet. Through the process of creating this first map students will be introduced to many of the core features of the leaflet package, including adding different map tiles, setting the center point and zoom level, plotting single points based on latitude and longitude coordinates, and storing leaflet maps as objects. Chapter 1 will conclude with students geocoding DataCamp’s headquarters, and creating a leaflet map that plots the headquarters and displays a popup describing the location.

1. Creating an Interactive Web Map¶

Similar to the packages in the tidyverse, the leaflet package makes use of the pipe operator (i.e., %>%) from the magrittr package to chain function calls together. This means we can pipe the result of one function into another without having to store the intermediate output in an object. For example, one way to find every car in the mtcars data set with a mpg >= 25 is to pipe the data through a series of functions.

{r}
mtcars  %>% 
    mutate(car = rownames(.))  %>% 
    select(car, mpg)  %>% 
    filter(mpg >= 25)

To create a web map in R, you will chain together a series of function calls using the %>% operator. Our first function leaflet() will initialize the htmlwidget then we will add a map tile using the addTiles() function.

# Load the leaflet library
library(leaflet)

{r}
# Create a leaflet map with default map tile using addTiles()
library(htmlwidgets)
leaflet() %>% addTiles()

2. Provider Tiles¶

In the previous exercise, addTiles() added the default OpenStreetMap (OSM) tile to your leaflet map. Map tiles weave multiple map images together. The map tiles presented adjust when a user zooms or pans the map enabling the interactive features you experimented with in exercise 2.

The leaflet package comes with more than 100 map tiles that you can use. These tiles are stored in a list called providers and can be added to your map using addProviderTiles() instead of addTiles().

The leaflet and tidyverse libraries have been loaded for you.

pkgs <- c("tidyverse", "leaflet")
sapply(pkgs, require, character.only = TRUE)

Loading required package: tidyverse
── Attaching packages ─────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.0     ✔ purrr   0.2.5
✔ tibble  1.4.2     ✔ dplyr   0.7.8
✔ tidyr   0.8.2     ✔ stringr 1.3.1
✔ readr   1.3.1     ✔ forcats 0.3.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

# Print the providers list included in the leaflet library
providers[1:5]

# Print only the names of the map tiles in the providers list 
names(providers)

# Use str_detect() to determine if the name of each provider tile contains the string "CartoDB"
str_detect(names(providers), "CartoDB")

# Use str_detect() to print only the provider tile names that include the string "CartoDB"
names(providers)[str_detect(names(providers), "CartoDB")]

3. Adding a Custom Map Tile¶

Did any tile names look familiar? If you have worked with the mapping software you may recognize the name ESRI or CartoDB.

We create our first leaflet map using the default OSM map tile.

{r}
leaflet() %>% 
    addTiles()

We will primarily use CartoDB provider tiles, but feel free to try others, like Esri. To add a custom provider tile to our map we will use the addProviderTiles() function. The first argument to addProviderTiles() is your leaflet map, which allows us to pipe leaflet() output directly into addProviderTiles(). The second argument is provider, which accepts any of the map tiles included in the providers list.

Familiarize yourself with the SCRIPT.R and HTML VIEWER tabs. Click back and forth to type your code and view your maps.

{r}
leaflet() %>% 
    addProviderTiles("Esri")

{r}
leaflet() %>% 
    addProviderTiles("CartoDB.PositronNoLabels")

4. A Map with a View I¶

You may have noticed that, by default, maps are zoomed out to the farthest level. Rather than manually zooming and panning, we can load the map centered on a particular point using the setView() function.

{r}
leaflet()  %>% 
    addProviderTiles("CartoDB")  %>% 
    setView(lat = 40.7, lng = -74.0, zoom = 10)

Currently, DataCamp has offices at the following locations:

350 5th Ave, Floor 77, New York, NY 10118

Martelarenlaan 38, 3010 Kessel-Lo, Belgium

These addresses were converted to coordinates using the geocode() function in the ggmaps package.

NYC: (-73.98575, 40.74856) Belgium: (4.717863, 50.881363)

{r}
leaflet()  %>% 
    addProviderTiles("CartoDB")  %>% 
    setView(lng = -73.98575, lat = 40.74856, zoom = 6)

{r}
hc_dq <- data.frame(hq = c("DataCamp - NYC", "DataCamp - Belgium"), 
                   lon = c(-74.0, 4.72), 
                   lat = c(40.7, 50.9))
leaflet() %>% 
    addProviderTiles("CartoDB.PositronNoLabels") %>% 
    setView(lng = hc_dq$lon[2], lat = hc_dq$lat[2], zoom = 4)

5. A Map with a Narrower View¶

We can limit users' ability to pan away from the map's focus using the options argument in the leaflet() function. By setting minZoom anddragging, we can create an interactive web map that will always be focused on a specific area.

{r}
leaflet(options = 
        leafletOptions(minZoom = 14, dragging = FALSE))  %>% 
  addProviderTiles("CartoDB")  %>% 
  setView(lng = -73.98575, lat = 40.74856, zoom = 14)

Alternatively, if we want our users to be able to drag the map while ensuring that they do not stray too far, we can set the maps maximum boundaries by specifying two diagonal corners of a rectangle.

You'll use dc_hq to create a map with the "CartoDB" provider tile that is centered on DataCamp's Belgium office.

{r}
leaflet(options = leafletOptions(
                    # Set minZoom and dragging 
                    minZoom = 12, dragging = TRUE))  %>% 
  addProviderTiles("CartoDB")  %>% 

  # Set default zoom level 
  setView(lng = hc_dq$lon[2], lat = hc_dq$lat[2], zoom = 14) %>% 

  # Set max bounds of map 
  setMaxBounds(lng1 = hc_dq$lon[2] + .05, 
               lat1 = hc_dq$lat[2] + .05, 
               lng2 = hc_dq$lon[2] - .05, 
               lat2 = hc_dq$lat[2] - .05)

6. Mark it¶

So far we have been creating maps with a single layer: a base map. We can add layers to this base map similar to how you add layers to a plot in ggplot2. One of the most common layers to add to a leaflet map is location markers, which you can add by piping the result of addTiles() or addProviderTiles() into the add markers function.

For example, if we plot DataCamp's NYC HQ by passing the coordinates to addMarkers() as numeric vectors with one element, our web map will place a blue drop pin at the coordinate. In chapters 2 and 3, we will review some options for customizing these markers.

{r}
leaflet()  %>% 
    addProviderTiles("CartoDB")  %>% 
    addMarkers(lng = -73.98575, lat = 40.74856)

The dc_hq tibble is available in your work space.

{r}
# Plot DataCamp's NYC HQ
hc_dq <- data.frame(hq = c("DataCamp - NYC", "DataCamp - Belgium"), 
                   lon = c(-74.0, 4.72), 
                   lat = c(40.7, 50.9))

leaflet() %>% 
    addProviderTiles("CartoDB") %>% 
    addMarkers(lng = hc_dq$lon[1], lat = hc_dq$lat[1])

{r}
# Plot DataCamp's NYC HQ with zoom of 12    
leaflet() %>% 
    addProviderTiles("CartoDB") %>% 
    addMarkers(lng = -73.98575, lat = 40.74856)  %>% 
    setView(lng = -73.98575, lat = 40.74856, zoom = 12)

{r}
# Plot both DataCamp's NYC and Belgium locations
leaflet() %>% 
    addProviderTiles("CartoDB") %>% 
    addMarkers(lng = dc_hq$lon, lat = dc_hq$lat)

7. Adding Popups and Storing your Map¶

To make our map more informative we can add popups. To add popups that appear when a marker is clicked we need to specify the popup argument in the addMarkers() function. Once we have a map we would like to preserve, we can store it in an object. Then we can pipe this object into functions to add or edit the map's layers.

{r}
dc_nyc <- 
    leaflet() %>% 
        addTiles() %>% 
        addMarkers(lng = -73.98575, lat = 40.74856, 
                   popup = "DataCamp - NYC") 

dc_nyc %>% 
    setView(lng = -73.98575, lat = 40.74856, 
            zoom = 2)

Let's try adding popups to both DataCamp location markers and storing our map in an object.

# Store leaflet hq map in an object called map
# Plot DataCamp's NYC HQ
pkgs <- c("tidyverse", "leaflet", "htmlwidgets", "webshot")
sapply(pkgs, require, character.only = TRUE)

dc_hq <- data.frame(hq = c("DataCamp - NYC", "DataCamp - Belgium"), 
                   lon = c(-74.0, 4.72), 
                   lat = c(40.7, 50.9))

map <- leaflet() %>%
          addProviderTiles("CartoDB") %>%
          # Use dc_hq to add the hq column as popups
          addMarkers(lng = dc_hq$lon, lat = dc_hq$lat,
                     popup = dc_hq$hq)

# Center the view of map on the Belgium HQ with a zoom of 5 
map_zoom <- map %>%
      setView(lat = 50.881363, lng = 4.717863,
              zoom = 5)

# Print map_zoom
# map_zoom

# saving leaflet
## create .html and .png
## save html to png
saveWidget(map_zoom, "chapter1_mapZoom.html", selfcontained = FALSE)
webshot("chapter1_mapZoom.html", file = "chapter1_mapZoom.png",
        cliprect = "viewport")

Loading required package: tidyverse
── Attaching packages ─────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.0       ✔ purrr   0.2.5  
✔ tibble  2.0.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.2       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0  
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
Loading required package: leaflet
Loading required package: htmlwidgets
Loading required package: webshot

[R Markdown] Customized Report (DataCamp) (0)	2019.05.04
[R] Interactive Data Visualisation using leaflet (1) (0)	2019.03.02
How to draw graph for two numeric variables in R (0)	2019.01.17
[R] Beginner Graph for Communication - Label (0)	2019.01.03

[R Markdown] Customized Report (DataCamp) (0)	2019.05.04
[R] Interactive Data Visualisation using leaflet (2) (0)	2019.03.03
How to draw graph for two numeric variables in R (0)	2019.01.17
[R] Beginner Graph for Communication - Label (0)	2019.01.03

[R] Classification Trees (0)	2019.02.03
[R] k-Nearest Neighbors (kNN) (0)	2019.01.20

state	n
New York	122
Pennsylvania	114
California	93
Texas	70
Ohio	60
Massachusetts	59

state	n
Alabama	28
Alaska	4
Arizona	8
Arkansas	20
California	93
Colorado	20

cozyDS

R

R에서 하는 딥러닝을 해보자

1단계: 패키지 설치

2단계: 데이터 불러오기

3단계: 간단하게 상관관계 그래프 그리기

4단계: 데이터 전처리 및 가공

5단계: 딥러닝 모형 데이터셋 분리

6단계: 딥러닝 모형 개발

7단계: 딥러닝 모형 컴파일

8단계: 딥러닝 모형 적합

9단계: 예측 모형 테스트

10단계: 모형 평가

1일차: 데이터 전처리와 시각화

2일차: 기초통계 및 회귀분석 ML모형

3일차: 분류모형 및 h2o 패키지 소개

4일차: 모형성능 향상 및 딥러닝 예제

[R] Interactive Data Visualisation using leaflet (2)

Chapter 1. Cleaning up the Base Map¶

Store leaflet hq map in an object called map¶

Plot DataCamp's NYC HQ¶

Center the view of map on the Belgium HQ with a zoom of 5¶

Chapter 2. Exploring the IPEDS Data¶

Chapter 3. Exploring the IPEDS Data II¶

4. California Colleges¶

Chapter 5. The City of Colleges¶

Chapter 6. Circle Markers¶

7. Making our Map Pop¶

Chapter 8. Building a Better Pop-up¶

9. Swapping Popups for Labels¶

Chapter 10. Creating a Color Palette using colorFactor¶

Chapter 11. A Legendary Map¶

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[R] Interactive Data Visualisation using leaflet (1)

1. Creating an Interactive Web Map¶

2. Provider Tiles¶

3. Adding a Custom Map Tile¶

4. A Map with a View I¶

5. A Map with a Narrower View¶

6. Mark it¶

7. Adding Popups and Storing your Map¶

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4. Limitation of Accuracy

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6. Dummy Coding Categorical Data

7. Handling Missing Data

8. Building a more sophisticated model

9. The dangers of stepwise regression

10. Building a stepwise regression model

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