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Mapping with R
- Welcome to Mapping with R (01_01)
- Making Maps is Complex (01_02)
- mapview for Quick Maps (01_03)
- sf for Simple Features (01_04)
- Turning Data Frames into sf Objects (01_05)
- Importing Shapefiles (01_06)
- Joining Geospatial Datasets (01_07)
- Disambiguating Country Names (01_08)
- Converting Addresses to Coordinates (01_09)
- U.S.-Specific Datasets (01_10)
- Advice on Finding International Datasets (01_11)
- CRS and Projections: Geographic and Projected CRS (01_12)
- CRS and Projections: How to Choose a CRS (01_13)
- Introducing Raster GIS with raster and stars (01_14)
- Basics of Using the raster Package (01_15)
- ggplot2 Essentials (02_01)
- Starting a Map in ggplot2 (02_02)
- Labelling ggplot2 Maps (02_03)
- Compare Locations/Events with Geobubble Charts (02_04)
- Highlight a Region in a Country with ggplot2 (02_05)
- Make a Choropleth Map of Discrete Variables with ggplot2 (02_06)
- Make a Choropleth Map of Continuous Variables with ggplot2 (02_07)
- Faceting Choropleth Maps with ggplot2 (02_08)
- Visualize Raster Data with ggplot2 (02_09)
- Adding Scale Bars and North Arrows with ggplot2 (02_10)
- What is leaflet? (03_01)
- Starting a Map in leaflet (03_02)
- Necessary HTML for Labelling leaflet Maps (03_03)
- Highlight a Region in a Country with leaflet (03_04)
- Compare Locations/Events with Geobubble Charts in leaflet (03_05)
- Make a Choropleth Map of Discrete Variables with leaflet (03_06)
- Make a Choropleth Map of Continuous Variables with leaflet (03_07)
- Visualize Raster Data with leaflet (03_08)
- You Did It!
Turning Data Frames into sf Objects (01_05)
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This lesson is called Turning Data Frames into sf Objects (01_05), part of the Mapping with R course. This lesson is called Turning Data Frames into sf Objects (01_05), part of the Mapping with R course.
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library(tidyverse) library(readxl) library(sf) library(mapview) airport_locations <- read_excel("data/airport-locations.xlsx") airport_locations %>% st_as_sf(coords = c("longitude", "latitude"), crs = 4326) %>% mapview()
Open the 01_05.Rproj file to open the appropriate project.
Open the airport-locations.R file from the
Visualise the locations of the airports in the airports-with-most-seat-kilometers_2015.xlsx file
Import the file with
st_as_sf()to convert the tibble into an sf object
mapviewto show the airport locations
In this video I used the
tribble() function for creating a dataset in R code. It’s convenient to use this function instead of data.frame() as it allows us to create datasets by row instead of by column.
tribble() also creates a tibble instead of a data.frame. The tidyverse introduced the tibble as a modern version of the data.frame, it looks prettier in the console and it allows functions like
group_by() to work. If you want a more technical description check out the package website.