Get access to all lessons in this course.
- Welcome to Mapping with R (01_01)
-
Geospatial Data
- 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)
-
Static Maps
- 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)
-
Interactive Maps
- 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)
-
Wrapping Up
- You Did It!
Mapping with R
Turning Data Frames into sf Objects (01_05)
This lesson is locked
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.
Transcript
Click on the transcript to go to that point in the video. Please note that transcripts are auto generated and may contain minor inaccuracies.
Your Turn
Open the 01_05.Rproj file to open the appropriate project.
Open the airport-locations.R file from the worked-examples/01_05
folder.
Visualise the locations of the airports in the airports-with-most-seat-kilometers_2015.xlsx file
Import the file with
read_excel()
Use
st_as_sf()
to convert the tibble into an sf objectUse
mapview
to show the airport locations
Learn More
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.
You need to be signed-in to comment on this post. Login.
Haresh Suppiah
March 1, 2022
Hi,
I just wanted to point out a couple of things. It seems like the github solution on this page is not synced with the solution used in the video. Also, the file name for the student exercise should be 'your-turn.R' instead of 'airport-locations.R'.
These edits might help others. Cheers,
David Keyes
March 2, 2022
Thanks for the heads up! I've fixed both things.