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This tutorial is a part of a series!

Click below to view all lessons in the series!


LiDAR (9)
R programming (70)
Remote Sensing (12)
Data Visualization (4)
Hyperspectral Remote Sensing (7)
Hierarchical Data Formats (HDF5) (24)
Spatial Data & GIS (18)
Time Series (15)
Phenology (7)
Raster Data (8)
Vector Data (6)
Metadata (1)
Git & GitHub (6)
(1) (1) (1)

Tutorial by R Package

dplyr (8)
ggplot2 (17)
h5py (1)
lubridate (time series) (6)
maps (1)
maptools (3)
plyr (2)
raster (32)
rasterVis (raster time series) (3)
rgdal (GIS) (23)
rgeos (5)
rhdf5 (21)
sp (7)
scales (4)
gridExtra (4)
ggtheme (0)
grid (2)
reshape2 (3)
plotly (6)

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After completing the previous tutorial, you have an R object p that contains a ggplot plot. We’ll now make an interactive plot.

If you have not completed the previous tutorial, the code for p can be found in the downloadable R code for this lesson, or complete the previous tutorial.

Now we can create an interactive plot from the p R object.

Create Interactive Plot is a free to use, online interactive data viz site. If you have the library installed, you can quickly export a ggplot graphic into! (NOTE: it also works for python matplotlib)!! To use plotly, you need to setup an account. Once you’ve setup an account, you can get your key from the site to make the code below work.

Plotly R Documentation

# install.packages("plotly")

# your ggplot - object

Create a local version of a plotly plot!

# plot your plot using plot_ly locally

Push a plot plot to your plotly account! Feel free to use NEON’s credentials for this week!

# setup your credentials

# generate the plot
            filename='NEON SJER CHM vs Insitu Tree Height') # let anyone in the world see the plot!

Check out the results!

NEON Remote Sensing Data compared to NEON Terrestrial Measurements for the SJER field site.

<b> LiDAR CHM Derived vs Measured Tree Height </b>

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