Extracting Colors from .tif Files in R Using Raster and Dplyr Libraries

Extracting Colors from .tif in R

As a data analyst, working with geospatial data can be both fascinating and frustrating. One of the most common challenges is extracting meaningful information from raster images such as .tif files. In this blog post, we will delve into the world of R programming language and explore how to extract colors from .tif files.

Introduction

Raster images are two-dimensional representations of data that are composed of pixels with specific values. These values can represent various characteristics such as brightness, color intensity, or even elevation. In the context of geospatial analysis, raster images are often used to store and analyze spatial data. However, working with raster images can be challenging due to their complex structure.

R provides a range of libraries and functions that make it easy to work with raster images. Two of the most popular libraries are raster and dplyr. In this blog post, we will focus on using these two libraries to extract colors from .tif files.

Prerequisites

Before we begin, ensure you have R installed on your computer. Additionally, make sure you have the necessary libraries installed, including:

raster
dplyr

You can install these libraries by running the following commands in the R console:

install.packages("raster")
install.packages("dplyr")

Reading .tif Files

To begin working with raster images, you need to read them into your R session. The raster library provides a convenient function called brick() that reads raster images from various formats.

Here’s an example of how to read a .tif file using the brick() function:

library(raster)
pic <- raster::brick(x="SUB_IMG_8020 (1)_A_36x36.tif")

In this code snippet, we first load the raster library and then use the brick() function to read the .tif file named “SUB_IMG_8020 (1)_A_36x36.tif”.

Understanding Raster Data

Raster images are composed of three primary bands: red, green, and blue. These bands contain information about the brightness and color intensity of each pixel in the image.

The raster library provides a range of functions to extract information from these bands. In our case, we’re interested in extracting colors from the .tif file.

Extracting Colors

To extract colors from the .tif file, you need to convert it into a data frame that can be used by the dplyr library.

Here’s an example of how to do this:

library(dplyr)
test_spdf <- as(pic, "SpatialPixelsDataFrame")

In this code snippet, we first load the dplyr library and then use the as() function from the raster library to convert the raster image into a data frame called test_spdf.

Arranging Data

Once you have converted your raster image into a data frame, you need to arrange it in a way that allows for easy access to its columns. The dplyr library provides a range of functions to do this.

Here’s an example of how to arrange the data:

library(dplyr)
test_df <- test_spdf %>%
  arrange(cols) %>%
  distinct(cols) %>%
  pull(cols)

In this code snippet, we first load the dplyr library and then use the pipe operator %>% to chain together a series of functions. The functions arranged(), distinct(), and pull() are used to sort the data by its columns, remove duplicate rows, and extract the column values into a vector called test_df.

Using Scale Fill Identity

To display the extracted colors in your R plot, you need to use the scale_fill_identity() function.

Here’s an example of how to do this:

ggplot(test_df, aes(x=x, y=y)) + 
  geom_raster(aes(fill=cols)) +
  theme_void() +
  theme(legend.position="none") +
  scale_fill_identity()

In this code snippet, we first load the ggplot2 library and then create a plot using the ggplot() function. We use the aes() function to map the color aesthetic to the columns extracted from our data frame. Finally, we use the scale_fill_identity() function to display the colors in our plot.

Conclusion

In this blog post, we explored how to extract colors from .tif files using R programming language and the raster and dplyr libraries. We covered the following topics:

Reading .tif files using the brick() function
Understanding raster data and its bands
Extracting colors from the .tif file using the as() function
Arranging the data for easy access to its columns
Using scale_fill_identity() to display the extracted colors in our R plot

With this knowledge, you can now work with geospatial data and extract meaningful information from raster images.

Last modified on 2024-03-21