Data for farming

Welcome! My name is Benjamin Nowak and I am a French lecturer in agronomy. This website is for sharing my research projects and courses with engineering students. Most of the posts here describe applications of digital tools to agriculture, based on specific agronomic case studies.

2022

[R] Where does your coffee come from? 2022-01-07
In this post, we will see how we can convert a static plot created with {ggplot2} to an interactive plot with {ggiraph}, using the example of the top 10 coffee-growing countries.

2021

[R] Riding tables with {gt} and {gtExtras} 2021-10-04
The {gtExtras} extension adds many customization options for tables created with the {gt} extension. In this tutorial we will illustrate some possibilities of these packages on a dataset about the Tour de France riders.

[R] Network analysis with {tidygraph} 2021-09-30
{tidygraph} allows to use tidyverse tools to perform network analysis. In this tutorial how we can produce and analyze a network of co-authorship of scientific paper.

[R] Hybrid map: Chloropleth X Time series 2021-09-28
{geofacet} allows to position plots in a similar pattern to the original geography, which provide more information than classical chloropleths but is also harder to read. In this tutorial we will see a simple approach to mix both approaches.

[GEE] Terrain features with Digital Elevation Model 2021-09-27
In this tutorial, we will see how we can use the Google Earth Engine to derived elevation, slope and aspect from the Digital Elevation Model of NASA Space Shuttle Radar Topography Mission (SRTM).

[R] Web scraping with {rvest} 2021-09-18
Web scraping refers to various methods used to collect data across the web. In this tutorial, we will see how we can use {rvest} to perform web scraping and answer one question: how do women perform compared to men in ultra-trail running races?

[R] PCA with tidyverse 2021-09-15
In this post, we will perform a Principal Component Analysis (PCA) to explore the evolution of songs' features over the years, using {tidyverse} tools and syntax

[GEE] NDVI time series 2021-09-10
Thanks to JavaScript API, Google Earth Engine allows to calculate NDVI for various sites of interest without having to download Sentinel-2 raw data. We will see here how to calculate NDVI time series with GEE.

[R] Text customization with {ggplot2} 2021-09-05
Text customization is an important step in the creation of a graph. In this post, we will see how we can change text fonts with {showtext} and add more changes with {ggtext}.

[R] Pretty polar barplots 2021-08-31
Polar barplots can be an alternative to standard barplots but several steps are required to obtain a nice layout. In this post, we will see how to customize such barplots.

[R] Labelling area plots 2021-08-10
Inserting the legend directly into a graph often makes it easier to read. In this post, we will see a quick example on how to annotate directly single or faceted area plots.

[QGIS] How to create NDVI maps from Sentinel-2 raw data 2021-08-09
The Normalized Difference Vegetation Index (NDVI) is the most commonly used vegetation monitoring index in agriculture. After some basic elements on the rationale behind NDVI, this tutorial shows how to download Sentinel-2 images and how to create NDVI maps with QGIS from these data.

[R] Soil profiles and chronosequence with ggplot2 2021-08-03
This post presents a simple framework to help you quickly transform your soil profiles observations into a graph, with {ggplot2} and {ggpattern}. after the plotting one soil profile, we see how to represent a whole chronosequence.

[R] Starting with ggplot2: evolution of nitrogen use efficiency 2021-08-02
The {ggplot2}] extension, developed by Hadley Wickham, is a powerful tool for making plots with R. In this introduction to {ggplot2}, we will see here how to use this extension to compare nitrogen fertilizer use efficiencies between different countries of the world.

[R] Plotting soil diagrams 2021-07-26
Soil texture diagrams are widely used in agronomy but can be difficult to plot. Let’s see how we can use {ggplot2} and {ggtern} to estimate and represent an important parameter related to soil texture: the water storage capacity.

[R] Crop model with tidyverse (Part III) 2021-07-14
In this third part of the tutorial, we will now evaluate the error associated with the model predictions (the Root Mean Square Error, RMSE), and then try to decrease this error by optimizing one of the model parameter.

[R] Crop model with tidyverse (Part II) 2021-07-11
In this second part of the tutorial, we will investigate the influence of the different parameters on the model’s results. You will learn how to perform a sensivity analysis and an uncertainty analysis.

[R] Crop model with tidyverse (Part I) 2021-07-01
The tidyverse is a collection of extensions designed to work together and based on a common philosophy, making data manipulation and plotting easier. In this tutorial, following the tidyverse philosophy, we will see how we can program the first part of a crop model: the estimation of the number of plant leaves from temperature data.