R is a programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.
This video delivers viewers the ability to conduct data analysis in practical contexts with R, using core language packages and tools. The end goal is to provide analysts and data scientists a comprehensive learning course on how to manipulate and analyse small and large sets of data with R. It will introduce how CRAN works and will demonstrate why viewers should use them.
You will start with the most basic importing techniques, to downloading compressed data from the web and learn of more advanced ways to handle even the most difficult datasets to import. Next, you will move on to create static plots, while the second will show how to plot spatial data on interactive web platforms such as Google Maps and Open Street maps. Finally, you will learn to implement your learning with real-world examples of data analysis.
This video will lay the foundations for deeper applications of data analysis, and pave the way for advanced data science.
About The Author
Fabio Veronesi obtained a Ph.D. in digital soil mapping from Cranfield University and then moved to ETH Zurich, where he has been working for the past three years as a postdoc. In his career, Dr. Veronesi worked at several topics related to environmental research: digital soil mapping, cartography and shaded relief, renewable energy and transmission line siting. During this time Dr. Veronesi specialized in the application of spatial statistical techniques to environmental data.
Accessing and importing open access environmental data is a crucial skill for data scientists. This section teaches you how to download data from the Web, import it in R and check it for consistency.
Often times, datasets are provided for free, but on FTP, websites and practitioners need to be able to access them. R is perfectly capable of downloading and importing data from FTP sites.
Not all text files can be opened easily with read.table. The fixed-width format is still popular but requires a bit more work in R.
Some data files are simply too difficult to be imported with simple functions. Luckily R provides the readLines function that allows importing of even the most difficult tables.
Most open data is generated automatically and therefore may contain NA or other values that need to be removed. R has various functions to deal with this problem.
To follow the exercises in the book viewers would need to install several important packages. This video will explain how to do and where to find information about them.
Vector data are very popular and widespread and require some thoughts before importing. R has dedicated tools to import these data and work with them.
Often times, spatial data is provided in tables and needs to be transformed before it can be used for analysis. This can be done simply with the sp package.
Many datasets have a temporal component and practitioners need to know how to deal with it. R provides functions to do that in a very easy way.
Raster data is fundamentally different from vector data, since its values refer to specific areas (cells) and no single locations. This video will clearly explain this difference and teach users how to import this data in R.
The NetCDF format is becoming very popular, since it allows to store 4D datasets. This requires some technical skills to be accessed and this video will teach viewers to open and import NetCDF files.
Many raster datasets we download from the web are distributed in tiles, meaning a single raster for each subset of the area. To obtain a full raster for the study area we are interested to cover we can create a mosaic.
Mosaicking involves merging rasters based on location. Spatio-temporal datasets include also multiple rasters for the same location but different times. To merge these we need to use the stacking function.
Once we complete our analysis we often need to export our results and share them with colleagues. Popular formats are CSV and TXT files, which we learn how to export in this video.
If we work with vector data and we want to share the same format with our co-workers, we need to learn how to export in vector formats. This will be covered here.
Many raster datasets we download from the Web are distributed in tiles, meaning a single raster for each subset of the area. To obtain a full raster for the study area we are interested in covering, we can create a mosaic.
Nowadays WebGIS applications are extremely popular. However, to use our data for WebGIS, we first need to export them in the correct format. This video will show how to do that.
In the previous volume we explored the basics R functions and syntaxes to import various types of data. In this video we will put these functions together, and overcome some unexpected challenges, to import a full year of NOAA data.
Before we can start analyzing our data we first need to properly understand what we are dealing with. The first step we have to take in this direction is describe our data with simple statistical indexes.
Numerical summaries are very useful but certainly not ideal to provide us with a direct feeling for the dataset in hands. Plots are much more informative and thus being able to produce them is certainly a crucial skill for data analysts.
For multivariate data we are often interested in assessing correlation between variables. This can be done in R very easily, and ggplot2 can also be used to produce more informative plots.
Detecting outliers is another basic skill that every data analyst should have and master. R provides a lot of technical tools to help us in finding outliers.
This Section will be dedicated entirely to manipulating vector data. However, viewers first need to familiarize with some basic concepts, otherwise they may not be able to understand the rest of the section.
In volume 1 we learned how to set the projection of our spatial data. However, in many cases we have to change this projection to successfully complete our analysis, and this requires some specific knowledge.
In many cases we may be interested in understanding the relation between spatial objects. One of such relations is the intersection, where we first want to know how two objects intersect, and then also extract only the part of one of these object that is included or outside the first.
Other important GIS operations that users have to master involve creating buffers and calculating distances between objects.
The last two GIS functions that anybody should master are used to merge different geometries and spatial objects and overlay
Raster objects are imported in R as rectangular matrixes. Users needs to be aware of this to properly work on these data, otherwise it may create some issues during the data analysis.
In many cases open data are not distributed directly in raster formats and they need to be converted. This can be easily done with the right functions.
Working with raster data often means extracting data for particular locations for further analysis, or crop the data to reduce their size. These are essential skills to master for any data analyst.
Sometimes we may need to filter out some values of our raster. It may seem tricky but only because it requires some skills.
Creating new raster by calculating their value is extremely important for spatial data analysis. Doing so is simple but can be difficult to understand at first.
Syntactically plotting spatial data in R is no different than plotting other types of data. Therefore, users need to know the basics of plotting before they can start making maps.
Creating multilayer plot can be difficult because we need to take care of several different aspects at once. However, learning that is very easy.
When plotting spatial data we are often interested in using colors to show the values of some variables. This can be done manually but producing the right color scale may be difficult. This issue can be solved employing automatic methods.
Creating multivariate plots not only means adding layers, but also using legends so that the viewer understands what the plot is showing. Creating legends in R is tricky because it requires a lot of tweaking, which will be explained here.
Temporal data need to be treated with specific procedures to highlight this additional component. This may be done in different ways depending on the scope of the analysis and R provides the right platform for this.
Being able to plot spatial data on web maps is certainly helpful and a crucial skill to have, but it can be difficult since it requires knowledge of different technologies. R makes this process very easy with dedicated functions that allow us to plot on web GIS services a breeze.
Plotting data with the function plotGoogleMaps is not as easy as using the function plot. With a simple step by step guide we can achieve good command of the function, so that users can plot whatever data they choose.
An interactive map with just one layer is hardly useful for our purposes. Many times we are faced with the challenge of plotting several data at once. This requires some additional work and understanding, but it is definitely not hard in R.
Plotting raster data on Google maps can be tricky. The function plotGoogleMaps does not handle rasters very well and if not done correctly the visualization will fail. This video will show users how to plot rasters successfully.
Plotting on Google Maps is easy but Google Maps are commercial products therefore if we want to use the on our commercial website we would need to pay. OpenStreetMaps are free to use, therefore knowing how to use them is certainly an advantage.
Using open data for our analysis requires a deep knowledge of the data provider and the actual data we are using. Without this knowledge we may end up with erroneous results.
Downloading data from the World Bank can be difficult since it requires users to know the acronym used to refer to these data. However, with some help this process becomes very easy.
To create a spatial map of the World Bank data we just have to download and we need to transform them into spatial data. However, in the dataset there are no coordinates of other information that may help us do that. The solution is to use the geocoding information from another dataset for this purpose.
Using the world bank data just to plot a static spatial map is very limitative. There are tons of other uses that researchers can do with these data and this video serves to provide some guidance into these additional avenue of research.
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