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2017-06-21 19:18:48

Learning Path: R: Powerful Data Analysis with R

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Learn advanced techniques of R to solve real-world problems in data analysis

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Current price: $10
Original price: $200
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95% off

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- 9.5 hours on-demand video
- 1 Supplemental Resource
- Full lifetime access
- Access on mobile and TV

- Certificate of Completion

What Will I Learn?

- Import and export data in various formats in R
- Perform advanced statistical data analysis
- Visualize your data on Google or OpenStreetMap
- Enhance your data analysis skills and learn to handle even the most complex datasets
- Learn how to handle vector and raster data in R
- Delve into data visualization and regression-based methods with R/RStudio.
- Tackle multiple linear regression with R
- Explore multinomial logistic regression with categorical response variables at three levels

Requirements

- You need to be familiar with the R programming language.
- You should have RStudio installed on your system.

Description

There’s an increasing number of data being produced every day. This has led to the demand for skilled professionals who can analyze these data and make decisions. R is one of the popular tools which is widely used by data analysts for performing data analysis on real-world data.

This Learning Path is the complete learning process to play with data. You will start with the most basic importing techniques for downloading compressed data from the Web. You will get introduced to how CRAN works and will demonstrate why viewers should use them.

Next, you will learn to create static plots. Then, you will understand how to plot spatial data on interactive web platforms such as Google Maps and OpenStreetMap.

You will learn advanced data analysis concepts such as cluster analysis, time-series analysis, association mining, PCA, handling missing data, sentiment analysis, spatial data analysis with R and QGIS, and advanced data visualization with R’s ggplot2 library.

Finally, you will implement the various topics learned so far to analyze real-world datasets from various industry sectors.

By the end of this Learning Path, you will learn how to perform data analysis on real-world data.

For this course, we have combined the best works of these esteemed authors:

**Fabio Veronesi**

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.

**Dr. Bharatendra Rai**

Dr. Bharatendra Rai is Professor of Business Statistics and Operations Management in the Charlton College of Business at UMass Dartmouth. He teaches courses on topics such as Analyzing Big Data, Business Analytics and Data Mining, Twitter and Text Analytics, Applied Decision Techniques, Operations Management, and Data Science for Business.

Who is the target audience?

- This Video Learning Path is for those who are familiar with R and want to learn data analysis from scratch to an advanced level.

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Curriculum For This Course

98 Lectures

09:42:37
+
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Learning Data Analysis with R
75 Lectures
05:59:07

This video provides an overview of the entire course.

Preview
04:16

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.

Importing Data from Tables (read.table)

02:30

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.

Downloading Open Data from FTP Sites

04:03

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.

Preview
04:24

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.

Importing with read.lines (The Last Resort)

03:20

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.

Cleaning Your Data

02:36

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.

Preview
04:09

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.

Importing Vector Data (ESRI shp and GeoJSON)

04:02

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.

Transforming from data.frame to SpatialPointsDataFrame

02:50

Geographical projections are very important and need to be handled carefully. R provides robust functions to do so successfully.

Understanding Projections

03:06

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.

Basic time/dates formats

03:50

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.

Preview
04:58

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.

Reading Raster Data in NetCDF

06:10

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

02:52

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.

Stacking to Include the Temporal Component

04:10

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.

Preview
03:12

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.

Exporting Vector Data (ESRI shp File)

02:21

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.

Exporting Rasters in Various Formats (GeoTIFF, ASCII Grids)

02:42

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.

Exporting Data for WebGIS Systems (GeoJSON, KML)

02:40

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.

Preview
07:44

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.

Measuring Spread (Standard Deviation and Standard Distance)

03:23

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.

Understanding Your Data with Plots

05:50

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.

Plotting for Multivariate Data

03:02

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.

Finding Outliers

03:50

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.

Preview
03:37

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.

Re-Projecting Your Data

02:54

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.

Intersection

03:07

Other important GIS operations that users have to master involve creating buffers and calculating distances between objects.

Buffer and Distance

03:22

The last two GIS functions that anybody should master are used to merge different geometries and spatial objects and overlay.

Union and Overlay

03:32

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.

Preview
04:43

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.

Converting Vector/Table Data into Raster

04:00

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.

Subsetting and Selection

03:16

Sometimes we may need to filter out some values of our raster. It may seem tricky but only because it requires some skills.

Filtering

04:58

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.

Raster Calculator

04:44

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.

Plotting Basics

05:15

Creating multilayer plot can be difficult because we need to take care of several different aspects at once. However, learning that is very easy.

Adding Layers

05:44

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.

Color Scale

04:51

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.

Creating Multivariate Plots

09:09

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.

Handling the Temporal Component

03:20

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.

Introduction

02:32

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.

Plotting Vector Data on Google Maps

05:45

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.

Adding Layers

04:41

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 Raster Data on Google Maps

04:19

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 Leaflet to Plot on Open Street Maps

09:03

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.

Introduction

02:21

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.

Importing Data from the World Bank

05:08

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.

Adding Geocoding Information

05:38

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.

Concluding Remarks

03:48

Executing a point pattern analysis is technically easy in R. However, it is extremely important that practitioners understand the theory behind a point pattern analysis to ensure the correctness of the results. This video illustrates this theory.

Theoretical Background

07:31

In many cases practitioners start their analysis by applying complex statistics without even looking at their data. This is a problem that may affect the correctness of their results. This video will teach the correct order to start a point pattern analysis.

Introduction

07:37

Calculating intensity and density of a point pattern can be done in many ways. Finding the best for the dataset in hand can be challenging. The package spatstat and the literature provides some tips to do it correctly.

Intensity and Density

07:38

By looking at the plot we created in the previous videos, we started understanding the spatial distribution of our data. However, we now need to prove quantitatively that our ideas are correct.

Spatial Distribution

10:02

In many cases we may want to model a point pattern to try and explain its location intensity in a way that would allow us to predict it outside our study area. This requires a general understanding of the modelling process, which will be explained here.

Modelling

06:41

Cluster analysis is commonly used in many fields. The problem is that in order to use it correctly we need to understand the clustering process, which is what this video is about.

Theoretical Background

04:30

As in every data analysis the data preparation plays a crucial role in guaranteeing its success. This video will prepare the data to be used for clustering.

Data Preparation

05:50

Clustering algorithms are extremely simple to apply. The challenge is interpret their results and try to understand what the algorithm is telling us in terms of insights into our data.

K-Means Clustering

05:26

When applying the k-means algorithms we need to specify the number of clusters in which we want our dataset to be divided. However, since it is often used as explanatory test, we may not know the optimal number of clusters.

Optimal Number of Clusters

05:17

Hierarchical clustering allows us to see how all of our points are related to each other with a bottom-up approach. However, determining the optimal number of clusters is not so trivial with this method.

Hierarchical Clustering

06:33

Determining the best clustering algorithm for our data is probably the most challenging part of such an analysis. This video will show the sort of reasoning users will need to make that decision.

Concluding

04:32

Time series analysis is another important technique to master. However, it requires some specific knowledge to understand the process and what this technique can actually do.

Theoretical Background

04:34

Time-series can be imported and analyzed using two formats: ts and xts. Both have their pros and cons and users need to be able to master both if they want to perform the best time-series analysis.

Reading Time-Series in R

06:37

Dealing with time-series sometimes means extracting data according to their location along the time line. This can be done in R but require some explanation to do it correctly.

Subsetting and Temporal Functions

05:15

Another important aspect of time-series analysis is decomposition and correlation. This allows us to draw important conclusions about our data. Technically this is not difficult to do, but it requires careful consideration if we want to do it right.

Decomposition and Correlation

07:33

The final step of time-series analysis is forecasting, where we try to simulate future events. This is extremely useful but requires adequate knowledge of the methods available, their pros and cons.

Forecasting

04:32

There are numerous geostatistical interpolation techniques that can be used to map environmental data. Kriging is probably the most famous but it not the only one available. It is important to know every technique to understand where to use what.

Theoretical Background

04:42

The first challenge of any geostatistical analysis is the data preparation. We cannot just download data, but we need to clean them and prepare them for analysis.

Data Preparation

06:20

Simple interpolation is easy to use and easy to interpret, therefore it is still commonly used. The package gstat allows us to use inverse distance, but to do so we need to follow some simple but precise rules.

Mapping with Deterministic Estimators

06:56

Before we can interpolate our data using kriging, we need to take care of some important steps. For example, we need to check if our data has a trend and then test for normality, because kriging can only be applied to normally distributed data.

Analyzing Trend and Checking Normality

04:57

Variogram is the keystone of kriging interpolation and users need to know how to compute and fit a model to it. These things require careful considerations that we are going to explore here.

Variogram Analysis

05:52

In this video, all concepts learned previously will be merged to perform a kriging interpolation. The problem in this case is making sure that everything works correctly and the process is smooth.

Mapping with kriging

06:17

There are numerous statistical learning algorithms that can be used to map environmental data. It is important to know every technique to understand where to use what.

Theoretical Background

04:08

Once again for data analysis, getting to know our data is the most important thing we need to do once we start. This can be done by looking at the data provider and using some explanatory techniques.

Dataset

02:36

Many users start a data analysis by testing complex methods. This is a problem though, because many times a simpler method can help us better understand our data. This video shows how to fit these simple models.

Linear Regression

06:06

Regression trees are extremely powerful algorithms, but sometimes are considered as black boxes. This is a problem because only expert users can understand their output. This may change simply by understanding how these algorithms work.

Regression Trees

04:13

Support vector machine is another important algorithm that is sometimes difficult to train. In this video we will look at the methods in the package caret to do that using an additional cross-validation.

Support Vector Machines

05:05

Test Your Knowledge

8 questions

+
–

Mastering Data Analysis with R
23 Lectures
03:43:30

This video will give an overview of entire course

Preview
03:24

The aim of this video is to introduce R/RStudio to those using it for the first time.

Getting Started and Data Exploration with R/RStudio

28:16

The aim of this video is to introduce commonly used visualization tools in R.

Introduction to Visualization

20:29

The aim of this video is to introduce the interactive visualization package “plotly” in R.

Interactive Visualization

10:35

The aim of this video is to introduce the “googleVis” package in R.

Geographic Plots

10:03

The aim of this video is to introduce visualization with ggplot2, d3heatmap, and googleVis packages.

Advanced Visualization

11:00

The aim of this video is to introduce the idea of regression, logistic regression, and data partitioning.

Getting Introductory Concepts

06:46

The aim of this video is to introduce data partitioning.

Data Partitioning with R

13:49

The aim of this video is to present steps for multiple linear regression

Multiple Linear Regression with R

11:59

The aim of this video is to introduce multicollinearity issues with regression models.

Multicollinearity Issues

07:31

The aim of this video is to introduce logistic regression using R.

Logistic Regression with Categorical Response Variables at two Levels

13:46

The aim of this video is to provide a logistic model interpretation.

Logistic Regression Model and Interpretation

04:23

The aim of this video is to show calculation for confusion matrix and misclassification error.

Misclassification Error and Confusion Matrix

06:40

The aim of this video is to show how to create ROC curves in R.

ROC Curves

06:02

The aim of this video is to provide an overall view of prediction and model assessment.

Prediction and Model Assessment

08:43

The aim of this video is to introduce multinomial logistic regression using R.

Multinomial Logistic Regression with Categorical Response Variables at 3Levels

07:29

The aim of this video is to provide the interpretation to the multinomial logistic model.

Multinomial Logistic Regression Model and Its Interpretation

08:14

The aim of this video is to show calculation for confusion matrix and misclassification error.

Misclassification Error and Confusion Matrix

06:33

The aim of this video is to provide an overall view of prediction and model assessment.

Prediction and Model Assessment

09:54

The aim of this video is to introduce ordinal logistic regression using R.

Ordinal Logistic Regression with R

12:54

The aim of this video is to provide ordinal logistic model interpretation.

Ordinal Logistic Regression Model and Interpretation

04:40

The aim of this video is to show calculation for the confusion matrix and misclassification error.

The Misclassification Error and Confusion Matrix

04:28

The aim of this video is to provide an overall view of the prediction and model assessment.

Prediction and Model Assessment

05:52

Test Your Knowledge

5 questions

About the Instructor

Tech Knowledge in Motion

Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

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