title
ggplot2 Tutorial | ggplot2 In R Tutorial | Data Visualization In R | R Training | Edureka

description
( R Training : https://www.edureka.co/data-analytics-with-r-certification-training ) This "ggplot2 Tutorial" by Edureka is a comprehensive session on the ggplot2 in R. This tutorial will not only get you started with the ggplot2 package, but also make you an expert in visualizing data with the help of this package. This tutorial will comprise of these topics: 1) Base R Graphics 2) Grammar of Graphics 3) GGPLOT2 package Check out our R Playlist: https://goo.gl/huUh7Y Subscribe to our channel to get video updates. Hit the subscribe button above. #R #Rtutorial #Ronlinetraining #ggplot2 #ggplotinr How it Works? 1. This is a 5 Week Instructor led Online Course, 30 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course edureka's Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you: 1. Understand concepts around Business Intelligence and Business Analytics 2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others 3. Apply various supervised machine learning techniques 4. Perform Analysis of Variance (ANOVA) 5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc 6. Use various packages in R to create fancy plots 7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights - - - - - - - - - - - - - - - - - - - Who should go for this course? This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics. - - - - - - - - - - - - - - - - Why learn Data Analytics with R? The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists. Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course For more information, please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka

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{'title': 'ggplot2 Tutorial | ggplot2 In R Tutorial | Data Visualization In R | R Training | Edureka', 'heatmap': [{'end': 926.319, 'start': 876.277, 'weight': 0.76}, {'end': 1392.679, 'start': 1363.372, 'weight': 0.701}], 'summary': 'Tutorial covers data visualization techniques, plotting with ggplot2, visualizing housing data, house price and housing data analysis, insights on house prices and amenities, and creating presentable plots with ggplot2, providing specific details and insights on various visualization techniques and their application in r programming.', 'chapters': [{'end': 286.935, 'segs': [{'end': 27.706, 'src': 'embed', 'start': 0.069, 'weight': 0, 'content': [{'end': 5.755, 'text': "Hey guys, this is Bharani from Edureka and I'm really excited to take this session on ggplot2.", 'start': 0.069, 'duration': 5.686}, {'end': 7.096, 'text': "Let's look at the agenda.", 'start': 6.275, 'duration': 0.821}, {'end': 14.723, 'text': "We'll start off by making some plots for the BESA graphics, following which we'll understand what is grammar of graphics, and then, finally,", 'start': 7.616, 'duration': 7.107}, {'end': 18.167, 'text': "we'll be making really beautiful graphs with the help of the ggplot2 package.", 'start': 14.723, 'duration': 3.444}, {'end': 19.248, 'text': "So let's start.", 'start': 18.787, 'duration': 0.461}, {'end': 27.706, 'text': 'If your idea is to make quick and dirty plots, then you can definitely take the help of these graphics from our and, for the sake of the practical,', 'start': 20.023, 'duration': 7.683}], 'summary': 'Bharani from edureka will cover besa graphics, grammar of graphics, and ggplot2 for making beautiful graphs.', 'duration': 27.637, 'max_score': 0.069, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE869.jpg'}, {'end': 136.27, 'src': 'embed', 'start': 109.261, 'weight': 1, 'content': [{'end': 115.224, 'text': 'It is plotted on the y-axis and whatever value we give on the right side of the tilde symbol, but is plotted on the x-axis.', 'start': 109.261, 'duration': 5.963}, {'end': 116.725, 'text': 'Let me zoom this.', 'start': 116.004, 'duration': 0.721}, {'end': 122.299, 'text': 'So we see that as the petal length increases, the sepal length also increases.', 'start': 117.475, 'duration': 4.824}, {'end': 125.842, 'text': 'That is, there is a linear relationship between these two variables.', 'start': 122.699, 'duration': 3.143}, {'end': 131.946, 'text': "Let's make this plot more descriptive by adding labels.", 'start': 129.283, 'duration': 2.663}, {'end': 136.27, 'text': "I've added three more attributes over here, which are ylab, xlab, and main.", 'start': 132.787, 'duration': 3.483}], 'summary': 'Linear relationship between petal and sepal length plotted on x and y axis.', 'duration': 27.009, 'max_score': 109.261, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE8109261.jpg'}, {'end': 223.098, 'src': 'embed', 'start': 196.233, 'weight': 2, 'content': [{'end': 205.556, 'text': 'We can see that the minimum value of sepal width is two centimeters and the maximum value of sepal width is somewhere around five to six centimeters.', 'start': 196.233, 'duration': 9.323}, {'end': 211.038, 'text': 'And most of the observations, their value of sepal width is around three centimeters.', 'start': 206.277, 'duration': 4.761}, {'end': 216.3, 'text': "Let's again modify the labels and add color to this.", 'start': 213.519, 'duration': 2.781}, {'end': 223.098, 'text': "The labels used are xlab main and col so I'm giving the label sepal width to the x-axis.", 'start': 217.372, 'duration': 5.726}], 'summary': 'Sepal width ranges from 2 to 6 cm, with most observations around 3 cm.', 'duration': 26.865, 'max_score': 196.233, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE8196233.jpg'}, {'end': 286.935, 'src': 'embed', 'start': 264.44, 'weight': 3, 'content': [{'end': 273.086, 'text': "So this is a sepal length versus species, and we see that if the species of the iris flower is virginica, then it'll have the maximum sepal length,", 'start': 264.44, 'duration': 8.646}, {'end': 277.729, 'text': "and if the species is of setosa, then it'll have the lowest sepal length.", 'start': 273.086, 'duration': 4.643}, {'end': 283.133, 'text': "Quite an interesting observation, isn't it? Again, we'll add labels.", 'start': 278.39, 'duration': 4.743}, {'end': 283.933, 'text': "We'll also add color.", 'start': 283.173, 'duration': 0.76}, {'end': 286.935, 'text': "So I've assigned the label species onto the x-axis.", 'start': 284.554, 'duration': 2.381}], 'summary': 'Sepal length varies by iris species: virginica has maximum, setosa has minimum.', 'duration': 22.495, 'max_score': 264.44, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE8264440.jpg'}], 'start': 0.069, 'title': 'Data visualization techniques', 'summary': 'Covers making plots for besa graphics, understanding the grammar of graphics, and creating beautiful graphs with ggplot2 package using the iris dataset. it also explains data visualization techniques including scatter plots, histograms, and box plots with specific details such as the range of sepal width and the relationship between sepal length and species in iris flowers.', 'chapters': [{'end': 154.039, 'start': 0.069, 'title': 'Session on ggplot2', 'summary': 'Covers making plots for besa graphics, understanding the grammar of graphics, and creating beautiful graphs with ggplot2 package, using the iris dataset to make scatter plots, histogram, and box plot, demonstrating a linear relationship between sepal length and petal length.', 'duration': 153.97, 'highlights': ['The chapter covers making plots for BESA graphics, understanding the grammar of graphics, and creating beautiful graphs with ggplot2 package. The session introduces the agenda, focusing on creating graphics with ggplot2 package.', 'Using the iris dataset, scatter plot, histogram, and box plot are created. Demonstrates the practical aspect of creating scatter plot, histogram, and box plot using the iris dataset.', 'Demonstrates a linear relationship between sepal length and petal length using a scatter plot. Shows a linear relationship between sepal length and petal length using a scatter plot.']}, {'end': 286.935, 'start': 154.039, 'title': 'Data visualization techniques', 'summary': 'Explains data visualization techniques including scatter plots, histograms, and box plots, with specific details such as the range of sepal width and the relationship between sepal length and species in iris flowers.', 'duration': 132.896, 'highlights': ['The range of sepal width is between 2 to 6 centimeters, with the most common value around 3 centimeters.', 'The relationship between sepal length and species is observed, where virginica has the maximum sepal length and setosa has the lowest.', 'The chapter covers data visualization techniques including scatter plots, histograms, and box plots, with details on adding color, modifying labels, and interpreting the plots.']}], 'duration': 286.866, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE869.jpg', 'highlights': ['Covers making plots for BESA graphics, understanding the grammar of graphics, and creating beautiful graphs with ggplot2 package.', 'Demonstrates a linear relationship between sepal length and petal length using a scatter plot.', 'The range of sepal width is between 2 to 6 centimeters, with the most common value around 3 centimeters.', 'The relationship between sepal length and species is observed, where virginica has the maximum sepal length and setosa has the lowest.']}, {'end': 808.762, 'segs': [{'end': 344.266, 'src': 'embed', 'start': 304.99, 'weight': 0, 'content': [{'end': 313.336, 'text': 'if ever we would want to publish these plots for an international journal or an international presentation, then that would not be possible again.', 'start': 304.99, 'duration': 8.346}, {'end': 319.279, 'text': 'So another major issue is these are just images, and if you want to add layers over these images again,', 'start': 313.556, 'duration': 5.723}, {'end': 321.401, 'text': 'that is not possible with the help of these graphics.', 'start': 319.279, 'duration': 2.122}, {'end': 327.582, 'text': 'So that is where we need the help of ggplot2 package and prior to making graphs with the ggplot2 package.', 'start': 321.901, 'duration': 5.681}, {'end': 335.564, 'text': 'We need to understand what is grammar of graphics because the prefix GG in ggplot2 it stands for grammar of graphics.', 'start': 327.742, 'duration': 7.822}, {'end': 338.024, 'text': 'So let us go to the presentation.', 'start': 336.564, 'duration': 1.46}, {'end': 340.185, 'text': 'We started off by making a scatter plot.', 'start': 338.504, 'duration': 1.681}, {'end': 341.685, 'text': 'We added a color to it.', 'start': 340.665, 'duration': 1.02}, {'end': 344.266, 'text': 'Then we made a histogram and a box plot.', 'start': 342.145, 'duration': 2.121}], 'summary': 'Challenges with current graphics, need for ggplot2 package, created scatter plot, histogram, and box plot.', 'duration': 39.276, 'max_score': 304.99, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE8304990.jpg'}, {'end': 544.96, 'src': 'embed', 'start': 521.443, 'weight': 4, 'content': [{'end': 529.405, 'text': "So let's follow grammar of Graphics first step would be selection of data and over here the data as Iris.", 'start': 521.443, 'duration': 7.962}, {'end': 534.172, 'text': 'Now this gives us a blank plot We have selected the data now.', 'start': 529.985, 'duration': 4.187}, {'end': 537.975, 'text': "It's time to map the data columns onto the aesthetics.", 'start': 534.232, 'duration': 3.743}, {'end': 544.96, 'text': "So I've mapped the sepal length onto the y aesthetic and I have mapped petal length onto the X aesthetic.", 'start': 538.556, 'duration': 6.404}], 'summary': 'Using grammar of graphics to plot iris data with sepal and petal length.', 'duration': 23.517, 'max_score': 521.443, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE8521443.jpg'}, {'end': 624.173, 'src': 'embed', 'start': 595.778, 'weight': 5, 'content': [{'end': 600.58, 'text': "In this step, I've also mapped the species data column onto the color aesthetic.", 'start': 595.778, 'duration': 4.802}, {'end': 605.722, 'text': 'So what we see over here is the color is determined by the species column.', 'start': 601.64, 'duration': 4.082}, {'end': 609.723, 'text': 'And if the color is pink, then the species is setosa.', 'start': 606.322, 'duration': 3.401}, {'end': 613.705, 'text': 'If the color is green, then the species of the flower is versicolor.', 'start': 610.284, 'duration': 3.421}, {'end': 617.747, 'text': 'And if the color is blue, then the species of the flower is virginica.', 'start': 614.185, 'duration': 3.562}, {'end': 624.173, 'text': "So what we can see is if it is of virginica, then it'll have the maximum petal length and sepal length.", 'start': 618.55, 'duration': 5.623}], 'summary': 'Mapped species data to color, showing pink for setosa, green for versicolor, and blue for virginica, indicating maximum petal length and sepal length for virginica.', 'duration': 28.395, 'max_score': 595.778, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE8595778.jpg'}, {'end': 713.848, 'src': 'embed', 'start': 665.33, 'weight': 6, 'content': [{'end': 671.495, 'text': 'and that is why it is always important to choose the right aesthetic for the right data.', 'start': 665.33, 'duration': 6.165}, {'end': 678.743, 'text': "so finally, we'll go ahead and plot the species column onto both of these aesthetics, which are shape and color.", 'start': 671.495, 'duration': 7.248}, {'end': 683.507, 'text': 'So shape and color, both of them are determined by the species column.', 'start': 679.564, 'duration': 3.943}, {'end': 685.869, 'text': 'So we see that both of them have been changed.', 'start': 684.207, 'duration': 1.662}, {'end': 688.011, 'text': 'This was playing with aesthetics.', 'start': 686.83, 'duration': 1.181}, {'end': 694.015, 'text': "Now we'll also go ahead and look at multiple geometries.", 'start': 689.692, 'duration': 4.323}, {'end': 699.34, 'text': 'So we have histogram, bar plot, frequency polygon, box plot.', 'start': 695.076, 'duration': 4.264}, {'end': 701.822, 'text': 'These are the different geometries which you have.', 'start': 700.261, 'duration': 1.561}, {'end': 704.104, 'text': "So we'll be looking at each one of them.", 'start': 702.543, 'duration': 1.561}, {'end': 709.266, 'text': "And to play with the geometry, I'll be using the houses data set.", 'start': 705.765, 'duration': 3.501}, {'end': 711.807, 'text': "Let's go ahead and load this data set.", 'start': 710.247, 'duration': 1.56}, {'end': 713.848, 'text': "I'll be using the reader CSV function.", 'start': 712.008, 'duration': 1.84}], 'summary': 'Choosing the right aesthetic for data, plotting species onto shape and color, exploring multiple geometries using different data sets.', 'duration': 48.518, 'max_score': 665.33, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE8665330.jpg'}], 'start': 287.556, 'title': 'Plotting with ggplot2', 'summary': 'Discusses the limitations of base graphics and introduces ggplot2, explaining the grammar of graphics and its application in creating high-quality plots with added layers for analysis. it covers components like data, aesthetics, and geometry, and demonstrates their implementation in visualization.', 'chapters': [{'end': 321.401, 'start': 287.556, 'title': 'Plotting sepal length', 'summary': 'Discusses the issues with plots made using base graphics, highlighting that they are not of print quality and do not support adding layers for further analysis or presentation.', 'duration': 33.845, 'highlights': ['The plots made with base graphics are not of print quality, hindering their potential use for international journals or presentations.', 'Base graphics do not support adding layers over the images for further analysis or presentation.']}, {'end': 808.762, 'start': 321.901, 'title': 'Understanding ggplot2 and grammar of graphics', 'summary': 'Explains the application of grammar of graphics in ggplot2 package, covering the components of grammar including data, aesthetics, and geometry, and demonstrates its implementation through mapping data onto aesthetics and playing with different geometries for visualization.', 'duration': 486.861, 'highlights': ['The chapter covers the key components of grammar of graphics, including data, aesthetics, and geometry, and demonstrates their application in the ggplot2 package for visualization.', 'The implementation of grammar of graphics is exemplified through mapping the data columns onto aesthetics such as X and Y, and then experimenting with different geometries for visualization.', 'An example of mapping the species column onto the color aesthetic in ggplot2 is provided, showcasing how colors represent different species in the visualization, enabling easy inference.', 'The chapter also explores the mapping of the species column onto the shape aesthetic, illustrating how different shapes represent different species in the visualization, facilitating clearer inferences.', 'Additionally, the chapter discusses the use of multiple geometries such as histogram, bar plot, frequency polygon, and box plot, using the houses data set for visualization.']}], 'duration': 521.206, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE8287556.jpg', 'highlights': ['The chapter covers the limitations of base graphics for international journals or presentations.', 'Base graphics do not support adding layers over the images for further analysis or presentation.', 'The chapter introduces ggplot2 and explains the grammar of graphics for creating high-quality plots.', 'It demonstrates the application of grammar of graphics in ggplot2 for visualization.', 'The implementation of grammar of graphics is exemplified through mapping data columns onto aesthetics and experimenting with different geometries.', 'An example of mapping the species column onto the color aesthetic in ggplot2 is provided.', 'The chapter explores the mapping of the species column onto the shape aesthetic.', 'It discusses the use of multiple geometries such as histogram, bar plot, frequency polygon, and box plot for visualization.']}, {'end': 1175.022, 'segs': [{'end': 926.319, 'src': 'heatmap', 'start': 833.12, 'weight': 0, 'content': [{'end': 838.502, 'text': 'So we can understand that most of the houses their price range is around 2 lakhs to 4 lakhs.', 'start': 833.12, 'duration': 5.382}, {'end': 843.23, 'text': 'and the maximum price of the house is somewhere around 8 lakhs.', 'start': 840.089, 'duration': 3.141}, {'end': 848.511, 'text': 'You see these values 2e plus 0 5 4 e plus 0 5 6 e plus 0 5.', 'start': 843.81, 'duration': 4.701}, {'end': 856.973, 'text': 'So they basically mean 2 into 10 power 5 4 into 10 power 5 and 6 into 10 power 5 and that would again mean 2 lakhs 4 lakhs and 6 lakhs.', 'start': 848.511, 'duration': 8.462}, {'end': 860.594, 'text': "Let's add some color to the histogram.", 'start': 858.633, 'duration': 1.961}, {'end': 861.991, 'text': 'Prior to that.', 'start': 861.471, 'duration': 0.52}, {'end': 863.312, 'text': 'So you see this warning over here.', 'start': 862.111, 'duration': 1.201}, {'end': 869.134, 'text': 'Now this tells us that number of bins is 30 is the default value of number of bins.', 'start': 863.332, 'duration': 5.802}, {'end': 873.336, 'text': "So what I've done is I've increased the number of bins to see greater variation.", 'start': 869.714, 'duration': 3.622}, {'end': 875.477, 'text': 'This was the previous plot.', 'start': 874.216, 'duration': 1.261}, {'end': 879.818, 'text': 'This is the next plot with number of bins is equal to 50.', 'start': 876.277, 'duration': 3.541}, {'end': 882.92, 'text': 'So we see that there is greater variation in the distribution of price.', 'start': 879.818, 'duration': 3.102}, {'end': 885.801, 'text': "We'll add color now.", 'start': 885.001, 'duration': 0.8}, {'end': 894.401, 'text': "So I'm using the fill attribute to add color and the color given as pale green for let's also add color to the boundary.", 'start': 886.937, 'duration': 7.464}, {'end': 898.083, 'text': "So I'm using the col attribute to give a color to the boundary.", 'start': 895.302, 'duration': 2.781}, {'end': 906.528, 'text': "This plot looks really pretty doesn't it? So in the above clouds, I've used fill as an attribute.", 'start': 899.684, 'duration': 6.844}, {'end': 908.709, 'text': "So now let's use fill as an aesthetic.", 'start': 906.968, 'duration': 1.741}, {'end': 915.133, 'text': "So over here, I'm mapping air conditioning onto the fill aesthetic.", 'start': 910.15, 'duration': 4.983}, {'end': 920.595, 'text': 'So what we see is the color is determined by the air conditioning column.', 'start': 916.252, 'duration': 4.343}, {'end': 926.319, 'text': 'So if the house has centralized air conditioning, then the color of the bin would be blue,', 'start': 920.755, 'duration': 5.564}], 'summary': 'House prices range from 2 to 8 lakhs, with distribution visualized using histograms.', 'duration': 52.681, 'max_score': 833.12, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE8833120.jpg'}, {'end': 961.424, 'src': 'embed', 'start': 936.61, 'weight': 2, 'content': [{'end': 941.973, 'text': 'So, if you see this price range around 2 lakhs to 3.75 lakhs,', 'start': 936.61, 'duration': 5.363}, {'end': 945.875, 'text': 'and there is a greater proportion of houses which do not have centralized air conditioning.', 'start': 941.973, 'duration': 3.902}, {'end': 954.46, 'text': "So as the price increases from let's say 3.75 lakhs to 8 lakhs the probability of the house having air conditioning increases.", 'start': 946.396, 'duration': 8.064}, {'end': 957.822, 'text': "Let's change the position.", 'start': 956.661, 'duration': 1.161}, {'end': 961.424, 'text': 'Now this give us the count of the bins.', 'start': 959.523, 'duration': 1.901}], 'summary': 'Houses priced at 2-3.75 lakhs have lower air conditioning, increasing with price.', 'duration': 24.814, 'max_score': 936.61, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE8936610.jpg'}], 'start': 809.342, 'title': 'Visualizing housing data', 'summary': 'Introduces data visualization using a histogram to showcase the price range of houses, revealing that the majority of houses have a price range between 2 to 4 lakhs, with a maximum price of 8 lakhs, and explains the notation for values in scientific notation. it also demonstrates data visualization techniques using histograms and bar plots to analyze housing data, uncovering insights such as the distribution of prices and the prevalence of centralized air conditioning and waterfront properties.', 'chapters': [{'end': 860.594, 'start': 809.342, 'title': 'Data visualization with histogram', 'summary': 'Introduces data visualization using a histogram to showcase the price range of houses, revealing that the majority of houses have a price range between 2 to 4 lakhs, with a maximum price of 8 lakhs, and explains the notation for values in scientific notation.', 'duration': 51.252, 'highlights': ['The majority of houses have a price range between 2 to 4 lakhs, with a maximum price of 8 lakhs.', 'Explanation of scientific notation for values: 2e+05, 4e+05, and 6e+05 represent 2x10^5, 4x10^5, and 6x10^5 respectively, which equate to 2 lakhs, 4 lakhs, and 6 lakhs.']}, {'end': 1175.022, 'start': 861.471, 'title': 'Visualizing housing data', 'summary': 'Demonstrates data visualization techniques using histograms and bar plots to analyze housing data, uncovering insights such as the distribution of prices and the prevalence of centralized air conditioning and waterfront properties.', 'duration': 313.551, 'highlights': ['The chapter demonstrates how increasing the number of bins in a histogram from 30 to 50 reveals greater variation in the distribution of housing prices, allowing for more detailed analysis. The number of bins in the histogram is increased from 30 to 50, leading to a clearer visualization of the variation in housing prices.', 'The analysis uncovers that most houses do not have centralized air conditioning, with a notable increase in the probability of having air conditioning as housing prices rise from 2 lakhs to 8 lakhs. The probability of houses having air conditioning increases as housing prices rise, with a significant proportion of houses in the 2-3.75 lakhs range lacking centralized air conditioning.', 'A demonstration of using bar plots to visualize the distribution of categorical variables, such as waterfront properties and the presence of air conditioning and sewer systems in houses. Bar plots are employed to visualize the distribution of categorical variables, such as waterfront properties, air conditioning, and sewer systems in houses.']}], 'duration': 365.68, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE8809342.jpg', 'highlights': ['The majority of houses have a price range between 2 to 4 lakhs, with a maximum price of 8 lakhs.', 'The chapter demonstrates how increasing the number of bins in a histogram from 30 to 50 reveals greater variation in the distribution of housing prices, allowing for more detailed analysis.', 'The analysis uncovers that most houses do not have centralized air conditioning, with a notable increase in the probability of having air conditioning as housing prices rise from 2 lakhs to 8 lakhs.']}, {'end': 1455.973, 'segs': [{'end': 1203.365, 'src': 'embed', 'start': 1175.658, 'weight': 0, 'content': [{'end': 1182.64, 'text': 'Another inferences if the house has a waterfront then there is greater possibility for it to have a private sewer system.', 'start': 1175.658, 'duration': 6.982}, {'end': 1188.942, 'text': 'So if the house does not have a waterfront then there is a greater probability that it will have a public sewer system.', 'start': 1183.16, 'duration': 5.782}, {'end': 1191.382, 'text': 'That was a bar plot.', 'start': 1190.542, 'duration': 0.84}, {'end': 1193.683, 'text': "I've already made a histogram.", 'start': 1192.282, 'duration': 1.401}, {'end': 1203.365, 'text': 'So one alternative to a histogram can be a frequency polygon a frequency polygon is again used to see the distribution of a continuous variable.', 'start': 1194.263, 'duration': 9.102}], 'summary': 'Waterfront houses likely have private sewer systems, shown by bar plot and histogram alternatives like frequency polygon.', 'duration': 27.707, 'max_score': 1175.658, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE81175658.jpg'}, {'end': 1355.447, 'src': 'embed', 'start': 1327.575, 'weight': 1, 'content': [{'end': 1333.797, 'text': 'the number of rooms in the house is 2 and its price will be somewhere around 1 lakh dollars.', 'start': 1327.575, 'duration': 6.222}, {'end': 1342.699, 'text': 'and if the number of rooms is 12, then its price, or average price, would be somewhere around 3 lakh dollars to 4 lakh dollars.', 'start': 1333.797, 'duration': 8.902}, {'end': 1344.56, 'text': 'these dots, which you see, are outliers.', 'start': 1342.699, 'duration': 1.861}, {'end': 1349.303, 'text': 'So when I say outliers that would mean they are beyond the average values.', 'start': 1345.18, 'duration': 4.123}, {'end': 1355.447, 'text': 'So these are values where the price of the house is closer to 8 lakh dollars, but that is not the norm.', 'start': 1349.823, 'duration': 5.624}], 'summary': 'House price increases with more rooms, outliers at 8 lakh dollars.', 'duration': 27.872, 'max_score': 1327.575, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE81327575.jpg'}, {'end': 1405.046, 'src': 'heatmap', 'start': 1363.372, 'weight': 2, 'content': [{'end': 1365.193, 'text': "Let's go ahead and add color to this.", 'start': 1363.372, 'duration': 1.821}, {'end': 1369.476, 'text': "So I've mapped the number of rooms onto the fill aesthetic.", 'start': 1366.334, 'duration': 3.142}, {'end': 1372.778, 'text': 'So that is why the color is determined by the number of rooms over here.', 'start': 1370.116, 'duration': 2.662}, {'end': 1379.993, 'text': "Now I'm mapping the air conditioning column onto the fill aesthetic.", 'start': 1375.987, 'duration': 4.006}, {'end': 1385.562, 'text': 'So what we see is there are two box plots so we can easily compare.', 'start': 1380.454, 'duration': 5.108}, {'end': 1387.104, 'text': "So let's take this case.", 'start': 1385.862, 'duration': 1.242}, {'end': 1388.867, 'text': 'So in the house has 12 rooms.', 'start': 1387.304, 'duration': 1.563}, {'end': 1392.679, 'text': 'We see that if the house is centrally air-conditioned,', 'start': 1389.477, 'duration': 3.202}, {'end': 1397.241, 'text': 'then its price will be much higher than the house which does not have centrally air conditioning.', 'start': 1392.679, 'duration': 4.562}, {'end': 1405.046, 'text': 'So the case is same for if the house has 11 rooms 10 rooms 9 rooms and so on so we can do multivariate analysis over here.', 'start': 1397.602, 'duration': 7.444}], 'summary': 'Mapped number of rooms and air conditioning onto fill aesthetic, showing higher prices for centrally air-conditioned houses with more rooms.', 'duration': 41.674, 'max_score': 1363.372, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE81363372.jpg'}], 'start': 1175.658, 'title': 'House price and housing data analysis', 'summary': 'Presents an analysis of house prices, revealing that houses without waterfronts are more likely to have a public sewer system. it also demonstrates the use of different plots, showing that most houses fall within the price range of 1.5 to 4.5 lakhs with variations based on the number of bins. additionally, the chapter discusses visualizing housing data using frequency polygon, box plot, and multivariate analysis to understand the relationship between variables such as price, number of rooms, air conditioning, and sewer system.', 'chapters': [{'end': 1243.448, 'start': 1175.658, 'title': 'House price distribution analysis', 'summary': 'Presents an analysis of house price distribution, concluding that houses without waterfronts are more likely to have a public sewer system, and demonstrates the use of different plots, showing that most houses fall within the price range of 1.5 to 4.5 lakhs with variations in distribution based on the number of bins.', 'duration': 67.79, 'highlights': ['The majority of houses in the dataset have a price range of 1.5 to 4.5 lakhs, as demonstrated by the distribution analysis.', 'Houses without waterfronts have a greater probability of having a public sewer system, as indicated by the inference drawn from the analysis.', 'Variation in the distribution of house prices is observed by manipulating the number of bins in the plots, showing the impact of bin size on the visualization of data.']}, {'end': 1455.973, 'start': 1243.448, 'title': 'Visualizing housing data analysis', 'summary': 'Discusses visualizing housing data using frequency polygon, box plot, and multivariate analysis to understand the relationship between variables such as price, number of rooms, air conditioning, and sewer system.', 'duration': 212.525, 'highlights': ['The chapter discusses how the number of rooms affects the price of the house, with an increase in rooms leading to a higher price, ranging from 1 to 4 lakh dollars, and identifies outliers with prices closer to 8 lakh dollars. The number of rooms is mapped to the x-axis and price to the y-axis, showing a direct correlation between the number of rooms and the price of the house, with outliers observed at prices closer to 8 lakh dollars.', 'The analysis compares the prices of houses with and without centralized air conditioning, showing that houses with centralized air conditioning have significantly higher prices across different room numbers. Multivariate analysis compares the prices of houses with centralized air conditioning to those without, revealing that the former consistently have higher prices across various room numbers.', 'The data visualization also identifies the impact of sewer systems on house prices, demonstrating that houses with private sewer systems command higher prices, with the number of rooms influencing the type of sewer system. The visualization shows that houses with private sewer systems command higher prices, and the number of rooms influences whether a house has a private or public sewer system.']}], 'duration': 280.315, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE81175658.jpg', 'highlights': ['Houses without waterfronts have a greater probability of having a public sewer system, as indicated by the inference drawn from the analysis.', 'The majority of houses in the dataset have a price range of 1.5 to 4.5 lakhs, as demonstrated by the distribution analysis.', 'The analysis compares the prices of houses with and without centralized air conditioning, showing that houses with centralized air conditioning have significantly higher prices across different room numbers.', 'The number of rooms is mapped to the x-axis and price to the y-axis, showing a direct correlation between the number of rooms and the price of the house, with outliers observed at prices closer to 8 lakh dollars.']}, {'end': 1872.314, 'segs': [{'end': 1488.251, 'src': 'embed', 'start': 1455.973, 'weight': 2, 'content': [{'end': 1459.515, 'text': 'then its price will be higher than the house which has a private sewer system.', 'start': 1455.973, 'duration': 3.542}, {'end': 1466.219, 'text': 'If we take a look at the house, which has nine rooms, then if the house does not have a sewer system, then its price will be lower.', 'start': 1460.196, 'duration': 6.023}, {'end': 1468.466, 'text': 'Now these two are quite comparable.', 'start': 1466.903, 'duration': 1.563}, {'end': 1473.436, 'text': 'So whether the house has a private sewer system or public sewer system, its price would be somewhere similar.', 'start': 1468.767, 'duration': 4.669}, {'end': 1475.56, 'text': 'That was box plot.', 'start': 1474.518, 'duration': 1.042}, {'end': 1478.486, 'text': "Let's go ahead and make a smooth line.", 'start': 1476.382, 'duration': 2.104}, {'end': 1488.251, 'text': 'A smooth line is used to see how does one continuous variable change with respect to another continuous variable.', 'start': 1481.303, 'duration': 6.948}], 'summary': 'House price impacted by sewer system, number of rooms, comparable price, continuous variable analysis.', 'duration': 32.278, 'max_score': 1455.973, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE81455973.jpg'}, {'end': 1565.268, 'src': 'embed', 'start': 1538.928, 'weight': 0, 'content': [{'end': 1543.869, 'text': 'and again we can say that if the house has centralized air conditioning, its price will be higher.', 'start': 1538.928, 'duration': 4.941}, {'end': 1551.17, 'text': 'if we see the price for this, it stops somewhere around 4.5 lakhs and this price goes above 6 lakhs.', 'start': 1543.869, 'duration': 7.301}, {'end': 1554.303, 'text': "Let's make the next plot.", 'start': 1553.283, 'duration': 1.02}, {'end': 1558.605, 'text': 'So now the color is determined by the heat data column.', 'start': 1556.024, 'duration': 2.581}, {'end': 1565.268, 'text': 'So we have three heating types electric hot air and hot water and so the green line.', 'start': 1560.266, 'duration': 5.002}], 'summary': 'Houses with centralized air conditioning fetch higher prices, ranging from 4.5 to 6 lakhs; heat type determines color in the plot.', 'duration': 26.34, 'max_score': 1538.928, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE81538928.jpg'}, {'end': 1640.229, 'src': 'embed', 'start': 1614.389, 'weight': 1, 'content': [{'end': 1621.214, 'text': 'So again as the size of living area increases the price of the house also increases and that is verified by this linear model over here.', 'start': 1614.389, 'duration': 6.825}, {'end': 1626.278, 'text': 'Again, this linear model tells us that there is a linear relationship between the living area and the price of house.', 'start': 1621.575, 'duration': 4.703}, {'end': 1629.861, 'text': "Let's make a similar plot.", 'start': 1628.92, 'duration': 0.941}, {'end': 1640.229, 'text': "So over here, I'm using another aesthetic which is color and I'm mapping the air conditioning data column onto the color aesthetic again.", 'start': 1632.323, 'duration': 7.906}], 'summary': 'Linear model confirms price increase with living area. aesthetic color maps air conditioning data.', 'duration': 25.84, 'max_score': 1614.389, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE81614389.jpg'}, {'end': 1683.345, 'src': 'embed', 'start': 1656.408, 'weight': 3, 'content': [{'end': 1664.452, 'text': 'So where these dots are of red color, that would be houses which do not have centralized air conditioning, and these dots which are of blue color,', 'start': 1656.408, 'duration': 8.044}, {'end': 1667.254, 'text': 'they determine the houses which have centralized air conditioning.', 'start': 1664.452, 'duration': 2.802}, {'end': 1673.057, 'text': 'Again, we can make a similar inference that if the house has centralized air conditioning, its price will be high.', 'start': 1667.914, 'duration': 5.143}, {'end': 1675.601, 'text': "So that was John's mood.", 'start': 1674.58, 'duration': 1.021}, {'end': 1677.802, 'text': "So you've looked at different geometries.", 'start': 1676.101, 'duration': 1.701}, {'end': 1683.345, 'text': "Now it's time to facet our data and we see that this plot is quite chaotic.", 'start': 1678.362, 'duration': 4.983}], 'summary': 'Geospatial analysis shows correlation between centralized air conditioning and house prices.', 'duration': 26.937, 'max_score': 1656.408, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE81656408.jpg'}, {'end': 1742.786, 'src': 'embed', 'start': 1721.129, 'weight': 4, 'content': [{'end': 1732.878, 'text': 'So the first facet tells us that all of these observations do not have centralized air conditioning and the second facet tells us that all of these observations have centralized air conditioning.', 'start': 1721.129, 'duration': 11.749}, {'end': 1737.081, 'text': 'now, making inference from this plot is much easier than the previous plot.', 'start': 1732.878, 'duration': 4.203}, {'end': 1742.786, 'text': 'So we can easily say that if the house has centralized air conditioning then its price will be higher.', 'start': 1737.582, 'duration': 5.204}], 'summary': 'Houses with centralized air conditioning have higher prices.', 'duration': 21.657, 'max_score': 1721.129, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE81721129.jpg'}], 'start': 1455.973, 'title': 'House price analysis and visualizing housing data insights', 'summary': 'Discusses the impact of factors such as sewer system, living area size, air conditioning, and heating type on house prices, emphasizing the influence of amenities on price variations, with an increase of up to 1.5 lakhs for centralized air conditioning and a decrease of up to 1.5 lakhs for hot water heating. it also explores visualizing housing data using scatter plots, facet layering, and color mapping to reveal insights on the relationship between house features and prices.', 'chapters': [{'end': 1629.861, 'start': 1455.973, 'title': 'House price analysis', 'summary': 'Discusses the impact of various factors such as sewer system, living area size, air conditioning, and heating type on house prices, emphasizing the influence of amenities like air conditioning and heating type on price variations, with an increase of up to 1.5 lakhs for houses with centralized air conditioning and a decrease of up to 1.5 lakhs for those with hot water heating, while also highlighting the linear relationship between living area size and house prices.', 'duration': 173.888, 'highlights': ['The impact of amenities such as air conditioning and heating type on house prices is emphasized, with an increase of up to 1.5 lakhs for houses with centralized air conditioning and a decrease of up to 1.5 lakhs for those with hot water heating.', 'The linear relationship between living area size and house prices is highlighted, indicating that as the size of living area increases, the price of the house also increases.', 'The influence of sewer system on house prices is noted, with houses having a private sewer system being priced higher than those without, while the presence of a public sewer system leads to similar pricing.', 'The use of a smooth line to demonstrate the linear relationship between living area size and house prices, with the removal of error by setting SE to false, is explained.', 'The impact of amenities like air conditioning and heating type on house prices is illustrated using different colored lines for houses with and without centralized air conditioning, and for different heating types, showing the varying price ranges based on these amenities.']}, {'end': 1872.314, 'start': 1632.323, 'title': 'Visualizing housing data insights', 'summary': 'Explores visualizing housing data using scatter plots, facet layering, and color mapping to reveal insights on the relationship between house features and prices, with emphasis on centralized air conditioning, number of fireplaces, and house age.', 'duration': 239.991, 'highlights': ['The color aesthetic is used to map air conditioning data onto the scatter plot, revealing that houses with centralized air conditioning have higher prices, as indicated by the blue dots. Relationship between centralized air conditioning and house prices, differentiation of house prices based on air conditioning, correlation between air conditioning and house prices.', 'Faceting the data based on air conditioning column provides a clearer inference on the relationship between centralized air conditioning and house prices, with the plot showing that houses with centralized air conditioning have higher prices. Clearer visualization of the correlation between centralized air conditioning and higher house prices.', 'Analysis of house prices based on the number of fireplaces reveals a positive correlation between living area and price across different fireplace counts. Correlation between living area, number of fireplaces, and house prices.', "Visualization of house age's impact on price indicates that older houses tend to have lower prices, while newer houses have higher prices. Inference on the relationship between house age and prices."]}], 'duration': 416.341, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE81455973.jpg', 'highlights': ['The impact of amenities such as air conditioning and heating type on house prices is emphasized, with an increase of up to 1.5 lakhs for houses with centralized air conditioning and a decrease of up to 1.5 lakhs for those with hot water heating.', 'The linear relationship between living area size and house prices is highlighted, indicating that as the size of living area increases, the price of the house also increases.', 'The influence of sewer system on house prices is noted, with houses having a private sewer system being priced higher than those without, while the presence of a public sewer system leads to similar pricing.', 'The color aesthetic is used to map air conditioning data onto the scatter plot, revealing that houses with centralized air conditioning have higher prices, as indicated by the blue dots.', 'Faceting the data based on air conditioning column provides a clearer inference on the relationship between centralized air conditioning and house prices, with the plot showing that houses with centralized air conditioning have higher prices.']}, {'end': 2428.552, 'segs': [{'end': 1918.745, 'src': 'embed', 'start': 1892.506, 'weight': 2, 'content': [{'end': 1898.927, 'text': 'What we need to understand is all of these are objects so they can be grouped together and stored in one object.', 'start': 1892.506, 'duration': 6.421}, {'end': 1906.19, 'text': "So I'm storing the result in the object and naming it as obj1 and I can build further layers onto this object.", 'start': 1899.148, 'duration': 7.042}, {'end': 1913.903, 'text': 'So on to the obj1 I am adding another layer and this layer is of labels.', 'start': 1906.83, 'duration': 7.073}, {'end': 1918.745, 'text': "So in the labs function, I'm giving the title of the plot.", 'start': 1914.884, 'duration': 3.861}], 'summary': 'Data can be grouped and stored in objects. additional layers and labels can be added to the object for further organization.', 'duration': 26.239, 'max_score': 1892.506, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE81892506.jpg'}, {'end': 1972.998, 'src': 'embed', 'start': 1944.01, 'weight': 0, 'content': [{'end': 1947.211, 'text': 'So theme layer helps us to give a theme to a plot.', 'start': 1944.01, 'duration': 3.201}, {'end': 1952.372, 'text': 'So you see this panel dot background attribute over here with the help of this attribute.', 'start': 1947.891, 'duration': 4.481}, {'end': 1954.013, 'text': 'We can add a background to the panel.', 'start': 1952.392, 'duration': 1.621}, {'end': 1958.394, 'text': 'So this is the panel and these are the different functions which we can use.', 'start': 1954.033, 'duration': 4.361}, {'end': 1964.336, 'text': 'So we have functions such as element blank, element gr, OB, element line and element rect.', 'start': 1958.534, 'duration': 5.802}, {'end': 1969.097, 'text': "if you want to change any rectangular space, then we'll be using the element rect function.", 'start': 1964.336, 'duration': 4.761}, {'end': 1972.998, 'text': 'If you want to change the text of something, will be using the element text.', 'start': 1969.717, 'duration': 3.281}], 'summary': 'Theme layer adds background, functions like element blank, gr, line, rect, and text can be used.', 'duration': 28.988, 'max_score': 1944.01, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE81944010.jpg'}, {'end': 2327.52, 'src': 'embed', 'start': 2295.687, 'weight': 1, 'content': [{'end': 2299.169, 'text': "Let's again store this into a new object which is P3.", 'start': 2295.687, 'duration': 3.482}, {'end': 2302.15, 'text': 'So this is the final theme layer.', 'start': 2300.55, 'duration': 1.6}, {'end': 2306.713, 'text': 'So onto the P3 object we made two changes using the theme layer.', 'start': 2302.39, 'duration': 4.323}, {'end': 2314.116, 'text': "So over here, I'm changing the title of the plot and again, I'm giving a background to the entire plot.", 'start': 2307.233, 'duration': 6.883}, {'end': 2317.538, 'text': 'So the title given is frequency polygon for price.', 'start': 2314.576, 'duration': 2.962}, {'end': 2319.878, 'text': "So over here, I'm adjusting.", 'start': 2318.217, 'duration': 1.661}, {'end': 2321.418, 'text': "I'm Center aligning the title.", 'start': 2319.958, 'duration': 1.46}, {'end': 2327.52, 'text': 'So initially it was on the left side now using the head just is equal to 0.5.', 'start': 2321.978, 'duration': 5.542}], 'summary': "P3 object has two changes: title 'frequency polygon for price' and center-aligned title.", 'duration': 31.833, 'max_score': 2295.687, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE82295687.jpg'}, {'end': 2372.819, 'src': 'embed', 'start': 2345.529, 'weight': 3, 'content': [{'end': 2348.252, 'text': "So let's recap what we did in the entire practical.", 'start': 2345.529, 'duration': 2.723}, {'end': 2354.38, 'text': 'We started off by making a scatter plot, histogram, and box plot with the help of base graphics.', 'start': 2348.272, 'duration': 6.108}, {'end': 2357.744, 'text': 'Then we understood that these are not up to the mark.', 'start': 2355.141, 'duration': 2.603}, {'end': 2365.853, 'text': 'so. then we took the help of ggplot2 and we played around a bit with the aesthetics of ggplot2.', 'start': 2358.527, 'duration': 7.326}, {'end': 2372.819, 'text': 'finally, we took the houses data set and in the houses data set we tried to explore the data with different geometries.', 'start': 2365.853, 'duration': 6.966}], 'summary': 'Explored data visualization techniques using base graphics and ggplot2, and experimented with different geometries in the houses dataset.', 'duration': 27.29, 'max_score': 2345.529, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE82345529.jpg'}], 'start': 1873.195, 'title': 'Creating presentable plots with ggplot2', 'summary': 'Explains creating presentable plots using the theme layer in ggplot2, demonstrating adding layers, modifying backgrounds, and changing plot titles. it also showcases enhancing data visualization with ggplot2 for housing data, including modifying labels, adding themes, and utilizing different geometries, resulting in more publishable quality plots for international journals.', 'chapters': [{'end': 2051.022, 'start': 1873.195, 'title': 'Creating presentable plots with theme layer', 'summary': 'Explains how to make plots presentable using the theme layer in ggplot2, demonstrating the process of adding layers, modifying the background, and changing the plot title.', 'duration': 177.827, 'highlights': ["The theme layer helps to give a theme to a plot, allowing modification of the background using functions such as element rect and specifying attributes like background color, as demonstrated by adding a pale green background to the plot. The theme layer in ggplot2 enables modification of the plot's background using functions like element rect, as shown by adding a pale green background to the plot.", 'Demonstrating the process of adding layers onto objects and modifying the plot title using the element text function to center align, bold, and change the color of the title, showcasing the flexibility of ggplot2 in creating visually appealing plots. The chapter showcases the flexibility of ggplot2 by demonstrating the process of adding layers onto objects and modifying the plot title, including center aligning, bolding, and changing the color of the title.', 'Explaining the process of grouping objects together and storing them in one object, showcasing the benefit of ggplot2 in adding multiple layers to create presentable plots. The chapter explains the process of grouping objects together and storing them in one object, highlighting the benefit of ggplot2 in adding multiple layers to create presentable plots.']}, {'end': 2428.552, 'start': 2051.442, 'title': 'Enhancing data visualization with ggplot2', 'summary': 'Demonstrates the use of ggplot2 to create presentable plots for housing data, including modifying labels, adding themes, and utilizing different geometries, resulting in more publishable quality plots for international journals.', 'duration': 377.11, 'highlights': ['The chapter demonstrates the use of ggplot2 to create presentable plots for housing data. ', 'The plots were modified to have proper x-axis, y-axis, and legend labels, resulting in more presentable plots suitable for International Journals. ', 'The use of ggplot2 enabled the creation of print-quality plots for housing data. ', 'Different geometries like frequency polygon and smooth line were used to create beautiful and alternative plots for housing data. ']}], 'duration': 555.357, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N5gYo43oLE8/pics/N5gYo43oLE81873195.jpg', 'highlights': ["The theme layer in ggplot2 enables modification of the plot's background using functions like element rect, as shown by adding a pale green background to the plot.", 'The chapter showcases the flexibility of ggplot2 by demonstrating the process of adding layers onto objects and modifying the plot title, including center aligning, bolding, and changing the color of the title.', 'The chapter explains the process of grouping objects together and storing them in one object, highlighting the benefit of ggplot2 in adding multiple layers to create presentable plots.', 'The use of ggplot2 enabled the creation of print-quality plots for housing data, including modifying labels, adding themes, and utilizing different geometries, resulting in more presentable plots suitable for International Journals.']}], 'highlights': ['The use of ggplot2 for creating high-quality plots with the grammar of graphics.', 'Demonstrating the linear relationship between sepal length and petal length using a scatter plot.', 'The range of sepal width is between 2 to 6 centimeters, with the most common value around 3 centimeters.', 'The relationship between sepal length and species, with virginica having the maximum sepal length and setosa having the lowest.', 'Exploring the limitations of base graphics for international journals or presentations.', 'Introduction to ggplot2 and the grammar of graphics for creating high-quality plots.', 'Demonstrating the application of grammar of graphics in ggplot2 for visualization.', 'Mapping data columns onto aesthetics and experimenting with different geometries in ggplot2.', 'Exploring the mapping of the species column onto the color aesthetic and shape aesthetic in ggplot2.', 'Using multiple geometries such as histogram, bar plot, frequency polygon, and box plot for visualization.', 'The majority of houses have a price range between 2 to 4 lakhs, with a maximum price of 8 lakhs.', 'Demonstrating the impact of increasing the number of bins in a histogram for detailed analysis of housing prices.', 'The analysis revealing the probability of having air conditioning as housing prices rise from 2 lakhs to 8 lakhs.', 'Inference drawn from the analysis that houses without waterfronts have a greater probability of having a public sewer system.', 'Comparing the prices of houses with and without centralized air conditioning, showing significantly higher prices for houses with centralized air conditioning.', 'Mapping the number of rooms to the x-axis and price to the y-axis, showing a direct correlation between the number of rooms and the price of the house.', 'Emphasizing the impact of amenities such as air conditioning and heating type on house prices.', 'Highlighting the linear relationship between living area size and house prices.', 'Noting the influence of sewer system on house prices based on the type of sewer system.', 'Using the color aesthetic to map air conditioning data onto the scatter plot, revealing the relationship between centralized air conditioning and house prices.', 'Faceting the data based on air conditioning column to provide a clearer inference on the relationship between centralized air conditioning and house prices.', "Demonstrating the use of the theme layer in ggplot2 for modifying the plot's background and adding layers onto objects.", 'Showcasing the flexibility of ggplot2 by adding layers onto objects, modifying the plot title, and grouping objects together to create presentable plots.']}