title
R Language For Beginners In Hindi | R Tutorial | Learn R Programming In 2 Hours | Great Learning

description
🔥1000+ Free Courses With Free Certificates: https://www.mygreatlearning.com/academy?ambassador_code=GLYT_DES_SBhpLnPuNlI&utm_source=GLYT&utm_campaign=GLYT_DES_SBhpLnPuNlI 🔥Build a career in Data Science & Business Analytics: https://www.mygreatlearning.com/pg-program-data-science-and-business-analytics-course?ambassador_code=GLYT_DES_Middle_SEP22&utm_source=GLYT&utm_campaign=GLYT_DES_Middle_SEP53 Great Learning offers a range of extensive Data Science courses that enable candidates for diverse work professions in Data Science and other trending domains. The faculty team of the Data Science Courses comprises top academicians in Data Science along with many skilled industry practitioners from leading organizations that practice Data Science. Over 500+ Hiring Partners & 8000+ career transitions over varied domains. Know More: https://glacad.me/3r9y7rA This R Language For Beginners video by Great Learning will act as a comprehensive guide in helping you master fundamental and core concepts of R Programming and help pave way for a career in Data Science in R Programming. Following pointers will be covered in this session: • Agenda- 00:00:00 • Installing R and R Studio- 00:01:41 • R Basics- 00:04:13 • R Data Structures- 00:09:29 • Inbuilt Functions in R- 00:46:55 • Flow Control Statements in R- 00:52:29 • User Defined Functions In R- 00:59:58 • Data Manipulation In R- 01:01:08 • Data Visualization In R- 01:21:39 🔥Check Our Free Courses with free certificate: 📌 Get your free certificate of completion for the Introduction to R course, Register Now: https://glacad.me/3dRgl6H ⚡ About Great Learning Academy: Visit Great Learning Academy to get access to 1000+ free courses with free certificate on Data Science, Data Analytics, Digital Marketing, Artificial Intelligence, Big Data, Cloud, Management, Cybersecurity, Software Development, and many more. These are supplemented with free projects, assignments, datasets, quizzes. You can earn a certificate of completion at the end of the course for free. ⚡ About Great Learning: With more than 5.4 Million+ learners in 170+ countries, Great Learning, a part of the BYJU'S group, is a leading global edtech company for professional and higher education offering industry-relevant programs in the blended, classroom, and purely online modes across technology, data and business domains. These programs are developed in collaboration with the top institutions like Stanford Executive Education, MIT Professional Education, The University of Texas at Austin, NUS, IIT Madras, IIT Bombay & more. SOCIAL MEDIA LINKS: 🔹 For more interesting tutorials, don't forget to subscribe to our channel: https://glacad.me/YTsubscribe 🔹 For more updates on courses and tips follow us on: ✅ Telegram: https://t.me/GreatLearningAcademy ✅ Facebook: https://www.facebook.com/GreatLearningOfficial/ ✅ LinkedIn: https://www.linkedin.com/school/great-learning/mycompany/verification/ ✅ Follow our Blog: https://glacad.me/GL_Blog #RProgrammingLanguageInHindi #RProgramming #RTutorial #GreatLearning

detail
{'title': 'R Language For Beginners In Hindi | R Tutorial | Learn R Programming In 2 Hours | Great Learning', 'heatmap': [], 'summary': 'Learn r programming in 2 hours with this comprehensive tutorial covering installation, variables, data types, structures, functions, looping, data extraction, visualization techniques, boxplot aesthetics, faceting, data frame manipulation, and analysis, as well as in-depth analysis and visualization of pokemon data with insights on different types, generations, and statistics.', 'chapters': [{'end': 469.472, 'segs': [{'end': 50.699, 'src': 'embed', 'start': 10.85, 'weight': 0, 'content': [{'end': 12.451, 'text': 'Hey guys, I welcome you all to this session.', 'start': 10.85, 'duration': 1.601}, {'end': 18.653, 'text': "In today's R Programming for Beginners in Hindi session, we will learn comprehensively about R.", 'start': 12.531, 'duration': 6.122}, {'end': 23.874, 'text': 'R is a language created by statisticians for statisticians.', 'start': 18.653, 'duration': 5.221}, {'end': 31.297, 'text': 'It means that whatever statistical analysis you want to perform, R should be a go-to language.', 'start': 24.034, 'duration': 7.263}, {'end': 40.347, 'text': 'Before we start the session, I would like to tell you that Great Learning has launched a free learning platform called Great Learning Academy.', 'start': 31.897, 'duration': 8.45}, {'end': 48.296, 'text': 'On Great Learning Academy, you will get more than 80 free courses with respect to different domains like Data Science, Machine Learning,', 'start': 40.647, 'duration': 7.649}, {'end': 50.699, 'text': 'Artificial Intelligence, Cloud Computing and so on.', 'start': 48.296, 'duration': 2.403}], 'summary': 'R programming for beginners in hindi covers statistical analysis. great learning academy offers 80+ free courses.', 'duration': 39.849, 'max_score': 10.85, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI10850.jpg'}, {'end': 104.432, 'src': 'embed', 'start': 76.603, 'weight': 1, 'content': [{'end': 79.644, 'text': 'And then we will work with flow control statements.', 'start': 76.603, 'duration': 3.041}, {'end': 85.965, 'text': 'Then moving forward we will see how to create user defined functions in R.', 'start': 79.684, 'duration': 6.281}, {'end': 91.427, 'text': 'And then we will see how to do data manipulation with the deployer package.', 'start': 85.965, 'duration': 5.462}, {'end': 95.028, 'text': 'Then we will see how to perform data visualization with the ggplot2 package.', 'start': 91.447, 'duration': 3.581}, {'end': 99.069, 'text': 'And finally we will apply a case study on the pokemon dataset.', 'start': 95.388, 'duration': 3.681}, {'end': 100.43, 'text': 'So we will start the session now.', 'start': 99.089, 'duration': 1.341}, {'end': 104.432, 'text': 'First of all, we will start this session by installing R.', 'start': 101.889, 'duration': 2.543}], 'summary': 'R session covers flow control, user functions, deployer package, ggplot2, and pokemon dataset.', 'duration': 27.829, 'max_score': 76.603, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI76603.jpg'}, {'end': 300.498, 'src': 'embed', 'start': 277.578, 'weight': 3, 'content': [{'end': 285.806, 'text': 'To store this data, many programming languages give you a feature named variable and this variable is just a temporary storage space.', 'start': 277.578, 'duration': 8.228}, {'end': 289.469, 'text': "To understand the concept of variables, let's take this as an example.", 'start': 286.426, 'duration': 3.043}, {'end': 293.932, 'text': 'Suppose you are looking at a basket or a shopping bag.', 'start': 289.629, 'duration': 4.303}, {'end': 296.474, 'text': 'Suppose this is our variable.', 'start': 293.952, 'duration': 2.522}, {'end': 300.498, 'text': 'First of all, we will take the phone and store it in it.', 'start': 296.514, 'duration': 3.984}], 'summary': 'Programming languages use variables as temporary storage spaces for data.', 'duration': 22.92, 'max_score': 277.578, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI277578.jpg'}, {'end': 380.864, 'src': 'embed', 'start': 349.897, 'weight': 2, 'content': [{'end': 352.181, 'text': 'So, here there are four consoles.', 'start': 349.897, 'duration': 2.284}, {'end': 354.181, 'text': 'This is our first console.', 'start': 352.659, 'duration': 1.522}, {'end': 356.923, 'text': 'We will call it script window.', 'start': 354.201, 'duration': 2.722}, {'end': 360.166, 'text': 'We will write the entire code here.', 'start': 356.943, 'duration': 3.223}, {'end': 363.55, 'text': 'Then we have this console window.', 'start': 360.387, 'duration': 3.163}, {'end': 367.213, 'text': 'If we want to execute the code, we will write it here.', 'start': 363.59, 'duration': 3.623}, {'end': 369.035, 'text': 'Then we have this history pane.', 'start': 367.533, 'duration': 1.502}, {'end': 374.74, 'text': 'We can see all the commands that we have run so far in the history.', 'start': 369.135, 'duration': 5.605}, {'end': 377.083, 'text': 'Again we have the environment here.', 'start': 374.78, 'duration': 2.303}, {'end': 380.864, 'text': 'For example, we can use variables here or we can import a data set from here.', 'start': 377.463, 'duration': 3.401}], 'summary': 'Introduction to four consoles for code execution and management.', 'duration': 30.967, 'max_score': 349.897, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI349897.jpg'}], 'start': 10.85, 'title': 'R programming basics and variables in rstudio', 'summary': "Provides an overview of r programming, including installation instructions for r and rstudio, and explains the concept of variables in rstudio, emphasizing their role and providing an overview of rstudio's interface and features.", 'chapters': [{'end': 248.05, 'start': 10.85, 'title': 'R programming basics & installation', 'summary': 'Covers an overview of r programming, including its importance, key features, and the agenda for the session. it also provides instructions for installing r and rstudio, emphasizing platform independence and the significance of ides in simplifying coding tasks.', 'duration': 237.2, 'highlights': ['R is a language created by statisticians for statisticians, emphasizing its suitability for statistical analysis tasks. None', 'Great Learning has launched Great Learning Academy, offering over 80 free courses in domains like Data Science, Machine Learning, Artificial Intelligence, and Cloud Computing. Over 80 free courses', 'The session agenda includes topics such as installing R and RStudio, learning R basics and data structures, working with inbuilt functions, flow control statements, user-defined functions, data manipulation, data visualization with ggplot2 package, and a case study on the pokemon dataset. None', 'R is platform-independent, available for Linux, Mac, and Windows systems. None', 'Instructions for installing R and RStudio, highlighting the significance of IDEs in simplifying coding tasks. None']}, {'end': 469.472, 'start': 249.636, 'title': 'Understanding variables in rstudio', 'summary': "Explains the concept of variables in r, emphasizing their role as temporary storage spaces with the analogy of a basket or shopping bag, and also provides an overview of rstudio's interface and features including the console, history pane, environment pane, and plots pane.", 'duration': 219.836, 'highlights': ['The concept of variables is explained with the analogy of a basket or shopping bag, highlighting their role as a temporary storage space for values that can change over time.', "RStudio's interface and features are described, including the script window, console window, history pane, environment pane, and plots pane, providing an overview of their functions and capabilities.", 'R provides over 10,000 packages, and the process of installing a package, such as ggplot2, is demonstrated by clicking on the Install button and automatically installing the package.', 'The process of accessing help and information about inbuilt datasets, such as the diamond dataset, is shown, along with the ability to manage and access files within RStudio.', "The customization of RStudio's appearance, including changing the theme color from white to dark blue, is explained by navigating through Tools, Global Options, and Appearance."]}], 'duration': 458.622, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI10850.jpg', 'highlights': ['Great Learning has launched Great Learning Academy, offering over 80 free courses in domains like Data Science, Machine Learning, Artificial Intelligence, and Cloud Computing.', 'The session agenda includes topics such as installing R and RStudio, learning R basics and data structures, working with inbuilt functions, flow control statements, user-defined functions, data manipulation, data visualization with ggplot2 package, and a case study on the pokemon dataset.', "RStudio's interface and features are described, including the script window, console window, history pane, environment pane, and plots pane, providing an overview of their functions and capabilities.", 'The concept of variables is explained with the analogy of a basket or shopping bag, highlighting their role as a temporary storage space for values that can change over time.', 'R is a language created by statisticians for statisticians, emphasizing its suitability for statistical analysis tasks.']}, {'end': 1253.911, 'segs': [{'end': 597.144, 'src': 'embed', 'start': 541.498, 'weight': 2, 'content': [{'end': 546.039, 'text': 'And if I print A now, then you can see that its value has become pen.', 'start': 541.498, 'duration': 4.541}, {'end': 549.219, 'text': 'Similarly, I will go to A and store makeup.', 'start': 546.419, 'duration': 2.8}, {'end': 551.2, 'text': 'Now I will write makeup in it.', 'start': 549.279, 'duration': 1.921}, {'end': 556.902, 'text': 'And as you can see, let me just print it out.', 'start': 553.86, 'duration': 3.042}, {'end': 562.886, 'text': 'So, the variable is just a temporary storage space and you can keep changing the values in it.', 'start': 557.082, 'duration': 5.804}, {'end': 566.128, 'text': 'So, this is the basic example of variables.', 'start': 562.906, 'duration': 3.222}, {'end': 568.889, 'text': "Now, let's understand what are the data types in R.", 'start': 566.148, 'duration': 2.741}, {'end': 574.912, 'text': 'So whenever you create a variable, you store a specific type of data in it.', 'start': 570.35, 'duration': 4.562}, {'end': 578.274, 'text': 'And there are many types of data present in this world.', 'start': 575.012, 'duration': 3.262}, {'end': 582.456, 'text': 'So we will also look at some different types of data from the perspective of R.', 'start': 578.294, 'duration': 4.162}, {'end': 590.66, 'text': 'So now if there are some numbers like 3.1475 or 500, So, all these numbers are of numeric type.', 'start': 582.456, 'duration': 8.204}, {'end': 597.144, 'text': 'Then, if there are characters, then some names like Sam, Bob or you said some sentences.', 'start': 590.701, 'duration': 6.443}], 'summary': 'Introduction to variables and data types in r, including numeric and character types.', 'duration': 55.646, 'max_score': 541.498, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI541498.jpg'}, {'end': 830.578, 'src': 'embed', 'start': 797.723, 'weight': 0, 'content': [{'end': 802.384, 'text': 'So, this is our dynamic type programming language.', 'start': 797.723, 'duration': 4.661}, {'end': 804.965, 'text': 'So, these are our variables and data types.', 'start': 802.924, 'duration': 2.041}, {'end': 808.207, 'text': 'After this, we will learn some operators in R.', 'start': 805.045, 'duration': 3.162}, {'end': 810.568, 'text': 'So, these are our different operators.', 'start': 808.207, 'duration': 2.361}, {'end': 816.471, 'text': 'We have Assignment operators, Arithmetic operators, Relational operators and Logical operators.', 'start': 810.708, 'duration': 5.763}, {'end': 818.752, 'text': 'So, we will see all of them one by one.', 'start': 816.491, 'duration': 2.261}, {'end': 822.634, 'text': 'First of all, we will start with assignment operators.', 'start': 820.613, 'duration': 2.021}, {'end': 830.578, 'text': 'We have three assignment operators, one is equal to, then this symbol is less than and hyphen.', 'start': 822.654, 'duration': 7.924}], 'summary': 'Introduction to r programming language, covering variables, data types, and operators.', 'duration': 32.855, 'max_score': 797.723, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI797723.jpg'}], 'start': 469.773, 'title': 'Variables and data types in r', 'summary': 'Introduces the concept of variables and data types in r, covering themes, font size, examples of variables, types of data (numeric, character, logical, complex), dynamic data types, and operators (assignment, arithmetic, relational, logical) with examples.', 'chapters': [{'end': 1253.911, 'start': 469.773, 'title': 'Introduction to variables and data types in r', 'summary': 'Introduces the concept of variables and data types in r, covering themes, font size, examples of variables, types of data (numeric, character, logical, complex), dynamic data types, and operators (assignment, arithmetic, relational, logical) with examples.', 'duration': 784.138, 'highlights': ['The chapter introduces the concept of variables and data types in R It covers the themes, font size, examples of variables, types of data (numeric, character, logical, complex), dynamic data types, and operators (assignment, arithmetic, relational, logical) with examples.', 'Types of data include numeric, character, logical, and complex values It explains the various types of data present in R, such as numeric, character, logical, and complex values.', 'Examples of dynamic data types in R It explains that in R, the data type of a variable is automatically set according to the stored value, demonstrating dynamic data types with examples.', 'Detailed explanation of different operators in R with examples It provides a detailed explanation of assignment, arithmetic, relational, and logical operators in R, supported by illustrative examples.']}], 'duration': 784.138, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI469773.jpg', 'highlights': ['The chapter introduces the concept of variables and data types in R, covering themes, font size, examples of variables, types of data (numeric, character, logical, complex), dynamic data types, and operators (assignment, arithmetic, relational, logical) with examples.', 'Detailed explanation of different operators in R with examples, including assignment, arithmetic, relational, and logical operators.', 'Examples of dynamic data types in R, demonstrating that the data type of a variable is automatically set according to the stored value.', 'Types of data include numeric, character, logical, and complex values, explaining the various types of data present in R.']}, {'end': 2026.999, 'segs': [{'end': 1310.962, 'src': 'embed', 'start': 1254.771, 'weight': 0, 'content': [{'end': 1262.837, 'text': 'So here I will write log2 and log2 and here I will get false because false or false is equal to false.', 'start': 1254.771, 'duration': 8.066}, {'end': 1265.438, 'text': 'So all these are my logical operators here.', 'start': 1263.437, 'duration': 2.001}, {'end': 1273.329, 'text': 'We have learnt the basics of R, now we will learn the data structures of R.', 'start': 1266.684, 'duration': 6.645}, {'end': 1277.892, 'text': 'We have vector, list, matrix, array, factor and data frame.', 'start': 1273.329, 'duration': 4.563}, {'end': 1281.755, 'text': 'We will start with vector.', 'start': 1278.212, 'duration': 3.543}, {'end': 1285.718, 'text': 'Vector is a homogenous single dimensional data frame.', 'start': 1281.895, 'duration': 3.823}, {'end': 1292.884, 'text': 'So single dimensional means there will be only one single dimensional data structure in rows and columns.', 'start': 1286.218, 'duration': 6.666}, {'end': 1302.252, 'text': 'And when I say that this vector is a homogenous data frame, it means that all the values you are storing should be of the same data type.', 'start': 1292.924, 'duration': 9.328}, {'end': 1306.437, 'text': "You can't store different types of elements in it.", 'start': 1303.793, 'duration': 2.644}, {'end': 1310.962, 'text': 'So this is the basic definition of vector.', 'start': 1306.477, 'duration': 4.485}], 'summary': 'Learning r data structures: vector, list, matrix, array, factor, data frame.', 'duration': 56.191, 'max_score': 1254.771, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI1254771.jpg'}, {'end': 1506.763, 'src': 'embed', 'start': 1478.604, 'weight': 2, 'content': [{'end': 1490.892, 'text': 'If you store different types of elements here, then those different types of elements will coerce in the higher priority of the data type.', 'start': 1478.604, 'duration': 12.288}, {'end': 1501.739, 'text': 'So when we talk about logical and numeric data types here, then the precedence of numeric data types will be more than that of logical data types.', 'start': 1491.012, 'duration': 10.727}, {'end': 1506.763, 'text': "So that's why all the logical data types coerced in numeric data types and we got a vector.", 'start': 1502.02, 'duration': 4.743}], 'summary': 'Different data types coerced, logical to numeric, resulting in a vector.', 'duration': 28.159, 'max_score': 1478.604, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI1478604.jpg'}, {'end': 1942.004, 'src': 'embed', 'start': 1915.799, 'weight': 1, 'content': [{'end': 1919.62, 'text': 'So in this numeric vector I will pass values 1, 2 and 3.', 'start': 1915.799, 'duration': 3.821}, {'end': 1927.981, 'text': 'Then my second vector will be character and in this I will pass values A, B and C.', 'start': 1919.62, 'duration': 8.361}, {'end': 1932.402, 'text': 'Then I will create a third vector which will be a logical vector.', 'start': 1927.981, 'duration': 4.421}, {'end': 1934.462, 'text': 'Then I will store values in it.', 'start': 1932.522, 'duration': 1.94}, {'end': 1936.023, 'text': 'True, false and true.', 'start': 1934.822, 'duration': 1.201}, {'end': 1937.963, 'text': 'So as you can see I have stored three elements.', 'start': 1936.403, 'duration': 1.56}, {'end': 1940.323, 'text': 'The first element is numeric vector.', 'start': 1938.003, 'duration': 2.32}, {'end': 1942.004, 'text': 'The second element is character vector.', 'start': 1940.383, 'duration': 1.621}], 'summary': 'Creating vectors with 3 elements: numeric, character, and logical.', 'duration': 26.205, 'max_score': 1915.799, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI1915799.jpg'}], 'start': 1254.771, 'title': 'R data structures and manipulation', 'summary': 'Introduces r data structures such as vectors, lists, matrix, array, factor, and data frame, emphasizing homogeneity. it also covers working with vectors and lists, including storing different data types, coercing elements, and maintaining individual data types.', 'chapters': [{'end': 1310.962, 'start': 1254.771, 'title': 'Introduction to r data structures', 'summary': 'Covers the basics of logical operators, followed by a detailed introduction to the data structures of r, including vector, list, matrix, array, factor, and data frame, emphasizing the homogeneity requirement for elements within a vector.', 'duration': 56.191, 'highlights': ["The chapter introduces the basics of logical operators, demonstrating the evaluation of 'false or false' resulting in false.", 'The chapter provides an overview of the data structures of R, including vector, list, matrix, array, factor, and data frame.', 'Vector is highlighted as a homogenous single dimensional data structure in R, where all values stored should be of the same data type.']}, {'end': 2026.999, 'start': 1311.002, 'title': 'Working with vectors and lists in r', 'summary': 'Covers creating and manipulating vectors and lists in r, including storing different data types, coercing elements, extracting elements by index, and maintaining individual data types in lists.', 'duration': 715.997, 'highlights': ['Creating and storing different types of elements in a vector The chapter demonstrates creating numeric, character, and logical vectors in R and shows how different data types coerce when stored in a vector.', 'Coercion of elements in a vector based on data type precedence It explains how storing different types of elements in a vector results in coercion, with numeric data types having precedence over logical data types, and character data types having precedence over numeric data types.', 'Extracting individual elements and sequences from a vector It illustrates extracting elements by index from a vector, including extracting single elements and sequences of elements, while emphasizing the 1-based indexing in R.', 'Creation and manipulation of lists in R The chapter introduces lists as a heterogeneous data structure, showcasing the preservation of individual data types when storing elements in a list and extracting values from a nested list.']}], 'duration': 772.228, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI1254771.jpg', 'highlights': ["The chapter introduces the basics of logical operators, demonstrating the evaluation of 'false or false' resulting in false.", 'Creating and storing different types of elements in a vector The chapter demonstrates creating numeric, character, and logical vectors in R and shows how different data types coerce when stored in a vector.', 'Coercion of elements in a vector based on data type precedence It explains how storing different types of elements in a vector results in coercion, with numeric data types having precedence over logical data types, and character data types having precedence over numeric data types.', 'Vector is highlighted as a homogenous single dimensional data structure in R, where all values stored should be of the same data type.', 'The chapter provides an overview of the data structures of R, including vector, list, matrix, array, factor, and data frame.']}, {'end': 3412.891, 'segs': [{'end': 2078.911, 'src': 'embed', 'start': 2051.373, 'weight': 3, 'content': [{'end': 2055.476, 'text': 'So, vector and list were our single-dimensional data structure.', 'start': 2051.373, 'duration': 4.103}, {'end': 2061.782, 'text': 'But when we talk about matrix, then matrix is a two-dimensional homogeneous data structure.', 'start': 2055.837, 'duration': 5.945}, {'end': 2065.245, 'text': 'So, two-dimensional means it has rows and columns.', 'start': 2062.042, 'duration': 3.203}, {'end': 2069.909, 'text': 'And because it is homogenous, you can store elements of the same type in it.', 'start': 2066.208, 'duration': 3.701}, {'end': 2073.05, 'text': 'So, this is the basic definition of matrix.', 'start': 2070.15, 'duration': 2.9}, {'end': 2077.091, 'text': 'So, if we want to create a matrix, we will create it with the matrix function.', 'start': 2073.411, 'duration': 3.68}, {'end': 2078.911, 'text': 'So, I will create a matrix in M1.', 'start': 2077.11, 'duration': 1.801}], 'summary': 'Matrix is a two-dimensional homogeneous data structure with rows and columns.', 'duration': 27.538, 'max_score': 2051.373, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI2051373.jpg'}, {'end': 2281.858, 'src': 'embed', 'start': 2251.09, 'weight': 1, 'content': [{'end': 2254.892, 'text': 'And your array is a multi-dimensional homogeneous data structure.', 'start': 2251.09, 'duration': 3.802}, {'end': 2261.575, 'text': 'And in other words, it can have more than two dimensions in your array.', 'start': 2255.392, 'duration': 6.183}, {'end': 2265.297, 'text': 'Or you can also say that your array is a composition of matrices.', 'start': 2261.635, 'duration': 3.662}, {'end': 2269.198, 'text': 'That means you can stack another matrix on top of another matrix.', 'start': 2265.317, 'duration': 3.881}, {'end': 2277.035, 'text': 'If we want to create an array, we will use the array method.', 'start': 2271.552, 'duration': 5.483}, {'end': 2281.858, 'text': 'We will create a vector of vectors and then we will set the dimensions.', 'start': 2277.075, 'duration': 4.783}], 'summary': 'Arrays can be multi-dimensional and composed of matrices, created using the array method.', 'duration': 30.768, 'max_score': 2251.09, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI2251090.jpg'}, {'end': 2552.405, 'src': 'embed', 'start': 2517.567, 'weight': 0, 'content': [{'end': 2519.868, 'text': 'Suppose we have this character vector here, blue, green, yellow.', 'start': 2517.567, 'duration': 2.301}, {'end': 2528.518, 'text': 'and I have to build a machine learning model on it.', 'start': 2525.857, 'duration': 2.661}, {'end': 2534.44, 'text': 'So, if I have to build a machine learning model on it, then the machine learning model will not understand it.', 'start': 2528.898, 'duration': 5.542}, {'end': 2544.063, 'text': 'So, what I will do is, I will convert it into a factor and that factor will set it to a level and that level will be alphabetical wise.', 'start': 2534.96, 'duration': 9.103}, {'end': 2552.405, 'text': 'So, what it will do is, it will set blue to level 0, green to level 1 and yellow to level 3.', 'start': 2544.083, 'duration': 8.322}], 'summary': 'Converting character vector into factor with alphabetical levels for machine learning model.', 'duration': 34.838, 'max_score': 2517.567, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI2517567.jpg'}, {'end': 2700.621, 'src': 'embed', 'start': 2674.412, 'weight': 4, 'content': [{'end': 2679.893, 'text': 'So one vector is fruitName in which we have fruit names like apple, banana, guava.', 'start': 2674.412, 'duration': 5.481}, {'end': 2688.355, 'text': 'Then we have fruitCost and these fruitCosts are 10, 20 and 30 and we are storing it in this object named fruits.', 'start': 2679.953, 'duration': 8.402}, {'end': 2692.536, 'text': 'And we are actually enclosing it in this data.frame.', 'start': 2688.975, 'duration': 3.561}, {'end': 2697.117, 'text': 'So if you want to make a data frame then you have to use this data.frame method.', 'start': 2692.956, 'duration': 4.161}, {'end': 2700.621, 'text': 'So you just write data dot frame.', 'start': 2698.68, 'duration': 1.941}], 'summary': 'Data frame contains fruit names and costs: apple, banana, guava with respective costs 10, 20, 30.', 'duration': 26.209, 'max_score': 2674.412, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI2674412.jpg'}, {'end': 2843.674, 'src': 'embed', 'start': 2815.438, 'weight': 2, 'content': [{'end': 2820.482, 'text': 'So, we have seen all the basic data structures in R, so now we will work with some inbuilt functions.', 'start': 2815.438, 'duration': 5.044}, {'end': 2826.646, 'text': 'So, these are some inbuilt functions like structure, head, tail, table, etc.', 'start': 2820.662, 'duration': 5.984}, {'end': 2829.168, 'text': 'So, now we will work with all these functions.', 'start': 2826.686, 'duration': 2.482}, {'end': 2833.331, 'text': 'So, I will come back to RStudio and show you all these functions.', 'start': 2829.328, 'duration': 4.003}, {'end': 2843.674, 'text': 'To implement all these functions, we will work with a dataFrame and the name of that dataFrame is iris.', 'start': 2837.708, 'duration': 5.966}], 'summary': 'Introduction to inbuilt functions in r, including structure, head, tail, and table, to be demonstrated using the iris dataframe.', 'duration': 28.236, 'max_score': 2815.438, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI2815438.jpg'}], 'start': 2026.999, 'title': 'R data structures and functions', 'summary': 'Delves into understanding matrices and arrays in r, and data transformation techniques including the creation of data frames. it also covers working with inbuilt functions on the iris data frame, providing insights into data structure and arithmetic functions.', 'chapters': [{'end': 2221.653, 'start': 2026.999, 'title': 'Understanding matrices in r', 'summary': 'Explains the concept of matrices in r, highlighting its two-dimensional homogeneous data structure, creation using the matrix function, and extraction of elements using indices.', 'duration': 194.654, 'highlights': ['Matrix is a two-dimensional homogeneous data structure with rows and columns, allowing storage of elements of the same type.', 'Creation of a matrix in R using the matrix function, specifying the number of rows and columns.', 'Demonstration of extracting elements from a matrix using indices like 1,2 and 2,3.']}, {'end': 2491.464, 'start': 2221.933, 'title': 'Array data structure', 'summary': 'Discusses the array data structure, highlighting its definition, difference from a matrix, creation using vectors, setting dimensions, and accessing values, emphasizing its multi-dimensional homogeneous nature and the process of creating and accessing values through examples.', 'duration': 269.531, 'highlights': ['The array is a multi-dimensional homogeneous data structure, allowing more than two dimensions and can be considered a composition of matrices.', 'Creation of an array involves using the array method to create a vector of vectors and setting the dimensions, exemplified by creating two vectors with values from 1 to 6 and 7 to 12 and then setting the dimensions as 2x3x2.', 'Accessing values from the array involves specifying the row, column, and dimension, demonstrated by extracting values like 7, 12, and 3 from the array.', 'The chapter also mentions the difference between a matrix and an array, highlighting the two-dimensional nature of a matrix and the multi-dimensional nature of an array.']}, {'end': 2811.687, 'start': 2491.804, 'title': 'Data transformation and data frame creation', 'summary': 'Explains the importance of converting categorical data into factors for machine learning models and demonstrates the creation of a data frame in r using heterogeneous data types and accessing individual columns.', 'duration': 319.883, 'highlights': ['Converting categorical data into factors is crucial for machine learning models, as they require numerical data, and factors set the level of categories internally. Converting categorical data into factors is crucial for machine learning models, as they require numerical data and factors set the level of categories internally, allowing easy fitting of machine learning models.', 'Demonstration of converting a character vector into a factor by setting levels alphabetically, enabling easy fitting of machine learning models. The demonstration showcases the conversion of a character vector into a factor by setting levels alphabetically, enabling easy fitting of machine learning models.', 'Creation of a data frame in R using heterogeneous data types, with an example of creating a data frame with fruit names and their respective costs. The explanation includes the creation of a data frame in R using heterogeneous data types, with an example of creating a data frame with fruit names and their respective costs.', "Accessing individual columns from a data frame by using the '$' symbol followed by the column name, allowing for extraction of specific data from the data frame. The chapter explains how to access individual columns from a data frame by using the '$' symbol followed by the column name, enabling the extraction of specific data from the data frame."]}, {'end': 3412.891, 'start': 2815.438, 'title': 'Working with inbuilt functions in r', 'summary': 'Covers working with inbuilt functions like structure, head, tail, table, etc. on the iris data frame in rstudio, providing insights into the data structure, viewing top and bottom records, frequency tabulation, and arithmetic functions like minimum, maximum, mean, and range, followed by an explanation of decision-making and looping statements.', 'duration': 597.453, 'highlights': ['The chapter covers working with inbuilt functions like structure, head, tail, table, etc. on the iris data frame in RStudio, providing insights into the data structure, viewing top and bottom records, frequency tabulation, and arithmetic functions like minimum, maximum, mean, and range.', "The data frame 'iris' contains 150 observations of 5 variables, representing the different species of iris flower (Cetosa, Vosicolor, and Virginica) along with their respective sepal length, sepal width, petal length, and petal width values.", 'The STR function provides a detailed summary of the data frame, including the number of observations and variables, column names, types, and sample values.', 'The head method is used to display the first few records of a data frame, and the tail method shows the last few records, providing flexibility to specify the number of records to be displayed.', "The table method is utilized for frequency tabulation of categorical columns, displaying the levels and corresponding record counts, such as the distribution of iris flower species in the 'iris' data frame.", "Arithmetic functions like min, max, mean, and range are applied to numeric columns, enabling the calculation of minimum, maximum, average, and range values in the 'iris' data frame.", 'The transcript further delves into decision-making and looping statements, illustrating the use of if-else conditions and looping for iterative tasks through real-life examples like playing football based on weather conditions and bucket filling.']}], 'duration': 1385.892, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI2026999.jpg', 'highlights': ['Demonstration of converting a character vector into a factor by setting levels alphabetically, enabling easy fitting of machine learning models.', 'The array is a multi-dimensional homogeneous data structure, allowing more than two dimensions and can be considered a composition of matrices.', 'The chapter covers working with inbuilt functions like structure, head, tail, table, etc. on the iris data frame in RStudio, providing insights into the data structure, viewing top and bottom records, frequency tabulation, and arithmetic functions like minimum, maximum, mean, and range.', 'Matrix is a two-dimensional homogeneous data structure with rows and columns, allowing storage of elements of the same type.', 'Creation of a data frame in R using heterogeneous data types, with an example of creating a data frame with fruit names and their respective costs.']}, {'end': 3849.228, 'segs': [{'end': 3437.77, 'src': 'embed', 'start': 3412.911, 'weight': 3, 'content': [{'end': 3422.401, 'text': 'So in this way we can use for and while loops so that if we have to repeat any task then we can do it with these two.', 'start': 3412.911, 'duration': 9.49}, {'end': 3425.225, 'text': 'So now we will see the examples of for and while.', 'start': 3422.742, 'duration': 2.483}, {'end': 3427.447, 'text': 'So here we have an example of for loop.', 'start': 3425.245, 'duration': 2.202}, {'end': 3434.769, 'text': 'First of all, I am creating a vector with values from 1 to 9.', 'start': 3429.706, 'duration': 5.063}, {'end': 3437.77, 'text': 'Then I am writing i in vec1 in the for loop.', 'start': 3434.769, 'duration': 3.001}], 'summary': 'Using for and while loops for task repetition, with example of creating a vector and writing in for loop.', 'duration': 24.859, 'max_score': 3412.911, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI3412911.jpg'}, {'end': 3520.721, 'src': 'embed', 'start': 3492.221, 'weight': 4, 'content': [{'end': 3497.543, 'text': 'So, I copy this entire syntax from here and show you the result by printing it here.', 'start': 3492.221, 'duration': 5.322}, {'end': 3500.304, 'text': 'And you can see the result here.', 'start': 3497.603, 'duration': 2.701}, {'end': 3505.226, 'text': 'So, we are printing from 6 to 14 with the help of this for loop.', 'start': 3500.344, 'duration': 4.882}, {'end': 3508.889, 'text': 'Similarly, we will work with while as well.', 'start': 3505.666, 'duration': 3.223}, {'end': 3512.092, 'text': 'We are doing the same sort of analysis in while.', 'start': 3508.989, 'duration': 3.103}, {'end': 3516.957, 'text': 'Here we have set a variable i is equal to 1.', 'start': 3512.152, 'duration': 4.805}, {'end': 3520.721, 'text': 'Then we are checking the condition with while loop.', 'start': 3516.957, 'duration': 3.764}], 'summary': 'Demonstrated printing from 6 to 14 using a for loop and analyzed while loop with variable i starting at 1.', 'duration': 28.5, 'max_score': 3492.221, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI3492221.jpg'}, {'end': 3679.868, 'src': 'embed', 'start': 3650.258, 'weight': 5, 'content': [{'end': 3654.442, 'text': 'And I am passing a value in add phi because it is the name of the function.', 'start': 3650.258, 'duration': 4.184}, {'end': 3658.265, 'text': 'For example, if I pass 10 in it, now this value has become 15.', 'start': 3654.462, 'duration': 3.803}, {'end': 3660.728, 'text': 'Similarly, if I pass 100 in it, this value has become 105.', 'start': 3658.265, 'duration': 2.463}, {'end': 3664.891, 'text': 'So, in this way, we can work with this user defined function.', 'start': 3660.728, 'duration': 4.163}, {'end': 3674.245, 'text': 'So, we saw different types of looping statements, flow control statements and we also saw how to make a user-defined function.', 'start': 3667.381, 'duration': 6.864}, {'end': 3679.868, 'text': 'So, now we will understand what data manipulation is and how to do data manipulation with the deployer package.', 'start': 3674.405, 'duration': 5.463}], 'summary': 'Demonstrated user-defined function with example inputs resulting in 15 and 105; introduced looping and flow control statements.', 'duration': 29.61, 'max_score': 3650.258, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI3650258.jpg'}, {'end': 3723.907, 'src': 'embed', 'start': 3692.935, 'weight': 0, 'content': [{'end': 3707.042, 'text': 'And if there is a huge data frame out of that huge data frame if we want to extract all the employees whose salary Suppose he is more than 10 lakh rupees and his age is more than 30..', 'start': 3692.935, 'duration': 14.107}, {'end': 3714.904, 'text': "So will you manually check each and every record of that employee's age and salary?", 'start': 3707.042, 'duration': 7.862}, {'end': 3719.425, 'text': 'This is a very tiring job and it will be a stupidity from your side.', 'start': 3714.944, 'duration': 4.481}, {'end': 3723.907, 'text': 'So, this is where data manipulation comes.', 'start': 3721.926, 'duration': 1.981}], 'summary': 'Data manipulation automates extraction of employees with salary > 10 lakh and age > 30, avoiding manual checking.', 'duration': 30.972, 'max_score': 3692.935, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI3692935.jpg'}, {'end': 3782.22, 'src': 'embed', 'start': 3749.721, 'weight': 1, 'content': [{'end': 3751.742, 'text': 'First of all, we have to load the deployer package.', 'start': 3749.721, 'duration': 2.021}, {'end': 3753.343, 'text': 'I will use the library here.', 'start': 3751.762, 'duration': 1.581}, {'end': 3757.486, 'text': 'The library is used to load different libraries or packages in R.', 'start': 3753.363, 'duration': 4.123}, {'end': 3759.607, 'text': 'I give the name of the package in it.', 'start': 3757.486, 'duration': 2.121}, {'end': 3763.989, 'text': 'And we will perform data manipulation operations with the package deployer.', 'start': 3759.647, 'duration': 4.342}, {'end': 3773.935, 'text': 'And we will implement all these data manipulation operations on a laptop.csv file.', 'start': 3768.792, 'duration': 5.143}, {'end': 3776.256, 'text': 'So, I am loading it right now.', 'start': 3773.975, 'duration': 2.281}, {'end': 3782.22, 'text': 'So, if I want to load a csv file, then I will have to use this read.csv method.', 'start': 3776.296, 'duration': 5.924}], 'summary': 'Loading deployer package to perform data manipulation operations on laptop.csv file using r.', 'duration': 32.499, 'max_score': 3749.721, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI3749721.jpg'}, {'end': 3831.664, 'src': 'embed', 'start': 3800.77, 'weight': 2, 'content': [{'end': 3804.032, 'text': 'This is our laptop data frame and these are the columns.', 'start': 3800.77, 'duration': 3.262}, {'end': 3808.234, 'text': 'This is the company, which company does the laptop belong to.', 'start': 3804.052, 'duration': 4.182}, {'end': 3811.436, 'text': 'Apple, HP, Acer, then the product.', 'start': 3808.654, 'duration': 2.782}, {'end': 3820.721, 'text': "In Apple, there are MacBook Pro, MacBook Air, HP's product 250G6, then the type name, which type of laptop it is, what is Ultrabook.", 'start': 3811.776, 'duration': 8.945}, {'end': 3826.462, 'text': 'Then what are the inches, how many inches is this laptop, it is telling me here.', 'start': 3821.981, 'duration': 4.481}, {'end': 3831.664, 'text': 'Then the screen resolution of this laptop, it is telling me here.', 'start': 3826.582, 'duration': 5.082}], 'summary': 'Describes laptop data frame with company, product, type, inches, and screen resolution.', 'duration': 30.894, 'max_score': 3800.77, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI3800770.jpg'}], 'start': 3412.911, 'title': 'Looping and data manipulation', 'summary': 'Explains for and while loops along with user-defined functions, and then discusses data manipulation using the deployer package in r to extract specific data, saving manual efforts and enhancing efficiency, with an example of extracting employees with salary > 10 lakhs and age > 30 from a dataset of 1m rows and 10,000 columns.', 'chapters': [{'end': 3674.245, 'start': 3412.911, 'title': 'Looping and user-defined functions', 'summary': 'Explains the concepts of for and while loops with examples, demonstrating the iteration process and outputs, followed by the creation and use of a user-defined function.', 'duration': 261.334, 'highlights': ['The chapter demonstrates the for loop by iterating through a vector from 1 to 9, incrementing the values by 5, and printing the results, resulting in an output from 6 to 14.', 'The while loop is illustrated by starting from 1 and incrementing by 1 until the value reaches 10, printing the results from 6 to 14.', "The creation and use of a user-defined function 'add5' is depicted, showcasing the ability to pass different values and obtain the corresponding output, exemplifying the concept of user-defined functions."]}, {'end': 3849.228, 'start': 3674.405, 'title': 'Data manipulation with deployer package', 'summary': 'Discusses the importance of data manipulation, demonstrating how to extract specific data from a large dataset using the deployer package in r, reducing manual efforts and increasing efficiency, with an example of extracting employees with salary greater than 10 lakhs and age over 30 from a dataset of 1 million rows and 10,000 columns.', 'duration': 174.823, 'highlights': ['The importance of data manipulation is demonstrated through the example of extracting employees with salary greater than 10 lakhs and age over 30 from a dataset of 1 million rows and 10,000 columns, showcasing the efficiency of using data manipulation operations with just one line of command. Illustrates the significance of data manipulation in efficiently handling large datasets, showcasing the capability to extract specific employee data from a dataset of 1 million rows and 10,000 columns using just one line of command.', 'Using the deployer package in R, different data manipulation operations are applied to efficiently extract specific data from a laptop.csv file, demonstrating the process of loading the package and the file, and using read.csv method to load the csv file. Describes the utilization of the deployer package in R to perform data manipulation operations on a laptop.csv file, detailing the process of loading the package, loading the file using read.csv method, and displaying the laptop data frame and its columns.', 'The laptop.csv file contains data regarding the company, product, type name, inches, screen resolution, CPU, and RAM of different laptops, showcasing the variety of information available for data manipulation and analysis. Provides an overview of the content within the laptop.csv file, including details about the company, product, type name, inches, screen resolution, CPU, and RAM, demonstrating the diverse data available for manipulation and analysis.']}], 'duration': 436.317, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI3412911.jpg', 'highlights': ['The importance of data manipulation is demonstrated through the example of extracting employees with salary greater than 10 lakhs and age over 30 from a dataset of 1 million rows and 10,000 columns, showcasing the efficiency of using data manipulation operations with just one line of command.', 'Using the deployer package in R, different data manipulation operations are applied to efficiently extract specific data from a laptop.csv file, demonstrating the process of loading the package and the file, and using read.csv method to load the csv file.', 'The laptop.csv file contains data regarding the company, product, type name, inches, screen resolution, CPU, and RAM of different laptops, showcasing the variety of information available for data manipulation and analysis.', 'The chapter demonstrates the for loop by iterating through a vector from 1 to 9, incrementing the values by 5, and printing the results, resulting in an output from 6 to 14.', 'The while loop is illustrated by starting from 1 and incrementing by 1 until the value reaches 10, printing the results from 6 to 14.', "The creation and use of a user-defined function 'add5' is depicted, showcasing the ability to pass different values and obtain the corresponding output, exemplifying the concept of user-defined functions."]}, {'end': 5159.141, 'segs': [{'end': 4008.58, 'src': 'embed', 'start': 3975.232, 'weight': 1, 'content': [{'end': 3980.956, 'text': 'From that entire data frame, I have only extracted the first two columns which are Company and Product.', 'start': 3975.232, 'duration': 5.724}, {'end': 3984.358, 'text': 'Suppose I have to extract sequence of columns from 3rd to 6th column.', 'start': 3981.596, 'duration': 2.762}, {'end': 3987.741, 'text': 'I have to extract all the columns sequentially from 3rd, 4th, 5th, and 6th.', 'start': 3984.418, 'duration': 3.323}, {'end': 3988.722, 'text': 'Let me show you.', 'start': 3987.761, 'duration': 0.961}, {'end': 3989.062, 'text': '3, 4, 5, and 6.', 'start': 3988.802, 'duration': 0.26}, {'end': 4008.58, 'text': 'If I want to extract all these columns, I just have to select I will write 3 colon 6 in the function.', 'start': 3989.062, 'duration': 19.518}], 'summary': 'Extracted company and product columns, then extracted columns 3 to 6 sequentially.', 'duration': 33.348, 'max_score': 3975.232, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI3975232.jpg'}, {'end': 4127.923, 'src': 'embed', 'start': 4096.078, 'weight': 0, 'content': [{'end': 4098.899, 'text': 'So, first of all, you give the name of the data frame here.', 'start': 4096.078, 'duration': 2.821}, {'end': 4102.84, 'text': 'After the name of the data frame, you use the pipe operator.', 'start': 4098.979, 'duration': 3.861}, {'end': 4104.441, 'text': 'Again, use the select method.', 'start': 4103, 'duration': 1.441}, {'end': 4109.363, 'text': 'Then whatever columns are there, give the names of the columns that you have to extract.', 'start': 4104.68, 'duration': 4.683}, {'end': 4112.124, 'text': 'First of all, I have to extract the company.', 'start': 4109.383, 'duration': 2.741}, {'end': 4113.545, 'text': 'Here I give the name of the column.', 'start': 4112.144, 'duration': 1.401}, {'end': 4115.185, 'text': 'That has become the company.', 'start': 4113.965, 'duration': 1.22}, {'end': 4116.886, 'text': 'Then after this, the product.', 'start': 4115.265, 'duration': 1.621}, {'end': 4118.506, 'text': 'I just have to write the product here.', 'start': 4116.906, 'duration': 1.6}, {'end': 4122.368, 'text': 'Then after this, I have to extract the price in euros column.', 'start': 4118.527, 'duration': 3.841}, {'end': 4127.923, 'text': 'So I have selected all these columns, then I have to store it in a new one.', 'start': 4123.956, 'duration': 3.967}], 'summary': 'Extract company, product, and price columns from data frame using select method.', 'duration': 31.845, 'max_score': 4096.078, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI4096078.jpg'}, {'end': 4373.019, 'src': 'embed', 'start': 4344.522, 'weight': 2, 'content': [{'end': 4349.203, 'text': 'So, we can extract some specific records from filter function.', 'start': 4344.522, 'duration': 4.681}, {'end': 4356.711, 'text': 'So, as we were extracting columns from select, Similarly, we can extract some specific records from the filter on the basis of a condition.', 'start': 4349.283, 'duration': 7.428}, {'end': 4358.352, 'text': 'So, we just have to use the filter function.', 'start': 4356.731, 'duration': 1.621}, {'end': 4365.435, 'text': "So, let's assume that we have to extract Dell laptops from this whole laptop's data frame.", 'start': 4358.492, 'duration': 6.943}, {'end': 4368.317, 'text': 'So, here we will set the condition in the filter function.', 'start': 4365.575, 'duration': 2.742}, {'end': 4373.019, 'text': 'So, here our condition will be where company is equal to Dell.', 'start': 4368.337, 'duration': 4.682}], 'summary': 'Filter function extracts specific records based on condition, e.g. dell laptops.', 'duration': 28.497, 'max_score': 4344.522, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI4344522.jpg'}, {'end': 4529.403, 'src': 'embed', 'start': 4498.274, 'weight': 3, 'content': [{'end': 4501.457, 'text': 'So here I will just use AND operator in the filter function.', 'start': 4498.274, 'duration': 3.183}, {'end': 4502.818, 'text': 'Here we have two conditions.', 'start': 4501.517, 'duration': 1.301}, {'end': 4509.977, 'text': 'The first condition will be where company is equal to del and then the second condition will be inches is greater than 15.', 'start': 4502.938, 'duration': 7.039}, {'end': 4513.678, 'text': 'So now again I have to give name of data frame which will be laptops.', 'start': 4509.977, 'duration': 3.701}, {'end': 4515.058, 'text': 'Then I will use pipe operator here.', 'start': 4513.738, 'duration': 1.32}, {'end': 4518.8, 'text': 'After that I will have to use filter method again.', 'start': 4515.159, 'duration': 3.641}, {'end': 4521.14, 'text': 'Here I will specify company.', 'start': 4518.84, 'duration': 2.3}, {'end': 4523.621, 'text': 'So here company will be del.', 'start': 4521.58, 'duration': 2.041}, {'end': 4529.403, 'text': 'Then after this I will use and operator and I will set inches.', 'start': 4523.661, 'duration': 5.742}], 'summary': 'Using and operator to filter laptops with company=del and inches>15.', 'duration': 31.129, 'max_score': 4498.274, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI4498274.jpg'}, {'end': 4799.599, 'src': 'embed', 'start': 4772.3, 'weight': 4, 'content': [{'end': 4776.841, 'text': 'So, now we will perform data visualization with the ggplot2 package.', 'start': 4772.3, 'duration': 4.541}, {'end': 4779.742, 'text': 'So, first of all we have to load this library.', 'start': 4776.981, 'duration': 2.761}, {'end': 4781.703, 'text': 'So, I will write here library of.', 'start': 4779.782, 'duration': 1.921}, {'end': 4785.564, 'text': 'Here I have to give the name of the package which will be ggplot2.', 'start': 4782.243, 'duration': 3.321}, {'end': 4787.984, 'text': 'So, I have loaded the package in this way.', 'start': 4785.924, 'duration': 2.06}, {'end': 4793.946, 'text': 'So, now this ggplot2 package provides us with a data set whose name is diamonds data set.', 'start': 4788.324, 'duration': 5.622}, {'end': 4797.287, 'text': 'So, I will just show you this data set here, view of.', 'start': 4794.326, 'duration': 2.961}, {'end': 4799.599, 'text': 'I will write diamonds here.', 'start': 4798.579, 'duration': 1.02}], 'summary': 'Performing data visualization using ggplot2 package with diamonds dataset', 'duration': 27.299, 'max_score': 4772.3, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI4772300.jpg'}, {'end': 5159.141, 'src': 'embed', 'start': 5123.176, 'weight': 5, 'content': [{'end': 5124.857, 'text': 'As you can see, we have more than 15,000 here.', 'start': 5123.176, 'duration': 1.681}, {'end': 5125.938, 'text': '15,000 or 12,500, whose price is around 1,000 Euros.', 'start': 5124.877, 'duration': 1.061}, {'end': 5128.62, 'text': 'And as you can see, this is around 5,000 US Dollars.', 'start': 5126.458, 'duration': 2.162}, {'end': 5130.922, 'text': 'And we have around 2,500 diamonds whose price is around 5,000 US Dollars.', 'start': 5128.66, 'duration': 2.262}, {'end': 5152.057, 'text': 'And here the price is 10,000, so there are very few diamonds whose price is 10,000.', 'start': 5146.915, 'duration': 5.142}, {'end': 5159.141, 'text': 'And here we will have 2 or 3 diamonds whose price will be around 18,000 USD.', 'start': 5152.057, 'duration': 7.084}], 'summary': '15,000 items at 1,000 euros, 2,500 diamonds at 5,000 usd, some at 10,000 and 2-3 at 18,000 usd', 'duration': 35.965, 'max_score': 5123.176, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI5123176.jpg'}], 'start': 3849.948, 'title': 'Data extraction and visualization in r', 'summary': 'Describes methods for extracting specific columns from a data frame using the select function, including extraction by index, range, and column names, as well as filtering records with the filter function. it also covers data visualization using ggplot2 to analyze the price distribution of diamonds dataset, with over 15,000 diamonds priced around 1,000 euros and a few diamonds priced at 18,000 usd.', 'chapters': [{'end': 4344.422, 'start': 3849.948, 'title': 'Extracting columns from data frame', 'summary': 'Describes how to extract specific columns from a data frame in r using the select function from the dplyr package, including examples of extracting columns by index, range, and column names, as well as using attributes like starts with and ends with, showcasing the process of creating new data frames with the extracted columns.', 'duration': 494.474, 'highlights': ['The chapter describes how to extract specific columns from a data frame in R using the select function from the dplyr package. This is the main topic of the transcript, providing an overview of the process being discussed.', 'Examples of extracting columns by index, range, and column names are provided. The transcript provides examples of extracting columns by different methods, showcasing the versatility of the select function.', 'The process of creating new data frames with the extracted columns is demonstrated. The transcript explains how the extracted columns are stored in new data frames, showcasing the practical application of the extraction process.', 'Attributes like starts with and ends with are utilized to extract columns based on specific patterns in their names. The use of attributes to extract columns based on specific patterns demonstrates advanced functionality of the select function.']}, {'end': 4496.394, 'start': 4344.522, 'title': 'Data extraction with filter function', 'summary': "Demonstrates how to use the filter function to extract specific records, such as 297 dell laptops and laptops with more than 15 inches, from a laptop's data frame.", 'duration': 151.872, 'highlights': ["The filter function is used to extract specific records from a data frame based on a condition, such as extracting 297 Dell laptops by setting the condition 'company is equal to Dell'.", "Another example demonstrates the extraction of laptops with a size greater than 15 inches using the filter function and storing them in a new data frame called 'laptops 15 inch'."]}, {'end': 4772.16, 'start': 4498.274, 'title': 'Using select and filter functions', 'summary': 'Demonstrates using the select and filter functions in r to extract specific columns and apply multiple conditions to filter the data, resulting in 297 entries for dell laptops and all lenovo laptops with at least 4gb ram.', 'duration': 273.886, 'highlights': ["Using the filter function with AND operator to extract laptops where company is 'del' and inches are greater than 15, resulting in the creation of a data frame 'laptops_del_15_inch'.", "Demonstrating the use of both select and filter functions simultaneously to extract only three columns (company, product, and price in euros) and filtering records where company is 'dell', resulting in a new data frame with 297 entries for Dell laptops.", "Applying select and filter functions together to extract specific columns (company, product, CPU, and RAM) and filter records for Lenovo laptops with 4GB RAM, resulting in the creation of a data frame 'laptop_final_2' with all entries meeting the specified conditions."]}, {'end': 5159.141, 'start': 4772.3, 'title': 'Data visualization with ggplot2', 'summary': "Covers the process of loading the ggplot2 package and visualizing the diamonds dataset, providing insights into the dataset's attributes and demonstrating the creation of a histogram to understand the price distribution, with over 15,000 diamonds priced around 1,000 euros and a few diamonds priced at 18,000 usd.", 'duration': 386.841, 'highlights': ["The chapter covers the process of loading the ggplot2 package and visualizing the diamonds dataset, providing insights into the dataset's attributes. Loading ggplot2 package, displaying the diamonds dataset with its attributes, understanding the dataset's columns such as price, carrot, cut, color, clarity, and dimensions.", 'Demonstrating the creation of a histogram to understand the price distribution, with over 15,000 diamonds priced around 1,000 Euros and a few diamonds priced at 18,000 USD. Creating a histogram to analyze the price distribution, revealing over 15,000 diamonds priced around 1,000 Euros, few diamonds priced at 18,000 USD, and various price distribution insights.']}], 'duration': 1309.193, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI3849948.jpg', 'highlights': ['The chapter describes how to extract specific columns from a data frame in R using the select function from the dplyr package.', 'Examples of extracting columns by index, range, and column names are provided, showcasing the versatility of the select function.', "The filter function is used to extract specific records from a data frame based on a condition, such as extracting 297 Dell laptops by setting the condition 'company is equal to Dell'.", "Using the filter function with AND operator to extract laptops where company is 'del' and inches are greater than 15, resulting in the creation of a data frame 'laptops_del_15_inch'.", "The chapter covers the process of loading the ggplot2 package and visualizing the diamonds dataset, providing insights into the dataset's attributes.", 'Creating a histogram to analyze the price distribution, revealing over 15,000 diamonds priced around 1,000 Euros, few diamonds priced at 18,000 USD, and various price distribution insights.']}, {'end': 5718.294, 'segs': [{'end': 5388.684, 'src': 'embed', 'start': 5358.3, 'weight': 2, 'content': [{'end': 5363.023, 'text': 'Similarly, we have created a bar plot and we will assign a color to it.', 'start': 5358.3, 'duration': 4.723}, {'end': 5367.026, 'text': 'This will be the command and I will have to use the fill attribute.', 'start': 5363.043, 'duration': 3.983}, {'end': 5375.433, 'text': 'With the fill attribute, I will set the color and it will be pale green 4.', 'start': 5367.086, 'duration': 8.347}, {'end': 5377.014, 'text': 'As you can see, I have set the color here.', 'start': 5375.433, 'duration': 1.581}, {'end': 5381.078, 'text': 'You can use fill as an attribute or as an aesthetic.', 'start': 5377.074, 'duration': 4.004}, {'end': 5388.684, 'text': 'Suppose I want to determine the color with a column, I will remove the fill attribute from here.', 'start': 5381.158, 'duration': 7.526}], 'summary': 'Creating a bar plot with the color set as pale green 4 using the fill attribute.', 'duration': 30.384, 'max_score': 5358.3, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI5358300.jpg'}, {'end': 5444.472, 'src': 'embed', 'start': 5417.426, 'weight': 0, 'content': [{'end': 5423.754, 'text': 'So here we have 5 types of cuts, Fair, Good, Very Good, Premium and Ideal and all these have different colours.', 'start': 5417.426, 'duration': 6.328}, {'end': 5426.477, 'text': 'So this is our bar plot.', 'start': 5423.774, 'duration': 2.703}, {'end': 5428.92, 'text': 'Now we will make a scatter plot.', 'start': 5426.497, 'duration': 2.423}, {'end': 5433.006, 'text': 'Scatter plot is used to find out the relationship between two numeric variables.', 'start': 5428.941, 'duration': 4.065}, {'end': 5440.431, 'text': 'So here we have the carrot column and the price column.', 'start': 5436.57, 'duration': 3.861}, {'end': 5441.991, 'text': 'So these are the numeric columns.', 'start': 5440.471, 'duration': 1.52}, {'end': 5444.472, 'text': 'So I will make a scatter plot between these two.', 'start': 5442.011, 'duration': 2.461}], 'summary': 'The presentation covers 5 types of cuts and demonstrates creating bar and scatter plots for analyzing diamond data.', 'duration': 27.046, 'max_score': 5417.426, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI5417426.jpg'}, {'end': 5573.638, 'src': 'embed', 'start': 5546.317, 'weight': 1, 'content': [{'end': 5551.061, 'text': 'On the x-axis, we have a carrot column and on the y-axis, we have a price column.', 'start': 5546.317, 'duration': 4.744}, {'end': 5554.443, 'text': 'We have seen that as the carrot value increases, the price also increases.', 'start': 5551.101, 'duration': 3.342}, {'end': 5560.248, 'text': 'And the colors you see here are telling us that the majority of the diamonds are of the ideal type.', 'start': 5554.784, 'duration': 5.464}, {'end': 5562.99, 'text': 'And these are of the fair type.', 'start': 5560.728, 'duration': 2.262}, {'end': 5566.673, 'text': 'So, it seems that the price of the fair type is high.', 'start': 5563.03, 'duration': 3.643}, {'end': 5569.595, 'text': 'And the carrot value of the fair type will also be high.', 'start': 5567.273, 'duration': 2.322}, {'end': 5571.477, 'text': 'This is an interesting observation.', 'start': 5569.735, 'duration': 1.742}, {'end': 5573.638, 'text': 'Again, the yellow ones are very varied.', 'start': 5571.497, 'duration': 2.141}], 'summary': 'Carrot value affects price, majority diamonds ideal type, fair type price and carrot value high.', 'duration': 27.321, 'max_score': 5546.317, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI5546317.jpg'}], 'start': 5159.341, 'title': 'Data visualization techniques', 'summary': 'Discusses techniques for visualizing data such as histogram, barplot, scatter plot, and box plot, using a diamond dataset to analyze price, distribution, and relationships, demonstrating insights including carat-price relationship and diamond cut impact.', 'chapters': [{'end': 5357.119, 'start': 5159.341, 'title': 'Data visualization with histogram and barplot', 'summary': 'Discusses data visualization techniques using histogram and barplot to analyze the price and distribution of diamonds, demonstrating how to assign colors and understand the difference between histogram and barplot.', 'duration': 197.778, 'highlights': ['The chapter demonstrates how to assign colors to a histogram using fill and call attributes, with green as the fill color and orange as the boundary color, providing visual representation of the data distribution and price analysis.', 'The chapter also highlights the implementation of barplot to represent categorical values, showcasing the distribution of diamond categories such as Fair, Good, Very Good, Premium, and Ideal, with more than 20,000 ideal types of diamonds and very few fair types, indicating a higher quality in the dataset.']}, {'end': 5718.294, 'start': 5358.3, 'title': 'Data visualization techniques', 'summary': 'Explains how to create bar plots, scatter plots, and box plots to visualize relationships and variations in a dataset, using examples from a diamond dataset, including insights such as the relationship between carat and price, the impact of diamond cut on price, and the variation of carat values across different clarity types.', 'duration': 359.994, 'highlights': ['The chapter demonstrates creating bar plots to visualize the impact of diamond cut on color, with 5 types of cuts (Fair, Good, Very Good, Premium, and Ideal) having different colors, providing insights into the relationship between cut and color (pale green 4) and the distribution of cuts (Fair, Good, Very Good, Premium, and Ideal) with different colors.', 'It illustrates the creation of scatter plots to understand the relationship between carat and price, showcasing that as carat value increases, the price also increases, and further assigning colors based on the cut column to observe the distribution of diamonds with different cuts and their corresponding carat and price values.', 'The chapter also covers the generation of box plots to analyze the variation between a continuous value (carat) and a categorical value (clarity), revealing insights such as the median carat values for different clarities, the presence of outliers, and the likelihood of L1 clarity having a higher maximum carat value.', 'The chapter provides practical examples and explanations of using aesthetics, attributes, and geometries to create effective visualizations, while also offering valuable insights into the relationships and variations within the diamond dataset.']}], 'duration': 558.953, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI5159341.jpg', 'highlights': ['The chapter demonstrates creating bar plots to visualize the impact of diamond cut on color, providing insights into the relationship between cut and color.', 'It illustrates the creation of scatter plots to understand the relationship between carat and price, showcasing that as carat value increases, the price also increases.', 'The chapter provides practical examples and explanations of using aesthetics, attributes, and geometries to create effective visualizations.']}, {'end': 6338.131, 'segs': [{'end': 5769.356, 'src': 'embed', 'start': 5718.314, 'weight': 2, 'content': [{'end': 5722.056, 'text': 'We have made a boxplot, now I will assign it as fill.', 'start': 5718.314, 'duration': 3.742}, {'end': 5728.699, 'text': 'So, fill will be determined by clarity again.', 'start': 5724.017, 'duration': 4.682}, {'end': 5733.301, 'text': 'Every type of boxplot will have a different color of different types of clarity.', 'start': 5728.719, 'duration': 4.582}, {'end': 5734.861, 'text': "So, I'll go back to the Aesthetic layer.", 'start': 5733.321, 'duration': 1.54}, {'end': 5740.583, 'text': "I'll use Fill Aesthetic and I'll map the clarity column in it.", 'start': 5735.281, 'duration': 5.302}, {'end': 5741.384, 'text': "Let's see the result.", 'start': 5740.603, 'duration': 0.781}, {'end': 5748.727, 'text': 'As you can see, we have different types of clarity and different types of clarity have different colors.', 'start': 5741.544, 'duration': 7.183}, {'end': 5755.472, 'text': 'So, this is our box plot and finally we will work with facetting.', 'start': 5750.831, 'duration': 4.641}, {'end': 5765.255, 'text': 'So, if we have very complicated data, meaning if we have to plot many things in one visualization, it will be difficult to understand.', 'start': 5755.492, 'duration': 9.763}, {'end': 5766.916, 'text': 'So, here we use facetting.', 'start': 5765.575, 'duration': 1.341}, {'end': 5769.356, 'text': 'Facetting means different groups.', 'start': 5767.556, 'duration': 1.8}], 'summary': 'Utilized boxplot with fill assigned by clarity and facetting for visualizing different groups.', 'duration': 51.042, 'max_score': 5718.314, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI5718314.jpg'}, {'end': 6014.587, 'src': 'embed', 'start': 5987.47, 'weight': 6, 'content': [{'end': 5993.176, 'text': "So, first of all, let's see how many rows and columns are there in this data frame.", 'start': 5987.47, 'duration': 5.706}, {'end': 5998.901, 'text': 'So, if I want to see the number of rows in this entire data frame, then I will have to use the nRow method.', 'start': 5993.236, 'duration': 5.665}, {'end': 6001.023, 'text': 'I will pass this data frame in the nRow method.', 'start': 5999.042, 'duration': 1.981}, {'end': 6007.465, 'text': 'It is telling me that there are 801 rows in this data frame.', 'start': 6001.584, 'duration': 5.881}, {'end': 6010.826, 'text': 'It means that there are 801 different Pokémon in this data frame.', 'start': 6007.485, 'duration': 3.341}, {'end': 6014.587, 'text': 'Similarly, if I want to see the number of columns, I will use the ncall attribute.', 'start': 6010.846, 'duration': 3.741}], 'summary': 'The data frame contains 801 rows and columns and represents 801 different pokémon.', 'duration': 27.117, 'max_score': 5987.47, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI5987470.jpg'}, {'end': 6095.181, 'src': 'embed', 'start': 6070.153, 'weight': 0, 'content': [{'end': 6082.397, 'text': 'I am passing this legendary column and it is telling me that 731 pokemons, which is total 801 pokemons out of that,', 'start': 6070.153, 'duration': 12.244}, {'end': 6087.699, 'text': '731 pokemons are not legendary and only 70 pokemons are legendary.', 'start': 6082.397, 'duration': 5.302}, {'end': 6095.181, 'text': 'in this way, if I want to know which pokemon or how many pokemon belong to which generation, I can use table method again.', 'start': 6087.699, 'duration': 7.482}], 'summary': 'Out of 801 pokemons, 731 are non-legendary and 70 are legendary.', 'duration': 25.028, 'max_score': 6070.153, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI6070153.jpg'}, {'end': 6189.365, 'src': 'embed', 'start': 6160.04, 'weight': 1, 'content': [{'end': 6161.841, 'text': 'Again, we have 105 Normal-Type Pokemons.', 'start': 6160.04, 'duration': 1.801}, {'end': 6174.011, 'text': 'So, if we look at this data, This shows that the most water-type Pokemon are present in this data frame.', 'start': 6161.861, 'duration': 12.15}, {'end': 6176.053, 'text': 'So, there are 114 water-type Pokemon present.', 'start': 6174.051, 'duration': 2.002}, {'end': 6179.196, 'text': 'And the least number of flying-type Pokemon are present here.', 'start': 6176.113, 'duration': 3.083}, {'end': 6181.578, 'text': 'So, we have only 3 flying-type Pokemon.', 'start': 6179.236, 'duration': 2.342}, {'end': 6183.7, 'text': 'So, these are some interesting observations.', 'start': 6181.598, 'duration': 2.102}, {'end': 6185.942, 'text': 'So, these are the categorical columns.', 'start': 6183.74, 'duration': 2.202}, {'end': 6187.984, 'text': 'Now, we will see the numeric columns.', 'start': 6185.982, 'duration': 2.002}, {'end': 6189.365, 'text': 'So, we had the HP column.', 'start': 6188.004, 'duration': 1.361}], 'summary': '105 normal-type, 114 water-type, and 3 flying-type pokemons observed.', 'duration': 29.325, 'max_score': 6160.04, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI6160040.jpg'}, {'end': 6260.093, 'src': 'embed', 'start': 6210.745, 'weight': 3, 'content': [{'end': 6215.429, 'text': 'and it is the lowest HP when compared to all the Pokemons.', 'start': 6210.745, 'duration': 4.684}, {'end': 6221.473, 'text': 'Similarly, if I want to find the maximum value, I will use the max function and I will pass the Pokemon$HP in it.', 'start': 6215.829, 'duration': 5.644}, {'end': 6223.154, 'text': 'And it is telling me that the maximum HP value is 255.', 'start': 6221.513, 'duration': 1.641}, {'end': 6226.356, 'text': 'So, there is a Pokemon whose HP is 255 and this is the maximum HP.', 'start': 6223.154, 'duration': 3.202}, {'end': 6238.363, 'text': 'Similarly, if I want to find minimum and maximum speed, I can do the same thing.', 'start': 6234.722, 'duration': 3.641}, {'end': 6240.004, 'text': 'Again, I will use MIN here.', 'start': 6238.383, 'duration': 1.621}, {'end': 6242.005, 'text': 'This will give me the minimum value.', 'start': 6240.024, 'duration': 1.981}, {'end': 6244.325, 'text': 'Here, I will pass Pokémon Dollar Speed.', 'start': 6242.045, 'duration': 2.28}, {'end': 6248.367, 'text': 'And it is telling me that the minimum speed value of Pokémon is 5.', 'start': 6244.465, 'duration': 3.902}, {'end': 6252.368, 'text': 'Again, if I want to find the maximum speed value, I will write MAX here.', 'start': 6248.367, 'duration': 4.001}, {'end': 6253.889, 'text': 'Again, I will pass Pokémon here.', 'start': 6252.428, 'duration': 1.461}, {'end': 6260.093, 'text': 'And here it is telling me that the maximum speed of a Pokemon is 180.', 'start': 6255.109, 'duration': 4.984}], 'summary': 'The lowest hp of a pokemon is 1, and the maximum hp is 255. the minimum speed is 5, and the maximum speed is 180.', 'duration': 49.348, 'max_score': 6210.745, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI6210745.jpg'}, {'end': 6312.988, 'src': 'embed', 'start': 6286.471, 'weight': 7, 'content': [{'end': 6292.335, 'text': 'So, if I want to go inside the Abilities column, if there are any null values present or not.', 'start': 6286.471, 'duration': 5.864}, {'end': 6295.157, 'text': 'So, as you can see, I am getting a complete false here.', 'start': 6292.355, 'duration': 2.802}, {'end': 6299.82, 'text': 'So, if it is true, it means that na is present there.', 'start': 6295.177, 'duration': 4.643}, {'end': 6301.441, 'text': 'It means that it is a null value.', 'start': 6299.84, 'duration': 1.601}, {'end': 6304.463, 'text': 'If it is false, it means that null value is not present.', 'start': 6301.461, 'duration': 3.002}, {'end': 6311.627, 'text': 'then we will get to know that there is no null value in all 801 records.', 'start': 6304.963, 'duration': 6.664}, {'end': 6312.988, 'text': 'So this is good.', 'start': 6311.907, 'duration': 1.081}], 'summary': 'Checked for null values in abilities column, found none in 801 records', 'duration': 26.517, 'max_score': 6286.471, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI6286471.jpg'}], 'start': 5718.314, 'title': 'Boxplot aesthetics and facetting', 'summary': 'Demonstrates the use of fill aesthetics for boxplot colors based on clarity, and facetting for creating visualization groups, with an example based on the cut column. additionally, it covers data visualization and analysis of pokemon dataset, including 801 rows and 41 columns, categorical and numeric column analysis, identification of null values, and key insights on pokemon types distribution and hp and speed values range.', 'chapters': [{'end': 5818.79, 'start': 5718.314, 'title': 'Boxplot aesthetics and facetting', 'summary': 'Demonstrates how to use fill aesthetics to assign different colors to a boxplot based on clarity, and how to use facetting to create different groups in visualizations, with an example of facetting based on the cut column of a dataset.', 'duration': 100.476, 'highlights': ['The chapter shows how to use fill aesthetics to assign different colors to a boxplot based on clarity, enhancing visualization clarity and differentiation.', 'It also explains the concept of facetting, demonstrating how to create different groups in visualizations, with an example of facetting based on the cut column of a dataset to visualize data in distinct groups.']}, {'end': 6338.131, 'start': 5819.23, 'title': 'Data visualization and analysis of pokemon dataset', 'summary': 'Covers data visualization of pokemon dataset, including 801 rows and 41 columns, analysis of categorical and numeric columns, and identification of null values, with key insights such as the distribution of pokemon types and the range of hp and speed values.', 'duration': 518.901, 'highlights': ['801 rows and 41 columns present in the Pokemon dataset, providing a comprehensive dataset for analysis.', "The 'Legendary' column analysis reveals 731 non-legendary and 70 legendary Pokemon, highlighting the rarity of legendary Pokemon in the dataset.", 'Water-type Pokemon are the most prevalent with 114 instances, while flying-type Pokemon are the least with only 3 instances, offering valuable insights into the distribution of Pokemon types.', 'The minimum HP value is 1, and the maximum HP value is 255, showcasing the range of HP values among the Pokemon.', 'The minimum speed value is 5, and the maximum speed value is 180, indicating the diversity in speed attributes among the Pokemon.', 'The analysis confirms the absence of null values in the dataset, ensuring data integrity and reliability for further analysis.']}], 'duration': 619.817, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI5718314.jpg', 'highlights': ["The 'Legendary' column analysis reveals 731 non-legendary and 70 legendary Pokemon, highlighting the rarity of legendary Pokemon in the dataset.", 'Water-type Pokemon are the most prevalent with 114 instances, while flying-type Pokemon are the least with only 3 instances, offering valuable insights into the distribution of Pokemon types.', 'The chapter shows how to use fill aesthetics to assign different colors to a boxplot based on clarity, enhancing visualization clarity and differentiation.', 'The minimum HP value is 1, and the maximum HP value is 255, showcasing the range of HP values among the Pokemon.', 'The minimum speed value is 5, and the maximum speed value is 180, indicating the diversity in speed attributes among the Pokemon.', 'It also explains the concept of facetting, demonstrating how to create different groups in visualizations, with an example of facetting based on the cut column of a dataset to visualize data in distinct groups.', '801 rows and 41 columns present in the Pokemon dataset, providing a comprehensive dataset for analysis.', 'The analysis confirms the absence of null values in the dataset, ensuring data integrity and reliability for further analysis.']}, {'end': 7367.345, 'segs': [{'end': 6473.001, 'src': 'embed', 'start': 6445.472, 'weight': 0, 'content': [{'end': 6449.274, 'text': 'So, I execute this command here, call names.', 'start': 6445.472, 'duration': 3.802}, {'end': 6452.634, 'text': 'If I write Pokemon, it will give us a list of columns.', 'start': 6450.433, 'duration': 2.201}, {'end': 6455.075, 'text': 'Then I will use parenthesis here.', 'start': 6452.674, 'duration': 2.401}, {'end': 6456.975, 'text': 'In parenthesis, I will again call names.', 'start': 6455.155, 'duration': 1.82}, {'end': 6461.757, 'text': 'And which call names do I want? I want the call names of the Pokemon DataFrame.', 'start': 6457.035, 'duration': 4.722}, {'end': 6466.179, 'text': 'And here I am checking if the column name is type 1.', 'start': 6461.897, 'duration': 4.282}, {'end': 6471.04, 'text': 'And where true is also found, I will change it.', 'start': 6466.179, 'duration': 4.861}, {'end': 6473.001, 'text': 'And this will be the primary type.', 'start': 6471.24, 'duration': 1.761}], 'summary': 'Executing command to extract and modify column data in pokemon dataframe.', 'duration': 27.529, 'max_score': 6445.472, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI6445472.jpg'}, {'end': 6717.885, 'src': 'embed', 'start': 6677.461, 'weight': 1, 'content': [{'end': 6685.542, 'text': 'so here I I have to write a condition here.', 'start': 6677.461, 'duration': 8.081}, {'end': 6689.583, 'text': 'Primary type is equal to grass.', 'start': 6686.002, 'duration': 3.581}, {'end': 6693.364, 'text': 'And I will store it in grass pokemon.', 'start': 6689.683, 'duration': 3.681}, {'end': 6696.324, 'text': 'Now I will see the result.', 'start': 6693.424, 'duration': 2.9}, {'end': 6698.645, 'text': 'View of grass pokemon.', 'start': 6696.384, 'duration': 2.261}, {'end': 6702.326, 'text': 'Now I will check the primary type column.', 'start': 6699.805, 'duration': 2.521}, {'end': 6710.419, 'text': 'And as you can see, we have 78 entries, which means 78 grass-type Pokemons.', 'start': 6704.594, 'duration': 5.825}, {'end': 6715.663, 'text': 'So out of the 801 Pokemons, we have 78 grass-type Pokemons.', 'start': 6710.759, 'duration': 4.904}, {'end': 6717.885, 'text': 'So we just implemented this simple command.', 'start': 6715.943, 'duration': 1.942}], 'summary': 'There are 78 grass-type pokemons out of 801 entries.', 'duration': 40.424, 'max_score': 6677.461, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI6677461.jpg'}, {'end': 6800.952, 'src': 'embed', 'start': 6769.198, 'weight': 2, 'content': [{'end': 6774.001, 'text': 'I will write Grass Pokemon, Dollar, and I will set the speed here.', 'start': 6769.198, 'duration': 4.803}, {'end': 6778.505, 'text': 'And it is telling me that the maximum speed of a Grass-type Pokemon is 145.', 'start': 6774.061, 'duration': 4.444}, {'end': 6779.205, 'text': 'So I will write 145 here.', 'start': 6778.505, 'duration': 0.7}, {'end': 6790.17, 'text': 'In this way, I have found out the minimum speed value of Grass Pokemon and the maximum speed value of Grass Pokemon.', 'start': 6783.448, 'duration': 6.722}, {'end': 6800.952, 'text': 'Similarly, if I want to know the average special attack and special defense of Grass Pokemon, then I will have to use the mean.', 'start': 6790.35, 'duration': 10.602}], 'summary': 'Max speed of grass pokemon is 145, mean for special attack & defense can be found similarly.', 'duration': 31.754, 'max_score': 6769.198, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI6769198.jpg'}, {'end': 6845.485, 'src': 'embed', 'start': 6817.767, 'weight': 3, 'content': [{'end': 6826.576, 'text': 'Here I will use the mean method and again it will be grass Pokemon and the column I am selecting will be SP defense.', 'start': 6817.767, 'duration': 8.809}, {'end': 6830.44, 'text': 'And the average special defense we are getting is 69 of grass Pokemon.', 'start': 6827.337, 'duration': 3.103}, {'end': 6834.002, 'text': 'Here I will write 69.', 'start': 6830.48, 'duration': 3.522}, {'end': 6837.163, 'text': 'So, this is the basic inference of Grass Pokemons.', 'start': 6834.002, 'duration': 3.161}, {'end': 6840.223, 'text': 'Now, I will visualize some stats of these Grass Pokemons.', 'start': 6837.203, 'duration': 3.02}, {'end': 6845.485, 'text': 'And if I want to visualize, I will have to load the ggplot2 library.', 'start': 6840.283, 'duration': 5.202}], 'summary': 'The average special defense of grass pokemon is 69.', 'duration': 27.718, 'max_score': 6817.767, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI6817767.jpg'}, {'end': 7052.283, 'src': 'embed', 'start': 7021.374, 'weight': 4, 'content': [{'end': 7024.215, 'text': 'And we will have 3 Pokemon whose height is 2 meter.', 'start': 7021.374, 'duration': 2.841}, {'end': 7027.536, 'text': 'And one Pokemon whose height is more than 3 meter.', 'start': 7024.315, 'duration': 3.221}, {'end': 7029.956, 'text': 'So this is about the height.', 'start': 7027.556, 'duration': 2.4}, {'end': 7031.997, 'text': 'After that we will analyze the weight.', 'start': 7029.976, 'duration': 2.021}, {'end': 7038.098, 'text': 'If we want to analyze the weight then we will have to map the weight KG column on X-Aesthetic.', 'start': 7032.117, 'duration': 5.981}, {'end': 7039.378, 'text': 'So I will write GGplot here.', 'start': 7038.118, 'duration': 1.26}, {'end': 7043.079, 'text': 'And I will map the grass Pokemon on the data layer.', 'start': 7039.478, 'duration': 3.601}, {'end': 7052.283, 'text': 'Then I will get the Aesthetic layer and I will map the weight Kg column on it.', 'start': 7045.88, 'duration': 6.403}], 'summary': 'Analyzing pokemon data: 3 are 2m tall, 1 over 3m, then analyzing weight using ggplot.', 'duration': 30.909, 'max_score': 7021.374, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI7021374.jpg'}, {'end': 7173.522, 'src': 'embed', 'start': 7144.259, 'weight': 5, 'content': [{'end': 7149.634, 'text': 'It is telling us that mostly grass-type Pokemon are not legendary.', 'start': 7144.259, 'duration': 5.375}, {'end': 7157.753, 'text': 'So we might have 65 other Pokemon which are not legendary, only 2-3 Pokemon which are legendary in grass type.', 'start': 7150.168, 'duration': 7.585}, {'end': 7161.175, 'text': 'So this has been an interesting observation about grass type Pokemon.', 'start': 7157.773, 'duration': 3.402}, {'end': 7168.339, 'text': 'Similarly, we have extracted grass type Pokemon, similarly we will extract fire type Pokemon.', 'start': 7161.195, 'duration': 7.144}, {'end': 7173.522, 'text': 'So to extract fire type Pokemon, we will have to use the filter method again.', 'start': 7168.359, 'duration': 5.163}], 'summary': 'Most grass-type pokemon are not legendary, with only 2-3 being legendary. this observation applies to other types as well.', 'duration': 29.263, 'max_score': 7144.259, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI7144259.jpg'}, {'end': 7248.968, 'src': 'embed', 'start': 7193.138, 'weight': 6, 'content': [{'end': 7196.26, 'text': 'Now I will see it view of fire pokemon.', 'start': 7193.138, 'duration': 3.122}, {'end': 7197.882, 'text': "And let's see what is the result here.", 'start': 7196.44, 'duration': 1.442}, {'end': 7203.778, 'text': 'As you can see, we have 52 entries.', 'start': 7200.616, 'duration': 3.162}, {'end': 7210.381, 'text': 'If you scroll through all the entries, you will see that all the primary types are fire type.', 'start': 7203.858, 'duration': 6.523}, {'end': 7213.783, 'text': 'So, we have extracted all the fire type Pokemon.', 'start': 7210.421, 'duration': 3.362}, {'end': 7222.527, 'text': 'Now, just like we did with the grass type Pokemon, we have to know the minimum speed, maximum speed,', 'start': 7213.843, 'duration': 8.684}, {'end': 7225.369, 'text': 'average special attack and average special defense of the fire type Pokemon.', 'start': 7222.527, 'duration': 2.842}, {'end': 7229.692, 'text': 'So I will use the min function and pass fire pokemon in it.', 'start': 7225.809, 'duration': 3.883}, {'end': 7232.014, 'text': 'And I will set the speed here.', 'start': 7229.792, 'duration': 2.222}, {'end': 7237.679, 'text': 'Because I want to know the minimum value of speed of fire pokemon.', 'start': 7232.034, 'duration': 5.645}, {'end': 7239.08, 'text': 'And it is giving me 20.', 'start': 7237.899, 'duration': 1.181}, {'end': 7242.763, 'text': 'Then I want to know the maximum speed value.', 'start': 7239.08, 'duration': 3.683}, {'end': 7245.505, 'text': 'Here I will use max and pass fire pokemon in it.', 'start': 7242.783, 'duration': 2.722}, {'end': 7248.968, 'text': 'And I will set the speed in the column.', 'start': 7245.685, 'duration': 3.283}], 'summary': 'Extracted 52 fire type pokemon with minimum speed of 20 and maximum speed value.', 'duration': 55.83, 'max_score': 7193.138, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI7193138.jpg'}, {'end': 7305.843, 'src': 'embed', 'start': 7273.91, 'weight': 8, 'content': [{'end': 7278.113, 'text': 'Similarly I will have to know the average special defense of a fire pokemon.', 'start': 7273.91, 'duration': 4.203}, {'end': 7283.277, 'text': 'Here I will write mean of fire pokemon dollar SP defense.', 'start': 7278.273, 'duration': 5.004}, {'end': 7289.184, 'text': 'So, the average Special Defense of Fire Pokemon is 71.', 'start': 7284.497, 'duration': 4.687}, {'end': 7292.769, 'text': "So, I'll add a comment of 71.", 'start': 7289.184, 'duration': 3.585}, {'end': 7295.814, 'text': 'So, this is the basic analysis.', 'start': 7292.769, 'duration': 3.045}, {'end': 7299.499, 'text': "Now, we'll go and visualize these Fire-type Pokemon.", 'start': 7295.834, 'duration': 3.665}, {'end': 7305.843, 'text': 'So we have to use GG plot again and this time I have to know how many generations these fire type pokemon belong to.', 'start': 7300.879, 'duration': 4.964}], 'summary': 'Average special defense of fire pokemon is 71, visualizing generations using gg plot.', 'duration': 31.933, 'max_score': 7273.91, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI7273910.jpg'}], 'start': 6338.151, 'title': 'Data frame manipulation and analysis', 'summary': 'Covers renaming, checking, and changing column names in a data frame, filtering pokemon based on their types, analyzing 78 grass-type pokemons out of 801, visualizing attributes, and identifying 52 fire-type pokemons with various statistics.', 'chapters': [{'end': 6677.461, 'start': 6338.151, 'title': 'Data frame manipulation and analysis', 'summary': 'Covers renaming columns in a data frame, checking and changing column names, and filtering pokemon based on their types using the dplyr package.', 'duration': 339.31, 'highlights': ['The chapter covers renaming columns in a data frame, checking and changing column names, and filtering Pokemon based on their types using the dplyr package. The chapter primarily focuses on renaming and changing column names in a data frame, as well as filtering Pokémon based on their types using the dplyr package.', 'Grass-type Pokemon are filtered from the data frame using the dplyr filter method from the dplyr package. The chapter demonstrates the use of the dplyr filter method to extract grass-type Pokémon from the data frame.', 'The chapter also mentions the use of the dplyr library for data manipulation. The dplyr library is mentioned for data manipulation, specifically for filtering Pokémon based on their types.']}, {'end': 7144.199, 'start': 6677.461, 'title': 'Analyzing grass-type pokemon', 'summary': 'Demonstrates the analysis of grass-type pokemon, identifying 78 out of 801 pokemons as grass-type, finding their minimum and maximum speed as 10 and 145 respectively, average special attack and special defense as 74 and 69, visualizing their hp, height, weight, and distinguishing legendary and non-legendary pokemons.', 'duration': 466.738, 'highlights': ['Identified 78 out of 801 Pokemons as Grass-type The chapter demonstrates the analysis of Grass-type Pokemon, identifying 78 out of 801 Pokemons as Grass-type.', 'Found minimum and maximum speed of Grass-type Pokemon as 10 and 145 respectively Discovered the minimum and maximum speed of Grass-type Pokemon as 10 and 145 respectively.', 'Obtained average special attack and defense of Grass-type Pokemon as 74 and 69 Calculated the average special attack and defense of Grass-type Pokemon as 74 and 69.', 'Visualized HP, height, and weight of Grass-type Pokemon Visualized the distribution of HP, height, and weight of Grass-type Pokemon using histograms.', 'Differentiated between legendary and non-legendary Grass-type Pokemon Distinguished between legendary and non-legendary Grass-type Pokemons using a bar plot.']}, {'end': 7367.345, 'start': 7144.259, 'title': 'Analyzing and visualizing fire-type pokemon', 'summary': 'Demonstrates the extraction and analysis of fire-type pokemon data, revealing 52 fire-type pokemon with a minimum speed of 20, maximum speed of 126, average special attack of 87, and average special defense of 71, leading to the visualization of the distribution of fire-type pokemon across 7 generations using ggplot.', 'duration': 223.086, 'highlights': ['The maximum speed of fire-type Pokemon is 126, as determined using the max function.', 'The average special attack of fire-type Pokemon is 87, calculated using the mean function.', 'The average special defense of fire-type Pokemon is 71, based on analysis using the mean function.', 'A total of 52 fire-type Pokemon were extracted using the filter method.', 'The minimum speed of fire-type Pokemon is 20, as determined using the min function.']}], 'duration': 1029.194, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI6338151.jpg', 'highlights': ['The chapter covers renaming columns, checking and changing column names, and filtering Pokemon based on their types using the dplyr package.', 'Identified 78 out of 801 Pokemons as Grass-type.', 'Found minimum and maximum speed of Grass-type Pokemon as 10 and 145 respectively.', 'Obtained average special attack and defense of Grass-type Pokemon as 74 and 69.', 'Visualized HP, height, and weight of Grass-type Pokemon.', 'Differentiated between legendary and non-legendary Grass-type Pokemon.', 'A total of 52 fire-type Pokemon were extracted using the filter method.', 'The maximum speed of fire-type Pokemon is 126.', 'The average special attack of fire-type Pokemon is 87.', 'The average special defense of fire-type Pokemon is 71.', 'The minimum speed of fire-type Pokemon is 20.']}, {'end': 8213.892, 'segs': [{'end': 7426.342, 'src': 'embed', 'start': 7398.738, 'weight': 0, 'content': [{'end': 7402.541, 'text': 'We have only 5 Pokemon that belong to Generation 4..', 'start': 7398.738, 'duration': 3.803}, {'end': 7411.55, 'text': 'Similarly, we have only 5 Fire-type Pokemon that belong to Generation 7.', 'start': 7402.541, 'duration': 9.009}, {'end': 7413.291, 'text': 'We have done such an inference of this thing.', 'start': 7411.55, 'duration': 1.741}, {'end': 7417.355, 'text': "Now let's know how the secondary type of these fire type pokemons is varying.", 'start': 7413.311, 'duration': 4.044}, {'end': 7421.298, 'text': 'So this time I will map the secondary type on the X aesthetic.', 'start': 7417.415, 'duration': 3.883}, {'end': 7426.342, 'text': 'And again I will have to use the Jom bar here.', 'start': 7421.378, 'duration': 4.964}], 'summary': 'There are 5 generation 4 and 5 generation 7 fire-type pokemon. analyzing the variation of secondary types using x aesthetic and jom bar.', 'duration': 27.604, 'max_score': 7398.738, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI7398738.jpg'}, {'end': 7528.499, 'src': 'embed', 'start': 7496.96, 'weight': 2, 'content': [{'end': 7500.721, 'text': 'Similarly, we have 5-6 Fire Pokemon whose secondary type is Flying.', 'start': 7496.96, 'duration': 3.761}, {'end': 7506.103, 'text': 'If we take this, we will have only one Fire Pokemon whose secondary type is Water.', 'start': 7500.761, 'duration': 5.342}, {'end': 7511.144, 'text': 'Similarly, if we take this, we will have only one Fire Pokemon whose secondary type is Steel.', 'start': 7506.463, 'duration': 4.681}, {'end': 7522.433, 'text': "So we have analyzed the secondary type, now let's see how the special attack and special defense of fire type pokemons are changing.", 'start': 7512.944, 'duration': 9.489}, {'end': 7528.499, 'text': 'Because these are both numeric columns, I will have to make a scatter plot and for that I will use the germ point method.', 'start': 7522.473, 'duration': 6.026}], 'summary': 'Analysis of fire pokemon: 5-6 with flying type, only 1 with water, and 1 with steel. assessing special attack and defense using scatter plot and germ point method.', 'duration': 31.539, 'max_score': 7496.96, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI7496960.jpg'}, {'end': 7593.369, 'src': 'embed', 'start': 7550.174, 'weight': 1, 'content': [{'end': 7552.715, 'text': 'And on y axis I will use sp defense.', 'start': 7550.174, 'duration': 2.541}, {'end': 7556.057, 'text': 'So I will write sp defense here.', 'start': 7552.915, 'duration': 3.142}, {'end': 7557.298, 'text': 'So I have mapped these two.', 'start': 7556.077, 'duration': 1.221}, {'end': 7560.179, 'text': 'After this I will have to use jom point.', 'start': 7557.418, 'duration': 2.761}, {'end': 7563.241, 'text': "Let's set its color to Coral.", 'start': 7562.12, 'duration': 1.121}, {'end': 7564.401, 'text': "Let's zoom in again.", 'start': 7563.261, 'duration': 1.14}, {'end': 7569.544, 'text': 'We have a special attack on the X-axis and a special defense on the Y-axis.', 'start': 7564.461, 'duration': 5.083}, {'end': 7574.846, 'text': 'It is telling us that as the value of the Fire-type Pokémon is increasing, so is the value of its special defense.', 'start': 7569.604, 'duration': 5.242}, {'end': 7576.127, 'text': 'This is an interesting observation.', 'start': 7574.866, 'duration': 1.261}, {'end': 7593.369, 'text': 'Similarly, I want to know if there is any interesting relationship between height and weight.', 'start': 7585.564, 'duration': 7.805}], 'summary': 'Plotted special attack vs. special defense, noticing increasing values for fire-type pokémon. considering relationship between height and weight.', 'duration': 43.195, 'max_score': 7550.174, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI7550174.jpg'}, {'end': 7886.486, 'src': 'embed', 'start': 7856.633, 'weight': 3, 'content': [{'end': 7866.439, 'text': 'This tells me that I have around 80 Pokemon against Poison as Rating 1 and I have around 5-10 Pokemon against Poison as Rating 2.', 'start': 7856.633, 'duration': 9.806}, {'end': 7873.204, 'text': 'So it seems that water type Pokemon will perform better against poison type Pokemon when compared to ice type Pokemon.', 'start': 7866.439, 'duration': 6.765}, {'end': 7882.902, 'text': "Similarly, let's see how to perform against water-type Pokemon and grass-type Pokemon.", 'start': 7879.139, 'duration': 3.763}, {'end': 7886.486, 'text': 'Again, the command will be ggplot.', 'start': 7882.923, 'duration': 3.563}], 'summary': 'Around 80 pokemon have rating 1 against poison, 5-10 have rating 2. water types perform better against poison than ice types.', 'duration': 29.853, 'max_score': 7856.633, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI7856633.jpg'}, {'end': 8021.327, 'src': 'embed', 'start': 7993.957, 'weight': 4, 'content': [{'end': 7999.339, 'text': 'If I want to find out the average Special Attack and Special Defense of Psychic Pokemon,', 'start': 7993.957, 'duration': 5.382}, {'end': 8004.801, 'text': 'I will have to use the mean method and pass these columns in the mean method.', 'start': 7999.339, 'duration': 5.462}, {'end': 8010.283, 'text': 'The average Special Attack of Psychic Pokemon is 92 and the average Special Defense of Psychic Pokemon is 85.', 'start': 8004.821, 'duration': 5.462}, {'end': 8016.405, 'text': 'It seems that when we compare Psychic Pokemon with Water, Grass or Fire, they are more powerful.', 'start': 8010.283, 'duration': 6.122}, {'end': 8021.327, 'text': 'Now we will go and visualize.', 'start': 8019.346, 'duration': 1.981}], 'summary': 'The average special attack of psychic pokemon is 92 and the average special defense is 85, making them more powerful than water, grass, or fire types.', 'duration': 27.37, 'max_score': 7993.957, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI7993957.jpg'}], 'start': 7368.911, 'title': 'Analyzing and visualizing pokemon data', 'summary': 'Explains the analysis of fire type pokemon with 12 belonging to generation 1, 5 to generation 4 and 7, and varying secondary types such as fighting and flying, and covers visual analysis of pokemon data using ggplot2, including scatter plots for sp attack vs sp defense, height vs weight, histograms for type matchups, and statistics and visualization for water, psychic, and grass type pokemon.', 'chapters': [{'end': 7522.433, 'start': 7368.911, 'title': 'Analyzing fire type pokemon', 'summary': 'Explains the analysis of fire type pokemon, with 12 belonging to generation 1, 5 to generation 4 and 7, and varying secondary types such as fighting and flying, with 5-6 pokemon for each, and the examination of special attack and special defense.', 'duration': 153.522, 'highlights': ['The chapter explains the analysis of fire type Pokemon, with 12 belonging to Generation 1, 5 to Generation 4 and 7, and varying secondary types such as fighting and flying, with 5-6 Pokemon for each.', 'The chapter highlights the examination of special attack and special defense of fire type pokemons, revealing the changing trends.']}, {'end': 8213.892, 'start': 7522.473, 'title': 'Visual analysis of pokemon data', 'summary': 'Covers the visual analysis of pokemon data using ggplot2, including scatter plots for sp attack vs sp defense, height vs weight, histograms for type matchups, and statistics and visualization for water, psychic, and grass type pokemon, where psychic type pokemon are observed to have higher average special attack and defense compared to other types.', 'duration': 691.419, 'highlights': ['The scatter plot for SP attack vs SP defense of Fire-type Pokemon shows a positive correlation, indicating that as the value of special attack increases, so does the value of special defense.', 'The average special attack and special defense of water Pokemon are found to be 74 and 71 respectively, with a maximum speed of 132.', 'Water type Pokemon are observed to perform better against poison type and grass type Pokemon compared to ice type Pokemon, as indicated by the rating distributions for type matchups.', 'Psychic Pokemon are found to have an average special attack of 92, average special defense of 85, and a broader range of base total values, indicating their higher power compared to other types.', "The visualization of psychic Pokemon's special defense by generation shows that 4th generation psychic Pokemon have the highest median special defense, while 5th generation psychic Pokemon have the lowest median special defense."]}], 'duration': 844.981, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SBhpLnPuNlI/pics/SBhpLnPuNlI7368911.jpg', 'highlights': ['The chapter explains the analysis of fire type Pokemon, with 12 belonging to Generation 1, 5 to Generation 4 and 7, and varying secondary types such as fighting and flying, with 5-6 Pokemon for each.', 'The scatter plot for SP attack vs SP defense of Fire-type Pokemon shows a positive correlation, indicating that as the value of special attack increases, so does the value of special defense.', 'The chapter highlights the examination of special attack and special defense of fire type pokemons, revealing the changing trends.', 'Water type Pokemon are observed to perform better against poison type and grass type Pokemon compared to ice type Pokemon, as indicated by the rating distributions for type matchups.', 'Psychic Pokemon are found to have an average special attack of 92, average special defense of 85, and a broader range of base total values, indicating their higher power compared to other types.']}], 'highlights': ['Learn r programming in 2 hours with this comprehensive tutorial covering installation, variables, data types, structures, functions, looping, data extraction, visualization techniques, boxplot aesthetics, faceting, data frame manipulation, and analysis, as well as in-depth analysis and visualization of pokemon data with insights on different types, generations, and statistics.', 'Great Learning has launched Great Learning Academy, offering over 80 free courses in domains like Data Science, Machine Learning, Artificial Intelligence, and Cloud Computing.', 'R is a language created by statisticians for statisticians, emphasizing its suitability for statistical analysis tasks.', 'The concept of variables is explained with the analogy of a basket or shopping bag, highlighting their role as a temporary storage space for values that can change over time.', 'The session agenda includes topics such as installing R and RStudio, learning R basics and data structures, working with inbuilt functions, flow control statements, user-defined functions, data manipulation, data visualization with ggplot2 package, and a case study on the pokemon dataset.', 'The chapter introduces the concept of variables and data types in R, covering themes, font size, examples of variables, types of data (numeric, character, logical, complex), dynamic data types, and operators (assignment, arithmetic, relational, logical) with examples.', "The chapter introduces the basics of logical operators, demonstrating the evaluation of 'false or false' resulting in false.", 'Demonstration of converting a character vector into a factor by setting levels alphabetically, enabling easy fitting of machine learning models.', 'The importance of data manipulation is demonstrated through the example of extracting employees with salary greater than 10 lakhs and age over 30 from a dataset of 1 million rows and 10,000 columns, showcasing the efficiency of using data manipulation operations with just one line of command.', 'The chapter describes how to extract specific columns from a data frame in R using the select function from the dplyr package.', "The 'Legendary' column analysis reveals 731 non-legendary and 70 legendary Pokemon, highlighting the rarity of legendary Pokemon in the dataset.", 'Water-type Pokemon are the most prevalent with 114 instances, while flying-type Pokemon are the least with only 3 instances, offering valuable insights into the distribution of Pokemon types.', 'The chapter covers renaming columns, checking and changing column names, and filtering Pokemon based on their types using the dplyr package.', 'The chapter explains the analysis of fire type Pokemon, with 12 belonging to Generation 1, 5 to Generation 4 and 7, and varying secondary types such as fighting and flying, with 5-6 Pokemon for each.']}