{"product_id":"154743","title":"Self-study R data analysis ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jYDPLHEuLChseyROSMOljP2E8I5SV.png?v=1765081642\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e Self-study R data analysis \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/106175850\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eSelf-study is enough! Learn R data analysis with a one-on-one tutoring tutorial.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e This book is designed to help beginners learning R data analysis on their own learn the essentials.\u003cbr\u003e It considers the vague minds of beginners who don't even know 'what' or 'how' to learn, and kindly, like a private tutor, but only points out the essential content. \u003cbr\u003eFrom the moment you open the book to the last page, you'll feel confident and assured that you can learn data analysis on your own!\u003cbr\u003e\u003cbr\u003e Verified by 30 beta readers, this is a customized book for beginners that was 'created together', and was composed with 30 beta readers to actively reflect the difficulty level, length, and learning elements that are suitable for beginners.\u003cbr\u003e Difficult terms and concepts are explained again, and complex explanations are explained with easy-to-see pictures.\u003cbr\u003e The greatest strength of this book is that the beginner's perspective and perspective of many beginners who have 'studied on their own' are reflected throughout the book.\u003cbr\u003e\n\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eChapter 01 Big Data and R\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e : Introduce the R language and learn why it is used in data analysis.\u003cbr\u003e 01-1 Big Data and the R Language\u003cbr\u003e __The era of big data\u003cbr\u003e Introducing the __R language\u003cbr\u003e Pros and Cons of __R \u003cbr\u003e[Key points summarized in three keywords]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e 01-2 Development Environment Installation\u003cbr\u003e Download the __R installation file\u003cbr\u003e Installing __R\u003cbr\u003e Running __R\u003cbr\u003e __Download the R Studio installation file\u003cbr\u003e Installing __R Studio\u003cbr\u003e [Learn more] R Studio Cloud\u003cbr\u003e [Key points summarized in four keywords]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e 01-3 R Studio Interface and Environment Settings\u003cbr\u003e __R Studio interface\u003cbr\u003e __Settings\u003cbr\u003e __Setting up the required working environment\u003cbr\u003e __Create and save a script\u003cbr\u003e __Run the code\u003cbr\u003e [Learn more] Using Help\u003cbr\u003e [Key Points Summarized in 5 Keywords]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 02: Laying the Groundwork for Data Analysis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e : Learn about the data analysis process and what data is.\u003cbr\u003e 02-1 Data Analysis Process\u003cbr\u003e Step 1: Designing the Data Analysis\u003cbr\u003e Step 2: Prepare the data\u003cbr\u003e Step 3: Processing the Data\u003cbr\u003e Step 4: Analyzing the Data\u003cbr\u003e Step 5: Draw conclusions  \u003cbr\u003e[Key Points Summarized in 5 Keywords]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e 02-2 Appearance of data\u003cbr\u003e __Relationships between data structures and data types\u003cbr\u003e __vector\u003cbr\u003e __Categorical data\u003cbr\u003e __Matrix and Array\u003cbr\u003e __Lists and Data Frames\u003cbr\u003e [Key Points Organized into 6 Keywords]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 03 Learning R Programming\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e : Learn the basic syntax of R programming.\u003cbr\u003e 03-1 Variables and Functions\u003cbr\u003e __Create a variable\u003cbr\u003e __Calling a function\u003cbr\u003e __Using built-in functions\u003cbr\u003e __Creating a custom function\u003cbr\u003e Why use the __return( ) function?\u003cbr\u003e [Key Points Summarized in 5 Keywords]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e Package 03-2\u003cbr\u003e Installing the __package\u003cbr\u003e __Check installed packages\u003cbr\u003e __Loading the package\u003cbr\u003e __Delete package\u003cbr\u003e __Using the main package\u003cbr\u003e [Learn more] Find the package you need\u003cbr\u003e [Key points summarized in four keywords]\u003cbr\u003e [Key functions summarized in a table]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e 03-3 Conditional statements and loops\u003cbr\u003e __operator\u003cbr\u003e __if-else conditional statement\u003cbr\u003e __loop  \u003cbr\u003e[Learn More] Troubleshooting R Code Errors\u003cbr\u003e [Key Points Organized into 6 Keywords]\u003cbr\u003e [Key functions summarized in a table]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 04 Handling Data\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e : Learn how to collect, observe, and explore data to understand its characteristics.\u003cbr\u003e 04-1 Collecting Data\u003cbr\u003e __Enter data directly\u003cbr\u003e __Import external data: TXT file\u003cbr\u003e __Import external data: CSV files\u003cbr\u003e __Import external data: Excel file\u003cbr\u003e __Import external data: XML, JSON files\u003cbr\u003e [Key points summarized in four keywords]\u003cbr\u003e [Key functions summarized in a table]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e 04-2 Observing Data\u003cbr\u003e __Check all data\u003cbr\u003e __Check the data summary\u003cbr\u003e __Check descriptive statistics\u003cbr\u003e __Analyzing data frequency\u003cbr\u003e [Key Points Organized into 6 Keywords]\u003cbr\u003e [Key functions summarized in a table]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e 04-3 Exploring Data\u003cbr\u003e __Drawing a bar graph\u003cbr\u003e __Drawing a box\u003cbr\u003e __Drawing a histogram  \u003cbr\u003e__Drawing a pie chart\u003cbr\u003e __Drawing a Stem and Leaf Picture\u003cbr\u003e __Drawing a scatter plot\u003cbr\u003e [Key Points Organized into 6 Keywords]\u003cbr\u003e [Key functions summarized in a table]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 05 Data Processing\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e : Extract, sort, or restructure data to make data analysis easier.\u003cbr\u003e 05-1 dplyr package\u003cbr\u003e Installing and loading the __dplyr package\u003cbr\u003e __Extracting and Sorting Data\u003cbr\u003e __Add data and remove duplicate data\u003cbr\u003e __Summary data and extract samples\u003cbr\u003e __Pipe operator: %〉%\u003cbr\u003e [Key points summarized in two keywords]\u003cbr\u003e [Key functions summarized in a table]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e 05-2 Data Processing\u003cbr\u003e __Extract the required data\u003cbr\u003e __Sorting data\u003cbr\u003e __Summarize data\u003cbr\u003e __Combine data\u003cbr\u003e [Key points summarized in four keywords]\u003cbr\u003e [Key functions summarized in a table]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e 05-3 Transforming Data Structures\u003cbr\u003e __Converting wide-shaped data to long-shaped data: melt( ) function  \u003cbr\u003e__Converting long data to wide data: cast( ) function\u003cbr\u003e [Learn more] Summarizing data with the cast( ) function\u003cbr\u003e [Key points summarized in two keywords]\u003cbr\u003e [Key functions summarized in a table]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e 05-4 Data Cleaning\u003cbr\u003e __Check missing values\u003cbr\u003e __Exclude missing values\u003cbr\u003e __Check the number of missing values\u003cbr\u003e __Remove missing values\u003cbr\u003e __Improve missing values\u003cbr\u003e __Check for outliers\u003cbr\u003e __Handling outliers\u003cbr\u003e [Key points summarized in three keywords]\u003cbr\u003e [Key functions summarized in a table]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 06 Data Visualization: The ggplot2 Package\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e : Let's draw a graph using the ggplot2 package, the flower of data visualization.\u003cbr\u003e 06-1 Drawing a graph\u003cbr\u003e __Creating the basic graph frame: ggplot( ) function\u003cbr\u003e __Drawing a Scatterplot: geom_point( ) function\u003cbr\u003e Drawing a line graph: geom_line( ) function\u003cbr\u003e __Drawing a bar graph: geom_bar( ) function\u003cbr\u003e __Drawing a Box Plot: geom_boxplot( ) Function\u003cbr\u003e __Drawing a histogram: geom_histogram( ) function  \u003cbr\u003e[Learn more 1] Breaking lines of code connected by operators\u003cbr\u003e [Learn more 2] Adding a graph to a graph\u003cbr\u003e [Key points summarized in three keywords]\u003cbr\u003e [Key functions summarized in a table]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e 06-2 Adding Objects to the Graph\u003cbr\u003e __Drawing diagonal lines: geom_abline( ) function\u003cbr\u003e __Drawing Parallel Lines: geom_hline( ) Function\u003cbr\u003e __Drawing a vertical line: geom_vline( ) function\u003cbr\u003e __Enter a label: geom_text( ) function\u003cbr\u003e __Inserting shapes and arrows: annotate( ) function\u003cbr\u003e [Learn more 1] Adding titles to graphs and axes and applying design themes\u003cbr\u003e [Learn More 2] Finding the Intercept and Slope: Regression Analysis\u003cbr\u003e [Key points summarized in three keywords]\u003cbr\u003e [Key functions summarized in a table]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e 06-3 Map Visualization: The ggmap Package\u003cbr\u003e __Get a Google Maps API key\u003cbr\u003e Using Google Maps with the __ggmap package\u003cbr\u003e [Key points summarized in three keywords]\u003cbr\u003e [Key functions summarized in a table]\u003cbr\u003e [Confirmation question]\u003cbr\u003e \u003cbr\u003e\u003cb\u003eChapter 07 Developing Skills through Projects\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e : Based on what we learned above, let's analyze public data ourselves.\u003cbr\u003e 07-1 Comparing the distribution of domestic recreational forests by region\u003cbr\u003e __Data Collection: Download National Recreation Forest Standard Data\u003cbr\u003e __Data Processing: Preprocessing with Excel\u003cbr\u003e __Data Analysis: Frequency Analysis and Visualization\u003cbr\u003e [Key points summarized in the analysis phase]\u003cbr\u003e [Key functions summarized in a table]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e 07-2 Check the trend of overseas arrivals\u003cbr\u003e __Data Collection: Downloading Entry Statistics Data\u003cbr\u003e __Data Processing (1): Preprocessing with Excel\u003cbr\u003e __Data Processing (2): Restructuring Data\u003cbr\u003e __Data Analysis: Visualization\u003cbr\u003e [Key points summarized in the analysis phase]\u003cbr\u003e [Key functions summarized in a table]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e 07-3 Check the locations of COVID-19 screening clinics on the map.\u003cbr\u003e __Data Collection: Download COVID-19 Screening Clinic Location Information\u003cbr\u003e __Data Processing: Extracting the Required Data  \u003cbr\u003e__Data Analysis (1): Frequency Analysis\u003cbr\u003e Data Analysis (2): Map Visualization\u003cbr\u003e [Key points summarized in the analysis phase]\u003cbr\u003e [Key functions summarized in a table]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e 07-4 Comparing differences in fine dust concentrations by region in Seoul\u003cbr\u003e __Data Collection: Download Seoul's Daily Fine Dust Data\u003cbr\u003e __Data Processing (1): Preprocessing with Excel\u003cbr\u003e __Data Processing (2): Extracting the Required Data\u003cbr\u003e __Data Analysis (1): Exploring and Visualizing Data\u003cbr\u003e __Data Analysis (2): Hypothesis Testing\u003cbr\u003e [Learn More] Testing Mean Differences Between Three or More Groups: Analysis of Variance\u003cbr\u003e [Key points summarized in the analysis phase]\u003cbr\u003e [Key functions summarized in a table]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 08 Sharing Data Analysis Reports\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e : Learn how to effectively share your data analysis results.\u003cbr\u003e 08-1 Sharing Data Analysis Results with RPubs\u003cbr\u003e Creating an __R Markdown Document\u003cbr\u003e __R Markdown document preview  \u003cbr\u003e__Change the save format of the R Markdown document\u003cbr\u003e __Deploy to RPubs\u003cbr\u003e [Learn more] R Markdown syntax\u003cbr\u003e [Key points summarized in four keywords]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e 08-2 Creating Interactive Web Apps with Shiny\u003cbr\u003e __Create Shiny File\u003cbr\u003e __A look at the SHINee app structure\u003cbr\u003e __Distributing the SHINee app\u003cbr\u003e __input control widget\u003cbr\u003e [Key points summarized in four keywords]\u003cbr\u003e [Key functions summarized in a table]\u003cbr\u003e [Confirmation question]\u003cbr\u003e\u003cbr\u003e Appendix A: Introduction to Data Analysis Tools\u003cbr\u003e Answer and explanation\u003cbr\u003e Search\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate3738\/MidCate008\/373771883(1).jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eWho is this book for?\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e - Beginner learning both programming and statistics\u003cbr\u003e - Non-majors who want to start data analysis using the R language\u003cbr\u003e - A statistics major who took R language classes in college but still has regrets\u003cbr\u003e -Workers who lack the time and resources to learn data analysis through academies or lectures.\u003cbr\u003e -Anyone interested in data analysis\u003cbr\u003e \u003cbr\u003e\u003cb\u003eBook Features\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003eFirst, a solid learning design that systematically repeats the \"7-step structure tailored for beginners\"!\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e This book is structured so that the core contents of R data analysis can be naturally memorized through repeated learning in seven steps.\u003cbr\u003e In each section, we warm up with the representative concepts of each section's topic through [Key Keywords] and [Before You Begin], then go through the core theories and practices of data analysis in earnest, and at the end, we review them all at once with [Key Points] and [Confirmation Questions].\u003cbr\u003e If you follow the curriculum that allows you to study on your own, even beginners in R data analysis who are new to programming and statistics will be able to finish the book without difficulty!\u003cbr\u003e\u003cbr\u003e \u003cb\u003eSecond, learn core grammar through 193 hands-on \"hand-coding\" exercises, and develop your data analysis skills through four projects!\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003eIt contains 193 carefully selected practical examples that allow you to read and understand the core grammar and theory with ease, and learn the R coding sense through hands-on experience.\u003cbr\u003e By following the repetitive learning and practice that beginners need most, you can make the code in the book into 'your own code.'\u003cbr\u003e Finally, by analyzing public data using the R language, you can personally learn the entire data analysis process, from data collection to analysis results.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003eThird, video lectures and learning sites to empower \"honkong\" (studying together).\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e http:\/\/hongong.hanbit.co.kr\u003cbr\u003e For beginners who still find it difficult to learn from books alone, we also provide videos of lectures directly from the author.\u003cbr\u003e We also provide a learning site so that you can ask questions at any time while learning.\u003cbr\u003e The author personally answers each question and also shares the latest technologies and information related to the R language. \u003cbr\u003eIn addition, we operate a self-study group for those who want to study alone but lack confidence in doing so, and we provide maximum support so that readers can complete the course without giving up.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eFourth, we provide a glossary of essential terms for studying alone, so you can read them anytime, anywhere.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e We provide a [Terminology Note] that organizes only the key concepts and terms that you must remember.\u003cbr\u003e Through beta readers, we have confirmed that the reason beginners find programming difficult is because of unfamiliar terminology.\u003cbr\u003e However, it is not difficult, but rather confusing due to unfamiliarity, so whenever you have trouble remembering a term or concept, feel free to open your glossary.\u003cbr\u003e Another fun part of the process is completing your own glossary by adding new terms in addition to the ones provided.\u003cbr\u003e \u003cbr\u003e·This book is like an 'alphabet of data analysis' that is easy to follow and helps you approach data analysis.\u003cbr\u003e - Beta leader Kwak Kyung-tae\u003cbr\u003e ·If you don't know where to ask questions about data analysis and are at a loss as to where to start, this book will be a great guide.\u003cbr\u003e - Beta leader Park Jo-eun\u003cbr\u003e ·When you read this book, you will experience the entire process of data analysis.\u003cbr\u003e - Beta leader Son Ji-min\u003cbr\u003e · We provide helpful explanations on how to resolve errors that may occur during practice, so you can focus solely on learning.\u003cbr\u003e - Beta leader Yang Min-hyeok\u003cbr\u003e ·You can learn by reading the explanations and coding right away, and you can confirm the concepts with the conclusion at the end of each section.\u003cbr\u003e - Beta leader Lee Dong-hee\u003cbr\u003e ·Detailed explanations of development environment settings, terminology, and code are provided to avoid frustration for beginners before they even begin.\u003cbr\u003e - Beta leader Im Hyeok \u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e January 17, 2022\u003c\/div\u003e\n\n \u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e- \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 444 pages | 954g | 188*257*20mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791162245019\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN10:\u003c\/strong\u003e 1162245018 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE 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