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Data wrangling with R

Data wrangling with R (3회 대출)

Boehmke, Bradley.
서명 / 저자사항
Data wrangling with R / Bradley C. Boehmke.
Cham :   Springer,   2016.  
xii, 238 p. : ill. ; 24 cm.
Use R
Includes bibliographical references and index.
Mathematical statistics --Data processing.
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001 000045894670
005 20170214113450
008 170210s2016 sz a b 001 0 eng d
020 ▼a 9783319455983
040 ▼a 211009 ▼c 211009 ▼d 211009
082 0 4 ▼a 519.50285 ▼2 23
084 ▼a 519.50285 ▼2 DDCK
090 ▼a 519.50285 ▼b B671d
100 1 ▼a Boehmke, Bradley.
245 1 0 ▼a Data wrangling with R / ▼c Bradley C. Boehmke.
260 ▼a Cham : ▼b Springer, ▼c 2016.
300 ▼a xii, 238 p. : ▼b ill. ; ▼c 24 cm.
490 1 ▼a Use R
504 ▼a Includes bibliographical references and index.
650 0 ▼a Mathematical statistics ▼x Data processing.
830 0 ▼a Use R.
945 ▼a KLPA


No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/서고7층/ 청구기호 519.50285 B671d 등록번호 111767138 도서상태 대출가능 반납예정일 예약 서비스 B M



1. Preface 2. Introduction a. The Role of Data Wrangling i. Introduction to R 1. Open Source 2. Flexibility 3. Community ii. R Basics 1. Assignment & Evaluation 2. Vectorization 3. Getting help 4. Workspace 5. Working with packages 6. Style guide 3. Working with Different Types of Data in R a. Dealing with Numbers i. Integer vs. Double ii. Generating sequence of non-random numbers iii. Generating sequence of random numbers iv. Setting the seed for reproducible random numbers v. Comparing numeric values vi. Rounding numbers b. Dealing with Character Strings i. Character string basics ii. String manipulation with base R iii. String manipulation with stringr iv. Set operatons for character strings c. Dealing with Regular Expressions i. Regex Syntax ii. Regex Functions iii. Additional resources d. Dealing with Factors i. Creating, converting & inspecting factors ii. Ordering levels iii. Revalue levels iv. Dropping levels e. Dealing with Dates i. Getting current date & time ii. Converting strings to dates iii. Extract & manipulate parts of dates iv. Creating date sequences v. Calculations with dates vi. Dealing with time zones & daylight savings vii. Additional resources % i. Pipe (%%) Operator ii. Additional Functions iii. Additional Pipe Operators iv. Additional Resources 7. Shaping & Transforming Your Data with R a. Reshaping Your Data with tidyr i. Making wide data long ii. Making long data wide iii. Splitting a single column into multiple columns iv. Combining multiple columns into a single column v. Additional tidyr functions vi. Sequencing your tidyr operations vii. Additional resources b. Transforming Your Data with dplyr i. Selecting variables of interest ii. Filtering rows iii. Grouping data by categorical variables iv. Performing summary statistics on variables v. Arranging variables by value vi. Joining datasets vii. Creating new variables viii. Additional resources

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