R Primer

Claus Thorn Ekstrom

• Publisher: Chapman and Hall/CRC
• Year: 2017
• eBook ISBN: 9781351650458
• Topic: Mathematics
• Subtopic: Applied Mathematics
• Index: Mathematics

Newcomers to R are often intimidated by the command-line interface, the vast number of functions and packages, or the processes of importing data and performing a simple statistical analysis. The R Primer provides a collection of concise examples and solutions to R problems frequently encountered by new users of this statistical software. This new edition adds coverage of R Studio and reproducible research.

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Contents

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Preface

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CHAPTER 1 ▪ Importing and exporting data

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1.1 IMPORT AN R DATASET FROM A PACKAGE

1.2 LOAD AND SAVE R DATA FILES

1.3 READ AND WRITE TEXT FILES

1.4 READ AND WRITE CSV FILES

1.5 READ DATA FROM THE CLIPBOARD

SPREADSHEETS

1.6 READ AND WRITE DATA FROM A SPREADSHEET

1.7 READ AND WRITE EXCEL FILES

STATISTICAL SOFTWARE PROGRAMS

1.8 IMPORT AND EXPORT SAS DATASETS

1.9 IMPORT AND EXPORT SPSS DATASETS

1.10 IMPORT OR EXPORT A STATA DATASET

1.11 IMPORT A SYSTAT DATASET

DATA EXCHANGE FORMATS AND DATABASES

1.12 IMPORT A JSON DATASET

1.13 READ DATA FROM A SIMPLE XML FILE

1.14 READ DATA FROM AN XML FILE

1.15 EXPORT A DATA FRAME TO XML

1.16 IMPORT DATA FROM AN HTML TABLE

1.17 SCRAPE DATA FROM AN HTML WEB PAGE

1.18 IMPORT FROM A MYSQL/POSTGRESQL DATABASE

1.19 READ DATA FROM AN SQL DATABASE USING ODBC

CHAPTER 2 ▪ Manipulating data

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2.1 USE MATHEMATICAL FUNCTIONS

2.2 USE COMMON VECTOR OPERATIONS

2.3 WORK WITH CHARACTER VECTORS

2.4 READ NON-ASCII CHARACTER VECTORS

2.5 SORT AND ORDER DATA

2.6 TRANSFORM A VARIABLE

2.7 FIND THE VALUE OF X CORRESPONDING TO THE MAXIMUM OR MINIMUM OF Y

2.8 CHECK IF ELEMENTS IN ONE OBJECT ARE PRESENT IN ANOTHER OBJECT

2.9 APPLY A FUNCTION TO SUBSETS OF A VECTOR

2.10 FILL IN MISSING VALUES WITH PREVIOUS VALUES

2.11 CONVERT COMMA AS DECIMAL MARK TO PERIOD

2.12 LAG OR SHIFT A VECTOR

2.13 CALCULATE THE AREA UNDER A CURVE

MATRICES, ARRAYS, AND TABLES

2.14 APPLY FUNCTION TO MARGINS OF A MATRIX/ARRAY

2.15 COMPUTE A MATRIX/ARRAY OF PROPORTIONS

2.16 TRANSPOSE A MATRIX (OR DATA FRAME)

2.17 CREATE A TABLE OF COUNTS

2.18 CONVERT A TABLE OF COUNTS TO A DATA FRAME

DATES AND TIMES

2.19 PARSING DATES AND TIMES

2.20 EXTRACT AND FORMAT DATE/TIME INFORMATION

FACTORS

2.21 CONVERT A FACTOR TO NUMERIC

2.22 CONVERT A FACTOR TO CHARACTER STRINGS

2.23 ADD A NEW LEVEL TO AN EXISTING FACTOR

2.24 COMBINE THE LEVELS OF A FACTOR

2.25 REMOVE UNUSED LEVELS OF A FACTOR

2.26 CHANGE THE REFERENCE LEVEL

2.27 CUT A NUMERIC VECTOR INTO A FACTOR

DATA FRAMES AND LISTS

2.28 SELECT A SUBSET OF A DATA FRAME

2.29 SELECT THE COMPLETE CASES OF A DATA FRAME

2.30 DELETE A VARIABLE FROM A DATA FRAME

2.31 APPLY FUNCTION TO EACH VARIABLE IN A DATA FRAME

2.32 SPLIT DATA FRAME INTO SUBSETS AND APPLY FUNCTION TO EACH PART

2.33 APPLY FUNCTION TO EACH ROW OF A DATA FRAME

2.34 COMBINE TWO DATASETS

2.35 MERGE DATASETS

2.36 ADD NEW OBSERVATIONS TO A DATA FRAME

2.37 STACK THE COLUMNS OF A DATA FRAME TOGETHER

2.38 RESHAPE A DATA FRAME FROM WIDE TO LONG FORMAT OR VICE VERSA

2.39 CONVERT A DATA FRAME TO A VECTOR

CHAPTER 3 ▪ STATISTICAL ANALYSES

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DESCRIPTIVE STATISTICS

3.1 COMPUTE SUMMARY STATISTICS

3.2 CREATE DESCRIPTIVE TABLE

LINEAR MODELS

3.3 FIT A LINEAR REGRESSION MODEL

3.4 FIT A MULTIPLE LINEAR REGRESSION MODEL

3.5 FIT A POLYNOMIAL REGRESSION MODEL

3.6 FIT A ONE-WAY ANALYSIS OF VARIANCE

3.7 MAKE POST-HOC PAIRWISE COMPARISONS

3.8 FIT A TWO-WAY ANALYSIS OF VARIANCE

3.9 FIT A LINEAR NORMAL MODEL

3.10 FIT A PENALIZED REGRESSION MODEL

GENERALIZED LINEAR MODELS

3.11 FIT A LOGISTIC REGRESSION MODEL

3.12 FIT A CONDITIONAL LOGISTIC REGRESSION MODEL

3.13 FIT AN ORDINAL LOGISTIC REGRESSION MODEL

3.14 FIT A MULTINOMIAL LOGISTIC REGRESSION MODEL

3.15 FIT A POISSON REGRESSION MODEL

METHODS FOR ANALYSIS OF REPEATED MEASUREMENTS

3.16 FIT A LINEAR MIXED-EFFECTS MODEL

3.17 FIT A LINEAR MIXED-EFFECTS MODEL WITH SERIAL CORRELATION

3.18 FIT A GENERALIZED LINEAR MIXED MODEL

3.19 FIT A GENERALIZED ESTIMATING EQUATION MODEL

3.20 ANALYZE TIME SERIES USING AN ARIMA MODEL

3.21 DECOMPOSE A TIME SERIES INTO TREND, SEASONAL, AND RESIDUAL COMPONENTS

SPECIFIC METHODS

3.22 COMPARE POPULATIONS USING T Test

3.23 FIT A NONLINEAR MODEL

3.24 FIT A CENSORED REGRESSION MODEL

3.25 FIT A ZERO-INFLATED REGRESSION MODEL

3.26 Fit a Smooth Curve

3.27 FIT A GENERALIZED ADDITIVE MODEL

3.28 USE META ANALYSIS TO COMBINE AND SUMMARIZE THE RESULTS FROM SEVERAL STUDIES

3.29 USE RANDOM FOREST FOR CLASSIFICATION AND REGRESSION

3.30 FIT A LINEAR QUANTILE REGRESSION MODEL

MODEL VALIDATION

3.31 TEST FOR NORMALITY OF A SINGLE SAMPLE

3.32 TEST VARIANCE HOMOGENEITY ACROSS GROUPS

3.33 VALIDATE A LINEAR OR GENERALIZED LINEAR MODEL

CONTINGENCY TABLES

3.34 ANALYZE TWO-DIMENSIONAL CONTINGENCY TABLES

3.35 ANALYZE TWO-DIMENSIONAL CONTINGENCY TABLES WITH ORDINAL CATEGORIES

3.36 ANALYZE TWO-DIMENSIONAL CONTINGENCY TABLES WITH PAIRED MEASUREMENTS

3.37 ANALYZE CONTINGENCY TABLES USING LOG-LINEAR MODELS

AGREEMENT

3.38 CREATE A BLAND–ALTMAN PLOT OF AGREEMENT TO COMPARE TWO QUANTITATIVE METHODS

3.39 DETERMINE AGREEMENT AMONG SEVERAL METHODS OF A QUANTITATIVE MEASUREMENT

3.40 CALCULATE COHEN’S KAPPA FOR AGREEMENT

MULTIVARIATE METHODS

3.41 FIT A MULTIVARIATE REGRESSION MODEL

3.42 CLUSTER OBSERVATIONS

3.43 USE PRINCIPAL COMPONENT ANALYSIS TO REDUCE DATA DIMENSI...

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