Basic Statistical Analysis Using the R Statistical Package
Contents
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Basic Statistical Analysis Using the R Statistical Package
Introduction
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1.2 The assign operator and inputting a data vector into R
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1.3 Bringing data into R from an Excel file or a text file
1.3.1 Bringing data into R from an Excel file using the read.csv(file.choose()) command
1.3.2 (Optional) Bringing data into R from an Excel file using the read.csv() command
1.3.3. Accessing individual variables from an imported data set
The 'dataframename$variablename' convention
The attach( ) command
1.3.4 Viewing or editing a data frame using the R data editor
1.3.5 (Optional) Bringing data into R from a space-delimited text file
1.3.6 (Optional) Specifying the default folder for R
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1.4 Creating new variables in R
1.4.1 Calculating new variables
1.4.2 Creating categorical variables
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1.5 Saving an R dataframe as a .csv file
1.6 The help( ) and help.search( ) functions
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1.7 Finding means, medians and standard deviations
1.8 Finding frequencies and proportions for categorical variables
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1.9 Subgroup analyses: finding means and standard deviations for subgroups
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1.10 Handling missing data in R
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1.11 Graphing histograms and box plots
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1.12 Statistical tables in R
The standard normal (z) distribution
The t distribution
The chi-square distribution
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2.1 Confidence Intervals for a Single Group
2.1.1 Confidence interval for a mean
2.1.2 Confidence interval for a proportion
Confidence Intervals for Comparing Means
2.1.3 Confidence interval for a difference in means, independent samples
2.1.4 Confidence interval for a mean difference, paired samples
Confidence Intervals for Comparing Frequencies
2.1.5 Confidence interval for the difference in proportions, independent samples
2.1.6 Confidence interval for a risk ratio
2.1.6.1 Confidence interval for a RR from a per-subject data set
2.1.6.2 Inputting counts from a 2x2 table into R for calculation of a RR
2.1.7 Confidence interval for an odds ratio
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2.2 t-tests for means of measurement outcomes
2.2.1 The one-sample t-test for a mean
2.2.2 The independent samples t-test to compare two means
2.2.3 The paired samples t-test
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2.3 z-tests for proportions, categorical outcomes
2.3.1 One-sample z-test for a proportion
2.3.2 Two-sample z-test comparing two proportions
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2.4 One factor ANOVA comparing means across several groups
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2.5 Chi-square tests for categorical outcoomes
2.5.1 The chi-square goodness-of-fit test for one sample
2.5.2 Contingency table analysis and the chi-square test of independence
2.5.2.1 The chi-square test of independence from per-subject data
2.5.2.2 The chi-square test of independence from tabled data
2.5.2.3 Fisher's exact test for small cell sizes
2.5.2.4 Relative Risk and Confidence interval for the RR
2.5.2.5 Odds ratios and 95% CI for the OR
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2.6 Nonparametric statistics for comparing medians of non-normal outcomes
2.6.1 Wilcoxon rank sum test for independent samples
2.6.2 Wilcoxon signed rank test for paired samples
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Section 3: Power and sample size calculations
3.1 Comparing means between groups
3.2 Comparing proportions between groups
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Section 4: Association between variables and multivariable methods to control for confounding
4.1 Simple correlation and regression
4.1.1 Scatterplots
4.1.2 Correlation
4.1.3 Simple regression analysis
4.1.4 Spearman's nonparametric correlation coefficient
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4.2 Multiple linear regression for a measurement outcome
4.2.1 Multiple regression analysis
4.2.2 Multiple regression with categorical predictors
4.2.3 Finding standardized regression coefficients in R
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4.3 Logistic regression for a Yes/No outcome
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5 Survival Analysis
5.1 Kaplan-Meier plots for one group
5.2 Kaplan-Meier plots and log-rank test for two groups
5.3 Cox's proportional hazards regression for survival data
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Section 6: p-value adjustment for multiple comparisons
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Section 7: User-defined functions in R
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