Home-> R Code
| chapter | Title |
R code |
|---|---|---|
| 1 | How this book works |
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| 2 | Statistics and R - Setting the scene |
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| 3 | R - What is it? Two ways to use it |
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| 4 | Downloading and installing the R software - free! |
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| 5 | Starting R | |
| 6 | R Commander the graphical front end to R |
✔ |
| 7 | Packages: the apps | ✔ |
| 8 | A quick tutorial: Analysing data shipped with R | ✔ |
| 9 | A quick introduction to the R language |
✔ |
| Basic Statistical techniques | ||
| 11 | Summary statistics | ✔ |
| 12 | Graphing Distributions of single variables: histograms and density plots | ✔ |
| 13 | Densityplots for subgroups defined by factor levels | ✔ |
| 14 | Boxplots | ✔ |
| 15 | Percentages for each category/factor level |
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| Samples and Populations | ||
| 17 | Comparing a sample mean to a population mean - Single sample t test | ✔ |
| 18 | Comparing pre-post test means - Paired samples t test | ✔ |
| 19 | Comparing 2 sample means - independent samples t test | ✔ |
| 20 | Comparing pre-post test median difference - Wilcoxon Matched Pairs Statistic | ✔ |
| 21 | Comparing 2 distributions - Mann Whitney U | ✔ |
| 22 | Comparing an observed proportion to a population value - The Binomial test | ✔ |
| 23 | Several independent proportions compared with the average: two way tables | ✔ |
| 24 | Comparing several independent categories: Contingency tables | ✔ |
| 25 | Measuring the degree to which two variables co-vary: Correlation |
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| 26 | Measuring the influence of one variable on another: Regression | ✔ |
Health Statistics |
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| 28 | Risk and Odds ratios |
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| 29 | Number needed to treat/harm | ✔ |
| 30 | Sensitivity, Specificity and predictive values |
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| 31 | Levels of agreement - Kappa, Krippendorff and the ICC | ✔ |
| 32 | Bland - Altman plots |
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| 33 | Meta-analysis: the basics |
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| 34 | Plotting survival over time: KM (Kaplan-Meier) plots | ✔ |
| 35 | Investigating effects upon survival over time: Cox PH regression | ✔ |
| 36 | Graphical summaries of data | ✔ |
| 37 | Paired nominal data: comparing proportions using McNemar's test | ✔ |
Managing your data and R |
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| 39 | Creating datasets and distributions in R Commander and R |
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| 40 | Importing your data into R |
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| 41 | Cutting and Pasting from Excel/Word to the R Data editor |
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| 42 | Saving and exporting your work and data | ✔ |
| 43 | R Script files (.r) | ✔ |
| 44 | Manipulating variables (columns) in R Commander and R | ✔ |
| 45 | Manipulating cases (rows) in R Commander and R | ✔ |
| 46 | Expanding tables of counts into flat files | ✔ |
| 47 | Installing non-CRANS packages | ✔ |
| 48 | Workspaces, objects and history files | ✔ |
| 49 | Developing R Code – Rstudio and NppToR | ✔ |
More ways of analysing your data |
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| 51 | Mosaic and extended association plots |
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| 52 | Multiway tables and Crosstabs | ✔ |
| 53 | Re-sampling – Permutations, Jackknifes and Bootstrap’s | ✔ |
| 54 (part 1) | Repeated measures: Mixed models and Gee | ✔ |
| 54 (part 2) | Repeated measures: Mixed models and Gee | ✔ |
| 55 | Sample size requirements |
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| 56 | Confidence intervals for effect sizes - Noncentral distributions | ✔ |
| 57 | Publication quality graphics | ✔ |
More Regression Techniques |
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| 59 | Multiple Linear Regression: Measuring the influence of several variables on one continuous variable |
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| 60 | Logistic regression: a binary outcome | ✔ |
| 61 | Poisson (Log linear) Regression | ✔ |
| 62 | Conditional Logistic Regression | ✔ |
| 63 | Factorial ANOVA |
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| 64 | Factor Analysis | ✔ |
| 65 | Structural Equation Modelling (SEM) | ✔ |
| 66 | Summary | |