As part of Learn Data analysis using R tutorials. This post explains how to use help in R or how to find help inside R.

There is extensive online help in the *R system*, the best starting point is to run the function `help.start()`

. This will launch a local page inside your browser with links to the *R manuals*, *R FAQ*, a search engine and other links.

### Help function

Now let’s see how to get help on a particular function. In the R Console, the function `help`

can be used to see the help file of a specific function.

### Example: Getting help for mean function in R

Use the following command to get help on `mean`

function.

help(mean)

You will get the following Output explaining arguments available in function and examples on how to use the function.

Arithmetic Mean Description: Generic function for the (trimmed) arithmetic mean. Usage: mean(x, ...) ## Default S3 method: mean(x, trim = 0, na.rm = FALSE, ...) Arguments: x: An R object. Currently there are methods for numeric/logical vectors and date, date-time and time interval objects. Complex vectors are allowed for â€˜trim = 0â€™, only. trim: the fraction (0 to 0.5) of observations to be trimmed from each end of â€˜xâ€™ before the mean is computed. Values of trim outside that range are taken as the nearest endpoint. na.rm: a logical value indicating whether â€˜NAâ€™ values should be stripped before the computation proceeds. ...: further arguments passed to or from other methods. Value: If â€˜trimâ€™ is zero (the default), the arithmetic mean of the values in â€˜xâ€™ is computed, as a numeric or complex vector of length one. If â€˜xâ€™ is not logical (coerced to numeric), numeric (including integer) or complex, â€˜NA_real_â€™ is returned, with a warning. If â€˜trimâ€™ is non-zero, a symmetrically trimmed mean is computed with a fraction of â€˜trimâ€™ observations deleted from each end before the mean is computed. References: Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) _The New S Language_. Wadsworth & Brooks/Cole. See Also: â€˜weighted.meanâ€™, â€˜mean.POSIXctâ€™, â€˜colMeansâ€™ for row and column means. Examples: x <- c(0:10, 50) xm <- mean(x) c(xm, mean(x, trim = 0.10))

### help.search Function

Use the function `help.search`

to list help files that contain a certain word. Use the following command to get help on word “linear regression”.

help.search("linear regression")

You will get the following Output

Help files with alias or concept or title matching â€˜linear regressionâ€™ using fuzzy matching: datasets::anscombe Anscombe's Quartet of 'Identical' Simple Linear Regressions KernSmooth::dpill Select a Bandwidth for Local Linear Regression MASS::area Adaptive Numerical Integration Concepts: Non-linear Regression MASS::rms.curv Relative Curvature Measures for Non-Linear Regression Concepts: Non-linear Regression stats::D Symbolic and Algorithmic Derivatives of Simple Expressions Concepts: Non-linear Regression stats::getInitial Get Initial Parameter Estimates Concepts: Non-linear Regression stats::nlm Non-Linear Minimization Concepts: Non-linear Regression stats::nls Nonlinear Least Squares Concepts: Non-linear Regression stats::nls.control Control the Iterations in nls Concepts: Non-linear Regression stats::optim General-purpose Optimization Concepts: Non-linear Regression stats::plot.profile.nls Plot a profile.nls Object Concepts: Non-linear Regression stats::predict.nls Predicting from Nonlinear Least Squares Fits Concepts: Non-linear Regression stats::profile.nls Method for Profiling nls Objects Concepts: Non-linear Regression stats::vcov Calculate Variance-Covariance Matrix for a Fitted Model Object Concepts: Non-linear Regression Type â€™help(FOO, package = PKG)â€™ to inspect entry â€™FOO(PKG) TITLEâ€™.

Each package in R comes up with manual which can be accessed from R or can be read from CRAN.