Note that this applies to head(mpg, n = 10) and not mpg itself. Notice that mpg is a tibble already, so the output from head() indicates there are only 10 observations. The head() function was more useful before tibbles. The function head() will display the first n observations of the data frame. # manufacturer model displ year cyl trans drv cty hwy fl class Also, if working on a larger collaborative project, you should use whatever style is already in place.īecause vectors must contain elements that are all the same type, R will automatically coerce to a single type when attempting to create a vector that combines multiple types. No matter what you choose, the more important thing is that you stay consistent. Some users like to keep assignment ( <-) and argument passing ( =) separate. If you wish to use <-, you will still need to use =, however only for argument passing. If you are interested in the weird cases where the difference matters, check out The R Inferno. The pros and cons of these two are well beyond the scope of this book, but know that for our purposes you will have no issue if you simply use =. (Which preceded R.) For simplicity we will use =, but know that often you will see <- as the assignment operator. X = c( 1, 3, 5, 7, 8, 9) x # 1 3 5 7 8 9Īs an aside, there is a long history of the assignment operator in R, partially due to the keys available on the keyboards of the creators of the S language. 17.3.6 Confidence Intervals for Mean Response.16 Variable Selection and Model Building.14.2.5 poly() Function and Orthogonal Polynomials.14.1.1 Variance Stabilizing Transformations.11.3.1 Factors with More Than Two Levels.11 Categorical Predictors and Interactions.9.2.3 Confidence Intervals for Mean Response.8.10 Significance of Regression, F-Test.8.8 Prediction Interval for New Observations.8.7 Confidence Interval for Mean Response.8.6.2 Significance of Regression, t-Test.8.4 Confidence Intervals for Slope and Intercept.
8.2.1 Simulating Sampling Distributions.8 Inference for Simple Linear Regression.7.5 Maximum Likelihood Estimation (MLE) Approach.6.4 Quick Comparisons to Other Languages.
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