HIGHLIGHT LINE GRAPH R FULL
HIGHLIGHT LINE GRAPH R SERIES

(e.g. the colours of points, or the linetypes used in line plots). Step 2 is to map the variables we want to axes or other features of the plot Step 2: Map variables to axes, colours, and other features This guide is restricted to examples likely to be useful and effective for Instead, you should be guidedīy the evidence from basic studies of human perception, and applied data on howĭifferent types of infromation displays are really used by readers. Number or type of variables you want to display. You should be guided in choosing plots not by mechanical rules based on the Within the bar, even though this is typically not the case). (specifically, when bar graphs are used to display estimates which containĮrror, readers assume points above the bar are less likely than points Perhaps most importantly, even when they include error bars, readersĬonsistently misinterpret the quantitative information in bar graphs

HIGHLIGHT LINE GRAPH R SERIES

Make comparisons between multiple data series very difficult (for example in But bar graphs have numerousĭisadvantages over other plots which can show the same information.Īre low in information density (and so inefficient in use of space)

Which plots are the most effective forms of communication.įor example, almost all guides to plotting, and especially R textbooks, will

Plot types are perhaps not as useful as those which reflect our knowledge of

HIGHLIGHT LINE GRAPH R FULL

However, guides which attempt to be comprehensive and show you a full range of

‘Show me the numbers’ chart guide (pdf).

There are various simple chart selection guides available online, of which these

Like maps, models are imperfect but useful.

Dichotomising continuous predictors (or outcomes).

Forgetting that the CI depends on sample size.

‘Poking around’ with $ and Save time: use a broom.

Making prdictions for margins ( effects of predictors).

For multilevel or generalised linear models.

Baysian fitting of linear models via MCMC methods.

Mediation example after Baron and Kenny.

Convergence problems and simplifying the random effects structure.

Contrasts and followup tests using lmer.

3 level models with ‘partially crossed’ random effects.

Variance partition coefficients and intraclass correlations.

Allow the effect of sleep deprivation to vary for different participants.

Repeated measures or ‘split plot’ designs.

Describing statistical models using formulae.

Aggregating and reshaping at the same time.

Which package should you use to reshape data?.

Differences in quantity: numeric variables.

Highlight line graph r