First you have to install the module Rj from the jamovi library. This will create a «R»-icon in the icon bar.
Running R commands
|Click on the «R» icon and select «Rj Editor».
||This opens an input field on the left side where you can use R commands.|
You can access your dataset with «data». The first line selects the first three columns of your dataset. Alternatively, you can use variable names as shown in the second line.
summary(data[1:3]) summary(data[, c('var1', 'var2', 'var3')])
But you can also use functions from R libraries:
stats::aggregate(as.numeric(data[, 'var1']), list(data[, 'gender']), mean)
It may take a bit of time to figure out how to use these functions, especially to discover, e.g., which format is required for the data matrices you use as input to functions (those may need to be converted to numbers with «as.numeric» or to a list with «list»). But once you managed that, you have unlimited access to (almost) any kind of statistical analysis you can imagine.
This is not at least because there come already a wealth of R libraries installed with jamovi and Rj: abind, acepack, afex, arm, assertthat, backports, base, base64enc, BayesFactor, BDgraph, BH, bindr, bindrcpp, bitops, boot, ca, car, carData, caTools, cellranger, checkmate, class, cli, clipr, cluster, coda, codetools, colorspace, compiler, contfrac, corpcor, crayon, curl, d3Network, data.table, datasets, deSolve, digest, dplyr, ellipsis, elliptic, emmeans, estimability, evaluate, evaluate, exact2x2, exactci, fansi, fdrtool, forcats, foreign, Formula, gdata, GGally, ggm, ggplot2, ggridges, glasso, glue, gnm, GPArotation, gplots, graphics, grDevices, grid, gridExtra, gtable, gtools, haven, highr, Hmisc, hms, htmlTable, htmltools, htmlwidgets, huge, hypergeo, igraph, jmv, jmvcore, jpeg, jsonlite, KernSmooth, knitr, kutils, labeling, lattice, latticeExtra, lavaan, lazyeval, lisrelToR, lme4, lmerTest, lmtest, magrittr, maptools, markdown, MASS, Matrix, matrixcalc, MatrixModels, methods, mgcv, mi, mime, minqa, mnormt, multcomp, munsell, mvnormtest, mvtnorm, nlme, nloptr, nnet, numDeriv, OpenMx, openxlsx, parallel, pbapply, pbivnorm, pbkrtest, pillar, pkgconfig, pkgconfig, plogr, plyr, PMCMR, png, praise, prettyunits, progress, psych, purrr, qgraph, quantreg, qvcalc, R6, RColorBrewer, Rcpp, RcppArmadillo, RcppEigen, RCurl, readr, readxl, regsem, relimp, rematch, reshape, reshape2, RInside, rio, Rj, rjson, rlang, rockchalk, ROCR, rpart, rpf, RProtoBuf, Rsolnp, rstudioapi, RUnit, sandwich, scales, sem, semPlot, semTools, sp, SparseM, spatial, splines, ssanv, StanHeaders, stats, stats4, stringi, stringr, survival, tcltk, testthat, TH.data, tibble, tidyselect, tools, truncnorm, utf8, utils, vcd, vcdExtra, vctrs, viridis, viridisLite, whisker, withr, xfun, XML, xtable, yaml, zeallot, zip, zoo.
Some of these libraries are especially interesting:
- stats to get access to a wealth of statistical analyses (e.g., stats::glm for fitting Generalized Linear Models or stats:kmeans for running k-means cluster analyses)
- lme4 to calculate Linear, Generalized Linear, and Nonlinear mixed modells
- MASS implements analyses from Venables og Ripley's famous book «Modern Applied Statistics with S» (which includes, e.e., linear discriminant analysis MASS::lda)
- lavaan to run a wide range of analyses with latent variables (incl. confirmatory factor analysis, structural equation modeling and latent growth curve models)
- BayesFactor to run several types of Bayes-analyses to complement your «classical» (frequentist) statistics (e.g. the Bayes-equivalent of the t-test; NB: it is of course easier to use the jamovi-module «jsq» for that)
- cluster to run several types of cluster analyses
- ggplot2 to produce (almost) any type of fancy figures you can imagine
- there are also several of Hadley Wickham's tidyverse libraries included, e.g., dplyr, stringr eller tidyselect