R-library jmv

The real power of using jamovi and the jmv-library (described below) comes with the opportunity to integrate it with other R-functions. Such R-functions could, e.g., be used to extract and manipulate data from log files of software used to conduct experiments (PsychoPy, e-prime, etc.).


To install the library that contains the functions used by jamovi (and in the examples below) open R and type the first line (the second line is required if you want to read or write SPSS files, the >-mark at the begin of the line is the input-mark of R and must not be copied / typed):

> install.packages('jmv')
> install.packages('foreign')

Use of jamovi syntax in R

First, you have to enable the syntax mode by pressing the properties icon Icon Properties.png in the top-right corner. Set a tick at syntax mode in the properties window.
Close the properties with the arrow-icon at the top-right Icon Arrow.png.

Jamovi SyntaxMode1.png The main window changes to text mode and you can run analyses and afterwards right-click on the command the appears at the top of each analysis to export or copy the syntax.
Jamovi SyntaxMode2.pngJamovi SyntaxMode3.png

Alternatively, you can write syntax directly. To do this, open R or RStudio and type the command in the first line (while the second line is required for if you want to use SPSS files):

> library(jmv)
> library(foreign)

Afterwards you are ready to analyze your data. Typically, you have to load a dataset first. Do this using the first line if you have a CSV file («sep» has to be set to the separator between data cells, e.g., ",", ";", etc.) or with the second line for loading SPSS data:

> data = read.csv("data.csv", header = TRUE, sep = ",")
> data = read.spss("data.sav", to.data.frame = TRUE)

Afterwards are you ready to run whatever analysis you like (here is an overview of available functions). For example, to run a simple descriptive-statistics-analysis:

> descriptives(data = data, vars = vars(var1, var2))

or for a correlation between to variables (quite basic in the first and more advanced - adding two non-parametric measures and plots - in the second line; please note that pearson = TRUE is not necessary because it is the default):

> corrMatrix(data = data, vars = vars(var1, var2), pearson = TRUE, sig = TRUE)
> corrMatrix(data = data, vars = vars(var1, var2), spearman = TRUE, kendall = TRUE, sig = FALSE, flag = TRUE, plots = TRUE)