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 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 .
| 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.|
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)