The iLCM allows you to include other functionalities to the existing environment. Here we will show you the steps necessary. The integration of a keyness-analysis will be utilized as a running example. Several Steps need to be considered.

# 1. UI

The architecture of the Shiny App is split in 2 parts. The UI part, where the userinterface is definded, and the server part, where the logic behind the ui is given. When integrating a new analysis, we start by adding this analysis as a choice in the task sheduler.

In the Task Scheduler we specify the input parameters, which are necessary to execute the script. At first we add keyness analysis to the choices of the input analysis_selected inside ui/tab_Task_Scheduler.R.

selectInput(inputId = "analysis_selected",label = "Analysis:",
choices = c("Cooccurrence_Analysis",
"Frequency_Extraction",
"Volatility_Analysis",
"Topic_Model",
"Dictionary_Extraction",
"Classification",
"Sentiment Analysis",
"Save Collection as token object",
"Save Collection as meta object",
"Keyness Analysis")


Then we add a conditional panel, which creates an UI for the input parameters needed for our analysis.

In the next step we describe the parameters which can be put in. For this, we can adopt some standard parameters from the other analysis. We need to add a file in the server directory, that describes our predefinded ui-element Analysis_Parameter_KA.

Even though we define a ui element, we need to make changes on the server side, because here we might need to render some ui-elements that are dependent to reactive variables.