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From 2013.igem.org

Creation of several design suggestions

During a meeting we decided to create three design proposals, so that the team could vote which one they like best. Out of the three one should be a modern design similar to other web pages, having a virtual third dimension. With regard to the Hitchhiker's Guide to the Galaxy we wanted one "spacy" design and lust but not least one alchemical / old fashioned design to fit our team logo.
Note: Since we finally used the same design as on our wiki, it's not worth showing the other design proposals or the introductory texts at this point. These can all be found on our wiki.

Draft of filtering options

The drafts for most of the filtering functions were implemented. Table 1 gives an overview of the different parameters and their basic filter design.

Table 1: Overview of all filters.

Parameter	Type	Options*	Status
Year	numeric	2007-2012	draft
Region	string	levels	draft
Track	string	levels	draft
Students	numeric	0, 5, 10, 15, 20, >20	draft
Advisors	numeric	0, 2, 5, 10, 15, >15	draft
Instructors	numeric	0, 2, 5, 10, 15, >15	draft
Biobricks	numeric	0, 5, 10, 20, 50, 100, 200, >200	draft
Championship	character vector	levels	draft
Regional	character vector	levels	draft
Medals	string	bronze, silver, medal	missing
Score	numeric	0-100 (steps of 10)	draft
Abstract	binary	provided/missing	missing
* Levels means all possible values the parameter can have.

The general code for filtering a numerical range parameter X using R looks like this:

FilterForX <- function(data) {
	if (input$FILX_min == ">max") data <- data[-which(data$X < max),]
	else if (input$FILX_max == ">max") data <- data[-which(data$X < input$FILX_min),]
	else data <- data[-which(data$X < input$FILX_min | data$X > input$X_max),]
	return(data)
}

The general code for filtering a single string parameter Y using R looks like this:

FilterForY <- function(data) {
	matchY <- rep(0, times=length(data$Y))
	for (i in 1:length(input$FILY)) {
		matchY[which(data$Y == input$FILY[i])] <- 1
	}
	data <- data[-which(matchY == 0),]
	rm(matchY)
	return(data)
}

For each filter the corresponding selection elements (max, min or multiple selection) are added underneath each other below the plot of the app. They are grouped as teams success and team composition.