Team:Heidelberg/Tempaltes/iGEM42-W-18a

From 2013.igem.org

(Difference between revisions)

Latest revision as of 01:12, 5 October 2013

Filters fixed

The drafts for most of the filtering functions had already been implemented, but ended up non-functional. This was due to data removal using empty vectors, which produces a severe error in R. Table 18.1 gives an overview of the different parameters and their basic filter design.

**Table 18.1:** Overview of all filters.
Parameter	Type	Options*	Status
Year	numeric	2007-2012	final
Region	string	levels	final
Track	string	levels	final
Students	numeric	0, 5, 10, 15, 20, >20	final
Advisors	numeric	0, 2, 5, 10, 15, >15	final
Instructors	numeric	0, 2, 5, 10, 15, >15	final
Biobricks	numeric	0, 5, 10, 20, 50, 100, 200, >200	final
Championship	character vector	levels	working
Regional	character vector	levels	working
Medals	string	bronze, silver, medal	missing
Score	numeric	0-100 (steps of 10)	final
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 after this update is displayed below (changes are displayed in red).

FilterForX <- function(data) {
	if (input$FILX_min == ">max") data <- data[-which(data$X < max),]
	else if (input$FILX_max == ">max" & input$FilX_min != "min") data <- data[-which(data$X < input$FILX_min),]
	else if (input$FILX_max == ">max" & input$FilX_min == "min") return(data)
	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 now 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
	}
	delete <- which(matchY == 0)
	if (length(delete) != 0) data <- data[-delete,]
	rm(delete)
	rm(matchY)
	return(data)
}

The code for matching the awards is slighly more complicated, since the awards are saved in the data-list and we want to reduce the data-frame. This is the pseudocode for the functions:

iterate through all awards to be included {
	(re-)set a vector for deleting those items from the data-list
	iterate through all the teams in the data-list {
		if the award matches one of the team awards:
		- expand the vector for the teams to keep with the name
		- expand the vector for the teams already matched
	}
	delete those teams from the data-list, that were already positive in order to save runtime
}
reduce data-frame according to the matching result

All the filter functions and corresponding layout elements were put together in one file to easily copy and paste them to the different apps.

@@ Line 1: / Line 1: @@
 == Filters fixed ==
-The drafts for most of the filtering functions had already been implemented, but ended up non-functional. This was due to data removal using empty vectors, which produces a severe error in R. Table 1 gives an overview of the different parameters and their basic filter design.
+The drafts for most of the filtering functions had already been implemented, but ended up non-functional. This was due to data removal using empty vectors, which produces a severe error in R. Table 18.1 gives an overview of the different parameters and their basic filter design.
 {| class="wikitable"
-|+ '''Table 1:''' Overview of all filters.
+|+ '''Table 18.1:''' Overview of all filters.
 |-
 ! Parameter !! Type !! Options* !! Status