X-Git-Url: https://code.communitydata.science/coldcallbot-discord.git/blobdiff_plain/95a99775721432eca7480aff7249449c5e0b35f3..499ed62bce2e13aaf3e4395931b4683d05fcb473:/assessment_and_tracking/track_participation.R?ds=sidebyside diff --git a/assessment_and_tracking/track_participation.R b/assessment_and_tracking/track_participation.R index a3a94eb..37898e7 100644 --- a/assessment_and_tracking/track_participation.R +++ b/assessment_and_tracking/track_participation.R @@ -1,114 +1,127 @@ setwd("~/online_communities/coldcallbot/data/") -library(ggplot2) library(data.table) -gs <- read.delim("student_information.tsv") -d <- gs[,c(2,4)] -colnames(d) <- c("student.num", "teams.name") +################################################ +## LOAD call_list TSV data +################################################ call.list <- do.call("rbind", lapply(list.files(".", pattern="^call_list-.*tsv$"), function (x) {read.delim(x, stringsAsFactors=FALSE)[,1:4]})) colnames(call.list) <- gsub("_", ".", colnames(call.list)) -table(call.list$unique_name) - -call.list$day <- as.Date(call.list$timestamp) +table(call.list$unique.name[call.list$answered]) ## drop calls where the person wasn't present call.list.full <- call.list call.list[!call.list$answered,] call.list <- call.list[call.list$answered,] -call.counts <- data.frame(table(call.list$discord.name)) -colnames(call.counts) <- c("discord.name", "num.calls") +## show the distribution of assessments +prop.table(table(call.list$assessment)) -d <- merge(d, call.counts, all.x=TRUE, all.y=TRUE, by="discord.name"); d +call.counts <- data.frame(table(call.list$unique.name)) +colnames(call.counts) <- c("unique.name", "num.calls") -## set anything that's missing to zero -d$num.calls[is.na(d$num.calls)] <- 0 - -attendance <- unlist(lapply(list.files(".", pattern="^attendance-.*tsv$"), function (x) {d <- read.delim(x); strsplit(d[[2]], ",")})) - -file.to.attendance.list <- function (x) { - tmp <- read.delim(x) - d.out <- data.frame(discord.name=unlist(strsplit(tmp[[2]], ","))) - d.out$day <- rep(as.Date(tmp[[1]][1]), nrow(d.out)) - return(d.out) -} +## create list of folks who are missing in class w/o reporting it +absence.data.cols <- c("unique.name", "date.absent", "reported") -attendance <- do.call("rbind", - lapply(list.files(".", pattern="^attendance-.*tsv$"), - file.to.attendance.list)) +missing.in.class <- call.list.full[!call.list.full$answered, + c("unique.name", "timestamp")] +missing.in.class$date.absent <- as.Date(missing.in.class$timestamp) +missing.in.class$reported <- FALSE +missing.in.class <- missing.in.class[,absence.data.cols] +missing.in.class <- unique(missing.in.class) -## create list of folks who are missing in class -missing.in.class <- call.list.full[is.na(call.list.full$answered) | - (!is.na(call.list.full$answered) & !call.list.full$answered), - c("discord.name", "day")] +################################################ +## LOAD absence data TSV data +################################################ -missing.in.class <- unique(missing.in.class) +absence.google <- read.delim("absence_poll_data.tsv") +colnames(absence.google) <- c("timestamp", "unique.name", "date.absent") +absence.google$date.absent <- as.Date(absence.google$date.absent, format="%m/%d/%Y") +absence.google$reported <- TRUE +absence.google <- absence.google[,absence.data.cols] +absence.google <- unique(absence.google) + +## combine the two absence lists and then create a unique subset +absence <- rbind(missing.in.class[,absence.data.cols], + absence.google[,absence.data.cols]) + +## these are people that show up in both lists (i.e., probably they +## submitted too late but it's worth verifying before we penalize +## them. i'd actually remove them from the absence sheet to suppress +## this error +absence[duplicated(absence[,1:2]),] +absence <- absence[!duplicated(absence[,1:2]),] -setDT(attendance) -setkey(attendance, discord.name, day) -setDT(missing.in.class) -setkey(missing.in.class, discord.name, day) +## print total questions asked and absences +absence.count <- data.frame(table(unique(absence[,c("unique.name", "date.absent")])[,"unique.name"])) +colnames(absence.count) <- c("unique.name", "absences") -## drop presence for people on missing days -attendance[missing.in.class,] -attendance <- as.data.frame(attendance[!missing.in.class,]) -attendance.counts <- data.frame(table(attendance$discord.name)) -colnames(attendance.counts) <- c("discord.name", "num.present") +## load up the full class list +gs <- read.delim("student_information.tsv") +d <- gs[,c("Your.UW.student.number", "Name.you.d.like.to.go.by.in.class")] +colnames(d) <- c("unique.name", "short.name") + +## merge in the call counts +d <- merge(d, call.counts, all.x=TRUE, all.y=FALSE, by="unique.name") +d <- merge(d, absence.count, by="unique.name", all.x=TRUE, all.y=FALSE) + +d + +## set anything that's missing to zero +d$num.calls[is.na(d$num.calls)] <- 0 +d$absences[is.na(d$absences)] <- 0 + +################################################ +## list people who have been absent often or called on a lot +################################################ -d <- merge(d, attendance.counts, - all.x=TRUE, all.y=TRUE, - by="discord.name") -days.list <- lapply(unique(attendance$day), function (day) { - day.total <- table(call.list.full$day == day)[["TRUE"]] - lapply(d$discord.name, function (discord.name) { - num.present <- nrow(attendance[attendance$day == day & attendance$discord.name == discord.name,]) - if (num.present/day.total > 1) {print(day)} - data.frame(discord.name=discord.name, - days.present=(num.present/day.total)) - }) -}) +## list students sorted in terms of (a) absences and (b) prev questions +d[sort.list(d$absences),] -days.tmp <- do.call("rbind", lapply(days.list, function (x) do.call("rbind", x))) +d[sort.list(d$num.calls, decreasing=TRUE),] -days.tbl <- tapply(days.tmp$days.present, days.tmp$discord.name, sum) +################################################ +## build visualizations +################################################ -attendance.days <- data.frame(discord.name=names(days.tbl), - days.present=days.tbl, - days.absent=length(list.files(".", pattern="^attendance-.*tsv$"))-days.tbl) -d <- merge(d, attendance.days, - all.x=TRUE, all.y=TRUE, by="discord.name") +library(ggplot2) -d[sort.list(d$days.absent), c("discord.name", "num.calls", "days.absent")] +color.gradient <- scales::seq_gradient_pal("yellow", "magenta", "Lab")(seq(0,1,length.out=range(d$absences)[2]+1)) -## make some visualizations of whose here/not here -####################################################### +table(d$num.calls, d$absences) -png("questions_absence_histogram_combined.png", units="px", width=800, height=600) +png("questions_absence_histogram_combined.png", units="px", width=600, height=400) ggplot(d) + - aes(x=as.factor(num.calls), fill=days.absent, group=days.absent) + + aes(x=as.factor(num.calls), fill=as.factor(absences)) + geom_bar(color="black") + - scale_x_discrete("Number of questions asked") + + stat_count() + + scale_x_discrete("Number of questions answered") + scale_y_continuous("Number of students") + - scale_fill_continuous("Days absent", low="red", high="blue")+ + ##scale_fill_brewer("Absences", palette="Blues") + + scale_fill_manual("Absences", values=color.gradient) + theme_bw() dev.off() -png("questions_absenses_boxplots.png", units="px", width=800, height=600) +absence.labeller <- function (df) { + lapply(df, function (x) { paste("Absences:", x) }) +} -ggplot(data=d) + - aes(x=as.factor(num.calls), y=days.absent) + - geom_boxplot() + - scale_x_discrete("Number of questions asked") + - scale_y_continuous("Days absent") +## png("questions_absence_histogram_facets.png", units="px", width=600, height=400) -dev.off() +## ggplot(d) + +## aes(x=as.factor(num.calls)) + +## geom_bar() + +## stat_count() + +## scale_x_discrete("Number of questions answered") + +## scale_y_continuous("Number of students") + +## theme_bw() + +## facet_wrap(.~absences, ncol=5, labeller="absence.labeller")