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September 5, 2017 09:24
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The code for the blog post about students' data.
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library("ggplot2") | |
log_alm <- read.csv(file = "/Users/irudnyts/Documents/projects/data/alm_logs.csv", | |
stringsAsFactors = FALSE) | |
exercises <- as.Date(c("2016-10-18", "2016-11-02", "2016-11-16", "2016-12-07")) | |
before_exercises <- exercises - 7 | |
midterm <- as.Date("2016-11-23") | |
colnames(log_alm) <- tolower(colnames(log_alm)) | |
log_alm$time <- as.POSIXct(strptime(log_alm$time, "%d/%m/%y, %H:%M")) | |
log_alm$date <- as.Date(log_alm$time) | |
log_alm <- log_alm[log_alm$date < as.Date("2016-12-23"), ] | |
ggplot(data = log_alm) + geom_bar(aes(x = date)) + | |
theme_bw() + | |
theme(text = element_text(size = 24)) | |
smr <- data.frame(date = unique(log_alm$date), n_logins = NA) | |
for(date in unique(log_alm$date)) { | |
smr[smr$date == date, "n_logins"] <- length(unique(log_alm[log_alm$date == date, | |
"user.full.name"])) | |
} | |
smr$class <- "no_class" | |
smr$class[weekdays(smr$date) %in% c("Tuesday", "Wednesday")] <- "lecture" | |
smr$class[smr$date %in% exercises] <- "exercise" | |
smr$class[smr$date %in% before_exercises] <- "before_exercise" | |
smr$class[smr$date == midterm] <- "midterm" | |
ggplot(data = smr, mapping = aes(x = date, y = n_logins)) + | |
geom_bar(aes(fill = class), stat="identity") + | |
theme_bw() + | |
theme(text = element_text(size = 24)) | |
ggplot(data = smr, mapping = aes(x = date, y = n_logins)) + | |
geom_line() + | |
stat_smooth(method ="auto", level = 0.95, span = 0.4) + | |
theme_bw() + | |
theme(text = element_text(size = 24)) | |
# students' info | |
std <- sort(table(log_alm$user.full.name)) | |
std <- data.frame(name = names(std), logins = as.vector(std)) | |
std$name <- tolower(std$name) | |
grades <- read.csv(file = "/Users/irudnyts/Documents/projects/data/alm_grades.csv") | |
colnames(grades) <- c("id", "surname", "name", "grade", "pres") | |
grades$name <- tolower(paste(grades$name, grades$surname)) | |
info <- merge(grades[, c("grade", "name")], | |
std, | |
all = TRUE) | |
info <- info[complete.cases(info), ] | |
info <- info[info$grade != 0, ] | |
model <- lm(info, formula = grade ~ logins) | |
summary(model) | |
# clustering analysis | |
cl <- kmeans(x = info[, -1], centers = 3) | |
info$cl <- cl$cluster | |
ggplot(data = info, | |
mapping = aes(x = grade, y = logins, color = as.factor(cl))) + | |
geom_point(size = 3) + | |
theme_bw() + | |
theme(text = element_text(size = 24)) | |
info$grade_std <- (info$grade - mean(info$grade)) / sd(info$grade) | |
info$logins_std <- (info$logins - mean(info$logins)) / sd(info$logins) | |
cl2 <- kmeans(x = info[, c("logins_std", "grade_std")], centers = 3) | |
info$cl2 <- cl2$cluster | |
ggplot(data = info, | |
mapping = aes(x = grade, y = logins, color = as.factor(cl2))) + | |
geom_point(size = 3) + | |
theme_bw() + | |
theme(text = element_text(size = 24)) |
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