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---
title: "Analytics // WLM 2023"
author: "A. Angelgardt"
format:
dashboard:
scrolling: true
logo: pics/logo.png
---
::: {hidden=true}
## Chunk opts
```{r opts, echo=FALSE, eval=TRUE, message=FALSE}
knitr::opts_chunk$set(echo=FALSE,
message=FALSE,
eval=TRUE,
warning=FALSE,
cache = FALSE
)
```
## Install pkgs
```{r install-pkgs, eval=FALSE}
# install.packages(c("rystats", "googlesheets4"))
# if (!("remotes" %in% installed.packages())) {
# install.packages("remotes")
# }
# remotes::install_github("datatrail-jhu/rgoogleclassroom")
```
## Libraries
```{r pkgs, cache=FALSE}
library(rytstat)
library(googlesheets4)
library(rgoogleclassroom)
library(tidyverse)
library(plotly)
```
```{r ggplot-theme, cache=FALSE}
theme_set(theme_bw())
theme_update(legend.position = "bottom")
```
```{r auth}
## youtube
ryt_auth('[email protected]')
# ryt_deauth()
# ryt_has_token()
# ryt_user()
## gs4 auth
gs4_auth("[email protected]")
## classroom
# token <- authorize()
# save.image("/Users/antonangelgardt/tokens/classroom_token.Rdata")
# load("/home/nglgrdt/tokens/classroom_token.Rdata")
# load("/home/angelgardt/tokens/classroom_token.Rdata")
load("/Users/antonangelgardt/tokens/classroom_token.Rdata")
auth_from_secret(access_token = token$credentials$access_token,
refresh_token = token$credentials$refresh_token)
```
## Load data
### Spec
```{r get-spec-data}
## homeworks
hw_spec <- read_sheet("https://docs.google.com/spreadsheets/d/1iCy8MDz-ER95OfylV-xAY6lIxfbCZiHq5NM3i4smm5M/edit?usp=sharing",
sheet = "HW") %>%
select(task, level)
## quizes
q_sp <- read_sheet("https://docs.google.com/spreadsheets/d/1iCy8MDz-ER95OfylV-xAY6lIxfbCZiHq5NM3i4smm5M/edit?usp=sharing",
sheet = "Q") %>%
filter(!is.na(task_code))
q_nulls <- read_sheet("https://docs.google.com/spreadsheets/d/1iCy8MDz-ER95OfylV-xAY6lIxfbCZiHq5NM3i4smm5M/edit?usp=sharing",
sheet = "Q_NULLS")
```
### Calendar
```{r get-calendar-data}
read_sheet("https://docs.google.com/spreadsheets/d/1k9QtNjZLzTrbYlkXrWFWxglXTG5kol1DHSPIN9stLiE/edit?usp=sharing",
sheet = "План", skip = 2) -> calendar
calendar %>%
select(date, code_1) %>%
filter(str_detect(code_1, "^L|^P|^C|^A")) %>%
mutate(date = as_date(date, format = "%d/%m/%y")) %>%
rename("code" = "code_1") -> calendar_activities
calendar %>%
select(code_2, deadline_soft, deadline_hard) %>%
filter(str_detect(code_2, "^Q|^HW")) %>%
mutate(deadline_soft = as_datetime(deadline_soft, format = "%d/%m/%y %H:%M") %>% date(),
deadline_hard = as_datetime(deadline_hard, format = "%d/%m/%y %H:%M") %>% date()) %>%
rename("code" = "code_2") %>%
pivot_longer(-code, names_to = "deadline", values_to = "date") %>%
mutate(deadline = str_remove(deadline, "deadline_")) -> calendar_assessment
```
### Journal
```{r get-journal-data}
## practice
journal <- read_sheet("https://docs.google.com/spreadsheets/d/1mNT6A3qJTnS5EXQJg6MKFNinRqOVmrGErMZdrfsN9To/edit?usp=sharing",
sheet = "Journal", col_types = "c")
journal %>%
select(ID, Stream, starts_with("P")) -> pr_journal
## calculate number of participants
pr_journal %>%
filter(Stream == "main") %>%
summarise(n = n()) %>%
pull(n) -> n_partic
## practice presence
pr_journal %>%
filter(Stream == "main") %>%
select(-Stream) %>%
pivot_longer(-ID,
names_to = "practice",
values_to = "present") %>%
separate(practice, into = c("practice", "half"), sep = "-") %>%
group_by(practice, half) %>%
mutate(present = as.numeric(present),
practice = factor(practice,
ordered = TRUE,
levels = paste0("P", 1:17))) %>%
replace_na(list(present = 0)) %>%
summarise(count = sum(present),
percentage = mean(present) %>% round(2)) -> pr
## consulations
journal %>%
select(ID, Stream, starts_with("C"), -Certificate) -> c_journal
## consulations presence
c_journal %>%
filter(Stream == "main") %>%
select(-Stream) %>%
pivot_longer(-ID,
names_to = "consultation",
values_to = "present") %>%
group_by(consultation) %>%
mutate(present = as.numeric(present),
consultation = factor(consultation,
ordered = TRUE,
levels = paste0("C", 1:17))) %>%
replace_na(list(present = 0)) %>%
summarise(count = sum(present),
percentage = mean(present) %>% round(2)) -> cons
## homeworks
journal %>%
select(ID, Stream, matches("^HW\\d+")) -> hw_journal
## homeworks not submitted
hw_journal %>%
filter(Stream == "main") %>%
pivot_longer(cols = -c(ID, Stream),
names_to = "hw",
values_to = "score") %>%
filter(score != "NS") %>%
group_by(hw) %>%
summarise(n = n()) -> hws_ns
## homeworks scores
hw_journal %>%
filter(Stream == "main") %>%
select(-Stream) %>%
pivot_longer(cols = -ID,
names_to = "hw",
values_to = "score") %>%
filter(score != "NS") %>%
mutate(score = as.numeric(score),
hw = factor(hw,
ordered = TRUE,
levels = paste0("HW", 1:16))) -> hws_scores
## homework sheets
sheet_names("https://docs.google.com/spreadsheets/d/1mNT6A3qJTnS5EXQJg6MKFNinRqOVmrGErMZdrfsN9To/edit?usp=sharing") %>%
str_extract("^HW\\d+") %>%
na.omit() -> hws_sheets
## homework tasks
hw_tsks <- tibble()
for (i in 1:length(hws_sheets)) {
read_sheet("https://docs.google.com/spreadsheets/d/1mNT6A3qJTnS5EXQJg6MKFNinRqOVmrGErMZdrfsN9To/edit?usp=sharing",
sheet = hws_sheets[i], col_types = "c") %>%
select(ID, starts_with("hw")) %>%
pivot_longer(-ID, names_to = "task", values_to = "score") %>%
bind_rows(hw_tsks) -> hw_tsks
}
## homework tasks preprocess
hw_tsks %>%
left_join(hw_spec,
join_by(task)) %>%
separate(task, into = c("hw", "task")) %>%
mutate(task = factor(task,
ordered = TRUE,
levels = as.character(1:15)),
level = factor(level,
ordered = TRUE,
levels = c("easy", "medium", "hard", "extreme"))) %>%
mutate(hw = toupper(hw)) -> hw_tasks
## quizes
# q_journal <- read_sheet("https://docs.google.com/spreadsheets/d/1mNT6A3qJTnS5EXQJg6MKFNinRqOVmrGErMZdrfsN9To/edit?usp=sharing",
# sheet = "Journal") %>%
# select(ID, Stream, starts_with("Q"))
# q_scores <- tibble()
# for(i in paste0("Q", 1:2)) {
# if(i == "Q1") {
# read_sheet("https://docs.google.com/spreadsheets/d/1mNT6A3qJTnS5EXQJg6MKFNinRqOVmrGErMZdrfsN9To/edit?usp=sharing",
# sheet = i) %>%
# select(ID, matches("q\\d+-\\d+"), TOTAL) -> q_scores
# } else {
# read_sheet("https://docs.google.com/spreadsheets/d/1mNT6A3qJTnS5EXQJg6MKFNinRqOVmrGErMZdrfsN9To/edit?usp=sharing",
# sheet = i) %>%
# select(ID, matches("q\\d+-\\d+"), TOTAL) %>%
# full_join(q_scores, join_by(ID)) -> q_scores
# }
# }
## J project
read_sheet("https://docs.google.com/spreadsheets/d/1mNT6A3qJTnS5EXQJg6MKFNinRqOVmrGErMZdrfsN9To/edit?usp=sharing",
sheet = "J", col_types = "c") %>%
anti_join(journal %>%
select(Stream, ID) %>%
filter(Stream == "test"),
join_by(ID)
) -> j_journal
## F project
read_sheet("https://docs.google.com/spreadsheets/d/1mNT6A3qJTnS5EXQJg6MKFNinRqOVmrGErMZdrfsN9To/edit?usp=sharing",
sheet = "F", col_types = "c") %>%
anti_join(journal %>%
select(Stream, ID) %>%
filter(Stream == "test"),
join_by(ID)
) -> f_journal
```
### YouTube
```{r get-yt-data}
## custom load function
## package function seems to have a bug
ryt_get_analytics_custom <- function (start_date = Sys.Date() - 14,
end_date = Sys.Date(),
metrics = c(
"views",
"likes",
"dislikes",
"estimatedMinutesWatched",
"averageViewDuration",
"averageViewPercentage"),
dimensions = "day",
filters = NULL, ...)
{
require(cli)
require(gargle)
cli_alert_info("Compose params")
metrics <- paste0(metrics, collapse = ",")
dimensions <- paste0(dimensions, collapse = ",")
out <- request_build(method = "GET",
params = list(startDate = start_date,
endDate = end_date, ids = "channel==MINE",
dimensions = dimensions,
filters = filters,
metrics = metrics),
token = ryt_token(),
path = "v2/reports",
base_url = "https://youtubeanalytics.googleapis.com/")
cli_alert_info("Send query")
ans <- request_retry(out, encode = "json")
resp <- response_process(ans)
cli_alert_info("Parse result")
suppressMessages({
data <- tibble(response = resp$rows) %>%
unnest_wider(.data$response, ...)
})
if (nrow(data) == 0) {
cli_alert_warning("Empty answer")
return(tibble())
}
headers <- tibble(response = resp$columnHeaders) %>%
unnest_wider(.data$response, ...)
data <- set_names(data, headers$name)
cli_alert_success(str_glue("Success, loading {nrow(data)} rows."))
return(data)
}
ryt_get_videos() %>%
select(id_videoId, title) %>%
filter(!str_detect(title, "внутряк") & str_detect(title, "WLM2023")) -> videos
# video_id_list <- paste0(head(videos$id_video_id, 500), collapse = ',')
dimensions <- c('day', 'video')
metrics <- c("views", "likes", "dislikes", "estimatedMinutesWatched", "averageViewDuration", "averageViewPercentage")
ryt_get_analytics_custom(
start_date = '2023-11-06', end_date = Sys.Date(),
dimensions = dimensions,
metrics = metrics,
filters = str_glue('video=={str_c(head(videos$id_videoId, 500), collapse=",")}'), names_sep = ""
) %>%
set_names(c(dimensions, metrics)) %>%
left_join(videos, join_by("video" == "id_videoId")) -> basics_by_videos
basics_by_videos %>%
mutate(day = as_date(day)) %>%
filter(str_detect(title, "^L")) %>%
separate(title, into = c("code", "name", "course", "lab"), sep = " // ") %>%
select(-course, - lab) -> lec
basics_by_videos %>%
mutate(day = as_date(day)) %>%
filter(str_detect(title, "^P")) %>%
separate(title, into = c("code", "name", "course", "lab"), sep = " // ") %>%
select(-course, - lab) -> pr_videos
basics_by_videos %>%
mutate(day = as_date(day)) %>%
filter(str_detect(title, "^С|^C")) %>%
separate(title, into = c("code", "course", "lab"), sep = " // ") %>%
select(-course, - lab) -> cons_videos
basics_by_videos %>%
mutate(day = as_date(day)) %>%
filter(str_detect(title, "^A|^А")) %>%
separate(title, into = c("code", "name", "course", "lab"), sep = " // ") %>%
select(-course, - lab) -> a_videos
```
### Classroom
```{r get-classroom-data}
get_course_list() %>%
.$courses %>%
filter(name == "WLM 2023 // HSE UX LAB") %>%
pull(id) -> course_id
get_coursework_list(course_id)$courseWork %>%
select(id, title) %>%
filter(str_detect(title, "^Q")) %>%
mutate(code = str_extract(title, "^Q\\d+")) -> q_ids
tibble(
Q1 = "https://docs.google.com/forms/d/1WrlABMSqcQY03Wefboe3E9uTXSxPW1o-Z6esk4qPSuM/edit#responses",
Q2 = "https://docs.google.com/forms/d/1muDdJjr0ZIwHFYuDRK9YBbQlsxXfd8fJM1dNdyFOaMk/edit#responses",
Q3 = "https://docs.google.com/forms/d/1lRFq_QXwI-LSXNDsZksgZLJ7in33fANNslYmlS3qUk0/edit#responses",
Q4 = "https://docs.google.com/forms/d/1VqekS1ApzQ74QxaQ1O-B9oPjjJ8c2QyRwe1DS9PEZKg/edit#responses",
Q5 = "https://docs.google.com/forms/d/1uq3i_Zz-ArIG0ILGxlLmFIMqGscVkKv7PHZe6LoFF4c/edit#responses",
Q6 = "https://docs.google.com/forms/d/17fPOx52-RK-hxDUsrD5hei0Uw7ScdWwfKpD_1fb8AWw/edit#responses",
Q7 = "https://docs.google.com/forms/d/1Ywo9r_EF9qEeV9PbG70EMm95ci2XM2S-b9IgCPjYBLI/edit#responses",
Q8 = "https://docs.google.com/forms/d/1JItuzbpNIemDLYFYI43pi_GGy_vmdpLWAauZzLNv0dE/edit#responses",
Q9 = "https://docs.google.com/forms/d/177eGZsat4ntKKFoS-YKbZ3GuZnzTaB4-vwh2lYTYwK0/edit#responses",
Q10 = "https://docs.google.com/forms/d/1EzNjUV5653DYEEsFXeIdgT98pEqEmcMh0hTVwlna-3Q/edit#responses",
Q11 = "https://docs.google.com/forms/d/1KTfflQIUL_VMsSB8W3KgXmLVu32VagUVycnOe08UN6o/edit#responses",
Q12 = "https://docs.google.com/forms/d/1WfDmr8Sds-hCrnL5oaBVGx_C2EzwvTBcCL1Ph2Dxebw/edit#responses",
Q13 = "https://docs.google.com/forms/d/1ffHuvBGiM7ctNozoH6TJgrU_Asp1oMaJMoMvFDiURbw/edit#responses",
Q14 = "https://docs.google.com/forms/d/1HBJ78eluyw29GNnRHlG5Yqtc_inclX9tJ_S5FpwNwNk/edit#responses",
Q15 = "https://docs.google.com/forms/d/1dG1AAOkA8vT8GyfPKklK5oeEAXRNBIiQcHyasQIYfxk/edit#responses"
) %>% pivot_longer(cols = everything(),
names_to = "code",
values_to = "link") -> q_links
# get_coursework_properties(course_id, coursework_id)
# get_form_properties(form_id = NULL, form_url = NULL)
```
```{r quizzes-preproc}
q_props <- list()
for(i in 1:15) {
get_form_properties(form_url = q_links$link[i]) %>%
.$items %>%
mutate(code = paste0("Q", i)) -> q_props[[i]]
}
q_resps <- list()
for(i in 1:15) {
get_form_responses(form_url = q_links$link[i])$responses %>%
filter(month(as_date(lastSubmittedTime)) > 10 | year(as_date(lastSubmittedTime)) > 2023) %>%
mutate(code = paste0("Q", i))-> q_resps[[i]]
}
q_resps %>%
map(function(x) {x %>% select(responseId, totalScore, lastSubmittedTime, code)}) -> q_ts
q_total_scores <- tibble()
for (i in 1:length(q_ts)) {
q_ts[[i]] %>%
bind_rows(q_total_scores) -> q_total_scores
}
q_ts %>%
map(function(x) nrow(x)) %>%
as_tibble(.name_repair = "universal") %>%
rename_with(str_replace_all, pattern = "\\.\\.\\.", replacement = "Q") %>%
pivot_longer(cols = everything(),
names_to = "code",
values_to = "n") -> q_n
```
```{r q-task-export, eval=FALSE}
# q_tasks <- tibble()
# for (i in 1:length(q_props)) {
# q_props[[i]] %>%
# select(itemId, title, code) %>%
# slice(-1) %>%
# bind_rows(q_tasks) -> q_tasks
# }
# q_tasks %>% write_sheet("https://docs.google.com/spreadsheets/d/1iCy8MDz-ER95OfylV-xAY6lIxfbCZiHq5NM3i4smm5M/edit?usp=sharing",
# sheet = "Q")
# q_questions <- tibble()
# for (i in 1:length(q_props)) {
# q_props[[i]]$questionItem$question %>%
# select(questionId) %>%
# mutate(code = paste0("Q", i)) %>%
# slice(-1) %>%
# bind_rows(q_questions) -> q_questions
# }
# q_questions %>% write_sheet("https://docs.google.com/spreadsheets/d/1iCy8MDz-ER95OfylV-xAY6lIxfbCZiHq5NM3i4smm5M/edit?usp=sharing",
# sheet = "Q_questions")
```
```{r q-fix, eval=FALSE}
# q_props[[1]]$questionItem$question$grading
# q_props[[1]]$questionItem$question %>% select(questionId)
# q_resps[[1]]$answers[[2]]$grade
# q_resps[[1]]
#
# q_props[[1]] %>% str()
# q_resps[[1]]$answers$`430dd10a`$grade$correct
#
# (q_resps[[7]]$answers %>% names())[
# !(q_resps[[7]]$answers %>% names()) %in% (q_props[[7]]$questionItem$question %>% select(questionId) %>% pull(questionId))
# ]
#
# # "57cc894c" "45438f30" "7e2f5172" "0cef7aff" "400b60f4" "704c90b2" "6d08b363"
# q_resps[[13]]$answers$`57cc894c`$textAnswers$answers %>% unlist()
```
```{r quizzes-spec-preproc}
## quiz specification preprocess
q_sp %>%
summarise(max_total_score = sum(max_score),
.by = code) -> q_max_total_score
### quizes preproc
# source("scripts/q-preproc/q1_preproc.R")
# source("scripts/q-preproc/q2_preproc.R")
# map(
# q_resps[[4]]$answers,
# function(x) x$grade$correct
# ) %>% enframe() %>% unnest("value", keep_empty = TRUE) %>% filter(name == "410b5ff6")
# map(
# q_resps[[2]]$answers[-1],
# function(x) x$grade$correct
# ) %>%
# as_tibble() %>%
# mutate(responseId = q_resps[[1]]$responseId,
# code = q_resps[[1]]$code) %>%
# pivot_longer(cols = -c(responseId, code),
# names_to = "questionId",
# values_to = "correct") %>%
# replace_na(list(correct = FALSE))
#
#
# map(
# q_resps[[7]]$answers[-17],
# function(x) x$grade$correct
# ) %>% as_tibble() %>% select("0621265c")
# q1_total_scores %>%
# bind_rows(q2_total_scores) %>%
# left_join(q_max_total_scores,
# join_by(code)) %>%
# mutate(lastSubmittedTime = as_datetime(lastSubmittedTime),
# totalScore100 = (totalScore / max_total_score * 100) %>% round()) %>%
# filter(month(lastSubmittedTime) > 10) -> q_total_scores
#
# q1_item_score %>%
# bind_rows(q2_item_score) -> q_item_scores
# get_form_responses(form_url = q_links$link[15]) -> q15
q_questions <- tibble()
for (i in 1:15) {
q_resps[[i]]$answers -> current_ans
map(
current_ans[!names(current_ans) %in% (q_nulls %>% filter(code == paste0("Q", i)) %>% pull(questionId))],
function(x) x$grade$correct
) %>%
as_tibble() %>%
mutate(responseId = q_resps[[i]]$responseId,
code = q_resps[[i]]$code) %>%
pivot_longer(cols = -c(responseId, code),
names_to = "questionId",
values_to = "correct") %>%
replace_na(list(correct = FALSE)) %>%
bind_rows(q_questions) -> q_questions
}
nrow(q_questions)
nrow(q_sp)
q_sp %>%
filter(!is.na(questionId)) %>% # nrow()
separate(questionId, into = paste0("questionId", 1:7), sep = "\n") %>%
pivot_longer(cols = paste0("questionId", 1:7),
names_to = "questionId_num", values_to = "questionId") %>%
filter(!is.na(questionId)) %>%
# full_join(q_questions, ## produce NA from buged questions q14_3 & q4_6
# join_by(code, questionId)) %>% # View()
right_join(q_questions,
join_by(code, questionId)) %>% # View()
# nrow()
# pull(max_score) %>% is.na() %>% sum()
# pull(correct) %>% is.na() %>% sum()
# filter(is.na(correct))
group_by(code, responseId, itemId, task_code) %>%
summarise(score = sum(correct)) %>%
full_join(
q_sp %>% select(task_code, max_score),
join_by(task_code)
) %>% # nrow()
full_join(q_total_scores %>% select(code, responseId, totalScore),
join_by(code, responseId)) -> q_questions_full
q_questions_full %>% # sapply(is.na) %>% apply(2, sum)
filter(!is.na(code)) %>%
ungroup() %>%
group_by(code, task_code) %>%
summarise(mean_score = mean(score)) %>% # nrow()
left_join(q_sp %>% select(code, task_code, max_score),
join_by(code, task_code)) %>%
mutate(difficulty = mean_score / max_score) -> q_difficulty
q_questions_full %>% # sapply(is.na) %>% apply(2, sum)
filter(!is.na(code)) %>%
ungroup() %>%
group_by(code, task_code) %>%
summarise(discrimination = cor(score, totalScore)) %>%
replace_na(list(discrimination = 0)) -> q_discrimination
q_discrimination %>%
full_join(q_difficulty %>% select(code, task_code, difficulty),
join_by(code, task_code)) -> q_psymet_props
```
```{r get-aesthetics}
aesth <- yaml::read_yaml("aesthetics.yml")
```
### FB5
```{r get-fb5}
fb5 <- read_sheet("https://docs.google.com/spreadsheets/d/1mttm7MFNfpopTqAj6KDmLSDiSsqNFMtzhDobhXazHFc/edit?usp=sharing",
skip = 1) # %>% filter(!str_detect(name, "test"))
fb5 %>%
select(fb_o1, fb_o2, fb_o4) %>%
mutate(fb_o1_groups = ifelse(fb_o1 >= 6, "good", "bad"),
fb_o2_groups = ifelse(fb_o2 >= 8, "enth", ifelse(fb_o2 <= 5, "dis", "norm")),
fb_o4_groups = ifelse(fb_o4 >= 9, "prom", ifelse(fb_o4 <= 6, "det", "neut"))) -> fb5_groups
fb5_groups %>% pull(fb_o1) %>% mean() %>% round() %>% paste0(" / 10") -> csi
fb5_groups %>% summarise(n = n(),
.by = fb_o1_groups) %>%
mutate(csat = round(n / sum(n) * 100)) %>%
filter(fb_o1_groups == "good") %>% pull(csat) %>% paste0("%") -> csat
fb5_groups %>% summarise(n = n(),
.by = fb_o2_groups) %>%
mutate(ers_perc = round(n / sum(n) * 100)) %>%
full_join(tibble(fb_o2_groups = c("norm", "enth", "dis"),
ers_perc_ = 0),
join_by(fb_o2_groups)) %>%
mutate(ers_perc = ifelse(is.na(ers_perc), ers_perc_, ers_perc)) %>%
select(-n, -ers_perc_) %>%
pivot_wider(names_from = fb_o2_groups,
values_from = ers_perc) %>%
transmute(ers = enth - dis) %>%
pull(ers) -> ers
fb5_groups %>% summarise(n = n(),
.by = fb_o4_groups) %>%
mutate(nps_perc = round(n / sum(n) * 100)) %>%
full_join(tibble(fb_o4_groups = c("prom", "neut", "det"),
nps_perc_ = 0),
join_by(fb_o4_groups)) %>%
mutate(nps_perc = ifelse(is.na(nps_perc), nps_perc_, nps_perc)) %>%
select(-n, -nps_perc_) %>%
pivot_wider(names_from = fb_o4_groups,
values_from = nps_perc) %>%
transmute(nps = prom - det) %>%
pull(nps) -> nps
```
:::
# Метрики
```{r merics}
journal %>%
filter(Stream == "main") %>%
select(ID, Stream, matches("^HW\\d|^P\\d|^Q\\d|^C\\d|^J$|^F$")) %>%
pivot_longer(cols = -c(ID, Stream),
names_to = "code") %>%
separate(code, into = c("code", "part"), sep = "-") %>%
mutate(value = as.numeric(value)) %>%
summarise(done = sum(value, na.rm = TRUE) > 0,
.by = c(ID, code)) %>%
summarise(n = sum(done),
.by = code) -> n_activities
calendar %>%
select(date, week, block, code_1, code_2) %>%
filter(week != "—" &
week != "0" &
code_1 != code_2) %>%
pivot_longer(cols = -c(date, week, block),
values_to = "code") %>%
filter(code != "—") %>%
right_join(n_activities,
join_by(code)) -> n_activities_weeks_blocks
n_activities_weeks_blocks %>%
summarise(n_retention = max(n),
.by = week) %>%
mutate(`Absolute Retention` = round(n_retention / n_partic * 100, 2)) %>%
filter(week != 7) %>%
mutate(previous_n_retention = lag(n_retention),
`Relative Retention` = round(n_retention / previous_n_retention * 100, 2)) -> retention_weeks
retention_weeks %>%
filter(week == 1) %>%
select(week, `Absolute Retention`) -> activation
retention_weeks %>%
filter(week == "21–22") %>%
select(week, `Absolute Retention`) -> completion
n_activities_weeks_blocks %>%
summarise(n_retention = max(n),
.by = block) %>%
mutate(previous_n_retention = lag(n_retention),
`Absolute Retention` = round(n_retention / n_partic * 100, 2),
`Relative Retention` = round(n_retention / previous_n_retention * 100, 2)) %>%
filter(!is.na(block)) -> retention_blocks
```
## Row
```{r}
#| content: valuebox
#| title: "Activation"
list(
icon = "check",
color = "#9AEBC6",
value = paste0(activation$`Absolute Retention`, "%")
)
```
```{r}
#| content: valuebox
#| title: "Completion Rate"
list(
icon = "trophy",
color = "#95B4F5",
value = paste0(completion$`Absolute Retention`, "%")
)
```
```{r}
#| content: valuebox
#| title: "Customer Satisfaction Index"
list(
icon = "bar-chart",
color = "#F0C281",
value = csi
)
```
```{r}
#| content: valuebox
#| title: "Customer Satisfaction Score"
list(
icon = "bar-chart",
color = "#F0C281",
value = csat
)
```
## Row
```{r}
#| content: valuebox
#| title: "Net Promoter Score"
list(
icon = "bar-chart",
color = "#F0E790",
value = as.character(nps)
)
```
```{r}
#| content: valuebox
#| title: "Expectation/Reality Score"
list(
icon = "bar-chart",
color = "#F0E790",
value = as.character(ers)
)
```
## Row
```{r retention-weeks}
retention_weeks %>% pull(week) -> weeks
retention_weeks %>%
pivot_longer(cols = c(`Absolute Retention`, `Relative Retention`)) %>%
mutate(week = factor(week,
ordered = TRUE,
levels = weeks)) %>%
ggplot(aes(week, value, color = name)) +
geom_point() +
geom_line(aes(group = name)) +
# scale_color_discrete(labels = c(rel_retention = "Относительное удержание",
# abs_retention = "Абcолютное удержание")) +
# geom_point(data = activation,
# aes(week, `Absolute Retention`),
# size = 2, color = "green3") +
# geom_point(data = completion,
# aes(week, `Absolute Retention`),
# size = 2, color = "blue3") +
labs(title = "Retention // Недели",
x = "Неделя", y = "Процент",
color = "Метрика") -> retention_weeks_plot
retention_weeks_plot %>%
ggplotly() %>%
layout(legend = list(
orientation = "h",
x = 0, y = -.3
))
```
## Row
```{r retention-blocks}
retention_blocks %>% pull(block) -> blocks
retention_blocks %>%
pivot_longer(cols = c(`Absolute Retention`, `Relative Retention`)) %>%
mutate(block = factor(block,
ordered = TRUE,
levels = blocks)) %>%
ggplot(aes(block, value, color = name)) +
geom_point() +
geom_line(aes(group = name)) +
scale_x_discrete(labels = c("R",
"Промежуточный\nпроект",
"Математика\nи статистика",
"Анализ данных",
"Итоговый\nпроект")) +
# scale_color_discrete(labels = c(rel_retention = "Относительное удержание",
# abs_retention = "Абcолютное удержание")) +
labs(title = "Retention // Блоки",
x = "Неделя", y = "Процент",
color = "Метрика") -> retention_blocks_plot
retention_blocks_plot %>%
ggplotly() %>%
layout(legend = list(
orientation = "h",
x = 0, y = -.3
))
```
# L // Лекции {#lec}
## Row
```{r lectures-views-by-day}
calendar_activities %>%
filter(str_detect(code, "^P")) %>%
ggplot() +
geom_line(data = lec,
aes(day, views, color = code)) +
geom_vline(aes(xintercept = as.numeric(date)),
linetype = "dashed",
color = "gray70") +
geom_text(aes(x = date,
y = 0,
label = code)) +
labs(
x = "Дата",
y = "Количество просмотров",
color = "Лекция",
title = "Динамика просмотров лекций"
) +
scale_x_date(date_breaks = "week",
date_minor_breaks = "day") +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90)) -> lec_views_dynamics
lec_views_dynamics %>%
ggplotly() %>%
layout(legend = list(
orientation = "h",
x = 0, y = -.3
))
```
```{r lectures-graph-cumsum-views}
calendar_activities %>%
filter(str_detect(code, "^P")) %>%
ggplot() +
geom_line(
data = lec %>%
group_by(video) %>%
mutate(cum_views = cumsum(views)),
aes(day, cum_views, color = code)
) +
geom_vline(aes(xintercept = as.numeric(date)),
linetype = "dashed",
color = "gray70") +
geom_text(aes(x = date,
y = 0,
label = code)) +
labs(
x = "Дата",
y = "Количество просмотров",
color = "Лекция",
title = "Кумулятивная сумма просмотров лекций"
) +
scale_x_date(date_breaks = "week",
date_minor_breaks = "day") +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90)) -> lec_cumsum_views
lec_cumsum_views %>%
ggplotly() %>%
layout(legend = list(
orientation = "h",
x = 0, y = -.3
))
```
## Row
```{r lectures-ave-view-dur-by-day}
calendar_activities %>%
filter(str_detect(code, "^P")) %>%
ggplot() +
geom_line(data = lec,
aes(day, averageViewDuration, color = code)) +
geom_vline(aes(xintercept = as.numeric(date)),
linetype = "dashed",
color = "gray70") +
geom_text(aes(x = date,
y = 0,
label = code)) +
labs(
x = "Дата",
y = "Средняя продолжительность\nвоспроизведения видео, с",
color = "Лекция",
title = "Абсолютная продолжительность просмотра лекций"
) +
scale_x_date(date_breaks = "week",
date_minor_breaks = "day") +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90)) -> lec_ave_views_dur
lec_ave_views_dur %>%
ggplotly() %>%
layout(legend = list(
orientation = "h",
x = 0, y = -.3
))
```
```{r lectures-ave-view-perc-by-day}
calendar_activities %>%
filter(str_detect(code, "^P")) %>%
ggplot() +
geom_line(data = lec,
aes(day, averageViewPercentage, color = code)) +
geom_vline(aes(xintercept = as.numeric(date)),
linetype = "dashed",
color = "gray70") +
geom_text(aes(x = date,
y = 0,
label = code)) +
labs(
x = "Дата",
y = "Средняя доля\nвоспроизведения видео, %",
color = "Лекция",
title = "Относительная продолжительность просмотра лекций"
) +
scale_x_date(date_breaks = "week",
date_minor_breaks = "day") +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90)) -> lec_ave_views_perc
lec_ave_views_perc %>%
ggplotly() %>%
layout(legend = list(
orientation = "h",
x = 0, y = -.3
))
```
# P // Практики {#pr}
## Row
```{r pr-graph-visits}
pr %>%
ggplot(aes(practice, percentage, color = half, group = half)) +
geom_line() +
geom_point() +
scale_y_continuous(limits = c(0, 1)) +
scale_color_discrete(labels = c(`1` = "первая", `2` = "вторая")) +
labs(
x = "Практика",
y = "Доля присутствовавших",
color = "Часть практики",
title = "Динамика посещения практик"
) -> pr_visits
pr_visits %>%
ggplotly() %>%
layout(legend = list(
orientation = "h",
x = 0, y = -.3
))
```
## Row
```{r practice-views-by-day}
calendar_assessment %>%
filter(str_detect(code, "^HW")) %>%
ggplot() +
geom_line(data = pr_videos,
aes(day, views, color = code)) +
geom_vline(aes(xintercept = as.numeric(date),
linetype = deadline),
color = "gray70") +
geom_text(aes(x = date,
y = 0,