blog post on burnout

blogContent/posts/other/flirting-with-burnout-at-rit.md

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+"Your absolutely crazy," my boyfriend exclaimed as he gazed at my schedule. Eighteen credit hours, two part-time jobs, and three clubs-- my spring semester was shaping up to be one hell of a ride. That semester I flew too high, burned my wings, and was then was saved by Covid-19.
+
+Indulge me as I recount what happened during this crazy semester and
+reconcile what I've learned while pushing my limits at RIT. 
+
+Going into this semester, I knew that  I was signing up for more than
+usual. I was trying to pack my schedule with six classes so that I can
+stay on track to graduate a semester early. Usually, students hover
+around 12 to 15 credit hours.  
+
+![calendar](media/burnout/schedule.png)
+
+Despite having little free time, I prioritized a healthy diet, sleep,
+and exercise. Those things stretched the limits of what I could do
+before getting burned-out. Like all plans, I deviated from my plan a
+bit. Although it was naive to plan on going to the gym early in the
+morning and eating overnight oats every day for breakfast,  I ended up
+maintaining my schedule for most of the semester. Putting everything
+on the calendar was quintessential for me that semester-- my tether to
+reality. If I could manage to schedule a time for it, it was
+manageable. 
+
+# The Fallout
+
+The folly of my plan was to block everything in one big chunk. My day
+started at 5:45 AM when I woke up and went to the gym, and it ended
+around 7 PM when I got back to my apartment. Laying out this
+continuous segment of time to work on homework, jobs, and classes made
+my day efficient, but it was exhausting. After an 11 hour day, I got
+back to my apartment and wanted to collapse. Nevertheless, structuring
+my time like this ended up giving me free time later at night and on
+the weekends-- which is usually when people typically hung out.  
+
+I recognized that I was getting burned after three consecutive weeks
+of working 70 hours. I was becoming less productive, caffeine had less
+impact, and it was hard to focus. When I went to Brickhack as a club
+representative, everything felt like a haze; I tried to think and get
+work done, but all my thoughts slipped me. That day I had four energy
+drinks(a personal record), but they didn't even phase me: my mind was
+still cloudy. Nothing is worse than trying to work for 6 hours, but
+only getting 20 minutes of work done.  
+
+![brick hack picture](media/burnout/brickHack.jpg)
+
+# Saved by COVID
+
+By the time spring break rolled around, I was exhausted: all energy
+and motivation were depleted from my system. Recognizing that I was
+burned out, I took time to rest and re-cooperate by spending time with
+my boyfriend. Spring break was magical, all the stresses of school
+melted off my shoulders. The little work that I did do was focused and
+efficient. 
+
+Then RIT decided to extend spring break a week and transition classes
+online due to COVID-19. This event got coined by my friends as "spring
+break v.2 electric boogaloo." This transition introduced a new element
+of anxiety because I had to find an apartment and move ASAP; however,
+at the same time, it gave me an additional week to re-cooperate. In
+just a few days, I signed an apartment lease and moved across the
+state. 
+
+After transitioning to online courses, I felt like I had more energy.
+Before COVID, I was spending 18 hours a week sitting in a classroom,
+but after the change, I was only spending 5 hours a week in structured
+"class," while the amount of time spent on homework remained
+equivalent. This change was huge. 
+
+# Tracking my work
+
+Being the geek that I am, I tracked every single hour that I worked
+this semester. In addition to hours, I also kept track of some basic
+metrics like perceived productivity, fatigue, diet, and stress levels.
+Tracking my work helped me stay focused during the allotted times that
+I record for a specific task, and it let me know empirically when I've
+worked too much and need a break. Using a quick and dirty solution, I
+kept track of all my hours in a spreadsheet with aggregating functions
+to calculate weekly totals for each column.  
+
+![excel sheet](media/burnout/sheet.png)
+
+At the end of the semester, I exported all my data as a single CSV
+file and imported it into R for examination. 
+
+```R
+library(tidyverse)
+library(plyr)
+library(lubridate)
+
+data <- read_csv("data.csv", col_names=TRUE)
+```
+
+In my spreadsheet, empty cells were exported to CSV as NA, and useful
+numbers only appear on every other line. The task of data preparation
+is straightforward to do in R. 
+
+```R
+# Remove rows that are empty
+data <- data %>% drop_na(date)
+
+# Convert class col to be numeric-- auto import miss impoted this
+data$class <- as.numeric(data$class)
+
+# replace any NA values with zero
+data[is.na(data)] = 0
+
+# parse date from string
+data$date <- parse_date(data$date, "%m/%d/%y")
+
+# calculates week of year and creates its own col
+data$ymd = lubridate::isoweek(ymd(data$date))
+
+# creates a new col with the week of day numerically
+data$wday = wday(data$date)
+```
+
+Transforming the data makes it easier to graph. When visualizing time
+series data, you typically add new columns to make grouping by that
+type intuitive; this is based on what you wish to display. 
+
+The most exciting graph to see would be a heatmap showing my daily
+hours worked. 
+
+```R
+ggplot(data, aes(ymd, wday))+ 
+    geom_tile(aes(fill= total_hours), color="purple") +
+    ggtitle("Daily Hours") +
+    labs(x="School Week", y="Day of Week") +
+    scale_y_continuous(name="Day of week",trans = "reverse",
+        breaks=c(1,2,3,4,5,6,7),
+        labels=c("Sun", "Mon", "Tue", "Wed","Thr","Fri","Sat")) +
+    theme_bw()
+```
+
+![Weekly heat map](media/burnout/weekly.png)
+
+This heatmap is interesting because it shows that I typically worked
+longer hours on weekdays and that the intensities change after spring
+break. 
+
+If you are not satisfied with a ggplot graph, you can use other
+scripts on the internet to plot calendar data as a heatmap. However, I
+like to solely use ggplot because it gives you very robust controls
+over how the data is displayed. 
+
+```R
+library(tidyquant) 
+source("https://raw.githubusercontent.com/iascchen/VisHealth/master/R/calendarHeat.R")
+
+r2g <- c("#D61818", "#FFAE63", "#FFFFBD", "#B5E384") 
+calendarHeat(data$date, data$total_hours, ncolors = 99, color = "g2r", varname="Daily Hours")
+```
+
+![Other Person's heatmap](media/burnout/heatmap.png)
+
+I wasn't a fan of this library because you couldn't scale the graph. 
+
+The next thing that I wanted to plot was a line graph showing my
+weekly totals over the semester. Note: when I exported the excel file
+as a CSV, it did not contain the cells that I added to compute the
+weekly totals, so we have to calculate the sums ourselves. A naive
+approach would loop over the data and create a new table using for or
+while loops. I am a massive shill for R and Tidyverse because the
+Tibble data structure is insanely powerful. Using Dplyr on tibbles we
+can create groupings on columns and then compute metrics on those
+groupings all while utilizing a pipeline data flow. I would highly
+recommend R and Tidyverse for anyone considering data science and
+visualizations. 
+
+```R
+data %>% group_by(ymd) %>%
+    dplyr::summarise(total = sum(total_hours),
+                     work_t = sum(work_total), 
+                     class_t = sum(class),
+                     hw_t = sum(hw)) %>%
+    gather(key,value, total, work_t, class_t, hw_t) %>%
+        ggplot(mapping=aes(x = ymd)) + 
+        ggtitle("Weekly Hours") +
+        geom_line(mapping=aes(y = value, colour = key)) +
+        labs(x="School Week", y="Hours") +
+        scale_colour_discrete(name="Categories",
+                         breaks=c("total", "work_t", "class_t", "hw_t"),
+                         labels=c("Total Hours", "Work", "In Class", "HW")) + 
+        theme_bw()
+```
+
+![Line graph of weekly hours](media/burnout/weeklyLineGraph.png)
+
+This is my favorite graph because it shows me the shift that my
+schedule took after classes went online. After the break, time in
+class dropped off, but the other metrics like time on homework
+remained about the same. 
+
+Using the same grouping method as we did for weekly hours, we can
+graph all the self-reported metrics. 
+
+```R
+data %>% group_by(ymd) %>%
+    dplyr::summarise(stress_a = mean(stress), 
+                     fatigue_a = mean(fatigue), 
+                     productivity_a = mean(productivity)) %>%
+    gather(key,value, stress_a, fatigue_a, productivity_a) %>%
+        ggplot(mapping=aes(x = ymd)) + 
+        ggtitle("Metrics Average") +
+        geom_line(mapping=aes(y = value, colour = key)) +
+        labs(x="School Week", y="Average (1-10)") +
+        scale_colour_discrete(name="Metrics",
+                         breaks=c("stress_a", "fatigue_a", "productivity_a"),
+                         labels=c("Stress", "Fatigue", "Productivity")) + 
+        theme_bw()
+```
+
+![Line graph metrics](media/burnout/weeklyLineGraphMetrics.png)
+
+The metrics' actual values are not that important since they are
+relative to personal experience and are very inaccurate. How
+self-reported metrics change over time is more insightful than the
+actual values. We can observe that spring break and the switch to
+online classes had a positive benefit on all my self reported metrics.
+
+The next graph we can generate is the daily distribution of hours
+spent on separate activities. If we wanted to get really crazy, we
+could also group by day of the week; however, we already see some of
+that information in the calendar heatmap. 
+
+```R
+data %>% 
+    group_by(date) %>%
+    gather(key,value, class, club, hw, work_total, total_hours)  %>%
+    ggplot(mapping=aes(x = date)) + 
+        ggtitle("Hourly Breakdowns") +
+        geom_boxplot(mapping=aes(y = value, colour = key)) +
+        labs(y="Hours") +
+        scale_colour_discrete(name="Categories",
+             breaks=c("total_hours", "hw", "work_total", "class", "club"),
+             labels=c("Total Hours", "HW", "Work", "Class", "Club")) + 
+        theme_bw() +
+        theme(axis.title.x=element_blank(),
+            axis.text.x=element_blank(),
+            axis.ticks.x=element_blank())
+```
+
+![Box plot of hours](media/burnout/hourlyBoxPlots.png)
+
+Unsurprisingly, we see that work and homework consumed the majority of
+my time. 
+
+I created the same boxplot view for the metrics. 
+
+
+```R
+data %>% 
+    group_by(date) %>%
+    gather(key,value, stress, fatigue, productivity)  %>%
+    ggplot(mapping=aes(x = date)) + 
+        ggtitle("Metrics Breakdowns") +
+        geom_boxplot(mapping=aes(y = value, colour = key)) +
+        labs(y="Metric") +
+        scale_colour_discrete(name="Metrics",
+                         breaks=c("stress", "fatigue", "productivity"),
+                         labels=c("Stress", "Fatigue", "Productivity")) + 
+        theme_bw() +
+        theme(axis.title.x=element_blank(),
+            axis.text.x=element_blank(),
+            axis.ticks.x=element_blank())
+```
+
+![Metrics Box Plot](media/burnout/metricsBoxPlots.png)
+
+What surprised me was that each of these three metrics had a
+relatively similar distribution. As mentioned before, self-recorded
+metrics are not accurate, but they provide insight when observing how
+they change over time.  
+
+# Remarks
+
+This was a laborious post to compose; I don't want to sound bashful or
+boastful or anything along those lines-- this is a sensitive subject.
+Sharing my experience and reflecting on this semester is my way of
+reconciling what I've learned, and hopefully, it teaches someone else
+about the nuances of burnout.  
+
+Although this definitely has had an impact on my mental health, I 
+pulled through the semester and got a 4.0 GPA. I don't think I could
+have faced burnout so defiantly without my amazing friends and loving
+boyfriend. If COVID didn't force classes online, I don't know how this
+semester would have ended for me. I feel confident in my ability to
+achieve academically, but it is hard to do so while burned out. This
+experience has taught me that I can work 50-60 hours a week without
+getting burned out, but 70 is the number that **will** break the
+camels back.  
+
+Since the very start of the semester, I knew that I would end up
+writing this post since I was collecting the data for it; however, I
+didn't know to what extent I would actually get affected by burnout. I
+still don't have a great way of describing what this experience was
+like. In extreme cases of burnout, people have passed out and gone to
+the hospital. In this country, we have a romanticized view of working
+long hours and pulling all-nighters. I've learned first hand that it
+is best to prioritize your mental health above all else.  
+
+College doesn't have to be this hard. RIT is known for having a
+rigorous course load, and lots of students here get burned out.
+Keeping to a regular course load and not maxing out on jobs and clubs
+should be enough to prevent most people from getting burned out.
+Looking back at my first three semesters of college, I had soo much
+free time. A large part of avoiding burnout is about knowing your
+limits and planning your calendar to accommodate that. In the future,
+I won't flirt with a schedule that will inevitably cause me to get
+burnout.