Personal Data Project

RStudio

Data Science

UCSB

A data science project based on data I collected on my life.

Author

Jessi Rumkin

Published

May 28, 2025

A project for UCSB ENV S 193DS. I collected data on my study habits with several variables to answer my main question: Am I more productive when I work by myself or with at least one other person?

Use side navigation to jump to code or interactive figures.

Set Up

library(tidyverse) #package for making R easier to use, includes many packages
library(here) #package for file management
library(gt) #package for making tables
library(janitor) #package to clean data
library(plotly) #package for making interactive figures
library(DT) #package for making interactive tables

mydata <- read.csv("C:/Users/jlrum/MyGit/jrumkin.github.io/posts/personal-data-project/mydata.csv") #read in mydata file

#summarizing personal data
mydata_summary <-  mydata |> #make an object called mydata_summary using mydata file
  group_by(accompanied) |> #group the sumary by company type
  summarise(mean_score = round(mean(final_productivity_score),digits = 1), #find mean
            sd = round(sd(final_productivity_score), digits = 1), #find standard deviation
            se = round((sd(final_productivity_score)/sqrt(length(final_productivity_score))),digits = 1), #find standard error
            ci_lower = round(mean_score - 1.96 * (sd(final_productivity_score)/ sqrt(length(final_productivity_score))),digits = 1), #find lower confidence interval at 95%
            ci_upper = round(mean_score + 1.96 * (sd(final_productivity_score) / sqrt(length(final_productivity_score))),digits = 1) #find upper confidence interval at 95%
  ) |> 
  mutate(marker = paste0("Type: ", accompanied, "<br>", #adding a column called marker which will be called on later by the interactive figure to display the mean and confidence interval
                         "Mean: ", mean_score, "<br>",
                         "95% Confidence interval for mean: ", ci_upper, " - ", ci_lower))

Note on the Marker Column

This column is created to be the hover-text/tooltip in the interactive figure. This marker column uses defined “text”, defined line breaks “
”, and response variables that call to an existing field.

Ex: “Type:” will print exaclty as that and be the first thing in the marker. Next is accompanied, which calls on that field and will print “Accompanied” or “Alone” depending on where the curser is hovering.

The mutate function to create a marker column is used again in the Data Visualization Code

Interactive data table

Brief data dictionary for mydata

Response Variable: Final Productivity Score Pre-selected predictor variable: accompanied (Accompanied or Alone)

Other variables collected: Company type (Alone, Friend, or Acquaintance), Sleep (decimal hour), deadline type (self imposed or external), and weather (Warm, Cold, Wind, or Rain)

Focus Score was self determined using this guideline: (1 is the lowest, 5 is the highest, discrete numeric data)

1: Constantly distracted + brain fog, low critical thinking applied

2: Frequently distracted, trouble staying on task, bored, needed to re-do or re-read instructions many times

3: Occasionally distracted, able to return to the task at hand without severe backtracking

4: Mostly focused, some distractions but they are usually intentional breaks

5: Highly focused, very few to no distractions, efficient workflow

#Using package DT to create an interactive table
datatable(data = mydata)

Data Visualization Code

# new mydata object with marker column
mydata_marker <- mydata |>  
  # create a new column/field called marker 
  mutate(marker = paste0("Type: ", company_type, "<br>",
                         "Score: ", focus_score, " × ", "Duration (hr): ", duration_decimal_hour, "<br>",
                         "= ", "Final Productivity Score: ", final_productivity_score)) #this marker will be displayed when the cursur hovers over a data point in the interactive figure. It displays the data values and how they are used to calculate the productivity score, which is the value each data point is being plotted with

#creating static visualizations for personal data

mydata_static <- ggplot(data = mydata_marker, #using ggplot to create a static figure
                       aes(x = accompanied, #x axis is by accompanied group
                           y = final_productivity_score, #y axis is by final productivity score
                           color = accompanied, #color the data by accompanied group
                           text = marker)) + #this text field is not used in ggplot directly, but will be called on later by plotly to create the hover-text. This is saying the hover-text should display the marker field - which was created earlier for this purpose
  geom_jitter(height = 0, #add geometry of jitter plot, 0 height jitter
              width = 0.2, #0.2 jitter width
              shape = 20) + #shape is open circles for the jitter points
  geom_errorbar(data = mydata_summary, #add a geometry of an error bar from data of mydata_summary
                aes(x = accompanied, #x axis by accompanied
                    y = mean_score, # y axis by average productivity scores
                    ymin = ci_lower, #error bar lower
                    ymax = ci_upper, #error bar upper
                    width = 0.12), #manual width of the bars
                    color = "black") + #color them black for visual ease
  geom_point(data = mydata_summary, #add geometry of a point from mydata_summary of the mean productivity score
             aes(x = accompanied, #x axis by accompnied
                 y = mean_score), # y axis is the mean productivity score
             color = "black", #point showing the mean is black
             size = 2) +
  labs(x = "Company Type", #labling the x and y axis and the title
       y = "Final Productivity Score",
       title = "Mean Productivity Score between \nAccompanied and Alone study sessions") +
  scale_color_manual(values = c("Accompanied" = "#377b42", #manual colors for groups 
                                "Alone" = "#AA71BA")) +
  theme_minimal() + #minimal theme
  theme(legend.position = "none") #remove legend because it's redundent


#using plotly package to make the figure interactive
mydata_interactive <- ggplotly(mydata_static, tooltip = "text") |> 
  layout( #edit the layout of the figure and details
    font = list(family = "Times"), #use Times fonts for the figure axis and title
  
    # editing the marker/tooltip/hover-text 
    hoverlabel = list(
      # editing the font for the marker hover-text
      font = list(
        family = "Times",
        size = 13,
        color = "#FFFFFF",
        align = "left"
      )))

Mean Productivity Interactive Figure

Affective Data Visualization

Artist Statement

This affective data visualization captures my study habits and productivity over the Spring 2025 quarter. Each data point is a small doodle: green for studying with others, purple for studying alone. For each observation the height represents my productivity and the count of dots inside or around them indicate my focus score. The larger single dot represents the mean for each category.

I was inspired by Dear Data by Stefanie Posavec and Giorgia Lupi. I drew the piece in Procreate on my iPad, beginning with layout sketches then adapting data from a jitter and mean plot I created in R. As I explored the data, I noticed that my study time per day increased over the quarter. That realization shaped the final design: the included mountain range represents the slope of my productivity over time, shifting the focus away from comparison and instead to a visual celebration of the effort I put in throughout the term.