Title: Figure Replication Author: Yaffet Output: html_document

library(ggplot2)
library(readr)

data <- read_csv("attentive_cursor_simulated.csv")

## Rows: 2909 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): age_group
## dbl (3): user_id, attention, num_coords
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

ggplot(data, aes(x = num_coords)) +
  geom_histogram(bins = 30) +
  labs(
    title = "Distribution of Cursor Activity",
    x = "Number of Cursor Coordinates",
    y = "Count"
  ) +
  theme_minimal()

knitr::include_graphics("figure1.png")

The original figure is the cursor trajectories over search engine result pages and it tracks the users attention over that given page. Since the dataset used in this project doesn’t include the raw cursor path coordinates, I used the nums_coords variable which tracks the cursor activity based on cursor coordinates. Using this I was able to make a histogram that showed the distribution of cursor movement across users.

ggplot(data, aes(x = age_group)) +
  geom_bar() +
  labs(
    title = "Participant Age Distribution",
    x = "Age Group",
    y = "Count"
  ) +
  theme_minimal()

knitr::include_graphics("table1.png")

The original table presented a demographic information about the participants and this included different variables one of them being age distribution. This bar chart recreates that information by the number of participants in each age group.