# -------------------------------------------------
# 1️⃣ Load required packages
# -------------------------------------------------
install.packages(c("jsonlite", "tidyverse", "lubridate"))
library(jsonlite)
library(tidyverse)
library(lubridate)
# -------------------------------------------------
# 2️⃣ Read the JSON log
# -------------------------------------------------
log_path <- "C:/Users/YourName/Documents/DeadlyFugitive/Logs/DF-2024-07-12.json"
raw_log <- fromJSON(log_path, flatten = TRUE)
# -------------------------------------------------
# 3️⃣ Tidy the events table
# -------------------------------------------------
events <- raw_log$events %>%
mutate(
time = hms(time), # convert "00:01:04" → period object
seconds = as.numeric(time) # seconds since mission start
) %>%
as_tibble()
print(events)
#> # A tibble: 5 × 5
#> time type zone level distance
#> <Period> <chr> <chr> <dbl> <dbl>
#> 1 12s enter_zone warehouse NA NA
#> 2 64s noise NA 3.2 NA
#> 3 105s guard_spotted NA NA 5.8
#> 4 150s takedown NA NA NA
#> 5 180s extraction NA NA NA
# -------------------------------------------------
# 4️⃣ Simple analysis – average detection distance
# -------------------------------------------------
avg_dist <- events %>%
filter(type == "guard_spotted") %>%
summarise(mean_distance = mean(distance, na.rm = TRUE))
cat("Average guard detection distance:", round(avg_dist$mean_distance, 2), "meters\n")
#> Average guard detection distance: 5.8 meters
# -------------------------------------------------
# 5️⃣ Plot a timeline of events
# -------------------------------------------------
ggplot(events, aes(x = seconds, y = fct_rev(factor(type)))) +
geom_point(size = 3, colour = "#2E86AB") +
labs(
title = paste0("Mission Timeline – ", raw_log$mission_id),
x = "Seconds since start",
y = "Event type"
) +
theme_minimal()
Running the script produces a horizontal timeline where each dot marks an in‑game event, ordered by time. You can quickly spot bottlenecks (e.g., many noises early on) and decide where to tweak your playstyle or mod the AI.
Meta Description: Struggling with the PKF Studios Ashley Lane “Deadly Fugitive” R install? This comprehensive guide covers system requirements, step-by-step installation, troubleshooting the R setup, and optimizing your gameplay. pkf studios ashley lane deadly fugitive r install
| Feature | Why it matters | |---------|----------------| | Narrative‑first design | Story drives gameplay, not the other way around. | | Open data pipelines | Most games ship with JSON or CSV logs, encouraging community analytics. | | Cross‑platform releases | PC (Windows/macOS/Linux) and consoles (Xbox, Switch). | | Community‑driven mods | Modders can hook into the same data streams the devs use. | Running the script produces a horizontal timeline where
PKF’s most popular titles before Deadly Fugitive were “Echoes of the Void” (a roguelike shooter) and “Neon Drift” (a cyber‑racing sim). Both games featured robust telemetry that players loved to mine for high scores, speedrun strategies, and even AI training. troubleshooting the R setup