TidyTuesday Assignment

#| label: load-libraries-data
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library(tidyverse)    # For ggplot, dplyr, and friends

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✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.4     
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ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library (ggridges) # For the ridgeline plot
library (ggrepel) # For clean annotations
library(plotly)

Warning: package 'plotly' was built under R version 4.5.1

Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout

library(tinytex)

Warning: package 'tinytex' was built under R version 4.5.1

Load TidyTuesday MTA data

MTA_raw <- read_csv("data/MTA_Permanent_Art_Catalog__Beginning_1980_20250724.csv")

Rows: 381 Columns: 9
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (8): Agency, Station Name, Line, Artist, Art Title, Art Material, Art De...
dbl (1): Art Date

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.

MTAart_filtered <- MTA_raw |>
  filter(`Art Date` >= 2013 & `Art Date` <= 2023)

Plotting a bar chart to visualize the number of artwork by year over a 10-year period

Art_display <- ggplot(MTAart_filtered, aes(x = `Art Date`, fill = Agency)) +
  geom_bar(position = "dodge") +
  labs(title = "Artwork display per year by agency (2013-2023)",
       x = "Year",
       y = "Count", caption= "Source: MTA Permanent Art Catalog (Beginning 1980) — https://www.mta.info/open-data.\n\nAfter 2020, there’s a noticeable decline across agencies, possibly reflecting disruptions due to the COVID-19 pandemic.") +
  theme_minimal()+
  theme(
    panel.border = element_blank(),
    plot.background = element_blank(),
    panel.grid.major = element_line(linetype = "blank"),
    panel.grid.minor = element_line(linetype = "blank"),
    axis.line = element_line(color = "black"))

Art_display

ggsave("Art_display.png", plot = Art_display, width = 10, height = 6, units = "in", dpi = 300)

Public_art <- ggplot(MTAart_filtered, aes(x= `Art Date`, y= Agency, fill =Agency))+
    geom_density_ridges(alpha = 0.7, scale = 2, rel_min_height = 0.01) +
  labs(
    title = "MTA Public Art Displays by Agency (2013–2023)",
    x = "Year",
    y = "MTA Agency", caption="Source: MTA Permanent Art Catalog (Beginning 1980) — https://www.mta.info/open-data.\n\nThis plot illustrates the distribution of public art displays across MTA agencies from 2013 to 2023.\nPeaks in 2017–2019 reflect high activity; the decline after 2020 likely reflects pandemic disruptions.") +
  theme_minimal()+
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    plot.title = element_text(face = "bold"),
    plot.subtitle = element_text(hjust = 0),
    legend.position = "none"
  )
ggsave("Public_art.png", plot = Public_art, width = 10, height = 6, units = "in", dpi = 300)

Picking joint bandwidth of 1.17

# Print the plot
ggplotly(Public_art)

Picking joint bandwidth of 1.17