Introduction

The 1940s were an extremely significant time period for historical events, especially in the US. Marking the end of the great depression, World War II, and dawn of the nuclear age. This project analyzes a subset of Tate artwork collection containing artworks dating from 1940-1950. The goal of this project is to analyze the differences in artwork production throughout the decade. Using R, stringr, and tidyverse, I was able to clean the data set to find specific production across years which is specifically measured by the year variable. I then visualized the artwork production, allowing clear trends to be observed over time. The results will allow us to see how these historical events may have shaped the production and sharing of art.

Sources

The source I used for this project was the tate_artwork_data_1940-1950.csv, which gave insight to the quantity of paintings that were produced in the tate artwork collection over the decade. The data set includes the artist name, title of the artwork, dateText, which gives the year of the artwork, and acquisitionYear. These variables all play a key role in understanding the trends of artwork production in the 1940s, with certain artists and years producing more than others.

Cleaning and Processes

Below, I also included my data cleaning, that allowed me to observe mediums of artwork, year of acquisitions, and other valuable metrics to observe trends over a ten year timeline. I was able to mutate the year and acquisition year to show up as numerical values to use in a quantitative visualization below.

Once the data was cleaned, I then created my visualization that observed the quantity of paintings by year. This allow us to understand the state of the art industry and various other insights about the market including the supply and demand present. A line chart seemed to be appropriate in order to analyze year to year differences between the artwork quantities. I used color to differentiate each year in a clear, simple way that is easy to interpret for viewers.

library(stringr)

clean_art <- art |>
  mutate(
    year = as.integer(year),
    acquisitionYear = as.integer(acquisitionYear),
    artist = str_trim(artist),
    medium = str_trim(medium)
  ) |>
  filter(!is.na(year),
         year >= 1940,
         year <= 1950)

Presentation

The project is presented as an HTML site generated from a R Markdown file, which was produced from R Studio. The theme, cosmo, that was used for the project was a light, simple theme for clarity and cleanliness throughout the project. I also implemented these same themes throughout the visualization process, using the minimal theme from ggplot2 to evoke the same trend of light and simple. The goal of the presentation was to reduce visual noise and improve the analysis and interpretation possible for viewers.

Significance

The data set of Tate artwork collection can be used to tell many meaningful stories about the role of artwork through war and turmoil. This data set allowed us to examine the production of art in the most infamous war in history while also examining the impact it had on postwar society. This data set allows for us to examine this period of history from a different perspective than most, which can help us understand a wider scope of art performance. This work exemplifies the work of digital humanities because rather than replacing traditional art analysis, the computational analysis allows us to uncover underlying trends that may not have been possible to detect. This provides more context and understanding of historical trends, which can help for future predictions and reasonings.