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+# Project Idea
+# David Whitmer
+For my three-course sequence to complete the Data Analytics major, I chose English. The goal of this project is to apply data-driven methods to examine narrative time and setting in Karl Ove Knausgaard’s A Death in the Family. Specifically, this project seeks to map the non-chronological movement of the narrative by tracking shifts in temporal position and setting throughout the text. The proposed research question is: How does Knausgaard’s use of non-linear chronology and recurring settings structure the reader’s experience of memory and narration in A Death in the Family? Ultimately, the goal of the project is to help literary scholars and readers make sense of the novel’s fragmented temporal structure by visualizing how the narrative moves across time and place.
+The primary text for this project is A Death in the Family, the first volume of Knausgaard’s My Life series. I own a digital copy of the novel, and because the book spans approximately 600 pages, it provides a substantial and ethically sound dataset for analysis. The length and density of the text make it particularly well suited for a data analytics approach, as the narrative repeatedly shifts between different moments in Knausgaard’s life and revisits key locations across decades.
+The data for this project will be created through close reading and structured annotation of the text. Each unit of analysis (such as a paragraph or page range) will be tagged for narrative time (for example, childhood, adolescence, early adulthood, or present-day narration following the father’s death) and setting (such as the childhood home, the father’s house, or other significant locations). These annotations will then be organized into a dataset that reflects both the reading order of the novel and the underlying chronological order of the events being narrated.
+Once the dataset is constructed, the primary analytical method will be visualization rather than inferential modeling. The central output of the project will be an interactive timeline that displays narrative time and setting in chronological order. When a user hovers over a specific date or time period on the timeline, a representative quotation from the novel will appear, allowing readers to see how Knausgaard’s prose inhabits that moment. By comparing reading order to narrative chronology, the visualization will make visible the extent to which the novel resists linear storytelling and instead organizes memory spatially and temporally.
+The intended audience for this project includes literary scholars, students, and readers of contemporary autofiction who may struggle to track the novel’s complex temporal structure through traditional close reading alone. By providing a visual and interactive tool, the project offers a new way to engage with the text that complements, rather than replaces, interpretive literary analysis. The results may also contribute to broader discussions in literary studies about memory, autobiography, and non-linear narrative form.
+Ethically, the project poses minimal concerns. Because I own a digital copy of the text, the data is accessed legally and for educational purposes. The project does not involve personal data from living individuals beyond the author himself, and all quotations used in the visualization will be properly cited and limited in length. The goal is not to reproduce the text but to analyze and contextualize it. Overall, this project demonstrates how data analytics methods can be used responsibly to illuminate complex narrative structures in contemporary literature.
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diff --git a/presentations/David Whitmer Test.Rmd b/presentations/David Whitmer Test.Rmd
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+++ b/presentations/David Whitmer Test.Rmd
@@ -0,0 +1,20 @@
+---
+title: "My presentation"
+subtitle: "⚔ with xaringan"
+author: "David Whitmer"
+institute: "RStudio, PBC"
+date: "2016/12/12 (updated: `r Sys.Date()`)"
+output:
+ xaringan::moon_reader:
+ css: xaringan-themer.css
+ lib_dir: libs
+ nature:
+ highlightStyle: github
+ highlightLines: true
+ countIncrementalSlides: false
+---
+
+
+
+
+# This is a test presentation!
diff --git a/presentations/test123.html b/presentations/David Whitmer.html
similarity index 98%
rename from presentations/test123.html
rename to presentations/David Whitmer.html
index 41fee02..a431152 100644
--- a/presentations/test123.html
+++ b/presentations/David Whitmer.html
@@ -3,7 +3,7 @@
My presentation
-
+
@@ -18,7 +18,7 @@
## ⚔ with xaringan
]
.author[
-### Eren Bilen
+### David Whitmer
]
.institute[
### RStudio, PBC
diff --git a/presentations/libs/header-attrs-2.30/header-attrs.js b/presentations/libs/header-attrs-2.30/header-attrs.js
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--- /dev/null
+++ b/presentations/libs/header-attrs-2.30/header-attrs.js
@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
+ }
+});
diff --git a/presentations/test123.Rmd b/presentations/test123.Rmd
index 1cdb3e7..97802d7 100644
--- a/presentations/test123.Rmd
+++ b/presentations/test123.Rmd
@@ -1,7 +1,7 @@
---
title: "My presentation"
subtitle: "⚔ with xaringan"
-author: "Eren Bilen"
+author: "David Whitmer"
institute: "RStudio, PBC"
date: "2016/12/12 (updated: `r Sys.Date()`)"
output:
@@ -14,15 +14,7 @@ output:
countIncrementalSlides: false
---
-```{r xaringan-themer, include=FALSE, warning=FALSE}
-library(xaringanthemer)
-style_mono_accent(
- base_color = "#1c5253",
- header_font_google = google_font("Josefin Sans"),
- text_font_google = google_font("Montserrat", "300", "300i"),
- code_font_google = google_font("Fira Mono")
-)
-```
+
# This is a test presentation!
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