Create Blog-Vizzes-Final.r

JoeLollo21 · Jul 12, 2023 · 6765fa0 · 6765fa0
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+## James Baldwin's presence in public library checkouts:
+# Install relevant packages -- once per machine.
+install.packages("tidyverse")
+install.packages("plotly")
+install.packages("stopwords")
+
+# Load libraries -- once per script.
+library("tidyverse")
+library("stopwords") # Doesn't work without quotes
+library("ggplot2")
+library("plotly")
+library("stringr")
+
+# Load the James Baldwin checkout data as a data frame.
+# Note: this data was cleaned to make all of them say the same Creator and 
+#make the Title easier to analyze. This was all done with OpenRefine.
+spl_df <- read.csv("https://raw.githubusercontent.com/JoeLollo21/Viral-Lit-Project/main/data/Checkouts-By-Title-Refined.csv", stringsAsFactors = FALSE)
+
+# Visulaization 1: All the Checkouts, Over Time
+spl_df_by_year <- spl_df %>% group_by(CheckoutMonth, CheckoutYear) %>%
+  summarize(TotalCheckouts = sum(Checkouts))
+
+# Convert month/year to date
+spl_df_monthly <- spl_df_by_year %>% mutate(date = paste0(CheckoutYear, "-", CheckoutMonth,  "-01" ))
+spl_df_monthly$date <- as.Date(spl_df_monthly$date, format = "%Y-%m-%d")
+spl_df_shorter <- spl_df_monthly %>% filter(CheckoutYear > 2011)
+
+spl_plot <- ggplot(spl_df_shorter) + geom_line(aes(x = date, y = TotalCheckouts)) + labs(x = "Checkout Year", y = "Checkouts", title = "Total Checkouts of James Baldwin's Work at SPL, 2012-2022")
+ggplotly(spl_plot)
+
+# Visualization 2: Material Type Checkouts Over Time
+# Group the data by material type and year, create new data frame from it.
+spl_materials <- spl_df %>% group_by(MaterialType, CheckoutYear) %>% summarize(TotalCheckouts = sum(Checkouts))
+
+# Graph the checkouts over time by material type.
+material_plot <- ggplot(spl_materials) + geom_line(aes(x = CheckoutYear, y = TotalCheckouts, color = MaterialType)) + 
+  geom_point(aes(x = CheckoutYear, y = TotalCheckouts, color = MaterialType)) +
+  labs(x = "Checkout Year", y = "Checkouts", title= "Checkouts of James Baldwin's Work at SPL by Material Type, 2005-2022")
+
+ggplotly(material_plot)
+
+# Create monthly column.
+spl_materials_monthly <- spl_df %>% group_by(MaterialType, CheckoutYear, CheckoutMonth) %>% summarize(TotalCheckouts = sum(Checkouts))
+
+# Create date column in the same way as the last one.
+spl_materials_monthly <- spl_materials_monthly %>% mutate(date = paste0(CheckoutYear, "-", CheckoutMonth,  "-01" ))
+
+spl_materials_monthly$date <- as.Date(spl_materials_monthly$date, format = "%Y-%m-%d")
+
+# Filter for the most recent months/years.
+spl_materials_shorter <- spl_materials_monthly %>% filter(CheckoutYear > 2011)
+
+# Visualize the monthly plot instead.
+material_plot_alt <- ggplot(spl_materials_shorter) + geom_line(aes(x = date, y = TotalCheckouts, color = MaterialType)) +
+  labs(x = "Checkout Year", y = "Checkouts", title = "Total Checkouts by Material Type, 2012-2022")
+
+ggplotly(material_plot_alt)
+
+# Visualization 3: Top Title Checkouts
+# Group the data by (general) title, create new data frame from it.
+spl_gen_titles <- spl_df %>% group_by(Gen.Title) %>%
+  summarize(TotalCheckouts = sum(Checkouts))
+
+# Graph each title's total checkouts.
+title_plot <- ggplot(spl_gen_titles) + geom_col(aes(x = TotalCheckouts, y = reorder(Gen.Title, +TotalCheckouts)), fill = "#FF0000") +
+  labs(x = "Checkouts", y = "", title = "Number of Checkouts by Title, 2005-2022")
+
+ggplotly(title_plot) 
+
+# Visualization 4: Top Checkouts and Trends - Time Series Analysis
+# Filter for the top 6 titles and create new data frame.
+spl_titles_monthly <- spl_df %>% group_by(Gen.Title, CheckoutYear, CheckoutMonth) %>% summarize(TotalCheckouts = sum(Checkouts))
+
+spl_titles <- spl_df %>% group_by(Gen.Title, CheckoutYear) %>% summarize(TotalCheckouts = sum(Checkouts))
+
+# Create list of top titles:
+top_titles <- c("Another Country", "Giovanni's Room", "Go Tell it On the Mountain", "If Beale Street Could Talk", "Notes of a Native Son", "The Fire Next Time")
+
+spl_top_titles_alt <- spl_titles %>% filter(Gen.Title %in% top_titles)
+
+spl_top_titles <- spl_titles_monthly %>% filter(Gen.Title %in% top_titles)
+
+# Convert to date:
+spl_top_titles <- spl_top_titles %>% mutate(date = paste0(CheckoutYear, "-", CheckoutMonth,  "-01" ))
+
+spl_top_titles$date <- as.Date(spl_top_titles$date, format = "%Y-%m-%d")
+
+# Same thing but only with 2012 onward, to make the top titles shorter.
+spl_top_titles_shorter <- spl_top_titles %>% filter(CheckoutYear > 2011)
+
+# Convert to date
+spl_top_titles_shorter <- spl_top_titles_shorter %>% mutate(date = paste0(CheckoutYear, "-", CheckoutMonth,  "-01" ))
+
+spl_top_titles_shorter$date <- as.Date(spl_top_titles_shorter$date, format = "%Y-%m-%d")
+
+# Extra vizzes for the blog: The Fire Next Time and Beale Street
+#(these will make sense if you read the blog!)
+just_fire <- spl_top_titles_shorter %>% filter(Gen.Title == "The Fire Next Time")
+just_beale <- spl_top_titles_shorter %>% filter(Gen.Title == "If Beale Street Could Talk")
+
+# Identify top titles:
+# spl_top_titles <- spl_titles %>% filter(TotalCheckouts > 200)
+# This one took lots of trial and error. There is probably a MUCH better way
+#to do it.
+# Another Country, Giovanni's Room, Go Tell it On the Mountain, If Beale Street Could Talk, Notes of a Native Son, The Fire Next Time
+
+# Make visualization of the top titles.
+top_titles_alt <- ggplot(spl_top_titles_alt) + geom_line(aes(x = CheckoutYear, y = TotalCheckouts, color = Gen.Title)) + geom_point(aes(x = CheckoutYear, y = TotalCheckouts, color = Gen.Title)) +
+  labs(x = "Checkout Year", y = "Checkouts", color = "Title", title = "Top Baldwin Titles Checked Out at SPL, 2005-2022")
+
+ggplotly(top_titles_alt)
+
+top_titles_plot <- ggplot(spl_top_titles_shorter) + geom_line(aes(x = date, y = TotalCheckouts, color = Gen.Title)) +
+  labs(x = "Checkout Year", y = "Checkouts", title = "Top Titles by James Baldwin Checked Out at SPL, 2012-2022")
+
+ggplotly(top_titles_plot)
+
+# Make vizzes of individual titles, using the same colors as the normal viz.
+fire_plot <- ggplot(just_fire) + geom_line(aes(x = date, y = TotalCheckouts), color = "purple") + 
+  labs(x = "Checkout Year", y = "Checkouts", title = "Checkouts of 'The Fire Next Time' at SPL, 2012-2022")
+
+beale_st_plot <- ggplot(just_beale) + geom_line(aes(x = date, y = TotalCheckouts), color = "darkgreen") +
+  labs(x = "Checkout Year", y = "Checkouts", title = "Checkouts of 'If Beale Street Could Talk' at SPL, 2012-2022")
+
+ggplotly(fire_plot)
+ggplotly(beale_st_plot)
+
+# See what titles are checked out the most.
+# (just some thoughts for now)
+# What surprises me?
+## All books have a significant decrease in 2022 except Giovanni's Room, might be
+##the content being relevant to the state of the world or some specific cultures?
+
+# Significance of "The Fire Next Time"?
+## Quoted significantly in BLM Tweets, Twitter activists might be encouraging
+##interested readers in checking the book out.
+
+# Search for big spikes or surprising directions.
+## BIG spike in If Beale Street Could Talk checkouts in 2019 when the movie 
+##came out.
+## Surprising increase in Giovanni's Room in 2021-2022.
+
+# Visualization 5: Top Titles as a Heatmap
+# Create heatmap of the titles. Create separate data frame.
+spl_titles_by_type <- spl_df %>% group_by(Gen.Title, MaterialType) %>% summarize(TotalCheckouts = sum(Checkouts))
+
+spl_top_titles_by_type <- spl_titles_by_type %>% filter(Gen.Title %in% top_titles)
+
+top_titles_map <- ggplot(spl_top_titles_by_type) +
+  geom_tile(aes(x = MaterialType, y = Gen.Title, fill = TotalCheckouts), color = "#000000") +
+  geom_text(aes(x = MaterialType, y = Gen.Title, label = TotalCheckouts), color = "#FFFFFF") +
+  scale_fill_gradient(low = "#000000", high = "#0094FF") +
+  labs(x = "Material Type", y = "Title", fill = "Total Checkouts", title = "Top Titles Checked Out, by Material Type")
+
+ggplotly(top_titles_map)
+
+# Upload ggplotly plots to the Plotly API:
+Sys.setenv("plotly_username" = "JoeLollo21")
+Sys.setenv("plotly_api_key" = "StG9GebmLPqXjbdj7kyB")
+
+api_create(spl_plot, filename = "Total Checkouts of James Baldwin's Work at SPL, 2012-2022")
+api_create(material_plot_alt, filename = "Total Checkouts by Material Type, 2012-2022")
+api_create(top_titles_plot, fielname = "Top Titles Checked Out, 2012-2022")
+api_create(fire_plot, filename = "Checkouts of The Fire Next Time, 2012-2022")
+api_create(beale_st_plot, filename = "Checkouts of Beale Street, 2012-2022")
+api_create(top_titles_map, filename = "Top Titles Checked Out, by Material Type")