library(shiny)
library(tidyverse)
library(here)
library(foreach)
library(english)
# Load Data
load("data/R_data/kmeans_nfact.RData")
files <- list.files(
paste0(here(), "/data/full_history_data"),
pattern = "*.json",
full.names = TRUE
)
full_streaming_history <- foreach(
file = files,
.packages = c("jsonlite"),
.combine = rbind
) %do% {
fromJSON(file, flatten = TRUE)
} %>%
rename(
track_name = "master_metadata_track_name",
artist_name = "master_metadata_album_artist_name"
) %>%
mutate(
track_uri = gsub("spotify:track:", "", spotify_track_uri),
month = ts %>%
substring(1, 7) %>%
paste0("-01") %>%
ymd()
) %>%
select(-spotify_track_uri) %>%
filter(month >= ymd("2019-04-01"))
min_date <- full_streaming_history %>%
pull(month) %>%
min()
max_date <- full_streaming_history %>%
pull(month) %>%
max()
# UI
ui <- fluidPage(
titlePanel("Spotify Clustering"),
sidebarLayout(
sidebarPanel(
selectInput(
"index",
"Clustering Performance Rank",
choices = seq(1, nrow(kmeans_nfact_save))
),
sliderInput(
"dates",
"Streaming History Date Range",
min = min_date,
max = max_date,
value = c(max_date %m-% months(6), max_date)
)
),
mainPanel(
tabsetPanel(
type = "tabs",
tabPanel(
"Cluster Plot",
h1(textOutput("cluster_title")),
textOutput("cluster_description"),
h2("Cluster Centres"),
tableOutput("cluster_table"),
h2("Cluster Plot"),
plotOutput("cluster_plot")
),
tabPanel(
"Cluster History Plot",
h1("Cluster History Plot"),
p("This plot shows the proportion of hours listened for each cluster."),
plotOutput("cluster_history_plot")
),
tabPanel(
"Track Clusters",
h1("Track Clusters"),
textOutput("track_clusters_desc"),
dataTableOutput("track_clusters")
)
)
)
)
)
# SERVER
server <- function(input, output) {
output$cluster_title <- renderText({
rank <- input$index
paste0(
"K-means Cluster: Rank ",
rank
)
})
output$cluster_description <- renderText({
total_withinss <- kmeans_nfact_save %>%
slice(as.numeric(input$index)) %>%
pull(total_withinss) %>%
round(digits = 2)
bsstssRatio <- kmeans_nfact_save %>%
slice(as.numeric(input$index)) %>%
pull(bsstssRatio) %>%
round(digits = 2)
index_ordinal <- input$index %>%
as.numeric() %>%
ordinal()
paste0(
"The graph below shows the k-means cluster with the ",
index_ordinal,
" highest BSS/TSS ratio of ",
bsstssRatio,
" and a total within sum of squares of ",
total_withinss,
"."
)
})
output$track_clusters_desc <- renderText({
start_date <- input$dates[1]
end_date <- input$dates[2]
paste0(
"This table shows the cluster of all tracks listened to from ",
start_date,
" to ",
end_date,
"."
)
})
output$track_clusters <- renderDataTable({
kmeans_nfact_save %>%
slice(as.numeric(input$index)) %>%
pull(km) %>%
pluck(1) %>%
pluck("cluster") %>%
as.data.frame() %>%
rownames_to_column("track_artist") %>%
rename(cluster = 2) %>%
mutate(cluster = paste0("Cluster ", cluster)) %>%
left_join(
full_streaming_history %>%
filter(
month %>%
between(input$dates[1], input$dates[2])
) %>%
select(
track_name,
artist_name
) %>%
na.omit() %>%
distinct() %>%
mutate(
track_artist = paste(
track_name,
artist_name,
sep = " - "
)
),
.,
by = "track_artist"
) %>%
select(
cluster,
artist_name,
track_name
) %>%
arrange(
cluster,
artist_name,
track_name
) %>%
rename(
Cluster = "cluster",
`Artist Name` = "artist_name",
`Track Name` = "track_name"
)
})
output$cluster_plot <- renderPlot({
kmeans_nfact_save %>%
slice(as.numeric(input$index)) %>%
pull(km_plot)
})
output$cluster_table <- renderTable({
kmeans_nfact_save %>%
slice(as.numeric(input$index)) %>%
pull(km) %>%
pluck(1) %>%
pluck("centers") %>%
as.data.frame() %>%
rownames_to_column("cluster") %>%
mutate(cluster = paste0("Cluster ", cluster))
})
output$cluster_history_plot <- renderPlot({
track_clusters <- kmeans_nfact_save %>%
slice(as.numeric(input$index)) %>%
pull(km) %>%
pluck(1) %>%
pluck("cluster") %>%
as.data.frame() %>%
rownames_to_column("track_artist") %>%
rename(cluster = 2) %>%
mutate(cluster = paste0("Cluster ", cluster)) %>%
left_join(
full_streaming_history %>%
select(
track_name,
artist_name
) %>%
na.omit() %>%
distinct() %>%
mutate(
track_artist = paste(
track_name,
artist_name,
sep = " - "
)
),
.,
by = "track_artist"
) %>%
select(-track_artist) %>%
arrange(
artist_name,
track_name
)
cluster_summary <- full_streaming_history %>%
select(
month,
track_name,
artist_name,
ms_played
) %>%
na.omit() %>%
filter(
month %>%
between(input$dates[1], input$dates[2])
) %>%
left_join(
track_clusters,
by = c("track_name", "artist_name")
) %>%
group_by(
month,
cluster
) %>%
summarise(hours_listened = sum(ms_played/(1000*60)))
ggplot(cluster_summary, aes(x = month, y = hours_listened, fill = cluster, label = cluster)) +
xlab("Date") +
ylab("Proportion") +
geom_bar(position = "fill", stat = "identity")
})
}
shinyApp(ui = ui, server = server)
