1/7/2024 0 Comments Spotify listening history 2015![]() ![]() Mins_listen = ( ( sum_ms_listen / 1000 ) / 60 ) ) %>% arrange ( desc ( mins_listen ) ) %>% filter ( date = "" ) %>% count ( track_name ) # A tibble: 4 × 3ġ Episode 156: The Matthew Coleman Murders 1 I don’t know if any song is “listen to nearly 1,200 minutes of it in the same day” good, but if anything is… = "plot" ) + labs (x = "", y = "Minutes Listened", title = "Luca Stricagnoli\'s Cover of Sweet Child O' Mine Ruled That Day" )Īs I’d suspected, I’d listened to an obscene amount of instrumental acoustic guitar with a You’re Wrong About episode sprinkled in for flavor.īefore you laugh too much (ok fair, after you finish laughing) I recommend you check out this video. Plot.title = element_text (hjust = 0.5 ), Plot.background = element_rect (fill = "black" ), Track_name = as.factor ( track_name ) ) %>% select ( - n ) %>% ggplot ( aes ( fct_reorder ( track_name, mins_listen ), fill = track_name ) ) + scale_fill_viridis_d (option = "plasma" ) + geom_col ( aes (y = mins_listen ), alpha = 0.8 ) + geom_text ( aes (y = mins_listen, label = mins_listen ), hjust = - 0.2, family = "oswald", color = "white", size = 5 ) + scale_y_continuous (labels = comma, limits = c ( 0, 1500 ) ) + theme_dark ( ) + coord_flip ( ) + theme ( = element_blank ( ), Mins_listen = ( ( sum_ms_listen / 1000 ) / 60 ) ) %>% arrange ( desc ( mins_listen ) ) %>% filter ( date = "" ) %>% group_by ( track_name ) %>% tally ( ms_played ) %>% mutate (mins_listen = as.integer ( ( ( n / 1000 ) / 60 ) ), Listen_data %>% group_by ( date ) %>% mutate (sum_ms_listen = sum ( ms_played ), I had an intuition of what I was listening to, but before I assumed too much I decided to visualize the data for that day. ![]() I was working on a huge task I needed to finish before I left to get married the following week! ![]() What on Earth Happened on August 28, 2021? Title = "By Far My Heaviest Spotify Listening Happened on August 28, 2021" ) = "plot" ) + labs (x = "", y = "Minutes Listened", ![]() Panel.background = element_rect (fill = "black" ),Īxis.text = element_text (color = "white" ),Ī = element_text (angle = 25 ),Ī = element_text (size = 15, hjust = 1.2 ), Text = element_text (family = "oswald", color = "white", size = 15 ), Mins_listen = ( ( sum_ms_listen / 1000 ) / 60 ) ) %>% ggplot ( aes (x = date, y = mins_listen, color = mins_listen ) ) + geom_line ( ) + scale_color_viridis_c (option = "plasma" ) + theme_dark ( ) + scale_x_date (limits = as.Date ( c ( "", "" ) ),ĭate_labels = "%B" ) + theme (plot.background = element_rect (fill = "black" ), They look cool and they’re pre-assessed for accessibility.įamily = "oswald" ) showtext :: showtext_auto ( ) listen_data %>% group_by ( date ) %>% mutate (sum_ms_listen = sum ( ms_played ), The custom font via the showtext package made a huge difference, and I highly recommend using the color schemes from the viridis palettes. I created this and all the other visualizations in this plot using ggplot2. Spotify Wrapped focuses on totals throughout the year, and I was curious about what my listening habits looked like at different times of year. TRUE ~ NA_character_ ) ) ) %>% filter ( date >= "" & date % arrange ( desc ( date ) ) %>% select ( - day_week_temp ) skim ( listen_data ) Table 1: Data summary Name Weekend = factor ( case_when ( day_week_temp = 6 | day_week_temp = 7 ~ "Weekend",ĭay_week_temp = 1 | day_week_temp = 2 | day_week_temp = 3 | day_week_temp = 4 | day_week_temp = 5 ~ "Weekday", Listen_data % clean_names ( ) %>% mutate (end_time = with_tz ( end_time, tzone = "America/New_York" ), ![]()
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