Kruzlic Class Project 1

final project 1

Qasim Abbas

dplyr

Author

Bryn Kruzlic

Published

July 17, 2023

These are the libraries being used in the following project.

Code

library(readxl)
library(tidyverse)
library(lubridate)
library(ggplot2)
library(stringr)
library(readr)
library(cluster)
library(dplyr)
library(AER)


knitr::opts_chunk$set(echo = TRUE)

Read in file- top Spotify chart data

Code

top10s <- read_csv("C:/Users/Bryn Kruzlic/OneDrive/Desktop/MS_DACSS/DACSS603/top10s.csv")

New names:
Rows: 603 Columns: 15
── Column specification
──────────────────────────────────────────────────────── Delimiter: "," chr
(3): title, artist, top genre dbl (12): ...1, year, bpm, nrgy, dnce, dB, live,
val, dur, acous, spch, pop
ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
Specify the column types or set `show_col_types = FALSE` to quiet this message.
• `` -> `...1`

Code

View(top10s)

Eliminate NA values within the data set

Code

top10s %>% 
  select_all() %>% 
  filter(bpm == 0 |
           nrgy == 0 |
           dnce == 0 |
           dB == 0 |
           live == 0 |
           dur == 0 |
           spch == 0 |
           pop  == 0 )

# A tibble: 5 × 15
   ...1 title artist `top genre`  year   bpm  nrgy  dnce    dB  live   val   dur
  <dbl> <chr> <chr>  <chr>       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1    51 Hello Marti… big room     2010   128    98    67    -3    10    45   191
2   139 Blow… P!nk   dance pop    2012   114    92    60    -3    25    75   256
3   268 Not … Justi… dance pop    2014    86    73    59    -6    38    46   266
4   363 L.A.… Fergie dance pop    2015   202    39    48    -8    26    27   193
5   443 Mill… Adele  british so…  2016     0     0     0   -60     0     0   227
# ℹ 3 more variables: acous <dbl>, spch <dbl>, pop <dbl>

Code

summary(top10s)

      ...1          title              artist           top genre        
 Min.   :  1.0   Length:603         Length:603         Length:603        
 1st Qu.:151.5   Class :character   Class :character   Class :character  
 Median :302.0   Mode  :character   Mode  :character   Mode  :character  
 Mean   :302.0                                                           
 3rd Qu.:452.5                                                           
 Max.   :603.0                                                           
      year           bpm             nrgy           dnce      
 Min.   :2010   Min.   :  0.0   Min.   : 0.0   Min.   : 0.00  
 1st Qu.:2013   1st Qu.:100.0   1st Qu.:61.0   1st Qu.:57.00  
 Median :2015   Median :120.0   Median :74.0   Median :66.00  
 Mean   :2015   Mean   :118.5   Mean   :70.5   Mean   :64.38  
 3rd Qu.:2017   3rd Qu.:129.0   3rd Qu.:82.0   3rd Qu.:73.00  
 Max.   :2019   Max.   :206.0   Max.   :98.0   Max.   :97.00  
       dB               live            val             dur       
 Min.   :-60.000   Min.   : 0.00   Min.   : 0.00   Min.   :134.0  
 1st Qu.: -6.000   1st Qu.: 9.00   1st Qu.:35.00   1st Qu.:202.0  
 Median : -5.000   Median :12.00   Median :52.00   Median :221.0  
 Mean   : -5.579   Mean   :17.77   Mean   :52.23   Mean   :224.7  
 3rd Qu.: -4.000   3rd Qu.:24.00   3rd Qu.:69.00   3rd Qu.:239.5  
 Max.   : -2.000   Max.   :74.00   Max.   :98.00   Max.   :424.0  
     acous            spch             pop       
 Min.   : 0.00   Min.   : 0.000   Min.   : 0.00  
 1st Qu.: 2.00   1st Qu.: 4.000   1st Qu.:60.00  
 Median : 6.00   Median : 5.000   Median :69.00  
 Mean   :14.33   Mean   : 8.358   Mean   :66.52  
 3rd Qu.:17.00   3rd Qu.: 9.000   3rd Qu.:76.00  
 Max.   :99.00   Max.   :48.000   Max.   :99.00

Additional edit; song listed twice

Code

top10s <- top10s %>% 
  filter(title != 'The Hills' | year != 2016)

#Fixing and grouping

Code

genre <- top10s["top genre"]

unique(top10s$'top genre')

 [1] "neo mellow"                "detroit hip hop"          
 [3] "dance pop"                 "pop"                      
 [5] "canadian pop"              "hip pop"                  
 [7] "barbadian pop"             "atl hip hop"              
 [9] "australian pop"            "indie pop"                
[11] "art pop"                   "colombian pop"            
[13] "big room"                  "british soul"             
[15] "chicago rap"               "acoustic pop"             
[17] "permanent wave"            "boy band"                 
[19] "baroque pop"               "celtic rock"              
[21] "electro"                   "complextro"               
[23] "canadian hip hop"          "candy pop"                
[25] "alaska indie"              "folk-pop"                 
[27] "metropopolis"              "house"                    
[29] "australian hip hop"        "electropop"               
[31] "australian dance"          "hollywood"                
[33] "canadian contemporary r&b" "irish singer-songwriter"  
[35] "tropical house"            "belgian edm"              
[37] "french indie pop"          "hip hop"                  
[39] "danish pop"                "latin"                    
[41] "canadian latin"            "electronic trap"          
[43] "edm"                       "electro house"            
[45] "downtempo"                 "brostep"                  
[47] "contemporary country"      "moroccan pop"             
[49] "escape room"               "alternative r&b"

Code

top10s <-top10s%>%separate((`top genre`),c("variable","genre"),extra='merge')

Warning: Expected 2 pieces. Missing pieces filled with `NA` in 97 rows [5, 12, 24, 55,
58, 60, 65, 106, 108, 110, 114, 118, 119, 138, 148, 150, 153, 155, 163, 168,
...].

Code

other <- c("complextro", "downtempo", "brostep", "electro", "electronic", "escape", "folk-pop", "hollywood", "house", "irish", "permanent", "neo", "metropolis", "tropical")
top10s$genre[top10s$variable %in% other] <- 'other'
top10s$genre[top10s$variable == 'hip'] <- 'hip hop'
top10s$genre[top10s$variable == 'latin'] <- 'latin'
top10s$genre[top10s$variable == 'boy'] <- 'pop'
top10s$genre[top10s$variable == 'french'] <- 'pop'
top10s$genre[top10s$variable == 'electropop'] <- 'pop'
top10s$genre[top10s$variable == 'pop'] <- 'pop'
top10s$genre[top10s$genre == 'contemporary r&b'] <- 'r&b'
top10s$genre[top10s$genre == 'room'] <- 'other'
table(top10s$genre)


country   dance     edm hip hop   indie   latin   other     pop     r&b     rap 
      1       6       2      20       1       5      46     493       9       1 
   rock    soul 
      1      11

Code

miss <- colSums(is.na(top10s))
print(miss[miss>0])

genre 
    6

Code

top10s <-na.omit(top10s)

Grouping and Analysis

Top Artists

Code

pop_artist <- top10s%>%arrange(desc(pop))%>%group_by(artist)
pop_artist

# A tibble: 596 × 16
# Groups:   artist [181]
    ...1 title   artist variable genre  year   bpm  nrgy  dnce    dB  live   val
   <dbl> <chr>   <chr>  <chr>    <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1   573 "Memor… Maroo… pop      pop    2019    91    32    76    -7     8    57
 2   574 "Lose … Selen… dance    pop    2019   102    34    51    -9    21     9
 3   575 "Someo… Lewis… pop      pop    2019   110    41    50    -6    11    45
 4   576 "Se\xf… Shawn… canadian pop    2019   117    54    76    -6     9    75
 5   577 "How D… Sam S… pop      pop    2019   111    68    48    -5     8    35
 6   578 "South… Ed Sh… pop      pop    2019    98    62    86    -6     9    67
 7   579 "Tramp… SHAED  electro… pop    2019   127    46    62    -6    14    50
 8   580 "Happi… Marsh… brostep  other  2019   100    79    69    -3    17    67
 9   581 "Truth… Lizzo  escape   other  2019   158    62    72    -3    12    41
10   582 "Good … Lizzo  escape   other  2019    96    89    67    -3    74    48
# ℹ 586 more rows
# ℹ 4 more variables: dur <dbl>, acous <dbl>, spch <dbl>, pop <dbl>

Code

top_artist<-top10s%>%
  group_by(`year`)%>%
  count(artist)%>%
  mutate(prop=n/sum(n))

most_popular_artist <- pop_artist%>%count(artist)%>%arrange(desc(n))
most_popular_artist%>%head(15)%>%ggplot(aes(x=reorder(artist,n),y=n))+geom_col(fill="skyblue")+ggtitle("15 Most Popular Artist")+coord_flip()

Song duration

Code

duration <-top10s%>%ggplot(aes(x=reorder(genre,dur),y=dur))+geom_boxplot(fill="limegreen")+ggtitle("Song Duration")+theme_light()
duration

Code

duration <- top10s %>% 
  ggplot(aes(x = reorder(genre, dur), y = dur)) +
  geom_point() +
  ggtitle("Song Duration") +
  xlab("Genre") +
  ylab("Duration") +
  theme_light() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1.2))
duration

Code

bpm <- top10s%>%ggplot(aes(x=reorder(genre,bpm),y=bpm))+geom_boxplot(fill="violet")+ggtitle("bpm Distribution")+theme_light()+theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1.2))
bpm

Code

summarized_data_genre <- top10s %>% 
  group_by(genre) %>% 
  summarize(avg_duration = mean(dur)) %>% 
  arrange(desc(avg_duration))
View(summarized_data_genre)

Most and least popular songs

Code

max_pop <-top10s%>%filter(pop>=60)%>%select(artist,pop,title,genre)%>%arrange(desc(pop))
max_pop

# A tibble: 448 × 4
   artist          pop title                                               genre
   <chr>         <dbl> <chr>                                               <chr>
 1 Maroon 5         99 "Memories"                                          pop  
 2 Selena Gomez     97 "Lose You To Love Me"                               pop  
 3 Lewis Capaldi    96 "Someone You Loved"                                 pop  
 4 Shawn Mendes     95 "Se\xf1orita"                                       pop  
 5 Sam Smith        93 "How Do You Sleep?"                                 pop  
 6 Ed Sheeran       92 "South of the Border (feat. Camila Cabello & Cardi… pop  
 7 SHAED            92 "Trampoline (with ZAYN)"                            pop  
 8 Marshmello       90 "Happier"                                           other
 9 Lizzo            90 "Truth Hurts"                                       other
10 Lizzo            90 "Good as Hell (feat. Ariana Grande) - Remix"        other
# ℹ 438 more rows

Code

min_pop <-top10s%>%filter(pop<=59)%>%select(artist,pop,title,genre)%>%arrange(desc(pop))
min_pop

# A tibble: 148 × 4
   artist                pop title                                         genre
   <chr>               <dbl> <chr>                                         <chr>
 1 Adam Lambert           59 If I Had You                                  pop  
 2 The Black Eyed Peas    59 Rock That Body                                pop  
 3 Britney Spears         59 Hold It Against Me                            pop  
 4 Jennifer Lopez         59 I'm Into You                                  pop  
 5 Pitbull                59 Don't Stop the Party (feat. TJR)              pop  
 6 Alicia Keys            59 Girl On Fire (feat. Nicki Minaj) - Inferno V… hip …
 7 Pharrell Williams      59 Come Get It Bae                               pop  
 8 Lost Frequencies       59 Reality (feat. Janieck Devy) - Radio Edit     edm  
 9 Fifth Harmony          59 Angel                                         pop  
10 Little Mix             59 Touch (feat. Kid Ink)                         pop  
# ℹ 138 more rows

Artists with most number of songs

Code

summarized_songs_artist_1 <- top10s %>% 
  group_by(artist) %>% 
  summarize(no.of.songs = n()) %>% 
  arrange(desc(no.of.songs)) 

summarized_songs_artist <- summarized_songs_artist_1 %>% 
  slice_max(no.of.songs,n=10)
view(summarized_songs_artist)

Plot PDF and CDF

Code

var1_pdf <- prop.table(table(top10s$genre))
var1_cdf <- cumsum(var1_pdf)
var1_pdf_cdf <- data.frame(Genre = names(var1_pdf), Probability = var1_pdf, Cumulative = var1_cdf)
print(var1_pdf_cdf)

          Genre Probability.Var1 Probability.Freq  Cumulative
country country          country      0.001677852 0.001677852
dance     dance            dance      0.010067114 0.011744966
edm         edm              edm      0.003355705 0.015100671
hip hop hip hop          hip hop      0.033557047 0.048657718
indie     indie            indie      0.001677852 0.050335570
latin     latin            latin      0.008389262 0.058724832
other     other            other      0.077181208 0.135906040
pop         pop              pop      0.827181208 0.963087248
r&b         r&b              r&b      0.015100671 0.978187919
rap         rap              rap      0.001677852 0.979865772
rock       rock             rock      0.001677852 0.981543624
soul       soul             soul      0.018456376 1.000000000

Code

var2_pdf <- density(top10s$dur)
var2_pdf <- prop.table(table(top10s$dur))
var2_cdf <- cumsum(top10s$dur)


var2_pdf <- prop.table(table(top10s$dur))
var2_cdf <- cumsum(var2_pdf)
var2_pdf_cdf <- data.frame(Duration = unique(top10s$dur),
                      Probability = var2_pdf,
                      Cumulative = var2_cdf)

print(var2_pdf_cdf)

    Duration Probability.Var1 Probability.Freq  Cumulative
134      217              134      0.001677852 0.001677852
148      263              148      0.001677852 0.003355705
157      200              157      0.001677852 0.005033557
158      295              158      0.001677852 0.006711409
159      221              159      0.003355705 0.010067114
160      214              160      0.001677852 0.011744966
162      203              162      0.001677852 0.013422819
164      225              164      0.001677852 0.015100671
166      216              166      0.001677852 0.016778523
169      235              169      0.003355705 0.020134228
170      230              170      0.001677852 0.021812081
171      213              171      0.003355705 0.025167785
172      269              172      0.001677852 0.026845638
173      202              173      0.003355705 0.030201342
175      308              175      0.001677852 0.031879195
176      274              176      0.003355705 0.035234899
177      187              177      0.001677852 0.036912752
178      284              178      0.003355705 0.040268456
179      227              179      0.001677852 0.041946309
180      215              180      0.001677852 0.043624161
181      238              181      0.008389262 0.052013423
182      212              182      0.006711409 0.058724832
183      231              183      0.005033557 0.063758389
184      228              184      0.010067114 0.073825503
185      192              185      0.005033557 0.078859060
186      172              186      0.008389262 0.087248322
187      258              187      0.003355705 0.090604027
188      249              188      0.006711409 0.097315436
189      196              189      0.011744966 0.109060403
190      264              190      0.005033557 0.114093960
191      253              191      0.010067114 0.124161074
192      185              192      0.013422819 0.137583893
193      273              193      0.021812081 0.159395973
194      251              194      0.005033557 0.164429530
195      193              195      0.001677852 0.166107383
196      257              196      0.008389262 0.174496644
197      186              197      0.006711409 0.181208054
198      220              198      0.013422819 0.194630872
199      329              199      0.013422819 0.208053691
200      191              200      0.018456376 0.226510067
201      285              201      0.010067114 0.236577181
202      252              202      0.020134228 0.256711409
203      236              203      0.016778523 0.273489933
204      201              204      0.011744966 0.285234899
205      267              205      0.008389262 0.293624161
206      223              206      0.015100671 0.308724832
207      260              207      0.011744966 0.320469799
208      379              208      0.015100671 0.335570470
209      254              209      0.008389262 0.343959732
210      262              210      0.011744966 0.355704698
211      205              211      0.016778523 0.372483221
212      219              212      0.008389262 0.380872483
213      242              213      0.021812081 0.402684564
214      208              214      0.010067114 0.412751678
215      199              215      0.018456376 0.431208054
216      321              216      0.013422819 0.444630872
217      210              217      0.015100671 0.459731544
218      307              218      0.011744966 0.471476510
219      232              219      0.013422819 0.484899329
220      197              220      0.015100671 0.500000000
221      265              221      0.018456376 0.518456376
222      229              222      0.010067114 0.528523490
223      247              223      0.011744966 0.540268456
224      255              224      0.013422819 0.553691275
225      279              225      0.020134228 0.573825503
226      245              226      0.006711409 0.580536913
227      233              227      0.020134228 0.600671141
228      188              228      0.018456376 0.619127517
229      222              229      0.013422819 0.632550336
230      248              230      0.023489933 0.656040268
231      211              231      0.013422819 0.669463087
232      206              232      0.010067114 0.679530201
233      198              233      0.010067114 0.689597315
234      277              234      0.010067114 0.699664430
235      171              235      0.015100671 0.714765101
236      243              236      0.016778523 0.731543624
237      237              237      0.003355705 0.734899329
238      256              238      0.008389262 0.743288591
239      224              239      0.005033557 0.748322148
240      209              240      0.005033557 0.753355705
241      271              241      0.003355705 0.756711409
242      184              242      0.008389262 0.765100671
243      177              243      0.008389262 0.773489933
244      183              244      0.005033557 0.778523490
245      354              245      0.011744966 0.790268456
246      272              246      0.003355705 0.793624161
247      326              247      0.003355705 0.796979866
248      280              248      0.008389262 0.805369128
249      157              249      0.005033557 0.810402685
250      241              250      0.005033557 0.815436242
251      207              251      0.010067114 0.825503356
252      424              252      0.006711409 0.832214765
253      244              253      0.008389262 0.840604027
254      353              254      0.001677852 0.842281879
255      286              255      0.005033557 0.847315436
256      259              256      0.003355705 0.850671141
257      250              257      0.006711409 0.857382550
258      270              258      0.005033557 0.862416107
259      173              259      0.001677852 0.864093960
260      190              260      0.006711409 0.870805369
261      179              261      0.003355705 0.874161074
262      194              262      0.003355705 0.877516779
263      240              263      0.008389262 0.885906040
264      246              264      0.008389262 0.894295302
265      178              265      0.001677852 0.895973154
266      204              266      0.001677852 0.897651007
267      319              267      0.001677852 0.899328859
268      226              268      0.001677852 0.901006711
269      268              269      0.006711409 0.907718121
270      323              270      0.003355705 0.911073826
271      169              271      0.003355705 0.914429530
272      160              272      0.006711409 0.921140940
273      239              273      0.005033557 0.926174497
274      266              274      0.005033557 0.931208054
277      282              277      0.003355705 0.934563758
279      234              279      0.006711409 0.941275168
280      218              280      0.001677852 0.942953020
282      293              282      0.003355705 0.946308725
284      189              284      0.001677852 0.947986577
285      134              285      0.006711409 0.954697987
286      320              286      0.003355705 0.958053691
289      159              289      0.001677852 0.959731544
291      292              291      0.001677852 0.961409396
292      309              292      0.001677852 0.963087248
293      182              293      0.001677852 0.964765101
294      181              294      0.001677852 0.966442953
295      166              295      0.003355705 0.969798658
304      170              304      0.001677852 0.971476510
307      291              307      0.001677852 0.973154362
308      403              308      0.001677852 0.974832215
309      310              309      0.001677852 0.976510067
310      289              310      0.001677852 0.978187919
319      176              319      0.001677852 0.979865772
320      261              320      0.001677852 0.981543624
321      158              321      0.001677852 0.983221477
323      180              323      0.001677852 0.984899329
326      175              326      0.001677852 0.986577181
329      341              329      0.003355705 0.989932886
341      164              341      0.001677852 0.991610738
353      195              353      0.001677852 0.993288591
354      304              354      0.001677852 0.994966443
379      294              379      0.001677852 0.996644295
403      148              403      0.001677852 0.998322148
424      162              424      0.001677852 1.000000000

Variable defining

Code

variable_2 <- top10s %>% 
  group_by(genre) %>% 
  summarize(avg_duration = mean(dur)) %>% 
  arrange(desc(avg_duration))
View(variable_2)

colnames(variable_2) <- c("Song Genre", "Average Song Duration")
view(variable_2)

mean(top10s$dur)

[1] 224.745

Code

sd(top10s$dur)

[1] 34.26896

Standard errors and Confidence intervals

Code

standard_errors <- aggregate(dur ~ genre, data = top10s, FUN = function(x) sd(x) / sqrt(length(x)))
sufficient_observations <- aggregate(dur ~ genre, data = top10s, FUN = function(x) length(x) >= 2)


genre_proportions <- prop.table(table(top10s$genre))
genre_sample_sizes <- as.integer(table(top10s$genre))

Confidence intervals using the binom.test() function

Code

confidence_intervals <- lapply(names(genre_proportions), function(g) {
  x <- as.integer(genre_proportions[g] * genre_sample_sizes[g])
  n <- genre_sample_sizes[g]
  conf_int <- binom.test(x, n)$conf.int
  data.frame(genre = g, conf_int)
})

Error in binom.test(x, n): 'x' must be nonnegative and integer

Code

confidence_intervals <- do.call(rbind, confidence_intervals)

Error in do.call(rbind, confidence_intervals): object 'confidence_intervals' not found

Code

print(confidence_intervals)

Error in print(confidence_intervals): object 'confidence_intervals' not found

Generating a plot

Code

average_duration <- aggregate(dur ~ genre, data = top10s, FUN = mean)
barplot(average_duration$dur, names.arg = average_duration$genre, xlab = "Genre", ylab = "Duration", main = "Average Duration by Genre", col = "steelblue")

Regression analysis

Code

top10s$dur <- as.numeric(top10s$dur)
missing_values <- is.na(top10s$dur)
subset_data <- top10s[!missing_values, ]


barplot(table(top10s$genre, useNA = "ifany"), beside = TRUE, col = "lightgray", xlab = "Genre", ylab = "Count", main = "Top 10s - Genre Distribution")


boxplot(dur ~ genre, data = top10s, col = "maroon", xlab = "Genre", ylab = "Duration", abline(h = median(top10s$dur), col = "blue", lwd = 2, lty = 2), main = "Top 10s - Duration by Genre")

Code

model <- lm(dur ~ genre, data = top10s)
summary(model)


Call:
lm(formula = dur ~ genre, data = top10s)

Residuals:
    Min      1Q  Median      3Q     Max 
-88.789 -21.789  -3.789  14.053 201.211 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)    196.00      32.75   5.985 3.78e-09 ***
genredance      47.00      35.37   1.329   0.1845    
genreedm       -37.50      40.11  -0.935   0.3502    
genrehip hop    62.70      33.56   1.868   0.0622 .  
genreindie      54.00      46.31   1.166   0.2441    
genrelatin      18.60      35.87   0.518   0.6043    
genreother      27.04      33.10   0.817   0.4143    
genrepop        26.79      32.78   0.817   0.4141    
genrer&b        25.67      34.52   0.744   0.4575    
genrerap       183.00      46.31   3.951 8.72e-05 ***
genrerock        7.00      46.31   0.151   0.8799    
genresoul       59.27      34.20   1.733   0.0836 .  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 32.75 on 584 degrees of freedom
Multiple R-squared:  0.1037,    Adjusted R-squared:  0.08678 
F-statistic:  6.14 on 11 and 584 DF,  p-value: 1.396e-09

Code

plot(top10s$genre, top10s$dur)

Warning in xy.coords(x, y, xlabel, ylabel, log): NAs introduced by coercion

Warning in min(x): no non-missing arguments to min; returning Inf

Warning in max(x): no non-missing arguments to max; returning -Inf

Error in plot.window(...): need finite 'xlim' values

Correlations

Code

nrgy_dnce <-top10s%>%ggplot(aes(x=nrgy,y=dnce))+geom_point()+ggtitle("Energy/Danceability")+theme_light()+geom_smooth(se=FALSE)
nrgy_dnce

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Code

cor(top10s$nrgy, top10s$dnce)

[1] 0.1666149

Code

val_nrgy <-top10s%>%ggplot(aes(x=val,y=nrgy))+geom_point()+ggtitle("Positivity/Energy")+theme_light()+geom_smooth(se=FALSE)
val_nrgy

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Code

cor(top10s$val, top10s$nrgy)

[1] 0.4109696

Code

bpm_dnce <-top10s%>%ggplot(aes(x=bpm,y=dnce))+geom_point()+ggtitle("BPM/Danceability")+theme_light()+geom_smooth(se=FALSE)
bpm_dnce

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Code

cor(top10s$bpm, top10s$dnce)

[1] -0.1322216

Code

bpm_pop <- top10s%>%ggplot(aes(x=bpm,y=pop))+geom_point()+ggtitle("BPM/Popularity")+theme_minimal()+geom_smooth(se=FALSE)
bpm_pop

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Code

cor(top10s$bpm, top10s$pop)

[1] 0.02314881