Week 4: Apply 3

Import data

# excel file
data <- read_excel("data/myData.xlsx")
data

## # A tibble: 691 × 22
##    Column1 sort_name clean_name album rank_2003 rank_2012 rank_2020 differential
##      <dbl> <chr>     <chr>      <chr> <chr>     <chr>     <chr>            <dbl>
##  1       1 Sinatra,… Frank Sin… "In … 100       101       282               -182
##  2       2 Diddley,… Bo Diddley "Bo … 214       216       455               -241
##  3       3 Presley,… Elvis Pre… "Elv… 55        56        332               -277
##  4       4 Sinatra,… Frank Sin… "Son… 306       308       NA                -195
##  5       5 Little R… Little Ri… "Her… 50        50        227               -177
##  6       6 Beyonce   Beyonce    "Lem… NA        NA        32                 469
##  7       7 Winehous… Amy Wineh… "Bac… NA        451       33                 468
##  8       8 Crickets  Buddy Hol… "The… 421       420       NA                 -80
##  9       9 Bush, Ka… Kate Bush  "Hou… NA        NA        68                 433
## 10      10 Davis, M… Miles Dav… "Kin… 12        12        31                 -19
## # ℹ 681 more rows
## # ℹ 14 more variables: release_year <dbl>, genre <chr>, type <chr>,
## #   weeks_on_billboard <chr>, peak_billboard_position <dbl>,
## #   spotify_popularity <chr>, spotify_url <chr>, artist_member_count <dbl>,
## #   artist_gender <chr>, artist_birth_year_sum <dbl>,
## #   debut_album_release_year <dbl>, ave_age_at_top_500 <dbl>,
## #   years_between <dbl>, album_id <chr>

Plot Data

data %>%
    
    ggplot(aes(artist_gender)) +
    geom_bar()