Paso.1 Instalar paquetes y llamar librerias

#install.packages("DataExplorer")
library(DataExplorer)
spotify <- read.csv("C:/Users/valer/OneDrive/Escritorio/IA con impacto empresarial/Base de datos/Most Streamed Spotify Songs 2024.csv")

Paso.2 Crear reporte

create_report(spotify)
## 
## 
## processing file: report.rmd
##   |                                             |                                     |   0%  |                                             |.                                    |   2%                                   |                                             |..                                   |   5% [global_options]                  |                                             |...                                  |   7%                                   |                                             |....                                 |  10% [introduce]                       |                                             |....                                 |  12%                                   |                                             |.....                                |  14% [plot_intro]                      |                                             |......                               |  17%                                   |                                             |.......                              |  19% [data_structure]                  |                                             |........                             |  21%                                   |                                             |.........                            |  24% [missing_profile]                 |                                             |..........                           |  26%                                   |                                             |...........                          |  29% [univariate_distribution_header]  |                                             |...........                          |  31%                                   |                                             |............                         |  33% [plot_histogram]                  |                                             |.............                        |  36%                                   |                                             |..............                       |  38% [plot_density]                    |                                             |...............                      |  40%                                   |                                             |................                     |  43% [plot_frequency_bar]              |                                             |.................                    |  45%                                   |                                             |..................                   |  48% [plot_response_bar]               |                                             |..................                   |  50%                                   |                                             |...................                  |  52% [plot_with_bar]                   |                                             |....................                 |  55%                                   |                                             |.....................                |  57% [plot_normal_qq]                  |                                             |......................               |  60%                                   |                                             |.......................              |  62% [plot_response_qq]                |                                             |........................             |  64%                                   |                                             |.........................            |  67% [plot_by_qq]                      |                                             |..........................           |  69%                                   |                                             |..........................           |  71% [correlation_analysis]            |                                             |...........................          |  74%                                   |                                             |............................         |  76% [principal_component_analysis]    |                                             |.............................        |  79%                                   |                                             |..............................       |  81% [bivariate_distribution_header]   |                                             |...............................      |  83%                                   |                                             |................................     |  86% [plot_response_boxplot]           |                                             |.................................    |  88%                                   |                                             |.................................    |  90% [plot_by_boxplot]                 |                                             |..................................   |  93%                                   |                                             |...................................  |  95% [plot_response_scatterplot]       |                                             |.................................... |  98%                                   |                                             |.....................................| 100% [plot_by_scatterplot]           
## output file: C:/Users/valer/OneDrive/Escritorio/IA con impacto empresarial/RStudio/Portafolio/report.knit.md
## "C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/pandoc" +RTS -K512m -RTS "C:\Users\valer\OneDrive\ESCRIT~1\IACONI~1\RStudio\PORTAF~1\REPORT~1.MD" --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output pandoc54dc1e254bfe.html --lua-filter "C:\Users\valer\AppData\Local\R\win-library\4.4\rmarkdown\rmarkdown\lua\pagebreak.lua" --lua-filter "C:\Users\valer\AppData\Local\R\win-library\4.4\rmarkdown\rmarkdown\lua\latex-div.lua" --embed-resources --standalone --variable bs3=TRUE --section-divs --table-of-contents --toc-depth 6 --template "C:\Users\valer\AppData\Local\R\win-library\4.4\rmarkdown\rmd\h\default.html" --no-highlight --variable highlightjs=1 --variable theme=yeti --mathjax --variable "mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML" --include-in-header "C:\Users\valer\AppData\Local\Temp\Rtmp8WSsAs\rmarkdown-str54dc71126bc8.html"
## 
## Output created: report.html

Paso.3 Obtener una gráfica en particular

introduce(spotify)
##   rows columns discrete_columns continuous_columns all_missing_columns
## 1 4600      29               22                  6                   1
##   total_missing_values complete_rows total_observations memory_usage
## 1                 7941             0             133400      5679272
plot_intro(spotify)

plot_missing(spotify)

plot_histogram(spotify)

plot_bar(spotify)
## 22 columns ignored with more than 50 categories.
## Track: 4370 categories
## Album.Name: 4005 categories
## Artist: 2000 categories
## Release.Date: 1562 categories
## ISRC: 4598 categories
## All.Time.Rank: 4577 categories
## Spotify.Streams: 4426 categories
## Spotify.Playlist.Count: 4208 categories
## Spotify.Playlist.Reach: 4479 categories
## YouTube.Views: 4291 categories
## YouTube.Likes: 4284 categories
## TikTok.Posts: 3319 categories
## TikTok.Likes: 3616 categories
## TikTok.Views: 3617 categories
## YouTube.Playlist.Reach: 3459 categories
## AirPlay.Spins: 3268 categories
## SiriusXM.Spins: 690 categories
## Deezer.Playlist.Reach: 3559 categories
## Pandora.Streams: 3492 categories
## Pandora.Track.Stations: 2976 categories
## Soundcloud.Streams: 1266 categories
## Shazam.Counts: 4003 categories

plot_correlation(spotify)
## Warning in dummify(data, maxcat = maxcat): Ignored all discrete features since
## `maxcat` set to 20 categories!
## Warning: Removed 28 rows containing missing values or values outside the scale range
## (`geom_text()`).

Conclusión

DataExplorer simplifica la exploración de datos en R, generando automáticamente gráficos y resúmenes clave. Esto te ayuda a entender rápidamente la estructura y los patrones en tus datos, facilitando un análisis más eficiente.

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