Data Explorer - NYC Flights 13
Valeria Nango - A01174106
2024-08-12
Paso 1. Instalar paquetes y llamar librerias
#install.packages("DataExplorer")
library("DataExplorer")
#install.packages("nycflights13")
library(nycflights13)Paso 2. Crear bases de datos
flights <- flights
weather <- weather
planes <- planes
airports <- airports
airlines <- airlinesPaso 3. Crear reporte
create_report(flights)##
##
## 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 pandoc6adc6db667a6.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\RtmpK8cVSH\rmarkdown-str6adc436b4500.html"##
## Output created: report.htmlPaso 4. Obtener una gráfica en particular
introduce(flights)## # A tibble: 1 × 9
## rows columns discrete_columns continuous_columns all_missing_columns
## <int> <int> <int> <int> <int>
## 1 336776 19 5 14 0
## # ℹ 4 more variables: total_missing_values <int>, complete_rows <int>,
## # total_observations <int>, memory_usage <dbl>plot_intro(flights)
plot_missing(flights)
plot_histogram(flights)
plot_bar(flights)## 3 columns ignored with more than 50 categories.
## tailnum: 4044 categories
## dest: 105 categories
## time_hour: 6936 categories
plot_correlation(flights)## 3 features with more than 20 categories ignored!
## tailnum: 4044 categories
## dest: 105 categories
## time_hour: 6936 categories## Warning in cor(x = structure(list(year = c(2013L, 2013L, 2013L, 2013L, 2013L, :
## La desviación estándar es cero
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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