cargamos librerias
library(readxl)buscamos el archivo de nuestro interes en este caso el libro de excel que necesitaremo, usamos la funcion file.choose para poder abirlo, para que nos de la ruta, despues read_xlxs y abrimos.
en este caso tenemos df (443x17) y df1 (84x17)
#seleccionar archivo
file.choose()## [1] "C:\\Users\\ferna\\Documents\\Documentos\\proyectos R\\excel\\Rconsolidados.html"# ver el nombre de las pestañas
excel_sheets("C:\\Users\\ferna\\Downloads\\supermarket_sales - Sheet1.xlsx")## [1] "supermarket_sales - Sheet1" "Hoja1"# con el archivo en la pestaña por default
df <- read_xlsx("C:\\Users\\ferna\\Downloads\\supermarket_sales - Sheet1.xlsx")
df## # A tibble: 443 × 17
## `Invoice ID` Branch City `Customer type` Gender `Product line` `Unit price`
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 731-59-7531 B Manda… Member Male Health and be… 72.6
## 2 676-39-6028 A Yangon Member Female Electronic ac… 64.4
## 3 502-05-1910 A Yangon Normal Male Health and be… 65.2
## 4 485-30-8700 A Yangon Normal Female Sports and tr… 33.3
## 5 598-47-9715 C Naypy… Normal Male Electronic ac… 84.1
## 6 701-69-8742 B Manda… Normal Male Sports and tr… 34.4
## 7 575-67-1508 A Yangon Normal Male Electronic ac… 38.6
## 8 541-08-3113 C Naypy… Normal Male Food and beve… 66.0
## 9 246-11-3901 C Naypy… Normal Female Electronic ac… 32.8
## 10 674-15-9296 A Yangon Normal Male Sports and tr… 37.1
## # ℹ 433 more rows
## # ℹ 10 more variables: Quantity <dbl>, `Tax 5%` <dbl>, Total <dbl>, Date <chr>,
## # Time <dttm>, Payment <chr>, cogs <dbl>, `gross margin percentage` <dbl>,
## # `gross income` <dbl>, Rating <dbl># el archivo con la pestaña de nuestro interes
df1 <-read_xlsx("C:\\Users\\ferna\\Downloads\\supermarket_sales - Sheet1.xlsx" , sheet = "Hoja1")
df1## # A tibble: 84 × 17
## `Invoice ID` Branch City `Customer type` Gender `Product line` `Unit price`
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 722-13-2115 C Naypy… Member Male Sports and tr… 42.8
## 2 749-81-8133 A Yangon Normal Female Fashion acces… 94.7
## 3 777-67-2495 B Manda… Normal Male Home and life… 69.0
## 4 636-98-3364 B Manda… Member Female Electronic ac… 26.3
## 5 246-55-6923 C Naypy… Member Female Home and life… 35.8
## 6 181-82-6255 B Manda… Normal Female Home and life… 16.4
## 7 838-02-1821 C Naypy… Member Female Home and life… 12.7
## 8 887-42-0517 C Naypy… Normal Female Sports and tr… 83.1
## 9 457-12-0244 C Naypy… Member Female Sports and tr… 35.2
## 10 226-34-0034 B Manda… Normal Female Electronic ac… 13.8
## # ℹ 74 more rows
## # ℹ 10 more variables: Quantity <dbl>, `Tax 5%` <dbl>, Total <dbl>, Date <chr>,
## # Time <dttm>, Payment <chr>, cogs <dbl>, `gross margin percentage` <dbl>,
## # `gross income` <dbl>, Rating <dbl>okay esos archivos vamos a consolidarlos facilmente usando la funcion rbind, facil sencillo y listos para trabajar y en teoria la suma de df + df1 nos tendria que dar igual 527x17 lo que es realmente lo que nos muestra.
#cosolidacion de la informacion
dfc = rbind(df,df1)
dfc## # A tibble: 527 × 17
## `Invoice ID` Branch City `Customer type` Gender `Product line` `Unit price`
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 731-59-7531 B Manda… Member Male Health and be… 72.6
## 2 676-39-6028 A Yangon Member Female Electronic ac… 64.4
## 3 502-05-1910 A Yangon Normal Male Health and be… 65.2
## 4 485-30-8700 A Yangon Normal Female Sports and tr… 33.3
## 5 598-47-9715 C Naypy… Normal Male Electronic ac… 84.1
## 6 701-69-8742 B Manda… Normal Male Sports and tr… 34.4
## 7 575-67-1508 A Yangon Normal Male Electronic ac… 38.6
## 8 541-08-3113 C Naypy… Normal Male Food and beve… 66.0
## 9 246-11-3901 C Naypy… Normal Female Electronic ac… 32.8
## 10 674-15-9296 A Yangon Normal Male Sports and tr… 37.1
## # ℹ 517 more rows
## # ℹ 10 more variables: Quantity <dbl>, `Tax 5%` <dbl>, Total <dbl>, Date <chr>,
## # Time <dttm>, Payment <chr>, cogs <dbl>, `gross margin percentage` <dbl>,
## # `gross income` <dbl>, Rating <dbl>