Walmart
Maria Del Bosque
2025-02-27
Instalar paquetes y llamar librerías
#install.packages("tidyverse")
library(tidyverse)Importar la base de datos
df <- read.csv("/Users/mariadelbosque/Desktop/TEC/CONCENTRACION/MODULO 2/walmart.csv")Entender y limpiar la base de datos
df$Date <- as.Date(df$Date, format="%d-%m-%Y")
summary(df)## Store Date Weekly_Sales Holiday_Flag
## Min. : 1 Min. :2010-02-05 Min. : 209986 Min. :0.00000
## 1st Qu.:12 1st Qu.:2010-10-08 1st Qu.: 553350 1st Qu.:0.00000
## Median :23 Median :2011-06-17 Median : 960746 Median :0.00000
## Mean :23 Mean :2011-06-17 Mean :1046965 Mean :0.06993
## 3rd Qu.:34 3rd Qu.:2012-02-24 3rd Qu.:1420159 3rd Qu.:0.00000
## Max. :45 Max. :2012-10-26 Max. :3818686 Max. :1.00000
## Temperature Fuel_Price CPI Unemployment
## Min. : -2.06 Min. :2.472 Min. :126.1 Min. : 3.879
## 1st Qu.: 47.46 1st Qu.:2.933 1st Qu.:131.7 1st Qu.: 6.891
## Median : 62.67 Median :3.445 Median :182.6 Median : 7.874
## Mean : 60.66 Mean :3.359 Mean :171.6 Mean : 7.999
## 3rd Qu.: 74.94 3rd Qu.:3.735 3rd Qu.:212.7 3rd Qu.: 8.622
## Max. :100.14 Max. :4.468 Max. :227.2 Max. :14.313str(df)## 'data.frame': 6435 obs. of 8 variables:
## $ Store : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Date : Date, format: "2010-02-05" "2010-02-12" ...
## $ Weekly_Sales: num 1643691 1641957 1611968 1409728 1554807 ...
## $ Holiday_Flag: int 0 1 0 0 0 0 0 0 0 0 ...
## $ Temperature : num 42.3 38.5 39.9 46.6 46.5 ...
## $ Fuel_Price : num 2.57 2.55 2.51 2.56 2.62 ...
## $ CPI : num 211 211 211 211 211 ...
## $ Unemployment: num 8.11 8.11 8.11 8.11 8.11 ...Agregar variables a la base de datos
df$Year <- format(df$Date, "%Y")
df$Year <- as.integer(df$Year)
df$Month <- format(df$Date, "%m")
df$Month <- as.integer(df$Month)
# df$WeekYear <- format(df$Date, "%W") # Iniciando en lunes
# df$WeekYear <- as.integer(df$WeekYear)
# df$WeekDay <- format(df$Date, "%u") # Iniciando en lunes
# df$WeekDay <- as.integer(df$WeekDay)
df$Day <- format(df$Date, "%d")
df$Day <- as.integer(df$Day)
str(df)## 'data.frame': 6435 obs. of 11 variables:
## $ Store : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Date : Date, format: "2010-02-05" "2010-02-12" ...
## $ Weekly_Sales: num 1643691 1641957 1611968 1409728 1554807 ...
## $ Holiday_Flag: int 0 1 0 0 0 0 0 0 0 0 ...
## $ Temperature : num 42.3 38.5 39.9 46.6 46.5 ...
## $ Fuel_Price : num 2.57 2.55 2.51 2.56 2.62 ...
## $ CPI : num 211 211 211 211 211 ...
## $ Unemployment: num 8.11 8.11 8.11 8.11 8.11 ...
## $ Year : int 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 ...
## $ Month : int 2 2 2 2 3 3 3 3 4 4 ...
## $ Day : int 5 12 19 26 5 12 19 26 2 9 ...summary(df)## Store Date Weekly_Sales Holiday_Flag
## Min. : 1 Min. :2010-02-05 Min. : 209986 Min. :0.00000
## 1st Qu.:12 1st Qu.:2010-10-08 1st Qu.: 553350 1st Qu.:0.00000
## Median :23 Median :2011-06-17 Median : 960746 Median :0.00000
## Mean :23 Mean :2011-06-17 Mean :1046965 Mean :0.06993
## 3rd Qu.:34 3rd Qu.:2012-02-24 3rd Qu.:1420159 3rd Qu.:0.00000
## Max. :45 Max. :2012-10-26 Max. :3818686 Max. :1.00000
## Temperature Fuel_Price CPI Unemployment
## Min. : -2.06 Min. :2.472 Min. :126.1 Min. : 3.879
## 1st Qu.: 47.46 1st Qu.:2.933 1st Qu.:131.7 1st Qu.: 6.891
## Median : 62.67 Median :3.445 Median :182.6 Median : 7.874
## Mean : 60.66 Mean :3.359 Mean :171.6 Mean : 7.999
## 3rd Qu.: 74.94 3rd Qu.:3.735 3rd Qu.:212.7 3rd Qu.: 8.622
## Max. :100.14 Max. :4.468 Max. :227.2 Max. :14.313
## Year Month Day
## Min. :2010 Min. : 1.000 Min. : 1.00
## 1st Qu.:2010 1st Qu.: 4.000 1st Qu.: 8.00
## Median :2011 Median : 6.000 Median :16.00
## Mean :2011 Mean : 6.448 Mean :15.68
## 3rd Qu.:2012 3rd Qu.: 9.000 3rd Qu.:23.00
## Max. :2012 Max. :12.000 Max. :31.00Generar regresión lineal
regresion <- lm(Weekly_Sales ~., data=df)
summary(regresion)##
## Call:
## lm(formula = Weekly_Sales ~ ., data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1094800 -382464 -42860 375406 2587123
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.384e+09 9.127e+09 -0.261 0.7940
## Store -1.538e+04 5.202e+02 -29.576 < 2e-16 ***
## Date -3.399e+03 1.266e+04 -0.268 0.7883
## Holiday_Flag 4.773e+04 2.706e+04 1.763 0.0779 .
## Temperature -1.817e+03 4.053e+02 -4.484 7.47e-06 ***
## Fuel_Price 6.124e+04 2.876e+04 2.130 0.0332 *
## CPI -2.109e+03 1.928e+02 -10.941 < 2e-16 ***
## Unemployment -2.209e+04 3.967e+03 -5.569 2.67e-08 ***
## Year 1.212e+06 4.633e+06 0.262 0.7937
## Month 1.177e+05 3.858e+05 0.305 0.7604
## Day 2.171e+03 1.269e+04 0.171 0.8642
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 520900 on 6424 degrees of freedom
## Multiple R-squared: 0.1495, Adjusted R-squared: 0.1482
## F-statistic: 113 on 10 and 6424 DF, p-value: < 2.2e-16Ajustar regresión lineal
df_ajustada <- df %>% select(-Store, - Date, -Fuel_Price , -Year:-Day)
regresion_ajustada <- lm(Weekly_Sales ~., data=df_ajustada)
summary(regresion_ajustada)##
## Call:
## lm(formula = Weekly_Sales ~ ., data = df_ajustada)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1020421 -477999 -115859 396128 2800875
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1687798.2 52515.7 32.139 < 2e-16 ***
## Holiday_Flag 75760.1 27605.3 2.744 0.00608 **
## Temperature -773.1 393.2 -1.966 0.04930 *
## CPI -1570.0 189.9 -8.267 < 2e-16 ***
## Unemployment -41235.7 3942.0 -10.460 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 557300 on 6430 degrees of freedom
## Multiple R-squared: 0.02538, Adjusted R-squared: 0.02477
## F-statistic: 41.86 on 4 and 6430 DF, p-value: < 2.2e-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