Walmart
José Arturo Silva Flores A01198049
2024-02-27
Librerías
library(dplyr)Importar la base de datos
df = read.csv("C:\\Users\\Silva\\Downloads\\Walmart_Store_sales.csv")Entender 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$Year = as.integer(format(df$Date, "%Y"))
df$Month = as.integer(format(df$Date, "%m"))
df$Day = as.integer(format(df$Date, "%d"))
df$WeekDay = as.integer(format(df$Date, "%u"))
str(df)## 'data.frame': 6435 obs. of 12 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 ...
## $ WeekDay : int 5 5 5 5 5 5 5 5 5 5 ...Realizar la 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: (1 not defined because of singularities)
## 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
## WeekDay NA NA NA NA
## ---
## 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 la regresión
df_ajustada = df %>%
select(-Store, -Fuel_Price, - Date, -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: (1 not defined because of singularities)
## 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 ***
## WeekDay NA NA NA NA
## ---
## 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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