Importar la base de dato

bd <- read.csv("/Users/marcogonzalez/Desktop/Programacion Bootcamp/Datos Arca Continental Original.csv")

Limpiar base de datos

summary(bd)
##        ID              Año        Territorio        Sub.Territorio    
##  Min.   :     1   Min.   :2016   Length:466509      Length:466509     
##  1st Qu.:116628   1st Qu.:2017   Class :character   Class :character  
##  Median :233255   Median :2018   Mode  :character   Mode  :character  
##  Mean   :233255   Mean   :2018                                        
##  3rd Qu.:349882   3rd Qu.:2019                                        
##  Max.   :466509   Max.   :2019                                        
##      CEDI             Cliente             Nombre          Tamaño.Cte.Industria
##  Length:466509      Length:466509      Length:466509      Length:466509       
##  Class :character   Class :character   Class :character   Class :character    
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character    
##                                                                               
##                                                                               
##                                                                               
##  Segmento.Det          Marca           Presentacion          Tamaño         
##  Length:466509      Length:466509      Length:466509      Length:466509     
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##  Retornable_NR         Enero             Febrero             Marzo          
##  Length:466509      Length:466509      Length:466509      Length:466509     
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##     Abril               Mayo              Junio              Julio          
##  Length:466509      Length:466509      Length:466509      Length:466509     
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##     Agosto           Septiembre          Octubre           Noviembre        
##  Length:466509      Length:466509      Length:466509      Length:466509     
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##   Diciembre        
##  Length:466509     
##  Class :character  
##  Mode  :character  
##                    
##                    
## 
str(bd)
## 'data.frame':    466509 obs. of  25 variables:
##  $ ID                  : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Año                 : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
##  $ Territorio          : chr  "Guadalajara" "Guadalajara" "Guadalajara" "Guadalajara" ...
##  $ Sub.Territorio      : chr  "Belenes" "Belenes" "Belenes" "Belenes" ...
##  $ CEDI                : chr  "Suc. Belenes" "Suc. Belenes" "Suc. Belenes" "Suc. Belenes" ...
##  $ Cliente             : chr  "77737" "77737" "77737" "77737" ...
##  $ Nombre              : chr  "ABARR" "ABARR" "ABARR" "ABARR" ...
##  $ Tamaño.Cte.Industria: chr  "Extra Grande" "Extra Grande" "Extra Grande" "Extra Grande" ...
##  $ Segmento.Det        : chr  "Agua Mineral" "Agua Purificada" "Agua Purificada" "Agua Saborizada" ...
##  $ Marca               : chr  "Topo Chico A.M." "Ciel Agua Purificada" "Ciel Agua Purificada" "Ciel Exprim" ...
##  $ Presentacion        : chr  "600 ml NR" "1 Ltro. N.R." "1.5 Lts. NR" "600 ml NR" ...
##  $ Tamaño              : chr  "Individual" "Individual" "Individual" "Individual" ...
##  $ Retornable_NR       : chr  "No Retornable" "No Retornable" "No Retornable" "No Retornable" ...
##  $ Enero               : chr  "" "" "" "" ...
##  $ Febrero             : chr  "" "2" "" "" ...
##  $ Marzo               : chr  "" "8" "3" "" ...
##  $ Abril               : chr  "" "4" "6" "" ...
##  $ Mayo                : chr  "" "4" "3" "" ...
##  $ Junio               : chr  "" "2" "3" "" ...
##  $ Julio               : chr  "" "2" "3" "" ...
##  $ Agosto              : chr  "" "2" "3" "" ...
##  $ Septiembre          : chr  "" "2" "3" "" ...
##  $ Octubre             : chr  "" "2" "3" "" ...
##  $ Noviembre           : chr  "" "4" "3" "" ...
##  $ Diciembre           : chr  "1" "2" "3" "1" ...
bd$Cliente <- as.integer(bd$Cliente)
## Warning: NAs introduced by coercion
bd$Enero <- as.integer(bd$Enero)
## Warning: NAs introduced by coercion
bd$Febrero <- as.integer(bd$Febrero)
## Warning: NAs introduced by coercion
bd$Marzo <- as.integer(bd$Marzo)
## Warning: NAs introduced by coercion
bd$Abril <- as.integer(bd$Abril)
## Warning: NAs introduced by coercion
bd$Mayo <- as.integer(bd$Mayo)
## Warning: NAs introduced by coercion
bd$Junio <- as.integer(bd$Junio)
## Warning: NAs introduced by coercion
bd$Julio <- as.integer(bd$Julio)
## Warning: NAs introduced by coercion
bd$Agosto <- as.integer(bd$Agosto)
## Warning: NAs introduced by coercion
bd$Septiembre <- as.integer(bd$Septiembre)
## Warning: NAs introduced by coercion
bd$Octubre <- as.integer(bd$Octubre)
## Warning: NAs introduced by coercion
bd$Noviembre <- as.integer(bd$Noviembre)
## Warning: NAs introduced by coercion
bd$Diciembre <- as.integer(bd$Diciembre)
## Warning: NAs introduced by coercion

Regresion Lineal

#library(dplyr)
#bd2 <- filter(bd, Tamaño.Cte.Industria=="Micro")
#summary(bd2)

regresion <- lm(Enero~Segmento.Det + Presentacion + Tamaño + Retornable_NR, data = bd)

summary(regresion)
## 
## Call:
## lm(formula = Enero ~ Segmento.Det + Presentacion + Tamaño + 
##     Retornable_NR, data = bd)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -71.26  -5.95  -0.86   1.33 966.00 
## 
## Coefficients: (2 not defined because of singularities)
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                        0.15243    0.63718   0.239 0.810933    
## Segmento.DetAgua Purificada        4.66404    0.70937   6.575 4.88e-11 ***
## Segmento.DetAgua Saborizada       -1.42197    0.74162  -1.917 0.055191 .  
## Segmento.DetBebidas de Fruta       0.98787    0.63032   1.567 0.117056    
## Segmento.DetBebidas de Soya        0.94708    2.50157   0.379 0.704989    
## Segmento.DetBebidas Energeticas   11.90635    0.93125  12.785  < 2e-16 ***
## Segmento.DetCafe Listo Para Bebe  -1.71183    2.02938  -0.844 0.398937    
## Segmento.DetColas Light            7.50047    0.63610  11.791  < 2e-16 ***
## Segmento.DetColas Regular         21.27155    0.61327  34.686  < 2e-16 ***
## Segmento.DetIsotónicos Light      -2.19258    1.58286  -1.385 0.165992    
## Segmento.DetIsotónicos Regular    -0.71862    0.69519  -1.034 0.301277    
## Segmento.DetJugos y Néctares       2.15427    0.77962   2.763 0.005723 ** 
## Segmento.DetLeche UHT Especializ  -1.26888    1.74705  -0.726 0.467657    
## Segmento.DetLeche UHT Regular     -1.37030    1.72970  -0.792 0.428234    
## Segmento.DetLeche UHT Saborizada   0.99574    2.78360   0.358 0.720557    
## Segmento.DetPolvos                 1.21821   19.83101   0.061 0.951017    
## Segmento.DetSabores Light         -0.97784    0.75695  -1.292 0.196418    
## Segmento.DetSabores Regular       -2.05300    0.58646  -3.501 0.000464 ***
## Segmento.DetTé Light              -1.73539    3.44607  -0.504 0.614553    
## Segmento.DetTé Regular            -0.39498    0.72665  -0.544 0.586743    
## Presentacion1 Ltro. Tetra          3.13124    1.49935   2.088 0.036763 *  
## Presentacion1.250 Lts NR           0.26963    0.57907   0.466 0.641486    
## Presentacion1.5 Lts. NR            2.56855    0.33239   7.727 1.10e-14 ***
## Presentacion1.5 Lts. Ret          11.58011    0.47716  24.269  < 2e-16 ***
## Presentacion1.750 Lts NR          -8.64830    0.71312 -12.127  < 2e-16 ***
## Presentacion100 ml NR Tetra       -3.98617    2.42464  -1.644 0.100172    
## Presentacion12 Oz. NR Pet        -12.69190    0.59197 -21.440  < 2e-16 ***
## Presentacion12 Oz. NR Vidrio       2.71821    5.88685   0.462 0.644266    
## Presentacion12 Oz. Ret            -3.78984    0.71010  -5.337 9.46e-08 ***
## Presentacion125 ml NR Tetra       -4.05397    1.79426  -2.259 0.023859 *  
## Presentacion2 Lts. NR              5.57947    0.33367  16.721  < 2e-16 ***
## Presentacion2 Lts. Ret            -4.07079    0.76137  -5.347 8.97e-08 ***
## Presentacion2.5 Lts. NR           12.85447    0.41190  31.208  < 2e-16 ***
## Presentacion2.5 Lts. Ret Pet      49.83402    0.46392 107.420  < 2e-16 ***
## Presentacion200 ml Tetra          -3.39505    2.77434  -1.224 0.221055    
## Presentacion235 ml NR Vid        -15.64521    0.61869 -25.288  < 2e-16 ***
## Presentacion237 ml NR Pet         -4.92657    1.25527  -3.925 8.69e-05 ***
## Presentacion237 ml NR Vid         -9.62600    3.54052  -2.719 0.006552 ** 
## Presentacion250 ml Tetra          -4.00227    0.78509  -5.098 3.44e-07 ***
## Presentacion250 ml. NR PET        -1.26010    0.53056  -2.375 0.017549 *  
## Presentacion250 ML. NR VID        -4.02729    0.96716  -4.164 3.13e-05 ***
## Presentacion3 Lts. NR              7.89587    3.38598   2.332 0.019705 *  
## Presentacion300 ML. NR PET        -3.53675    0.84617  -4.180 2.92e-05 ***
## Presentacion350 ML NR PET         -7.66011   10.59819  -0.723 0.469819    
## Presentacion355 Ml NR Pet         -5.25305    2.19872  -2.389 0.016889 *  
## Presentacion400 ml NR             -0.77727    0.52836  -1.471 0.141267    
## Presentacion413 ml NR VId         -1.59460    0.73113  -2.181 0.029184 *  
## Presentacion473 ml NR             -3.13329    2.31070  -1.356 0.175102    
## Presentacion5 Lts. NR              4.87934    1.31673   3.706 0.000211 ***
## Presentacion500 ml NR PET         -7.85446    0.62317 -12.604  < 2e-16 ***
## Presentacion500 ml NR Vidrio      -4.88632    0.54094  -9.033  < 2e-16 ***
## Presentacion500 ml Ret             9.63179    0.48915  19.691  < 2e-16 ***
## Presentacion6.5 Oz. Ret          -22.22859    2.06983 -10.739  < 2e-16 ***
## Presentacion600 ml NR             -0.01699    0.42048  -0.040 0.967778    
## Presentacion710 ml NR            -13.09879    2.48719  -5.267 1.39e-07 ***
## Presentacion8 Oz. NR             -15.54148    0.73295 -21.204  < 2e-16 ***
## Presentacion946 ml NR Tetra        0.74832    2.19366   0.341 0.733007    
## PresentacionBag In Box             0.43004   16.30382   0.026 0.978957    
## PresentacionLata                 -14.03884    0.60528 -23.194  < 2e-16 ***
## PresentacionLata 16 Oz.          -18.02910    0.71216 -25.316  < 2e-16 ***
## PresentacionLata 222 ml           -2.52967    7.51573  -0.337 0.736431    
## PresentacionLata 235 ml          -11.85441    0.56203 -21.092  < 2e-16 ***
## PresentacionLata 237 ml          -14.96592    9.38096  -1.595 0.110635    
## PresentacionLata 335 ml           -3.95048    2.05074  -1.926 0.054060 .  
## PresentacionLata 340 ml          -14.90506    1.77138  -8.414  < 2e-16 ***
## PresentacionLata 450 ml          -14.05582    1.55716  -9.027  < 2e-16 ***
## PresentacionLata 453 ml           -3.29515    2.14134  -1.539 0.123848    
## PresentacionLATA 680 ML           -1.29590    3.50407  -0.370 0.711512    
## PresentacionLata 8 OZ.            -0.66468    2.28915  -0.290 0.771540    
## PresentacionSobres 907 grs              NA         NA      NA       NA    
## TamañoIndividual                   3.12936    0.49310   6.346 2.21e-10 ***
## Retornable_NRRetornable                 NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 28.03 on 232887 degrees of freedom
##   (233552 observations deleted due to missingness)
## Multiple R-squared:  0.2019, Adjusted R-squared:  0.2016 
## F-statistic: 853.7 on 69 and 232887 DF,  p-value: < 2.2e-16

Evaluar, y en caso necesario, ajustar la Regresión Lineal

regresion_ajustada<- lm(Enero ~ Segmento.Det + Presentacion + Tamaño, data = bd)

summary(regresion_ajustada)
## 
## Call:
## lm(formula = Enero ~ Segmento.Det + Presentacion + Tamaño, data = bd)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -71.26  -5.95  -0.86   1.33 966.00 
## 
## Coefficients: (1 not defined because of singularities)
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                        0.15243    0.63718   0.239 0.810933    
## Segmento.DetAgua Purificada        4.66404    0.70937   6.575 4.88e-11 ***
## Segmento.DetAgua Saborizada       -1.42197    0.74162  -1.917 0.055191 .  
## Segmento.DetBebidas de Fruta       0.98787    0.63032   1.567 0.117056    
## Segmento.DetBebidas de Soya        0.94708    2.50157   0.379 0.704989    
## Segmento.DetBebidas Energeticas   11.90635    0.93125  12.785  < 2e-16 ***
## Segmento.DetCafe Listo Para Bebe  -1.71183    2.02938  -0.844 0.398937    
## Segmento.DetColas Light            7.50047    0.63610  11.791  < 2e-16 ***
## Segmento.DetColas Regular         21.27155    0.61327  34.686  < 2e-16 ***
## Segmento.DetIsotónicos Light      -2.19258    1.58286  -1.385 0.165992    
## Segmento.DetIsotónicos Regular    -0.71862    0.69519  -1.034 0.301277    
## Segmento.DetJugos y Néctares       2.15427    0.77962   2.763 0.005723 ** 
## Segmento.DetLeche UHT Especializ  -1.26888    1.74705  -0.726 0.467657    
## Segmento.DetLeche UHT Regular     -1.37030    1.72970  -0.792 0.428234    
## Segmento.DetLeche UHT Saborizada   0.99574    2.78360   0.358 0.720557    
## Segmento.DetPolvos                 1.21821   19.83101   0.061 0.951017    
## Segmento.DetSabores Light         -0.97784    0.75695  -1.292 0.196418    
## Segmento.DetSabores Regular       -2.05300    0.58646  -3.501 0.000464 ***
## Segmento.DetTé Light              -1.73539    3.44607  -0.504 0.614553    
## Segmento.DetTé Regular            -0.39498    0.72665  -0.544 0.586743    
## Presentacion1 Ltro. Tetra          3.13124    1.49935   2.088 0.036763 *  
## Presentacion1.250 Lts NR           0.26963    0.57907   0.466 0.641486    
## Presentacion1.5 Lts. NR            2.56855    0.33239   7.727 1.10e-14 ***
## Presentacion1.5 Lts. Ret          11.58011    0.47716  24.269  < 2e-16 ***
## Presentacion1.750 Lts NR          -8.64830    0.71312 -12.127  < 2e-16 ***
## Presentacion100 ml NR Tetra       -3.98617    2.42464  -1.644 0.100172    
## Presentacion12 Oz. NR Pet        -12.69190    0.59197 -21.440  < 2e-16 ***
## Presentacion12 Oz. NR Vidrio       2.71821    5.88685   0.462 0.644266    
## Presentacion12 Oz. Ret            -3.78984    0.71010  -5.337 9.46e-08 ***
## Presentacion125 ml NR Tetra       -4.05397    1.79426  -2.259 0.023859 *  
## Presentacion2 Lts. NR              5.57947    0.33367  16.721  < 2e-16 ***
## Presentacion2 Lts. Ret            -4.07079    0.76137  -5.347 8.97e-08 ***
## Presentacion2.5 Lts. NR           12.85447    0.41190  31.208  < 2e-16 ***
## Presentacion2.5 Lts. Ret Pet      49.83402    0.46392 107.420  < 2e-16 ***
## Presentacion200 ml Tetra          -3.39505    2.77434  -1.224 0.221055    
## Presentacion235 ml NR Vid        -15.64521    0.61869 -25.288  < 2e-16 ***
## Presentacion237 ml NR Pet         -4.92657    1.25527  -3.925 8.69e-05 ***
## Presentacion237 ml NR Vid         -9.62600    3.54052  -2.719 0.006552 ** 
## Presentacion250 ml Tetra          -4.00227    0.78509  -5.098 3.44e-07 ***
## Presentacion250 ml. NR PET        -1.26010    0.53056  -2.375 0.017549 *  
## Presentacion250 ML. NR VID        -4.02729    0.96716  -4.164 3.13e-05 ***
## Presentacion3 Lts. NR              7.89587    3.38598   2.332 0.019705 *  
## Presentacion300 ML. NR PET        -3.53675    0.84617  -4.180 2.92e-05 ***
## Presentacion350 ML NR PET         -7.66011   10.59819  -0.723 0.469819    
## Presentacion355 Ml NR Pet         -5.25305    2.19872  -2.389 0.016889 *  
## Presentacion400 ml NR             -0.77727    0.52836  -1.471 0.141267    
## Presentacion413 ml NR VId         -1.59460    0.73113  -2.181 0.029184 *  
## Presentacion473 ml NR             -3.13329    2.31070  -1.356 0.175102    
## Presentacion5 Lts. NR              4.87934    1.31673   3.706 0.000211 ***
## Presentacion500 ml NR PET         -7.85446    0.62317 -12.604  < 2e-16 ***
## Presentacion500 ml NR Vidrio      -4.88632    0.54094  -9.033  < 2e-16 ***
## Presentacion500 ml Ret             9.63179    0.48915  19.691  < 2e-16 ***
## Presentacion6.5 Oz. Ret          -22.22859    2.06983 -10.739  < 2e-16 ***
## Presentacion600 ml NR             -0.01699    0.42048  -0.040 0.967778    
## Presentacion710 ml NR            -13.09879    2.48719  -5.267 1.39e-07 ***
## Presentacion8 Oz. NR             -15.54148    0.73295 -21.204  < 2e-16 ***
## Presentacion946 ml NR Tetra        0.74832    2.19366   0.341 0.733007    
## PresentacionBag In Box             0.43004   16.30382   0.026 0.978957    
## PresentacionLata                 -14.03884    0.60528 -23.194  < 2e-16 ***
## PresentacionLata 16 Oz.          -18.02910    0.71216 -25.316  < 2e-16 ***
## PresentacionLata 222 ml           -2.52967    7.51573  -0.337 0.736431    
## PresentacionLata 235 ml          -11.85441    0.56203 -21.092  < 2e-16 ***
## PresentacionLata 237 ml          -14.96592    9.38096  -1.595 0.110635    
## PresentacionLata 335 ml           -3.95048    2.05074  -1.926 0.054060 .  
## PresentacionLata 340 ml          -14.90506    1.77138  -8.414  < 2e-16 ***
## PresentacionLata 450 ml          -14.05582    1.55716  -9.027  < 2e-16 ***
## PresentacionLata 453 ml           -3.29515    2.14134  -1.539 0.123848    
## PresentacionLATA 680 ML           -1.29590    3.50407  -0.370 0.711512    
## PresentacionLata 8 OZ.            -0.66468    2.28915  -0.290 0.771540    
## PresentacionSobres 907 grs              NA         NA      NA       NA    
## TamañoIndividual                   3.12936    0.49310   6.346 2.21e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 28.03 on 232887 degrees of freedom
##   (233552 observations deleted due to missingness)
## Multiple R-squared:  0.2019, Adjusted R-squared:  0.2016 
## F-statistic: 853.7 on 69 and 232887 DF,  p-value: < 2.2e-16
plot(regresion_ajustada)

### Contruir un modelo de prediccion

datos_nuevos <- data.frame(Segmento.Det= "Colas Regular" , Presentacion= "2.5 Lts. Ret Pet", Tamaño= "Familiar")
predict(regresion_ajustada,datos_nuevos)
## Warning in predict.lm(regresion_ajustada, datos_nuevos): prediction from a
## rank-deficient fit may be misleading
##      1 
## 71.258

Conclusion

En conclusion podemos realizar la regresiones lineales para generar modelos predictivos de variables de interes como las ventas. En este caso, se recomienda tener mas variables y de mayor impacto sobre las ventas, para que el modelo sea mas confiable (actualmente 38%)

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