#Libraries Required
library(fpp2)
## Warning: package 'fpp2' was built under R version 3.5.3
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 3.5.3
## Loading required package: forecast
## Warning: package 'forecast' was built under R version 3.5.2
## Loading required package: fma
## Warning: package 'fma' was built under R version 3.5.2
## Loading required package: expsmooth
## Warning: package 'expsmooth' was built under R version 3.5.2
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.5.1
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(lubridate)
## Warning: package 'lubridate' was built under R version 3.5.1
##
## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
##
## date
library(readr)
## Warning: package 'readr' was built under R version 3.5.1
library(gridExtra)
## Warning: package 'gridExtra' was built under R version 3.5.3
##
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
##
## combine
library(urca)
## Warning: package 'urca' was built under R version 3.5.2
#Data Preparation
raw.data<- read.csv("C:\\Users\\nidhi\\OneDrive\\Desktop\\Forecasting project2\\Sales_Transactions_Dataset_Weekly.csv")
head(raw.data)
## Product_Code W0 W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 W11 W12 W13 W14 W15 W16
## 1 P1 11 12 10 8 13 12 14 21 6 14 11 14 16 9 9 9 14
## 2 P2 7 6 3 2 7 1 6 3 3 3 2 2 6 2 0 6 2
## 3 P3 7 11 8 9 10 8 7 13 12 6 14 9 4 7 12 8 7
## 4 P4 12 8 13 5 9 6 9 13 13 11 8 4 5 4 15 7 11
## 5 P5 8 5 13 11 6 7 9 14 9 9 11 18 8 4 13 8 10
## 6 P6 3 3 2 7 6 3 8 6 6 3 1 1 5 4 3 5 3
## W17 W18 W19 W20 W21 W22 W23 W24 W25 W26 W27 W28 W29 W30 W31 W32 W33 W34
## 1 9 3 12 5 11 7 12 5 9 7 10 5 11 7 10 12 6 5
## 2 7 7 9 4 7 2 4 5 3 5 8 5 5 3 1 3 2 3
## 3 11 10 7 7 13 11 8 10 8 14 5 3 13 11 9 7 8 7
## 4 9 15 4 6 7 11 7 9 6 10 10 2 6 7 2 5 12 5
## 5 15 6 13 11 6 10 9 8 12 8 9 13 3 5 3 5 5 9
## 6 5 10 8 4 9 7 5 4 2 1 3 2 4 0 3 2 11 2
## W35 W36 W37 W38 W39 W40 W41 W42 W43 W44 W45 W46 W47 W48 W49 W50 W51 MIN
## 1 14 10 9 12 17 7 11 4 7 8 10 12 3 7 6 5 10 3
## 2 10 5 2 7 3 2 5 2 4 5 1 1 4 5 1 6 0 0
## 3 9 6 12 12 9 3 5 6 14 5 5 7 8 14 8 8 7 3
## 4 19 8 6 8 8 12 6 9 10 3 4 6 8 14 8 7 8 2
## 5 7 4 8 8 5 5 8 7 11 7 12 6 6 5 11 8 9 3
## 6 1 4 4 3 2 5 4 4 2 4 3 6 5 3 3 10 6 0
## MAX Normalized.0 Normalized.1 Normalized.2 Normalized.3 Normalized.4
## 1 21 0.44 0.50 0.39 0.28 0.56
## 2 10 0.70 0.60 0.30 0.20 0.70
## 3 14 0.36 0.73 0.45 0.55 0.64
## 4 19 0.59 0.35 0.65 0.18 0.41
## 5 18 0.33 0.13 0.67 0.53 0.20
## 6 11 0.27 0.27 0.18 0.64 0.55
## Normalized.5 Normalized.6 Normalized.7 Normalized.8 Normalized.9
## 1 0.50 0.61 1.00 0.17 0.61
## 2 0.10 0.60 0.30 0.30 0.30
## 3 0.45 0.36 0.91 0.82 0.27
## 4 0.24 0.41 0.65 0.65 0.53
## 5 0.27 0.40 0.73 0.40 0.40
## 6 0.27 0.73 0.55 0.55 0.27
## Normalized.10 Normalized.11 Normalized.12 Normalized.13 Normalized.14
## 1 0.44 0.61 0.72 0.33 0.33
## 2 0.20 0.20 0.60 0.20 0.00
## 3 1.00 0.55 0.09 0.36 0.82
## 4 0.35 0.12 0.18 0.12 0.76
## 5 0.53 1.00 0.33 0.07 0.67
## 6 0.09 0.09 0.45 0.36 0.27
## Normalized.15 Normalized.16 Normalized.17 Normalized.18 Normalized.19
## 1 0.33 0.61 0.33 0.00 0.50
## 2 0.60 0.20 0.70 0.70 0.90
## 3 0.45 0.36 0.73 0.64 0.36
## 4 0.29 0.53 0.41 0.76 0.12
## 5 0.33 0.47 0.80 0.20 0.67
## 6 0.45 0.27 0.45 0.91 0.73
## Normalized.20 Normalized.21 Normalized.22 Normalized.23 Normalized.24
## 1 0.11 0.44 0.22 0.50 0.11
## 2 0.40 0.70 0.20 0.40 0.50
## 3 0.36 0.91 0.73 0.45 0.64
## 4 0.24 0.29 0.53 0.29 0.41
## 5 0.53 0.20 0.47 0.40 0.33
## 6 0.36 0.82 0.64 0.45 0.36
## Normalized.25 Normalized.26 Normalized.27 Normalized.28 Normalized.29
## 1 0.33 0.22 0.39 0.11 0.44
## 2 0.30 0.50 0.80 0.50 0.50
## 3 0.45 1.00 0.18 0.00 0.91
## 4 0.24 0.47 0.47 0.00 0.24
## 5 0.60 0.33 0.40 0.67 0.00
## 6 0.18 0.09 0.27 0.18 0.36
## Normalized.30 Normalized.31 Normalized.32 Normalized.33 Normalized.34
## 1 0.22 0.39 0.50 0.17 0.11
## 2 0.30 0.10 0.30 0.20 0.30
## 3 0.73 0.55 0.36 0.45 0.36
## 4 0.29 0.00 0.18 0.59 0.18
## 5 0.13 0.00 0.13 0.13 0.40
## 6 0.00 0.27 0.18 1.00 0.18
## Normalized.35 Normalized.36 Normalized.37 Normalized.38 Normalized.39
## 1 0.61 0.39 0.33 0.50 0.78
## 2 1.00 0.50 0.20 0.70 0.30
## 3 0.55 0.27 0.82 0.82 0.55
## 4 1.00 0.35 0.24 0.35 0.35
## 5 0.27 0.07 0.33 0.33 0.13
## 6 0.09 0.36 0.36 0.27 0.18
## Normalized.40 Normalized.41 Normalized.42 Normalized.43 Normalized.44
## 1 0.22 0.44 0.06 0.22 0.28
## 2 0.20 0.50 0.20 0.40 0.50
## 3 0.00 0.18 0.27 1.00 0.18
## 4 0.59 0.24 0.41 0.47 0.06
## 5 0.13 0.33 0.27 0.53 0.27
## 6 0.45 0.36 0.36 0.18 0.36
## Normalized.45 Normalized.46 Normalized.47 Normalized.48 Normalized.49
## 1 0.39 0.50 0.00 0.22 0.17
## 2 0.10 0.10 0.40 0.50 0.10
## 3 0.18 0.36 0.45 1.00 0.45
## 4 0.12 0.24 0.35 0.71 0.35
## 5 0.60 0.20 0.20 0.13 0.53
## 6 0.27 0.55 0.45 0.27 0.27
## Normalized.50 Normalized.51
## 1 0.11 0.39
## 2 0.60 0.00
## 3 0.45 0.36
## 4 0.29 0.35
## 5 0.33 0.40
## 6 0.91 0.55
Data.Copy <- raw.data
#Data Manipulation
#Raw Data Transpose
Prod_Name <- raw.data$Product_Code
rownames(raw.data) <- raw.data$Product_Code
raw.data$Product_Code <- NULL
raw.data.t<- as.data.frame(t(as.matrix(raw.data)))
head(raw.data.t)
## P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 P16 P17 P18 P19 P20
## W0 11 7 7 12 8 3 4 8 14 22 15 3 12 14 19 30 49 40 26 13
## W1 12 6 11 8 5 3 8 6 9 19 7 4 10 12 45 27 40 38 31 17
## W2 10 3 8 13 13 2 3 10 10 19 15 1 9 9 47 27 40 39 45 11
## W3 8 2 9 5 11 7 7 9 7 29 14 6 6 11 42 43 28 38 36 10
## W4 13 7 10 9 6 6 8 6 11 20 17 4 10 13 29 29 40 39 31 7
## W5 12 1 8 6 7 3 7 8 15 16 7 3 11 12 44 32 47 33 28 11
## P21 P22 P23 P24 P25 P26 P27 P28 P29 P30 P31 P32 P33 P34 P35 P36 P37 P38
## W0 12 8 3 36 26 14 44 34 13 46 7 15 15 47 34 41 36 37
## W1 5 14 5 42 28 14 34 32 10 36 17 13 12 42 37 32 39 36
## W2 9 8 4 27 33 9 33 36 12 45 6 10 11 24 26 39 43 43
## W3 8 9 3 33 32 8 39 41 17 34 7 4 17 55 27 45 42 52
## W4 9 17 3 40 20 9 34 31 17 35 9 13 10 42 49 38 38 61
## W5 6 6 2 48 33 7 30 31 11 36 7 7 18 23 48 40 37 50
## P39 P40 P41 P42 P43 P44 P45 P46 P47 P48 P49 P50 P51 P52 P53 P54 P55 P56
## W0 31 41 35 42 28 34 40 27 40 29 37 12 19 40 2 41 34 31
## W1 21 27 27 27 43 27 29 46 42 51 28 3 14 44 5 42 30 28
## W2 28 27 26 40 40 28 39 31 27 40 42 15 17 37 6 37 42 41
## W3 39 51 43 27 43 36 34 38 28 33 38 8 27 48 8 40 42 36
## W4 53 37 44 32 54 50 37 40 33 43 37 12 14 28 3 29 29 45
## W5 39 38 38 42 33 28 23 40 29 28 35 7 18 40 1 52 24 25
## P57 P58 P59 P60 P61 P62 P63 P64 P65 P66 P67 P68 P69 P70 P71 P72 P73 P74
## W0 38 32 5 37 25 12 37 34 14 35 36 14 26 31 7 26 35 8
## W1 26 34 13 41 30 27 39 37 10 41 30 11 43 39 11 34 30 9
## W2 37 38 10 46 32 18 54 29 9 45 38 9 46 50 10 39 36 6
## W3 43 41 11 27 47 22 44 42 10 34 44 9 39 31 8 41 46 15
## W4 51 24 10 51 45 19 30 31 11 50 47 5 41 37 10 39 34 4
## W5 39 29 11 28 50 19 36 36 9 40 37 19 31 30 14 38 29 12
## P75 P76 P77 P78 P79 P80 P81 P82 P83 P84 P85 P86 P87 P88 P89 P90 P91 P92
## W0 34 27 7 39 37 38 14 10 46 29 39 37 30 41 24 42 10 26
## W1 45 34 7 40 34 29 6 12 40 26 34 33 38 28 26 34 11 36
## W2 37 43 6 31 45 32 13 17 34 31 28 33 35 32 44 40 7 56
## W3 27 45 2 39 41 40 5 13 38 39 38 32 39 30 31 39 13 40
## W4 40 28 7 44 37 29 8 14 36 28 33 36 37 33 37 47 5 31
## W5 22 35 5 34 23 36 13 8 45 44 37 26 42 41 37 31 9 44
## P93 P94 P95 P96 P97 P98 P99 P100 P101 P102 P103 P104 P105 P106 P107
## W0 11 14 12 31 29 4 16 13 26 34 7 4 2 11 19
## W1 9 9 15 35 37 2 11 6 51 41 5 4 3 10 19
## W2 10 10 15 36 36 4 9 7 45 22 7 5 3 11 15
## W3 13 9 13 44 40 4 21 9 47 32 7 5 2 16 24
## W4 5 5 17 36 33 7 9 9 40 36 14 3 5 16 16
## W5 11 11 12 38 29 2 5 15 36 27 9 7 4 7 23
## P108 P109 P110 P111 P112 P113 P114 P115 P116 P117 P118 P119 P120 P121
## W0 0 9 9 5 31 22 7 9 13 6 16 30 37 7
## W1 2 8 12 4 36 26 6 9 7 2 13 40 35 10
## W2 2 8 12 7 32 31 7 12 8 3 10 46 27 8
## W3 0 5 6 4 51 37 8 13 8 3 10 33 30 10
## W4 3 13 15 4 39 32 10 14 7 3 16 28 31 11
## W5 1 9 10 5 33 25 12 13 12 2 15 41 42 10
## P122 P123 P124 P125 P126 P127 P128 P129 P130 P131 P132 P133 P134 P135
## W0 7 5 4 6 3 4 36 21 19 29 39 30 32 38
## W1 10 5 6 9 8 0 43 36 53 32 36 48 41 32
## W2 7 3 0 5 8 2 28 44 43 32 45 29 50 36
## W3 8 6 7 10 3 2 39 47 41 35 41 38 35 37
## W4 11 4 4 6 5 2 41 26 39 49 43 40 36 42
## W5 2 6 3 9 3 1 37 33 46 38 37 42 35 33
## P136 P137 P138 P139 P140 P141 P142 P143 P144 P145 P146 P147 P148 P149
## W0 39 37 36 32 28 27 23 31 9 10 7 10 3 11
## W1 37 41 44 36 36 45 29 35 13 12 13 4 2 9
## W2 39 43 22 32 41 40 40 29 17 9 9 9 8 9
## W3 52 41 35 38 46 34 43 42 12 9 13 9 3 6
## W4 38 40 34 47 34 31 34 31 8 12 10 15 5 13
## W5 28 47 34 31 38 35 30 35 21 6 12 7 7 8
## P150 P151 P152 P153 P154 P155 P156 P157 P158 P159 P160 P161 P162 P163
## W0 4 3 11 22 12 5 1 12 8 2 9 5 13 6
## W1 3 7 10 9 14 7 4 8 3 2 8 6 9 5
## W2 3 8 8 11 11 4 6 14 3 9 10 5 12 6
## W3 4 8 8 10 6 3 6 7 2 5 13 7 9 6
## W4 6 6 13 10 8 4 3 8 3 2 14 1 11 7
## W5 2 8 4 5 7 3 8 10 3 7 10 3 11 5
## P164 P165 P166 P167 P168 P169 P170 P171 P172 P173 P174 P175 P176 P177
## W0 9 9 7 33 34 38 29 6 32 34 37 30 38 25
## W1 5 13 10 45 29 31 37 7 48 25 42 40 44 38
## W2 8 9 9 42 34 22 32 13 44 42 30 48 41 37
## W3 9 16 11 41 59 43 43 6 40 47 43 28 26 48
## W4 14 12 5 32 27 29 30 4 31 37 48 30 28 34
## W5 10 9 8 45 39 25 27 12 40 40 44 34 36 40
## P178 P179 P180 P181 P182 P183 P184 P185 P186 P187 P188 P189 P190 P191
## W0 35 44 40 32 42 25 30 32 36 31 33 32 54 48
## W1 34 43 38 44 28 37 37 28 34 38 34 34 41 36
## W2 40 35 34 31 34 42 38 36 37 47 36 46 51 42
## W3 36 36 43 38 28 50 30 40 36 36 52 30 45 35
## W4 45 39 43 37 26 18 32 40 41 36 42 39 51 44
## W5 37 42 34 36 35 32 32 50 33 27 49 51 46 39
## P192 P193 P194 P195 P196 P197 P198 P199 P200 P201 P202 P203 P204 P205
## W0 24 36 35 4 43 6 7 4 18 1 10 9 1 11
## W1 33 22 37 4 34 8 19 1 14 3 10 7 2 7
## W2 30 32 31 7 39 8 4 1 21 3 10 3 1 7
## W3 35 45 23 5 32 16 9 6 17 5 11 6 1 13
## W4 36 35 37 5 45 8 8 3 26 5 11 6 1 8
## W5 43 44 42 3 35 11 12 7 14 5 11 9 3 6
## P206 P207 P208 P209 P210 P211 P212 P213 P214 P215 P216 P217 P218 P219
## W0 1 2 30 12 16 15 0 0 0 0 0 0 0 0
## W1 0 5 26 6 12 12 0 0 0 0 0 0 0 0
## W2 1 8 36 6 9 9 0 0 0 0 0 0 0 0
## W3 3 7 33 11 14 12 0 0 0 0 0 0 0 1
## W4 1 3 33 16 7 14 0 1 0 0 0 0 0 0
## W5 3 5 30 12 11 14 1 0 1 0 0 0 0 0
## P220 P221 P222 P223 P224 P225 P226 P227 P228 P229 P230 P231 P232 P233
## W0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## W1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## W2 0 0 0 0 0 1 0 0 0 1 1 0 0 0
## W3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## W4 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## W5 0 0 0 0 1 1 0 0 0 0 0 0 0 0
## P234 P235 P236 P237 P238 P239 P240 P241 P242 P243 P244 P245 P246 P247
## W0 0 0 0 0 0 0 0 0 0 0 0 2 0 1
## W1 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## W2 0 0 0 0 0 1 1 0 0 0 0 0 0 0
## W3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## W4 1 0 0 0 0 0 0 1 0 0 0 0 0 0
## W5 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## P248 P249 P250 P251 P252 P253 P254 P255 P256 P257 P258 P259 P260 P261
## W0 0 0 0 0 0 0 0 1 0 0 0 0 0 20
## W1 0 0 0 0 0 0 0 0 0 0 0 0 0 36
## W2 0 0 0 0 0 0 0 1 0 0 0 0 0 17
## W3 0 0 0 0 0 0 0 0 0 0 0 0 1 19
## W4 0 0 0 0 0 0 0 1 0 1 0 0 0 16
## W5 0 0 0 0 0 0 1 1 0 1 0 0 0 23
## P262 P263 P264 P265 P266 P267 P268 P269 P270 P271 P272 P273 P274 P275
## W0 25 18 8 0 6 8 18 7 26 3 0 0 0 0
## W1 31 12 16 1 8 14 18 11 16 3 0 0 0 1
## W2 33 20 9 1 7 14 18 6 23 4 0 1 0 0
## W3 23 21 9 5 15 5 14 12 19 6 3 0 0 0
## W4 38 12 9 2 17 8 18 15 29 1 0 0 0 0
## W5 34 24 10 1 10 5 24 11 21 8 1 0 0 0
## P276 P277 P278 P279 P280 P281 P282 P283 P284 P285 P286 P287 P288 P289
## W0 0 0 0 0 0 1 0 0 26 11 17 0 1 0
## W1 0 0 0 0 1 0 0 0 17 12 19 0 1 1
## W2 0 0 0 0 0 3 0 0 17 6 13 0 0 0
## W3 0 0 0 0 0 1 0 0 22 6 15 0 0 0
## W4 0 0 0 0 0 0 3 0 23 8 16 1 1 0
## W5 0 0 0 0 0 0 1 0 24 12 23 0 0 0
## P290 P291 P292 P293 P294 P295 P296 P297 P298 P299 P300 P301 P302 P303
## W0 0 2 2 6 9 3 7 3 5 10 6 7 4 3
## W1 0 2 7 3 13 6 5 4 7 8 7 1 2 5
## W2 1 5 5 5 19 3 6 4 4 12 3 2 5 8
## W3 0 6 4 2 13 8 4 1 1 8 1 3 2 4
## W4 1 4 4 6 5 8 6 3 4 15 8 4 4 3
## W5 0 0 3 4 10 3 3 3 3 14 9 5 7 6
## P304 P305 P306 P307 P308 P309 P310 P311 P312 P313 P314 P315 P316 P317
## W0 7 8 4 4 7 9 5 8 3 10 6 5 4 2
## W1 6 7 2 5 3 5 4 6 2 1 17 7 5 3
## W2 6 3 3 6 2 8 4 1 3 6 9 2 9 3
## W3 8 1 8 5 4 8 2 9 8 5 14 5 2 7
## W4 9 3 7 3 6 14 5 5 3 4 10 7 1 9
## W5 9 2 4 2 3 7 1 1 6 7 8 8 8 7
## P318 P319 P320 P321 P322 P323 P324 P325 P326 P327 P328 P329 P330 P331
## W0 3 5 2 3 4 3 11 7 3 2 4 2 4 7
## W1 5 4 5 6 5 7 11 6 11 5 6 6 4 6
## W2 5 11 8 8 2 2 15 2 7 4 6 4 8 6
## W3 9 8 7 5 2 2 6 3 6 5 6 9 2 4
## W4 9 10 2 3 7 2 6 7 4 5 4 3 4 5
## W5 1 12 6 3 3 8 9 3 7 5 9 7 5 2
## P332 P333 P334 P335 P336 P337 P338 P339 P340 P341 P342 P343 P344 P345
## W0 10 9 11 6 2 3 2 0 0 5 3 0 1 2
## W1 14 9 13 4 3 6 2 0 0 1 2 0 2 3
## W2 8 5 15 2 5 3 1 1 0 3 2 2 0 2
## W3 12 8 8 4 3 1 3 2 0 1 2 1 3 0
## W4 6 11 16 3 1 1 4 4 0 1 3 0 0 7
## W5 10 13 16 2 4 7 2 2 0 3 0 5 0 1
## P346 P347 P348 P349 P350 P351 P352 P354 P355 P356 P357 P358 P359 P360
## W0 0 1 1 0 0 0 0 0 0 4 3 1 3 0
## W1 0 0 0 0 0 0 0 0 0 6 1 1 5 1
## W2 0 0 0 0 0 0 0 0 0 4 3 1 3 2
## W3 0 0 0 0 0 0 0 0 0 2 4 0 5 2
## W4 0 0 0 0 0 0 0 0 0 2 2 0 4 1
## W5 0 0 1 0 0 0 0 0 0 5 3 0 5 1
## P361 P362 P363 P364 P365 P366 P367 P368 P369 P370 P371 P372 P373 P374
## W0 6 1 2 10 6 2 0 0 3 6 2 1 0 1
## W1 3 0 1 18 5 2 2 5 2 2 4 2 3 1
## W2 3 0 0 10 5 8 2 5 2 3 3 0 0 2
## W3 2 1 0 8 1 3 1 3 3 5 1 1 0 2
## W4 3 1 0 11 5 5 1 2 3 11 2 2 1 1
## W5 0 1 0 18 2 3 5 2 7 5 2 4 1 0
## P375 P376 P377 P378 P379 P380 P381 P382 P383 P384 P386 P387 P388 P389
## W0 1 0 1 0 0 0 0 0 0 1 0 5 5 2
## W1 0 0 0 0 1 0 0 0 0 0 0 3 2 3
## W2 1 0 0 0 0 0 0 0 0 0 0 0 1 4
## W3 0 0 0 0 0 0 0 0 0 1 0 1 1 4
## W4 0 0 0 0 0 0 1 0 0 0 0 9 1 0
## W5 0 0 0 0 0 0 0 0 0 1 0 5 2 1
## P390 P391 P392 P393 P394 P395 P396 P397 P398 P399 P400 P401 P402 P403
## W0 4 0 3 1 1 1 0 10 14 17 20 3 1 12
## W1 4 1 3 0 4 1 1 7 12 11 11 5 0 8
## W2 3 0 7 1 2 0 1 8 10 8 19 5 6 12
## W3 2 4 5 0 9 2 4 13 8 9 16 7 0 9
## W4 3 3 5 2 1 2 3 7 14 8 22 2 3 12
## W5 1 0 4 2 2 0 1 10 8 5 20 2 4 18
## P404 P405 P406 P407 P408 P409 P410 P411 P412 P413 P414 P415 P416 P417
## W0 11 23 6 22 7 42 6 16 4 4 2 2 1 0
## W1 8 12 12 23 10 48 13 17 2 6 2 4 1 0
## W2 7 16 17 10 8 38 16 12 4 8 1 2 1 0
## W3 9 22 15 19 9 43 9 10 8 7 1 3 0 1
## W4 11 18 14 16 13 35 18 23 5 12 5 6 0 0
## W5 6 18 18 26 8 39 16 14 3 11 2 7 0 0
## P418 P419 P420 P421 P422 P423 P424 P425 P426 P427 P428 P429 P430 P431
## W0 0 0 0 0 1 0 0 0 0 0 0 16 13 3
## W1 0 0 0 0 1 0 0 0 0 0 0 9 8 6
## W2 0 1 0 0 0 0 1 0 1 0 0 9 14 4
## W3 0 0 0 0 1 0 0 0 0 0 0 10 12 2
## W4 1 0 0 0 0 0 1 0 0 0 0 7 15 5
## W5 0 0 1 0 0 0 0 0 0 0 0 11 12 6
## P432 P433 P434 P435 P436 P437 P438 P439 P440 P441 P442 P443 P444 P445
## W0 6 2 2 21 14 7 1 1 1 0 0 0 0 0
## W1 7 4 0 16 5 4 1 0 0 0 0 1 0 0
## W2 6 5 7 17 6 13 0 0 0 1 1 1 0 0
## W3 5 3 1 17 12 6 0 0 0 0 0 1 0 0
## W4 6 2 3 14 10 3 2 0 0 1 0 0 0 0
## W5 5 2 2 13 6 7 0 1 0 0 0 0 0 0
## P446 P447 P448 P449 P450 P451 P452 P453 P454 P455 P456 P457 P458 P459
## W0 0 0 0 0 1 1 0 0 0 0 0 0 0 0
## W1 1 0 2 0 0 0 0 0 0 0 0 0 0 1
## W2 1 0 0 0 0 0 0 0 0 1 0 0 0 0
## W3 0 0 2 0 0 0 0 0 0 0 2 0 0 0
## W4 0 0 1 0 1 0 1 1 0 0 0 0 0 0
## W5 0 0 0 0 3 0 0 0 0 0 0 0 0 0
## P460 P461 P462 P463 P464 P465 P466 P467 P468 P469 P470 P471 P472 P473
## W0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## W1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## W2 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## W3 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## W4 1 0 0 0 0 1 0 0 0 0 0 0 0 0
## W5 0 0 1 0 0 0 0 0 0 0 0 0 1 1
## P474 P475 P476 P477 P478 P479 P480 P481 P482 P483 P484 P485 P486 P487
## W0 0 3 1 1 1 0 0 1 0 0 3 15 16 7
## W1 0 5 0 0 0 3 0 2 0 0 5 9 30 10
## W2 0 1 1 1 0 1 0 0 1 0 3 8 25 15
## W3 0 2 1 0 2 1 0 0 0 0 6 11 11 12
## W4 0 2 3 0 3 0 0 0 1 2 10 12 23 14
## W5 0 3 0 1 0 2 0 0 0 1 5 12 19 10
## P488 P489 P490 P491 P492 P493 P494 P495 P496 P497 P498 P499 P500 P501
## W0 10 5 6 19 9 9 6 16 5 5 4 11 8 4
## W1 11 8 7 20 14 9 13 15 3 4 4 13 8 5
## W2 10 4 6 27 14 12 12 27 13 4 6 12 3 4
## W3 9 8 12 23 13 5 15 14 11 3 8 18 4 2
## W4 9 6 13 19 5 11 16 19 10 2 8 8 13 6
## W5 14 5 9 30 16 10 19 12 9 2 1 9 9 5
## P502 P503 P504 P505 P506 P507 P508 P509 P510 P511 P512 P513 P514 P515
## W0 11 13 10 23 23 11 9 9 9 25 17 22 7 10
## W1 15 19 13 4 16 27 10 5 4 23 17 23 14 15
## W2 19 15 11 16 9 11 9 5 10 30 16 25 13 15
## W3 15 13 5 24 11 22 12 2 6 33 16 21 11 14
## W4 14 22 13 19 16 9 11 9 5 20 27 25 9 12
## W5 11 23 12 15 19 15 17 3 11 27 18 17 19 8
## P516 P517 P518 P519 P520 P521 P522 P523 P524 P525 P526 P527 P528 P529
## W0 24 15 15 30 9 8 12 8 14 15 9 7 9 13
## W1 18 15 6 26 12 14 15 14 9 12 13 18 9 17
## W2 15 11 14 16 11 14 16 12 10 15 15 11 10 15
## W3 21 11 12 16 16 11 10 16 7 15 18 14 5 11
## W4 20 20 11 23 8 14 19 18 11 5 15 9 13 13
## W5 19 16 18 30 9 12 11 9 8 7 10 16 22 14
## P530 P531 P532 P533 P534 P535 P536 P537 P538 P539 P540 P541 P542 P543
## W0 20 4 15 28 6 15 14 20 7 9 23 11 12 9
## W1 11 8 5 33 10 26 9 16 7 10 19 17 14 6
## W2 22 3 10 21 9 11 13 17 13 14 21 5 7 16
## W3 14 2 8 37 7 15 13 20 14 7 23 14 13 9
## W4 15 4 12 20 7 19 3 19 11 5 20 10 11 6
## W5 20 5 8 28 9 15 21 20 11 16 21 10 13 15
## P544 P545 P546 P547 P548 P549 P550 P551 P552 P553 P554 P555 P556 P557
## W0 15 13 10 9 32 35 11 5 15 2 24 10 15 28
## W1 10 13 14 6 28 29 10 8 9 2 15 16 11 23
## W2 14 15 13 11 39 40 6 12 10 3 32 17 26 35
## W3 7 14 11 5 36 32 11 10 8 7 28 8 15 34
## W4 12 13 9 9 47 33 11 9 11 5 22 11 13 33
## W5 13 22 13 17 40 30 19 8 15 4 21 12 11 27
## P558 P559 P560 P561 P562 P563 P564 P565 P566 P567 P568 P569 P570 P571
## W0 15 11 9 6 6 5 13 4 24 3 4 1 2 4
## W1 10 13 9 5 5 19 4 7 26 7 2 3 2 3
## W2 15 4 3 1 0 5 8 10 22 1 1 4 0 6
## W3 13 19 7 3 4 11 9 11 24 9 5 1 2 6
## W4 16 10 11 1 4 10 6 8 20 6 2 2 0 4
## W5 8 13 8 7 4 15 10 13 13 5 2 1 2 6
## P572 P573 P574 P575 P576 P577 P578 P579 P580 P581 P582 P583 P584 P585
## W0 2 1 0 0 0 1 1 0 1 2 3 2 2 1
## W1 0 2 1 0 0 1 0 0 5 2 4 1 3 0
## W2 0 4 0 0 0 0 1 0 4 5 2 4 2 1
## W3 0 4 0 0 1 1 0 0 7 2 3 1 6 1
## W4 1 2 0 0 0 1 0 1 2 2 5 3 2 2
## W5 3 2 0 0 0 0 0 0 5 8 1 3 2 0
## P586 P587 P588 P589 P590 P591 P592 P593 P594 P595 P596 P597 P598 P599
## W0 7 4 5 0 3 2 2 4 1 1 2 1 10 2
## W1 2 4 2 1 4 2 0 5 2 2 3 2 6 1
## W2 4 2 1 1 4 2 0 1 3 3 5 0 6 1
## W3 6 5 0 0 6 2 1 4 5 3 4 0 15 1
## W4 8 2 1 2 4 1 0 0 3 1 3 2 10 3
## W5 8 3 2 1 4 3 0 1 4 5 3 1 14 2
## P600 P601 P602 P603 P604 P605 P606 P607 P608 P609 P610 P611 P612 P613
## W0 0 0 0 0 0 1 1 0 1 0 6 3 14 18
## W1 1 0 0 1 0 3 1 1 1 0 3 1 11 26
## W2 3 1 0 0 0 2 0 0 0 2 1 2 13 12
## W3 1 0 1 0 0 1 1 0 0 1 4 7 9 17
## W4 1 1 1 1 0 4 1 0 0 8 5 3 13 23
## W5 1 1 0 0 0 1 1 0 0 4 1 0 14 15
## P614 P615 P616 P617 P618 P619 P620 P621 P622 P623 P624 P625 P626 P627
## W0 4 17 6 36 40 38 31 31 29 38 5 11 10 12
## W1 4 16 6 33 35 31 27 33 33 27 10 6 6 11
## W2 2 19 3 40 49 31 37 29 39 36 13 11 8 12
## W3 3 20 2 46 31 41 29 38 40 36 6 15 13 9
## W4 4 19 6 39 34 41 34 36 40 35 12 7 9 11
## W5 6 19 2 39 45 36 39 27 37 39 10 12 16 8
## P628 P629 P630 P631 P632 P633 P634 P635 P636 P637 P638 P639 P640 P641
## W0 7 10 11 8 13 5 6 14 6 2 15 0 14 0
## W1 6 9 11 4 11 7 8 8 15 4 7 1 20 5
## W2 13 11 8 13 14 16 13 11 9 3 10 0 15 0
## W3 9 12 12 14 9 16 10 10 14 7 18 0 11 2
## W4 11 6 26 12 11 11 11 6 18 4 21 1 10 6
## W5 5 11 5 5 14 12 12 9 7 4 8 0 20 2
## P642 P643 P644 P646 P647 P649 P650 P651 P652 P653 P654 P655 P656 P657
## W0 5 0 0 0 0 0 0 0 0 0 0 0 0 0
## W1 3 0 0 0 0 0 0 0 0 0 0 1 0 0
## W2 4 0 0 0 0 0 0 0 0 0 0 0 0 0
## W3 2 0 0 0 0 0 0 0 0 1 0 0 0 0
## W4 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## W5 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## P658 P659 P660 P661 P662 P663 P664 P665 P666 P667 P668 P669 P670 P671
## W0 0 0 0 0 0 0 0 0 1 1 1 0 0 1
## W1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## W2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## W3 0 1 1 0 0 2 0 0 0 0 0 0 0 0
## W4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## W5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## P672 P673 P674 P675 P676 P677 P678 P679 P680 P681 P682 P683 P684 P685
## W0 0 1 4 2 1 0 0 1 0 0 0 0 0 0
## W1 0 3 4 1 1 1 0 0 0 1 0 0 0 0
## W2 0 1 8 4 3 0 0 0 0 0 0 0 0 0
## W3 0 2 7 0 2 0 0 0 0 0 0 0 0 0
## W4 0 3 2 2 5 0 0 0 0 0 0 0 0 0
## W5 0 0 6 5 4 0 0 0 0 0 0 0 0 0
## P686 P687 P688 P689 P690 P691 P692 P693 P694 P695 P696 P697 P698 P699
## W0 2 1 0 0 0 0 0 2 0 0 0 0 2 0
## W1 4 0 0 0 0 0 2 3 0 0 0 0 2 0
## W2 0 0 0 1 0 0 3 4 0 0 0 0 2 0
## W3 1 0 0 0 0 0 0 2 0 0 0 0 1 0
## W4 2 0 1 1 2 1 2 4 0 0 0 0 0 0
## W5 4 1 0 0 0 0 3 4 0 0 1 0 2 0
## P700 P701 P702 P703 P704 P705 P706 P707 P708 P711 P712 P713 P714 P715
## W0 1 5 3 2 0 1 0 1 0 0 0 0 0 0
## W1 0 3 1 3 0 3 0 0 1 0 0 2 0 0
## W2 0 1 4 4 2 0 0 0 0 0 0 0 0 0
## W3 5 2 4 4 1 1 0 0 0 1 0 0 0 0
## W4 0 1 4 0 0 4 0 0 0 0 0 0 1 0
## W5 1 3 6 3 0 3 1 1 0 0 0 0 1 0
## P716 P717 P718 P719 P720 P721 P722 P724 P726 P727 P728 P729 P730 P731
## W0 1 0 0 0 0 0 0 0 0 2 1 0 0 0
## W1 0 0 2 0 0 0 0 0 1 0 0 0 1 0
## W2 0 0 0 0 0 0 0 0 0 4 1 0 0 0
## W3 0 0 0 0 0 0 0 0 2 1 1 0 0 1
## W4 1 0 0 0 0 0 0 0 2 4 0 0 0 0
## W5 0 0 1 0 0 0 0 0 2 2 1 0 0 0
## P732 P733 P734 P735 P736 P737 P738 P739 P740 P741 P742 P743 P744 P745
## W0 0 0 1 0 1 0 0 0 0 0 0 1 0 2
## W1 1 0 0 0 1 0 0 0 0 0 0 1 0 10
## W2 1 0 0 0 0 3 0 0 0 0 1 3 0 6
## W3 1 0 1 0 0 1 0 1 0 0 0 0 1 2
## W4 1 2 0 0 0 0 0 0 1 0 1 2 0 2
## W5 1 0 0 0 2 1 0 0 0 0 1 0 0 2
## P746 P747 P748 P749 P750 P751 P752 P753 P754 P755 P756 P757 P758 P759
## W0 1 1 0 1 0 0 4 1 0 0 0 0 0 0
## W1 0 1 1 0 0 2 0 0 0 0 0 0 0 0
## W2 0 1 1 0 0 0 2 0 0 0 1 0 0 0
## W3 0 0 0 0 0 1 3 0 0 0 0 0 0 1
## W4 1 4 0 1 0 0 2 0 0 0 0 0 0 0
## W5 2 1 3 0 1 0 2 0 0 0 0 0 0 0
## P760 P761 P762 P763 P764 P765 P766 P767 P768 P769 P770 P771 P772 P773
## W0 0 0 2 0 1 0 0 0 0 0 2 0 0 0
## W1 0 0 3 0 2 0 1 0 0 0 0 0 0 0
## W2 0 0 2 0 5 0 1 1 2 2 0 0 0 0
## W3 0 0 1 0 3 0 0 1 8 3 0 0 0 0
## W4 0 0 0 0 0 0 0 0 2 0 0 0 1 0
## W5 0 0 2 0 3 0 1 2 5 1 1 0 1 0
## P774 P775 P776 P777 P778 P779 P780 P781 P782 P783 P784 P785 P786 P787
## W0 0 0 0 0 0 0 0 9 5 15 3 3 1 1
## W1 0 0 0 0 0 0 0 13 7 12 1 1 2 0
## W2 0 0 0 1 0 1 0 15 4 7 2 4 2 2
## W3 0 0 0 0 0 0 0 15 6 16 1 0 2 0
## W4 0 0 0 0 0 0 0 16 2 18 3 1 1 1
## W5 0 0 0 0 0 0 0 17 2 9 4 1 0 1
## P788 P789 P790 P791 P792 P793 P794 P795 P796 P797 P798 P799 P800 P801
## W0 4 2 3 4 0 0 0 0 2 2 6 3 3 3
## W1 3 1 1 3 1 1 0 0 5 3 0 3 2 2
## W2 3 1 5 4 0 1 1 0 0 2 3 5 1 0
## W3 5 6 2 2 1 4 0 1 3 2 1 3 0 3
## W4 1 1 0 6 0 2 0 0 3 2 0 5 0 2
## W5 4 4 4 6 3 1 0 0 0 3 2 4 0 3
## P802 P803 P804 P805 P806 P807 P808 P809 P810 P811 P812 P813 P814 P815
## W0 1 3 2 0 4 0 0 0 0 5 3 1 4 0
## W1 1 2 2 6 5 0 3 1 0 1 2 1 2 0
## W2 1 3 5 3 7 2 3 0 1 3 0 1 2 1
## W3 0 3 3 2 4 0 3 1 0 5 0 3 6 0
## W4 3 7 1 5 7 1 1 1 0 4 2 2 0 0
## W5 2 3 4 2 2 0 2 0 0 4 1 2 4 2
## P816 P817 P818 P819
## W0 0 1 0 0
## W1 1 0 0 1
## W2 0 0 0 0
## W3 0 0 1 0
## W4 1 1 0 0
## W5 2 1 0 0
#Slice the product weekly data based on sales
prod.wkly.sls <- raw.data.t[1:52,]
head(prod.wkly.sls)
## P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 P16 P17 P18 P19 P20
## W0 11 7 7 12 8 3 4 8 14 22 15 3 12 14 19 30 49 40 26 13
## W1 12 6 11 8 5 3 8 6 9 19 7 4 10 12 45 27 40 38 31 17
## W2 10 3 8 13 13 2 3 10 10 19 15 1 9 9 47 27 40 39 45 11
## W3 8 2 9 5 11 7 7 9 7 29 14 6 6 11 42 43 28 38 36 10
## W4 13 7 10 9 6 6 8 6 11 20 17 4 10 13 29 29 40 39 31 7
## W5 12 1 8 6 7 3 7 8 15 16 7 3 11 12 44 32 47 33 28 11
## P21 P22 P23 P24 P25 P26 P27 P28 P29 P30 P31 P32 P33 P34 P35 P36 P37 P38
## W0 12 8 3 36 26 14 44 34 13 46 7 15 15 47 34 41 36 37
## W1 5 14 5 42 28 14 34 32 10 36 17 13 12 42 37 32 39 36
## W2 9 8 4 27 33 9 33 36 12 45 6 10 11 24 26 39 43 43
## W3 8 9 3 33 32 8 39 41 17 34 7 4 17 55 27 45 42 52
## W4 9 17 3 40 20 9 34 31 17 35 9 13 10 42 49 38 38 61
## W5 6 6 2 48 33 7 30 31 11 36 7 7 18 23 48 40 37 50
## P39 P40 P41 P42 P43 P44 P45 P46 P47 P48 P49 P50 P51 P52 P53 P54 P55 P56
## W0 31 41 35 42 28 34 40 27 40 29 37 12 19 40 2 41 34 31
## W1 21 27 27 27 43 27 29 46 42 51 28 3 14 44 5 42 30 28
## W2 28 27 26 40 40 28 39 31 27 40 42 15 17 37 6 37 42 41
## W3 39 51 43 27 43 36 34 38 28 33 38 8 27 48 8 40 42 36
## W4 53 37 44 32 54 50 37 40 33 43 37 12 14 28 3 29 29 45
## W5 39 38 38 42 33 28 23 40 29 28 35 7 18 40 1 52 24 25
## P57 P58 P59 P60 P61 P62 P63 P64 P65 P66 P67 P68 P69 P70 P71 P72 P73 P74
## W0 38 32 5 37 25 12 37 34 14 35 36 14 26 31 7 26 35 8
## W1 26 34 13 41 30 27 39 37 10 41 30 11 43 39 11 34 30 9
## W2 37 38 10 46 32 18 54 29 9 45 38 9 46 50 10 39 36 6
## W3 43 41 11 27 47 22 44 42 10 34 44 9 39 31 8 41 46 15
## W4 51 24 10 51 45 19 30 31 11 50 47 5 41 37 10 39 34 4
## W5 39 29 11 28 50 19 36 36 9 40 37 19 31 30 14 38 29 12
## P75 P76 P77 P78 P79 P80 P81 P82 P83 P84 P85 P86 P87 P88 P89 P90 P91 P92
## W0 34 27 7 39 37 38 14 10 46 29 39 37 30 41 24 42 10 26
## W1 45 34 7 40 34 29 6 12 40 26 34 33 38 28 26 34 11 36
## W2 37 43 6 31 45 32 13 17 34 31 28 33 35 32 44 40 7 56
## W3 27 45 2 39 41 40 5 13 38 39 38 32 39 30 31 39 13 40
## W4 40 28 7 44 37 29 8 14 36 28 33 36 37 33 37 47 5 31
## W5 22 35 5 34 23 36 13 8 45 44 37 26 42 41 37 31 9 44
## P93 P94 P95 P96 P97 P98 P99 P100 P101 P102 P103 P104 P105 P106 P107
## W0 11 14 12 31 29 4 16 13 26 34 7 4 2 11 19
## W1 9 9 15 35 37 2 11 6 51 41 5 4 3 10 19
## W2 10 10 15 36 36 4 9 7 45 22 7 5 3 11 15
## W3 13 9 13 44 40 4 21 9 47 32 7 5 2 16 24
## W4 5 5 17 36 33 7 9 9 40 36 14 3 5 16 16
## W5 11 11 12 38 29 2 5 15 36 27 9 7 4 7 23
## P108 P109 P110 P111 P112 P113 P114 P115 P116 P117 P118 P119 P120 P121
## W0 0 9 9 5 31 22 7 9 13 6 16 30 37 7
## W1 2 8 12 4 36 26 6 9 7 2 13 40 35 10
## W2 2 8 12 7 32 31 7 12 8 3 10 46 27 8
## W3 0 5 6 4 51 37 8 13 8 3 10 33 30 10
## W4 3 13 15 4 39 32 10 14 7 3 16 28 31 11
## W5 1 9 10 5 33 25 12 13 12 2 15 41 42 10
## P122 P123 P124 P125 P126 P127 P128 P129 P130 P131 P132 P133 P134 P135
## W0 7 5 4 6 3 4 36 21 19 29 39 30 32 38
## W1 10 5 6 9 8 0 43 36 53 32 36 48 41 32
## W2 7 3 0 5 8 2 28 44 43 32 45 29 50 36
## W3 8 6 7 10 3 2 39 47 41 35 41 38 35 37
## W4 11 4 4 6 5 2 41 26 39 49 43 40 36 42
## W5 2 6 3 9 3 1 37 33 46 38 37 42 35 33
## P136 P137 P138 P139 P140 P141 P142 P143 P144 P145 P146 P147 P148 P149
## W0 39 37 36 32 28 27 23 31 9 10 7 10 3 11
## W1 37 41 44 36 36 45 29 35 13 12 13 4 2 9
## W2 39 43 22 32 41 40 40 29 17 9 9 9 8 9
## W3 52 41 35 38 46 34 43 42 12 9 13 9 3 6
## W4 38 40 34 47 34 31 34 31 8 12 10 15 5 13
## W5 28 47 34 31 38 35 30 35 21 6 12 7 7 8
## P150 P151 P152 P153 P154 P155 P156 P157 P158 P159 P160 P161 P162 P163
## W0 4 3 11 22 12 5 1 12 8 2 9 5 13 6
## W1 3 7 10 9 14 7 4 8 3 2 8 6 9 5
## W2 3 8 8 11 11 4 6 14 3 9 10 5 12 6
## W3 4 8 8 10 6 3 6 7 2 5 13 7 9 6
## W4 6 6 13 10 8 4 3 8 3 2 14 1 11 7
## W5 2 8 4 5 7 3 8 10 3 7 10 3 11 5
## P164 P165 P166 P167 P168 P169 P170 P171 P172 P173 P174 P175 P176 P177
## W0 9 9 7 33 34 38 29 6 32 34 37 30 38 25
## W1 5 13 10 45 29 31 37 7 48 25 42 40 44 38
## W2 8 9 9 42 34 22 32 13 44 42 30 48 41 37
## W3 9 16 11 41 59 43 43 6 40 47 43 28 26 48
## W4 14 12 5 32 27 29 30 4 31 37 48 30 28 34
## W5 10 9 8 45 39 25 27 12 40 40 44 34 36 40
## P178 P179 P180 P181 P182 P183 P184 P185 P186 P187 P188 P189 P190 P191
## W0 35 44 40 32 42 25 30 32 36 31 33 32 54 48
## W1 34 43 38 44 28 37 37 28 34 38 34 34 41 36
## W2 40 35 34 31 34 42 38 36 37 47 36 46 51 42
## W3 36 36 43 38 28 50 30 40 36 36 52 30 45 35
## W4 45 39 43 37 26 18 32 40 41 36 42 39 51 44
## W5 37 42 34 36 35 32 32 50 33 27 49 51 46 39
## P192 P193 P194 P195 P196 P197 P198 P199 P200 P201 P202 P203 P204 P205
## W0 24 36 35 4 43 6 7 4 18 1 10 9 1 11
## W1 33 22 37 4 34 8 19 1 14 3 10 7 2 7
## W2 30 32 31 7 39 8 4 1 21 3 10 3 1 7
## W3 35 45 23 5 32 16 9 6 17 5 11 6 1 13
## W4 36 35 37 5 45 8 8 3 26 5 11 6 1 8
## W5 43 44 42 3 35 11 12 7 14 5 11 9 3 6
## P206 P207 P208 P209 P210 P211 P212 P213 P214 P215 P216 P217 P218 P219
## W0 1 2 30 12 16 15 0 0 0 0 0 0 0 0
## W1 0 5 26 6 12 12 0 0 0 0 0 0 0 0
## W2 1 8 36 6 9 9 0 0 0 0 0 0 0 0
## W3 3 7 33 11 14 12 0 0 0 0 0 0 0 1
## W4 1 3 33 16 7 14 0 1 0 0 0 0 0 0
## W5 3 5 30 12 11 14 1 0 1 0 0 0 0 0
## P220 P221 P222 P223 P224 P225 P226 P227 P228 P229 P230 P231 P232 P233
## W0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## W1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## W2 0 0 0 0 0 1 0 0 0 1 1 0 0 0
## W3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## W4 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## W5 0 0 0 0 1 1 0 0 0 0 0 0 0 0
## P234 P235 P236 P237 P238 P239 P240 P241 P242 P243 P244 P245 P246 P247
## W0 0 0 0 0 0 0 0 0 0 0 0 2 0 1
## W1 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## W2 0 0 0 0 0 1 1 0 0 0 0 0 0 0
## W3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## W4 1 0 0 0 0 0 0 1 0 0 0 0 0 0
## W5 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## P248 P249 P250 P251 P252 P253 P254 P255 P256 P257 P258 P259 P260 P261
## W0 0 0 0 0 0 0 0 1 0 0 0 0 0 20
## W1 0 0 0 0 0 0 0 0 0 0 0 0 0 36
## W2 0 0 0 0 0 0 0 1 0 0 0 0 0 17
## W3 0 0 0 0 0 0 0 0 0 0 0 0 1 19
## W4 0 0 0 0 0 0 0 1 0 1 0 0 0 16
## W5 0 0 0 0 0 0 1 1 0 1 0 0 0 23
## P262 P263 P264 P265 P266 P267 P268 P269 P270 P271 P272 P273 P274 P275
## W0 25 18 8 0 6 8 18 7 26 3 0 0 0 0
## W1 31 12 16 1 8 14 18 11 16 3 0 0 0 1
## W2 33 20 9 1 7 14 18 6 23 4 0 1 0 0
## W3 23 21 9 5 15 5 14 12 19 6 3 0 0 0
## W4 38 12 9 2 17 8 18 15 29 1 0 0 0 0
## W5 34 24 10 1 10 5 24 11 21 8 1 0 0 0
## P276 P277 P278 P279 P280 P281 P282 P283 P284 P285 P286 P287 P288 P289
## W0 0 0 0 0 0 1 0 0 26 11 17 0 1 0
## W1 0 0 0 0 1 0 0 0 17 12 19 0 1 1
## W2 0 0 0 0 0 3 0 0 17 6 13 0 0 0
## W3 0 0 0 0 0 1 0 0 22 6 15 0 0 0
## W4 0 0 0 0 0 0 3 0 23 8 16 1 1 0
## W5 0 0 0 0 0 0 1 0 24 12 23 0 0 0
## P290 P291 P292 P293 P294 P295 P296 P297 P298 P299 P300 P301 P302 P303
## W0 0 2 2 6 9 3 7 3 5 10 6 7 4 3
## W1 0 2 7 3 13 6 5 4 7 8 7 1 2 5
## W2 1 5 5 5 19 3 6 4 4 12 3 2 5 8
## W3 0 6 4 2 13 8 4 1 1 8 1 3 2 4
## W4 1 4 4 6 5 8 6 3 4 15 8 4 4 3
## W5 0 0 3 4 10 3 3 3 3 14 9 5 7 6
## P304 P305 P306 P307 P308 P309 P310 P311 P312 P313 P314 P315 P316 P317
## W0 7 8 4 4 7 9 5 8 3 10 6 5 4 2
## W1 6 7 2 5 3 5 4 6 2 1 17 7 5 3
## W2 6 3 3 6 2 8 4 1 3 6 9 2 9 3
## W3 8 1 8 5 4 8 2 9 8 5 14 5 2 7
## W4 9 3 7 3 6 14 5 5 3 4 10 7 1 9
## W5 9 2 4 2 3 7 1 1 6 7 8 8 8 7
## P318 P319 P320 P321 P322 P323 P324 P325 P326 P327 P328 P329 P330 P331
## W0 3 5 2 3 4 3 11 7 3 2 4 2 4 7
## W1 5 4 5 6 5 7 11 6 11 5 6 6 4 6
## W2 5 11 8 8 2 2 15 2 7 4 6 4 8 6
## W3 9 8 7 5 2 2 6 3 6 5 6 9 2 4
## W4 9 10 2 3 7 2 6 7 4 5 4 3 4 5
## W5 1 12 6 3 3 8 9 3 7 5 9 7 5 2
## P332 P333 P334 P335 P336 P337 P338 P339 P340 P341 P342 P343 P344 P345
## W0 10 9 11 6 2 3 2 0 0 5 3 0 1 2
## W1 14 9 13 4 3 6 2 0 0 1 2 0 2 3
## W2 8 5 15 2 5 3 1 1 0 3 2 2 0 2
## W3 12 8 8 4 3 1 3 2 0 1 2 1 3 0
## W4 6 11 16 3 1 1 4 4 0 1 3 0 0 7
## W5 10 13 16 2 4 7 2 2 0 3 0 5 0 1
## P346 P347 P348 P349 P350 P351 P352 P354 P355 P356 P357 P358 P359 P360
## W0 0 1 1 0 0 0 0 0 0 4 3 1 3 0
## W1 0 0 0 0 0 0 0 0 0 6 1 1 5 1
## W2 0 0 0 0 0 0 0 0 0 4 3 1 3 2
## W3 0 0 0 0 0 0 0 0 0 2 4 0 5 2
## W4 0 0 0 0 0 0 0 0 0 2 2 0 4 1
## W5 0 0 1 0 0 0 0 0 0 5 3 0 5 1
## P361 P362 P363 P364 P365 P366 P367 P368 P369 P370 P371 P372 P373 P374
## W0 6 1 2 10 6 2 0 0 3 6 2 1 0 1
## W1 3 0 1 18 5 2 2 5 2 2 4 2 3 1
## W2 3 0 0 10 5 8 2 5 2 3 3 0 0 2
## W3 2 1 0 8 1 3 1 3 3 5 1 1 0 2
## W4 3 1 0 11 5 5 1 2 3 11 2 2 1 1
## W5 0 1 0 18 2 3 5 2 7 5 2 4 1 0
## P375 P376 P377 P378 P379 P380 P381 P382 P383 P384 P386 P387 P388 P389
## W0 1 0 1 0 0 0 0 0 0 1 0 5 5 2
## W1 0 0 0 0 1 0 0 0 0 0 0 3 2 3
## W2 1 0 0 0 0 0 0 0 0 0 0 0 1 4
## W3 0 0 0 0 0 0 0 0 0 1 0 1 1 4
## W4 0 0 0 0 0 0 1 0 0 0 0 9 1 0
## W5 0 0 0 0 0 0 0 0 0 1 0 5 2 1
## P390 P391 P392 P393 P394 P395 P396 P397 P398 P399 P400 P401 P402 P403
## W0 4 0 3 1 1 1 0 10 14 17 20 3 1 12
## W1 4 1 3 0 4 1 1 7 12 11 11 5 0 8
## W2 3 0 7 1 2 0 1 8 10 8 19 5 6 12
## W3 2 4 5 0 9 2 4 13 8 9 16 7 0 9
## W4 3 3 5 2 1 2 3 7 14 8 22 2 3 12
## W5 1 0 4 2 2 0 1 10 8 5 20 2 4 18
## P404 P405 P406 P407 P408 P409 P410 P411 P412 P413 P414 P415 P416 P417
## W0 11 23 6 22 7 42 6 16 4 4 2 2 1 0
## W1 8 12 12 23 10 48 13 17 2 6 2 4 1 0
## W2 7 16 17 10 8 38 16 12 4 8 1 2 1 0
## W3 9 22 15 19 9 43 9 10 8 7 1 3 0 1
## W4 11 18 14 16 13 35 18 23 5 12 5 6 0 0
## W5 6 18 18 26 8 39 16 14 3 11 2 7 0 0
## P418 P419 P420 P421 P422 P423 P424 P425 P426 P427 P428 P429 P430 P431
## W0 0 0 0 0 1 0 0 0 0 0 0 16 13 3
## W1 0 0 0 0 1 0 0 0 0 0 0 9 8 6
## W2 0 1 0 0 0 0 1 0 1 0 0 9 14 4
## W3 0 0 0 0 1 0 0 0 0 0 0 10 12 2
## W4 1 0 0 0 0 0 1 0 0 0 0 7 15 5
## W5 0 0 1 0 0 0 0 0 0 0 0 11 12 6
## P432 P433 P434 P435 P436 P437 P438 P439 P440 P441 P442 P443 P444 P445
## W0 6 2 2 21 14 7 1 1 1 0 0 0 0 0
## W1 7 4 0 16 5 4 1 0 0 0 0 1 0 0
## W2 6 5 7 17 6 13 0 0 0 1 1 1 0 0
## W3 5 3 1 17 12 6 0 0 0 0 0 1 0 0
## W4 6 2 3 14 10 3 2 0 0 1 0 0 0 0
## W5 5 2 2 13 6 7 0 1 0 0 0 0 0 0
## P446 P447 P448 P449 P450 P451 P452 P453 P454 P455 P456 P457 P458 P459
## W0 0 0 0 0 1 1 0 0 0 0 0 0 0 0
## W1 1 0 2 0 0 0 0 0 0 0 0 0 0 1
## W2 1 0 0 0 0 0 0 0 0 1 0 0 0 0
## W3 0 0 2 0 0 0 0 0 0 0 2 0 0 0
## W4 0 0 1 0 1 0 1 1 0 0 0 0 0 0
## W5 0 0 0 0 3 0 0 0 0 0 0 0 0 0
## P460 P461 P462 P463 P464 P465 P466 P467 P468 P469 P470 P471 P472 P473
## W0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## W1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## W2 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## W3 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## W4 1 0 0 0 0 1 0 0 0 0 0 0 0 0
## W5 0 0 1 0 0 0 0 0 0 0 0 0 1 1
## P474 P475 P476 P477 P478 P479 P480 P481 P482 P483 P484 P485 P486 P487
## W0 0 3 1 1 1 0 0 1 0 0 3 15 16 7
## W1 0 5 0 0 0 3 0 2 0 0 5 9 30 10
## W2 0 1 1 1 0 1 0 0 1 0 3 8 25 15
## W3 0 2 1 0 2 1 0 0 0 0 6 11 11 12
## W4 0 2 3 0 3 0 0 0 1 2 10 12 23 14
## W5 0 3 0 1 0 2 0 0 0 1 5 12 19 10
## P488 P489 P490 P491 P492 P493 P494 P495 P496 P497 P498 P499 P500 P501
## W0 10 5 6 19 9 9 6 16 5 5 4 11 8 4
## W1 11 8 7 20 14 9 13 15 3 4 4 13 8 5
## W2 10 4 6 27 14 12 12 27 13 4 6 12 3 4
## W3 9 8 12 23 13 5 15 14 11 3 8 18 4 2
## W4 9 6 13 19 5 11 16 19 10 2 8 8 13 6
## W5 14 5 9 30 16 10 19 12 9 2 1 9 9 5
## P502 P503 P504 P505 P506 P507 P508 P509 P510 P511 P512 P513 P514 P515
## W0 11 13 10 23 23 11 9 9 9 25 17 22 7 10
## W1 15 19 13 4 16 27 10 5 4 23 17 23 14 15
## W2 19 15 11 16 9 11 9 5 10 30 16 25 13 15
## W3 15 13 5 24 11 22 12 2 6 33 16 21 11 14
## W4 14 22 13 19 16 9 11 9 5 20 27 25 9 12
## W5 11 23 12 15 19 15 17 3 11 27 18 17 19 8
## P516 P517 P518 P519 P520 P521 P522 P523 P524 P525 P526 P527 P528 P529
## W0 24 15 15 30 9 8 12 8 14 15 9 7 9 13
## W1 18 15 6 26 12 14 15 14 9 12 13 18 9 17
## W2 15 11 14 16 11 14 16 12 10 15 15 11 10 15
## W3 21 11 12 16 16 11 10 16 7 15 18 14 5 11
## W4 20 20 11 23 8 14 19 18 11 5 15 9 13 13
## W5 19 16 18 30 9 12 11 9 8 7 10 16 22 14
## P530 P531 P532 P533 P534 P535 P536 P537 P538 P539 P540 P541 P542 P543
## W0 20 4 15 28 6 15 14 20 7 9 23 11 12 9
## W1 11 8 5 33 10 26 9 16 7 10 19 17 14 6
## W2 22 3 10 21 9 11 13 17 13 14 21 5 7 16
## W3 14 2 8 37 7 15 13 20 14 7 23 14 13 9
## W4 15 4 12 20 7 19 3 19 11 5 20 10 11 6
## W5 20 5 8 28 9 15 21 20 11 16 21 10 13 15
## P544 P545 P546 P547 P548 P549 P550 P551 P552 P553 P554 P555 P556 P557
## W0 15 13 10 9 32 35 11 5 15 2 24 10 15 28
## W1 10 13 14 6 28 29 10 8 9 2 15 16 11 23
## W2 14 15 13 11 39 40 6 12 10 3 32 17 26 35
## W3 7 14 11 5 36 32 11 10 8 7 28 8 15 34
## W4 12 13 9 9 47 33 11 9 11 5 22 11 13 33
## W5 13 22 13 17 40 30 19 8 15 4 21 12 11 27
## P558 P559 P560 P561 P562 P563 P564 P565 P566 P567 P568 P569 P570 P571
## W0 15 11 9 6 6 5 13 4 24 3 4 1 2 4
## W1 10 13 9 5 5 19 4 7 26 7 2 3 2 3
## W2 15 4 3 1 0 5 8 10 22 1 1 4 0 6
## W3 13 19 7 3 4 11 9 11 24 9 5 1 2 6
## W4 16 10 11 1 4 10 6 8 20 6 2 2 0 4
## W5 8 13 8 7 4 15 10 13 13 5 2 1 2 6
## P572 P573 P574 P575 P576 P577 P578 P579 P580 P581 P582 P583 P584 P585
## W0 2 1 0 0 0 1 1 0 1 2 3 2 2 1
## W1 0 2 1 0 0 1 0 0 5 2 4 1 3 0
## W2 0 4 0 0 0 0 1 0 4 5 2 4 2 1
## W3 0 4 0 0 1 1 0 0 7 2 3 1 6 1
## W4 1 2 0 0 0 1 0 1 2 2 5 3 2 2
## W5 3 2 0 0 0 0 0 0 5 8 1 3 2 0
## P586 P587 P588 P589 P590 P591 P592 P593 P594 P595 P596 P597 P598 P599
## W0 7 4 5 0 3 2 2 4 1 1 2 1 10 2
## W1 2 4 2 1 4 2 0 5 2 2 3 2 6 1
## W2 4 2 1 1 4 2 0 1 3 3 5 0 6 1
## W3 6 5 0 0 6 2 1 4 5 3 4 0 15 1
## W4 8 2 1 2 4 1 0 0 3 1 3 2 10 3
## W5 8 3 2 1 4 3 0 1 4 5 3 1 14 2
## P600 P601 P602 P603 P604 P605 P606 P607 P608 P609 P610 P611 P612 P613
## W0 0 0 0 0 0 1 1 0 1 0 6 3 14 18
## W1 1 0 0 1 0 3 1 1 1 0 3 1 11 26
## W2 3 1 0 0 0 2 0 0 0 2 1 2 13 12
## W3 1 0 1 0 0 1 1 0 0 1 4 7 9 17
## W4 1 1 1 1 0 4 1 0 0 8 5 3 13 23
## W5 1 1 0 0 0 1 1 0 0 4 1 0 14 15
## P614 P615 P616 P617 P618 P619 P620 P621 P622 P623 P624 P625 P626 P627
## W0 4 17 6 36 40 38 31 31 29 38 5 11 10 12
## W1 4 16 6 33 35 31 27 33 33 27 10 6 6 11
## W2 2 19 3 40 49 31 37 29 39 36 13 11 8 12
## W3 3 20 2 46 31 41 29 38 40 36 6 15 13 9
## W4 4 19 6 39 34 41 34 36 40 35 12 7 9 11
## W5 6 19 2 39 45 36 39 27 37 39 10 12 16 8
## P628 P629 P630 P631 P632 P633 P634 P635 P636 P637 P638 P639 P640 P641
## W0 7 10 11 8 13 5 6 14 6 2 15 0 14 0
## W1 6 9 11 4 11 7 8 8 15 4 7 1 20 5
## W2 13 11 8 13 14 16 13 11 9 3 10 0 15 0
## W3 9 12 12 14 9 16 10 10 14 7 18 0 11 2
## W4 11 6 26 12 11 11 11 6 18 4 21 1 10 6
## W5 5 11 5 5 14 12 12 9 7 4 8 0 20 2
## P642 P643 P644 P646 P647 P649 P650 P651 P652 P653 P654 P655 P656 P657
## W0 5 0 0 0 0 0 0 0 0 0 0 0 0 0
## W1 3 0 0 0 0 0 0 0 0 0 0 1 0 0
## W2 4 0 0 0 0 0 0 0 0 0 0 0 0 0
## W3 2 0 0 0 0 0 0 0 0 1 0 0 0 0
## W4 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## W5 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## P658 P659 P660 P661 P662 P663 P664 P665 P666 P667 P668 P669 P670 P671
## W0 0 0 0 0 0 0 0 0 1 1 1 0 0 1
## W1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## W2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## W3 0 1 1 0 0 2 0 0 0 0 0 0 0 0
## W4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## W5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## P672 P673 P674 P675 P676 P677 P678 P679 P680 P681 P682 P683 P684 P685
## W0 0 1 4 2 1 0 0 1 0 0 0 0 0 0
## W1 0 3 4 1 1 1 0 0 0 1 0 0 0 0
## W2 0 1 8 4 3 0 0 0 0 0 0 0 0 0
## W3 0 2 7 0 2 0 0 0 0 0 0 0 0 0
## W4 0 3 2 2 5 0 0 0 0 0 0 0 0 0
## W5 0 0 6 5 4 0 0 0 0 0 0 0 0 0
## P686 P687 P688 P689 P690 P691 P692 P693 P694 P695 P696 P697 P698 P699
## W0 2 1 0 0 0 0 0 2 0 0 0 0 2 0
## W1 4 0 0 0 0 0 2 3 0 0 0 0 2 0
## W2 0 0 0 1 0 0 3 4 0 0 0 0 2 0
## W3 1 0 0 0 0 0 0 2 0 0 0 0 1 0
## W4 2 0 1 1 2 1 2 4 0 0 0 0 0 0
## W5 4 1 0 0 0 0 3 4 0 0 1 0 2 0
## P700 P701 P702 P703 P704 P705 P706 P707 P708 P711 P712 P713 P714 P715
## W0 1 5 3 2 0 1 0 1 0 0 0 0 0 0
## W1 0 3 1 3 0 3 0 0 1 0 0 2 0 0
## W2 0 1 4 4 2 0 0 0 0 0 0 0 0 0
## W3 5 2 4 4 1 1 0 0 0 1 0 0 0 0
## W4 0 1 4 0 0 4 0 0 0 0 0 0 1 0
## W5 1 3 6 3 0 3 1 1 0 0 0 0 1 0
## P716 P717 P718 P719 P720 P721 P722 P724 P726 P727 P728 P729 P730 P731
## W0 1 0 0 0 0 0 0 0 0 2 1 0 0 0
## W1 0 0 2 0 0 0 0 0 1 0 0 0 1 0
## W2 0 0 0 0 0 0 0 0 0 4 1 0 0 0
## W3 0 0 0 0 0 0 0 0 2 1 1 0 0 1
## W4 1 0 0 0 0 0 0 0 2 4 0 0 0 0
## W5 0 0 1 0 0 0 0 0 2 2 1 0 0 0
## P732 P733 P734 P735 P736 P737 P738 P739 P740 P741 P742 P743 P744 P745
## W0 0 0 1 0 1 0 0 0 0 0 0 1 0 2
## W1 1 0 0 0 1 0 0 0 0 0 0 1 0 10
## W2 1 0 0 0 0 3 0 0 0 0 1 3 0 6
## W3 1 0 1 0 0 1 0 1 0 0 0 0 1 2
## W4 1 2 0 0 0 0 0 0 1 0 1 2 0 2
## W5 1 0 0 0 2 1 0 0 0 0 1 0 0 2
## P746 P747 P748 P749 P750 P751 P752 P753 P754 P755 P756 P757 P758 P759
## W0 1 1 0 1 0 0 4 1 0 0 0 0 0 0
## W1 0 1 1 0 0 2 0 0 0 0 0 0 0 0
## W2 0 1 1 0 0 0 2 0 0 0 1 0 0 0
## W3 0 0 0 0 0 1 3 0 0 0 0 0 0 1
## W4 1 4 0 1 0 0 2 0 0 0 0 0 0 0
## W5 2 1 3 0 1 0 2 0 0 0 0 0 0 0
## P760 P761 P762 P763 P764 P765 P766 P767 P768 P769 P770 P771 P772 P773
## W0 0 0 2 0 1 0 0 0 0 0 2 0 0 0
## W1 0 0 3 0 2 0 1 0 0 0 0 0 0 0
## W2 0 0 2 0 5 0 1 1 2 2 0 0 0 0
## W3 0 0 1 0 3 0 0 1 8 3 0 0 0 0
## W4 0 0 0 0 0 0 0 0 2 0 0 0 1 0
## W5 0 0 2 0 3 0 1 2 5 1 1 0 1 0
## P774 P775 P776 P777 P778 P779 P780 P781 P782 P783 P784 P785 P786 P787
## W0 0 0 0 0 0 0 0 9 5 15 3 3 1 1
## W1 0 0 0 0 0 0 0 13 7 12 1 1 2 0
## W2 0 0 0 1 0 1 0 15 4 7 2 4 2 2
## W3 0 0 0 0 0 0 0 15 6 16 1 0 2 0
## W4 0 0 0 0 0 0 0 16 2 18 3 1 1 1
## W5 0 0 0 0 0 0 0 17 2 9 4 1 0 1
## P788 P789 P790 P791 P792 P793 P794 P795 P796 P797 P798 P799 P800 P801
## W0 4 2 3 4 0 0 0 0 2 2 6 3 3 3
## W1 3 1 1 3 1 1 0 0 5 3 0 3 2 2
## W2 3 1 5 4 0 1 1 0 0 2 3 5 1 0
## W3 5 6 2 2 1 4 0 1 3 2 1 3 0 3
## W4 1 1 0 6 0 2 0 0 3 2 0 5 0 2
## W5 4 4 4 6 3 1 0 0 0 3 2 4 0 3
## P802 P803 P804 P805 P806 P807 P808 P809 P810 P811 P812 P813 P814 P815
## W0 1 3 2 0 4 0 0 0 0 5 3 1 4 0
## W1 1 2 2 6 5 0 3 1 0 1 2 1 2 0
## W2 1 3 5 3 7 2 3 0 1 3 0 1 2 1
## W3 0 3 3 2 4 0 3 1 0 5 0 3 6 0
## W4 3 7 1 5 7 1 1 1 0 4 2 2 0 0
## W5 2 3 4 2 2 0 2 0 0 4 1 2 4 2
## P816 P817 P818 P819
## W0 0 1 0 0
## W1 1 0 0 1
## W2 0 0 0 0
## W3 0 0 1 0
## W4 1 1 0 0
## W5 2 1 0 0
#Identify the total sales in an year of all the product
#Store the product names
Prdt<- colnames(prod.wkly.sls)
#Created a dataframe with total sales at product level
Tot.Sls.By.Prdt <- as.data.frame(colSums(prod.wkly.sls[,c(1:811)]))
names(Tot.Sls.By.Prdt)[1] <- "Yr.Sls"
Tot.Sls.By.Prdt <- cbind(Prdt,Tot.Sls.By.Prdt)
rownames(Tot.Sls.By.Prdt) <- NULL
summary(Tot.Sls.By.Prdt)
## Prdt Yr.Sls
## P1 : 1 Min. : 1.0
## P10 : 1 1st Qu.: 21.0
## P100 : 1 Median : 199.0
## P101 : 1 Mean : 462.7
## P102 : 1 3rd Qu.: 575.0
## P103 : 1 Max. :2220.0
## (Other):805
Tot.Sls.By.Prdt$Yr.Sls <- as.numeric(Tot.Sls.By.Prdt$Yr.Sls )
#Top5 Product by Yearly Sale
Top5.Prdt.Sls <- Tot.Sls.By.Prdt %>%
select(Prdt,Yr.Sls) %>% arrange(desc(Yr.Sls)) %>% top_n(5,Yr.Sls)
## Warning: package 'bindrcpp' was built under R version 3.5.1
# Plot top 5 Product by Sale
ggplot(Top5.Prdt.Sls, aes(x=reorder(Prdt,-Yr.Sls), y= Yr.Sls)) +
geom_bar(stat="identity", width=.5, fill="tomato3") +
labs(title="Top 5 Products by Yearly Sale",
subtitle="Yearly",
caption="Product:Sales",
y="Total Sales",
x="Product") +
theme(axis.text.x = element_text(angle=65, vjust=0.6))
#Quarterly sales of top 5 Product analysis
Top5.Prdt.Sls$Prdt
## [1] P409 P34 P178 P135 P43
## 811 Levels: P1 P10 P100 P101 P102 P103 P104 P105 P106 P107 P108 ... P99
Q1.Sls.By.Prdt <- as.data.frame(colSums(prod.wkly.sls[1:13,c(1:811)]))
names(Q1.Sls.By.Prdt)[1] <- "Q1.Sls"
Q2.Sls.By.Prdt <- as.data.frame(colSums(prod.wkly.sls[14:26,c(1:811)]))
names(Q2.Sls.By.Prdt)[1] <- "Q2.Sls"
Q3.Sls.By.Prdt <- as.data.frame(colSums(prod.wkly.sls[27:39,c(1:811)]))
names(Q3.Sls.By.Prdt)[1] <- "Q3.Sls"
Q4.Sls.By.Prdt <- as.data.frame(colSums(prod.wkly.sls[40:52,c(1:811)]))
names(Q4.Sls.By.Prdt)[1] <- "Q4.Sls"
Qtr.Sls.By.Prdt<-as.data.frame(cbind(Prdt,"Q1.Sls"=Q1.Sls.By.Prdt$Q1.Sls,"Q2.Sls"=Q2.Sls.By.Prdt$Q2.Sls,"Q3.Sls"=Q3.Sls.By.Prdt$Q3.Sls,"Q4.Sls"=Q4.Sls.By.Prdt$Q4.Sls))
Qtr.Sls.By.Prdt
## Prdt Q1.Sls Q2.Sls Q3.Sls Q4.Sls
## 1 P1 162 114 118 107
## 2 P2 51 58 59 39
## 3 P3 118 119 116 99
## 4 P4 116 111 100 103
## 5 P5 128 125 87 100
## 6 P6 54 69 40 57
## 7 P7 65 62 39 47
## 8 P8 110 126 115 99
## 9 P9 156 162 108 113
## 10 P10 291 281 216 222
## 11 P11 148 154 133 166
## 12 P12 55 51 52 45
## 13 P13 145 116 104 105
## 14 P14 155 212 112 136
## 15 P15 506 487 405 407
## 16 P16 480 511 446 438
## 17 P17 514 483 350 418
## 18 P18 470 465 371 376
## 19 P19 449 454 406 378
## 20 P20 141 119 92 113
## 21 P21 128 125 96 113
## 22 P22 150 123 101 113
## 23 P23 58 64 49 44
## 24 P24 515 495 437 430
## 25 P25 420 428 356 398
## 26 P26 143 155 108 129
## 27 P27 493 480 416 415
## 28 P28 460 424 401 411
## 29 P29 171 186 129 142
## 30 P30 491 453 378 368
## 31 P31 125 103 114 100
## 32 P32 138 116 112 107
## 33 P33 163 159 128 154
## 34 P34 518 505 443 466
## 35 P35 465 464 438 438
## 36 P36 531 491 405 416
## 37 P37 470 539 426 423
## 38 P38 606 462 412 412
## 39 P39 481 475 382 418
## 40 P40 514 503 416 431
## 41 P41 490 516 379 433
## 42 P42 447 485 366 372
## 43 P43 513 558 377 465
## 44 P44 420 489 395 388
## 45 P45 452 439 392 368
## 46 P46 502 460 352 402
## 47 P47 458 487 378 415
## 48 P48 488 495 393 428
## 49 P49 472 493 401 431
## 50 P50 129 110 99 121
## 51 P51 241 254 235 202
## 52 P52 495 475 330 415
## 53 P53 58 74 44 58
## 54 P54 525 479 406 450
## 55 P55 465 431 377 369
## 56 P56 497 440 357 407
## 57 P57 487 475 377 385
## 58 P58 451 472 393 422
## 59 P59 131 122 99 114
## 60 P60 478 479 381 377
## 61 P61 489 419 363 400
## 62 P62 232 223 190 189
## 63 P63 531 490 392 410
## 64 P64 467 465 356 361
## 65 P65 136 140 122 91
## 66 P66 516 489 425 412
## 67 P67 494 456 378 364
## 68 P68 128 128 104 82
## 69 P69 501 454 391 400
## 70 P70 466 448 364 390
## 71 P71 132 131 119 112
## 72 P72 482 535 389 407
## 73 P73 490 454 372 391
## 74 P74 128 135 93 113
## 75 P75 498 495 417 425
## 76 P76 481 465 362 375
## 77 P77 65 57 49 63
## 78 P78 482 486 359 334
## 79 P79 493 465 349 375
## 80 P80 456 467 341 334
## 81 P81 118 135 95 115
## 82 P82 132 99 100 113
## 83 P83 535 456 386 435
## 84 P84 477 478 389 408
## 85 P85 466 426 372 384
## 86 P86 472 426 391 415
## 87 P87 487 406 368 382
## 88 P88 474 428 370 354
## 89 P89 486 443 384 362
## 90 P90 517 415 385 392
## 91 P91 122 131 103 101
## 92 P92 506 552 421 417
## 93 P93 127 111 109 106
## 94 P94 107 136 99 115
## 95 P95 160 202 142 136
## 96 P96 480 487 396 440
## 97 P97 479 442 340 383
## 98 P98 54 66 31 59
## 99 P99 142 137 93 111
## 100 P100 127 179 104 169
## 101 P101 532 506 388 423
## 102 P102 434 461 375 373
## 103 P103 113 113 105 98
## 104 P104 74 45 41 46
## 105 P105 56 54 48 37
## 106 P106 149 152 119 137
## 107 P107 256 243 220 202
## 108 P108 27 42 36 43
## 109 P109 117 118 134 120
## 110 P110 134 114 112 101
## 111 P111 53 57 63 65
## 112 P112 495 467 435 411
## 113 P113 410 416 372 414
## 114 P114 133 120 122 112
## 115 P115 144 161 141 125
## 116 P116 123 137 103 122
## 117 P117 50 69 55 52
## 118 P118 147 153 102 100
## 119 P119 486 459 376 403
## 120 P120 446 462 375 443
## 121 P121 128 125 105 103
## 122 P122 112 130 109 113
## 123 P123 57 48 38 44
## 124 P124 67 64 46 63
## 125 P125 128 118 96 93
## 126 P126 63 46 54 62
## 127 P127 28 38 25 26
## 128 P128 519 452 406 448
## 129 P129 462 501 447 422
## 130 P130 525 464 402 374
## 131 P131 485 487 385 388
## 132 P132 511 477 408 423
## 133 P133 506 473 400 402
## 134 P134 478 489 438 436
## 135 P135 508 549 431 432
## 136 P136 516 488 410 445
## 137 P137 529 482 442 441
## 138 P138 443 435 394 391
## 139 P139 480 498 365 367
## 140 P140 462 436 431 406
## 141 P141 454 452 379 391
## 142 P142 459 434 349 421
## 143 P143 464 496 373 361
## 144 P144 149 131 106 107
## 145 P145 136 142 100 92
## 146 P146 135 94 95 115
## 147 P147 128 117 124 109
## 148 P148 55 42 46 58
## 149 P149 138 124 122 102
## 150 P150 50 59 43 53
## 151 P151 75 68 35 43
## 152 P152 128 117 104 125
## 153 P153 153 112 100 121
## 154 P154 117 124 110 83
## 155 P155 48 58 53 45
## 156 P156 56 43 44 44
## 157 P157 141 100 91 98
## 158 P158 59 44 32 45
## 159 P159 60 52 44 56
## 160 P160 137 105 107 124
## 161 P161 65 54 50 59
## 162 P162 134 128 94 93
## 163 P163 56 43 54 51
## 164 P164 117 133 110 105
## 165 P165 146 133 111 122
## 166 P166 134 133 115 120
## 167 P167 534 441 368 389
## 168 P168 495 474 416 401
## 169 P169 438 435 355 389
## 170 P170 426 447 403 435
## 171 P171 128 112 109 99
## 172 P172 493 490 418 403
## 173 P173 495 538 413 451
## 174 P174 522 508 391 465
## 175 P175 488 494 440 402
## 176 P176 499 490 381 342
## 177 P177 461 488 391 380
## 178 P178 511 562 405 447
## 179 P179 494 504 459 447
## 180 P180 521 484 418 430
## 181 P181 481 462 403 359
## 182 P182 458 421 342 416
## 183 P183 440 444 376 357
## 184 P184 496 513 392 382
## 185 P185 495 459 427 387
## 186 P186 500 492 405 402
## 187 P187 433 409 344 393
## 188 P188 487 443 360 388
## 189 P189 449 437 404 388
## 190 P190 549 500 433 430
## 191 P191 547 506 393 408
## 192 P192 443 470 374 397
## 193 P193 505 544 390 420
## 194 P194 461 432 338 382
## 195 P195 64 65 49 48
## 196 P196 492 408 386 411
## 197 P197 133 137 106 124
## 198 P198 137 166 109 120
## 199 P199 51 49 32 41
## 200 P200 228 275 165 237
## 201 P201 51 58 42 46
## 202 P202 167 247 164 227
## 203 P203 77 89 67 83
## 204 P204 22 38 34 28
## 205 P205 137 207 123 165
## 206 P206 27 42 23 45
## 207 P207 73 81 54 86
## 208 P208 404 517 293 431
## 209 P209 133 153 103 126
## 210 P210 170 192 125 166
## 211 P211 165 172 127 125
## 212 P212 3 4 7 8
## 213 P213 2 1 3 6
## 214 P214 2 2 6 3
## 215 P215 0 1 0 0
## 216 P216 0 1 5 4
## 217 P217 0 0 1 6
## 218 P218 1 0 0 4
## 219 P219 3 2 8 3
## 220 P220 0 1 5 7
## 221 P221 0 3 2 10
## 222 P222 2 2 4 3
## 223 P223 1 5 1 3
## 224 P224 4 5 3 12
## 225 P225 3 0 2 7
## 226 P226 1 4 2 2
## 227 P227 2 2 0 2
## 228 P228 1 0 0 1
## 229 P229 3 1 2 3
## 230 P230 1 0 0 1
## 231 P231 2 1 0 6
## 232 P232 0 1 1 1
## 233 P233 0 3 1 2
## 234 P234 1 1 0 2
## 235 P235 1 4 1 2
## 236 P236 0 2 2 1
## 237 P237 0 2 2 1
## 238 P238 1 4 1 8
## 239 P239 4 3 2 2
## 240 P240 1 4 2 4
## 241 P241 2 3 3 5
## 242 P242 1 3 0 8
## 243 P243 0 2 0 2
## 244 P244 3 2 1 3
## 245 P245 4 6 0 4
## 246 P246 0 4 2 1
## 247 P247 4 9 2 4
## 248 P248 0 5 3 4
## 249 P249 1 1 1 1
## 250 P250 0 1 0 1
## 251 P251 0 0 0 1
## 252 P252 0 0 1 7
## 253 P253 0 0 0 2
## 254 P254 1 0 0 0
## 255 P255 4 13 11 27
## 256 P256 1 0 1 2
## 257 P257 7 11 7 9
## 258 P258 0 2 3 0
## 259 P259 0 1 0 0
## 260 P260 2 3 0 0
## 261 P261 285 318 216 250
## 262 P262 400 534 291 456
## 263 P263 228 304 172 235
## 264 P264 131 149 92 118
## 265 P265 22 32 10 39
## 266 P266 136 125 106 125
## 267 P267 129 123 119 115
## 268 P268 237 276 186 261
## 269 P269 140 203 127 170
## 270 P270 264 309 218 239
## 271 P271 55 46 35 45
## 272 P272 4 6 6 5
## 273 P273 1 2 2 5
## 274 P274 1 1 0 5
## 275 P275 1 0 0 3
## 276 P276 1 0 0 2
## 277 P277 0 3 4 7
## 278 P278 1 1 2 3
## 279 P279 0 1 1 0
## 280 P280 1 4 0 2
## 281 P281 9 19 10 12
## 282 P282 7 6 9 11
## 283 P283 1 5 9 12
## 284 P284 257 262 189 222
## 285 P285 127 145 92 114
## 286 P286 225 314 178 243
## 287 P287 7 16 16 22
## 288 P288 9 12 14 9
## 289 P289 4 5 3 9
## 290 P290 4 0 5 5
## 291 P291 42 57 40 44
## 292 P292 56 42 51 44
## 293 P293 47 63 40 47
## 294 P294 148 144 94 99
## 295 P295 54 74 31 72
## 296 P296 55 55 49 60
## 297 P297 49 59 40 56
## 298 P298 65 53 33 54
## 299 P299 120 126 98 130
## 300 P300 64 43 57 59
## 301 P301 51 56 49 56
## 302 P302 59 46 55 57
## 303 P303 59 57 32 52
## 304 P304 113 120 95 102
## 305 P305 75 62 61 58
## 306 P306 68 53 44 47
## 307 P307 58 58 57 54
## 308 P308 58 56 43 50
## 309 P309 119 144 99 120
## 310 P310 48 67 64 53
## 311 P311 67 53 54 52
## 312 P312 45 54 57 56
## 313 P313 66 48 44 62
## 314 P314 159 136 90 119
## 315 P315 56 57 46 48
## 316 P316 69 60 49 41
## 317 P317 60 64 54 48
## 318 P318 69 42 30 50
## 319 P319 125 141 112 96
## 320 P320 64 53 48 54
## 321 P321 61 45 54 56
## 322 P322 62 57 37 37
## 323 P323 61 58 46 46
## 324 P324 134 139 101 98
## 325 P325 62 52 48 52
## 326 P326 70 65 47 30
## 327 P327 52 51 52 47
## 328 P328 55 69 40 45
## 329 P329 74 55 49 53
## 330 P330 58 57 39 55
## 331 P331 71 69 62 44
## 332 P332 141 127 102 90
## 333 P333 134 138 93 108
## 334 P334 154 145 104 110
## 335 P335 46 40 41 42
## 336 P336 42 55 36 49
## 337 P337 44 61 44 52
## 338 P338 35 36 20 45
## 339 P339 15 12 17 19
## 340 P340 1 3 9 17
## 341 P341 41 44 43 40
## 342 P342 25 39 20 36
## 343 P343 16 33 19 35
## 344 P344 15 21 19 27
## 345 P345 31 25 29 48
## 346 P346 1 5 5 6
## 347 P347 3 4 6 7
## 348 P348 5 4 9 5
## 349 P349 0 6 5 5
## 350 P350 0 2 2 4
## 351 P351 0 2 0 1
## 352 P352 1 2 1 2
## 353 P354 0 1 1 2
## 354 P355 1 0 1 2
## 355 P356 50 51 38 58
## 356 P357 35 32 20 27
## 357 P358 6 10 13 35
## 358 P359 62 81 54 68
## 359 P360 11 14 8 18
## 360 P361 28 40 27 37
## 361 P362 8 17 14 21
## 362 P363 6 6 6 9
## 363 P364 156 200 131 169
## 364 P365 54 55 37 45
## 365 P366 40 39 36 44
## 366 P367 24 37 20 36
## 367 P368 55 86 46 93
## 368 P369 48 75 36 45
## 369 P370 53 59 32 41
## 370 P371 27 35 18 34
## 371 P372 19 36 13 43
## 372 P373 17 5 24 29
## 373 P374 14 11 15 19
## 374 P375 7 6 8 9
## 375 P376 2 9 10 10
## 376 P377 3 7 14 7
## 377 P378 1 3 7 9
## 378 P379 3 3 2 5
## 379 P380 0 0 0 3
## 380 P381 1 1 0 0
## 381 P382 0 1 0 1
## 382 P383 0 1 0 1
## 383 P384 3 4 2 7
## 384 P386 0 2 0 1
## 385 P387 51 46 27 43
## 386 P388 33 54 29 37
## 387 P389 20 44 28 37
## 388 P390 39 43 22 35
## 389 P391 24 45 24 42
## 390 P392 60 75 66 78
## 391 P393 9 15 19 27
## 392 P394 37 31 25 37
## 393 P395 19 41 27 35
## 394 P396 26 33 23 40
## 395 P397 121 179 92 110
## 396 P398 148 199 123 154
## 397 P399 162 205 120 173
## 398 P400 196 264 129 229
## 399 P401 58 50 38 52
## 400 P402 34 56 45 35
## 401 P403 187 246 178 212
## 402 P404 129 133 97 113
## 403 P405 246 295 169 256
## 404 P406 180 205 115 191
## 405 P407 268 263 201 226
## 406 P408 137 192 116 180
## 407 P409 526 697 414 583
## 408 P410 192 276 155 237
## 409 P411 208 281 176 220
## 410 P412 45 53 39 45
## 411 P413 105 156 77 138
## 412 P414 44 47 34 42
## 413 P415 54 54 38 30
## 414 P416 9 12 12 24
## 415 P417 4 9 5 7
## 416 P418 1 4 5 6
## 417 P419 1 1 4 6
## 418 P420 1 2 1 1
## 419 P421 0 1 1 0
## 420 P422 8 10 7 16
## 421 P423 2 4 3 4
## 422 P424 3 5 1 7
## 423 P425 0 1 3 0
## 424 P426 1 3 1 1
## 425 P427 3 1 2 3
## 426 P428 0 1 0 1
## 427 P429 122 136 82 117
## 428 P430 166 172 131 174
## 429 P431 85 103 53 78
## 430 P432 86 126 80 122
## 431 P433 45 63 43 61
## 432 P434 28 36 25 47
## 433 P435 209 262 199 197
## 434 P436 110 154 84 114
## 435 P437 88 95 64 71
## 436 P438 6 3 7 7
## 437 P439 5 4 3 10
## 438 P440 4 3 3 5
## 439 P441 4 4 1 5
## 440 P442 4 2 5 10
## 441 P443 4 7 1 4
## 442 P444 0 2 1 1
## 443 P445 4 3 2 3
## 444 P446 3 2 1 6
## 445 P447 1 4 1 8
## 446 P448 7 1 6 11
## 447 P449 0 6 7 8
## 448 P450 5 1 6 5
## 449 P451 5 2 4 4
## 450 P452 5 5 2 3
## 451 P453 5 0 3 2
## 452 P454 2 1 1 4
## 453 P455 3 5 10 6
## 454 P456 3 6 2 14
## 455 P457 0 2 1 1
## 456 P458 1 5 1 6
## 457 P459 1 2 4 7
## 458 P460 3 4 4 8
## 459 P461 1 4 2 4
## 460 P462 1 0 3 3
## 461 P463 1 2 0 3
## 462 P464 1 4 1 1
## 463 P465 1 3 0 2
## 464 P466 0 1 0 2
## 465 P467 0 0 1 1
## 466 P468 0 0 0 2
## 467 P469 0 1 0 0
## 468 P470 0 3 0 4
## 469 P471 0 1 0 1
## 470 P472 3 4 3 2
## 471 P473 1 2 0 0
## 472 P474 0 1 0 2
## 473 P475 30 44 29 30
## 474 P476 14 8 10 27
## 475 P477 10 7 10 13
## 476 P478 9 18 10 18
## 477 P479 8 9 13 17
## 478 P480 1 6 9 5
## 479 P481 10 21 18 20
## 480 P482 4 11 6 12
## 481 P483 4 3 6 4
## 482 P484 63 59 47 59
## 483 P485 148 132 107 113
## 484 P486 276 252 206 211
## 485 P487 160 187 146 132
## 486 P488 143 175 114 127
## 487 P489 73 71 38 48
## 488 P490 108 125 86 114
## 489 P491 262 251 189 225
## 490 P492 140 163 118 135
## 491 P493 139 167 120 131
## 492 P494 166 181 138 128
## 493 P495 221 239 156 210
## 494 P496 140 127 76 124
## 495 P497 56 65 49 62
## 496 P498 70 48 47 34
## 497 P499 139 125 94 99
## 498 P500 100 84 80 60
## 499 P501 71 81 59 54
## 500 P502 176 173 137 165
## 501 P503 202 230 147 232
## 502 P504 156 166 120 140
## 503 P505 216 230 161 190
## 504 P506 208 187 110 126
## 505 P507 227 294 161 193
## 506 P508 128 130 99 99
## 507 P509 79 84 60 71
## 508 P510 104 117 65 93
## 509 P511 363 364 274 288
## 510 P512 235 290 172 266
## 511 P513 269 257 188 208
## 512 P514 148 169 123 154
## 513 P515 135 151 94 98
## 514 P516 271 269 207 222
## 515 P517 170 182 123 127
## 516 P518 153 146 100 104
## 517 P519 292 270 184 221
## 518 P520 168 184 123 158
## 519 P521 149 141 107 129
## 520 P522 159 158 122 116
## 521 P523 171 180 106 134
## 522 P524 146 152 95 120
## 523 P525 163 182 147 143
## 524 P526 188 275 154 199
## 525 P527 163 142 120 140
## 526 P528 169 168 95 134
## 527 P529 179 183 141 155
## 528 P530 215 245 148 212
## 529 P531 59 67 61 67
## 530 P532 114 146 105 108
## 531 P533 345 362 260 286
## 532 P534 108 157 100 107
## 533 P535 245 277 171 256
## 534 P536 146 183 123 151
## 535 P537 248 271 193 208
## 536 P538 154 205 137 163
## 537 P539 132 140 104 106
## 538 P540 248 265 182 213
## 539 P541 142 156 118 140
## 540 P542 147 149 113 149
## 541 P543 141 169 104 123
## 542 P544 162 164 127 147
## 543 P545 215 263 167 184
## 544 P546 157 156 123 166
## 545 P547 142 146 113 124
## 546 P548 471 467 378 375
## 547 P549 473 469 331 362
## 548 P550 150 116 106 106
## 549 P551 122 135 118 101
## 550 P552 139 116 97 100
## 551 P553 53 56 52 41
## 552 P554 290 247 207 212
## 553 P555 142 163 122 110
## 554 P556 201 241 156 188
## 555 P557 399 356 264 296
## 556 P558 164 181 132 142
## 557 P559 153 171 128 134
## 558 P560 125 149 96 129
## 559 P561 60 63 36 44
## 560 P562 58 50 48 64
## 561 P563 130 123 83 114
## 562 P564 128 138 112 102
## 563 P565 120 117 90 114
## 564 P566 276 253 213 204
## 565 P567 61 51 49 38
## 566 P568 37 52 34 45
## 567 P569 38 46 25 30
## 568 P570 26 50 25 30
## 569 P571 67 87 51 72
## 570 P572 11 22 16 23
## 571 P573 36 61 36 36
## 572 P574 5 9 7 7
## 573 P575 3 4 6 10
## 574 P576 1 1 6 7
## 575 P577 10 6 6 17
## 576 P578 3 7 2 2
## 577 P579 1 2 4 1
## 578 P580 41 52 30 49
## 579 P581 48 36 32 47
## 580 P582 37 31 30 26
## 581 P583 33 39 28 40
## 582 P584 46 56 20 38
## 583 P585 14 21 10 24
## 584 P586 70 104 64 76
## 585 P587 38 60 39 48
## 586 P588 33 34 15 27
## 587 P589 9 19 22 27
## 588 P590 43 65 31 49
## 589 P591 32 41 28 38
## 590 P592 9 16 13 25
## 591 P593 43 54 29 48
## 592 P594 37 59 28 47
## 593 P595 35 43 18 42
## 594 P596 55 100 52 78
## 595 P597 10 14 11 33
## 596 P598 147 204 112 173
## 597 P599 23 43 53 80
## 598 P600 16 23 26 49
## 599 P601 7 21 31 28
## 600 P602 9 10 13 9
## 601 P603 4 5 10 10
## 602 P604 2 4 6 2
## 603 P605 15 15 12 21
## 604 P606 10 9 14 11
## 605 P607 2 3 3 5
## 606 P608 5 4 2 12
## 607 P609 38 63 36 42
## 608 P610 54 55 37 49
## 609 P611 37 53 40 55
## 610 P612 186 199 121 175
## 611 P613 286 342 209 316
## 612 P614 60 90 54 80
## 613 P615 298 383 226 358
## 614 P616 56 85 47 66
## 615 P617 496 420 380 360
## 616 P618 512 437 422 411
## 617 P619 456 431 419 366
## 618 P620 429 439 352 373
## 619 P621 477 454 361 406
## 620 P622 451 440 383 419
## 621 P623 449 425 390 409
## 622 P624 140 114 105 103
## 623 P625 135 119 116 116
## 624 P626 133 105 105 121
## 625 P627 143 122 108 139
## 626 P628 119 105 109 104
## 627 P629 123 128 129 102
## 628 P630 143 109 103 95
## 629 P631 147 117 106 90
## 630 P632 131 168 110 111
## 631 P633 124 161 93 113
## 632 P634 138 157 109 114
## 633 P635 149 111 119 84
## 634 P636 144 171 122 156
## 635 P637 42 60 53 65
## 636 P638 175 208 103 181
## 637 P639 2 2 3 3
## 638 P640 197 260 190 220
## 639 P641 35 31 30 34
## 640 P642 44 57 33 43
## 641 P643 0 4 1 0
## 642 P644 0 1 1 0
## 643 P646 0 1 0 1
## 644 P647 1 3 1 1
## 645 P649 0 0 1 1
## 646 P650 0 2 0 1
## 647 P651 2 6 5 6
## 648 P652 0 3 1 6
## 649 P653 2 3 3 5
## 650 P654 0 0 0 2
## 651 P655 1 3 1 1
## 652 P656 0 1 0 1
## 653 P657 0 0 2 4
## 654 P658 2 1 1 4
## 655 P659 1 1 2 2
## 656 P660 1 2 1 3
## 657 P661 2 0 1 0
## 658 P662 0 0 0 4
## 659 P663 3 11 12 12
## 660 P664 2 6 4 10
## 661 P665 1 6 3 3
## 662 P666 3 5 0 3
## 663 P667 4 3 2 2
## 664 P668 1 1 1 0
## 665 P669 1 2 1 1
## 666 P670 4 10 11 14
## 667 P671 3 5 1 9
## 668 P672 0 4 1 6
## 669 P673 26 38 42 54
## 670 P674 62 89 91 139
## 671 P675 28 62 58 81
## 672 P676 30 40 38 54
## 673 P677 1 2 1 1
## 674 P678 1 2 1 2
## 675 P679 2 3 1 3
## 676 P680 0 1 0 0
## 677 P681 2 1 0 4
## 678 P682 1 0 0 3
## 679 P683 0 2 0 2
## 680 P684 0 0 0 1
## 681 P685 0 2 0 2
## 682 P686 22 48 27 36
## 683 P687 13 8 18 14
## 684 P688 6 8 11 7
## 685 P689 3 2 5 11
## 686 P690 3 6 6 8
## 687 P691 1 6 3 4
## 688 P692 25 45 11 37
## 689 P693 35 31 24 43
## 690 P694 0 2 3 11
## 691 P695 1 4 1 7
## 692 P696 1 4 1 5
## 693 P697 1 6 3 13
## 694 P698 27 39 28 27
## 695 P699 6 17 9 20
## 696 P700 13 6 8 19
## 697 P701 41 48 26 43
## 698 P702 62 77 69 93
## 699 P703 26 25 39 67
## 700 P704 6 8 12 13
## 701 P705 31 39 24 42
## 702 P706 10 15 16 25
## 703 P707 2 6 12 18
## 704 P708 1 0 1 0
## 705 P711 1 4 3 1
## 706 P712 0 1 0 2
## 707 P713 2 1 1 0
## 708 P714 2 2 3 0
## 709 P715 2 2 0 0
## 710 P716 2 1 0 0
## 711 P717 1 2 2 3
## 712 P718 6 2 3 5
## 713 P719 0 0 2 1
## 714 P720 0 6 0 2
## 715 P721 0 1 0 0
## 716 P722 0 1 0 1
## 717 P724 1 2 0 2
## 718 P726 12 12 16 18
## 719 P727 32 20 54 52
## 720 P728 5 10 11 15
## 721 P729 0 4 1 7
## 722 P730 2 0 2 1
## 723 P731 3 3 4 6
## 724 P732 12 9 18 31
## 725 P733 3 7 10 17
## 726 P734 4 4 7 7
## 727 P735 0 3 6 9
## 728 P736 9 14 16 23
## 729 P737 20 32 46 47
## 730 P738 3 8 8 16
## 731 P739 4 2 5 5
## 732 P740 1 2 2 5
## 733 P741 1 3 2 6
## 734 P742 7 14 10 21
## 735 P743 9 10 9 21
## 736 P744 1 2 2 4
## 737 P745 53 76 62 81
## 738 P746 13 11 15 24
## 739 P747 31 36 30 54
## 740 P748 7 7 3 18
## 741 P749 4 10 8 13
## 742 P750 2 3 6 7
## 743 P751 6 2 4 9
## 744 P752 25 39 18 28
## 745 P753 8 12 11 16
## 746 P754 5 14 12 18
## 747 P755 3 7 5 7
## 748 P756 5 3 1 8
## 749 P757 1 4 0 10
## 750 P758 0 3 0 9
## 751 P759 1 1 1 7
## 752 P760 0 1 0 1
## 753 P761 0 2 1 0
## 754 P762 18 23 16 32
## 755 P763 1 1 0 0
## 756 P764 25 49 27 64
## 757 P765 5 3 14 19
## 758 P766 4 6 6 9
## 759 P767 8 17 17 25
## 760 P768 35 51 46 74
## 761 P769 10 22 13 24
## 762 P770 5 2 3 5
## 763 P771 1 4 3 7
## 764 P772 2 3 5 4
## 765 P773 2 3 1 3
## 766 P774 0 3 1 2
## 767 P775 0 0 0 4
## 768 P776 2 4 6 4
## 769 P777 1 3 2 5
## 770 P778 0 1 1 6
## 771 P779 1 2 3 6
## 772 P780 0 7 1 0
## 773 P781 196 259 163 237
## 774 P782 52 46 45 46
## 775 P783 174 192 149 172
## 776 P784 29 57 36 54
## 777 P785 24 34 18 35
## 778 P786 32 27 29 30
## 779 P787 12 9 20 27
## 780 P788 43 48 33 53
## 781 P789 32 52 26 54
## 782 P790 31 42 26 27
## 783 P791 60 92 53 82
## 784 P792 14 13 23 24
## 785 P793 21 38 39 59
## 786 P794 4 2 9 19
## 787 P795 3 4 9 7
## 788 P796 39 58 30 46
## 789 P797 32 56 20 45
## 790 P798 25 34 28 32
## 791 P799 47 79 48 77
## 792 P800 9 16 22 29
## 793 P801 32 34 32 52
## 794 P802 9 8 6 15
## 795 P803 37 58 41 49
## 796 P804 32 48 31 41
## 797 P805 36 45 24 33
## 798 P806 61 64 55 89
## 799 P807 7 21 13 27
## 800 P808 27 38 40 57
## 801 P809 7 13 8 12
## 802 P810 2 1 11 6
## 803 P811 44 50 44 46
## 804 P812 24 18 15 23
## 805 P813 27 44 22 29
## 806 P814 58 69 54 85
## 807 P815 6 6 3 8
## 808 P816 17 27 37 61
## 809 P817 8 2 2 14
## 810 P818 2 5 6 4
## 811 P819 1 5 8 2
Qtr.Sls.By.Prdt$Q1.Sls <- as.numeric(Qtr.Sls.By.Prdt$Q1.Sls)
Qtr.Sls.By.Prdt$Q2.Sls <- as.numeric(Qtr.Sls.By.Prdt$Q2.Sls)
Qtr.Sls.By.Prdt$Q3.Sls <- as.numeric(Qtr.Sls.By.Prdt$Q3.Sls)
Qtr.Sls.By.Prdt$Q4.Sls <- as.numeric(Qtr.Sls.By.Prdt$Q4.Sls)
#Top5 Product by Q1 Sale
TopQ1.Prdt.Sls <- Qtr.Sls.By.Prdt %>%
select(Prdt,Q1.Sls) %>% filter(Prdt %in% c("P409", "P34" , "P178" ,"P135", "P43")) %>% arrange(desc(Q1.Sls)) %>% top_n(5,Q1.Sls)
TopQ2.Prdt.Sls <- Qtr.Sls.By.Prdt %>%
select(Prdt,Q2.Sls) %>% filter(Prdt %in% c("P409", "P34" , "P178" ,"P135", "P43")) %>% arrange(desc(Q2.Sls)) %>% top_n(5,Q2.Sls)
TopQ3.Prdt.Sls <- Qtr.Sls.By.Prdt %>%
select(Prdt,Q3.Sls) %>% filter(Prdt %in% c("P409", "P34" , "P178" ,"P135", "P43")) %>% arrange(desc(Q3.Sls)) %>% top_n(5,Q3.Sls)
TopQ4.Prdt.Sls <- Qtr.Sls.By.Prdt %>%
select(Prdt,Q4.Sls) %>% filter(Prdt %in% c("P409", "P34" , "P178" ,"P135", "P43")) %>% arrange(desc(Q4.Sls)) %>% top_n(5,Q4.Sls)
# Plot top 5 Product by Sale
p1<- ggplot(TopQ1.Prdt.Sls, aes(x=reorder(Prdt,-Q1.Sls), y= Q1.Sls)) +
geom_bar(stat="identity", width=.5, fill="tomato3") +
labs(title="Top 5 Products by Q1 Sale",
subtitle="Q1 Quarter",
caption="Product:Sales",
y="Total Sales",
x="Product") +
theme(axis.text.x = element_text(angle=65, vjust=0.6))
p2<- ggplot(TopQ2.Prdt.Sls, aes(x=reorder(Prdt,-Q2.Sls), y= Q2.Sls)) +
geom_bar(stat="identity", width=.5, fill="tomato3") +
labs(title="Top 5 Products by Q2 Sale",
subtitle="Q2 Quarter",
caption="Product:Sales",
y="Total Sales",
x="Product") +
theme(axis.text.x = element_text(angle=65, vjust=0.6))
p3<- ggplot(TopQ3.Prdt.Sls, aes(x=reorder(Prdt,-Q3.Sls), y= Q3.Sls)) +
geom_bar(stat="identity", width=.5, fill="tomato3") +
labs(title="Top 5 Products by Q3 Sale",
subtitle="Q3 Quarter",
caption="Product:Sales",
y="Total Sales",
x="Product") +
theme(axis.text.x = element_text(angle=65, vjust=0.6))
p4<- ggplot(TopQ4.Prdt.Sls, aes(x=reorder(Prdt,-Q4.Sls), y= Q4.Sls)) +
geom_bar(stat="identity", width=.5, fill="tomato3") +
labs(title="Top 5 Products by Q4 Sale",
subtitle="Q4 Quarter",
caption="Product:Sales",
y="Total Sales",
x="Product") +
theme(axis.text.x = element_text(angle=65, vjust=0.6))
library(gridExtra)
grid.arrange(p1, p2,p3,p4, ncol = 4)
#Total Sales by different buckets
Tot.Sls.Bkt<- Tot.Sls.By.Prdt %>%
select(Prdt,Yr.Sls) %>%
mutate(High=ifelse(Yr.Sls >575,1,0 ), Mid=ifelse(Yr.Sls>199 & Yr.Sls < 575,1,0),Low=ifelse(Yr.Sls>1 & Yr.Sls < 199,1,0) ) %>%
arrange(desc(Yr.Sls))
Sls.Bkt<- Tot.Sls.Bkt %>% select(High,Mid,Low) %>% summarise(High=sum(High),Mid=sum(Mid),Low=sum(Low))
Sls.Bkt.Plot<- as.data.frame(t(as.matrix(Sls.Bkt)))
names(Sls.Bkt.Plot)[1] <- "count"
bkt<- rownames(Sls.Bkt.Plot)
Sls.Bkt.Plot <- cbind(bkt,Sls.Bkt.Plot)
rownames(Sls.Bkt.Plot) <- NULL
ggplot(Sls.Bkt.Plot, aes('', count, fill = bkt)) +
geom_col(position = 'fill') +
geom_label(aes(label = paste(format(round( (count/sum(count))*100, 2), nsmall = 2),"%")), position = position_fill(vjust = 0.6)) +
coord_polar(theta = 'y')+
labs(x = NULL, y = NULL, fill = "Category", title = "Product % by Different Buckets")
#Plot the Sales counts by different buckets
Tol.Sls.Count.Bkt <- data.frame(Tot.Sls.Bkt %>% select(Yr.Sls,High) %>% filter(High==1) %>% summarise(Highcount=sum(Yr.Sls)),
Tot.Sls.Bkt %>% select(Yr.Sls,Mid) %>% filter(Mid==1) %>% summarise(Midcount=sum(Yr.Sls)) ,
Tot.Sls.Bkt %>% select(Yr.Sls,Low) %>% filter(Low==1) %>% summarise(Lowcount=sum(Yr.Sls)) )
Tol.Sls.Count.Bkt<- as.data.frame(t(as.matrix(Tol.Sls.Count.Bkt)))
names(Tol.Sls.Count.Bkt)[1] <- "count"
count.bkt<- c("High Count","Mid Count", "Low Count")
Tol.Sls.Count.plot <- cbind(count.bkt,Tol.Sls.Count.Bkt)
rownames(Tol.Sls.Count.plot) <- NULL
ggplot(Tol.Sls.Count.plot, aes('', count, fill = count.bkt)) +
geom_col(position = 'fill') +
geom_label(aes(label = paste(format(round( (count/sum(count))*100, 2), nsmall = 2),"%")), position = position_fill(vjust = 0.6)) +
coord_polar(theta = 'y')+
labs(x = NULL, y = NULL, fill = "Category", title = "Sales by Different Buckets")
#High Product Group Analysis
High.Prdt.List<- Tot.Sls.Bkt %>% select(Prdt,High) %>% filter(High==1)
head(High.Prdt.List)
## Prdt High
## 1 P409 1
## 2 P34 1
## 3 P178 1
## 4 P135 1
## 5 P43 1
## 6 P190 1
High.Dtl.Data<- Data.Copy %>% filter(Product_Code %in% c("P409","P34","P178","P135","P43","P190","P179","P173","P92","P137","P38","P174","P24","P16","P40","P54","P136","P193","P37","P191","P180","P101","P36","P66","P134","P75","P129","P128","P175","P63","P132","P41","P72","P83","P112","P15","P35","P27","P48","P172","P96","P186","P49","P168","P184","P618","P133","P185","P17","P130","P39","P84","P69","P131","P47","P58","P140","P167","P120","P57","P119","P177","P46","P52","P60","P176","P170","P139","P90","P73","P181","P86","P56","P621","P196","P28","P143","P622","P44","P67","P548","P30","P19","P192","P76","P18","P79","P262","P188","P189","P141","P89","P623","P619","P61","P42","P70","P138","P142","P78","P617","P45","P64","P85","P208","P97","P87","P102","P55","P182","P549","P88","P169","P183","P194","P113","P25","P80","P620","P187","P557","P511","P615","P533","P613","P261","P270","P10","P516","P519","P405","P512","P268","P286","P407","P554","P535","P566","P486","P263","P51","P284","P491","P513","P107","P537","P540","P200","P411","P507","P435","P640","P410","P781","P62","P545","P495","P403","P530","P400","P526","P503","P202","P505","P556","P406","P783","P612","P638","P399","P538","P529","P364","P210","P502","P430","P95","P269","P598","P525","P520","P205","P506","P29","P408","P487","P398","P558","P14","P494","P33","P536","P517","P546","P11","P544","P514","P636","P523","P211","P559","P504","P100")
)
head(High.Dtl.Data)
## Product_Code W0 W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 W11 W12 W13 W14 W15 W16
## 1 P10 22 19 19 29 20 16 26 20 24 20 31 22 23 19 15 19 22
## 2 P11 15 7 15 14 17 7 10 16 11 8 8 10 10 12 10 16 13
## 3 P14 14 12 9 11 13 12 8 12 13 10 10 17 14 14 25 18 13
## 4 P15 19 45 47 42 29 44 43 36 25 52 39 42 43 42 43 51 40
## 5 P16 30 27 27 43 29 32 49 41 49 38 42 30 43 43 54 48 34
## 6 P17 49 40 40 28 40 47 44 45 39 33 39 37 33 52 29 45 34
## W17 W18 W19 W20 W21 W22 W23 W24 W25 W26 W27 W28 W29 W30 W31 W32 W33 W34
## 1 23 20 33 16 23 23 16 25 27 12 15 15 11 14 29 23 12 16
## 2 10 13 13 18 12 15 9 8 5 11 8 9 10 7 12 12 9 16
## 3 22 12 21 15 17 11 15 12 17 11 5 7 7 8 9 5 11 9
## 4 44 30 39 39 36 32 39 30 22 25 34 28 29 24 29 25 38 40
## 5 36 44 43 44 28 35 29 43 30 27 30 29 35 42 39 38 38 38
## 6 43 40 50 29 36 32 28 35 30 22 23 20 17 24 26 20 30 28
## W35 W36 W37 W38 W39 W40 W41 W42 W43 W44 W45 W46 W47 W48 W49 W50 W51 MIN
## 1 9 23 22 15 18 13 17 14 17 11 24 13 16 18 23 18 20 9
## 2 11 12 10 6 19 6 12 11 16 12 11 13 8 17 10 10 21 5
## 3 8 13 12 7 8 12 7 14 6 3 9 15 8 11 13 12 18 3
## 4 36 42 22 33 30 21 31 38 35 33 30 37 30 30 34 38 20 19
## 5 34 32 31 33 32 36 34 40 30 33 39 42 45 31 31 23 22 22
## 6 35 41 29 35 30 40 28 36 31 36 35 43 28 30 30 26 25 17
## MAX Normalized.0 Normalized.1 Normalized.2 Normalized.3 Normalized.4
## 1 33 0.54 0.42 0.42 0.83 0.46
## 2 21 0.63 0.13 0.63 0.56 0.75
## 3 25 0.50 0.41 0.27 0.36 0.45
## 4 52 0.00 0.79 0.85 0.70 0.30
## 5 54 0.25 0.16 0.16 0.66 0.22
## 6 52 0.91 0.66 0.66 0.31 0.66
## Normalized.5 Normalized.6 Normalized.7 Normalized.8 Normalized.9
## 1 0.29 0.71 0.46 0.63 0.46
## 2 0.13 0.31 0.69 0.38 0.19
## 3 0.41 0.23 0.41 0.45 0.32
## 4 0.76 0.73 0.52 0.18 1.00
## 5 0.31 0.84 0.59 0.84 0.50
## 6 0.86 0.77 0.80 0.63 0.46
## Normalized.10 Normalized.11 Normalized.12 Normalized.13 Normalized.14
## 1 0.92 0.54 0.58 0.42 0.25
## 2 0.19 0.31 0.31 0.44 0.31
## 3 0.32 0.64 0.50 0.50 1.00
## 4 0.61 0.70 0.73 0.70 0.73
## 5 0.63 0.25 0.66 0.66 1.00
## 6 0.63 0.57 0.46 1.00 0.34
## Normalized.15 Normalized.16 Normalized.17 Normalized.18 Normalized.19
## 1 0.42 0.54 0.58 0.46 1.00
## 2 0.69 0.50 0.31 0.50 0.50
## 3 0.68 0.45 0.86 0.41 0.82
## 4 0.97 0.64 0.76 0.33 0.61
## 5 0.81 0.38 0.44 0.69 0.66
## 6 0.80 0.49 0.74 0.66 0.94
## Normalized.20 Normalized.21 Normalized.22 Normalized.23 Normalized.24
## 1 0.29 0.58 0.58 0.29 0.67
## 2 0.81 0.44 0.63 0.25 0.19
## 3 0.55 0.64 0.36 0.55 0.41
## 4 0.61 0.52 0.39 0.61 0.33
## 5 0.69 0.19 0.41 0.22 0.66
## 6 0.34 0.54 0.43 0.31 0.51
## Normalized.25 Normalized.26 Normalized.27 Normalized.28 Normalized.29
## 1 0.75 0.13 0.25 0.25 0.08
## 2 0.00 0.38 0.19 0.25 0.31
## 3 0.64 0.36 0.09 0.18 0.18
## 4 0.09 0.18 0.45 0.27 0.30
## 5 0.25 0.16 0.25 0.22 0.41
## 6 0.37 0.14 0.17 0.09 0.00
## Normalized.30 Normalized.31 Normalized.32 Normalized.33 Normalized.34
## 1 0.21 0.83 0.58 0.13 0.29
## 2 0.13 0.44 0.44 0.25 0.69
## 3 0.23 0.27 0.09 0.36 0.27
## 4 0.15 0.30 0.18 0.58 0.64
## 5 0.63 0.53 0.50 0.50 0.50
## 6 0.20 0.26 0.09 0.37 0.31
## Normalized.35 Normalized.36 Normalized.37 Normalized.38 Normalized.39
## 1 0.00 0.58 0.54 0.25 0.38
## 2 0.38 0.44 0.31 0.06 0.88
## 3 0.23 0.45 0.41 0.18 0.23
## 4 0.52 0.70 0.09 0.42 0.33
## 5 0.38 0.31 0.28 0.34 0.31
## 6 0.51 0.69 0.34 0.51 0.37
## Normalized.40 Normalized.41 Normalized.42 Normalized.43 Normalized.44
## 1 0.17 0.33 0.21 0.33 0.08
## 2 0.06 0.44 0.38 0.69 0.44
## 3 0.41 0.18 0.50 0.14 0.00
## 4 0.06 0.36 0.58 0.48 0.42
## 5 0.44 0.38 0.56 0.25 0.34
## 6 0.66 0.31 0.54 0.40 0.54
## Normalized.45 Normalized.46 Normalized.47 Normalized.48 Normalized.49
## 1 0.63 0.17 0.29 0.38 0.58
## 2 0.38 0.50 0.19 0.75 0.31
## 3 0.27 0.55 0.23 0.36 0.45
## 4 0.33 0.55 0.33 0.33 0.45
## 5 0.53 0.63 0.72 0.28 0.28
## 6 0.51 0.74 0.31 0.37 0.37
## Normalized.50 Normalized.51
## 1 0.38 0.46
## 2 0.31 1.00
## 3 0.41 0.68
## 4 0.58 0.03
## 5 0.03 0.00
## 6 0.26 0.23
High_Prod_Name <- High.Dtl.Data$Product_Code
rownames(High.Dtl.Data) <- High.Dtl.Data$Product_Code
High.Dtl.Data$Product_Code<- NULL
High.Dtl.Data.Bskt<- as.data.frame(t(as.matrix(High.Dtl.Data)))
High.Dtl.Data.Bskt <- High.Dtl.Data.Bskt[1:52,]
#Created a dataframe with total sales at product level
Tot.Sls.High.Bskt <- as.data.frame(colSums(High.Dtl.Data.Bskt[,c(1:203)]))
names(Tot.Sls.High.Bskt)[1] <- "Yr.Sls"
Tot.Sls.By.Prdt <- cbind(Prdt,Tot.Sls.By.Prdt)
rownames(Tot.Sls.By.Prdt) <- NULL
summary(Tot.Sls.High.Bskt)
## Yr.Sls
## Min. : 579.0
## 1st Qu.: 863.5
## Median :1651.0
## Mean :1360.6
## 3rd Qu.:1754.0
## Max. :2220.0
hist(Tot.Sls.High.Bskt$Yr.Sls, main = "No of Product vs Sales Distribution on High Category", xlab = "Sale")
boxplot(Tot.Sls.High.Bskt$Yr.Sls, main = "Plot", names = c("High"))
#Low Product Group Analysis
Low.Prdt.List<- Tot.Sls.Bkt %>% select(Prdt,Low) %>% filter(Low==1)
head(Low.Prdt.List)
## Prdt Low
## 1 P401 1
## 2 P201 1
## 3 P293 1
## 4 P356 1
## 5 P105 1
## 6 P610 1
Low.Prdt.List$Prdt
## [1] P401 P201 P293 P356 P105 P610 P292 P322 P318 P365 P782 P590 P123 P156
## [15] P370 P587 P611 P803 P811 P291 P336 P412 P271 P158 P609 P642 P788 P415
## [29] P784 P593 P199 P796 P580 P594 P402 P335 P573 P341 P568 P387 P414 P764
## [43] P789 P581 P676 P808 P584 P673 P366 P701 P727 P703 P793 P388 P797 P804
## [57] P747 P801 P108 P737 P816 P583 P390 P569 P591 P595 P805 P206 P338 P434
## [71] P705 P391 P345 P475 P686 P693 P361 P570 P394 P641 P389 P790 P582 P204
## [85] P395 P396 P813 P698 P342 P798 P692 P786 P127 P367 P357 P371 P600 P372
## [99] P785 P752 P588 P265 P343 P762 P601 P344 P812 P589 P800 P373 P792 P572
## [113] P393 P732 P481 P585 P769 P597 P787 P807 P767 P706 P358 P339 P592 P605
## [127] P746 P736 P287 P362 P374 P476 P726 P416 P255 P478 P687 P699 P742 P360
## [141] P281 P743 P754 P479 P753 P700 P288 P606 P422 P602 P728 P765 P477 P809
## [155] P577 P670 P704 P663 P707 P802 P733 P738 P748 P749 P257 P794 P282 P482
## [169] P688 P376 P377 P340 P375 P603 P574 P283 P363 P817 P417 P448 P456 P766
## [183] P224 P455 P348 P438 P575 P608 P690 P697 P795 P815 P212 P439 P664 P734
## [197] P755 P272 P289 P442 P449 P480 P689 P751 P347 P378 P810 P247 P460 P651
## [211] P671 P735 P750 P346 P450 P483 P756 P818 P219 P349 P384 P418 P424 P443
## [225] P694 P718 P731 P739 P776 P819 P221 P440 P451 P452 P576 P757 P770 P771
## [239] P238 P245 P277 P290 P441 P447 P459 P578 P604 P691 P772 P214 P220 P241
## [253] P379 P423 P458 P607 P653 P665 P695 P213 P225 P242 P248 P419 P445 P446
## [267] P472 P729 P741 P758 P779 P222 P239 P240 P461 P666 P667 P672 P696 P777
## [281] P216 P223 P273 P453 P639 P652 P740 P759 P226 P229 P231 P244 P427 P679
## [295] P711 P744 P773 P235 P252 P350 P454 P579 P658 P717 P720 P778 P780 P217
## [309] P246 P274 P278 P280 P462 P464 P470 P660 P681 P714 P227 P233 P352 P426
## [323] P463 P465 P647 P655 P657 P659 P678 P774 P218 P236 P237 P258 P260 P420
## [337] P643 P669 P677 P724 P730 P234 P243 P249 P256 P275 P354 P355 P425 P444
## [351] P457 P662 P682 P683 P685 P713 P715 P775 P232 P276 P351 P380 P386 P466
## [365] P473 P474 P650 P661 P668 P712 P716 P719 P761 P228 P230 P250 P253 P279
## [379] P381 P382 P383 P421 P428 P467 P468 P471 P644 P646 P649 P654 P656 P708
## [393] P722 P760 P763
## 811 Levels: P1 P10 P100 P101 P102 P103 P104 P105 P106 P107 P108 ... P99
Low.Dtl.Data<- Data.Copy %>% filter(Product_Code %in% c("P401","P201","P293","P356","P105","P610","P292","P322","P318","P365","P782","P590","P123","P156","P370","P587","P611","P803","P811","P291","P336","P412","P271","P158","P609","P642","P788","P415","P784","P593","P199","P796","P580","P594","P402","P335","P573","P341","P568","P387","P414","P764","P789","P581","P676","P808","P584","P673","P366","P701","P727","P703","P793","P388","P797","P804","P747","P801","P108","P737","P816","P583","P390","P569","P591","P595","P805","P206","P338","P434","P705","P391","P345","P475","P686","P693","P361","P570","P394","P641","P389","P790","P582","P204","P395","P396","P813","P698","P342","P798","P692","P786","P127","P367","P357","P371","P600","P372","P785","P752","P588","P265","P343","P762","P601","P344","P812","P589","P800","P373","P792","P572","P393","P732","P481","P585","P769","P597","P787","P807","P767","P706","P358","P339","P592","P605","P746","P736","P287","P362","P374","P476","P726","P416","P255","P478","P687","P699","P742","P360","P281","P743","P754","P479","P753","P700","P288","P606","P422","P602","P728","P765","P477","P809","P577","P670","P704","P663","P707","P802","P733","P738","P748","P749","P257","P794","P282","P482","P688","P376","P377","P340","P375","P603","P574","P283","P363","P817","P417","P448","P456","P766","P224","P455","P348","P438","P575","P608","P690","P697","P795","P815","P212","P439","P664","P734","P755","P272","P289","P442","P449","P480","P689","P751","P347","P378","P810","P247","P460","P651","P671","P735","P750","P346","P450","P483","P756","P818","P219","P349","P384","P418","P424","P443","P694","P718","P731","P739","P776","P819","P221","P440","P451","P452","P576","P757","P770","P771","P238","P245","P277","P290","P441","P447","P459","P578","P604","P691","P772","P214","P220","P241","P379","P423","P458","P607","P653","P665","P695","P213","P225","P242","P248","P419","P445","P446","P472","P729","P741","P758","P779","P222","P239","P240","P461","P666","P667","P672","P696","P777","P216","P223","P273","P453","P639","P652","P740","P759","P226","P229","P231","P244","P427","P679","P711","P744","P773","P235","P252","P350","P454","P579","P658","P717","P720","P778","P780","P217","P246","P274","P278","P280","P462","P464","P470","P660","P681","P714","P227","P233","P352","P426","P463","P465","P647","P655","P657","P659","P678","P774","P218","P236","P237","P258","P260","P420","P643","P669","P677","P724","P730","P234","P243","P249","P256","P275","P354","P355","P425","P444","P457","P662","P682","P683","P685","P713","P715","P775","P232","P276","P351","P380","P386","P466","P473","P474","P650","P661","P668","P712","P716","P719","P761","P228","P230","P250","P253","P279","P381","P382","P383","P421","P428","P467","P468","P471","P644","P646","P649","P654","P656","P708","P722","P760","P763")
)
head(Low.Dtl.Data)
## Product_Code W0 W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 W11 W12 W13 W14 W15 W16
## 1 P105 2 3 3 2 5 4 5 7 5 3 8 3 6 2 2 3 1
## 2 P108 0 2 2 0 3 1 2 1 6 3 0 3 4 3 5 1 5
## 3 P123 5 5 3 6 4 6 4 7 4 2 4 4 3 3 5 2 5
## 4 P127 4 0 2 2 2 1 3 2 1 3 1 0 7 2 6 1 4
## 5 P156 1 4 6 6 3 8 2 3 4 6 3 6 4 3 2 6 1
## 6 P158 8 3 3 2 3 3 4 5 2 7 7 3 9 3 4 6 3
## W17 W18 W19 W20 W21 W22 W23 W24 W25 W26 W27 W28 W29 W30 W31 W32 W33 W34
## 1 7 6 5 6 7 3 4 5 3 3 2 8 4 2 6 2 5 5
## 2 0 1 2 2 3 4 9 2 5 3 2 0 3 3 0 2 4 0
## 3 1 0 5 4 6 5 5 0 7 5 0 5 1 3 5 5 0 2
## 4 0 3 7 7 3 2 0 2 1 1 3 2 3 4 2 2 0 0
## 5 5 5 0 4 0 5 4 4 4 2 6 5 3 2 2 7 4 1
## 6 2 4 2 6 2 5 2 2 3 5 1 3 1 0 6 1 3 1
## W35 W36 W37 W38 W39 W40 W41 W42 W43 W44 W45 W46 W47 W48 W49 W50 W51 MIN
## 1 1 2 5 3 1 1 1 3 2 6 3 2 3 6 1 4 4 1
## 2 4 6 3 6 4 4 2 5 7 1 2 3 3 3 4 1 4 0
## 3 2 5 2 3 2 2 5 4 3 2 5 4 5 3 2 3 4 0
## 4 3 1 4 0 3 0 2 2 1 3 5 2 1 3 3 1 0 0
## 5 2 3 5 2 5 6 1 4 4 1 2 3 5 2 3 4 4 0
## 6 3 4 1 3 2 2 4 2 2 6 2 4 2 4 7 3 5 0
## MAX Normalized.0 Normalized.1 Normalized.2 Normalized.3 Normalized.4
## 1 8 0.14 0.29 0.29 0.14 0.57
## 2 9 0.00 0.22 0.22 0.00 0.33
## 3 7 0.71 0.71 0.43 0.86 0.57
## 4 7 0.57 0.00 0.29 0.29 0.29
## 5 8 0.13 0.50 0.75 0.75 0.38
## 6 9 0.89 0.33 0.33 0.22 0.33
## Normalized.5 Normalized.6 Normalized.7 Normalized.8 Normalized.9
## 1 0.43 0.57 0.86 0.57 0.29
## 2 0.11 0.22 0.11 0.67 0.33
## 3 0.86 0.57 1.00 0.57 0.29
## 4 0.14 0.43 0.29 0.14 0.43
## 5 1.00 0.25 0.38 0.50 0.75
## 6 0.33 0.44 0.56 0.22 0.78
## Normalized.10 Normalized.11 Normalized.12 Normalized.13 Normalized.14
## 1 1.00 0.29 0.71 0.14 0.14
## 2 0.00 0.33 0.44 0.33 0.56
## 3 0.57 0.57 0.43 0.43 0.71
## 4 0.14 0.00 1.00 0.29 0.86
## 5 0.38 0.75 0.50 0.38 0.25
## 6 0.78 0.33 1.00 0.33 0.44
## Normalized.15 Normalized.16 Normalized.17 Normalized.18 Normalized.19
## 1 0.29 0.00 0.86 0.71 0.57
## 2 0.11 0.56 0.00 0.11 0.22
## 3 0.29 0.71 0.14 0.00 0.71
## 4 0.14 0.57 0.00 0.43 1.00
## 5 0.75 0.13 0.63 0.63 0.00
## 6 0.67 0.33 0.22 0.44 0.22
## Normalized.20 Normalized.21 Normalized.22 Normalized.23 Normalized.24
## 1 0.71 0.86 0.29 0.43 0.57
## 2 0.22 0.33 0.44 1.00 0.22
## 3 0.57 0.86 0.71 0.71 0.00
## 4 1.00 0.43 0.29 0.00 0.29
## 5 0.50 0.00 0.63 0.50 0.50
## 6 0.67 0.22 0.56 0.22 0.22
## Normalized.25 Normalized.26 Normalized.27 Normalized.28 Normalized.29
## 1 0.29 0.29 0.14 1.00 0.43
## 2 0.56 0.33 0.22 0.00 0.33
## 3 1.00 0.71 0.00 0.71 0.14
## 4 0.14 0.14 0.43 0.29 0.43
## 5 0.50 0.25 0.75 0.63 0.38
## 6 0.33 0.56 0.11 0.33 0.11
## Normalized.30 Normalized.31 Normalized.32 Normalized.33 Normalized.34
## 1 0.14 0.71 0.14 0.57 0.57
## 2 0.33 0.00 0.22 0.44 0.00
## 3 0.43 0.71 0.71 0.00 0.29
## 4 0.57 0.29 0.29 0.00 0.00
## 5 0.25 0.25 0.88 0.50 0.13
## 6 0.00 0.67 0.11 0.33 0.11
## Normalized.35 Normalized.36 Normalized.37 Normalized.38 Normalized.39
## 1 0.00 0.14 0.57 0.29 0.00
## 2 0.44 0.67 0.33 0.67 0.44
## 3 0.29 0.71 0.29 0.43 0.29
## 4 0.43 0.14 0.57 0.00 0.43
## 5 0.25 0.38 0.63 0.25 0.63
## 6 0.33 0.44 0.11 0.33 0.22
## Normalized.40 Normalized.41 Normalized.42 Normalized.43 Normalized.44
## 1 0.00 0.00 0.29 0.14 0.71
## 2 0.44 0.22 0.56 0.78 0.11
## 3 0.29 0.71 0.57 0.43 0.29
## 4 0.00 0.29 0.29 0.14 0.43
## 5 0.75 0.13 0.50 0.50 0.13
## 6 0.22 0.44 0.22 0.22 0.67
## Normalized.45 Normalized.46 Normalized.47 Normalized.48 Normalized.49
## 1 0.29 0.14 0.29 0.71 0.00
## 2 0.22 0.33 0.33 0.33 0.44
## 3 0.71 0.57 0.71 0.43 0.29
## 4 0.71 0.29 0.14 0.43 0.43
## 5 0.25 0.38 0.63 0.25 0.38
## 6 0.22 0.44 0.22 0.44 0.78
## Normalized.50 Normalized.51
## 1 0.43 0.43
## 2 0.11 0.44
## 3 0.43 0.57
## 4 0.14 0.00
## 5 0.50 0.50
## 6 0.33 0.56
Low_Prod_Name <- Low.Dtl.Data$Product_Code
rownames(Low.Dtl.Data) <- Low.Dtl.Data$Product_Code
Low.Dtl.Data$Product_Code<- NULL
Low.Dtl.Data.Bskt<- as.data.frame(t(as.matrix(Low.Dtl.Data)))
Low.Dtl.Data.Bskt <- Low.Dtl.Data.Bskt[1:52,]
#Created a dataframe with total sales at product level
Tot.Sls.Low.Bskt <- as.data.frame(colSums(Low.Dtl.Data.Bskt[,c(1:395)]))
names(Tot.Sls.Low.Bskt)[1] <- "Yr.Sls"
summary(Tot.Sls.Low.Bskt)
## Yr.Sls
## Min. : 2.00
## 1st Qu.: 9.00
## Median : 21.00
## Mean : 55.64
## 3rd Qu.:110.50
## Max. :198.00
hist(Tot.Sls.Low.Bskt$Yr.Sls,main = "No of Product vs Sales Distribution on Low Category", xlab = "Sale")
boxplot(Tot.Sls.Low.Bskt$Yr.Sls, main = "Plot", names = c("Low"))
#Mid Product Group Analysis
Mid.Prdt.List<- Tot.Sls.Bkt %>% select(Prdt,Mid) %>% filter(Mid==1)
head(Mid.Prdt.List)
## Prdt Mid
## 1 P115 1
## 2 P528 1
## 3 P527 1
## 4 P488 1
## 5 P542 1
## 6 P106 1
Mid.Prdt.List$Prdt
## [1] P115 P528 P527 P488 P542 P106 P493 P492 P541 P522 P9 P543 P555 P26
## [15] P198 P521 P547 P632 P634 P209 P334 P524 P165 P627 P314 P518 P118 P166
## [29] P397 P1 P197 P485 P560 P71 P144 P266 P633 P264 P65 P109 P22 P114
## [43] P149 P153 P267 P625 P116 P294 P99 P309 P539 P629 P564 P147 P285 P515
## [57] P550 P413 P551 P152 P299 P319 P32 P160 P333 P532 P324 P404 P534 P13
## [71] P145 P74 P496 P59 P20 P164 P122 P626 P81 P635 P21 P436 P624 P110
## [85] P121 P332 P631 P50 P91 P94 P429 P499 P508 P93 P3 P552 P8 P563
## [99] P630 P162 P171 P82 P31 P68 P565 P5 P146 P628 P125 P154 P490 P4
## [113] P157 P304 P103 P432 P674 P510 P500 P431 P437 P203 P586 P702 P207 P509
## [127] P791 P596 P614 P368 P392 P571 P745 P806 P814 P359 P501 P305 P531 P616
## [141] P799 P331 P124 P111 P53 P77 P310 P497 P295 P329 P489 P675 P161 P484
## [155] P307 P117 P195 P311 P317 P126 P300 P151 P6 P313 P562 P637 P296 P316
## [169] P320 P302 P321 P23 P325 P7 P159 P301 P306 P312 P326 P433 P323 P98
## [183] P328 P330 P2 P308 P315 P104 P768 P150 P298 P155 P163 P297 P369 P12
## [197] P561 P327 P553 P148 P337 P303
## 811 Levels: P1 P10 P100 P101 P102 P103 P104 P105 P106 P107 P108 ... P99
Mid.Dtl.Data<- Data.Copy %>% filter(Product_Code %in% c("P115","P528","P527","P488","P542","P106","P493","P492","P541","P522","P9","P543","P555","P26","P198","P521","P547","P632","P634","P209","P334","P524","P165","P627","P314","P518","P118","P166","P397","P1","P197","P485","P560","P71","P144","P266","P633","P264","P65","P109","P22","P114","P149","P153","P267","P625","P116","P294","P99","P309","P539","P629","P564","P147","P285","P515","P550","P413","P551","P152","P299","P319","P32","P160","P333","P532","P324","P404","P534","P13","P145","P74","P496","P59","P20","P164","P122","P626","P81","P635","P21","P436","P624","P110","P121","P332","P631","P50","P91","P94","P429","P499","P508","P93","P3","P552","P8","P563","P630","P162","P171","P82","P31","P68","P565","P5","P146","P628","P125","P154","P490","P4","P157","P304","P103","P432","P674","P510","P500","P431","P437","P203","P586","P702","P207","P509","P791","P596","P614","P368","P392","P571","P745","P806","P814","P359","P501","P305","P531","P616","P799","P331","P124","P111","P53","P77","P310","P497","P295","P329","P489","P675","P161","P484","P307","P117","P195","P311","P317","P126","P300","P151","P6","P313","P562","P637","P296","P316","P320","P302","P321","P23","P325","P7","P159","P301","P306","P312","P326","P433","P323","P98","P328","P330","P2","P308","P315","P104","P768","P150","P298","P155","P163","P297","P369","P12","P561","P327","P553","P148","P337","P303")
)
head(Mid.Dtl.Data)
## Product_Code W0 W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 W11 W12 W13 W14 W15 W16
## 1 P1 11 12 10 8 13 12 14 21 6 14 11 14 16 9 9 9 14
## 2 P2 7 6 3 2 7 1 6 3 3 3 2 2 6 2 0 6 2
## 3 P3 7 11 8 9 10 8 7 13 12 6 14 9 4 7 12 8 7
## 4 P4 12 8 13 5 9 6 9 13 13 11 8 4 5 4 15 7 11
## 5 P5 8 5 13 11 6 7 9 14 9 9 11 18 8 4 13 8 10
## 6 P6 3 3 2 7 6 3 8 6 6 3 1 1 5 4 3 5 3
## W17 W18 W19 W20 W21 W22 W23 W24 W25 W26 W27 W28 W29 W30 W31 W32 W33 W34
## 1 9 3 12 5 11 7 12 5 9 7 10 5 11 7 10 12 6 5
## 2 7 7 9 4 7 2 4 5 3 5 8 5 5 3 1 3 2 3
## 3 11 10 7 7 13 11 8 10 8 14 5 3 13 11 9 7 8 7
## 4 9 15 4 6 7 11 7 9 6 10 10 2 6 7 2 5 12 5
## 5 15 6 13 11 6 10 9 8 12 8 9 13 3 5 3 5 5 9
## 6 5 10 8 4 9 7 5 4 2 1 3 2 4 0 3 2 11 2
## W35 W36 W37 W38 W39 W40 W41 W42 W43 W44 W45 W46 W47 W48 W49 W50 W51 MIN
## 1 14 10 9 12 17 7 11 4 7 8 10 12 3 7 6 5 10 3
## 2 10 5 2 7 3 2 5 2 4 5 1 1 4 5 1 6 0 0
## 3 9 6 12 12 9 3 5 6 14 5 5 7 8 14 8 8 7 3
## 4 19 8 6 8 8 12 6 9 10 3 4 6 8 14 8 7 8 2
## 5 7 4 8 8 5 5 8 7 11 7 12 6 6 5 11 8 9 3
## 6 1 4 4 3 2 5 4 4 2 4 3 6 5 3 3 10 6 0
## MAX Normalized.0 Normalized.1 Normalized.2 Normalized.3 Normalized.4
## 1 21 0.44 0.50 0.39 0.28 0.56
## 2 10 0.70 0.60 0.30 0.20 0.70
## 3 14 0.36 0.73 0.45 0.55 0.64
## 4 19 0.59 0.35 0.65 0.18 0.41
## 5 18 0.33 0.13 0.67 0.53 0.20
## 6 11 0.27 0.27 0.18 0.64 0.55
## Normalized.5 Normalized.6 Normalized.7 Normalized.8 Normalized.9
## 1 0.50 0.61 1.00 0.17 0.61
## 2 0.10 0.60 0.30 0.30 0.30
## 3 0.45 0.36 0.91 0.82 0.27
## 4 0.24 0.41 0.65 0.65 0.53
## 5 0.27 0.40 0.73 0.40 0.40
## 6 0.27 0.73 0.55 0.55 0.27
## Normalized.10 Normalized.11 Normalized.12 Normalized.13 Normalized.14
## 1 0.44 0.61 0.72 0.33 0.33
## 2 0.20 0.20 0.60 0.20 0.00
## 3 1.00 0.55 0.09 0.36 0.82
## 4 0.35 0.12 0.18 0.12 0.76
## 5 0.53 1.00 0.33 0.07 0.67
## 6 0.09 0.09 0.45 0.36 0.27
## Normalized.15 Normalized.16 Normalized.17 Normalized.18 Normalized.19
## 1 0.33 0.61 0.33 0.00 0.50
## 2 0.60 0.20 0.70 0.70 0.90
## 3 0.45 0.36 0.73 0.64 0.36
## 4 0.29 0.53 0.41 0.76 0.12
## 5 0.33 0.47 0.80 0.20 0.67
## 6 0.45 0.27 0.45 0.91 0.73
## Normalized.20 Normalized.21 Normalized.22 Normalized.23 Normalized.24
## 1 0.11 0.44 0.22 0.50 0.11
## 2 0.40 0.70 0.20 0.40 0.50
## 3 0.36 0.91 0.73 0.45 0.64
## 4 0.24 0.29 0.53 0.29 0.41
## 5 0.53 0.20 0.47 0.40 0.33
## 6 0.36 0.82 0.64 0.45 0.36
## Normalized.25 Normalized.26 Normalized.27 Normalized.28 Normalized.29
## 1 0.33 0.22 0.39 0.11 0.44
## 2 0.30 0.50 0.80 0.50 0.50
## 3 0.45 1.00 0.18 0.00 0.91
## 4 0.24 0.47 0.47 0.00 0.24
## 5 0.60 0.33 0.40 0.67 0.00
## 6 0.18 0.09 0.27 0.18 0.36
## Normalized.30 Normalized.31 Normalized.32 Normalized.33 Normalized.34
## 1 0.22 0.39 0.50 0.17 0.11
## 2 0.30 0.10 0.30 0.20 0.30
## 3 0.73 0.55 0.36 0.45 0.36
## 4 0.29 0.00 0.18 0.59 0.18
## 5 0.13 0.00 0.13 0.13 0.40
## 6 0.00 0.27 0.18 1.00 0.18
## Normalized.35 Normalized.36 Normalized.37 Normalized.38 Normalized.39
## 1 0.61 0.39 0.33 0.50 0.78
## 2 1.00 0.50 0.20 0.70 0.30
## 3 0.55 0.27 0.82 0.82 0.55
## 4 1.00 0.35 0.24 0.35 0.35
## 5 0.27 0.07 0.33 0.33 0.13
## 6 0.09 0.36 0.36 0.27 0.18
## Normalized.40 Normalized.41 Normalized.42 Normalized.43 Normalized.44
## 1 0.22 0.44 0.06 0.22 0.28
## 2 0.20 0.50 0.20 0.40 0.50
## 3 0.00 0.18 0.27 1.00 0.18
## 4 0.59 0.24 0.41 0.47 0.06
## 5 0.13 0.33 0.27 0.53 0.27
## 6 0.45 0.36 0.36 0.18 0.36
## Normalized.45 Normalized.46 Normalized.47 Normalized.48 Normalized.49
## 1 0.39 0.50 0.00 0.22 0.17
## 2 0.10 0.10 0.40 0.50 0.10
## 3 0.18 0.36 0.45 1.00 0.45
## 4 0.12 0.24 0.35 0.71 0.35
## 5 0.60 0.20 0.20 0.13 0.53
## 6 0.27 0.55 0.45 0.27 0.27
## Normalized.50 Normalized.51
## 1 0.11 0.39
## 2 0.60 0.00
## 3 0.45 0.36
## 4 0.29 0.35
## 5 0.33 0.40
## 6 0.91 0.55
Mid_Prod_Name <- Mid.Dtl.Data$Product_Code
rownames(Mid.Dtl.Data) <- Mid.Dtl.Data$Product_Code
Mid.Dtl.Data$Product_Code<- NULL
Mid.Dtl.Data.Bskt<- as.data.frame(t(as.matrix(Mid.Dtl.Data)))
Mid.Dtl.Data.Bskt <- Mid.Dtl.Data.Bskt[1:52,]
#Created a dataframe with total sales at product level
Tot.Sls.Mid.Bskt <- as.data.frame(colSums(Mid.Dtl.Data.Bskt[,c(1:202)]))
names(Tot.Sls.Mid.Bskt)[1] <- "Yr.Sls"
Tot.Sls.By.Prdt <- cbind(Prdt,Tot.Sls.By.Prdt)
rownames(Tot.Sls.By.Prdt) <- NULL
summary(Tot.Sls.Mid.Bskt)
## Yr.Sls
## Min. :200.0
## 1st Qu.:229.2
## Median :446.0
## Mean :378.8
## 3rd Qu.:482.0
## Max. :571.0
hist(Tot.Sls.Mid.Bskt$Yr.Sls, main = "No of Product vs Sales Distribution on Mid Category", xlab = "Sale")
boxplot(Tot.Sls.Mid.Bskt$Yr.Sls, main = "Plot", names = c("Mid"))
#Create timeseries of top 5 products
P409.df<-prod.wkly.sls %>% select(P409)
P409.ts <- ts(P409.df, frequency=52)
P34.df<-prod.wkly.sls %>% select(P34)
P34.ts <- ts(P34.df, frequency=52)
P178.df<-prod.wkly.sls %>% select(P178)
P178.ts <- ts(P178.df, frequency=52)
P135.df<-prod.wkly.sls %>% select(P135)
P135.ts <- ts(P135.df, frequency=52)
P43.df<-prod.wkly.sls %>% select(P43)
P43.ts <- ts(P43.df, frequency=52)
#Forecasting of P409
autoplot(P409.ts)+ ggtitle("P409")+ xlab("Week")+ylab("Sales")
P409_fit_ets <- ets(P409.ts)
summary(P409_fit_ets)
## ETS(M,N,N)
##
## Call:
## ets(y = P409.ts)
##
## Smoothing parameters:
## alpha = 0.4844
##
## Initial states:
## l = 40.3168
##
## sigma: 0.2375
##
## AIC AICc BIC
## 446.3975 446.8975 452.2512
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE ACF1
## Training set 0.8212682 10.57843 7.725092 -3.44772 19.74376 NaN 0.04469462
checkresiduals(P409_fit_ets)
##
## Ljung-Box test
##
## data: Residuals from ETS(M,N,N)
## Q* = 11.714, df = 8.4, p-value = 0.1894
##
## Model df: 2. Total lags used: 10.4
autoplot(forecast(P409_fit_ets, h = 4)) + xlab("Week") + ylab("Sales") + ggtitle("P409 Forecast using ETS Method")
summary(ur.kpss(P409.ts))
##
## #######################
## # KPSS Unit Root Test #
## #######################
##
## Test is of type: mu with 3 lags.
##
## Value of test-statistic is: 0.115
##
## Critical value for a significance level of:
## 10pct 5pct 2.5pct 1pct
## critical values 0.347 0.463 0.574 0.739
p1 <- ggAcf(P409.ts)
p2 <- ggPacf(P409.ts)
grid.arrange(p1, p2, ncol = 2)
p409_fit_arima <- auto.arima(P409.ts, seasonal = FALSE)
summary(p409_fit_arima)
## Series: P409.ts
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.5160 43.2755
## s.e. 0.1267 2.9042
##
## sigma^2 estimated as 110.2: log likelihood=-195.17
## AIC=396.34 AICc=396.84 BIC=402.2
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.01617023 10.29233 7.781377 -6.132657 19.99894 NaN
## ACF1
## Training set -0.04291878
checkresiduals(p409_fit_arima)
##
## Ljung-Box test
##
## data: Residuals from ARIMA(1,0,0) with non-zero mean
## Q* = 13.541, df = 8.4, p-value = 0.1113
##
## Model df: 2. Total lags used: 10.4
autoplot(forecast(p409_fit_arima, h = 4)) + xlab("Week") + ylab("Number of Transactions") + ggtitle("P409 Forecast using ARIMA")
forecast(p409_fit_arima,h=4)
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## 2.000000 58.61293 45.16157 72.06430 38.04084 79.18502
## 2.019231 51.18941 36.05295 66.32588 28.04018 74.33865
## 2.038462 47.35899 31.80462 62.91336 23.57063 71.14735
## 2.057692 45.38254 29.71879 61.04630 21.42690 69.33819
p1 <- autoplot(P409.ts) + autolayer(meanf(P409.ts, h = 4)) + xlab("Week") + ylab("Number of Transactions") + ggtitle("P409 Forecast using Average Method")
p2 <- autoplot(P409.ts) + autolayer(naive(P409.ts, h = 4)) + xlab("Week") + ylab("Number of Transactions") + ggtitle("P409 Forecast using Naive Method")
p3 <- autoplot(P409.ts) + autolayer(rwf(P409.ts, h = 4)) + xlab("Week") + ylab("Number of Transactions") + ggtitle("P409Forecast using Drift Method")
grid.arrange(p1, p2, p3, ncol = 3)
#Forecasting of P34
autoplot(P34.ts)+ ggtitle("P34")+ xlab("Week")+ylab("Sales")
P34_fit_ets <- ets(P34.ts)
summary(P34_fit_ets)
## ETS(M,N,N)
##
## Call:
## ets(y = P34.ts)
##
## Smoothing parameters:
## alpha = 0.0923
##
## Initial states:
## l = 41.2495
##
## sigma: 0.2045
##
## AIC AICc BIC
## 423.1565 423.6565 429.0103
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set -1.100599 7.789164 5.909122 -7.572166 17.78021 NaN
## ACF1
## Training set -0.1352629
checkresiduals(P34_fit_ets)
##
## Ljung-Box test
##
## data: Residuals from ETS(M,N,N)
## Q* = 19.527, df = 8.4, p-value = 0.01536
##
## Model df: 2. Total lags used: 10.4
autoplot(forecast(P34_fit_ets, h = 4)) + xlab("Week") + ylab("Sales") + ggtitle("P34 Forecast using ETS Method")
summary(ur.kpss(P34.ts))
##
## #######################
## # KPSS Unit Root Test #
## #######################
##
## Test is of type: mu with 3 lags.
##
## Value of test-statistic is: 0.5382
##
## Critical value for a significance level of:
## 10pct 5pct 2.5pct 1pct
## critical values 0.347 0.463 0.574 0.739
p1 <- ggAcf(P34.ts)
p2 <- ggPacf(P34.ts)
grid.arrange(p1, p2, ncol = 2)
p34_fit_arima <- auto.arima(P34.ts, seasonal = FALSE)
summary(p34_fit_arima)
## Series: P34.ts
## ARIMA(3,1,2)
##
## Coefficients:
## ar1 ar2 ar3 ma1 ma2
## -0.9397 -0.5901 -0.2055 -0.0596 -0.5850
## s.e. 0.3688 0.2525 0.2577 0.3408 0.2344
##
## sigma^2 estimated as 55.82: log likelihood=-173.4
## AIC=358.8 AICc=360.71 BIC=370.39
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set -0.582173 7.02675 5.616037 -5.177022 16.33428 NaN
## ACF1
## Training set -0.004282835
checkresiduals(p34_fit_arima)
##
## Ljung-Box test
##
## data: Residuals from ARIMA(3,1,2)
## Q* = 7.9037, df = 5.4, p-value = 0.1932
##
## Model df: 5. Total lags used: 10.4
autoplot(forecast(p34_fit_arima, h = 4)) + xlab("Week") + ylab("Number of Transactions") + ggtitle("P34 Forecast using ARIMA")
forecast(p34_fit_arima,h=4)
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## 2.000000 38.14782 28.57338 47.72226 23.50498 52.79066
## 2.019231 41.42214 31.84769 50.99658 26.77929 56.06498
## 2.038462 34.25893 24.42297 44.09490 19.21612 49.30174
## 2.057692 36.56186 26.10526 47.01846 20.56987 52.55385
p1 <- autoplot(P34.ts) + autolayer(meanf(P34.ts, h = 4)) + xlab("Week") + ylab("Number of Transactions") + ggtitle("P34 Forecast using Average Method")
p2 <- autoplot(P34.ts) + autolayer(naive(P34.ts, h = 4)) + xlab("Week") + ylab("Number of Transactions") + ggtitle("P34 Forecast using Naive Method")
p3 <- autoplot(P34.ts) + autolayer(rwf(P34.ts, h = 4)) + xlab("Week") + ylab("Number of Transactions") + ggtitle("P34Forecast using Drift Method")
grid.arrange(p1, p2, p3, ncol = 3)
#Forecasting of P178
autoplot(P178.ts)+ ggtitle("P178")+ xlab("Week")+ylab("Sales")
P178_fit_ets <- ets(P178.ts)
summary(P178_fit_ets)
## ETS(A,N,N)
##
## Call:
## ets(y = P178.ts)
##
## Smoothing parameters:
## alpha = 0.2736
##
## Initial states:
## l = 37.4499
##
## sigma: 7.4224
##
## AIC AICc BIC
## 417.8930 418.3930 423.7467
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set -0.4285944 7.278231 5.643992 -5.073001 16.56116 NaN
## ACF1
## Training set -0.01996414
checkresiduals(P178_fit_ets)
##
## Ljung-Box test
##
## data: Residuals from ETS(A,N,N)
## Q* = 4.4406, df = 8.4, p-value = 0.8438
##
## Model df: 2. Total lags used: 10.4
autoplot(forecast(P178_fit_ets, h = 4)) + xlab("Week") + ylab("Sales") + ggtitle("P178 Forecast using ETS Method")
summary(ur.kpss(diff(P178.ts)))
##
## #######################
## # KPSS Unit Root Test #
## #######################
##
## Test is of type: mu with 3 lags.
##
## Value of test-statistic is: 0.0901
##
## Critical value for a significance level of:
## 10pct 5pct 2.5pct 1pct
## critical values 0.347 0.463 0.574 0.739
p1 <- ggAcf(diff(P178.ts))
p2 <- ggPacf(diff(P178.ts))
grid.arrange(p1, p2, ncol = 2)
p178_fit_arima <- auto.arima(diff(P178.ts), seasonal = FALSE)
summary(p178_fit_arima)
## Series: diff(P178.ts)
## ARIMA(0,0,1) with zero mean
##
## Coefficients:
## ma1
## -0.7081
## s.e. 0.1116
##
## sigma^2 estimated as 55.12: log likelihood=-174.45
## AIC=352.9 AICc=353.15 BIC=356.77
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE ACF1
## Training set -0.3036805 7.351146 5.692531 -Inf Inf NaN -0.03417743
checkresiduals(p178_fit_arima)
##
## Ljung-Box test
##
## data: Residuals from ARIMA(0,0,1) with zero mean
## Q* = 4.3884, df = 9.2, p-value = 0.8943
##
## Model df: 1. Total lags used: 10.2
autoplot(forecast(p178_fit_arima, h = 4)) + xlab("Week") + ylab("Number of Transactions") + ggtitle("P178 Forecast using ARIMA")
forecast(p178_fit_arima,h=4)
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## 2.000000 5.084 -4.430615 14.59861 -9.467348 19.63535
## 2.019231 0.000 -11.658634 11.65863 -17.830343 17.83034
## 2.038462 0.000 -11.658634 11.65863 -17.830343 17.83034
## 2.057692 0.000 -11.658634 11.65863 -17.830343 17.83034
p1 <- autoplot(P178.ts) + autolayer(meanf(P178.ts, h = 4)) + xlab("Week") + ylab("Number of Transactions") + ggtitle("P178 Forecast using Average Method")
p2 <- autoplot(P178.ts) + autolayer(naive(P178.ts, h = 4)) + xlab("Week") + ylab("Number of Transactions") + ggtitle("P178 Forecast using Naive Method")
p3 <- autoplot(P178.ts) + autolayer(rwf(P178.ts, h = 4)) + xlab("Week") + ylab("Number of Transactions") + ggtitle("P178Forecast using Drift Method")
grid.arrange(p1, p2, p3, ncol = 3)
#Forecasting of P135
autoplot(P135.ts)+ ggtitle("P135")+ xlab("Week")+ylab("Sales")
P135_fit_ets <- ets(P135.ts)
summary(P135_fit_ets)
## ETS(A,N,N)
##
## Call:
## ets(y = P135.ts)
##
## Smoothing parameters:
## alpha = 0.2337
##
## Initial states:
## l = 37.6663
##
## sigma: 7.5356
##
## AIC AICc BIC
## 419.4677 419.9677 425.3215
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set -0.6751885 7.389274 5.741731 -6.331545 17.5548 NaN
## ACF1
## Training set -0.03564441
checkresiduals(P135_fit_ets)
##
## Ljung-Box test
##
## data: Residuals from ETS(A,N,N)
## Q* = 8.5352, df = 8.4, p-value = 0.4223
##
## Model df: 2. Total lags used: 10.4
autoplot(forecast(P135_fit_ets, h = 4)) + xlab("Week") + ylab("Sales") + ggtitle("P135 Forecast using ETS Method")
summary(ur.kpss(diff(P135.ts)))
##
## #######################
## # KPSS Unit Root Test #
## #######################
##
## Test is of type: mu with 3 lags.
##
## Value of test-statistic is: 0.1163
##
## Critical value for a significance level of:
## 10pct 5pct 2.5pct 1pct
## critical values 0.347 0.463 0.574 0.739
p1 <- ggAcf(diff(P135.ts))
p2 <- ggPacf(diff(P135.ts))
grid.arrange(p1, p2, ncol = 2)
P135_fit_arima <- auto.arima(diff(P135.ts), seasonal = FALSE)
summary(P135_fit_arima)
## Series: diff(P135.ts)
## ARIMA(0,0,1) with zero mean
##
## Coefficients:
## ma1
## -0.7394
## s.e. 0.1240
##
## sigma^2 estimated as 56.83: log likelihood=-175.28
## AIC=354.56 AICc=354.81 BIC=358.42
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE ACF1
## Training set -0.5964362 7.46462 5.886218 NaN Inf NaN -0.05414681
checkresiduals(P135_fit_arima)
##
## Ljung-Box test
##
## data: Residuals from ARIMA(0,0,1) with zero mean
## Q* = 9.1106, df = 9.2, p-value = 0.4462
##
## Model df: 1. Total lags used: 10.2
autoplot(forecast(P135_fit_arima, h = 4)) + xlab("Week") + ylab("Number of Transactions") + ggtitle("P135 Forecast using ARIMA")
forecast(P135_fit_arima,h=4)
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## 2.000000 12.78969 3.128202 22.45117 -1.986279 27.56565
## 2.019231 0.00000 -12.015849 12.01585 -18.376655 18.37665
## 2.038462 0.00000 -12.015849 12.01585 -18.376655 18.37665
## 2.057692 0.00000 -12.015849 12.01585 -18.376655 18.37665
p1 <- autoplot(P135.ts) + autolayer(meanf(P135.ts, h = 4)) + xlab("Week") + ylab("Number of Transactions") + ggtitle("P135 Forecast using Average Method")
p2 <- autoplot(P135.ts) + autolayer(naive(P135.ts, h = 4)) + xlab("Week") + ylab("Number of Transactions") + ggtitle("P135 Forecast using Naive Method")
p3 <- autoplot(P135.ts) + autolayer(rwf(P135.ts, h = 4)) + xlab("Week") + ylab("Number of Transactions") + ggtitle("P135Forecast using Drift Method")
grid.arrange(p1, p2, p3, ncol = 3)
#Forecasting of P43
autoplot(P43.ts)+ ggtitle("P43")+ xlab("Week")+ylab("Sales")
P43_fit_ets <- ets(P43.ts)
summary(P43_fit_ets)
## ETS(M,N,N)
##
## Call:
## ets(y = P43.ts)
##
## Smoothing parameters:
## alpha = 0.3196
##
## Initial states:
## l = 38.5878
##
## sigma: 0.2224
##
## AIC AICc BIC
## 428.1162 428.6162 433.9699
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set -0.4379003 8.04433 6.976893 -5.388537 20.01099 NaN
## ACF1
## Training set 0.02390699
checkresiduals(P43_fit_ets)
##
## Ljung-Box test
##
## data: Residuals from ETS(M,N,N)
## Q* = 11.824, df = 8.4, p-value = 0.1836
##
## Model df: 2. Total lags used: 10.4
autoplot(forecast(P43_fit_ets, h = 4)) + xlab("Week") + ylab("Sales") + ggtitle("P43 Forecast using ETS Method")
summary(ur.kpss(diff(P43.ts)))
##
## #######################
## # KPSS Unit Root Test #
## #######################
##
## Test is of type: mu with 3 lags.
##
## Value of test-statistic is: 0.1014
##
## Critical value for a significance level of:
## 10pct 5pct 2.5pct 1pct
## critical values 0.347 0.463 0.574 0.739
p1 <- ggAcf(diff(P43.ts))
p2 <- ggPacf(diff(P43.ts))
grid.arrange(p1, p2, ncol = 2)
p43_fit_arima <- auto.arima(diff(P43.ts), seasonal = FALSE)
summary(p43_fit_arima)
## Series: diff(P43.ts)
## ARIMA(1,0,1) with zero mean
##
## Coefficients:
## ar1 ma1
## 0.0796 -0.7157
## s.e. 0.2426 0.1910
##
## sigma^2 estimated as 68.48: log likelihood=-179.42
## AIC=364.85 AICc=365.36 BIC=370.64
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE ACF1
## Training set -0.08860345 8.111088 6.966843 Inf Inf NaN 0.01719383
checkresiduals(p43_fit_arima)
##
## Ljung-Box test
##
## data: Residuals from ARIMA(1,0,1) with zero mean
## Q* = 6.2418, df = 8.2, p-value = 0.6404
##
## Model df: 2. Total lags used: 10.2
autoplot(forecast(p43_fit_arima, h = 4)) + xlab("Week") + ylab("Number of Transactions") + ggtitle("P43 Forecast using ARIMA")
forecast(p43_fit_arima,h=4)
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## 2.000000 4.27327683 -6.331517 14.87807 -11.94536 20.49191
## 2.019231 0.34017058 -12.228501 12.90884 -18.88195 19.56230
## 2.038462 0.02707899 -12.553060 12.60722 -19.21258 19.26674
## 2.057692 0.00215560 -12.578056 12.58237 -19.23762 19.24193
p1 <- autoplot(P43.ts) + autolayer(meanf(P43.ts, h = 4)) + xlab("Week") + ylab("Number of Transactions") + ggtitle("P43 Forecast using Average Method")
p2 <- autoplot(P43.ts) + autolayer(naive(P43.ts, h = 4)) + xlab("Week") + ylab("Number of Transactions") + ggtitle("P43 Forecast using Naive Method")
p3 <- autoplot(P43.ts) + autolayer(rwf(P43.ts, h = 4)) + xlab("Week") + ylab("Number of Transactions") + ggtitle("P43Forecast using Drift Method")
grid.arrange(p1, p2, p3, ncol = 3)
