Dimensionality Reduction and Feature Selection
Oprah Muthoni
2022-04-01
Carrefour is a French multinational retail corporation headquartered in Massy, France. The eighth-largest retailer in the world by revenue, it operates a chain of hypermarkets, groceries stores and convenience stores, which as of January 2021, comprises its 12,225 stores in over 30 countries.
This project will inform the marketing department on the most relevant marketing strategies that will result in the highest no. of sales (total price including tax). The project has been divided into four parts where I will explore recent marketing dataset by performing various unsupervised learning techniques and later providing recommendations based on your insights.
Part 1: Dimensionality Reduction
Dimensionality reduction refers to techniques that reduce the number of input variables in a dataset. More input features often make a predictive modeling task more challenging to model, more generally referred to as the curse of dimensionality.
This section of the project entails reducing the dataset to a low dimensional dataset using PCA. Analysis will be performed and insights from your analysis provided.
- DATA UNDERSTANDING
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## liftPreviewing the dataset
# importing our dataset
sales_df<- read.csv('/home/oppy/Downloads/Supermarket_Dataset_1 - Sales Data.csv')
#previewing the dataset
head(sales_df)A summary of the dataset
summary(sales_df)## Invoice.ID Branch Customer.type Gender
## Length:1000 Length:1000 Length:1000 Length:1000
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Product.line Unit.price Quantity Tax
## Length:1000 Min. :10.08 Min. : 1.00 Min. : 0.5085
## Class :character 1st Qu.:32.88 1st Qu.: 3.00 1st Qu.: 5.9249
## Mode :character Median :55.23 Median : 5.00 Median :12.0880
## Mean :55.67 Mean : 5.51 Mean :15.3794
## 3rd Qu.:77.94 3rd Qu.: 8.00 3rd Qu.:22.4453
## Max. :99.96 Max. :10.00 Max. :49.6500
## Date Time Payment cogs
## Length:1000 Length:1000 Length:1000 Min. : 10.17
## Class :character Class :character Class :character 1st Qu.:118.50
## Mode :character Mode :character Mode :character Median :241.76
## Mean :307.59
## 3rd Qu.:448.90
## Max. :993.00
## gross.margin.percentage gross.income Rating Total
## Min. :4.762 Min. : 0.5085 Min. : 4.000 Min. : 10.68
## 1st Qu.:4.762 1st Qu.: 5.9249 1st Qu.: 5.500 1st Qu.: 124.42
## Median :4.762 Median :12.0880 Median : 7.000 Median : 253.85
## Mean :4.762 Mean :15.3794 Mean : 6.973 Mean : 322.97
## 3rd Qu.:4.762 3rd Qu.:22.4453 3rd Qu.: 8.500 3rd Qu.: 471.35
## Max. :4.762 Max. :49.6500 Max. :10.000 Max. :1042.65Info on the dataset
glimpse(sales_df)## Rows: 1,000
## Columns: 16
## $ Invoice.ID <chr> "750-67-8428", "226-31-3081", "631-41-3108", "…
## $ Branch <chr> "A", "C", "A", "A", "A", "C", "A", "C", "A", "…
## $ Customer.type <chr> "Member", "Normal", "Normal", "Member", "Norma…
## $ Gender <chr> "Female", "Female", "Male", "Male", "Male", "M…
## $ Product.line <chr> "Health and beauty", "Electronic accessories",…
## $ Unit.price <dbl> 74.69, 15.28, 46.33, 58.22, 86.31, 85.39, 68.8…
## $ Quantity <int> 7, 5, 7, 8, 7, 7, 6, 10, 2, 3, 4, 4, 5, 10, 10…
## $ Tax <dbl> 26.1415, 3.8200, 16.2155, 23.2880, 30.2085, 29…
## $ Date <chr> "1/5/2019", "3/8/2019", "3/3/2019", "1/27/2019…
## $ Time <chr> "13:08", "10:29", "13:23", "20:33", "10:37", "…
## $ Payment <chr> "Ewallet", "Cash", "Credit card", "Ewallet", "…
## $ cogs <dbl> 522.83, 76.40, 324.31, 465.76, 604.17, 597.73,…
## $ gross.margin.percentage <dbl> 4.761905, 4.761905, 4.761905, 4.761905, 4.7619…
## $ gross.income <dbl> 26.1415, 3.8200, 16.2155, 23.2880, 30.2085, 29…
## $ Rating <dbl> 9.1, 9.6, 7.4, 8.4, 5.3, 4.1, 5.8, 8.0, 7.2, 5…
## $ Total <dbl> 548.9715, 80.2200, 340.5255, 489.0480, 634.378…Checking the number of records
dim(sales_df)## [1] 1000 16Checking the columns
cols <-colnames(sales_df)
cols## [1] "Invoice.ID" "Branch"
## [3] "Customer.type" "Gender"
## [5] "Product.line" "Unit.price"
## [7] "Quantity" "Tax"
## [9] "Date" "Time"
## [11] "Payment" "cogs"
## [13] "gross.margin.percentage" "gross.income"
## [15] "Rating" "Total"Checking the datatypes
str(sales_df)## 'data.frame': 1000 obs. of 16 variables:
## $ Invoice.ID : chr "750-67-8428" "226-31-3081" "631-41-3108" "123-19-1176" ...
## $ Branch : chr "A" "C" "A" "A" ...
## $ Customer.type : chr "Member" "Normal" "Normal" "Member" ...
## $ Gender : chr "Female" "Female" "Male" "Male" ...
## $ Product.line : chr "Health and beauty" "Electronic accessories" "Home and lifestyle" "Health and beauty" ...
## $ Unit.price : num 74.7 15.3 46.3 58.2 86.3 ...
## $ Quantity : int 7 5 7 8 7 7 6 10 2 3 ...
## $ Tax : num 26.14 3.82 16.22 23.29 30.21 ...
## $ Date : chr "1/5/2019" "3/8/2019" "3/3/2019" "1/27/2019" ...
## $ Time : chr "13:08" "10:29" "13:23" "20:33" ...
## $ Payment : chr "Ewallet" "Cash" "Credit card" "Ewallet" ...
## $ cogs : num 522.8 76.4 324.3 465.8 604.2 ...
## $ gross.margin.percentage: num 4.76 4.76 4.76 4.76 4.76 ...
## $ gross.income : num 26.14 3.82 16.22 23.29 30.21 ...
## $ Rating : num 9.1 9.6 7.4 8.4 5.3 4.1 5.8 8 7.2 5.9 ...
## $ Total : num 549 80.2 340.5 489 634.4 ...2.DATA CLEANING
Checking for null values
sum(is.na(sales_df))## [1] 0There are no null values
Check for duplictes
sum(duplicated(sales_df))## [1] 0There are no duplicates
Checking for annomalies
anoms <- lapply(sales_df, unique)
anoms## $Invoice.ID
## [1] "750-67-8428" "226-31-3081" "631-41-3108" "123-19-1176" "373-73-7910"
## [6] "699-14-3026" "355-53-5943" "315-22-5665" "665-32-9167" "692-92-5582"
## [11] "351-62-0822" "529-56-3974" "365-64-0515" "252-56-2699" "829-34-3910"
## [16] "299-46-1805" "656-95-9349" "765-26-6951" "329-62-1586" "319-50-3348"
## [21] "300-71-4605" "371-85-5789" "273-16-6619" "636-48-8204" "549-59-1358"
## [26] "227-03-5010" "649-29-6775" "189-17-4241" "145-94-9061" "848-62-7243"
## [31] "871-79-8483" "149-71-6266" "640-49-2076" "595-11-5460" "183-56-6882"
## [36] "232-16-2483" "129-29-8530" "272-65-1806" "333-73-7901" "777-82-7220"
## [41] "280-35-5823" "554-53-8700" "354-25-5821" "228-96-1411" "617-15-4209"
## [46] "132-32-9879" "370-41-7321" "727-46-3608" "669-54-1719" "574-22-5561"
## [51] "326-78-5178" "162-48-8011" "616-24-2851" "778-71-5554" "242-55-6721"
## [56] "399-46-5918" "106-35-6779" "635-40-6220" "817-48-8732" "120-06-4233"
## [61] "285-68-5083" "803-83-5989" "347-34-2234" "199-75-8169" "853-23-2453"
## [66] "877-22-3308" "838-78-4295" "109-28-2512" "232-11-3025" "382-03-4532"
## [71] "393-65-2792" "796-12-2025" "510-95-6347" "841-35-6630" "287-21-9091"
## [76] "732-94-0499" "263-10-3913" "381-20-0914" "829-49-1914" "756-01-7507"
## [81] "870-72-4431" "847-38-7188" "480-63-2856" "787-56-0757" "360-39-5055"
## [86] "730-50-9884" "362-58-8315" "633-44-8566" "504-35-8843" "318-68-5053"
## [91] "565-80-5980" "225-32-0908" "873-51-0671" "152-08-9985" "512-91-0811"
## [96] "594-34-4444" "766-85-7061" "871-39-9221" "865-92-6136" "733-01-9107"
## [101] "163-56-7055" "189-98-2939" "551-21-3069" "212-62-1842" "716-39-1409"
## [106] "704-48-3927" "628-34-3388" "630-74-5166" "588-01-7461" "861-77-0145"
## [111] "479-26-8945" "210-67-5886" "227-78-1148" "645-44-1170" "237-01-6122"
## [116] "225-98-1496" "291-32-1427" "659-65-8956" "642-32-2990" "378-24-2715"
## [121] "638-60-7125" "659-36-1684" "219-22-9386" "336-78-2147" "268-27-6179"
## [126] "668-90-8900" "870-54-3162" "189-08-9157" "663-86-9076" "549-84-7482"
## [131] "191-10-6171" "802-70-5316" "695-51-0018" "590-83-4591" "483-71-1164"
## [136] "597-78-7908" "700-81-1757" "354-39-5160" "241-72-9525" "575-30-8091"
## [141] "731-81-9469" "280-17-4359" "338-65-2210" "488-25-4221" "239-10-7476"
## [146] "458-41-1477" "685-64-1609" "568-90-5112" "262-47-2794" "238-49-0436"
## [151] "608-96-3517" "584-86-7256" "746-94-0204" "214-17-6927" "400-89-4171"
## [156] "782-95-9291" "279-74-2924" "307-85-2293" "743-04-1105" "423-57-2993"
## [161] "894-41-5205" "275-28-0149" "101-17-6199" "423-80-0988" "548-46-9322"
## [166] "505-02-0892" "234-65-2137" "687-47-8271" "796-32-9050" "105-31-1824"
## [171] "249-42-3782" "316-55-4634" "733-33-4967" "608-27-6295" "414-12-7047"
## [176] "827-26-2100" "175-54-2529" "139-52-2867" "407-63-8975" "342-65-4817"
## [181] "130-98-8941" "434-83-9547" "851-28-6367" "824-88-3614" "586-25-0848"
## [186] "895-66-0685" "305-14-0245" "732-04-5373" "400-60-7251" "593-65-1552"
## [191] "284-34-9626" "437-58-8131" "286-43-6208" "641-43-2399" "831-07-6050"
## [196] "556-86-3144" "848-24-9445" "856-22-8149" "699-01-4164" "420-11-4919"
## [201] "606-80-4905" "542-41-0513" "426-39-2418" "875-46-5808" "394-43-4238"
## [206] "749-24-1565" "672-51-8681" "263-87-5680" "573-58-9734" "817-69-8206"
## [211] "888-02-0338" "677-11-0152" "142-63-6033" "656-16-1063" "891-58-8335"
## [216] "802-43-8934" "560-30-5617" "319-74-2561" "549-03-9315" "790-29-1172"
## [221] "239-36-3640" "468-01-2051" "389-25-3394" "279-62-1445" "213-72-6612"
## [226] "746-68-6593" "836-82-5858" "583-72-1480" "466-61-5506" "721-86-6247"
## [231] "289-65-5721" "545-46-3100" "418-02-5978" "269-04-5750" "157-13-5295"
## [236] "645-78-8093" "211-30-9270" "755-12-3214" "346-84-3103" "478-06-7835"
## [241] "540-11-4336" "448-81-5016" "142-72-4741" "217-58-1179" "376-02-8238"
## [246] "530-90-9855" "866-05-7563" "604-70-6476" "799-71-1548" "785-13-7708"
## [251] "845-51-0542" "662-47-5456" "883-17-4236" "290-68-2984" "704-11-6354"
## [256] "110-48-7033" "366-93-0948" "729-09-9681" "151-16-1484" "380-94-4661"
## [261] "850-41-9669" "821-07-3596" "655-85-5130" "447-15-7839" "154-74-7179"
## [266] "253-12-6086" "808-65-0703" "571-94-0759" "144-51-6085" "731-14-2199"
## [271] "783-09-1637" "687-15-1097" "126-54-1082" "633-91-1052" "477-24-6490"
## [276] "566-19-5475" "526-86-8552" "376-56-3573" "537-72-0426" "828-61-5674"
## [281] "136-08-6195" "523-38-0215" "490-29-1201" "667-92-0055" "565-17-3836"
## [286] "498-41-1961" "593-95-4461" "226-71-3580" "283-79-9594" "430-60-3493"
## [291] "139-20-0155" "558-80-4082" "278-97-7759" "316-68-6352" "585-03-5943"
## [296] "211-05-0490" "727-75-6477" "744-02-5987" "307-83-9164" "779-06-0012"
## [301] "446-47-6729" "573-10-3877" "735-06-4124" "439-54-7422" "396-90-2219"
## [306] "411-77-0180" "286-01-5402" "803-17-8013" "512-98-1403" "848-42-2560"
## [311] "532-59-7201" "181-94-6432" "870-76-1733" "423-64-4619" "227-07-4446"
## [316] "174-36-3675" "428-83-5800" "603-07-0961" "704-20-4138" "787-15-1757"
## [321] "649-11-3678" "622-20-1945" "372-94-8041" "563-91-7120" "746-54-5508"
## [326] "276-54-0879" "815-11-1168" "719-76-3868" "730-61-8757" "340-66-0321"
## [331] "868-81-1752" "634-97-8956" "566-71-1091" "442-48-3607" "835-16-0096"
## [336] "527-09-6272" "898-04-2717" "692-27-8933" "633-09-3463" "374-17-3652"
## [341] "378-07-7001" "433-75-6987" "873-95-4984" "416-13-5917" "150-89-8043"
## [346] "135-84-8019" "441-94-7118" "725-96-3778" "531-80-1784" "400-45-1220"
## [351] "860-79-0874" "834-61-8124" "115-99-4379" "565-67-6697" "320-49-6392"
## [356] "889-04-9723" "632-90-0281" "554-42-2417" "453-63-6187" "578-80-7669"
## [361] "612-36-5536" "605-72-4132" "471-41-2823" "462-67-9126" "272-27-9238"
## [366] "834-25-9262" "122-61-9553" "468-88-0009" "613-59-9758" "254-31-0042"
## [371] "201-86-2184" "261-12-8671" "730-70-9830" "382-25-8917" "422-29-8786"
## [376] "667-23-5919" "843-01-4703" "743-88-1662" "595-86-2894" "182-69-8360"
## [381] "289-15-7034" "462-78-5240" "868-52-7573" "153-58-4872" "662-72-2873"
## [386] "525-88-7307" "689-16-9784" "725-56-0833" "394-41-0748" "596-42-3999"
## [391] "541-89-9860" "173-82-9529" "563-36-9814" "308-47-4913" "885-17-6250"
## [396] "726-27-2396" "316-01-3952" "760-54-1821" "793-10-3222" "346-12-3257"
## [401] "110-05-6330" "651-61-0874" "236-86-3015" "831-64-0259" "587-03-7455"
## [406] "882-40-4577" "732-67-5346" "725-32-9708" "256-08-8343" "372-26-1506"
## [411] "244-08-0162" "569-71-4390" "132-23-6451" "696-90-2548" "472-15-9636"
## [416] "268-03-6164" "750-57-9686" "186-09-3669" "848-07-1692" "745-71-3520"
## [421] "266-76-6436" "740-22-2500" "271-88-8734" "301-81-8610" "489-64-4354"
## [426] "198-84-7132" "269-10-8440" "650-98-6268" "741-73-3559" "325-77-6186"
## [431] "286-75-7818" "574-57-9721" "459-50-7686" "616-87-0016" "837-55-7229"
## [436] "751-69-0068" "257-73-1380" "345-08-4992" "549-96-4200" "810-60-6344"
## [441] "450-28-2866" "394-30-3170" "138-17-5109" "192-98-7397" "301-11-9629"
## [446] "390-80-5128" "235-46-8343" "453-12-7053" "296-11-7041" "449-27-2918"
## [451] "891-01-7034" "744-09-5786" "727-17-0390" "568-88-3448" "187-83-5490"
## [456] "767-54-1907" "710-46-4433" "533-33-5337" "325-90-8763" "729-46-7422"
## [461] "639-76-1242" "234-03-4040" "326-71-2155" "320-32-8842" "470-32-9057"
## [466] "878-30-2331" "440-59-5691" "554-53-3790" "746-19-0921" "233-34-0817"
## [471] "767-05-1286" "340-21-9136" "405-31-3305" "731-59-7531" "676-39-6028"
## [476] "502-05-1910" "485-30-8700" "598-47-9715" "701-69-8742" "575-67-1508"
## [481] "541-08-3113" "246-11-3901" "674-15-9296" "305-18-3552" "493-65-6248"
## [486] "438-01-4015" "709-58-4068" "795-49-7276" "556-72-8512" "627-95-3243"
## [491] "686-41-0932" "510-09-5628" "608-04-3797" "148-82-2527" "437-53-3084"
## [496] "632-32-4574" "556-97-7101" "862-59-8517" "401-18-8016" "420-18-8989"
## [501] "277-63-2961" "573-98-8548" "620-02-2046" "282-35-2475" "511-54-3087"
## [506] "726-29-6793" "387-49-4215" "862-17-9201" "291-21-5991" "602-80-9671"
## [511] "347-72-6115" "209-61-0206" "595-27-4851" "189-52-0236" "503-07-0930"
## [516] "413-20-6708" "425-85-2085" "521-18-7827" "220-28-1851" "600-38-9738"
## [521] "734-91-1155" "451-28-5717" "609-81-8548" "133-14-7229" "534-01-4457"
## [526] "719-89-8991" "286-62-6248" "339-38-9982" "827-44-5872" "827-77-7633"
## [531] "287-83-1405" "435-13-4908" "857-67-9057" "236-27-1144" "892-05-6689"
## [536] "583-41-4548" "339-12-4827" "643-38-7867" "308-81-0538" "358-88-9262"
## [541] "460-35-4390" "343-87-0864" "173-50-1108" "243-47-2663" "841-18-8232"
## [546] "701-23-5550" "647-50-1224" "541-48-8554" "539-21-7227" "213-32-1216"
## [551] "747-58-7183" "582-52-8065" "210-57-1719" "399-69-4630" "134-75-2619"
## [556] "356-44-8813" "198-66-9832" "283-26-5248" "712-39-0363" "218-59-9410"
## [561] "174-75-0888" "866-99-7614" "134-54-4720" "760-90-2357" "514-37-2845"
## [566] "698-98-5964" "718-57-9773" "651-88-7328" "241-11-2261" "408-26-9866"
## [571] "834-83-1826" "343-61-3544" "239-48-4278" "355-34-6244" "550-84-8664"
## [576] "339-96-8318" "458-61-0011" "592-34-6155" "797-88-0493" "207-73-1363"
## [581] "390-31-6381" "443-82-0585" "339-18-7061" "359-90-3665" "375-72-3056"
## [586] "127-47-6963" "278-86-2735" "695-28-6250" "379-17-6588" "227-50-3718"
## [591] "302-15-2162" "788-07-8452" "560-49-6611" "880-35-0356" "585-11-6748"
## [596] "470-31-3286" "152-68-2907" "123-35-4896" "258-69-7810" "334-64-2006"
## [601] "219-61-4139" "881-41-7302" "373-09-4567" "642-30-6693" "484-22-8230"
## [606] "830-58-2383" "559-98-9873" "544-32-5024" "318-12-0304" "349-97-8902"
## [611] "421-95-9805" "277-35-5865" "789-23-8625" "284-54-4231" "443-59-0061"
## [616] "509-29-3912" "327-40-9673" "840-19-2096" "828-46-6863" "641-96-3695"
## [621] "420-97-3340" "436-54-4512" "670-79-6321" "852-62-7105" "598-06-7312"
## [626] "135-13-8269" "816-57-2053" "628-90-8624" "856-66-2701" "308-39-1707"
## [631] "149-61-1929" "655-07-2265" "589-02-8023" "420-04-7590" "182-88-2763"
## [636] "188-55-0967" "610-46-4100" "318-81-2368" "364-33-8584" "665-63-9737"
## [641] "695-09-5146" "155-45-3814" "794-32-2436" "131-15-8856" "273-84-2164"
## [646] "706-36-6154" "778-89-7974" "574-31-8277" "859-71-0933" "740-11-5257"
## [651] "369-82-2676" "563-47-4072" "742-04-5161" "149-15-7606" "133-77-3154"
## [656] "169-52-4504" "250-81-7186" "562-12-5430" "816-72-8853" "491-38-3499"
## [661] "322-02-2271" "842-29-4695" "725-67-2480" "641-51-2661" "714-02-3114"
## [666] "518-17-2983" "779-42-2410" "190-14-3147" "408-66-6712" "679-22-6530"
## [671] "588-47-8641" "642-61-4706" "576-31-4774" "556-41-6224" "811-03-8790"
## [676] "242-11-3142" "752-23-3760" "274-05-5470" "648-94-3045" "130-67-4723"
## [681] "528-87-5606" "320-85-2052" "370-96-0655" "105-10-6182" "510-79-0415"
## [686] "241-96-5076" "767-97-4650" "648-83-1321" "173-57-2300" "305-03-2383"
## [691] "394-55-6384" "266-20-6657" "689-05-1884" "196-01-2849" "372-62-5264"
## [696] "800-09-8606" "182-52-7000" "826-58-8051" "868-06-0466" "751-41-9720"
## [701] "626-43-7888" "176-64-7711" "191-29-0321" "729-06-2010" "640-48-5028"
## [706] "186-79-9562" "834-45-5519" "162-65-8559" "760-27-5490" "445-30-9252"
## [711] "786-94-2700" "728-88-7867" "183-21-3799" "268-20-3585" "735-32-9839"
## [716] "258-92-7466" "857-16-3520" "482-17-1179" "788-21-5741" "821-14-9046"
## [721] "418-05-0656" "678-79-0726" "776-68-1096" "592-46-1692" "434-35-9162"
## [726] "149-14-0304" "442-44-6497" "174-64-0215" "210-74-9613" "299-29-0180"
## [731] "247-11-2470" "635-28-5728" "756-49-0168" "438-23-1242" "238-45-6950"
## [736] "607-65-2441" "386-27-7606" "137-63-5492" "197-77-7132" "805-86-0265"
## [741] "733-29-1227" "451-73-2711" "373-14-0504" "546-80-2899" "345-68-9016"
## [746] "390-17-5806" "457-13-1708" "664-14-2882" "487-79-6868" "314-23-4520"
## [751] "210-30-7976" "585-86-8361" "807-14-7833" "775-72-1988" "288-38-3758"
## [756] "652-43-6591" "785-96-0615" "406-46-7107" "250-17-5703" "156-95-3964"
## [761] "842-40-8179" "525-09-8450" "410-67-1709" "587-73-4862" "787-87-2010"
## [766] "593-14-4239" "801-88-0346" "388-76-2555" "711-31-1234" "886-54-6089"
## [771] "707-32-7409" "759-98-4285" "201-63-8275" "471-06-8611" "200-16-5952"
## [776] "120-54-2248" "102-77-2261" "875-31-8302" "102-06-2002" "457-94-0464"
## [781] "629-42-4133" "534-53-3526" "307-04-2070" "468-99-7231" "516-77-6464"
## [786] "404-91-5964" "886-77-9084" "790-38-4466" "704-10-4056" "497-37-6538"
## [791] "651-96-5970" "400-80-4065" "744-16-7898" "263-12-5321" "702-72-0487"
## [796] "605-83-1050" "443-60-9639" "864-24-7918" "359-94-5395" "401-09-4232"
## [801] "751-15-6198" "324-41-6833" "474-33-8305" "759-29-9521" "831-81-6575"
## [806] "220-68-6701" "618-34-8551" "257-60-7754" "559-61-5987" "189-55-2313"
## [811] "565-91-4567" "380-60-5336" "815-04-6282" "674-56-6360" "778-34-2523"
## [816] "499-27-7781" "477-59-2456" "832-51-6761" "869-11-3082" "190-59-3964"
## [821] "366-43-6862" "186-43-8965" "784-21-9238" "276-75-6884" "109-86-4363"
## [826] "569-76-2760" "222-42-0244" "760-53-9233" "538-22-0304" "416-17-9926"
## [831] "237-44-6163" "636-17-0325" "343-75-9322" "528-14-9470" "427-45-9297"
## [836] "807-34-3742" "288-62-1085" "670-71-7306" "660-29-7083" "271-77-8740"
## [841] "497-36-0989" "291-59-1384" "860-73-6466" "549-23-9016" "896-34-0956"
## [846] "804-38-3935" "585-90-0249" "862-29-5914" "845-94-6841" "125-45-2293"
## [851] "843-73-4724" "409-33-9708" "658-66-3967" "866-70-2814" "160-22-2687"
## [856] "895-03-6665" "770-42-8960" "748-45-2862" "234-36-2483" "316-66-3011"
## [861] "848-95-6252" "840-76-5966" "152-03-4217" "533-66-5566" "124-31-1458"
## [866] "176-78-1170" "361-59-0574" "101-81-4070" "631-34-1880" "852-82-2749"
## [871] "873-14-6353" "584-66-4073" "544-55-9589" "166-19-2553" "737-88-5876"
## [876] "154-87-7367" "885-56-0389" "608-05-3804" "448-61-3783" "761-49-0439"
## [881] "490-95-0021" "115-38-7388" "311-13-6971" "291-55-6563" "548-48-3156"
## [886] "460-93-5834" "325-89-4209" "884-80-6021" "137-74-8729" "880-46-5796"
## [891] "389-70-2397" "114-35-5271" "607-76-6216" "715-20-1673" "811-35-1094"
## [896] "699-88-1972" "781-84-8059" "409-49-6995" "725-54-0677" "146-09-5432"
## [901] "377-79-7592" "509-10-0516" "595-94-9924" "865-41-9075" "545-07-8534"
## [906] "118-62-1812" "450-42-3339" "851-98-3555" "186-71-5196" "624-01-8356"
## [911] "313-66-9943" "151-27-8496" "453-33-6436" "522-57-8364" "459-45-2396"
## [916] "717-96-4189" "722-13-2115" "749-81-8133" "777-67-2495" "636-98-3364"
## [921] "246-55-6923" "181-82-6255" "838-02-1821" "887-42-0517" "457-12-0244"
## [926] "226-34-0034" "321-49-7382" "397-25-8725" "431-66-2305" "825-94-5922"
## [931] "641-62-7288" "756-93-1854" "243-55-8457" "458-10-8612" "501-61-1753"
## [936] "235-06-8510" "433-08-7822" "361-85-2571" "131-70-8179" "500-02-2261"
## [941] "720-72-2436" "702-83-5291" "809-69-9497" "449-16-6770" "333-23-2632"
## [946] "489-82-1237" "859-97-6048" "676-10-2200" "373-88-1424" "365-16-4334"
## [951] "503-21-4385" "305-89-2768" "574-80-1489" "784-08-0310" "200-40-6154"
## [956] "846-10-0341" "577-34-7579" "430-02-3888" "867-47-1948" "384-59-6655"
## [961] "256-58-3609" "324-92-3863" "593-08-5916" "364-34-2972" "794-42-3736"
## [966] "172-42-8274" "558-60-5016" "195-06-0432" "605-03-2706" "214-30-2776"
## [971] "746-04-1077" "448-34-8700" "452-04-8808" "531-56-4728" "744-82-9138"
## [976] "883-69-1285" "221-25-5073" "518-71-6847" "156-20-0370" "151-33-7434"
## [981] "728-47-9078" "809-46-1866" "139-32-4183" "148-41-7930" "189-40-5216"
## [986] "374-38-5555" "764-44-8999" "552-44-5977" "267-62-7380" "430-53-4718"
## [991] "886-18-2897" "602-16-6955" "745-74-0715" "690-01-6631" "652-49-6720"
## [996] "233-67-5758" "303-96-2227" "727-02-1313" "347-56-2442" "849-09-3807"
##
## $Branch
## [1] "A" "C" "B"
##
## $Customer.type
## [1] "Member" "Normal"
##
## $Gender
## [1] "Female" "Male"
##
## $Product.line
## [1] "Health and beauty" "Electronic accessories" "Home and lifestyle"
## [4] "Sports and travel" "Food and beverages" "Fashion accessories"
##
## $Unit.price
## [1] 74.69 15.28 46.33 58.22 86.31 85.39 68.84 73.56 36.26 54.84 14.48 25.51
## [13] 46.95 43.19 71.38 93.72 68.93 72.61 54.67 40.30 86.04 87.98 33.20 34.56
## [25] 88.63 52.59 33.52 87.67 88.36 24.89 94.13 78.07 83.78 96.58 99.42 68.12
## [37] 62.62 60.88 54.92 30.12 86.72 56.11 69.12 98.70 15.37 93.96 56.69 20.01
## [49] 18.93 82.63 91.40 44.59 17.87 15.43 16.16 85.98 44.34 89.60 72.35 30.61
## [61] 24.74 55.73 55.07 15.81 75.74 15.87 33.47 97.61 78.77 18.33 89.48 62.12
## [73] 48.52 75.91 74.67 41.65 49.04 78.31 20.38 99.19 96.68 19.25 80.36 48.91
## [85] 83.06 76.52 49.38 42.47 76.99 47.38 44.86 21.98 64.36 89.75 97.16 87.87
## [97] 12.45 52.75 82.70 48.71 78.55 23.07 58.26 30.35 88.67 27.38 62.13 33.98
## [109] 81.97 16.49 98.21 72.84 58.07 80.79 27.02 21.94 51.36 10.96 53.44 99.56
## [121] 57.12 99.96 63.91 56.47 93.69 32.25 31.73 68.54 90.28 39.62 92.13 34.84
## [133] 87.45 81.30 90.22 26.31 34.42 51.91 72.50 89.80 90.50 68.60 30.41 77.95
## [145] 46.26 30.14 66.14 71.86 32.46 91.54 83.24 16.48 80.97 92.29 72.17 50.28
## [157] 97.22 93.39 43.18 63.69 45.79 76.40 39.90 42.57 95.58 98.98 51.28 69.52
## [169] 70.01 80.05 20.85 52.89 19.79 33.84 22.17 22.51 73.88 86.80 64.26 38.47
## [181] 15.50 34.31 12.34 18.08 94.49 46.47 74.07 69.81 77.04 73.52 87.80 25.55
## [193] 32.71 74.29 43.70 25.29 41.50 71.39 19.15 57.49 61.41 25.90 17.77 23.03
## [205] 66.65 28.53 30.37 99.73 26.23 93.26 92.36 46.42 29.61 18.28 24.77 94.64
## [217] 94.87 57.34 45.35 62.08 11.81 12.54 43.25 87.16 69.37 37.06 90.70 63.42
## [229] 81.37 10.59 84.09 73.82 51.94 93.14 17.41 44.22 13.22 89.69 24.94 59.77
## [241] 93.20 62.65 93.87 47.59 81.40 17.94 77.72 73.06 46.55 35.19 14.39 23.75
## [253] 58.90 32.62 66.35 25.91 65.94 75.06 16.45 38.30 22.24 54.45 98.40 35.47
## [265] 74.60 70.74 35.54 67.43 21.12 21.54 12.03 99.71 47.97 21.82 95.42 70.99
## [277] 44.02 69.96 37.00 15.34 99.83 47.67 66.68 74.86 48.51 94.88 27.85 62.48
## [289] 36.36 18.11 51.92 28.84 78.38 60.01 88.61 99.82 39.01 48.61 51.19 14.96
## [301] 72.20 40.23 88.79 26.48 81.91 79.93 69.33 14.23 15.55 78.13 99.37 21.08
## [313] 74.79 29.67 44.07 22.93 39.42 15.26 61.77 21.52 97.74 99.78 94.26 51.13
## [325] 22.02 32.90 77.02 23.48 14.70 28.45 57.95 47.65 42.82 48.09 55.97 76.90
## [337] 97.03 44.65 77.93 71.95 89.25 26.02 13.50 99.30 51.69 54.73 27.00 30.24
## [349] 89.14 37.55 95.44 27.50 74.97 80.96 94.47 99.79 73.22 41.24 81.68 51.32
## [361] 14.36 21.50 26.26 60.96 70.11 42.08 67.09 96.70 35.38 95.49 96.98 23.65
## [373] 82.33 26.61 99.69 74.89 40.94 75.82 46.77 32.32 54.07 18.22 80.48 37.95
## [385] 76.82 52.26 79.74 77.50 54.27 13.59 41.06 19.24 39.43 46.22 13.98 39.75
## [397] 97.79 67.26 13.79 68.71 56.53 23.82 34.21 21.87 20.97 25.84 50.93 96.11
## [409] 45.38 81.51 57.22 25.22 38.60 84.05 97.21 25.42 16.28 40.61 53.17 20.87
## [421] 67.27 90.65 69.08 43.27 23.46 95.54 47.44 99.24 82.93 33.99 17.04 40.86
## [433] 17.44 88.43 89.21 12.78 19.10 27.66 45.74 27.07 39.12 74.71 22.01 63.61
## [445] 25.00 20.77 29.56 77.40 79.39 46.57 35.89 40.52 73.05 73.95 22.62 51.34
## [457] 54.55 37.15 37.02 21.58 98.84 83.77 40.05 43.13 72.57 64.44 65.18 33.26
## [469] 84.07 34.37 65.97 32.80 37.14 60.38 36.98 49.49 41.09 22.96 77.68 34.70
## [481] 19.66 25.32 12.12 99.89 75.92 63.22 90.24 98.13 51.52 73.97 31.90 69.40
## [493] 93.31 88.45 24.18 48.50 61.29 15.95 90.74 42.91 54.28 99.55 58.39 51.47
## [505] 54.86 39.39 34.73 71.92 45.71 83.17 37.44 62.87 81.71 91.41 39.21 59.86
## [517] 54.36 98.09 25.43 86.68 22.95 16.31 28.32 16.67 73.96 97.94 87.48 30.68
## [529] 75.88 20.18 18.77 71.20 38.81 29.42 60.95 51.54 66.06 57.27 54.31 58.24
## [541] 22.21 19.32 37.48 72.04 98.52 41.66 72.42 89.20 42.42 74.51 99.25 81.21
## [553] 49.33 65.74 79.86 73.98 82.04 26.67 10.13 72.39 85.91 81.31 60.30 31.77
## [565] 64.27 69.51 27.22 92.98 63.06 51.71 52.34 43.06 59.61 14.62 46.53 24.24
## [577] 45.58 75.20 96.80 14.82 52.20 46.66 36.85 70.32 83.08 64.99 77.56 54.51
## [589] 51.89 31.75 53.65 49.79 57.89 28.96 98.97 93.22 80.93 67.45 38.72 72.60
## [601] 87.91 98.53 43.46 71.68 91.61 94.59 83.25 91.35 78.88 60.87 82.58 53.30
## [613] 12.09 64.19 99.70 79.91 66.47 28.95 46.20 17.63 52.42 98.79 88.55 55.67
## [625] 72.52 12.05 19.36 70.21 33.63 15.49 75.66 55.81 72.78 37.32 60.18 15.69
## [637] 88.15 27.93 55.45 42.97 17.14 58.75 87.10 98.80 48.63 57.74 17.97 47.71
## [649] 40.62 56.04 93.40 73.41 33.64 45.48 64.08 73.47 58.95 39.48 34.81 49.32
## [661] 21.48 23.08 49.10 64.83 63.56 72.88 67.10 70.19 55.04 73.38 52.60 87.37
## [673] 27.04 62.19 69.58 97.50 60.41 19.77 80.47 88.39 71.77 43.00 68.98 15.62
## [685] 25.70 80.62 75.53 77.63 13.85 35.68 71.46 11.94 17.48 25.56 90.63 44.12
## [697] 36.77 23.34 28.50 55.57 69.74 97.26 52.18 22.32 56.00 19.70 53.72 81.95
## [709] 81.20 58.76 91.56 55.61 84.83 71.63 37.69 31.67 38.42 65.23 10.53 12.29
## [721] 81.23 27.28 17.42 73.28 84.87 97.29 35.74 96.52 18.85 55.39 77.20 72.13
## [733] 63.88 10.69 55.50 95.46 76.06 13.69 95.64 11.43 85.87 67.99 65.65 28.86
## [745] 65.31 93.38 25.25 21.80 94.76 30.62 44.01 10.16 74.58 71.89 10.99 60.47
## [757] 58.91 46.41 68.55 97.37 92.60 46.61 27.18 24.49 92.78 86.69 23.01 30.20
## [769] 67.39 48.96 75.59 77.47 93.18 50.23 17.75 62.18 10.75 40.26 64.97 95.15
## [781] 48.62 53.21 45.44 33.88 96.16 47.16 47.68 10.17 60.08 72.11 41.28 64.95
## [793] 74.22 10.56 62.57 11.85 91.30 40.73 52.38 38.54 44.63 55.87 29.22 39.47
## [805] 14.87 21.32 93.78 73.26 22.38 99.10 74.10 98.48 53.19 52.79 95.95 36.51
## [817] 28.31 57.59 47.63 86.27 12.76 11.28 51.07 79.59 33.81 90.53 62.82 24.31
## [829] 64.59 24.82 56.50 21.43 89.06 23.29 65.26 52.35 90.02 12.10 33.21 10.18
## [841] 31.99 83.34 87.90 12.19 76.92 83.66 57.91 92.49 28.38 50.45 99.16 60.74
## [853] 47.27 85.60 35.04 44.84 45.97 27.73 11.53 58.32 84.61 82.88 79.54 49.01
## [865] 29.15 56.13 93.12 99.60 35.49 42.85 94.67 68.97 35.79 16.37 12.73 83.14
## [877] 35.22 13.78 88.31 88.25 25.31 99.92 83.35 74.44 63.15 85.72 78.89 92.09
## [889] 57.29 66.52 45.68 50.79 10.08 93.88 84.25 53.78 35.81 26.43 39.91 21.90
## [901] 62.85 21.04 65.91 50.49 46.02 15.80 98.66 91.98 20.89 96.82 33.33 38.27
## [913] 33.30 81.01 34.49 84.63 36.91 87.08 80.08 86.13 49.92 74.66 26.60 25.45
## [925] 67.77 59.59 58.15 97.48 96.37 63.71 14.76 62.00 82.34 75.37 56.56 76.60
## [937] 58.03 17.49 40.35 97.38 31.84 65.82 88.34
##
## $Quantity
## [1] 7 5 8 6 10 2 3 4 1 9
##
## $Tax
## [1] 26.1415 3.8200 16.2155 23.2880 30.2085 29.8865 20.6520 36.7800 3.6260
## [10] 8.2260 2.8960 5.1020 11.7375 21.5950 35.6900 28.1160 24.1255 21.7830
## [19] 8.2005 4.0300 21.5100 13.1970 3.3200 8.6400 13.2945 21.0360 1.6760
## [28] 8.7670 22.0900 11.2005 23.5325 35.1315 33.5120 9.6580 19.8840 3.4060
## [37] 15.6550 27.3960 21.9680 12.0480 4.3360 5.6110 20.7360 39.4800 1.5370
## [46] 18.7920 25.5105 9.0045 5.6790 41.3150 31.9900 11.1475 3.5740 0.7715
## [55] 1.6160 34.3920 4.4340 35.8400 36.1750 9.1830 3.7110 16.7190 24.7815
## [64] 7.9050 15.1480 7.9350 3.3470 29.2830 39.3850 0.9165 44.7400 31.0600
## [73] 7.2780 22.7730 33.6015 20.8250 22.0680 39.1550 5.0950 29.7570 14.5020
## [82] 7.7000 16.0720 12.2275 29.0710 19.1300 17.2830 2.1235 23.0970 9.4760
## [91] 22.4300 7.6930 28.9620 4.4875 4.8580 43.9350 3.7350 7.9125 24.8100
## [100] 2.4355 35.3475 10.3815 17.4780 10.6225 44.3350 8.2140 18.6390 15.2910
## [109] 40.9850 1.6490 14.7315 25.4940 26.1315 36.3555 4.0530 5.4850 2.5680
## [118] 5.4800 5.3440 39.8240 19.9920 44.9820 25.5640 22.5880 32.7915 8.0625
## [127] 14.2785 27.4160 40.6260 13.8670 27.6390 6.9680 26.2350 24.3900 13.5330
## [136] 6.5775 10.3260 25.9550 29.0000 44.9000 45.2500 34.3000 1.5205 23.3850
## [145] 13.8780 15.0700 13.2280 28.7440 12.9840 18.3080 12.0960 37.4580 4.9440
## [154] 32.3880 23.0725 3.6085 12.5700 43.7490 28.0170 17.2720 3.1845 16.0265
## [163] 7.6400 19.9500 17.0280 47.7900 49.4900 15.3840 24.3320 17.5025 20.0125
## [172] 8.3400 15.8670 7.9160 15.2280 8.8680 7.8785 22.1640 13.0200 22.4910
## [181] 15.3880 7.7500 13.7240 4.3190 2.7120 37.7960 9.2940 3.7035 13.9620
## [190] 11.5560 7.3520 39.5100 5.1100 8.1775 3.7145 4.3700 1.2645 8.3000
## [199] 17.8475 5.7450 11.4980 21.4935 12.9500 4.4425 10.3635 29.9925 14.2650
## [208] 4.5555 44.8785 11.8035 41.9670 23.0900 6.9630 0.9140 6.1925 14.1960
## [217] 37.9480 8.6010 13.6050 21.7280 2.9525 0.6270 4.3250 8.7160 31.2165
## [226] 7.4120 27.2100 25.3680 8.1370 1.5885 37.8405 14.7640 25.9700 9.3140
## [235] 4.3525 11.0550 3.3050 4.4845 11.2230 5.9770 9.3200 12.5300 37.5480
## [244] 19.0360 12.2100 4.4850 15.5440 25.5710 20.9475 17.5950 1.4390 4.7500
## [253] 23.5600 6.5240 3.3175 7.7730 6.4500 13.1880 33.7770 3.2900 7.6600
## [262] 11.1200 2.7225 34.4400 7.0940 37.3000 14.1480 17.7700 16.8575 2.1120
## [271] 9.6930 1.2030 29.9130 16.7895 10.9100 19.0840 35.4950 22.0100 27.9840
## [280] 1.8500 0.7670 29.9490 9.5340 16.6700 3.7430 10.6875 16.9785 33.2080
## [289] 20.1500 9.7475 3.1240 3.6360 9.0550 12.9800 5.7680 23.5140 12.0020
## [298] 4.4305 9.9820 1.9505 2.4305 10.2380 5.9840 25.2700 14.0805 35.5160
## [307] 3.9720 8.1910 23.9790 6.9330 3.5575 6.9975 39.0650 9.9370 3.1620
## [316] 18.6975 10.3845 8.8140 10.3185 1.9710 4.5780 15.4425 6.4560 19.5480
## [325] 24.9450 18.8520 10.2260 7.2720 9.9090 4.9350 19.2550 2.3480 3.6750
## [334] 7.1125 34.3800 17.3850 7.1475 19.2690 7.2135 19.5895 26.9150 24.2575
## [343] 6.6975 35.0685 3.5975 35.7000 9.1070 6.7500 49.6500 18.0915 19.1555
## [352] 12.1500 1.5120 17.8280 18.7750 47.7200 4.1250 3.7485 32.3840 37.7880
## [361] 9.9790 21.9660 8.2480 16.3360 23.0940 7.1800 9.6750 9.1910 6.0960
## [370] 21.0330 12.6240 16.7725 24.1750 15.9210 33.4215 19.3960 4.7300 16.4660
## [379] 2.6610 24.9225 14.9780 10.2350 3.7910 14.0310 16.1600 24.3315 6.3770
## [388] 12.0720 18.9750 3.8410 26.1300 3.9870 19.3750 13.5675 6.1155 12.3180
## [397] 8.6580 11.8290 9.2440 0.6990 9.9375 34.2265 13.4520 3.4475 13.7420
## [406] 11.3060 5.9550 17.1050 2.1870 5.2425 3.8760 20.3720 4.8055 9.0760
## [415] 4.0755 5.7220 8.8270 5.7900 12.6075 48.6050 10.1680 0.8140 18.2745
## [424] 18.6095 3.1305 16.8175 45.3250 6.9080 4.3270 7.0380 33.4390 2.3720
## [433] 44.6580 16.5860 10.1970 3.4080 16.3440 4.3600 35.3720 40.1445 0.6390
## [442] 6.6850 0.9575 13.8300 6.8610 1.3535 1.9560 22.4130 6.6030 15.9025
## [451] 1.2500 4.1540 7.3900 34.8300 39.6950 23.2850 1.7945 10.1300 36.5250
## [460] 14.7900 1.1310 12.8350 27.2750 13.0025 11.1060 1.0790 4.9420 25.1310
## [469] 8.0100 21.5650 29.0280 16.1100 9.7770 8.3150 16.8140 17.1850 1.9300
## [478] 26.3880 16.4000 9.2850 30.1900 18.4900 9.8980 20.5450 7.4300 1.1480
## [487] 34.9560 3.4700 9.8300 10.1280 6.0600 9.9890 30.3680 6.3220 27.0720
## [496] 4.9065 20.6080 3.6985 1.5950 6.9400 9.3310 4.4225 9.6720 7.2750
## [505] 25.2150 15.3225 4.7850 31.7590 10.7275 18.9980 34.8425 20.4365 2.5735
## [514] 13.7150 9.8475 3.4730 17.9800 6.8565 24.9510 11.2320 6.2870 24.5130
## [523] 22.8525 7.8420 5.9860 27.1800 44.1405 7.6290 34.6720 11.4750 7.3395
## [532] 7.0800 5.8345 3.6980 4.8970 14.6100 26.2440 4.6020 3.7940 4.0360
## [541] 5.6310 3.5600 7.7620 14.7100 27.4275 12.8850 19.8180 8.5905 24.4395
## [550] 26.2080 6.6630 6.7620 5.6220 7.2040 49.2600 12.4980 10.8630 9.7110
## [559] 44.6000 16.9680 22.3530 9.9250 40.6050 24.6650 29.5830 27.9510 25.8930
## [568] 20.5100 13.3350 3.5455 7.2390 21.4775 28.4585 12.0600 6.3540 12.8540
## [577] 6.9510 4.0830 15.5360 9.2980 3.6160 9.4590 10.3420 7.8510 10.7650
## [586] 29.8050 3.6550 13.9590 8.4840 2.2790 11.2800 14.5200 2.2230 7.8300
## [595] 20.9970 9.2125 7.0320 3.2495 38.7800 16.3530 18.1615 6.3500 18.7775
## [604] 9.9580 1.5305 5.7890 1.4480 44.5365 13.9830 4.0465 33.7250 17.4240
## [613] 21.7800 21.9775 29.5590 13.0380 10.7520 4.5805 33.1065 41.6250 4.5675
## [622] 7.8880 6.0870 41.2900 7.9950 0.6045 32.0950 11.7465 8.3770 14.9550
## [631] 11.9865 33.2350 10.1325 2.3100 4.4075 7.8630 14.8185 35.4200 5.5670
## [640] 29.0080 3.0125 8.7120 21.0630 1.6815 1.5490 12.3700 18.9150 16.7430
## [649] 36.3900 16.7940 12.0360 2.3535 4.9845 13.2225 6.9825 2.7725 6.4455
## [658] 5.9990 17.6250 43.5500 9.8800 9.7260 8.6610 3.5940 14.3130 4.0620
## [667] 28.0200 9.3400 11.0115 13.4560 22.7400 22.4280 14.6940 29.4750 14.5500
## [676] 1.9740 1.7405 14.7960 2.1480 6.9240 4.9100 6.4830 31.7800 7.2880
## [685] 10.0650 31.5855 19.2640 24.3150 25.6830 23.6700 21.8425 5.4080 12.4380
## [694] 31.3110 48.7500 24.1640 4.8480 9.8850 36.2115 39.7755 25.1195 8.6000
## [703] 3.4490 6.2480 3.8550 24.1860 15.1060 34.9335 6.2325 8.9200 25.0110
## [712] 1.7910 6.8070 5.2440 8.9460 40.7835 6.6180 12.8695 4.6680 11.4000
## [721] 8.3355 34.8700 19.4520 18.2630 4.4640 8.4000 0.9850 26.5580 2.6860
## [730] 40.9750 28.4200 29.3800 36.6240 42.2820 19.4635 4.2415 7.1630 3.7690
## [739] 12.6680 1.9210 32.6150 2.6325 5.5305 28.4305 6.8200 8.7100 18.3200
## [748] 12.7305 38.9160 14.2960 28.9560 9.4250 11.0780 38.6000 36.0650 25.5520
## [757] 2.6725 11.1000 38.1840 11.4090 4.1070 19.1280 3.4290 19.1080 30.0545
## [766] 23.7965 2.6210 6.5650 7.2150 22.8585 4.6690 6.3125 39.5415 8.7200
## [775] 18.9520 1.5310 17.6040 2.5400 26.1030 28.7560 2.7475 9.0705 20.6185
## [784] 2.3205 13.7100 48.6850 32.4100 4.6610 2.7180 3.0435 12.2450 4.6390
## [793] 21.6725 6.9030 12.0800 23.5865 22.0320 34.0155 15.4940 9.3180 10.0460
## [802] 0.8875 31.0900 4.3000 20.1300 16.2425 4.7575 19.4480 21.2840 15.9040
## [811] 13.5520 19.2320 11.7900 10.5780 4.7680 0.5085 10.3065 21.0280 4.4020
## [820] 32.4495 6.1920 32.4750 37.1100 4.2240 12.5140 4.7400 4.5650 14.2555
## [829] 2.6190 9.6350 13.3890 27.9350 8.7660 7.7910 3.0150 3.9470 1.4870
## [838] 1.0660 14.0670 3.6630 1.1190 32.7960 29.7300 3.7050 9.8480 18.6165
## [847] 26.3950 23.9875 16.4295 8.4480 5.6620 17.2770 21.4335 4.3135 1.2760
## [856] 5.0760 17.8745 11.9385 5.0715 36.2120 6.2820 3.6465 12.9180 8.6870
## [865] 2.8250 10.7150 26.7180 4.6580 26.1040 2.6175 1.9875 36.0080 4.8400
## [874] 16.6050 4.0720 15.9950 8.3340 15.9530 4.3950 36.7350 4.8760 38.4600
## [883] 20.9150 23.1640 23.1225 7.0950 15.1350 39.6640 21.2590 14.1810 29.9600
## [892] 15.7680 20.1780 9.1940 6.9325 4.0355 5.8320 15.6760 42.3050 20.7200
## [901] 7.9540 24.5050 4.3725 11.2260 37.2480 20.5360 14.9400 10.6470 2.1425
## [910] 18.9340 10.3455 3.9390 16.1055 4.9110 1.2730 29.0990 10.5660 2.7560
## [919] 4.4155 17.8290 39.7125 2.5310 29.9760 8.3350 37.2200 18.9450 12.8580
## [928] 27.6115 22.3700 13.8135 17.1870 13.3040 44.9190 22.8400 12.6975 3.5280
## [937] 32.8580 8.4250 2.6890 8.9525 10.5720 5.9865 3.2850 4.2080 19.7730
## [946] 14.8995 22.7205 13.8060 7.9000 44.3970 4.5990 2.0890 0.7750 14.5230
## [955] 3.3330 3.8270 14.9850 12.1515 2.3700 8.6225 42.3150 12.9185 30.4780
## [964] 12.0120 8.6130 4.9920 14.9320 7.9800 1.2725 3.3885 11.9180 11.6300
## [973] 43.8660 34.9860 33.7295 15.9275 1.4760 24.8000 41.1700 30.1480 14.1400
## [982] 38.3000 5.8030 8.7450 3.0475 2.0175 48.6900 1.5920 3.2910 30.9190
##
## $Date
## [1] "1/5/2019" "3/8/2019" "3/3/2019" "1/27/2019" "2/8/2019" "3/25/2019"
## [7] "2/25/2019" "2/24/2019" "1/10/2019" "2/20/2019" "2/6/2019" "3/9/2019"
## [13] "2/12/2019" "2/7/2019" "3/29/2019" "1/15/2019" "3/11/2019" "1/1/2019"
## [19] "1/21/2019" "3/5/2019" "3/15/2019" "2/17/2019" "3/2/2019" "3/22/2019"
## [25] "3/10/2019" "1/25/2019" "1/28/2019" "1/7/2019" "3/23/2019" "1/17/2019"
## [31] "2/2/2019" "3/4/2019" "3/16/2019" "2/27/2019" "2/10/2019" "3/19/2019"
## [37] "2/3/2019" "3/7/2019" "2/28/2019" "3/27/2019" "1/20/2019" "3/12/2019"
## [43] "2/15/2019" "3/6/2019" "2/14/2019" "3/13/2019" "1/24/2019" "1/6/2019"
## [49] "2/11/2019" "1/22/2019" "1/13/2019" "1/9/2019" "1/12/2019" "1/26/2019"
## [55] "1/23/2019" "2/23/2019" "1/2/2019" "2/9/2019" "3/26/2019" "3/1/2019"
## [61] "2/1/2019" "3/28/2019" "3/24/2019" "2/5/2019" "1/19/2019" "1/16/2019"
## [67] "1/8/2019" "2/18/2019" "1/18/2019" "2/16/2019" "2/22/2019" "1/29/2019"
## [73] "1/4/2019" "3/30/2019" "1/30/2019" "1/3/2019" "3/21/2019" "2/13/2019"
## [79] "1/14/2019" "3/18/2019" "3/20/2019" "2/21/2019" "1/31/2019" "1/11/2019"
## [85] "2/26/2019" "3/17/2019" "3/14/2019" "2/4/2019" "2/19/2019"
##
## $Time
## [1] "13:08" "10:29" "13:23" "20:33" "10:37" "18:30" "14:36" "11:38" "17:15"
## [10] "13:27" "18:07" "17:03" "10:25" "16:48" "19:21" "16:19" "11:03" "10:39"
## [19] "18:00" "15:30" "11:24" "10:40" "12:20" "11:15" "17:36" "19:20" "15:31"
## [28] "12:17" "19:48" "15:36" "19:39" "12:43" "14:49" "10:12" "10:42" "12:28"
## [37] "19:15" "17:17" "13:24" "13:01" "18:45" "10:11" "13:03" "20:39" "19:47"
## [46] "17:24" "15:47" "12:45" "17:08" "10:19" "15:10" "14:42" "15:46" "11:49"
## [55] "19:01" "11:26" "11:28" "15:55" "20:36" "17:47" "10:55" "13:40" "12:27"
## [64] "14:35" "16:40" "15:43" "15:01" "10:04" "18:50" "12:46" "18:17" "18:21"
## [73] "17:04" "14:20" "15:48" "16:24" "18:56" "19:56" "18:37" "10:17" "14:31"
## [82] "10:23" "20:35" "16:57" "17:55" "19:54" "16:42" "12:09" "20:05" "20:38"
## [91] "13:11" "10:16" "18:14" "13:22" "11:27" "16:44" "18:19" "14:50" "20:54"
## [100] "20:19" "10:43" "14:30" "11:32" "10:41" "12:44" "20:07" "20:31" "12:29"
## [109] "15:26" "20:48" "12:02" "17:26" "19:52" "14:57" "18:44" "13:26" "16:17"
## [118] "15:57" "13:18" "20:34" "18:36" "14:40" "16:43" "20:59" "15:39" "12:21"
## [127] "19:25" "13:00" "13:48" "19:57" "10:36" "16:37" "17:11" "15:07" "16:07"
## [136] "11:56" "18:23" "13:05" "19:40" "13:58" "14:43" "19:18" "16:21" "19:44"
## [145] "19:42" "15:24" "14:12" "13:32" "16:20" "16:31" "11:36" "19:17" "17:34"
## [154] "12:04" "17:01" "10:50" "19:16" "16:47" "10:00" "11:51" "15:00" "11:19"
## [163] "19:46" "19:00" "10:53" "12:50" "20:50" "13:41" "19:08" "20:23" "11:30"
## [172] "19:30" "18:03" "10:13" "19:58" "10:01" "11:57" "10:02" "14:51" "12:42"
## [181] "17:38" "20:24" "18:08" "15:53" "15:05" "18:27" "16:55" "12:58" "18:59"
## [190] "13:44" "13:46" "18:06" "12:38" "15:56" "14:29" "19:14" "10:52" "12:55"
## [199] "19:28" "13:52" "10:54" "18:31" "18:24" "18:09" "15:16" "17:07" "19:26"
## [208] "11:20" "16:49" "12:01" "11:25" "18:42" "14:47" "19:43" "14:04" "16:11"
## [217] "19:06" "15:34" "11:22" "11:23" "10:46" "13:25" "14:53" "19:22" "11:00"
## [226] "19:24" "17:22" "20:55" "16:05" "13:34" "18:13" "11:44" "15:51" "16:52"
## [235] "20:52" "16:28" "13:29" "11:09" "15:02" "14:21" "18:01" "13:30" "14:38"
## [244] "17:37" "17:20" "20:29" "11:46" "13:42" "14:44" "14:16" "15:54" "10:21"
## [253] "16:46" "20:14" "17:09" "17:43" "19:05" "10:08" "13:12" "20:51" "17:29"
## [262] "11:34" "18:58" "20:26" "15:08" "13:21" "12:48" "19:53" "19:09" "16:30"
## [271] "13:07" "18:48" "17:27" "15:59" "11:21" "15:49" "13:02" "20:21" "15:04"
## [280] "16:10" "12:14" "11:06" "18:22" "19:02" "15:44" "20:01" "13:45" "15:40"
## [289] "16:58" "11:12" "15:12" "20:37" "17:44" "16:23" "12:12" "19:33" "14:28"
## [298] "17:54" "12:25" "12:52" "19:50" "15:32" "13:19" "13:37" "14:55" "12:31"
## [307] "10:26" "20:18" "20:04" "13:38" "17:30" "15:28" "19:07" "18:55" "19:36"
## [316] "10:57" "17:13" "13:57" "13:53" "16:53" "16:51" "15:37" "20:15" "19:35"
## [325] "15:42" "14:11" "17:58" "11:02" "15:09" "13:47" "16:59" "14:15" "15:19"
## [334] "18:33" "12:10" "11:40" "16:54" "15:25" "20:47" "18:20" "11:48" "14:14"
## [343] "11:17" "12:40" "17:53" "16:36" "10:48" "18:05" "12:07" "19:49" "15:52"
## [352] "20:46" "10:34" "13:55" "11:43" "16:03" "20:03" "19:41" "18:04" "10:31"
## [361] "13:28" "17:16" "18:43" "10:30" "20:40" "12:08" "17:45" "10:28" "10:49"
## [370] "12:34" "18:51" "19:38" "12:32" "10:33" "19:55" "14:33" "13:54" "12:15"
## [379] "12:37" "15:06" "15:58" "14:03" "16:38" "11:07" "12:23" "14:13" "19:11"
## [388] "18:53" "14:22" "10:06" "20:08" "12:56" "10:18" "11:45" "16:08" "12:24"
## [397] "19:51" "18:10" "15:27" "16:04" "14:41" "14:19" "14:08" "11:29" "12:16"
## [406] "20:00" "15:29" "14:58" "11:52" "17:46" "14:45" "11:39" "13:06" "20:43"
## [415] "16:34" "13:10" "17:10" "10:22" "19:29" "14:27" "12:22" "11:59" "17:59"
## [424] "12:51" "13:56" "19:45" "16:18" "18:57" "11:18" "14:06" "20:13" "15:14"
## [433] "16:06" "12:47" "20:42" "20:10" "14:24" "11:42" "17:49" "15:33" "10:38"
## [442] "12:39" "14:26" "12:41" "15:20" "16:33" "20:44" "11:16" "12:30" "17:48"
## [451] "20:30" "13:59" "11:58" "16:50" "18:02" "17:52" "20:32" "16:09" "11:33"
## [460] "15:15" "20:06" "16:26" "18:38" "16:45" "18:41" "17:12" "14:00" "16:32"
## [469] "10:10" "10:05" "18:15" "11:01" "15:21" "16:16" "11:05" "19:31" "18:35"
## [478] "13:51" "12:35" "11:55" "15:11" "14:48" "12:36" "13:35" "15:45" "14:25"
## [487] "15:18" "10:03" "13:14" "16:35" "20:57" "13:50" "17:35" "17:56" "10:44"
## [496] "10:09" "10:58" "13:49" "11:10" "13:33" "14:05" "16:27" "15:23" "18:18"
## [505] "15:17" "19:12"
##
## $Payment
## [1] "Ewallet" "Cash" "Credit card"
##
## $cogs
## [1] 522.83 76.40 324.31 465.76 604.17 597.73 413.04 735.60 72.52 164.52
## [11] 57.92 102.04 234.75 431.90 713.80 562.32 482.51 435.66 164.01 80.60
## [21] 430.20 263.94 66.40 172.80 265.89 420.72 33.52 175.34 441.80 224.01
## [31] 470.65 702.63 670.24 193.16 397.68 68.12 313.10 547.92 439.36 240.96
## [41] 86.72 112.22 414.72 789.60 30.74 375.84 510.21 180.09 113.58 826.30
## [51] 639.80 222.95 71.48 15.43 32.32 687.84 88.68 716.80 723.50 183.66
## [61] 74.22 334.38 495.63 158.10 302.96 158.70 66.94 585.66 787.70 18.33
## [71] 894.80 621.20 145.56 455.46 672.03 416.50 441.36 783.10 101.90 595.14
## [81] 290.04 154.00 321.44 244.55 581.42 382.60 345.66 42.47 461.94 189.52
## [91] 448.60 153.86 579.24 89.75 97.16 878.70 74.70 158.25 496.20 48.71
## [101] 706.95 207.63 349.56 212.45 886.70 164.28 372.78 305.82 819.70 32.98
## [111] 294.63 509.88 522.63 727.11 81.06 109.70 51.36 109.60 106.88 796.48
## [121] 399.84 899.64 511.28 451.76 655.83 161.25 285.57 548.32 812.52 277.34
## [131] 552.78 139.36 524.70 487.80 270.66 131.55 206.52 519.10 580.00 898.00
## [141] 905.00 686.00 30.41 467.70 277.56 301.40 264.56 574.88 259.68 366.16
## [151] 241.92 749.16 98.88 647.76 461.45 72.17 251.40 874.98 560.34 345.44
## [161] 63.69 320.53 152.80 399.00 340.56 955.80 989.80 307.68 486.64 350.05
## [171] 400.25 166.80 317.34 158.32 304.56 177.36 157.57 443.28 260.40 449.82
## [181] 307.76 155.00 274.48 86.38 54.24 755.92 185.88 74.07 279.24 231.12
## [191] 147.04 790.20 102.20 163.55 74.29 87.40 25.29 166.00 356.95 114.90
## [201] 229.96 429.87 259.00 88.85 207.27 599.85 285.30 91.11 897.57 236.07
## [211] 839.34 461.80 139.26 18.28 123.85 283.92 758.96 172.02 272.10 434.56
## [221] 59.05 12.54 86.50 174.32 624.33 148.24 544.20 507.36 162.74 31.77
## [231] 756.81 295.28 519.40 186.28 87.05 221.10 66.10 89.69 224.46 119.54
## [241] 186.40 250.60 750.96 380.72 244.20 89.70 310.88 511.42 418.95 351.90
## [251] 28.78 95.00 471.20 130.48 66.35 155.46 129.00 263.76 675.54 65.80
## [261] 153.20 222.40 54.45 688.80 141.88 746.00 282.96 355.40 337.15 42.24
## [271] 193.86 24.06 598.26 335.79 218.20 381.68 709.90 440.20 559.68 37.00
## [281] 15.34 598.98 190.68 333.40 74.86 213.75 339.57 664.16 403.00 194.95
## [291] 62.48 72.72 181.10 259.60 115.36 470.28 240.04 88.61 199.64 39.01
## [301] 48.61 204.76 119.68 505.40 281.61 710.32 79.44 163.82 479.58 138.66
## [311] 71.15 139.95 781.30 198.74 63.24 373.95 207.69 176.28 206.37 39.42
## [321] 91.56 308.85 129.12 390.96 498.90 377.04 204.52 145.44 198.18 98.70
## [331] 385.10 46.96 73.50 142.25 687.60 347.70 142.95 385.38 144.27 391.79
## [341] 538.30 485.15 133.95 701.37 71.95 714.00 182.14 135.00 993.00 361.83
## [351] 383.11 243.00 30.24 356.56 375.50 954.40 82.50 74.97 647.68 755.76
## [361] 199.58 439.32 164.96 326.72 461.88 143.60 193.50 183.82 121.92 420.66
## [371] 252.48 335.45 483.50 318.42 668.43 387.92 94.60 329.32 53.22 498.45
## [381] 299.56 204.70 75.82 280.62 323.20 486.63 127.54 241.44 379.50 76.82
## [391] 522.60 79.74 387.50 271.35 122.31 246.36 173.16 236.58 184.88 13.98
## [401] 198.75 684.53 269.04 68.95 274.84 226.12 119.10 342.10 43.74 104.85
## [411] 77.52 407.44 96.11 181.52 81.51 114.44 176.54 115.80 252.15 972.10
## [421] 203.36 16.28 365.49 372.19 62.61 336.35 906.50 138.16 86.54 140.76
## [431] 668.78 47.44 893.16 331.72 203.94 68.16 326.88 87.20 707.44 802.89
## [441] 12.78 133.70 19.15 276.60 137.22 27.07 39.12 448.26 132.06 318.05
## [451] 25.00 83.08 147.80 696.60 793.90 465.70 35.89 202.60 730.50 295.80
## [461] 22.62 256.70 545.50 260.05 222.12 21.58 98.84 502.62 160.20 431.30
## [471] 580.56 322.20 195.54 166.30 336.28 343.70 38.60 527.76 328.00 185.70
## [481] 603.80 369.80 197.96 410.90 148.60 22.96 699.12 69.40 196.60 202.56
## [491] 121.20 199.78 607.36 126.44 541.44 98.13 412.16 73.97 31.90 138.80
## [501] 186.62 88.45 193.44 145.50 504.30 306.45 95.70 635.18 214.55 379.96
## [511] 696.85 408.73 51.47 274.30 196.95 69.46 359.60 137.13 499.02 224.64
## [521] 125.74 490.26 457.05 156.84 119.72 543.60 882.81 152.58 693.44 229.50
## [531] 146.79 141.60 116.69 73.96 97.94 292.20 524.88 92.04 75.88 80.72
## [541] 112.62 71.20 155.24 294.20 548.55 257.70 396.36 171.81 488.79 524.16
## [551] 133.26 135.24 112.44 144.08 985.20 249.96 217.26 194.22 892.00 339.36
## [561] 447.06 198.50 812.10 493.30 591.66 559.02 517.86 410.20 266.70 70.91
## [571] 144.78 429.55 569.17 241.20 127.08 257.08 139.02 81.66 310.72 185.96
## [581] 72.32 189.18 206.84 157.02 215.30 596.10 73.10 279.18 169.68 45.58
## [591] 225.60 290.40 44.46 156.60 419.94 184.25 140.64 64.99 775.60 327.06
## [601] 363.23 127.00 375.55 199.16 30.61 115.78 28.96 890.73 279.66 80.93
## [611] 674.50 348.48 435.60 439.55 591.18 260.76 215.04 91.61 662.13 832.50
## [621] 91.35 157.76 121.74 825.80 159.90 12.09 641.90 234.93 167.54 299.10
## [631] 239.73 664.70 202.65 46.20 88.15 157.26 296.37 708.40 111.34 580.16
## [641] 60.25 174.24 421.26 33.63 30.98 247.40 378.30 334.86 727.80 335.88
## [651] 240.72 47.07 99.69 264.45 139.65 55.45 128.91 119.98 352.50 871.00
## [661] 197.60 194.52 173.22 71.88 286.26 81.24 560.40 186.80 220.23 269.12
## [671] 454.80 448.56 293.88 589.50 291.00 39.48 34.81 295.92 42.96 138.48
## [681] 98.20 129.66 635.60 145.76 201.30 631.71 385.28 486.30 513.66 473.40
## [691] 436.85 108.16 248.76 626.22 975.00 483.28 96.96 197.70 724.23 795.51
## [701] 502.39 172.00 68.98 124.96 77.10 483.72 302.12 698.67 124.65 178.40
## [711] 500.22 35.82 136.14 104.88 178.92 815.67 132.36 257.39 93.36 228.00
## [721] 166.71 697.40 389.04 365.26 89.28 168.00 19.70 531.16 53.72 819.50
## [731] 568.40 587.60 732.48 845.64 389.27 84.83 143.26 75.38 253.36 38.42
## [741] 652.30 52.65 110.61 568.61 136.40 174.20 366.40 254.61 778.32 285.92
## [751] 579.12 188.50 221.56 772.00 721.30 511.04 53.45 222.00 763.68 228.18
## [761] 82.14 382.56 68.58 382.16 601.09 475.93 52.42 131.30 144.30 457.17
## [771] 93.38 126.25 790.83 174.40 379.04 30.62 352.08 50.80 522.06 575.12
## [781] 54.95 181.41 412.37 46.41 274.20 973.70 648.20 93.22 54.36 60.87
## [791] 244.90 92.78 433.45 138.06 241.60 471.73 440.64 680.31 309.88 186.36
## [801] 200.92 17.75 621.80 86.00 402.60 324.85 95.15 388.96 425.68 318.08
## [811] 271.04 384.64 235.80 211.56 95.36 10.17 206.13 420.56 88.04 648.99
## [821] 123.84 649.50 742.20 84.48 250.28 94.80 91.30 285.11 52.38 192.70
## [831] 267.78 558.70 175.32 155.82 60.30 78.94 29.74 21.32 281.34 73.26
## [841] 22.38 655.92 594.60 74.10 196.96 372.33 527.90 479.75 328.59 168.96
## [851] 113.24 345.54 428.67 86.27 25.52 101.52 357.49 238.77 101.43 724.24
## [861] 125.64 72.93 258.36 173.74 56.50 214.30 534.36 93.16 522.08 52.35
## [871] 39.75 720.16 96.80 332.10 81.44 319.90 166.68 319.06 87.90 734.70
## [881] 97.52 769.20 418.30 463.28 462.45 141.90 302.70 793.28 425.18 283.62
## [891] 599.20 315.36 403.56 183.88 138.65 80.71 116.64 313.52 846.10 414.40
## [901] 159.08 490.10 87.45 224.52 744.96 410.72 298.80 212.94 42.85 378.68
## [911] 206.91 78.78 322.11 98.22 25.46 581.98 211.32 55.12 88.31 356.58
## [921] 794.25 50.62 599.52 166.70 744.40 378.90 257.16 552.23 447.40 276.27
## [931] 343.74 266.08 898.38 456.80 253.95 70.56 657.16 168.50 53.78 179.05
## [941] 211.44 119.73 65.70 84.16 395.46 297.99 454.41 276.12 158.00 887.94
## [951] 91.98 41.78 15.50 290.46 66.66 76.54 299.70 243.03 47.40 172.45
## [961] 846.30 258.37 609.56 240.24 172.26 99.84 298.64 159.60 25.45 67.77
## [971] 238.36 232.60 877.32 699.72 674.59 318.55 29.52 496.00 823.40 602.96
## [981] 282.80 766.00 116.06 174.90 60.95 40.35 973.80 31.84 65.82 618.38
##
## $gross.margin.percentage
## [1] 4.761905
##
## $gross.income
## [1] 26.1415 3.8200 16.2155 23.2880 30.2085 29.8865 20.6520 36.7800 3.6260
## [10] 8.2260 2.8960 5.1020 11.7375 21.5950 35.6900 28.1160 24.1255 21.7830
## [19] 8.2005 4.0300 21.5100 13.1970 3.3200 8.6400 13.2945 21.0360 1.6760
## [28] 8.7670 22.0900 11.2005 23.5325 35.1315 33.5120 9.6580 19.8840 3.4060
## [37] 15.6550 27.3960 21.9680 12.0480 4.3360 5.6110 20.7360 39.4800 1.5370
## [46] 18.7920 25.5105 9.0045 5.6790 41.3150 31.9900 11.1475 3.5740 0.7715
## [55] 1.6160 34.3920 4.4340 35.8400 36.1750 9.1830 3.7110 16.7190 24.7815
## [64] 7.9050 15.1480 7.9350 3.3470 29.2830 39.3850 0.9165 44.7400 31.0600
## [73] 7.2780 22.7730 33.6015 20.8250 22.0680 39.1550 5.0950 29.7570 14.5020
## [82] 7.7000 16.0720 12.2275 29.0710 19.1300 17.2830 2.1235 23.0970 9.4760
## [91] 22.4300 7.6930 28.9620 4.4875 4.8580 43.9350 3.7350 7.9125 24.8100
## [100] 2.4355 35.3475 10.3815 17.4780 10.6225 44.3350 8.2140 18.6390 15.2910
## [109] 40.9850 1.6490 14.7315 25.4940 26.1315 36.3555 4.0530 5.4850 2.5680
## [118] 5.4800 5.3440 39.8240 19.9920 44.9820 25.5640 22.5880 32.7915 8.0625
## [127] 14.2785 27.4160 40.6260 13.8670 27.6390 6.9680 26.2350 24.3900 13.5330
## [136] 6.5775 10.3260 25.9550 29.0000 44.9000 45.2500 34.3000 1.5205 23.3850
## [145] 13.8780 15.0700 13.2280 28.7440 12.9840 18.3080 12.0960 37.4580 4.9440
## [154] 32.3880 23.0725 3.6085 12.5700 43.7490 28.0170 17.2720 3.1845 16.0265
## [163] 7.6400 19.9500 17.0280 47.7900 49.4900 15.3840 24.3320 17.5025 20.0125
## [172] 8.3400 15.8670 7.9160 15.2280 8.8680 7.8785 22.1640 13.0200 22.4910
## [181] 15.3880 7.7500 13.7240 4.3190 2.7120 37.7960 9.2940 3.7035 13.9620
## [190] 11.5560 7.3520 39.5100 5.1100 8.1775 3.7145 4.3700 1.2645 8.3000
## [199] 17.8475 5.7450 11.4980 21.4935 12.9500 4.4425 10.3635 29.9925 14.2650
## [208] 4.5555 44.8785 11.8035 41.9670 23.0900 6.9630 0.9140 6.1925 14.1960
## [217] 37.9480 8.6010 13.6050 21.7280 2.9525 0.6270 4.3250 8.7160 31.2165
## [226] 7.4120 27.2100 25.3680 8.1370 1.5885 37.8405 14.7640 25.9700 9.3140
## [235] 4.3525 11.0550 3.3050 4.4845 11.2230 5.9770 9.3200 12.5300 37.5480
## [244] 19.0360 12.2100 4.4850 15.5440 25.5710 20.9475 17.5950 1.4390 4.7500
## [253] 23.5600 6.5240 3.3175 7.7730 6.4500 13.1880 33.7770 3.2900 7.6600
## [262] 11.1200 2.7225 34.4400 7.0940 37.3000 14.1480 17.7700 16.8575 2.1120
## [271] 9.6930 1.2030 29.9130 16.7895 10.9100 19.0840 35.4950 22.0100 27.9840
## [280] 1.8500 0.7670 29.9490 9.5340 16.6700 3.7430 10.6875 16.9785 33.2080
## [289] 20.1500 9.7475 3.1240 3.6360 9.0550 12.9800 5.7680 23.5140 12.0020
## [298] 4.4305 9.9820 1.9505 2.4305 10.2380 5.9840 25.2700 14.0805 35.5160
## [307] 3.9720 8.1910 23.9790 6.9330 3.5575 6.9975 39.0650 9.9370 3.1620
## [316] 18.6975 10.3845 8.8140 10.3185 1.9710 4.5780 15.4425 6.4560 19.5480
## [325] 24.9450 18.8520 10.2260 7.2720 9.9090 4.9350 19.2550 2.3480 3.6750
## [334] 7.1125 34.3800 17.3850 7.1475 19.2690 7.2135 19.5895 26.9150 24.2575
## [343] 6.6975 35.0685 3.5975 35.7000 9.1070 6.7500 49.6500 18.0915 19.1555
## [352] 12.1500 1.5120 17.8280 18.7750 47.7200 4.1250 3.7485 32.3840 37.7880
## [361] 9.9790 21.9660 8.2480 16.3360 23.0940 7.1800 9.6750 9.1910 6.0960
## [370] 21.0330 12.6240 16.7725 24.1750 15.9210 33.4215 19.3960 4.7300 16.4660
## [379] 2.6610 24.9225 14.9780 10.2350 3.7910 14.0310 16.1600 24.3315 6.3770
## [388] 12.0720 18.9750 3.8410 26.1300 3.9870 19.3750 13.5675 6.1155 12.3180
## [397] 8.6580 11.8290 9.2440 0.6990 9.9375 34.2265 13.4520 3.4475 13.7420
## [406] 11.3060 5.9550 17.1050 2.1870 5.2425 3.8760 20.3720 4.8055 9.0760
## [415] 4.0755 5.7220 8.8270 5.7900 12.6075 48.6050 10.1680 0.8140 18.2745
## [424] 18.6095 3.1305 16.8175 45.3250 6.9080 4.3270 7.0380 33.4390 2.3720
## [433] 44.6580 16.5860 10.1970 3.4080 16.3440 4.3600 35.3720 40.1445 0.6390
## [442] 6.6850 0.9575 13.8300 6.8610 1.3535 1.9560 22.4130 6.6030 15.9025
## [451] 1.2500 4.1540 7.3900 34.8300 39.6950 23.2850 1.7945 10.1300 36.5250
## [460] 14.7900 1.1310 12.8350 27.2750 13.0025 11.1060 1.0790 4.9420 25.1310
## [469] 8.0100 21.5650 29.0280 16.1100 9.7770 8.3150 16.8140 17.1850 1.9300
## [478] 26.3880 16.4000 9.2850 30.1900 18.4900 9.8980 20.5450 7.4300 1.1480
## [487] 34.9560 3.4700 9.8300 10.1280 6.0600 9.9890 30.3680 6.3220 27.0720
## [496] 4.9065 20.6080 3.6985 1.5950 6.9400 9.3310 4.4225 9.6720 7.2750
## [505] 25.2150 15.3225 4.7850 31.7590 10.7275 18.9980 34.8425 20.4365 2.5735
## [514] 13.7150 9.8475 3.4730 17.9800 6.8565 24.9510 11.2320 6.2870 24.5130
## [523] 22.8525 7.8420 5.9860 27.1800 44.1405 7.6290 34.6720 11.4750 7.3395
## [532] 7.0800 5.8345 3.6980 4.8970 14.6100 26.2440 4.6020 3.7940 4.0360
## [541] 5.6310 3.5600 7.7620 14.7100 27.4275 12.8850 19.8180 8.5905 24.4395
## [550] 26.2080 6.6630 6.7620 5.6220 7.2040 49.2600 12.4980 10.8630 9.7110
## [559] 44.6000 16.9680 22.3530 9.9250 40.6050 24.6650 29.5830 27.9510 25.8930
## [568] 20.5100 13.3350 3.5455 7.2390 21.4775 28.4585 12.0600 6.3540 12.8540
## [577] 6.9510 4.0830 15.5360 9.2980 3.6160 9.4590 10.3420 7.8510 10.7650
## [586] 29.8050 3.6550 13.9590 8.4840 2.2790 11.2800 14.5200 2.2230 7.8300
## [595] 20.9970 9.2125 7.0320 3.2495 38.7800 16.3530 18.1615 6.3500 18.7775
## [604] 9.9580 1.5305 5.7890 1.4480 44.5365 13.9830 4.0465 33.7250 17.4240
## [613] 21.7800 21.9775 29.5590 13.0380 10.7520 4.5805 33.1065 41.6250 4.5675
## [622] 7.8880 6.0870 41.2900 7.9950 0.6045 32.0950 11.7465 8.3770 14.9550
## [631] 11.9865 33.2350 10.1325 2.3100 4.4075 7.8630 14.8185 35.4200 5.5670
## [640] 29.0080 3.0125 8.7120 21.0630 1.6815 1.5490 12.3700 18.9150 16.7430
## [649] 36.3900 16.7940 12.0360 2.3535 4.9845 13.2225 6.9825 2.7725 6.4455
## [658] 5.9990 17.6250 43.5500 9.8800 9.7260 8.6610 3.5940 14.3130 4.0620
## [667] 28.0200 9.3400 11.0115 13.4560 22.7400 22.4280 14.6940 29.4750 14.5500
## [676] 1.9740 1.7405 14.7960 2.1480 6.9240 4.9100 6.4830 31.7800 7.2880
## [685] 10.0650 31.5855 19.2640 24.3150 25.6830 23.6700 21.8425 5.4080 12.4380
## [694] 31.3110 48.7500 24.1640 4.8480 9.8850 36.2115 39.7755 25.1195 8.6000
## [703] 3.4490 6.2480 3.8550 24.1860 15.1060 34.9335 6.2325 8.9200 25.0110
## [712] 1.7910 6.8070 5.2440 8.9460 40.7835 6.6180 12.8695 4.6680 11.4000
## [721] 8.3355 34.8700 19.4520 18.2630 4.4640 8.4000 0.9850 26.5580 2.6860
## [730] 40.9750 28.4200 29.3800 36.6240 42.2820 19.4635 4.2415 7.1630 3.7690
## [739] 12.6680 1.9210 32.6150 2.6325 5.5305 28.4305 6.8200 8.7100 18.3200
## [748] 12.7305 38.9160 14.2960 28.9560 9.4250 11.0780 38.6000 36.0650 25.5520
## [757] 2.6725 11.1000 38.1840 11.4090 4.1070 19.1280 3.4290 19.1080 30.0545
## [766] 23.7965 2.6210 6.5650 7.2150 22.8585 4.6690 6.3125 39.5415 8.7200
## [775] 18.9520 1.5310 17.6040 2.5400 26.1030 28.7560 2.7475 9.0705 20.6185
## [784] 2.3205 13.7100 48.6850 32.4100 4.6610 2.7180 3.0435 12.2450 4.6390
## [793] 21.6725 6.9030 12.0800 23.5865 22.0320 34.0155 15.4940 9.3180 10.0460
## [802] 0.8875 31.0900 4.3000 20.1300 16.2425 4.7575 19.4480 21.2840 15.9040
## [811] 13.5520 19.2320 11.7900 10.5780 4.7680 0.5085 10.3065 21.0280 4.4020
## [820] 32.4495 6.1920 32.4750 37.1100 4.2240 12.5140 4.7400 4.5650 14.2555
## [829] 2.6190 9.6350 13.3890 27.9350 8.7660 7.7910 3.0150 3.9470 1.4870
## [838] 1.0660 14.0670 3.6630 1.1190 32.7960 29.7300 3.7050 9.8480 18.6165
## [847] 26.3950 23.9875 16.4295 8.4480 5.6620 17.2770 21.4335 4.3135 1.2760
## [856] 5.0760 17.8745 11.9385 5.0715 36.2120 6.2820 3.6465 12.9180 8.6870
## [865] 2.8250 10.7150 26.7180 4.6580 26.1040 2.6175 1.9875 36.0080 4.8400
## [874] 16.6050 4.0720 15.9950 8.3340 15.9530 4.3950 36.7350 4.8760 38.4600
## [883] 20.9150 23.1640 23.1225 7.0950 15.1350 39.6640 21.2590 14.1810 29.9600
## [892] 15.7680 20.1780 9.1940 6.9325 4.0355 5.8320 15.6760 42.3050 20.7200
## [901] 7.9540 24.5050 4.3725 11.2260 37.2480 20.5360 14.9400 10.6470 2.1425
## [910] 18.9340 10.3455 3.9390 16.1055 4.9110 1.2730 29.0990 10.5660 2.7560
## [919] 4.4155 17.8290 39.7125 2.5310 29.9760 8.3350 37.2200 18.9450 12.8580
## [928] 27.6115 22.3700 13.8135 17.1870 13.3040 44.9190 22.8400 12.6975 3.5280
## [937] 32.8580 8.4250 2.6890 8.9525 10.5720 5.9865 3.2850 4.2080 19.7730
## [946] 14.8995 22.7205 13.8060 7.9000 44.3970 4.5990 2.0890 0.7750 14.5230
## [955] 3.3330 3.8270 14.9850 12.1515 2.3700 8.6225 42.3150 12.9185 30.4780
## [964] 12.0120 8.6130 4.9920 14.9320 7.9800 1.2725 3.3885 11.9180 11.6300
## [973] 43.8660 34.9860 33.7295 15.9275 1.4760 24.8000 41.1700 30.1480 14.1400
## [982] 38.3000 5.8030 8.7450 3.0475 2.0175 48.6900 1.5920 3.2910 30.9190
##
## $Rating
## [1] 9.1 9.6 7.4 8.4 5.3 4.1 5.8 8.0 7.2 5.9 4.5 6.8 7.1 8.2 5.7
## [16] 4.6 6.9 8.6 4.4 4.8 5.1 9.9 6.0 8.5 6.7 7.7 7.5 7.0 4.7 7.6
## [31] 7.9 6.3 5.6 9.5 8.1 6.5 6.1 6.6 5.4 9.3 10.0 6.4 4.3 4.0 8.7
## [46] 9.4 5.5 8.3 7.3 4.9 4.2 9.2 7.8 5.2 9.0 8.8 6.2 9.8 9.7 5.0
## [61] 8.9
##
## $Total
## [1] 548.9715 80.2200 340.5255 489.0480 634.3785 627.6165 433.6920
## [8] 772.3800 76.1460 172.7460 60.8160 107.1420 246.4875 453.4950
## [15] 749.4900 590.4360 506.6355 457.4430 172.2105 84.6300 451.7100
## [22] 277.1370 69.7200 181.4400 279.1845 441.7560 35.1960 184.1070
## [29] 463.8900 235.2105 494.1825 737.7615 703.7520 202.8180 417.5640
## [36] 71.5260 328.7550 575.3160 461.3280 253.0080 91.0560 117.8310
## [43] 435.4560 829.0800 32.2770 394.6320 535.7205 189.0945 119.2590
## [50] 867.6150 671.7900 234.0975 75.0540 16.2015 33.9360 722.2320
## [57] 93.1140 752.6400 759.6750 192.8430 77.9310 351.0990 520.4115
## [64] 166.0050 318.1080 166.6350 70.2870 614.9430 827.0850 19.2465
## [71] 939.5400 652.2600 152.8380 478.2330 705.6315 437.3250 463.4280
## [78] 822.2550 106.9950 624.8970 304.5420 161.7000 337.5120 256.7775
## [85] 610.4910 401.7300 362.9430 44.5935 485.0370 198.9960 471.0300
## [92] 161.5530 608.2020 94.2375 102.0180 922.6350 78.4350 166.1625
## [99] 521.0100 51.1455 742.2975 218.0115 367.0380 223.0725 931.0350
## [106] 172.4940 391.4190 321.1110 860.6850 34.6290 309.3615 535.3740
## [113] 548.7615 763.4655 85.1130 115.1850 53.9280 115.0800 112.2240
## [120] 836.3040 419.8320 944.6220 536.8440 474.3480 688.6215 169.3125
## [127] 299.8485 575.7360 853.1460 291.2070 580.4190 146.3280 550.9350
## [134] 512.1900 284.1930 138.1275 216.8460 545.0550 609.0000 942.9000
## [141] 950.2500 720.3000 31.9305 491.0850 291.4380 316.4700 277.7880
## [148] 603.6240 272.6640 384.4680 254.0160 786.6180 103.8240 680.1480
## [155] 484.5225 75.7785 263.9700 918.7290 588.3570 362.7120 66.8745
## [162] 336.5565 160.4400 418.9500 357.5880 1003.5900 1039.2900 323.0640
## [169] 510.9720 367.5525 420.2625 175.1400 333.2070 166.2360 319.7880
## [176] 186.2280 165.4485 465.4440 273.4200 472.3110 323.1480 162.7500
## [183] 288.2040 90.6990 56.9520 793.7160 195.1740 77.7735 293.2020
## [190] 242.6760 154.3920 829.7100 107.3100 171.7275 78.0045 91.7700
## [197] 26.5545 174.3000 374.7975 120.6450 241.4580 451.3635 271.9500
## [204] 93.2925 217.6335 629.8425 299.5650 95.6655 942.4485 247.8735
## [211] 881.3070 484.8900 146.2230 19.1940 130.0425 298.1160 796.9080
## [218] 180.6210 285.7050 456.2880 62.0025 13.1670 90.8250 183.0360
## [225] 655.5465 155.6520 571.4100 532.7280 170.8770 33.3585 794.6505
## [232] 310.0440 545.3700 195.5940 91.4025 232.1550 69.4050 94.1745
## [239] 235.6830 125.5170 195.7200 263.1300 788.5080 399.7560 256.4100
## [246] 94.1850 326.4240 536.9910 439.8975 369.4950 30.2190 99.7500
## [253] 494.7600 137.0040 69.6675 163.2330 135.4500 276.9480 709.3170
## [260] 69.0900 160.8600 233.5200 57.1725 723.2400 148.9740 783.3000
## [267] 297.1080 373.1700 354.0075 44.3520 203.5530 25.2630 628.1730
## [274] 352.5795 229.1100 400.7640 745.3950 462.2100 587.6640 38.8500
## [281] 16.1070 628.9290 200.2140 350.0700 78.6030 224.4375 356.5485
## [288] 697.3680 423.1500 204.6975 65.6040 76.3560 190.1550 272.5800
## [295] 121.1280 493.7940 252.0420 93.0405 209.6220 40.9605 51.0405
## [302] 214.9980 125.6640 530.6700 295.6905 745.8360 83.4120 172.0110
## [309] 503.5590 145.5930 74.7075 146.9475 820.3650 208.6770 66.4020
## [316] 392.6475 218.0745 185.0940 216.6885 41.3910 96.1380 324.2925
## [323] 135.5760 410.5080 523.8450 395.8920 214.7460 152.7120 208.0890
## [330] 103.6350 404.3550 49.3080 77.1750 149.3625 721.9800 365.0850
## [337] 150.0975 404.6490 151.4835 411.3795 565.2150 509.4075 140.6475
## [344] 736.4385 75.5475 749.7000 191.2470 141.7500 1042.6500 379.9215
## [351] 402.2655 255.1500 31.7520 374.3880 394.2750 1002.1200 86.6250
## [358] 78.7185 680.0640 793.5480 209.5590 461.2860 173.2080 343.0560
## [365] 484.9740 150.7800 203.1750 193.0110 128.0160 441.6930 265.1040
## [372] 352.2225 507.6750 334.3410 701.8515 407.3160 99.3300 345.7860
## [379] 55.8810 523.3725 314.5380 214.9350 79.6110 294.6510 339.3600
## [386] 510.9615 133.9170 253.5120 398.4750 80.6610 548.7300 83.7270
## [393] 406.8750 284.9175 128.4255 258.6780 181.8180 248.4090 194.1240
## [400] 14.6790 208.6875 718.7565 282.4920 72.3975 288.5820 237.4260
## [407] 125.0550 359.2050 45.9270 110.0925 81.3960 427.8120 100.9155
## [414] 190.5960 85.5855 120.1620 185.3670 121.5900 264.7575 1020.7050
## [421] 213.5280 17.0940 383.7645 390.7995 65.7405 353.1675 951.8250
## [428] 145.0680 90.8670 147.7980 702.2190 49.8120 937.8180 348.3060
## [435] 214.1370 71.5680 343.2240 91.5600 742.8120 843.0345 13.4190
## [442] 140.3850 20.1075 290.4300 144.0810 28.4235 41.0760 470.6730
## [449] 138.6630 333.9525 26.2500 87.2340 155.1900 731.4300 833.5950
## [456] 488.9850 37.6845 212.7300 767.0250 310.5900 23.7510 269.5350
## [463] 572.7750 273.0525 233.2260 22.6590 103.7820 527.7510 168.2100
## [470] 452.8650 609.5880 338.3100 205.3170 174.6150 353.0940 360.8850
## [477] 40.5300 554.1480 344.4000 194.9850 633.9900 388.2900 207.8580
## [484] 431.4450 156.0300 24.1080 734.0760 72.8700 206.4300 212.6880
## [491] 127.2600 209.7690 637.7280 132.7620 568.5120 103.0365 432.7680
## [498] 77.6685 33.4950 145.7400 195.9510 92.8725 203.1120 152.7750
## [505] 529.5150 321.7725 100.4850 666.9390 225.2775 398.9580 731.6925
## [512] 429.1665 54.0435 288.0150 206.7975 72.9330 377.5800 143.9865
## [519] 523.9710 235.8720 132.0270 514.7730 479.9025 164.6820 125.7060
## [526] 570.7800 926.9505 160.2090 728.1120 240.9750 154.1295 148.6800
## [533] 122.5245 77.6580 102.8370 306.8100 551.1240 96.6420 79.6740
## [540] 84.7560 118.2510 74.7600 163.0020 308.9100 575.9775 270.5850
## [547] 416.1780 180.4005 513.2295 550.3680 139.9230 142.0020 118.0620
## [554] 151.2840 1034.4600 262.4580 228.1230 203.9310 936.6000 356.3280
## [561] 469.4130 208.4250 852.7050 517.9650 621.2430 586.9710 543.7530
## [568] 430.7100 280.0350 74.4555 152.0190 451.0275 597.6285 253.2600
## [575] 133.4340 269.9340 145.9710 85.7430 326.2560 195.2580 75.9360
## [582] 198.6390 217.1820 164.8710 226.0650 625.9050 76.7550 293.1390
## [589] 178.1640 47.8590 236.8800 304.9200 46.6830 164.4300 440.9370
## [596] 193.4625 147.6720 68.2395 814.3800 343.4130 381.3915 133.3500
## [603] 394.3275 209.1180 32.1405 121.5690 30.4080 935.2665 293.6430
## [610] 84.9765 708.2250 365.9040 457.3800 461.5275 620.7390 273.7980
## [617] 225.7920 96.1905 695.2365 874.1250 95.9175 165.6480 127.8270
## [624] 867.0900 167.8950 12.6945 673.9950 246.6765 175.9170 314.0550
## [631] 251.7165 697.9350 212.7825 48.5100 92.5575 165.1230 311.1885
## [638] 743.8200 116.9070 609.1680 63.2625 182.9520 442.3230 35.3115
## [645] 32.5290 259.7700 397.2150 351.6030 764.1900 352.6740 252.7560
## [652] 49.4235 104.6745 277.6725 146.6325 58.2225 135.3555 125.9790
## [659] 370.1250 914.5500 207.4800 204.2460 181.8810 75.4740 300.5730
## [666] 85.3020 588.4200 196.1400 231.2415 282.5760 477.5400 470.9880
## [673] 308.5740 618.9750 305.5500 41.4540 36.5505 310.7160 45.1080
## [680] 145.4040 103.1100 136.1430 667.3800 153.0480 211.3650 663.2955
## [687] 404.5440 510.6150 539.3430 497.0700 458.6925 113.5680 261.1980
## [694] 657.5310 1023.7500 507.4440 101.8080 207.5850 760.4415 835.2855
## [701] 527.5095 180.6000 72.4290 131.2080 80.9550 507.9060 317.2260
## [708] 733.6035 130.8825 187.3200 525.2310 37.6110 142.9470 110.1240
## [715] 187.8660 856.4535 138.9780 270.2595 98.0280 239.4000 175.0455
## [722] 732.2700 408.4920 383.5230 93.7440 176.4000 20.6850 557.7180
## [729] 56.4060 860.4750 596.8200 616.9800 769.1040 887.9220 408.7335
## [736] 89.0715 150.4230 79.1490 266.0280 40.3410 684.9150 55.2825
## [743] 116.1405 597.0405 143.2200 182.9100 384.7200 267.3405 817.2360
## [750] 300.2160 608.0760 197.9250 232.6380 810.6000 757.3650 536.5920
## [757] 56.1225 233.1000 801.8640 239.5890 86.2470 401.6880 72.0090
## [764] 401.2680 631.1445 499.7265 55.0410 137.8650 151.5150 480.0285
## [771] 98.0490 132.5625 830.3715 183.1200 397.9920 32.1510 369.6840
## [778] 53.3400 548.1630 603.8760 57.6975 190.4805 432.9885 48.7305
## [785] 287.9100 1022.3850 680.6100 97.8810 57.0780 63.9135 257.1450
## [792] 97.4190 455.1225 144.9630 253.6800 495.3165 462.6720 714.3255
## [799] 325.3740 195.6780 210.9660 18.6375 652.8900 90.3000 422.7300
## [806] 341.0925 99.9075 408.4080 446.9640 333.9840 284.5920 403.8720
## [813] 247.5900 222.1380 100.1280 10.6785 216.4365 441.5880 92.4420
## [820] 681.4395 130.0320 681.9750 779.3100 88.7040 262.7940 99.5400
## [827] 95.8650 299.3655 54.9990 202.3350 281.1690 586.6350 184.0860
## [834] 163.6110 63.3150 82.8870 31.2270 22.3860 295.4070 76.9230
## [841] 23.4990 688.7160 624.3300 77.8050 206.8080 390.9465 554.2950
## [848] 503.7375 345.0195 177.4080 118.9020 362.8170 450.1035 90.5835
## [855] 26.7960 106.5960 375.3645 250.7085 106.5015 760.4520 131.9220
## [862] 76.5765 271.2780 182.4270 59.3250 225.0150 561.0780 97.8180
## [869] 548.1840 54.9675 41.7375 756.1680 101.6400 348.7050 85.5120
## [876] 335.8950 175.0140 335.0130 92.2950 771.4350 102.3960 807.6600
## [883] 439.2150 486.4440 485.5725 148.9950 317.8350 832.9440 446.4390
## [890] 297.8010 629.1600 331.1280 423.7380 193.0740 145.5825 84.7455
## [897] 122.4720 329.1960 888.4050 435.1200 167.0340 514.6050 91.8225
## [904] 235.7460 782.2080 431.2560 313.7400 223.5870 44.9925 397.6140
## [911] 217.2555 82.7190 338.2155 103.1310 26.7330 611.0790 221.8860
## [918] 57.8760 92.7255 374.4090 833.9625 53.1510 629.4960 175.0350
## [925] 781.6200 397.8450 270.0180 579.8415 469.7700 290.0835 360.9270
## [932] 279.3840 943.2990 479.6400 266.6475 74.0880 690.0180 176.9250
## [939] 56.4690 188.0025 222.0120 125.7165 68.9850 88.3680 415.2330
## [946] 312.8895 477.1305 289.9260 165.9000 932.3370 96.5790 43.8690
## [953] 16.2750 304.9830 69.9930 80.3670 314.6850 255.1815 49.7700
## [960] 181.0725 888.6150 271.2885 640.0380 252.2520 180.8730 104.8320
## [967] 313.5720 167.5800 26.7225 71.1585 250.2780 244.2300 921.1860
## [974] 734.7060 708.3195 334.4775 30.9960 520.8000 864.5700 633.1080
## [981] 296.9400 804.3000 121.8630 183.6450 63.9975 42.3675 1022.4900
## [988] 33.4320 69.1110 649.2990#combine year and month into one column
unite(sales_df, Date.Time, c(Date, Time))drop <- c("Time")
#dro time column
sales_df = sales_df[,!(names(sales_df) %in% drop)]Checking for outliars
sales_df$Unit.price <- as.numeric(sales_df$Unit.price)
sales_df$Quantity <- as.numeric(sales_df$Quantity)
sales_df$Tax <- as.numeric(sales_df$Tax)
sales_df$cogs <- as.numeric(sales_df$cogs)
sales_df$gross.margin.percentage <- as.numeric(sales_df$gross.margin.percentage)
sales_df$gross.income <- as.numeric(sales_df$gross.income)
sales_df$Rating <- as.numeric(sales_df$Rating)
sales_df$Total <- as.numeric(sales_df$Total)boxplot(sales_df[6:8])
boxplot(sales_df[12])
boxplot(sales_df[13:15])
The data has outliers that will be retained
UNIVARIATE ANALYSIS
num_df <- subset(sales_df, select = c(6, 7, 8, 12, 13, 14, 15))
num_dfHistograms
#hist plot
for (i in 1:ncol(num_df)){
hist(num_df[[i]], main=paste("Plot ", i), xlab = paste("Values Plot",i)) # Create multiple histigram plots
box(lty = "solid")
}
Bar Charts
#Bar Charts
plot(factor(sales_df$"Branch"), xlab="Branch", ylab="Count", col="Cornflowerblue",
main="Branch")
Supermarket Branch A was the most popular
#Bar Charts
plot(factor(sales_df$"Customer.type"), xlab="Customer.type", ylab="Count", col="Cornflowerblue",
main="Customer.type")
Normal and member customer types shopped an equal number of times
#Bar Charts
plot(factor(sales_df$"Gender"), xlab="Gender", ylab="Count", col="Cornflowerblue",
main="Gender")
There was an equal number of Female and Male shoppers
#Bar Charts
plot(factor(sales_df$"Product.line"), col="Cornflowerblue",
main="Product.line", las=2, cex.names = 0.9, horiz= T)
Fashion Accessories was the most common product
#Bar Charts
plot(factor(sales_df$"Payment"), xlab="Payment", ylab="Count", col="Cornflowerblue", main="Payment")
Most customers preferred E wallet and cash as payment methods
Measures of Symmetry
print("skewness")## [1] "skewness"skewness(num_df[])## Unit.price Quantity Tax
## 0.007066827 0.012921628 0.891230392
## gross.margin.percentage gross.income Rating
## NaN 0.891230392 0.008996129
## Total
## 0.891230392print("kurtosis")## [1] "kurtosis"kurtosis(num_df[])## Unit.price Quantity Tax
## 1.781499 1.784528 2.912530
## gross.margin.percentage gross.income Rating
## NaN 2.912530 1.848169
## Total
## 2.912530Measures of Dispersion
print("Variance")## [1] "Variance"var(num_df[])## Unit.price Quantity Tax
## Unit.price 701.9653313 0.83477848 196.6683401
## Quantity 0.8347785 8.54644645 24.1495704
## Tax 196.6683401 24.14957038 137.0965941
## gross.margin.percentage 0.0000000 0.00000000 0.0000000
## gross.income 196.6683401 24.14957038 137.0965941
## Rating -0.3996675 -0.07945646 -0.7333003
## Total 4130.0351420 507.14097799 2879.0284770
## gross.margin.percentage gross.income Rating
## Unit.price 0 196.6683401 -0.39966752
## Quantity 0 24.1495704 -0.07945646
## Tax 0 137.0965941 -0.73330028
## gross.margin.percentage 0 0.0000000 0.00000000
## gross.income 0 137.0965941 -0.73330028
## Rating 0 -0.7333003 2.95351823
## Total 0 2879.0284770 -15.39930581
## Total
## Unit.price 4130.03514
## Quantity 507.14098
## Tax 2879.02848
## gross.margin.percentage 0.00000
## gross.income 2879.02848
## Rating -15.39931
## Total 60459.59802BIVARIATE ANALYSIS
# Correlation matrix
cor(num_df)## Warning in cor(num_df): the standard deviation is zero## Unit.price Quantity Tax
## Unit.price 1.000000000 0.01077756 0.6339621
## Quantity 0.010777564 1.00000000 0.7055102
## Tax 0.633962089 0.70551019 1.0000000
## gross.margin.percentage NA NA NA
## gross.income 0.633962089 0.70551019 1.0000000
## Rating -0.008777507 -0.01581490 -0.0364417
## Total 0.633962089 0.70551019 1.0000000
## gross.margin.percentage gross.income Rating
## Unit.price NA 0.6339621 -0.008777507
## Quantity NA 0.7055102 -0.015814905
## Tax NA 1.0000000 -0.036441705
## gross.margin.percentage 1 NA NA
## gross.income NA 1.0000000 -0.036441705
## Rating NA -0.0364417 1.000000000
## Total NA 1.0000000 -0.036441705
## Total
## Unit.price 0.6339621
## Quantity 0.7055102
## Tax 1.0000000
## gross.margin.percentage NA
## gross.income 1.0000000
## Rating -0.0364417
## Total 1.0000000#scatter plot
cor <-plot(num_df)
#heatmap
heatmaply(num_df)DIMENSIONALITY REDUCTION
# Confirm the changes made
str(sales_df)## 'data.frame': 1000 obs. of 15 variables:
## $ Invoice.ID : chr "750-67-8428" "226-31-3081" "631-41-3108" "123-19-1176" ...
## $ Branch : chr "A" "C" "A" "A" ...
## $ Customer.type : chr "Member" "Normal" "Normal" "Member" ...
## $ Gender : chr "Female" "Female" "Male" "Male" ...
## $ Product.line : chr "Health and beauty" "Electronic accessories" "Home and lifestyle" "Health and beauty" ...
## $ Unit.price : num 74.7 15.3 46.3 58.2 86.3 ...
## $ Quantity : num 7 5 7 8 7 7 6 10 2 3 ...
## $ Tax : num 26.14 3.82 16.22 23.29 30.21 ...
## $ Date : chr "1/5/2019" "3/8/2019" "3/3/2019" "1/27/2019" ...
## $ Payment : chr "Ewallet" "Cash" "Credit card" "Ewallet" ...
## $ cogs : num 522.8 76.4 324.3 465.8 604.2 ...
## $ gross.margin.percentage: num 4.76 4.76 4.76 4.76 4.76 ...
## $ gross.income : num 26.14 3.82 16.22 23.29 30.21 ...
## $ Rating : num 9.1 9.6 7.4 8.4 5.3 4.1 5.8 8 7.2 5.9 ...
## $ Total : num 549 80.2 340.5 489 634.4 ...#changing datatypes to factor
sales_df$Branch <- as.factor(sales_df$Branch)
sales_df$Customer.type <- as.factor(sales_df$Customer.type)
sales_df$Gender <- as.factor(sales_df$Gender)
sales_df$Product.line <- as.factor(sales_df$Product.line)
sales_df$Payment <- as.factor(sales_df$Payment)
sales_df$Date <- as.factor(sales_df$Date)
sales_df$Invoice.ID <- as.factor(sales_df$Invoice)
# Convert our variables from factor to numeric datatype
sales_df$Branch <- as.numeric(sales_df$Branch)
sales_df$Customer.type <- as.numeric(sales_df$Customer.type)
sales_df$Gender <- as.numeric(sales_df$Gender)
sales_df$Product.line <- as.numeric(sales_df$Product.line)
sales_df$Payment <- as.numeric(sales_df$Payment)
sales_df$Date <- as.numeric(sales_df$Date)
sales_df$Invoice.ID <- as.numeric(sales_df$Invoice.ID)# Confirm the changes made
str(sales_df)## 'data.frame': 1000 obs. of 15 variables:
## $ Invoice.ID : num 815 143 654 19 340 734 316 265 703 727 ...
## $ Branch : num 1 3 1 1 1 3 1 3 1 2 ...
## $ Customer.type : num 1 2 2 1 2 2 1 2 1 1 ...
## $ Gender : num 1 1 2 2 2 2 1 1 1 1 ...
## $ Product.line : num 4 1 5 4 6 1 1 5 4 3 ...
## $ Unit.price : num 74.7 15.3 46.3 58.2 86.3 ...
## $ Quantity : num 7 5 7 8 7 7 6 10 2 3 ...
## $ Tax : num 26.14 3.82 16.22 23.29 30.21 ...
## $ Date : num 27 88 82 20 58 77 49 48 2 44 ...
## $ Payment : num 3 1 2 3 3 3 3 3 2 2 ...
## $ cogs : num 522.8 76.4 324.3 465.8 604.2 ...
## $ gross.margin.percentage: num 4.76 4.76 4.76 4.76 4.76 ...
## $ gross.income : num 26.14 3.82 16.22 23.29 30.21 ...
## $ Rating : num 9.1 9.6 7.4 8.4 5.3 4.1 5.8 8 7.2 5.9 ...
## $ Total : num 549 80.2 340.5 489 634.4 ...# PCA is trying to group things by maximizing variance there is no point in retaining these variables. They can easily be removed with
df_f <- sales_df[,apply(sales_df, 2, var, na.rm=TRUE) != 0]#we then pass df to the prcomp(). We also set two arguments, center and scale,
# to be TRUE then preview our object with summary
sales_pca<- prcomp(df_f[1:14], center = TRUE, scale. = T)
sales_pca## Standard deviations (1, .., p=14):
## [1] 2.220476e+00 1.079209e+00 1.033424e+00 1.026168e+00 1.019098e+00
## [6] 9.938777e-01 9.756570e-01 9.560176e-01 9.513337e-01 9.470558e-01
## [11] 2.993811e-01 3.000178e-16 1.639206e-16 1.251713e-16
##
## Rotation (n x k) = (14 x 14):
## PC1 PC2 PC3 PC4 PC5
## Invoice.ID -0.01342536 -0.42261553 -0.239822705 -0.47178372 0.009672586
## Branch -0.02240796 0.51283763 -0.243770773 -0.00101976 -0.071324038
## Customer.type 0.01232173 -0.32575192 -0.606332779 -0.12613319 0.125694174
## Gender 0.02813404 -0.44375402 -0.141689100 0.45171769 -0.259547012
## Product.line -0.01759696 -0.25643189 0.551284907 -0.28115036 -0.325285877
## Unit.price -0.29113185 -0.02683075 -0.071091946 0.36218894 -0.449682810
## Quantity -0.32436792 0.01669313 0.053810182 -0.30128241 0.398171419
## Tax -0.44928671 -0.01053368 -0.007796897 0.01265254 0.005865603
## Date 0.01580055 -0.08112857 0.071809742 0.48749122 0.611788893
## Payment 0.00772114 -0.42259774 0.169252962 0.12944245 0.177433664
## cogs -0.44928671 -0.01053368 -0.007796897 0.01265254 0.005865603
## gross.income -0.44928671 -0.01053368 -0.007796897 0.01265254 0.005865603
## Rating 0.01867790 0.05469315 -0.386567743 -0.03768018 -0.197948631
## Total -0.44928671 -0.01053368 -0.007796897 0.01265254 0.005865603
## PC6 PC7 PC8 PC9 PC10
## Invoice.ID -0.221369231 0.066264489 -0.498969214 -0.3766090777 0.310856568
## Branch -0.123130266 -0.502290744 -0.517077556 -0.0529771025 -0.366392983
## Customer.type -0.172427784 -0.068665451 0.146948767 0.6234705651 -0.222682007
## Gender -0.069710136 0.068724164 0.044716547 -0.4447830504 -0.548977936
## Product.line 0.105618930 0.087828983 -0.357049172 0.4018538199 -0.370556861
## Unit.price -0.131721607 -0.018360193 -0.139938148 0.2062328081 0.368100556
## Quantity 0.152685039 0.032036406 0.152360545 -0.1899480136 -0.333215828
## Tax 0.004335007 0.004028264 0.004454282 -0.0008560926 -0.002548641
## Date 0.036380099 0.286896868 -0.513395815 0.1692854677 0.012008722
## Payment 0.332117945 -0.774868715 0.020187985 0.0010125356 0.182572703
## cogs 0.004335007 0.004028264 0.004454282 -0.0008560926 -0.002548641
## gross.income 0.004335007 0.004028264 0.004454282 -0.0008560926 -0.002548641
## Rating 0.858908660 0.204942592 -0.160387976 -0.0071612714 0.028533248
## Total 0.004335007 0.004028264 0.004454282 -0.0008560926 -0.002548641
## PC11 PC12 PC13 PC14
## Invoice.ID -0.013989496 -4.293783e-17 2.673794e-18 -1.597958e-17
## Branch -0.010317761 3.698763e-17 3.782064e-17 5.931128e-17
## Customer.type -0.006539828 -6.110573e-17 1.077033e-16 5.326176e-17
## Gender 0.003270700 5.519765e-17 1.691874e-17 9.008435e-17
## Product.line -0.003045903 7.316011e-17 1.595629e-16 -3.752342e-17
## Unit.price -0.600643389 -5.164279e-16 -5.499326e-17 4.561093e-19
## Quantity -0.669054452 -4.901955e-16 -1.422751e-16 1.339127e-16
## Tax 0.218433331 7.232924e-02 -2.228567e-01 -8.337286e-01
## Date -0.008255564 7.198822e-17 -7.285992e-20 1.930345e-17
## Payment -0.001297822 -1.251341e-17 1.057872e-17 3.569691e-18
## cogs 0.218433331 -3.458485e-01 7.918346e-01 5.819568e-02
## gross.income 0.218433331 -5.104462e-01 -5.686235e-01 4.075683e-01
## Rating 0.017505244 -6.598585e-17 -5.965787e-17 -7.242685e-17
## Total 0.218433331 7.839655e-01 -3.544120e-04 3.679646e-01#to have a look at your PCA object
str(sales_pca)## List of 5
## $ sdev : num [1:14] 2.22 1.08 1.03 1.03 1.02 ...
## $ rotation: num [1:14, 1:14] -0.0134 -0.0224 0.0123 0.0281 -0.0176 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:14] "Invoice.ID" "Branch" "Customer.type" "Gender" ...
## .. ..$ : chr [1:14] "PC1" "PC2" "PC3" "PC4" ...
## $ center : Named num [1:14] 500.5 1.99 1.5 1.5 3.45 ...
## ..- attr(*, "names")= chr [1:14] "Invoice.ID" "Branch" "Customer.type" "Gender" ...
## $ scale : Named num [1:14] 288.819 0.818 0.5 0.5 1.715 ...
## ..- attr(*, "names")= chr [1:14] "Invoice.ID" "Branch" "Customer.type" "Gender" ...
## $ x : num [1:1000, 1:14] -2.04 2.32 -0.12 -1.45 -2.73 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : NULL
## .. ..$ : chr [1:14] "PC1" "PC2" "PC3" "PC4" ...
## - attr(*, "class")= chr "prcomp"# We will now plot our pca
ggbiplot(sales_pca)
ggbiplot(sales_pca, labels=rownames(df_f), obs.scale = 0.5, var.scale = 100)
# We now plot tosee the variables that contribute PCA1 and PCA2
ggbiplot(sales_pca, obs.scale = 1, var.scale = 1,
groups = sales_pca$Total, ellipse = TRUE, circle = TRUE,ellipse.prob = 0.68) +
scale_color_discrete(name = '') +
theme(legend.direction = 'horizontal', legend.position = 'top')
the Invoice ID, Product Line, Gender, Customer type,Payment and Date
contribute to PCA1 while gross income, quantity, and Unit price
contribute to PCA2
Part 2: Feature Selection This section will require feature selection through the use of the unsupervised learning methods. Analysis will be performed and insights provided on the features that contribute the most information to the dataset.
# Calculating the correlation matrix
# ---
correlationMatrix <- cor(df_f)
# Find attributes that are highly correlated
# ---
highlyCorrelated <- findCorrelation(correlationMatrix, cutoff=0.75)
# Highly correlated attributes
# ---
#
highlyCorrelated## [1] 8 11 12names(df_f[,highlyCorrelated])## [1] "Tax" "cogs" "gross.income"Tax, cogs and gross income are the variables that are the highly correlated
# We can remove the variables with a higher correlation
# and comparing the results graphically as shown below
# ---
#
# Removing Redundant Features
# ---
#
df2<-df_f[-highlyCorrelated]
# Performing our graphical comparison
# ---
#
par(mfrow = c(1, 2))
corrplot(correlationMatrix, order = "hclust")
corrplot(cor(df2), order = "hclust")
The second image shows a reduced correlation after feature selection. These are the variables that will be used in modelling for the best results
Using Wrapper Methods to see whether it will yield the same or better results
# Sequential forward greedy search (default)
# ---
#
out = clustvarsel(df_f, G = 1:16)
out## ------------------------------------------------------
## Variable selection for Gaussian model-based clustering
## Stepwise (forward/backward) greedy search
## ------------------------------------------------------
##
## Variable proposed Type of step BICclust Model G BICdiff Decision
## Quantity Add -4192.156 E 9 804.0515 Accepted
## Product.line Add -5948.616 EEV 14 2173.5392 Accepted
## Payment Add -9067.256 VEV 11 -639.4635 Rejected
## Product.line Remove -4192.156 E 9 2173.5392 Rejected
##
## Selected subset: Quantity, Product.line#model summary
summary(out)## Length Class Mode
## variables 14 -none- character
## subset 2 -none- numeric
## steps.info 7 data.frame list
## search 1 -none- character
## direction 1 -none- character
## model 16 Mclust listQuality and Product line are features that have been selected for subsetting and use in modelling
# The selection algorithm would indicate that the subset
# we use for the clustering model is composed of variables X1 and X2
# and that other variables should be rejected.
# Having identified the variables that we use, we proceed to build the clustering model:
# ---
#
Subset1 = df_f[,out$subset]
mod = Mclust(Subset1, G = 1:16)
summary(mod)## ----------------------------------------------------
## Gaussian finite mixture model fitted by EM algorithm
## ----------------------------------------------------
##
## Mclust VEV (ellipsoidal, equal shape) model with 7 components:
##
## log-likelihood n df BIC ICL
## -4093.452 1000 35 -8428.676 -8762.808
##
## Clustering table:
## 1 2 3 4 5 6 7
## 141 162 255 71 121 200 50plot(mod,c("classification"))
Conclusion:
the Invoice ID, Product Line, Gender, Customer type,Payment and Date contribute to PCA1 while gross income, quantity, and Unit price contribute to PCA2
Quantity and product line were the selected features by wrapping methods and
Most customers preferred E wallet and cash as payment methods
Supermarket Branch A was the most popular
There was an equal number of Female and Male shoppers
Normal and member customer types shopped an equal number of times
Fashion Accessories was the most common product
Reccommendation
Using the variables that contribute to PCA1 and PCA2 for modelling. It reduces the time and storage space required. It helps Remove multi-collinearity which improves the interpretation of the parameters of the machine learning model. It becomes easier to visualize the data when reduced to very low dimensions.
Customize the shopping experience for the customers with the above characteristics