# Load text file into local variable called 'data'
data = read.delim("C:\\Users\\Srirama Bonam\\Documents\\Mktg Data Analytics.txt", header = FALSE, sep = '\t', dec = '.')
data
## V1 V2 V3
## 1 760 25.00 2009-11-06
## 2 860 50.00 2012-09-28
## 3 1200 100.00 2005-10-25
## 4 1420 50.00 2009-07-09
## 5 1940 70.00 2013-01-25
## 6 1960 40.00 2013-10-29
## 7 2620 30.00 2006-03-09
## 8 3050 50.00 2007-04-12
## 9 3120 150.00 2006-11-08
## 10 3260 45.00 2010-10-20
## 11 3510 35.00 2013-05-04
## 12 3970 30.00 2007-06-07
## 13 4000 20.00 2005-11-25
## 14 4180 20.00 2010-09-22
## 15 4390 30.00 2011-04-15
## 16 4750 60.00 2013-12-02
## 17 4840 30.00 2005-10-14
## 18 4910 15.00 2006-12-13
## 19 4950 50.00 2010-05-19
## 20 4970 30.00 2006-12-01
## 21 5250 50.00 2005-12-20
## 22 5480 100.00 2014-06-06
## 23 5990 30.00 2006-03-17
## 24 6240 100.00 2006-11-09
## 25 6390 15.00 2005-10-25
## 26 6570 30.00 2012-11-10
## 27 6600 50.00 2005-09-23
## 28 6690 20.00 2010-03-10
## 29 6690 15.00 2010-11-04
## 30 6690 15.00 2013-12-10
## 31 6710 80.00 2013-05-03
## 32 6940 30.00 2013-12-31
## 33 7000 10.00 2013-04-05
## 34 7210 40.00 2009-10-28
## 35 7450 25.00 2008-05-23
## 36 7560 40.00 2008-12-31
## 37 7660 30.00 2006-06-03
## 38 7660 50.00 2013-03-28
## 39 7730 150.00 2010-10-22
## 40 7740 100.00 2007-08-09
## 41 7790 20.00 2014-07-04
## 42 8060 30.00 2007-04-12
## 43 8240 30.00 2007-06-07
## 44 8190 20.00 2006-12-09
## 45 8200 20.00 2011-07-13
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## 47 8560 100.00 2009-10-02
## 48 8590 25.00 2012-06-03
## 49 8620 60.00 2008-06-14
## 50 8720 50.00 2005-08-23
## 51 8950 30.00 2011-12-20
## 52 9010 15.00 2005-10-25
## 53 9230 30.00 2013-10-30
## 54 9250 25.00 2005-08-02
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## 105 13890 30.00 2011-04-27
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## 202 47850 152.45 2006-01-28
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## 222 52100 100.00 2012-12-07
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## 2005 74430 35.00 2007-05-31
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## 33214 9720 50.00 2013-04-11
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## 33270 12690 35.00 2010-09-10
## 33271 74420 30.00 2007-05-31
## 33272 120620 50.00 2013-10-25
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## 33329 77740 65.00 2012-03-08
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## 33331 101160 30.00 2009-12-12
## 33332 142150 30.00 2010-06-18
## 33333 128290 30.00 2012-03-08
## [ reached 'max' / getOption("max.print") -- omitted 17910 rows ]
# Display what has been loaded
head(data)
## V1 V2 V3
## 1 760 25 2009-11-06
## 2 860 50 2012-09-28
## 3 1200 100 2005-10-25
## 4 1420 50 2009-07-09
## 5 1940 70 2013-01-25
## 6 1960 40 2013-10-29
summary(data)
## V1 V2 V3
## Min. : 10 Min. : 5.00 2013-12-31: 864
## 1st Qu.: 57720 1st Qu.: 25.00 2006-12-31: 584
## Median :102440 Median : 30.00 2012-12-31: 583
## Mean :108935 Mean : 62.34 2011-12-31: 510
## 3rd Qu.:160525 3rd Qu.: 60.00 2008-12-31: 503
## Max. :264200 Max. :4500.00 2014-12-31: 485
## (Other) :47714
# Add headers and interpret the last column as a date, extract year of purchase
colnames(data) = c('customer_id', 'purchase_amount', 'date_of_purchase')
data$date_of_purchase = as.Date(data$date_of_purchase, "%Y-%m-%d")
data$year_of_purchase = as.numeric(format(data$date_of_purchase, "%Y"))
# Display the data set after transformation
head(data)
## customer_id purchase_amount date_of_purchase year_of_purchase
## 1 760 25 2009-11-06 2009
## 2 860 50 2012-09-28 2012
## 3 1200 100 2005-10-25 2005
## 4 1420 50 2009-07-09 2009
## 5 1940 70 2013-01-25 2013
## 6 1960 40 2013-10-29 2013
summary(data)
## customer_id purchase_amount date_of_purchase year_of_purchase
## Min. : 10 Min. : 5.00 Min. :2005-01-02 Min. :2005
## 1st Qu.: 57720 1st Qu.: 25.00 1st Qu.:2009-01-17 1st Qu.:2009
## Median :102440 Median : 30.00 Median :2011-11-23 Median :2011
## Mean :108935 Mean : 62.34 Mean :2011-07-14 Mean :2011
## 3rd Qu.:160525 3rd Qu.: 60.00 3rd Qu.:2013-12-29 3rd Qu.:2013
## Max. :264200 Max. :4500.00 Max. :2015-12-31 Max. :2015
# Explore the data using simple SQL statements
library(sqldf)
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
# Number of purchases per year
x = sqldf("SELECT year_of_purchase, COUNT(year_of_purchase) AS 'counter' FROM data GROUP BY 1 ORDER BY 1")
barplot(x$counter, names.arg = x$year_of_purchase)
# Average purchase amount per year
x = sqldf("SELECT year_of_purchase, AVG(purchase_amount) AS 'avg_amount' FROM data GROUP BY 1 ORDER BY 1")
barplot(x$avg_amount, names.arg = x$year_of_purchase)
# Total purchase amounts per year
x = sqldf("SELECT year_of_purchase, SUM(purchase_amount) AS 'sum_amount' FROM data GROUP BY 1 ORDER BY 1")
barplot(x$sum_amount, names.arg = x$year_of_purchase)
# All in one
x = sqldf("SELECT year_of_purchase,
COUNT(year_of_purchase) AS 'counter',
AVG(purchase_amount) AS 'avg_amount',
SUM(purchase_amount) AS 'sum_amount'
FROM data GROUP BY 1 ORDER BY 1")
print(x)
## year_of_purchase counter avg_amount sum_amount
## 1 2005 1470 55.82612 82064.4
## 2 2006 2182 52.25045 114010.5
## 3 2007 4674 49.26401 230260.0
## 4 2008 4331 53.07178 229853.9
## 5 2009 5054 50.74525 256466.5
## 6 2010 4939 58.74001 290116.9
## 7 2011 4785 63.51930 303939.8
## 8 2012 5960 62.91331 374963.3
## 9 2013 5912 67.93136 401610.2
## 10 2014 5739 75.39035 432665.2
## 11 2015 6197 77.19770 478394.1