LBB P4DS

This is my report of exploring retail data for my LBB

Reading Data

retail <- read.csv("data_input/retail.csv")

Data Wrangling

library(dplyr)

## 
## 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

retail <- retail %>% 
  mutate(Ship.Mode = as.factor(Ship.Mode), Category = as.factor(Category), Sub.Category = as.factor(Sub.Category), Segment = as.factor(Segment))
retail$Order.Date <- as.Date(retail$Order.Date,
                             "%m/%d/%y")
retail$Ship.Date <- as.Date(retail$Ship.Date,
                             "%m/%d/%y")
anyNA(retail)

## [1] FALSE

head(retail)

retail_clean <- retail %>% 
  select(Order.Date, Ship.Date, Ship.Mode, Segment, Category, Sub.Category, Product.Name, Sales, Quantity, Discount, Profit)
head(retail_clean)

In this section, I clean my data using DPLYR Package to make it easier and faster.

While cleaning the data, I did some changing in data type and also selecting the needed columns and removing columns that I don’t need.

Subsetting

In this section I wanna know: 1. Products that give big profit 2. Products that give big loss

retail_clean %>% 
  filter(Profit > 4500 | Profit < -4500)

Adding New Column

From the result above, I wanna make a new column about the profit status

retail_clean$Profit.Loss <- as.factor(ifelse(retail_clean$Profit < 0, "Loss", "Profit"))
head(retail_clean)

Analyzing

From the final data. I want to analyze the data.

1. Profits that are more than 2000 and is from the “Consumer” Segment
2. Profits that are more than 2000 and is from the “Coorporate” Segment
3. Profits that are more than 500 and is from the “Furniture” Category

retail_clean %>% 
  filter(Segment == "Consumer",
         Profit >= 2000)

retail_clean %>% 
  filter(Category == "Furniture",
         Profit >= 500)

retail_clean %>% 
  filter(Segment == "Consumer",
         Profit >= 500)

Summary

There are a few ways to pull a summary from a data and one of them is the “summary” function

summary(retail_clean)

##    Order.Date           Ship.Date                   Ship.Mode   
##  Min.   :2014-01-03   Min.   :2014-01-07   First Class   :1538  
##  1st Qu.:2015-05-23   1st Qu.:2015-05-27   Same Day      : 543  
##  Median :2016-06-26   Median :2016-06-29   Second Class  :1945  
##  Mean   :2016-04-30   Mean   :2016-05-03   Standard Class:5968  
##  3rd Qu.:2017-05-14   3rd Qu.:2017-05-18                        
##  Max.   :2017-12-30   Max.   :2018-01-05                        
##                                                                 
##         Segment                Category         Sub.Category 
##  Consumer   :5191   Furniture      :2121   Binders    :1523  
##  Corporate  :3020   Office Supplies:6026   Paper      :1370  
##  Home Office:1783   Technology     :1847   Furnishings: 957  
##                                            Phones     : 889  
##                                            Storage    : 846  
##                                            Art        : 796  
##                                            (Other)    :3613  
##  Product.Name           Sales              Quantity        Discount     
##  Length:9994        Min.   :    0.444   Min.   : 1.00   Min.   :0.0000  
##  Class :character   1st Qu.:   17.280   1st Qu.: 2.00   1st Qu.:0.0000  
##  Mode  :character   Median :   54.490   Median : 3.00   Median :0.2000  
##                     Mean   :  229.858   Mean   : 3.79   Mean   :0.1562  
##                     3rd Qu.:  209.940   3rd Qu.: 5.00   3rd Qu.:0.2000  
##                     Max.   :22638.480   Max.   :14.00   Max.   :0.8000  
##                                                                         
##      Profit          Profit.Loss  
##  Min.   :-6599.978   Loss  :1871  
##  1st Qu.:    1.729   Profit:8123  
##  Median :    8.666                
##  Mean   :   28.657                
##  3rd Qu.:   29.364                
##  Max.   : 8399.976                
## 

Here you can see the min, median, max, mean, and etc.

Data Aggregating

Here are some examples for aggregating

1. Table

table(retail_clean$Category, retail_clean$Segment == "Consumer")

##                  
##                   FALSE TRUE
##   Furniture        1008 1113
##   Office Supplies  2899 3127
##   Technology        896  951

2. Prop.Table

round(prop.table(table(retail_clean$Category, retail_clean$Segment))*100, digits = 2)

##                  
##                   Consumer Corporate Home Office
##   Furniture          11.14      6.46        3.62
##   Office Supplies    31.29     18.21       10.80
##   Technology          9.52      5.54        3.42

3. XTabs

# Profits each category and segment
xtabs(formula = Profit ~ Category + Segment, data = retail_clean)

##                  Segment
## Category           Consumer Corporate Home Office
##   Furniture        6991.079  7584.816    3875.378
##   Office Supplies 56330.321 40227.320   25933.160
##   Technology      70797.810 44166.998   30490.141

4. Aggregate

#  Profit's mean each category and segment
aggregate(formula = Profit ~ Category + Segment, data = retail_clean, FUN = mean)

#  Top profit's mean each segment and category
aggregate(formula = Profit ~ Category + Segment, data = retail_clean, FUN = max)

# Top loss' mean each segment and category
aggregate(formula = Profit ~ Category + Segment, data = retail_clean, FUN = min)

Maybe that is all for my little explanation of my retail data for my LBB. Thank you.