ODD Whole Data Analysis

Introduction

The dataset subject to our analysis includes retail sales of automobiles and commercial vehicles in Turkey based on brand in monthly basis. The dataset includes data from Jan’16 to Sep’19. The dataset incorporates 12 columns. Details of the column names & description is are represented below:

• brand_name: Name of the brand
• auto_dom : Sales amount of domestic automobiles
• auto_imp : Sales amount of imported automobiles
• auto_total : Total sales amount of automobiles
• comm_dom : Sales amount of domestic commercial vehicles
• comm_imp : Sales amount of imported commercial vehicles
• comm_total : Total sales amount of commercial vehicles
• total_dom : Total sales of all domestic product vehicles
• total_imp : Total sales of all imported vehicles
• total_total: Total sales amount of all vehicles
• year : Year
• month : Month

In order to perform a healthy analysis, the dataset has been adjusted in three aspects: 1. Delete the invalid row that contains a disclaimer 2. Delete the rows brand_name = “TOPLAM:” 3. Change of “ASTON MARTIN” to “ASTON MARTIN” in order to merge the two different brand names for the same brand.

library(tidyverse)

## -- Attaching packages ---------------------------------------------------------------------------------------- tidyverse 1.2.1 --

## <U+221A> ggplot2 3.0.0     <U+221A> purrr   0.2.5
## <U+221A> tibble  1.4.2     <U+221A> dplyr   0.7.6
## <U+221A> tidyr   0.8.1     <U+221A> stringr 1.3.1
## <U+221A> readr   1.1.1     <U+221A> forcats 0.3.0

## -- Conflicts ------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(scales)

## 
## Attaching package: 'scales'

## The following object is masked from 'package:purrr':
## 
##     discard

## The following object is masked from 'package:readr':
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##     col_factor

setwd('C:/Users/Turhal/Documents/Data Science/Big Data Essentials')
raw_data = read_rds("car_data_aggregate.rds")
raw_data = subset(raw_data,subset=brand_name != 'TOPLAM:' & !startsWith(brand_name, "ODD"))
raw_data$brand_name = str_replace(raw_data$brand_name, "MARTIN", "MARTIN")
#raw_data = raw_data %>% slice(-46)
raw_data[is.na(raw_data)] <- 0

head(raw_data)

## # A tibble: 6 x 12
##   brand_name auto_dom auto_imp auto_total comm_dom comm_imp comm_total
##   <chr>         <dbl>    <dbl>      <dbl>    <dbl>    <dbl>      <dbl>
## 1 ALFA ROMEO        0       13         13        0        0          0
## 2 ASTON MAR~        0        2          2        0        0          0
## 3 AUDI              0      350        350        0        0          0
## 4 BENTLEY           0        0          0        0        0          0
## 5 BMW               0      158        158        0        0          0
## 6 CITROEN           0      134        134        0      197        197
## # ... with 5 more variables: total_dom <dbl>, total_imp <dbl>,
## #   total_total <dbl>, year <dbl>, month <dbl>

raw_data1 =  
    raw_data %>% 
    filter(auto_total > 0 & comm_total > 0) %>%
    select(brand_name,auto_total,comm_total,total_total,year) %>%
    arrange(desc(year),desc(total_total)) %>%
    filter(brand_name != "TOPLAM:") %>% 
    filter(brand_name != "ODD*") %>%
    filter(year == "2016") %>%  
    group_by(brand_name) %>%
    summarize(year_total = sum(total_total)) %>%
    arrange(desc(year_total))

raw_data1 %>% top_n(5) %>% 
    mutate(prop=percent(year_total / sum(year_total))) %>%
    ggplot(data = ., aes(x="", y=prop, fill=brand_name))+ labs(x = "", y = "", title = "Top 5 Car Sales per Brand in 2016") +  
    geom_bar(width = 1, stat = "identity") + coord_polar("y", start=0) +
    theme(plot.title=element_text(hjust=0.5,face='bold',size=16)) +  
    theme_classic() + theme(axis.line = element_blank(), axis.text = element_blank(), axis.ticks = element_blank()) +
    geom_text(aes(label=prop),position=position_stack(vjust = 0.5)) + guides(fill=guide_legend(title="Brand Name")) 

## Selecting by year_total

#Total Annual Automobile Sales of FIAT and TOYOTA
raw_data %>%
  group_by(brand_name, year) %>% 
  filter(brand_name %in% c("FIAT", "TOYOTA")) %>%
  summarize(yearly_auto_total = sum(auto_total))%>%
  ggplot(data=., aes(x=year, y=yearly_auto_total, fill=brand_name)) +
  geom_bar(stat="identity", position=position_dodge())+
  labs(x="Years", y = "Number Of Sales", fill="Brands") +
  theme_minimal()