Car Sales Data Import and Analyze

In the 2nd week; I have done analysis on ODD Car Sales Data for one month. In This week, sales data for 2016,2017 and 2018 for all months is gathered together. The analysis is done for a bigger data.

Firstly, I call the libraries that we need, download the .rds file from github and do some cleaning on the data.

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(readxl)
library(dplyr)
library(ggplot2)
library(lubridate)
## 
## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
## 
##     date
tmp<-tempfile(fileext=".rds")

download.file("https://github.com/MEF-BDA503/mef-bda503.github.io/blob/master/files/car_data_aggregate.rds?raw=true",destfile=tmp,mode = 'wb')
raw_data<-read_rds(tmp)
file.remove(tmp)
## [1] TRUE
colnames(raw_data) <- c("brand_name","auto_dom","auto_imp","auto_total","comm_dom","comm_imp","comm_total","total_dom","total_imp","total_total","year","month")
car_data <- raw_data %>% mutate_if(is.numeric,funs(ifelse(is.na(.),0,.))) 

car_data <- car_data  %>% 
  filter(!(year==2017 & month==2 & total_dom==0 & total_imp==0 & total_total==0) ) %>%
  filter(brand_name != "TOPLAM:")
car_data <- car_data %>% mutate(day=1)
car_data <- car_data %>% mutate(date=paste(year, month, day, sep="-")) %>% mutate(date= ymd(date))

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>
colnames(raw_data) <- c("brand_name","auto_dom","auto_imp","auto_total","comm_dom","comm_imp","comm_total","total_dom","total_imp","total_total","year","month")
all_car_data <- raw_data %>% mutate_if(is.numeric,funs(ifelse(is.na(.),0,.))) 

all_car_data <- all_car_data  %>% 
  filter(!(year==2017 & month==2 & total_dom==0 & total_imp==0 & total_total==0) ) %>%
  filter(brand_name != "TOPLAM:")
all_car_data <- all_car_data %>% mutate(day=1)
all_car_data <- all_car_data %>% mutate(date=paste(year, month, day, sep="-")) %>% mutate(date= ymd(date))

Focus on The Commercial Vehicles

I choose 6 brands from the dataset and explore the sales trend in months for each year in the dataset.

comm_veh=all_car_data%>%filter(comm_total>0,brand_name %in% c("RENAULT", "FIAT", "FORD", "VOLKSWAGEN","ISUZU","IVECO"),month)%>%select(brand_name,comm_total,year,month)
View(comm_veh)

CONCLUSION

As it is clearly seen from the visuals, sales trends on the commercial vehicles get peak amount on the last months of the year. Contraversially In 2018, Sales numbers on the same months are strictly decreasing.