Odd-2 assignment

Ferdi Atesin

library(tidyverse)

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

## v ggplot2 3.0.0     v purrr   0.2.5
## v tibble  1.4.2     v dplyr   0.7.6
## v tidyr   0.8.1     v stringr 1.3.1
## v readr   1.1.1     v forcats 0.3.0

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

library(ggplot2)
library(lubridate)

## 
## Attaching package: 'lubridate'

## The following object is masked from 'package:base':
## 
##     date

Download raw data

we retrieve the data from github repository with analyzing it to a dataframe:

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

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>

tail(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 SSANGYONG         0       19         19        0        3          3
## 2 TATA              0        0          0        0        9          9
## 3 TOYOTA         1298      149       1447        0       34         34
## 4 VOLKSWAGEN        0     2792       2792        0     1736       1736
## 5 VOLVO             0      187        187        0        0          0
## 6 TOPLAM:        7375    15983      23358     4815     4540       9355
## # ... with 5 more variables: total_dom <dbl>, total_imp <dbl>,
## #   total_total <dbl>, year <dbl>, month <dbl>

I would like to investigate total sales trend for the best ten brand. Due to this cause, I need best ten brand in total sales.