AllOddDataAnalysis

Used This Libraries

library(readxl)
library(tidyverse)

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

## <U+221A> ggplot2 3.1.0     <U+221A> purrr   0.2.5
## <U+221A> tibble  1.4.2     <U+221A> dplyr   0.7.8
## <U+221A> tidyr   0.8.2     <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(ggplot2)

dat <- readRDS("car_data_aggregate.rds")

head and tail of excel data

head(dat)

## # 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(dat)

## # 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>

Time to make some analysis

dat %>% 
  select(brand_name,auto_dom) %>% 
  arrange(auto_dom) %>% 
  filter(auto_dom>0)

## # A tibble: 206 x 2
##    brand_name auto_dom
##    <chr>         <dbl>
##  1 FORD             91
##  2 FORD            139
##  3 FORD            153
##  4 FORD            155
##  5 FORD            229
##  6 FORD            242
##  7 FORD            280
##  8 FORD            287
##  9 FORD            304
## 10 FORD            315
## # ... with 196 more rows

On above, companies of maked car sales.

---

On the below, Some Filter Added to show sales of car clearly in April of 2017.

dat %>% 
  select(brand_name,auto_dom,comm_dom,total_imp,total_dom) %>% 
  arrange(auto_dom) %>% arrange(total_imp) %>%
  filter(comm_dom>0) %>% filter(auto_dom>0) %>% filter(total_imp >0)

## # A tibble: 78 x 5
##    brand_name auto_dom comm_dom total_imp total_dom
##    <chr>         <dbl>    <dbl>     <dbl>     <dbl>
##  1 FIAT           1979     1962       219      3941
##  2 FIAT            632      789       256      1421
##  3 FIAT           1602     1090       315      2692
##  4 FIAT           2275     2500       354      4775
##  5 FIAT           3720     1787       360      5507
##  6 FIAT           3106     1689       362      4795
##  7 FIAT           1515     1988       363      3503
##  8 FIAT           4175     3183       379      7358
##  9 FIAT           1465     1944       434      3409
## 10 FIAT           2678     2771       480      5449
## # ... with 68 more rows