2016/2017/2018 - OddDataAnalysis
Mercan Karacabey
19 11 2018
Used This Libraries
library(readxl)
library(tidyverse)## ── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──## ✔ ggplot2 3.1.0 ✔ purrr 0.2.5
## ✔ tibble 1.4.2 ✔ dplyr 0.7.7
## ✔ tidyr 0.8.2 ✔ stringr 1.3.1
## ✔ readr 1.1.1 ✔ forcats 0.3.0## ── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ 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 12 12 0 0 0
## 2 ASTON MAR… 0 2 2 0 0 0
## 3 AUDI 0 911 911 0 0 0
## 4 BENTLEY 0 0 0 0 0 0
## 5 BMW 0 496 496 0 0 0
## 6 CHERY 0 30 30 0 0 0
## # ... 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 SUZUKI 0 143 143 0 0 0
## 2 TOYOTA 1254 413 1667 0 451 451
## 3 VOLKSWAGEN 0 4903 4903 0 1599 1599
## 4 VOLVO 0 213 213 0 0 0
## 5 <NA> 0 0 0 0 0 0
## 6 "ODD, ver… 0 0 0 0 0 0
## # ... with 5 more variables: total_dom <dbl>, total_imp <dbl>,
## # total_total <dbl>, year <dbl>, month <dbl>Time to make some analysis
Firstly, we should learn brand names.
dat %>%
select(brand_name) ## # A tibble: 1,400 x 1
## brand_name
## <chr>
## 1 ALFA ROMEO
## 2 ASTON MARTÄ°N
## 3 AUDI
## 4 BENTLEY
## 5 BMW
## 6 CHERY
## 7 CITROEN
## 8 DACIA
## 9 DS
## 10 FERRARI
## # ... with 1,390 more rowsOn the below, automobiles which sold more than others in 2016,2017,2018.
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>%
arrange(desc(auto_total)) %>% filter(brand_name != 'TOPLAM:')## # A tibble: 1,389 x 8
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 RENAULT 18490 11492 0 9586 11492 2016 12
## 2 RENAULT 16240 11420 0 7634 11420 2017 12
## 3 VOLKSWAGEN 14539 0 0 18082 0 2016 12
## 4 VOLKSWAGEN 13003 0 0 16293 0 2017 12
## 5 RENAULT 11975 7692 0 6292 7692 2017 11
## 6 RENAULT 11275 7292 0 5449 7292 2016 5
## 7 RENAULT 11135 7905 0 4328 7905 2017 6
## 8 RENAULT 10831 6905 0 5855 6905 2016 11
## 9 RENAULT 10633 7297 0 4778 7297 2016 4
## 10 VOLKSWAGEN 10587 0 0 14359 0 2016 11
## # ... with 1,379 more rowsresult1 <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>%
arrange(desc(auto_total)) %>% filter(brand_name != 'TOPLAM:')
Analysis for ALFA ROMEO
Most automobile sales for Alfa Romeo.
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'ALFA ROMEO') %>% arrange(desc(auto_total))## # A tibble: 30 x 8
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ALFA ROMEO 149 0 0 149 0 2016 3
## 2 ALFA ROMEO 75 0 0 75 0 2016 2
## 3 ALFA ROMEO 68 0 0 68 0 2016 5
## 4 ALFA ROMEO 59 0 0 59 0 2017 3
## 5 ALFA ROMEO 58 0 0 58 0 2016 4
## 6 ALFA ROMEO 53 0 0 53 0 2016 11
## 7 ALFA ROMEO 43 0 0 43 0 2017 2
## 8 ALFA ROMEO 38 0 0 38 0 2016 12
## 9 ALFA ROMEO 33 0 0 33 0 2016 7
## 10 ALFA ROMEO 32 0 0 32 0 2016 9
## # ... with 20 more rowsFor 2018,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'ALFA ROMEO') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 8 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ALFA ROMEO 32 0 0 32 0 2018 4
## 2 ALFA ROMEO 25 0 0 25 0 2018 5
## 3 ALFA ROMEO 15 0 0 15 0 2018 8
## 4 ALFA ROMEO 14 0 0 14 0 2018 7
## 5 ALFA ROMEO 14 0 0 14 0 2018 2
## 6 ALFA ROMEO 13 0 0 13 0 2018 9
## 7 ALFA ROMEO 10 0 0 10 0 2018 6
## 8 ALFA ROMEO 5 0 0 5 0 2018 1
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'ALFA ROMEO') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2017,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'ALFA ROMEO') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 12 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ALFA ROMEO 59 0 0 59 0 2017 3
## 2 ALFA ROMEO 43 0 0 43 0 2017 2
## 3 ALFA ROMEO 27 0 0 27 0 2017 12
## 4 ALFA ROMEO 27 0 0 27 0 2017 4
## 5 ALFA ROMEO 26 0 0 26 0 2017 11
## 6 ALFA ROMEO 24 0 0 24 0 2017 6
## 7 ALFA ROMEO 24 0 0 24 0 2017 5
## 8 ALFA ROMEO 21 0 0 21 0 2017 10
## 9 ALFA ROMEO 19 0 0 19 0 2017 8
## 10 ALFA ROMEO 18 0 0 18 0 2017 7
## 11 ALFA ROMEO 17 0 0 17 0 2017 1
## 12 ALFA ROMEO 15 0 0 15 0 2017 9
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'ALFA ROMEO') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2016,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'ALFA ROMEO') %>% filter(year==2016) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 10 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ALFA ROMEO 149 0 0 149 0 2016 3
## 2 ALFA ROMEO 75 0 0 75 0 2016 2
## 3 ALFA ROMEO 68 0 0 68 0 2016 5
## 4 ALFA ROMEO 58 0 0 58 0 2016 4
## 5 ALFA ROMEO 53 0 0 53 0 2016 11
## 6 ALFA ROMEO 38 0 0 38 0 2016 12
## 7 ALFA ROMEO 33 0 0 33 0 2016 7
## 8 ALFA ROMEO 32 0 0 32 0 2016 9
## 9 ALFA ROMEO 31 0 0 31 0 2016 8
## 10 ALFA ROMEO 12 0 0 12 0 2016 1
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'ALFA ROMEO') %>% filter(year==2016) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
Analysis for ASTON MARTIN
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'ASTON MARTÄ°N') %>% arrange(desc(auto_total))## # A tibble: 1 x 8
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ASTON MARTÄ°N 2 0 0 2 0 2016 1Only, was done sale in 2016-Jan for Aston Martin.
Analysis for AUDI
Most automobile sales for Audi.
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'AUDI') %>% arrange(desc(auto_total))## # A tibble: 31 x 8
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AUDI 3785 0 0 3785 0 2017 10
## 2 AUDI 3189 0 0 3189 0 2016 11
## 3 AUDI 3124 0 0 3124 0 2017 12
## 4 AUDI 2992 0 0 2992 0 2016 12
## 5 AUDI 2448 0 0 2448 0 2016 10
## 6 AUDI 2352 0 0 2352 0 2016 5
## 7 AUDI 2326 0 0 2326 0 2017 11
## 8 AUDI 2177 0 0 2177 0 2017 6
## 9 AUDI 2148 0 0 2148 0 2016 4
## 10 AUDI 2109 0 0 2109 0 2017 5
## # ... with 21 more rowsFor 2018,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'AUDI') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 8 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AUDI 1630 0 0 1630 0 2018 5
## 2 AUDI 1625 0 0 1625 0 2018 4
## 3 AUDI 1033 0 0 1033 0 2018 2
## 4 AUDI 1013 0 0 1013 0 2018 1
## 5 AUDI 840 0 0 840 0 2018 7
## 6 AUDI 737 0 0 737 0 2018 8
## 7 AUDI 668 0 0 668 0 2018 6
## 8 AUDI 350 0 0 350 0 2018 9
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'AUDI') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2017,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'AUDI') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 12 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AUDI 3785 0 0 3785 0 2017 10
## 2 AUDI 3124 0 0 3124 0 2017 12
## 3 AUDI 2326 0 0 2326 0 2017 11
## 4 AUDI 2177 0 0 2177 0 2017 6
## 5 AUDI 2109 0 0 2109 0 2017 5
## 6 AUDI 1673 0 0 1673 0 2017 4
## 7 AUDI 1370 0 0 1370 0 2017 7
## 8 AUDI 1352 0 0 1352 0 2017 9
## 9 AUDI 1287 0 0 1287 0 2017 3
## 10 AUDI 1055 0 0 1055 0 2017 8
## 11 AUDI 702 0 0 702 0 2017 2
## 12 AUDI 625 0 0 625 0 2017 1
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'AUDI') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
Analysis for BENTLEY
Most automobile sales for BENTLEY.
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'BENTLEY') %>% arrange(desc(auto_total))## # A tibble: 31 x 8
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BENTLEY 4 0 0 4 0 2017 12
## 2 BENTLEY 3 0 0 3 0 2016 9
## 3 BENTLEY 3 0 0 3 0 2018 2
## 4 BENTLEY 3 0 0 3 0 2017 9
## 5 BENTLEY 2 0 0 2 0 2017 8
## 6 BENTLEY 2 0 0 2 0 2016 11
## 7 BENTLEY 2 0 0 2 0 2017 2
## 8 BENTLEY 1 0 0 1 0 2018 8
## 9 BENTLEY 1 0 0 1 0 2018 6
## 10 BENTLEY 1 0 0 1 0 2018 5
## # ... with 21 more rowsFor 2018,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'BENTLEY') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 8 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BENTLEY 3 0 0 3 0 2018 2
## 2 BENTLEY 1 0 0 1 0 2018 8
## 3 BENTLEY 1 0 0 1 0 2018 6
## 4 BENTLEY 1 0 0 1 0 2018 5
## 5 BENTLEY 0 0 0 0 0 2018 9
## 6 BENTLEY 0 0 0 0 0 2018 7
## 7 BENTLEY 0 0 0 0 0 2018 4
## 8 BENTLEY 0 0 0 0 0 2018 1
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'BENTLEY') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2017,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'BENTLEY') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 12 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BENTLEY 4 0 0 4 0 2017 12
## 2 BENTLEY 3 0 0 3 0 2017 9
## 3 BENTLEY 2 0 0 2 0 2017 8
## 4 BENTLEY 2 0 0 2 0 2017 2
## 5 BENTLEY 1 0 0 1 0 2017 10
## 6 BENTLEY 1 0 0 1 0 2017 6
## 7 BENTLEY 1 0 0 1 0 2017 5
## 8 BENTLEY 1 0 0 1 0 2017 4
## 9 BENTLEY 1 0 0 1 0 2017 1
## 10 BENTLEY 0 0 0 0 0 2017 11
## 11 BENTLEY 0 0 0 0 0 2017 7
## 12 BENTLEY 0 0 0 0 0 2017 3
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'BENTLEY') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2016,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'BENTLEY') %>% filter(year==2016) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 11 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BENTLEY 3 0 0 3 0 2016 9
## 2 BENTLEY 2 0 0 2 0 2016 11
## 3 BENTLEY 1 0 0 1 0 2016 10
## 4 BENTLEY 1 0 0 1 0 2016 7
## 5 BENTLEY 1 0 0 1 0 2016 5
## 6 BENTLEY 1 0 0 1 0 2016 2
## 7 BENTLEY 0 0 0 0 0 2016 1
## 8 BENTLEY 0 0 0 0 0 2016 12
## 9 BENTLEY 0 0 0 0 0 2016 8
## 10 BENTLEY 0 0 0 0 0 2016 4
## 11 BENTLEY 0 0 0 0 0 2016 3
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'BENTLEY') %>% filter(year==2016) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
Analysis for BMW
Most automobile sales for BMW.
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'BMW') %>% arrange(desc(auto_total))## # A tibble: 31 x 8
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BMW 4611 0 0 4611 0 2016 11
## 2 BMW 3498 0 0 3498 0 2017 12
## 3 BMW 3174 0 0 3174 0 2016 5
## 4 BMW 2399 0 0 2399 0 2017 11
## 5 BMW 2332 0 0 2332 0 2016 4
## 6 BMW 2230 0 0 2230 0 2016 8
## 7 BMW 2205 0 0 2205 0 2016 10
## 8 BMW 2080 0 0 2080 0 2016 3
## 9 BMW 2075 0 0 2075 0 2017 10
## 10 BMW 2042 0 0 2042 0 2016 2
## # ... with 21 more rowsFor 2018,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'BMW') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 8 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BMW 1910 0 0 1910 0 2018 5
## 2 BMW 1386 0 0 1386 0 2018 6
## 3 BMW 1270 0 0 1270 0 2018 4
## 4 BMW 1005 0 0 1005 0 2018 8
## 5 BMW 1003 0 0 1003 0 2018 2
## 6 BMW 760 0 0 760 0 2018 7
## 7 BMW 626 0 0 626 0 2018 1
## 8 BMW 158 0 0 158 0 2018 9
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'BMW') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2017,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'BMW') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 12 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BMW 3498 0 0 3498 0 2017 12
## 2 BMW 2399 0 0 2399 0 2017 11
## 3 BMW 2075 0 0 2075 0 2017 10
## 4 BMW 1904 0 0 1904 0 2017 7
## 5 BMW 1814 0 0 1814 0 2017 5
## 6 BMW 1487 0 0 1487 0 2017 9
## 7 BMW 1409 0 0 1409 0 2017 6
## 8 BMW 1346 0 0 1346 0 2017 3
## 9 BMW 1285 0 0 1285 0 2017 8
## 10 BMW 900 0 0 900 0 2017 2
## 11 BMW 877 0 0 877 0 2017 4
## 12 BMW 570 0 0 570 0 2017 1
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'BMW') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2016,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'BMW') %>% filter(year==2016) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 11 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BMW 4611 0 0 4611 0 2016 11
## 2 BMW 3174 0 0 3174 0 2016 5
## 3 BMW 2332 0 0 2332 0 2016 4
## 4 BMW 2230 0 0 2230 0 2016 8
## 5 BMW 2205 0 0 2205 0 2016 10
## 6 BMW 2080 0 0 2080 0 2016 3
## 7 BMW 2042 0 0 2042 0 2016 2
## 8 BMW 1856 0 0 1856 0 2016 7
## 9 BMW 1806 0 0 1806 0 2016 9
## 10 BMW 1694 0 0 1694 0 2016 12
## 11 BMW 496 0 0 496 0 2016 1
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'BMW') %>% filter(year==2016) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
Analysis for CHERY
Most automobile sales for CHERY.
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'CHERY') %>% arrange(desc(auto_total))## # A tibble: 25 x 8
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CHERY 30 0 0 30 0 2016 1
## 2 CHERY 23 0 0 23 0 2016 2
## 3 CHERY 22 0 0 22 0 2016 7
## 4 CHERY 22 0 0 22 0 2016 5
## 5 CHERY 12 0 0 12 0 2016 3
## 6 CHERY 9 0 0 9 0 2016 4
## 7 CHERY 1 0 0 1 0 2016 8
## 8 CHERY 0 0 0 0 0 2016 9
## 9 CHERY 0 0 0 0 0 2018 2
## 10 CHERY 0 0 0 0 0 2018 1
## # ... with 15 more rowsFor 2018,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'CHERY') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 2 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CHERY 0 0 0 0 0 2018 2
## 2 CHERY 0 0 0 0 0 2018 1
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'CHERY') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2017,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'CHERY') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 12 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CHERY 0 0 0 0 0 2017 12
## 2 CHERY 0 0 0 0 0 2017 11
## 3 CHERY 0 0 0 0 0 2017 10
## 4 CHERY 0 0 0 0 0 2017 9
## 5 CHERY 0 0 0 0 0 2017 8
## 6 CHERY 0 0 0 0 0 2017 7
## 7 CHERY 0 0 0 0 0 2017 6
## 8 CHERY 0 0 0 0 0 2017 5
## 9 CHERY 0 0 0 0 0 2017 4
## 10 CHERY 0 0 0 0 0 2017 3
## 11 CHERY 0 0 0 0 0 2017 1
## 12 CHERY 0 0 0 0 0 2017 2
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'CHERY') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2016,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'CHERY') %>% filter(year==2016) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 11 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CHERY 30 0 0 30 0 2016 1
## 2 CHERY 23 0 0 23 0 2016 2
## 3 CHERY 22 0 0 22 0 2016 7
## 4 CHERY 22 0 0 22 0 2016 5
## 5 CHERY 12 0 0 12 0 2016 3
## 6 CHERY 9 0 0 9 0 2016 4
## 7 CHERY 1 0 0 1 0 2016 8
## 8 CHERY 0 0 0 0 0 2016 9
## 9 CHERY 0 0 0 0 0 2016 12
## 10 CHERY 0 0 0 0 0 2016 11
## 11 CHERY 0 0 0 0 0 2016 10
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'CHERY') %>% filter(year==2016) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
Analysis for CITROEN
Most automobile sales for CITROEN.
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'CITROEN') %>% arrange(desc(auto_total))## # A tibble: 31 x 8
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CITROEN 2275 0 241 3285 241 2016 12
## 2 CITROEN 2017 0 274 2728 274 2016 11
## 3 CITROEN 1929 0 99 2327 99 2016 5
## 4 CITROEN 1765 0 1 2189 1 2017 3
## 5 CITROEN 1684 0 95 2354 95 2016 4
## 6 CITROEN 1654 0 0 2461 0 2017 4
## 7 CITROEN 1562 0 127 2314 127 2016 3
## 8 CITROEN 1544 0 0 2277 0 2017 10
## 9 CITROEN 1519 0 88 2047 88 2016 9
## 10 CITROEN 1504 0 0 2651 0 2017 5
## # ... with 21 more rowsFor 2018,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'CITROEN') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 8 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CITROEN 1128 0 0 1419 0 2018 2
## 2 CITROEN 980 0 0 1614 0 2018 5
## 3 CITROEN 922 0 0 1392 0 2018 7
## 4 CITROEN 615 0 0 1021 0 2018 4
## 5 CITROEN 499 0 0 1019 0 2018 6
## 6 CITROEN 495 0 0 693 0 2018 8
## 7 CITROEN 407 0 0 501 0 2018 1
## 8 CITROEN 134 0 0 331 0 2018 9
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'CITROEN') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2017,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'CITROEN') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 12 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CITROEN 1765 0 1 2189 1 2017 3
## 2 CITROEN 1654 0 0 2461 0 2017 4
## 3 CITROEN 1544 0 0 2277 0 2017 10
## 4 CITROEN 1504 0 0 2651 0 2017 5
## 5 CITROEN 1473 0 1 2649 1 2017 6
## 6 CITROEN 1433 0 0 1913 0 2017 9
## 7 CITROEN 1339 0 0 1818 0 2017 7
## 8 CITROEN 1321 0 0 2003 0 2017 8
## 9 CITROEN 761 0 20 1430 20 2017 2
## 10 CITROEN 745 0 0 1039 0 2017 11
## 11 CITROEN 655 0 0 1349 0 2017 12
## 12 CITROEN 515 0 3 953 3 2017 1
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'CITROEN') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2016,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'CITROEN') %>% filter(year==2016) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 11 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CITROEN 2275 0 241 3285 241 2016 12
## 2 CITROEN 2017 0 274 2728 274 2016 11
## 3 CITROEN 1929 0 99 2327 99 2016 5
## 4 CITROEN 1684 0 95 2354 95 2016 4
## 5 CITROEN 1562 0 127 2314 127 2016 3
## 6 CITROEN 1519 0 88 2047 88 2016 9
## 7 CITROEN 1381 0 126 2017 126 2016 10
## 8 CITROEN 1221 0 100 1698 100 2016 7
## 9 CITROEN 1080 0 131 1672 131 2016 8
## 10 CITROEN 394 0 41 601 41 2016 1
## 11 CITROEN 366 0 40 579 40 2016 2
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'CITROEN') %>% filter(year==2016) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
Analysis for DACIA
Most automobile sales for DACIA.
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'DACIA') %>% arrange(desc(auto_total))## # A tibble: 31 x 8
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 DACIA 6685 0 0 7449 0 2017 12
## 2 DACIA 6324 0 0 7197 0 2016 12
## 3 DACIA 4745 0 0 5492 0 2016 11
## 4 DACIA 4238 0 0 4724 0 2017 11
## 5 DACIA 4179 0 0 4751 0 2016 5
## 6 DACIA 3989 0 0 4238 0 2016 10
## 7 DACIA 3933 0 0 4575 0 2017 7
## 8 DACIA 3557 0 0 4172 0 2017 4
## 9 DACIA 3555 0 0 4067 0 2016 4
## 10 DACIA 3534 0 0 4057 0 2017 3
## # ... with 21 more rowsFor 2018,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'DACIA') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 8 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 DACIA 3199 0 0 3536 0 2018 4
## 2 DACIA 3193 0 0 3521 0 2018 5
## 3 DACIA 2343 0 0 2602 0 2018 7
## 4 DACIA 2254 0 0 2547 0 2018 6
## 5 DACIA 1298 0 0 1600 0 2018 2
## 6 DACIA 1220 0 0 1687 0 2018 8
## 7 DACIA 1141 0 0 1460 0 2018 9
## 8 DACIA 937 0 0 1191 0 2018 1
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'DACIA') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2017,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'DACIA') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 12 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 DACIA 6685 0 0 7449 0 2017 12
## 2 DACIA 4238 0 0 4724 0 2017 11
## 3 DACIA 3933 0 0 4575 0 2017 7
## 4 DACIA 3557 0 0 4172 0 2017 4
## 5 DACIA 3534 0 0 4057 0 2017 3
## 6 DACIA 3521 0 0 4063 0 2017 10
## 7 DACIA 3467 0 0 4005 0 2017 8
## 8 DACIA 3356 0 0 4014 0 2017 5
## 9 DACIA 2933 0 0 3583 0 2017 6
## 10 DACIA 2916 0 0 3371 0 2017 9
## 11 DACIA 2303 0 0 2651 0 2017 2
## 12 DACIA 1471 0 0 1706 0 2017 1
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'DACIA') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2016,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'DACIA') %>% filter(year==2016) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 11 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 DACIA 6324 0 0 7197 0 2016 12
## 2 DACIA 4745 0 0 5492 0 2016 11
## 3 DACIA 4179 0 0 4751 0 2016 5
## 4 DACIA 3989 0 0 4238 0 2016 10
## 5 DACIA 3555 0 0 4067 0 2016 4
## 6 DACIA 3402 0 0 3782 0 2016 3
## 7 DACIA 3120 0 0 3509 0 2016 8
## 8 DACIA 3116 0 0 3618 0 2016 9
## 9 DACIA 2680 0 0 3000 0 2016 7
## 10 DACIA 1963 0 0 2191 0 2016 2
## 11 DACIA 1235 0 0 1456 0 2016 1
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'DACIA') %>% filter(year==2016) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
Analysis for DS
Most automobile sales for DS.
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'DS') %>% arrange(desc(auto_total))## # A tibble: 31 x 8
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 DS 99 0 0 99 0 2016 12
## 2 DS 64 0 0 64 0 2018 8
## 3 DS 42 0 0 42 0 2016 11
## 4 DS 42 0 0 42 0 2016 3
## 5 DS 38 0 0 38 0 2017 7
## 6 DS 35 0 0 35 0 2016 4
## 7 DS 34 0 0 34 0 2016 8
## 8 DS 34 0 0 34 0 2016 2
## 9 DS 32 0 0 32 0 2016 9
## 10 DS 30 0 0 30 0 2017 12
## # ... with 21 more rowsFor 2018,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'DS') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 8 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 DS 64 0 0 64 0 2018 8
## 2 DS 20 0 0 20 0 2018 4
## 3 DS 10 0 0 10 0 2018 7
## 4 DS 9 0 0 9 0 2018 9
## 5 DS 9 0 0 9 0 2018 6
## 6 DS 9 0 0 9 0 2018 5
## 7 DS 0 0 0 0 0 2018 2
## 8 DS 0 0 0 0 0 2018 1
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'DS') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2017,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'DS') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 12 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 DS 38 0 0 38 0 2017 7
## 2 DS 30 0 0 30 0 2017 12
## 3 DS 21 0 0 21 0 2017 9
## 4 DS 13 0 0 13 0 2017 4
## 5 DS 9 0 0 9 0 2017 1
## 6 DS 5 0 0 5 0 2017 5
## 7 DS 1 0 0 1 0 2017 8
## 8 DS 1 0 0 1 0 2017 6
## 9 DS 1 0 0 1 0 2017 3
## 10 DS 0 0 0 0 0 2017 11
## 11 DS 0 0 0 0 0 2017 10
## 12 DS 0 0 0 0 0 2017 2
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'DS') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2016,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'DS') %>% filter(year==2016) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 11 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 DS 99 0 0 99 0 2016 12
## 2 DS 42 0 0 42 0 2016 11
## 3 DS 42 0 0 42 0 2016 3
## 4 DS 35 0 0 35 0 2016 4
## 5 DS 34 0 0 34 0 2016 8
## 6 DS 34 0 0 34 0 2016 2
## 7 DS 32 0 0 32 0 2016 9
## 8 DS 24 0 0 24 0 2016 5
## 9 DS 19 0 0 19 0 2016 7
## 10 DS 17 0 0 17 0 2016 10
## 11 DS 8 0 0 8 0 2016 1
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'DS') %>% filter(year==2016) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
Analysis for FERRARI
Most automobile sales for FERRARI.
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'FERRARI') %>% arrange(desc(auto_total))## # A tibble: 31 x 8
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FERRARI 4 0 0 4 0 2016 4
## 2 FERRARI 3 0 0 3 0 2016 1
## 3 FERRARI 3 0 0 3 0 2018 9
## 4 FERRARI 3 0 0 3 0 2018 4
## 5 FERRARI 3 0 0 3 0 2017 12
## 6 FERRARI 3 0 0 3 0 2017 8
## 7 FERRARI 3 0 0 3 0 2017 3
## 8 FERRARI 3 0 0 3 0 2016 5
## 9 FERRARI 2 0 0 2 0 2018 8
## 10 FERRARI 2 0 0 2 0 2018 7
## # ... with 21 more rowsFor 2018,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'FERRARI') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 8 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FERRARI 3 0 0 3 0 2018 9
## 2 FERRARI 3 0 0 3 0 2018 4
## 3 FERRARI 2 0 0 2 0 2018 8
## 4 FERRARI 2 0 0 2 0 2018 7
## 5 FERRARI 1 0 0 1 0 2018 6
## 6 FERRARI 1 0 0 1 0 2018 5
## 7 FERRARI 1 0 0 1 0 2018 2
## 8 FERRARI 1 0 0 1 0 2018 1
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'FERRARI') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2017,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'FERRARI') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 12 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FERRARI 3 0 0 3 0 2017 12
## 2 FERRARI 3 0 0 3 0 2017 8
## 3 FERRARI 3 0 0 3 0 2017 3
## 4 FERRARI 2 0 0 2 0 2017 11
## 5 FERRARI 2 0 0 2 0 2017 10
## 6 FERRARI 1 0 0 1 0 2017 6
## 7 FERRARI 1 0 0 1 0 2017 4
## 8 FERRARI 1 0 0 1 0 2017 2
## 9 FERRARI 0 0 0 0 0 2017 9
## 10 FERRARI 0 0 0 0 0 2017 7
## 11 FERRARI 0 0 0 0 0 2017 5
## 12 FERRARI 0 0 0 0 0 2017 1
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'FERRARI') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2016,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'FERRARI') %>% filter(year==2016) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 11 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FERRARI 4 0 0 4 0 2016 4
## 2 FERRARI 3 0 0 3 0 2016 1
## 3 FERRARI 3 0 0 3 0 2016 5
## 4 FERRARI 2 0 0 2 0 2016 12
## 5 FERRARI 2 0 0 2 0 2016 11
## 6 FERRARI 1 0 0 1 0 2016 9
## 7 FERRARI 1 0 0 1 0 2016 10
## 8 FERRARI 1 0 0 1 0 2016 2
## 9 FERRARI 0 0 0 0 0 2016 8
## 10 FERRARI 0 0 0 0 0 2016 7
## 11 FERRARI 0 0 0 0 0 2016 3
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'FERRARI') %>% filter(year==2016) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
## Analysis for FIAT
Most automobile sales for FIAT.
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'FIAT') %>% arrange(desc(auto_total))## # A tibble: 31 x 8
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FIAT 8348 7947 8821 1424 16768 2017 12
## 2 FIAT 7944 7522 7680 1496 15202 2016 12
## 3 FIAT 6946 6638 5595 996 12233 2016 11
## 4 FIAT 6543 6398 3767 525 10165 2017 6
## 5 FIAT 6512 6334 3384 583 9718 2017 5
## 6 FIAT 5664 5547 4562 643 10109 2017 7
## 7 FIAT 5487 5309 2731 540 8040 2018 5
## 8 FIAT 5474 5358 5006 642 10364 2017 10
## 9 FIAT 5358 5160 6076 830 11236 2017 11
## 10 FIAT 5065 4881 3885 861 8766 2017 8
## # ... with 21 more rowsFor 2018,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'FIAT') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 8 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FIAT 5487 5309 2731 540 8040 2018 5
## 2 FIAT 4305 4175 3183 379 7358 2018 4
## 3 FIAT 3832 3720 1787 360 5507 2018 7
## 4 FIAT 3245 3106 1689 362 4795 2018 6
## 5 FIAT 2377 2275 2500 354 4775 2018 2
## 6 FIAT 2035 1979 1962 219 3941 2018 1
## 7 FIAT 1681 1602 1090 315 2692 2018 8
## 8 FIAT 689 632 789 256 1421 2018 9
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'FIAT') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2017,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'FIAT') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 12 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FIAT 8348 7947 8821 1424 16768 2017 12
## 2 FIAT 6543 6398 3767 525 10165 2017 6
## 3 FIAT 6512 6334 3384 583 9718 2017 5
## 4 FIAT 5664 5547 4562 643 10109 2017 7
## 5 FIAT 5474 5358 5006 642 10364 2017 10
## 6 FIAT 5358 5160 6076 830 11236 2017 11
## 7 FIAT 5065 4881 3885 861 8766 2017 8
## 8 FIAT 4885 4700 3752 787 8452 2017 4
## 9 FIAT 4803 4692 4274 727 8966 2017 9
## 10 FIAT 4622 4393 4271 719 8664 2017 3
## 11 FIAT 2469 2282 2317 635 4599 2017 2
## 12 FIAT 1621 1515 1988 363 3503 2017 1
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'FIAT') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2016,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'FIAT') %>% filter(year==2016) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 11 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FIAT 7944 7522 7680 1496 15202 2016 12
## 2 FIAT 6946 6638 5595 996 12233 2016 11
## 3 FIAT 4818 4540 4370 611 8910 2016 4
## 4 FIAT 4597 4390 4036 702 8426 2016 10
## 5 FIAT 4565 4299 4728 609 9027 2016 5
## 6 FIAT 4268 3932 3517 951 7449 2016 3
## 7 FIAT 3703 3491 2790 733 6281 2016 9
## 8 FIAT 3325 3063 3951 706 7014 2016 8
## 9 FIAT 2849 2678 2771 480 5449 2016 7
## 10 FIAT 2765 2518 2363 621 4881 2016 2
## 11 FIAT 1627 1465 1944 434 3409 2016 1
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'FIAT') %>% filter(year==2016) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
Analysis for RENAULT
Most automobile sales for RENAULT.
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'RENAULT') %>% arrange(desc(auto_total))## # A tibble: 31 x 8
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 RENAULT 18490 11492 0 9586 11492 2016 12
## 2 RENAULT 16240 11420 0 7634 11420 2017 12
## 3 RENAULT 11975 7692 0 6292 7692 2017 11
## 4 RENAULT 11275 7292 0 5449 7292 2016 5
## 5 RENAULT 11135 7905 0 4328 7905 2017 6
## 6 RENAULT 10831 6905 0 5855 6905 2016 11
## 7 RENAULT 10633 7297 0 4778 7297 2016 4
## 8 RENAULT 10320 6690 0 4898 6690 2017 5
## 9 RENAULT 10310 6280 0 5013 6280 2016 3
## 10 RENAULT 10141 6468 0 5172 6468 2017 7
## # ... with 21 more rowsFor 2018,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'RENAULT') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 8 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 RENAULT 10034 7417 0 3744 7417 2018 4
## 2 RENAULT 9280 6586 0 3663 6586 2018 5
## 3 RENAULT 7427 5657 0 2475 5657 2018 6
## 4 RENAULT 6552 5000 0 2345 5000 2018 2
## 5 RENAULT 6485 4774 0 2371 4774 2018 7
## 6 RENAULT 4650 3637 0 1525 3637 2018 1
## 7 RENAULT 3634 2592 0 1510 2592 2018 8
## 8 RENAULT 2751 1969 0 1217 1969 2018 9
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'RENAULT') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2017,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'RENAULT') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 12 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 RENAULT 16240 11420 0 7634 11420 2017 12
## 2 RENAULT 11975 7692 0 6292 7692 2017 11
## 3 RENAULT 11135 7905 0 4328 7905 2017 6
## 4 RENAULT 10320 6690 0 4898 6690 2017 5
## 5 RENAULT 10141 6468 0 5172 6468 2017 7
## 6 RENAULT 9919 7357 0 4259 7357 2017 10
## 7 RENAULT 8866 5718 0 4475 5718 2017 3
## 8 RENAULT 8666 5787 0 4117 5787 2017 4
## 9 RENAULT 8531 4817 0 5089 4817 2017 8
## 10 RENAULT 7869 5094 0 4035 5094 2017 9
## 11 RENAULT 5437 3975 0 2180 3975 2017 2
## 12 RENAULT 4355 3511 0 1363 3511 2017 1
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'RENAULT') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2016,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'RENAULT') %>% filter(year==2016) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 11 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 RENAULT 18490 11492 0 9586 11492 2016 12
## 2 RENAULT 11275 7292 0 5449 7292 2016 5
## 3 RENAULT 10831 6905 0 5855 6905 2016 11
## 4 RENAULT 10633 7297 0 4778 7297 2016 4
## 5 RENAULT 10310 6280 0 5013 6280 2016 3
## 6 RENAULT 7399 4325 0 4557 4325 2016 10
## 7 RENAULT 6589 2516 0 4937 2516 2016 8
## 8 RENAULT 5560 3553 0 2609 3553 2016 2
## 9 RENAULT 5513 2006 0 4268 2006 2016 7
## 10 RENAULT 5277 2806 0 3660 2806 2016 9
## 11 RENAULT 4068 2810 0 1709 2810 2016 1
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'RENAULT') %>% filter(year==2016) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
Analysis for VOLKSWAGEN
Most automobile sales for VOLKSWAGEN.
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'VOLKSWAGEN') %>% arrange(desc(auto_total))## # A tibble: 27 x 8
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 VOLKSWAGEN 14539 0 0 18082 0 2016 12
## 2 VOLKSWAGEN 13003 0 0 16293 0 2017 12
## 3 VOLKSWAGEN 10587 0 0 14359 0 2016 11
## 4 VOLKSWAGEN 10228 0 0 12937 0 2017 11
## 5 VOLKSWAGEN 9597 0 0 12436 0 2016 5
## 6 VOLKSWAGEN 9211 0 0 12229 0 2016 10
## 7 VOLKSWAGEN 8438 0 0 11167 0 2016 8
## 8 VOLKSWAGEN 8197 0 0 11293 0 2016 3
## 9 VOLKSWAGEN 8028 0 0 10312 0 2016 9
## 10 VOLKSWAGEN 7811 0 0 10485 0 2016 4
## # ... with 17 more rowsFor 2018,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'VOLKSWAGEN') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 8 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 VOLKSWAGEN 5913 0 0 7844 0 2018 5
## 2 VOLKSWAGEN 5801 0 0 7790 0 2018 4
## 3 VOLKSWAGEN 4428 0 0 5521 0 2018 6
## 4 VOLKSWAGEN 4217 0 0 5515 0 2018 7
## 5 VOLKSWAGEN 3763 0 0 5279 0 2018 2
## 6 VOLKSWAGEN 2567 0 0 3585 0 2018 8
## 7 VOLKSWAGEN 2497 0 0 4127 0 2018 1
## 8 VOLKSWAGEN 1763 0 0 2239 0 2018 9
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'VOLKSWAGEN') %>% filter(year==2018) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2017,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'VOLKSWAGEN') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 8 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 VOLKSWAGEN 13003 0 0 16293 0 2017 12
## 2 VOLKSWAGEN 10228 0 0 12937 0 2017 11
## 3 VOLKSWAGEN 7213 0 0 9431 0 2017 6
## 4 VOLKSWAGEN 7006 0 0 9382 0 2017 7
## 5 VOLKSWAGEN 6957 0 0 9362 0 2017 8
## 6 VOLKSWAGEN 6729 0 0 8850 0 2017 3
## 7 VOLKSWAGEN 4903 0 0 6502 0 2017 2
## 8 VOLKSWAGEN 3059 0 0 4314 0 2017 1
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'VOLKSWAGEN') %>% filter(year==2017) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))
For 2016,
dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'VOLKSWAGEN') %>% filter(year==2016) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))## # A tibble: 11 x 9
## brand_name auto_total auto_dom comm_dom total_imp total_dom year month
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 VOLKSWAGEN 14539 0 0 18082 0 2016 12
## 2 VOLKSWAGEN 10587 0 0 14359 0 2016 11
## 3 VOLKSWAGEN 9597 0 0 12436 0 2016 5
## 4 VOLKSWAGEN 9211 0 0 12229 0 2016 10
## 5 VOLKSWAGEN 8438 0 0 11167 0 2016 8
## 6 VOLKSWAGEN 8197 0 0 11293 0 2016 3
## 7 VOLKSWAGEN 8028 0 0 10312 0 2016 9
## 8 VOLKSWAGEN 7811 0 0 10485 0 2016 4
## 9 VOLKSWAGEN 7239 0 0 9084 0 2016 7
## 10 VOLKSWAGEN 5763 0 0 8154 0 2016 2
## 11 VOLKSWAGEN 2792 0 0 4528 0 2016 1
## # ... with 1 more variable: meanOfAutoTotal <dbl>tabl <- dat %>%
select(brand_name,auto_total,auto_dom,comm_dom,total_imp,total_dom,year,month) %>% filter(brand_name == 'VOLKSWAGEN') %>% filter(year==2016) %>% mutate(meanOfAutoTotal = mean(auto_total)) %>% arrange(desc(auto_total))