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# Carregando o dataset 
data("mtcars")

cars <- mtcars

head(cars)
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1
# 1) Renomear coluna para facilitar leitura
cars <- cars %>%
  tibble::rownames_to_column(var = "model")


# 2) Criar a variável power_to_weight = hp / wt
cars <- cars %>%
  mutate(power_to_weight = hp / wt)


# 3) Filtrar carros com mpg >= 20 (economia razoável)
economicos <- cars %>% filter(mpg >= 20)


# 4) Ordenar por power_to_weight decrescente
cars_sorted <- cars %>% arrange(desc(power_to_weight))


# 5) Agrupar por número de cilindros e calcular médias
resumo_cil <- cars %>%
  group_by(cyl) %>%
  summarise(n = n(),
            mpg_mean = mean(mpg),
            hp_mean = mean(hp),
            wt_mean = mean(wt),
            ptw_mean = mean(power_to_weight))


list(economicos = economicos, head_sorted = head(cars_sorted), resumo_cil = resumo_cil)
## $economicos
##             model  mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## 1       Mazda RX4 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## 2   Mazda RX4 Wag 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## 3      Datsun 710 22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## 4  Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## 5       Merc 240D 24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## 6        Merc 230 22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## 7        Fiat 128 32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## 8     Honda Civic 30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## 9  Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## 10  Toyota Corona 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## 11      Fiat X1-9 27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## 12  Porsche 914-2 26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## 13   Lotus Europa 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## 14     Volvo 142E 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
##    power_to_weight
## 1         41.98473
## 2         38.26087
## 3         40.08621
## 4         34.21462
## 5         19.43574
## 6         30.15873
## 7         30.00000
## 8         32.19814
## 9         35.42234
## 10        39.35091
## 11        34.10853
## 12        42.52336
## 13        74.68605
## 14        39.20863
## 
## $head_sorted
##            model  mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## 1  Maserati Bora 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## 2 Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## 3   Lotus Europa 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## 4     Duster 360 14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## 5     Camaro Z28 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## 6   Ferrari Dino 19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
##   power_to_weight
## 1        93.83754
## 2        83.28076
## 3        74.68605
## 4        68.62745
## 5        63.80208
## 6        63.17690
## 
## $resumo_cil
## # A tibble: 3 × 6
##     cyl     n mpg_mean hp_mean wt_mean ptw_mean
##   <dbl> <int>    <dbl>   <dbl>   <dbl>    <dbl>
## 1     4    11     26.7    82.6    2.29     37.9
## 2     6     7     19.7   122.     3.12     39.9
## 3     8    14     15.1   209.     4.00     53.9
# Mostrar tabela interativa com DT (todas as colunas)
DT::datatable(cars,
              options = list(pageLength = 10,
                             searchHighlight = TRUE,
                             autoWidth = TRUE),
              rownames = FALSE)

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