Questao 1.

Vamos carregar um conjunto de dados simples, como o conjunto mtcars, que já vem pré-carregado no R.

# Carregando os dados
data("mtcars")
head(mtcars)
##                    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
# Ordenando os dados por mpg
mtcars_sorted <- mtcars[order(-mtcars$mpg), ]
head(mtcars_sorted)
##                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Fiat 128       32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Honda Civic    30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Fiat X1-9      27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
# Filtrando para mostrar apenas carros com 6 cilindros
mtcars_filtered <- subset(mtcars, cyl == 6)
head(mtcars_filtered)
##                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Valiant        18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Merc 280       19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C      17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
# Criando uma nova variável mpg_per_cyl
mtcars$mpg_per_cyl <- mtcars$mpg / mtcars$cyl
head(mtcars)
##                    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
##                   mpg_per_cyl
## Mazda RX4            3.500000
## Mazda RX4 Wag        3.500000
## Datsun 710           5.700000
## Hornet 4 Drive       3.566667
## Hornet Sportabout    2.337500
## Valiant              3.016667

Questao 2

# Carregando o pacote DT
library(DT)

# Criando a tabela interativa
datatable(mtcars, 
          options = list(pageLength = 10, 
                         autoWidth = TRUE, 
                         searchHighlight = TRUE))

Questao 3

Esta equação representa a transformada de Fourier de uma função: \[\mathcal{F}\{f(t)\} = F(\omega) = \int_{-\infty}^{\infty} f(t) e^{-i\omega t} \, dt\]

Esta equação representa a Equação de Schrödinger: \[i\hbar \frac{\partial \psi(x,t)}{\partial t} = -\frac{\hbar^2}{2m} \frac{\partial^2 \psi(x,t)}{\partial x^2} + V(x)\psi(x,t)\]

Esta é Equação de Onda: \[\frac{\partial^2 u}{\partial t^2} = c^2 \nabla^2 u\]

Esta é a Fórmula de Euler: \[e^{i\theta} = \cos(\theta) + i\sin(\theta)\]

Esta é a Equação de Maxwell para o Campo Elétrico:\[\nabla \times \mathbf{E} = -\frac{\partial \mathbf{B}}{\partial t}\]

Questao 4

Conceito de Ciência de Dados Fluxo de Trabalho em Ciência de Dados

Questao 5

Baumer and Udwin (2015)

Racine (2012)

Kronthaler and Z"ollner (2021)

Van der Loo (2012)

Campbell and Campbell (2019)

Referencias

Baumer, Benjamin, and Dana Udwin. 2015. “R Markdown.” Wiley Interdisciplinary Reviews: Computational Statistics 7 (3): 167–77.
Campbell, Matthew, and Matthew Campbell. 2019. “RStudio Projects.” Learn RStudio IDE: Quick, Effective, and Productive Data Science, 39–48.
Kronthaler, Franz, and Silke Z"ollner. 2021. “Data Analysis with RStudio.” Data Analysis with RStudio.
Racine, Jeffrey S. 2012. “RStudio: A Platform-Independent IDE for r and Sweave.” JSTOR.
Van der Loo, Mark PJ. 2012. Learning RStudio for r Statistical Computing. Packt Publishing Ltd.