Segundo documento

Autores
Roney Fraga Souza

Universidade Federal de Mato Grosso

Nome do Segundo Autor

Universidade Federal de Algum Lugar

Data de Publicação

2022-11-05

Quarto

Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see https://quarto.org.

Rodando Código

When you click the Render button a document will be generated that includes both content and the output of embedded code. You can embed code like this:

Código 
1 + 1
[1] 2

You can add options to executable code like this

[1] 4

The echo: false option disables the printing of code (only output is displayed).

R – código e figura

Figura 1 further explores the impact of temperature on ozone level.

Código 
library(ggplot2)

ggplot(airquality, aes(Temp, Ozone)) + 
  geom_point() + 
  geom_smooth(method = "loess"
)

Figura 1: Temperature and ozone level.

Figura no markdown

Figura 2: Fatu and Najin

Cross reference with Figura 2, more details in https://quarto.org/docs/authoring/figures.html.

Tabelas Markdown

Tabela 1: Demonstration of pipe table syntax
Default Left Right Center
12 12 12 12
123 123 123 123
1 1 1 1

Cross reference as Tabela 1.

Tabelas – knitr

Cross-reference with Tabela 2.

Código 
```{r}
#| label: tbl-example
#| tbl-cap: "Example"
#| tbl-subcap:
#|   - "Cars"
#|   - "Pressure"
#| layout-ncol: 2

library(knitr)
kable(head(cars))
kable(head(pressure))
```

Tabela 2: Example

(a) Cars
speed dist
4 2
4 10
7 4
7 22
8 16
9 10
(b) Pressure
temperature pressure
0 0.0002
20 0.0012
40 0.0060
60 0.0300
80 0.0900
100 0.2700

Tabelas – DT

DT

Código 
mtcars |>
    tibble::rownames_to_column(var = 'car') |>
    dplyr::select(1:5) |> 
    DT::datatable()

Tabelas – DT download

DT

Código 
mtcars |>
    tibble::rownames_to_column(var = 'car') |>
    dplyr::select(1:5) |> 
    DT::datatable(extensions = 'Buttons', rownames = F, 
                  options = list(
                        dom = 'Bfrtip', 
                        pageLength = 5, 
                        buttons = list(list(
                                            extend = 'collection', 
                                            buttons = list(list(extend = 'csv', filename = 'data'), 
                                                            list(extend = 'excel', filename = 'data')), 
                                                            text = 'Download'))))

Tabelas – gt

Opção a ser analisada gt.

Código 
library(gt)
library(tidyverse)
library(glue)

# Define the start and end dates for the data range
start_date <- "2010-06-07"
end_date <- "2010-06-14"

# Create a gt table based on preprocessed
# `sp500` table data
sp500 %>%
  filter(date >= start_date & date <= end_date) %>%
  select(-adj_close) %>%
  gt() %>%
  tab_header(
    title = "S&P 500",
    subtitle = glue("{start_date} to {end_date}")
  ) %>%
  fmt_date(
    columns = date,
    date_style = 3
  ) %>%
  fmt_currency(
    columns = c(open, high, low, close),
    currency = "USD"
  ) %>%
  fmt_number(
    columns = volume,
    suffixing = TRUE
  )
gt tables
S&P 500
2010-06-07 to 2010-06-14
date open high low close volume
Mon, Jun 14, 2010 $1,095.00 $1,105.91 $1,089.03 $1,089.63 4.43B
Fri, Jun 11, 2010 $1,082.65 $1,092.25 $1,077.12 $1,091.60 4.06B
Thu, Jun 10, 2010 $1,058.77 $1,087.85 $1,058.77 $1,086.84 5.14B
Wed, Jun 9, 2010 $1,062.75 $1,077.74 $1,052.25 $1,055.69 5.98B
Tue, Jun 8, 2010 $1,050.81 $1,063.15 $1,042.17 $1,062.00 6.19B
Mon, Jun 7, 2010 $1,065.84 $1,071.36 $1,049.86 $1,050.47 5.47B

Tabelas – gtsummary

Opção a ser analisada gtsummary.

Código 
library(gtsummary)
# make dataset with a few variables to summarize
trial2 <- trial %>% select(age, grade, response, trt)

# summarize the data with our package
tbl_summary(trial2)
gtsummary tables
Characteristic N = 2001
Age 47 (38, 57)
    Unknown 11
Grade
    I 68 (34%)
    II 68 (34%)
    III 64 (32%)
Tumor Response 61 (32%)
    Unknown 7
Chemotherapy Treatment
    Drug A 98 (49%)
    Drug B 102 (51%)
1 Median (IQR); n (%)
Código 
tbl_summary(
    trial2,
    by = trt, # split table by group
    missing = "no" # don't list missing data separately
  ) %>%
  add_n() %>% # add column with total number of non-missing observations
  add_p() %>% # test for a difference between groups
  modify_header(label = "**Variable**") %>% # update the column header
  bold_labels()
gtsummary tables 2
Variable N Drug A, N = 981 Drug B, N = 1021 p-value2
Age 189 46 (37, 59) 48 (39, 56) 0.7
Grade 200 0.9
    I 35 (36%) 33 (32%)
    II 32 (33%) 36 (35%)
    III 31 (32%) 33 (32%)
Tumor Response 193 28 (29%) 33 (34%) 0.5
1 Median (IQR); n (%)
2 Wilcoxon rank sum test; Pearson's Chi-squared test

Regressão – gtsummary

Código 
mod1 <- glm(response ~ trt + age + grade, trial, family = binomial)
t1 <- tbl_regression(mod1, exponentiate = TRUE)
t1
gtsummary regressão
Characteristic OR1 95% CI1 p-value
Chemotherapy Treatment
    Drug A — —
    Drug B 1.13 0.60, 2.13 0.7
Age 1.02 1.00, 1.04 0.10
Grade
    I — —
    II 0.85 0.39, 1.85 0.7
    III 1.01 0.47, 2.15 >0.9
1 OR = Odds Ratio, CI = Confidence Interval

Regressão lado a lado – gtsummary

Código 
library(survival)

# build survival model table
t2 <-
  coxph(Surv(ttdeath, death) ~ trt + grade + age, trial) %>%
  tbl_regression(exponentiate = TRUE)

# merge tables 
tbl_merge(
    tbls = list(t1, t2),
    tab_spanner = c("**Tumor Response**", "**Time to Death**")
  )
gtsummary regressão lado a lado
Characteristic Tumor Response Time to Death
OR1 95% CI1 p-value HR1 95% CI1 p-value
Chemotherapy Treatment
    Drug A — — — —
    Drug B 1.13 0.60, 2.13 0.7 1.30 0.88, 1.92 0.2
Age 1.02 1.00, 1.04 0.10 1.01 0.99, 1.02 0.3
Grade
    I — — — —
    II 0.85 0.39, 1.85 0.7 1.21 0.73, 1.99 0.5
    III 1.01 0.47, 2.15 >0.9 1.79 1.12, 2.86 0.014
1 OR = Odds Ratio, CI = Confidence Interval, HR = Hazard Ratio

Regressão – sjPlot

sjPlot

Código 
library(sjPlot)
library(sjlabelled)
library(sjmisc)
library(ggplot2)

data(efc)
theme_set(theme_sjplot())

y <- ifelse(efc$neg_c_7 < median(na.omit(efc$neg_c_7)), 0, 1)

# create data frame for fitting model
df <- data.frame(
  y = to_factor(y),
  sex = to_factor(efc$c161sex),
  dep = to_factor(efc$e42dep),
  barthel = efc$barthtot,
  education = to_factor(efc$c172code)
)

# set variable label for response
set_label(df$y) <- "High Negative Impact"

# fit model
m1 <- glm(y ~., data = df, family = binomial(link = "logit"))
tab_model(m1)
sjPlot regressão
  High Negative Impact
Predictors Odds Ratios CI p
(Intercept) 2.01 0.64 – 6.22 0.225
carer's gender: Female 1.91 1.33 – 2.76 <0.001
elder's dependency:
slightly dependent		 1.62 0.82 – 3.42 0.179
elder's dependency:
moderately dependent		 3.08 1.56 – 6.50 0.002
elder's dependency:
severely dependent		 2.48 1.06 – 6.05 0.039
Total score BARTHEL INDEX 0.97 0.96 – 0.98 <0.001
carer's level of
education: intermediate
level of education		 1.25 0.85 – 1.86 0.258
carer's level of
education: high level of
education		 1.33 0.82 – 2.17 0.255
Observations 815
R2 Tjur 0.209

Regressão – sjPlot gráficos

sjPlot

Código 
plot_model(m1, show.values = TRUE, value.offset = .3)

Figura 3: sjPlot opção de saída de regressão

Código 
plot_model(
  m1, 
  colors = "Accent", 
  show.values = TRUE,
  value.offset = .4,
  value.size = 4,
  dot.size = 3,
  line.size = 1.5,
  vline.color = "blue",
  width = 1.5
)

Figura 4: sjPlot opção de saída de regressão

Diagramas

flowchart LR
  A[Hard edge] --> B(Round edge)
  B --> C{Decision}
  C --> D[Result one]
  C --> E[Result two]

Citações

Citação direta: Lee, Iliev e Preston (2009), Pénin e Neicu (2018), Arnold (2015).

Citação indireta (Arnold, 2015; Lee, Iliev e Preston, 2009; Pénin e Neicu, 2018).

Texto entre parênteses (see Lee, Iliev e Preston, 2009, pp. 33–35; also Arnold, 2015, chap. 1).

Nome do autor e ano (2015).

Citar uma tabela ou figura com: Figura 2, Fig 2, ou 2.

Notas de rodapé

Here is a footnote reference,1 and another.2

Here is an inline note.3

Equações

Black-Scholes (Equação 1) is a mathematical model that seeks to explain the behavior of financial derivatives, most commonly options:

\[ \frac{\partial \mathrm C}{ \partial \mathrm t } + \frac{1}{2}\sigma^{2} \mathrm S^{2} \frac{\partial^{2} \mathrm C}{\partial \mathrm C^2} + \mathrm r \mathrm S \frac{\partial \mathrm C}{\partial \mathrm S}\ = \mathrm r \mathrm C \tag{1}\]

Código de Python

Obs.: necessário ter Python instalado, assim como, os pacotes numpy e matplotlib. Se tiver Python e Julia instalados, altere ‘|# eval:’ e ‘|# include:’ para ‘true’.

For a demonstration of a line plot on a polar axis.

import numpy as np
import matplotlib.pyplot as plt

r = np.arange(0, 2, 0.01)
theta = 2 * np.pi * r
fig, ax = plt.subplots(
  subplot_kw = {'projection': 'polar'} 
)
ax.plot(theta, r)
ax.set_rticks([0.5, 1, 1.5, 2])
ax.grid(True)
plt.show()

Código de Julia

Obs.: necessário ter Julia instalada, assim como, o pacote Plots.

Plot function pair (x(u), y(u)).

using Plots

plot(sin, 
     x->sin(2x), 
     0, 
     2π, 
     leg=false, 
     fill=(0,:lavender))

Código de C++

library(Rcpp)

# Define the function euclidean_distance()
cppFunction('
  double euclidean_distance(double x, double y) {
    return sqrt(x*x + y*y) ;
  }
')

# Calculate the euclidean distance
euclidean_distance(1.5, 2.5)

Referências

ARNOLD, A. J. A game-theoretic approach to modelling crop royalties. [s.l.] University of Adelaide, 2015.
LEE, B.; ILIEV, I.; PRESTON, F. Who owns our low carbon future? [s.l.] Royal Institute of International Affairs (RIIA), 2009.
PÉNIN, J.; NEICU, D. Patents and Open Innovation: Bad Fences Do Not Make Good Neighbors. Journal of Innovation Economics, v. 25, n. 1, p. 57, 2018.

Notas de rodapé

  1. Here is the footnote.↩︎

  2. Here’s one with multiple blocks.↩︎

  3. Inlines notes are easier to write, since you don’t have to pick an identifier and move down to type the note.↩︎