Quarto-Project-Example/example_codechunk_2.qmd at main · alicebeittel/Quarto-Project-Example · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
---
title: "Quarto Code Chunk Practice - Session 2"
author: "Alice Beittel"
format: html
---

## Quarto

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

## Running Code

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:

```{r}
1 + 1
```

You can add options to executable code like this

```{r}
#| echo: false
2 * 2
```

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

```{r}
#| label: simple-addition
#| eval: true
#| echo: true

#you can make a vertical line by doing shift and then press the \ key
# #| eval: false  = this disables printing of code. Only output is displayed.
# #| echo: false = this will remove code from being displayed and only show output

a <- 1+1
a

a <- 'this is a string in R'

```

You can use the \` on either side of text/numbers to indicate r code in line.

The sum of 1+1 is `r 1+1`.

The sum of 1+1 is `r 1+1`.

You can output a line of text in quarto document like this:

`r a`

```{r}
#| lable: fig-HR
#| fig-cap: 'This is an example figure'
library(tidyverse)
library(BayesFactor)

#Read data
df <- read_csv("data/processed/preprocessed-GARP-TSST-data.csv")

#Convert df to long-format
df_long <- df %>%
  pivot_longer(cols = c(HR_Baseline_Average, HR_TSST_Average),
               names_to = "Measurement",
               values_to = "HR")

#Drop missing values
df_long <- df_long %>% drop_na(HR)

#Make sure columns are coded as factors for analysis
df_long$VPN <- as.factor(df_long$VPN)
df_long$Measurement <- as.factor(df_long$Measurement)
df_long$Condition <- as.factor(df_long$Condition)

#Bayesian Analysis
BF <- anovaBF(formula = HR ~ Measurement*Condition + VPN,
              data = df_long,
              whichRandom = "VPN")

#Evidence for interaction term
BF_interaction <- BF[4]/BF[3]

BF_interaction

#Summarize to mean / SEM for plot
df_long2 <- df_long %>%
  group_by(Measurement, Condition) %>%
  summarize(mean_value = mean(HR, na.rm = T),
            sem = sd(HR, na.rm = T)/sqrt(n()))

#Create plot
plot <- ggplot(df_long2, aes(Measurement, mean_value, group = Condition, color = Condition)) +
  geom_pointrange(aes(ymin=mean_value-sem, ymax= mean_value+sem)) +
  geom_line() +
  theme_classic() +
  scale_x_discrete(labels = c("Baseline",
                              "TSST-G/Control")) +
  ylab("Mean Heartrate (BPM)") +
  scale_colour_grey(start = 0.5, end = 0.2) +
  theme(legend.position = "top")

#Print and save plots
plot

#ggsave("../output/heartrate.pdf", plot, width = 4, height = 3)
```