The original code for the desaster markdown file, as well as a corrected version

DesasterMarkdown.Rmd

---
title: "What a desaster!"
author: "Claudius"
date: '2022-04-06'
output: pdf_document
---

# Packages used
```{r}
library(tidyverse)
library(DataScienceExercises)
library(knitr)
```

# Exploring flight data 

In this short text we explore the following data set on flights departing 
from New York.

```{r}
base_data <- DataScienceExercises::nycflights21_small[1:200, ]
data.frame(head(DataScienceExercises::nycflights21_small, 50))
```

To have a first look on the relationship of the variables, consider the 
following scatter plots:

```{r, out.width='40%', out.height='40%', fig.pos="center"}

arrival_dep <- ggplot(data = base_data) +
  geom_point(mapping = aes(x=arr_delay, y=dep_delay), 
             alpha=0.5, color="#00395B") +
  ggplot2::theme_bw() +
  labs(x="Arrival delay", y="Departure delay") +
  theme(
    legend.position = "bottom",
    legend.title = ggplot2::element_blank(),
    panel.border = ggplot2::element_blank(),
    axis.line = ggplot2::element_line(colour = "grey"),
    axis.ticks = ggplot2::element_line(colour = "grey")
  )

arrival_dist <- ggplot(data = base_data) +
  geom_point(mapping = aes(x=arr_delay, y=distance), 
             alpha=0.5, color="#00395B") +
  ggplot2::theme_bw() +
  labs(x="Arrival delay", y="Departure delay") +
  theme(
    legend.position = "bottom",
    legend.title = ggplot2::element_blank(),
    panel.border = ggplot2::element_blank(),
    axis.line = ggplot2::element_line(colour = "grey"),
    axis.ticks = ggplot2::element_line(colour = "grey")
  )

arrival_month <- ggplot(data = base_data) +
  geom_point(mapping = aes(y=arr_delay, x=month), 
             alpha=0.5, color="#00395B") +
  ggplot2::theme_bw() +
  labs(x="Arrival delay", y="Departure delay") +
  theme(
    legend.position = "bottom",
    legend.title = ggplot2::element_blank(),
    panel.border = ggplot2::element_blank(),
    axis.line = ggplot2::element_line(colour = "grey"),
    axis.ticks = ggplot2::element_line(colour = "grey")
  )

arrival_carrier <- ggplot(data = base_data) +
  geom_point(mapping = aes(y=arr_delay, x=carrier), 
             alpha=0.5, color="#00395B") +
  ggplot2::theme_bw() +
  labs(x="Arrival delay", y="Departure delay") +
  theme(
    legend.position = "bottom",
    legend.title = ggplot2::element_blank(),
    panel.border = ggplot2::element_blank(),
    axis.line = ggplot2::element_line(colour = "grey"),
    axis.ticks = ggplot2::element_line(colour = "grey")
  )

ggpubr::ggarrange(
  arrival_dep, arrival_dist, 
  arrival_month, arrival_carrier, 
  ncol = 2, nrow = 2)
```

This suggests that there is a strong correlation between departure and arrival
delay. To compute the correlation we might use the following R code:

```{r, echo=FALSE}
cor(base_data$arr_delay, base_data$dep_delay)
```

There is indeed a very strong correlation. But is it significant? Lets check 
it using the Pearson correlation test:

```{r}
cor.test(base_data$arr_delay, base_data$dep_delay, method = "pearson")
```

Of course, these are just preliminary results, from a methodological point of
view there is still much to do...

NicerMarkdown.Rmd

---
title: "What a beauty!"
author: "Claudius"
date: '2022-04-06'
output: pdf_document
header-includes:
   - \usepackage{setspace}
   - \onehalfspacing
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  warning = FALSE, message = FALSE)
```


# Packages used
```{r}
library(tidyverse)
library(DataScienceExercises)
library(knitr)
```

# Exploring flight data 

In this short text we explore the following data set on flights departing 
from New York.

```{r, echo=FALSE}
base_data <- DataScienceExercises::nycflights21_small[1:200, ]
knitr::kable(head(DataScienceExercises::nycflights21_small, 5))

```

To have a first look on the relationship of the variables, consider the 
following scatter plots:

```{r, fig.align='center', out.width='70%', out.height='70%', echo=FALSE}

arrival_dep <- ggplot(data = base_data) +
  geom_point(mapping = aes(x=arr_delay, y=dep_delay), 
             alpha=0.5, color="#00395B") +
  ggplot2::theme_bw() +
  labs(x="Arrival delay", y="Departure delay") +
  theme(
    legend.position = "bottom",
    legend.title = ggplot2::element_blank(),
    panel.border = ggplot2::element_blank(),
    axis.line = ggplot2::element_line(colour = "grey"),
    axis.ticks = ggplot2::element_line(colour = "grey")
  )

arrival_dist <- ggplot(data = base_data) +
  geom_point(mapping = aes(x=arr_delay, y=distance), 
             alpha=0.5, color="#00395B") +
  ggplot2::theme_bw() +
  labs(x="Arrival delay", y="Departure delay") +
  theme(
    legend.position = "bottom",
    legend.title = ggplot2::element_blank(),
    panel.border = ggplot2::element_blank(),
    axis.line = ggplot2::element_line(colour = "grey"),
    axis.ticks = ggplot2::element_line(colour = "grey")
  )

arrival_month <- ggplot(data = base_data) +
  geom_point(mapping = aes(y=arr_delay, x=month), 
             alpha=0.5, color="#00395B") +
  ggplot2::theme_bw() +
  labs(x="Arrival delay", y="Departure delay") +
  theme(
    legend.position = "bottom",
    legend.title = ggplot2::element_blank(),
    panel.border = ggplot2::element_blank(),
    axis.line = ggplot2::element_line(colour = "grey"),
    axis.ticks = ggplot2::element_line(colour = "grey")
  )

arrival_carrier <- ggplot(data = base_data) +
  geom_point(mapping = aes(y=arr_delay, x=carrier), 
             alpha=0.5, color="#00395B") +
  ggplot2::theme_bw() +
  labs(x="Arrival delay", y="Departure delay") +
  theme(
    legend.position = "bottom",
    legend.title = ggplot2::element_blank(),
    panel.border = ggplot2::element_blank(),
    axis.line = ggplot2::element_line(colour = "grey"),
    axis.ticks = ggplot2::element_line(colour = "grey")
  )

ggpubr::ggarrange(
  arrival_dep, arrival_dist, 
  arrival_month, arrival_carrier, 
  ncol = 2, nrow = 2)
```

These plots suggests that there is a strong correlation between departure and 
arrival delay. To compute the correlation we might use the following R code:

```{r, echo=TRUE, results='hide'}
cor_coef <- cor(base_data$arr_delay, base_data$dep_delay)
```

This produces a correlation coefficient of `r round(cor_coef, 3)`, suggesting 
that there is indeed a very strong correlation. 

But is it significant? Lets check it using the Pearson correlation test:

```{r}
c_test <- cor.test(
  x = base_data$arr_delay, 
  y = base_data$dep_delay, 
  method = "pearson")
```

The most relevant statistics are:

```{r, echo=FALSE}
knitr::kable(tibble(
  "t-stat"=c_test$statistic, 
  "df"=c_test$parameter, 
  "p-val"=c_test$p.value, 
  "95% conf interval"=paste0(
    "[", 
    paste(round(c_test$conf.int, 3), collapse = "; "), 
    "]")
), align = "c")
```

Of course, these are just preliminary results, from a methodological point of
view there is still much to do...

# The corrections we did

To make this document look *much* nicer immediately, the following changes
were made:

* Supress warnings and messages by default
* Set line spacing to one and a half (just looked it up in the internet)
* Do not show the whole table in the beginning but only the first lines; 
* do not show the R code in this context since it is not meaningful; 
* use `knitr::kable()` to print tables
* Do not show the code for preparing the plot, it is not necessary to understand
the message
* Adjust `out.width` and `out.height` options in the plot chunk such that the
plot is easier to read
* Show the code use to compute the correlation coefficient, but in a readable 
way; but summarize the output concisely, focusing on what is relevant
* Report the result of the Pearson correlation test in a more concise way

Of course, the last sentence above is true: to analyze this data in a 
meaningful way, we must invest a bit more thinking into the correct analysis
method!