07-workshop-objects.Rmd

---
title: "Naming objects"
subtitle: "Session 7"
institute: "NHS-R Community"
output:
  xaringan::moon_reader:
    css: 
      - default
      - css/nhsr.css
      - css/nhsr-fonts.css
    lib_dir: libs
    seal: false
    self_contained: true
    nature:
      highlightStyle: googlecode
      highlightLines: true
      highlightLanguage: ["r"]
      countIncrementalSlides: false
      ratio: "16:9"
    includes:
      after_body: [css/insert-logo.html]
---

```{r libs, include=FALSE}
library(knitr)
library(magick)
library(tidyverse)
library(xaringan)
library(kableExtra)
library(icons)
library(xaringanExtra)

xaringanExtra::use_panelset()
xaringanExtra::use_clipboard()
xaringanExtra::use_share_again() # need to get the slide button on html view

opts_chunk$set(
  echo = TRUE,
  eval = FALSE,
  message = FALSE,
  warning = FALSE,
  fig.width = 7.252,
  fig.height = 4,
  dpi = 300,
  dev.args = list(type = "cairo")
)

# Load data -----------------------------------

beds_data <- read_csv(url("https://raw.githubusercontent.com/nhs-r-community/intro_r_data/main/beds_data.csv"), col_types = cols(date = col_date(format = "%d/%m/%Y")), 
    skip = 3)

```

class: title-slide, left, bottom

# `r rmarkdown::metadata$title`
----
## **`r rmarkdown::metadata$subtitle`**
### `r rmarkdown::metadata$author`
### `r rmarkdown::metadata$date`

.right-column[

]

---

# Let's revisit our dplyr session:

</br> The mean number of beds available by date

```{r eval=TRUE}
beds_data %>% 
  group_by(date) %>% 
  summarise(mean_beds = mean(beds_av, na.rm = TRUE))

```

</br> Output = Object = New data frame!

---

class: inverse, middle, center

# Next step: Visualise

---

# Piecing it together

We can put the output...

```{r }

    beds_data %>%
         group_by(date) %>% 
         summarise(mean_beds = mean(beds_av, na.rm = TRUE)) 

```

</br> ... into ggplot2 code...

```{r}
ggplot(
         beds_data %>% #<<
         group_by(date) %>% #<<
         summarise(mean_beds = mean(beds_av, na.rm = TRUE)) #<<
) + 
   geom_point(aes(x = date, y = mean_beds))

```

---

class: center, middle

# But, it's often better to:

## Keep wrangling separate

---

class: center, middle

# And it's <ins>always</ins> better to:

## Keep your code .green[as] readable .green[as possible]

---

# Solution:

This whole 'chunk' of code can be named

```{r}
# name = 

beds_data %>% 
  group_by(date) %>% 
  summarise(mean_beds = mean(beds_av, na.rm = TRUE))

```

--

</br> With this:

```{r}
beds_ts <- beds_data %>% #<<
  group_by(date) %>% 
  summarise(mean_beds = mean(beds_av, na.rm = TRUE))

```

---

# Good (object) names are:

</br> 1. Descriptive
</br>
</br> 2. Short.blue[(ish)]
</br>
</br> 3. Consistent .blue[with other names]

--

</br> 
</br> Whilst = will work, R convention is to use the following assignment operator:

```{r eval=FALSE}
<- 
```

Shortcut: <kbd> Alt - </kbd>
---

# What happens when the code is run?

The code creates an object which can be seen in the top right quadrant Environment pane

<img class="center" src="img/session07/object-creation.PNG" width="40%"/>

And this can be viewed by running the code line:

```{r}
beds_ts
```

---
# Returning to the plot

The long code used to create the dataset

```{r}
ggplot(data = beds_data %>% #<<
         group_by(date) %>% #<<
         summarise(mean_beds = mean(beds_av, na.rm = TRUE))) + #<<
     geom_point(aes(x = date, y = mean_beds)) #<<
```

--

</br> 
Becomes...

```{r}
ggplot(data = beds_ts) + #<<
     geom_point(aes(x = date, y = mean_beds))
```

---

# Your turn

Give your plot a name

```{r}
# plot = 

ggplot(data = beds_ts) +
     geom_point(aes(x = date, y = mean_beds))

```

Then run your plot

```{r}
# plot
```

---

# Solution

```{r}

plot <- ggplot(data = beds_ts) + #<<
     geom_point(aes(x = date, y = mean_beds))

plot #<<
```

---

# Naming style

The way names are written out is a question of style but it's best to be consistent. 

The two common forms in R are camelCase and snake_case but others are:

```{r}
camelCase # first letter is small case

PascalCase # every letter is capital

snake_case # lower case and words are separated with underline

kebab-case # lower case and hyphen, used in RMarkdown but not R scripts
```

---

#### This work is licensed as
</br> Creative Commons
</br> Attribution
</br> ShareAlike 4.0
</br> International
</br> To view a copy of this license, visit
</br> https://creativecommons.org/licenses/by/4.0/