commit 6d7860f004d4aa8a30750122d601c461d8d90f0b
parent bc5c3ae1941b254878d73fc00c77b5b5365de451
Author: Fgazzelloni <61802414+Fgazzelloni@users.noreply.github.com>
Date: Wed, 22 Jun 2022 16:06:58 +0200
chapter5 (#14)
* chapter5
* Add files via upload
* updating chapter5
Diffstat:
4 files changed, 218 insertions(+), 4 deletions(-)
diff --git a/05_Control_flow.Rmd b/05_Control_flow.Rmd
@@ -2,12 +2,226 @@
**Learning objectives:**
-- THESE ARE NICE TO HAVE BUT NOT ABSOLUTELY NECESSARY
+- What are the **tools** for controlling a flow
+- What is the difference between **choices (if, switch)** and **loops (for, while)**
+- How to use conditional tools in data analysis
+
+---
+
+## Introduction
+
+In this chapter we will see how to use conditions for making data analysis.
+There are two main group of conditional tools: **choices** and **loops**. These are both very useful for making iterating data analysis such as multiple substitutions matching predefined inputs or performing more or less flexible indexing.
+
+```{r echo=FALSE,fig.align='center',fig.dim="100%"}
+knitr::include_graphics("images/whatif.png")
+```
+
+---
+
+
+## Choices and Loops
+
+Iterators of objects pointing to an element inside the container
+
+Use if to specify a block of code to be executed, if a specified condition is true. Use else to specify a block of code to be executed, if the same condition is false. Use else if to specify a new condition to test, if the first condition is false.
+
+```{r echo=FALSE,fig.align='left',fig.dim="100%"}
+knitr::include_graphics("images/whatif2.png")
+```
+```{r echo=FALSE,fig.align='right',fig.dim="100%"}
+knitr::include_graphics("images/forloop.png")
+```
+
+----
+
+
+## Choises
+
+- `if()` and `ifelse()`
+
+
+```{r 05-lib,include=FALSE}
+library(DiagrammeR)
+```
+<center>
+```{r echo=FALSE, fig.align='center', fig.dim="100%"}
+DiagrammeR("
+graph TD
+A{if}-->B(condition)
+ B-->C(true action)
+
+D{if}-->E(condition)
+ E-->F(true action)
+E-->G((else))
+ G-->H(false action)
+
+I{ifelse}-->L(condition)
+ L-->M(true action)
+ L-->N(false action)
+
+style A fill:#f96
+style D fill:#f96
+style I fill:#f96
+style G fill:#f96
+style B fill:#bbf,stroke:#f66,stroke-width:2px,color:#fff,stroke-dasharray: 5 5
+style E fill:#bbf,stroke:#f66,stroke-width:2px,color:#fff,stroke-dasharray: 5 5
+style L fill:#bbf,stroke:#f66,stroke-width:2px,color:#fff,stroke-dasharray: 5 5
+
+ ",height = '100%', width = '100%')
+```
+</center>
+
+`if (condition) true_action`
+<br>
+`if (condition) true_action else false_action`
+
+```{r eval=FALSE, include=T}
+if (test_expression) {
+ yes
+ }
+
+
+if (test_expression) {
+ yes
+ } else if (other test_expression) {
+ no
+ } else {
+ other
+ }
+
+---
+ifelse(test, yes, no)
+```
+
+Note: **What is the difference?**
+
+`dplyr::if_else()` and base R `ifelse()`
+
+>if_else is more strict. It checks that both alternatives are of the same type and otherwise throws an error, while ifelse will promote types as necessary. This may be a benefit in some circumstances, but may otherwise break scripts if you don't check for errors or explicitly force type conversion.
+source: https://stackoverflow.com/questions/50646133/dplyr-if-else-vs-base-r-ifelse
+
+**For example:**
+
+```{r eval=FALSE, include=T}
+ifelse(c(TRUE,TRUE,FALSE),"a",3)
+dplyr::if_else(c(TRUE,TRUE,FALSE),"a",3)
+```
+
+It releases an error in `dplyr::if_else(c(TRUE, TRUE, FALSE), "a", 3)` :
+
+`must be a character vector, not a double vector.`
+
+
+
+An alternative to `ifelse` defined as **condition-vector pairs** and more broadly as a general vectorised `if` is:
+
+- `case_when()`
+
+It allows you to vectorise multiple `if_else()` statements
+
+
+<center>
+```{r echo=FALSE, fig.align='center', fig.dim="100%"}
+DiagrammeR("
+ graph TD
+
+I{ifelse}-->L(condition)
+
+A{case_when}-->B(condition)
+
+C{switch}-->D((list of conditions))
+
+style I fill:#f27,stroke:#f66,stroke-width:3px
+style A fill:#f99,stroke:#f66,stroke-width:3px
+style C fill:#f96,stroke:#f66,stroke-width:3px
+style L fill:#bbf,stroke:#f66,stroke-width:2px,color:#fff,stroke-dasharray: 5 5
+style B fill:#bbf,stroke:#f66,stroke-width:2px,color:#fff,stroke-dasharray: 5 5
+style D fill:#bbf,stroke:#f66,stroke-width:2px,color:#fff,stroke-dasharray: 5 5
+
+ ",height = '100%', width = '100%')
+```
+</center>
+
+Finally, the last tool just mentioned is `switch()`, here is an example on how to use it:
+
+```{r message=FALSE, warning=FALSE}
+require(stats)
+centre <- function(x, type) {
+ switch(type,
+ mean = mean(x),
+ median = median(x),
+ trimmed = mean(x, trim = .1))
+}
+
+set.seed(123)
+x <- rcauchy(10)
+
+require(ggplot2)
+ggplot(data = data.frame(x), aes(x))+
+ geom_density()+
+ geom_vline(xintercept = c(centre(x, "mean"),
+ centre(x, "median"),
+ centre(x, "trimmed")),
+ size=0.5,linetype="dashed",
+ color=c("darkgreen","red","blue"))+
+ xlim(-10,10)+
+ theme_bw()
+```
+
+
+## Loops
+
+Iteration of a set of values with:
+
+- `for (var in seq) expr`
+- `while (cond) expr`
+- `repeat expr`
+- `break`
+- `next`
+
+
+`for (item in vector) perform_action`
+
+```{r eval=FALSE, include=T}
+? for (variable in vector) {
+
+}
+```
+
+**First example**
+```{r}
+for(i in 1:5) {
+ print(1:i)
+}
+```
+
+
+**Second example**: terminate a *for loop* earlier
+```{r}
+for (i in 1:10) {
+ if (i < 3)
+ next
+
+ print(i)
+
+ if (i >= 5)
+ break
+}
+```
+
+
+**More tools**
+
+- `while(condition) action`: performs action while condition is TRUE.
+- `repeat(action)`: repeats action forever (i.e. until it encounters break).
+
+>Generally speaking you shouldn’t need to use for loops for data analysis tasks, as map() and apply() already provide less flexible solutions to most problems. You’ll learn more in Chapter 9.
+
+---
+
-## SLIDE 1
-- ADD SLIDES AS SECTIONS (`##`).
-- TRY TO KEEP THEM RELATIVELY SLIDE-LIKE; THESE ARE NOTES, NOT THE BOOK ITSELF.
## Meeting Videos
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