lesson_answers.Rmd

---
title: "Lesson"
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
#Benefits of coding
  -Reproducible
  -Accurate
  
#Rstudio
Rstudio is an integrated development environment (IDE) for R.
  Panes:
  -Console (like R x64 4.04)
  -Source (R files)
  -Environment, History, Connections, Tutorials
  -Files, Plots, Packages, Help, Viewer

#Rmarkdown
-Script: a text file for storing and running code (comments have a hash-mark before them)
-R Markdown:    interactive file for writing anything from notes to entire manuscripts
                contains code "chunks" for storing and running code
                can be easily converted to (knitted into) an html file, a word document, or a PDF

#Variables, Vectors, and Functions

  Definitions:
  -Variable: an object that stores information, i.e. a number, text, or a vector
  -Vector: a data structure that contains multiple elements of the same type (class)
  -Function: an object that takes arguments (aka numbers or vectors) as inputs, performs a task on those arguments, and sometimes has an output.  The output of a function can be stored as a variable
  

  Tricks:
  -Run button (green play button): Run all highlighted or current line of code
   Ctrl + Enter or Command+Enter: Run all highlighted or current line of code 
  -#: Comment out text, this allows you to type notes in a script.  It is considered good practice to explain your code as you write it
  -?function_name: Help
  
  Code from script:
```{r}
print(paste0("The sum of 3 and 2 is ", 3+2))  #add 3+2
mean(c(1,2,3)) #take the mean of 1, 2, and 3
y <- 3+2  #store the sum of 3 and 2 as a variable called y
y   #print y
remove(y)   #remove  y from the environment
y <- c(1,2,3)   #store the vector of 1, 2, and 3 as a variable called y
mean(y)   #Use the function, mean(), to calculate the mean of vector y (to calculate the mean of 1, 2, and 3) and print the answer (aka output of the function)
z <- mean(y) #Store the output from mean(y) in the variable, z
```

Character strings

Examples:
```{r}
my_name <- "Mercedes"
my_name
dogs <- c("rottweiler", "husky", "malamute", "chihuahua", "golden retriever")

```

**Exercises:**

1. Make a new variable containing the number 13
2. Make a new variable 1345 times the variable above (doing the calculation in code)
3. Make new variables containing the name of you and your friend
4. Make a vector containing your name and your friend's name.  Hint: use the variables you created in 3.

```{r}
new <- 13
new2 <- new*1345

my_name <- "Mercedes"
friends_name <- "Eli"

c(my_name, friends_name)
```

#Directories and R projects
  -R works with your computer's file system.
  -Where does R think we are?
```{r}
getwd()
```

  -R Project: a file that points R at whatever folder the file is saved in


The final pane:
  -Files
    Mini file explorer for R Projects
  

  -Now, after creating a project file, where does R think we are?
```{r}
getwd()
```


#Packages and the Tidyverse
Package: A collection of functions.  Note, sometimes packages contain multiple packages, or multiple collections of functions, i.e. tidyverse. 

- Install package by using `install.packages("package-name")`
- Update packages by using `update.packages("package-name")`
- Load packages by using `library("package-name")`
- tidyverse is a useful collection of packages
- Possible to install packages from places other than cran (e.g. github, r-forge, local computer)
  - ways to install

Good instructions [here if needed](https://datacarpentry.org/R-ecology-lesson/#setup_instructions)

Install tidyverse
```{r}
install.packages("tidyverse")
```

Load tidyverse into R: Even though it is installed, R cannot use any of the functions from the tidyverse package until it is loaded into the session.  
```{r}
library(tidyverse)
```
  You must reload packages every session so it is easiest to code out which packages you need in your R markdown or script and run the code at the beginning of each session.

The final pane:
  -Packages
    See all installed packages
    Loaded packages will have a tick mark next to them


#CSV files and Data Frames

```{r}
read_csv("Data/Bats_data.csv")
```

**Exercises:** Breakout Session

1. Store Bats_data.csv in a variable, i.e. bats 
Hint: You'll need the code above and the assign arrow.

2. Use the View() function to look at the data.  i.e.View(bats) 
Note: View() is a function that takes one argument.  You can use your variable as the argument just like when we used mean(y) when y was the variable name.
```{r}
bats <- read_csv("Data/Bats_data.csv")

View(bats)
```


#Manipulating Data

Each column in a data frame is a vector
  -$: extracts a vector from the data frame
  
Examples
```{r}
bats$Date
bats$Activity

```

## Logicals in R
  -   == Equal to
  -   < Less than
  -   > Greater than
  -   <= Less than or equal to 
  -   >= Greater than or equal to
  -   | or
  -   & and
  -   ! not
  
Examples
```{r}
5 == 5
x <- 5
x == 5
x > 5
x >= 5
x > 5 | x >=5
x > 5 & x >= 5
y<- "hello"
y == "hello"
x > 5 | y == "hello"
x > 5 & y == "hello"

```

##filter(data, expression)

Examples:
```{r}
filter(bats, bats$Treatment_thinned == "medium-term")
filter(bats, Treatment_thinned == "medium_term")

bats %>% 
  filter(Treatment_thinned == "medium-term") %>% 
  filter(Foraging > 10)

```
 
**Exercises:** Breakout Session

Find the maximum Activity observed on 7/01/2013
1. Filter by Date for 7/01/2013
2. Save the filtered data as a new variable, i.e. filtered_bats
3. Use the max() function on the column Activity from your new data frame.  Hint: use $ to access the vector

```{r}
filtered_bats <- bats %>% 
  filter(Date == "7/01/2013")

max(filtered_bats$Activity)
```

#Visualizing data: Teaser for future workshops

```{r}
##Boxplot of Treatment_thinned on the x-axis and Activity on the y-axis
bats %>% 
ggplot()+ #If no mapping in ggplot, any additional geom_ functions will need their own mapping statements, see below
  geom_boxplot(mapping = aes(x = Treatment_thinned, y = Activity)) #boxplot

#Boxplots separated out by date
bats %>% 
ggplot(mapping = aes(x = Treatment_thinned, y = Activity))+
  geom_boxplot()+ #boxplot
  facet_wrap(facets = vars(Date))

#or....
plot <- bats %>% 
  ggplot(mapping = aes(x = Treatment_thinned, y = Activity))+
  geom_boxplot() #boxplot

plot +
  facet_wrap(facets = vars(Date)) #Tip: facets = ~Date is Shorthand for facets = vars(Date).  

#_______________________________________________________________________________
##Scatter plot of 

plot2 <- bats %>% 
  ggplot(mapping = aes(x = log(Foraging), y = Activity))+ #if mapping in ggplot, x and y will be handed to any geom_ functions afterwards.
  geom_point()+ #scatter plot
  geom_smooth(method = "lm", se = FALSE) #plot a smooth curve of the linear regression line (linear model) of the data

plot

plot2
```

# Resources

Personal Recommendations:
- ["RStudio Primers" Step by step interactive tutorials](https://rstudio.cloud/learn/primers)
- ["R Resources for Beginners"](https://unsw-coders.netlify.app/resources/2021-03-22-beginner-resources/)
- ["Functions"](https://www.stat.berkeley.edu/~statcur/Workshop2/Presentations/functions.pdf)

Creating and manipulating variables

- ["Making new variables" on environmentalcomputing](http://environmentalcomputing.net/making-new-variables/)
- ["Creating R objects" on DataCarpentry](https://datacarpentry.org/R-ecology-lesson/01-intro-to-r.html)
- ["Software carpentry"'s intor to RStudio'](http://swcarpentry.github.io/r-novice-gapminder/01-rstudio-intro/index.html)

A few resources for loading other file types

- [readxl](https://readxl.tidyverse.org/reference/read_excel.html): Reading data direct from Excel
- [datapasta](https://cran.r-project.org/web/packages/datapasta/): when copying and pasting data from the web
- [googlesheets4](https://googlesheets4.tidyverse.org/): for reading data from google sheets

Online courses:

- [Environmental Computing](http://environmentalcomputing.net/) 
- [Software carpentry's R novice lesson](http://swcarpentry.github.io/r-novice-gapminder/)
- [Datacarpentry's lesson on R for ecology](https://datacarpentry.org/R-ecology-lesson/)
- [A paper looking at how much R is used in Ecology](https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.2567)
- [The tidyverse web page](https://www.tidyverse.org/)
- [Hadley Wickham's book R for data Science](https://r4ds.had.co.nz/)


Learning to code and need inspiration ?

- [Anyone can code](https://www.youtube.com/watch?v=qYZF6oIZtfc&list=PLzdnOPI1iJNe1WmdkMG-Ca8cLQpdEAL7Q)
- [Coding is the new literacy](https://www.youtube.com/watch?v=MwLXrN0Yguk&list=PLzdnOPI1iJNe1WmdkMG-Ca8cLQpdEAL7Q)
- [What most schools don't teach](https://www.youtube.com/watch?v=nKIu9yen5nc&feature=c4-overview-vl&list=PLzdnOPI1iJNe1WmdkMG-Ca8cLQpdEAL7Q)
- when coding saves you time, [from XKCD](http://xkcd.com/1205/)

Why good code matters

- [Why I want to write nice R code](http://nicercode.github.io/blog/2013-04-05-why-nice-code/)
- [Science has a credibility problem](http://www.economist.com/news/leaders/21588069-scientific-research-has-changed-world-now-it-needs-change-itself-how-science-goes-wrong)