class: center, middle, inverse, title-slide .title[ # Introduction to <br/><br/> <img src="img/Rlogo.png" width="200" /> ] .author[ ### Jason Thomas ] .institute[ ### R Working Group ] .date[ ### Jan. 24th, 2025 ] --- class:slide-font-25 # Welcome to the R Working Group * Website [https://buckipr.github.io/R_Working_Group/](https://buckipr.github.io/R_Working_Group/) * We are Slackers <br> (email Jason at thomas.3912 for an invite) * Let's jump right in + Go to the GitHub page → intro_R → 2025_01_24 + Download the rmarkdown.Rmd file. * Install R packages + Open R Studio: Tools → Install Packages... + install: rmarkdown + (maybe dplyr, haven, and ggplot2 as well) + (in R: Packages & Data → Package Installer) --- class: inverse, center, middle # R Studio --- # R Studio * Let's dive in by starting R Studio and opening a new R script + menu bar: `File` → `New File` → `R Script` + (in R: `File` → `New Script`) * You should now have 4 panes open (like on the next slide) + **Source** -- Our script where we will type and save our comments & commands + **Console** -- Where we can give R commands and where the output will appear + **Output** -- File explorer, plots, help files, and more! + **Environments** -- Useful information about the R session --- .center[<img src="img/rstudio-panes-labeled.jpeg" style="width: 75%" />] .center[.bottom[downloaded from [user guide on postit.co](https://docs.posit.co/ide/user/ide/guide/ui/ui-panes.html)]] --- class:slide-font-25 # R Studio: Good Habits * Add a comment to our new script: ``` r #------------------------------------------------------------------------ # File name: first_r_script.R last modified: 2024-09-13 (start comment with # # and R ignores the rest of the line) #------------------------------------------------------------------------ 3 + 3 # this useful part is for humans (R will add & ignore the rest) ``` * Save our script + menu bar: `File` → `Save As...` * Set our **working directory** + this is where R will start looking for & saving files (e.g., data files or plots) + menu bar: `Session` → `Set Working Directory` → <br>         `Choose Directory...` --- class: inverse, center, middle # Basic R Syntax --- class:slide-font-25 # Basic R Syntax * R syntax takes the form ``` r # object_name <- object_value mean_age <- 33 ``` -- * The symbol "`<-`" is called the assignment operator + we are creating a new variable called `mean_age` and assigning it a value of 33 + `mean_age = 33` will also work (but `<-` is the convention) * Useful(?) keyboard shortcut to produce `<-` + <kbd>Alt</kbd> + <kbd>-</kbd> (Windows) + <kbd>option</kbd> + <kbd>-</kbd> (Mac) --- class: slide-font-20 # Basic R Syntax (cont.) If we enter the name of a variable in the `Console`, then R will list the value(s) ``` r > Mean_age2 <- 22 ## note: object names are case-sensitive > mean_age2 ``` ``` ## Error in eval(expr, envir, enclos): object 'mean_age2' not found ``` ``` r > Mean_age2 ``` ``` ## [1] 22 ``` BUT we are in the business of good habits... * type this syntax into our script and (with the cursor on the same line) press the following keys together: + On a Mac: <kbd>command</kbd> + <kbd>return</kbd> + In Windows: <kbd>Ctrl</kbd> + <kbd>Enter</kbd>   (in R Studio) <br>           <kbd>Ctrl</kbd> + <kbd>R</kbd>       (in the R app) * these keyboard shortcuts will run the syntax on the line in the `Console` <br> (or you can highlight a region) --- class: codefs-50 # Basic R Syntax: help files * Google searches are a very effective way to find help + and so is asking the R Working Group 😎 * R documentation can be accessed in the `Help` tab in the `Output` pane * Some additional syntax and functions ``` r `?`(read.csv # show the help file for the function read.csv ) help.search("weighted mean") # search help files for the phrase 'weighted meant ``` * What does the `save` function do, and how do you use it? What about `save.image`? --- class: inverse, center, middle # Data Structures in R --- ## **Data Structures**: motivation We are not going to solve the world's problems with a single number... ``` r > all_ages <- c(22, 33, 44, 55) # c() concatenates numbers together > all_ages ``` ``` ## [1] 22 33 44 55 ``` ``` r > mean(all_ages) # calculate the mean ``` ``` ## [1] 38.5 ``` ``` r > all_ed <- c("HS", "Col", "Grad Sch", "HS") > all_ed ``` ``` ## [1] "HS" "Col" "Grad Sch" "HS" ``` --- ## **Data Structures**: indexing vectors We can access the `\(i^{th}\)` element in a vector with the syntax `vector_name[ i ]` ``` r > test_scores <- c(88, 92, 67, 79, 72, 85, 96) > test_scores[1] # first element ``` ``` ## [1] 88 ``` ``` r > test_scores[2] # second element ``` ``` ## [1] 92 ``` ``` r > 1:3 # a vector of c(1, 2, 3) ``` ``` ## [1] 1 2 3 ``` ``` r > # so what will test_scores[3:1] give us? ``` --- ## **Data Structures**: indexing vectors (cont.) What will the following command do? 🤔 ``` r test_scores[c(3, 5, 11)] ``` --- ## **Data Structures**: changing vectors We can use indexing to change vectors as well, e.g., reassign the first element ``` r > test_scores[1] <- NA # change the first element to NA > test_scores[1] ``` ``` ## [1] NA ``` Now on to the rmarkdown file