Example 01: Up and Running with R

We will be working with the most recent stable versions of R and RStudio, as well as with a number of additional packages. You will need to install R, RStudio, and the necessary packages on your own computer.

1. Install R on your computer

Begin by installing R (http://cloud.r-project.org). Choose the version appropriate for your computing platform:

If you use macOS with an Apple Silicon processor (i.e. an M1, M2, or M3 chip), then install R for macOS’s Apple Silicon build. This version does not work on older, Intel-based Macs.
If you use macOS with an Intel processor, then install R for macOS’s Intel build.
If you use Microsoft Windows, then install R for Windows.
If you use Linux, choose a distribution and install it.

2. Install RStudio on your computer

Follow this link and download RStudio Desktop for your computer. You will have already completed Step 1.

3. Installing some additional packages

Once R and RStudio are installed, launch RStudio. Either carefully type in or copy-and-paste the following lines of code at R’s command prompt, located in the RStudio window named “Console”, and then hit return. To copy this chunk of code, mouse over the code click the clipboard icon, , that appears in the top right corner of the chunk.

course_packages <- c("tidyverse", "babynames", "broom",
    "gapminder", "here", "janitor", "naniar", 
    "palmerpenguins", "skimr", "slider", "socviz",
    "usethis", "visdat","reprex", "remotes")

install.packages(course_packages, repos = "http://cran.rstudio.com")

data_packages <- c("kjhealy/covdata", "kjhealy/congress", "kjhealy/nycdogs", 
                   "kjhealy/ukelection2019", "kjhealy/uscenpops")


remotes::install_github(data_packages)

Installing these packages may take a little time. Once you have completed this step, you’ll be ready to begin.

4. Examples from the slides

Arithmetic:

Logic:

Take care:

But now try 3 < 5 & 1, where your intention is “Three is less than five and also less than one [True or False?]”

Instead:

You have to make your comparisons explicit.

Objects:

Functions are objects too.

Assignment:

Assignment with equals:

On the other hand, = has a different meaning when used in functions.

I’m going to use <- for assignment throughout.

Just be consistent either way.

Special operators

For example, matrix multiplication is %*%

Why %*%? In R the notation %<SOMETHING>% is used for some operators, including custom operators.

But the thing in between the % % can be lots of things. E.g.,

And we can define our own, too

Vector arithmetic

Packages

Packages are loaded into your working environment using the library() function:

You need only install a package once (and occasionally update it):

## Do at least once for each package. Once done, not needed each time.
install.packages("palmerpenguins", repos = "http://cran.rstudio.com")

## Needed sometimes, especially after an R major version upgrade.
update.packages(repos = "http://cran.rstudio.com")

But you must load the package in each R session before you can access its contents:

--- title: "Example 01: Up and Running with R" engine: knitr filters: - webr webr: packages: ['gapminder', 'palmerpenguins', 'tibble'] autoload-packages: false --- We will be working with the most recent stable versions of R and RStudio, as well as with a number of additional packages. You will need to install R, RStudio, and the necessary packages on your own computer. ### 1. Install R on your computer Begin by installing R (<http://cloud.r-project.org>). Choose the version appropriate for your computing platform: - If you use macOS with an Apple Silicon processor (i.e. an M1, M2, or M3 chip), then install [R for macOS's Apple Silicon build](https://cran.r-project.org/bin/macosx/big-sur-arm64/base/R-4.4.2-arm64.pkg). This version does not work on older, Intel-based Macs. - If you use macOS with an Intel processor, then install [R for macOS's Intel build](https://cran.r-project.org/bin/macosx/big-sur-x86_64/base/R-4.4.2-x86_64.pkg). - If you use Microsoft Windows, then install [R for Windows](https://cran.r-project.org/bin/windows/base/R-4.4.2-win.exe). - If you use Linux, [choose a distribution](https://cloud.r-project.org/bin/linux/) and install it. ### 2. Install RStudio on your computer - [Follow this link and download RStudio Desktop for your computer](https://posit.co/download/rstudio-desktop/). You will have already completed Step 1. ### 3. Installing some additional packages - Once R and RStudio are installed, launch RStudio. Either _carefully_ type in or copy-and-paste the following lines of code at R’s command prompt, located in the RStudio window named “Console”, and then hit return. To copy this chunk of code, mouse over the code click the clipboard icon, {{< fa clipboard >}}, that appears in the top right corner of the chunk. ```{r} #| label: setup-1 #| eval: FALSE #| echo: TRUE course_packages <- c("tidyverse", "babynames", "broom", "gapminder", "here", "janitor", "naniar", "palmerpenguins", "skimr", "slider", "socviz", "usethis", "visdat","reprex", "remotes") install.packages(course_packages, repos = "http://cran.rstudio.com") data_packages <- c("kjhealy/covdata", "kjhealy/congress", "kjhealy/nycdogs", "kjhealy/ukelection2019", "kjhealy/uscenpops") remotes::install_github(data_packages) ``` Installing these packages may take a little time. Once you have completed this step, you'll be ready to begin. ### 4. Examples from the slides #### Arithmetic: ```{webr-r} #| label: "02-about-r-7a" (31 * 12) / 2^4 ``` ```{webr-r} #| label: "02-about-r-8a" sqrt(25) ``` ```{webr-r} #| label: "02-about-r-9a" log(100) log10(100) ``` Logic: ```{webr-r} #| label: "02-about-r-10" 4 < 10 4 > 2 & 1 > 0.5 # The "&" means "and" 4 < 2 | 1 > 0.5 # The "|" means "or" 4 < 2 | 1 < 0.5 ``` ```{webr-r} #| label: "02-about-r-11" ## A logical test 2 == 2 # Write `=` twice ``` ```{webr-r} #| label: "02-about-r-12" #| eval: FALSE ## This will cause an error, because R will think you are trying to assign a value 2 = 2 ## Error in 2 = 2 : invalid (do_set) left-hand side to assignment ``` ```{webr-r} #| label: "02-about-r-13" 3 != 7 # Write `!` and then `=` to make `!=` ``` Take care: ```{webr-r} #| label: "02-about-r-14" 3 < 5 & 7 ``` But now try `3 < 5 & 1`, where your intention is "Three is less than five and also less than one [True or False?]" ```{webr-r} #| label: "02-about-r-15" 3 < 5 & 1 ``` Instead: ```{webr-r} #| label: "02-about-r-17" 3 < 5 & 3 < 1 ``` You have to make your comparisons explicit. #### Objects: ```{webr-r} #| label: "02-about-r-21" ## We made this before my_numbers <- c(1, 1, 2, 4, 1, 3, 1, 5) my_numbers letters # This one is built-in LETTERS # Different! pi # Also built-in ``` Functions are objects too. ```{webr-r} mean ``` #### Assignment: ```{webr-r} #| label: "02-about-r-26" ## name... gets ... this stuff my_numbers <- c(1, 2, 3, 1, 3, 5, 25, 10) ## name ... gets ... the output of the function `c()` your_numbers <- c(5, 31, 71, 1, 3, 21, 6, 52) ``` Assignment with equals: ```{webr-r} #| label: "02-about-r-27" my_numbers = c(1, 2, 3, 1, 3, 5, 25) my_numbers ``` On the other hand, [**`=`**]{.fg-pink} has a different meaning when used in functions. I'm going to use [**`<-`**]{.fg-pink} for assignment throughout. Just be consistent either way. #### Special operators For example, matrix multiplication is `%*%` ```{webr-r} x <- matrix(c(2,3,3,4,1,8), ncol = 2) x y <- matrix(c(1,2,3,4), nrow = 2) y x %*% y ``` Why `%*%`? In R the notation `%<SOMETHING>%` is used for some operators, including custom operators. But the thing in between the `% %` can be lots of things. E.g., ```{webr-r} x <- letters[1:10] y <- letters[5:15] x y x %in% y ``` And we can define our own, too ```{webr-r} ## Need to refer to the operator in a special way with backticks `%nin%` <- Negate(`%in%`) # Now we have "not in" x %nin% y ``` #### Vector arithmetic ```{webr-r} vec1 <- c(1:20) # the `+1` operation is recycled down the length of vec1 vec1 + 1 ``` ```{webr-r} vec2 <- c(100:120) # Vectors of the same length are added elementwise vec1 + vec2 ``` #### Packages Packages are loaded into your working environment using the `library()` function: ```{webr-r} #| label: "02-about-r-48" ## Display datatframes nicely library(tibble) ## A package containing a dataset rather than functions library(gapminder) gapminder ``` You need only _install_ a package once (and occasionally update it): ```{r} #| label: "02-about-r-49" #| eval: FALSE ## Do at least once for each package. Once done, not needed each time. install.packages("palmerpenguins", repos = "http://cran.rstudio.com") ## Needed sometimes, especially after an R major version upgrade. update.packages(repos = "http://cran.rstudio.com") ``` But you must _load_ the package in each R session before you can access its contents: ```{webr-r} #| label: "02-about-r-50" ## To load a package, usually at the start of your RMarkdown document or script file library(palmerpenguins) penguins ```