In EDS 222 labs, we will often be working with exercises based on Introduction to Modern Statistics (Çetinkaya-Rundel and Hardin, 2021). To be able to access the data and custom packages these exercises sometimes rely on, we need to do a bit of setup. This will get you all set for all future labs; you won’t need to do it each time.
_common.R
from our course website and put it in the Labs/ folder.suppressMessages({
install.packages(c(
"tidymodels",
"gghighlight",
"glue",
"ggmosaic",
"ggridges",
"gridExtra",
"infer",
"janitor",
"knitr",
"kableExtra",
"maps",
"openintro",
"patchwork",
"quantreg",
"tidyverse",
"scales",
"skimr",
"caret",
"palmerpenguins",
"survival",
"waffle",
"ggrepel",
"ggpubr",
"openintro"
))
})
_common.R
source file.2 Note: If you get a namespace load
error for tidyverse
, you want to remove the problem package, reinstall it, and then load tidyverse
again.# You probably already have these packages installed, so let's just load them
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.8.0 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(readr)
library(gt)
library(openintro)
## Loading required package: airports
## Loading required package: cherryblossom
## Loading required package: usdata
##
## Attaching package: 'openintro'
##
## The following object is masked from 'package:gt':
##
## sp500
# Where is the root directory where all your labs live? Change this filepath to work on your computer.
rootdir <- "~/Dropbox/Teaching/UCSB/EDS_222/EDS222_data" #~/Dropbox/EDS222_data/"
Take a look at the help files for source()
and file.path()
functions. We will use these a lot in Labs and homework assignments.
# `source()` is a helpful function for calling and running other R scripts.
?source
# `file.path()` is a helpful function for creating file paths from directory and file names
?file.path
Now, use source
to run the script _common.R
, which loads all the packages we’ll need for our labs and does some extra stuff we won’t use today. You’ll source()
this setup script before running all your labs.
source(file.path(rootdir,"labs","_common.R"))
## Warning: package 'glue' was built under R version 4.0.5
## Warning: package 'caret' was built under R version 4.0.5
For labs, we want to see all our code. Set your knitr
options to echo=TRUE
so you see all your code and output.
Done! First real lab on Thursday in class.