In this lab you can use the interactive console to explore or Knit the document. Remember anything you type here can be “sent” to the console with Cmd-Enter (OS-X) or Ctrl-Enter (Windows/Linux) in an R code chunk.

Part 1

1.1

Read in the SARS-CoV-2 wastewater data from URL https://daseh.org/data/SARS-CoV-2_Wastewater_Data.csv and assign it to an object named covid.

# General format
library(readr)
# OBJECT <- read_csv(FILE)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ 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
covid <- read_csv(file = "https://daseh.org/data/SARS-CoV-2_Wastewater_Data.csv")
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
## Rows: 776059 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (12): reporting_jurisdiction, sample_location, key_plot_id, county_names...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

1.2

Filter the dataset so that the “reporting_jurisdiction” column is equal to “Maryland”. Store the modified dataset as covid_filtered.

# General format
NEW_OBJECT <- OBJECT %>% filter(COLUMNNAME == CRITERIA)
covid_filtered <- covid %>% filter(reporting_jurisdiction == "Maryland")

1.3

Write out the covid_filtered object as a CSV file calling it “covid_filtered.csv”, using write_csv():

write_csv(covid_filtered, file = "covid_filtered.csv")

Practice on Your Own!

P.1

Copy your code from problem 1.3 and modify it to write to the data directory inside your R Project. Note: you may need to make a new folder named “data” if it doesn’t already exist.

getwd()
dir.create("data")
write_csv(covid_filtered, file = "data/covid_filtered.csv")

P.2

Write one of the objects in your Environment to your working directory in rds format. Call the file my_variable.rds.

y <- c(10, 20, 30, 40, 50, 60)
write_rds(y, file = "my_variable.rds")

P.3

Read the RDS file from your working directory back into your Environment. Call the file z.

z <- read_rds(file = "my_variable.rds")