eds212-day3-activitiesCreate a new Quarto document in your docs folder, saved as
conditionals_loops.qmd. Complete all tasks for Part 1 in
this .qmd.
Complete each of the following in a separate code chunk.
Create an object called pm2_5 with a value of 48
(representing Particulate Matter 2.5, an indicator for air quality, in
\(\frac{\mu g}{m^3}\) (see more about
PM2.5 here).
Write an if - else if - else statement that returns “Low
to moderate risk” if pm2_5 (for Particulate Matter 2.5) is
less than 100, “Unhealthy for sensitive groups” if PM 2.5 is 100 <=
pm2_5 < 150, and “Health risk present” if PM 2.5 is >= 150.
Test by changing the value of your pm2_5 object and re-running your statement to check.
Store the string “blue whale” as an object called
species. Write an if statement that returns “You found a
whale!” if the string “whale” is detected in species, otherwise return
nothing. Test by changing the species string & re-running to see
output.
Store the base price of a burrito as base_burrito with a
value of 6.50. Store main_ingredient with a starting string
of “veggie.” Write a statement that will return the price of a burrito
based on what a user specifies as “main_ingredient” (either “veggie”,
“chicken” or “steak”) given the following:
Complete each of the following in a separate code chunk.
Create a new vector called fish that contains the values
8, 10, 12, 23 representing counts of different fish types
in a fish tank (goldfish, tetras, guppies, and mollies, respectively).
Write a for loop that iterates through fish, and returns
what proportion of total fish in the tank are that species. Assume that
these counts represent all fish in the tank.
There is an existing vector in R called month.name that
contains all month names (just ry running month.name in the
Console to check it out). Write a for loop that
iterates over all months in month.name and prints “January
is month 1,” “February is month 2”, etc.
Hint: you can index values in the
month.name vector just like you would any other vector
(e.g., try running month.name[5]).
You will complete Part 3 in a separate .qmd.
Explore this data package from EDI, which contains a “Data file describing the biogeochemistry of samples collected at various sites near Toolik Lake, North Slope of Alaska”. Familiarize yourself with the metadata (particularly, View full metadata > expand ‘Data entities’ to learn more about the variables in the dataset).
Citation: Kling, G. 2016. Biogeochemistry data set for soil waters, streams, and lakes near Toolik on the North Slope of Alaska, 2011. ver 5. Environmental Data Initiative. https://doi.org/10.6073/pasta/362c8eeac5cad9a45288cf1b0d617ba7
Download the CSV containing the Toolik biogeochemistry data
Take a look at it - how are missing values stored? Keep that in mind.
Drop the CSV into your data folder of your project
Create a new Quarto document, save in docs as
toolik_chem.qmd
Attach the tidyverse, here, and
janitor packages in your setup code chunk
Read in the data as toolik_biochem. Remember, you’ll
want to specify here how NA values are stored. Pipe
directly into janitor::clean_names() following your import
code to get all column names into lower snake case.
Create a subset of the data that contains only observations from
the “Toolik Inlet” site, and that only contains the variables (columns)
for pH, dissolved organic carbon (DOC), and total dissolved nitrogen
(TDN) (hint: see dplyr::select()). Store
this subset as inlet_biochem. Make sure to look at the
subset you’ve created.
Find the mean value of each column in inlet_biochem
3 different ways:
apply,
across, or purrr::map_df) to find the mean for
each column.