Below we will use the provided ASV data to combine two data frames and perform some typical calculations.
Get temperature data from data frame
Isolate samples that have a temperature greater than 15 C.
asv_long %>%
filter(temp > 15)
# A tibble: 42,039 × 10
FeatureID Taxon SAMPLEID value VENT SITE Sample_or_Control SAMPLETYPE DEPTH
<chr> <chr> <chr> <int> <chr> <chr> <chr> <chr> <chr>
1 00056209… Euka… GordaRi… 0 Cand… Gord… Sample Vent 2730
2 00056209… Euka… GordaRi… 0 Sir … Gord… Sample Vent 2732
3 00056209… Euka… GordaRi… 0 Cand… Gord… Sample Vent 2730
4 00056209… Euka… GordaRi… 0 Sir … Gord… Sample Vent 2732
5 00056209… Euka… GordaRi… 0 Mt E… Gord… Sample Vent 2707
6 00056209… Euka… GordaRi… 0 Mt E… Gord… Sample Vent 2707
7 00056209… Euka… GordaRi… 0 Sir … Gord… Sample Vent 2732
8 00056209… Euka… GordaRi… 0 Mt E… Gord… Sample Vent 2707
9 00056209… Euka… GordaRi… 0 Cand… Gord… Sample Vent 2730
10 00096455… Euka… GordaRi… 0 Cand… Gord… Sample Vent 2730
# ℹ 42,029 more rows
# ℹ 1 more variable: temp <dbl>
# Make a list of vent sites from Gorda Ridge that had a temperature greater than 15 C.
What are the average temperatures at the vent, plume, versus background samples? And how many of each sample are there?
Calculate relative abundance
Create a table with sequence counts and ASV counts by supergroup.