Lab 0: Getting Started
Heidi NydamQuestion 1 I reopened R, closed my previous projects from the summer, and began working on this project. I followed this tutorial from Professor Baumann.Question 2 Question 3 Question 4
# install.packages("tidyverse") # install.packages("lubridate") # install.packages("performance") # install.packages("palmerpenguins") # install.packages("patchwork") # install.packages("ggsci") 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
library (lubridate)library (performance)library (palmerpenguins)library (patchwork)library (ggsci)
Question 5
<- read.csv ('https://raw.githubusercontent.com/jbaumann3/BIOL234_Biostats_MHC/main/belize_coral_survey_data_2016.csv' )head (labzero)
method site type lat transect diver life.history species
1 Video 1 Back Reef 3 1 4 Stress Tolerant SSID
2 Video 1 Back Reef 3 1 4 <NA> MCOM
3 Video 1 Back Reef 3 1 4 Stress Tolerant SSID
4 Video 1 Back Reef 3 1 4 Generalist OFAV
5 Video 1 Back Reef 3 1 4 Weedy UTEN
6 Video 1 Back Reef 3 1 4 Stress Tolerant SSID
percent.of.cover l w area pale bleached total.pb
1 0.30 5.62500 5.625 31.640625 0 0 0
2 0.90 10.80000 5.4 58.32 0 0 0
3 0.80 29.41176 12.94117647 380.6228374 0 0 0
4 0.90 26.71875 18.28125 488.4521484 0 0 0
5 0.30 11.78571 16.07142857 189.4132654 0 0 0
6 0.85 21.25000 13.66071429 290.2901787 0 0 0
percent.pb new trans old total.mort percent.mort disease percentdisease
1 0 0.7 0 0 16 70 0 0
2 0 0.0 0 0 2 0 0 0
3 0 0.0 0 0.4 10 40 0 0
4 0 0.0 0 0 2 0 0 0
5 0 0.0 0 0 2 0 0 0
6 0 0.1 0 0 4 10 0 0
method site type lat transect diver life.history species
5218 AGRRA 14 Patch Reef 1 6 3 Weedy PAST
5219 AGRRA 14 Patch Reef 1 6 3 <NA> PDIV
5220 AGRRA 14 Patch Reef 1 6 3 Stress Tolerant SSID
5221 AGRRA 14 Patch Reef 1 6 3 <NA> MCOM
5222 AGRRA 14 Patch Reef 1 6 3 Weedy PAST
5223 AGRRA 14 Patch Reef 1 6 3 Weedy PAST
percent.of.cover l w area pale bleached total.pb percent.pb new trans
5218 NA 20 10 200 0 0 0 0 0 0
5219 NA 25 15 375 0 0 0 0 0 0
5220 NA 40 30 1200 0 0 0 0 0 0
5221 NA 35 25 875 0 0 0 0 0 0
5222 NA 5 5 25 0 0 0 0 0 0
5223 NA 10 10 100 0 0 0 0 0 0
old total.mort percent.mort disease percentdisease
5218 0 2 0 0 0
5219 0 2 0 0 0
5220 0 2 0 0 0
5221 0 2 0 0 0
5222 0 2 0 0 0
5223 0 2 0 0 0
'data.frame': 5223 obs. of 23 variables:
$ method : chr "Video" "Video" "Video" "Video" ...
$ site : int 1 1 1 1 1 1 1 1 1 1 ...
$ type : chr "Back Reef" "Back Reef" "Back Reef" "Back Reef" ...
$ lat : int 3 3 3 3 3 3 3 3 3 3 ...
$ transect : int 1 1 1 1 1 1 1 1 1 1 ...
$ diver : int 4 4 4 4 4 4 4 4 4 4 ...
$ life.history : chr "Stress Tolerant" NA "Stress Tolerant" "Generalist" ...
$ species : chr "SSID" "MCOM" "SSID" "OFAV" ...
$ percent.of.cover: num 0.3 0.9 0.8 0.9 0.3 0.85 0.8 0.4 0.9 0.85 ...
$ l : num 5.62 10.8 29.41 26.72 11.79 ...
$ w : chr "5.625" "5.4" "12.94117647" "18.28125" ...
$ area : chr "31.640625" "58.32" "380.6228374" "488.4521484" ...
$ pale : num 0 0 0 0 0 0 0 0 0 0 ...
$ bleached : num 0 0 0 0 0 0 0 0 0 0 ...
$ total.pb : num 0 0 0 0 0 0 0 0 0 0 ...
$ percent.pb : num 0 0 0 0 0 0 0 0 0 0 ...
$ new : num 0.7 0 0 0 0 0.1 0 0 0 0 ...
$ trans : chr "0" "0" "0" "0" ...
$ old : chr "0" "0" "0.4" "0" ...
$ total.mort : int 16 2 10 2 2 4 2 2 2 2 ...
$ percent.mort : chr "70" "0" "40" "0" ...
$ disease : int 0 0 0 0 0 0 0 0 0 0 ...
$ percentdisease : int 0 0 0 0 0 0 0 0 0 0 ...
Min. 1st Qu. Median Mean 3rd Qu. Max.
2.57 8.75 14.22 22.19 26.92 600.00
In this section, I looked at both the first 6 rows with (head), the last 6 with (tail) and then creating some summary statistics using (summary). This allows for easy data visualization. Question 6
$ l= as.factor (labzero$ l)
This changes all of the data in the selected column (which is chosen by $), from numerical continuous value and then changes it to a new column where it is a factor. This is a different type of data and is non continuous and easier to graph.Question 7
write.csv (labzero,file= 'labzero.csv' )<- read.csv ('labzero.csv' )
Question 3