This is where you should prepare yourself for all of the work below. In other words, load all of the packages you might need to do the work you are doing below. It is generally a good idea to load data and packages at the top of an R document/script. Today we are just loading packages since reading in data is part of our tutorial.
#Loading ALL of my necessary packages here :)
library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.3.6 ✔ purrr 0.3.4
✔ tibble 3.1.8 ✔ dplyr 1.0.10
✔ tidyr 1.2.0 ✔ stringr 1.4.1
✔ readr 2.1.2 ✔ forcats 0.5.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()library(ggplot2)Need help making a proj? Item number 3 in This tutorial can help.
#My rproj working directory is:
getwd()[1] "/Users/julialewis-borgia/Documents/SDS300/r_labs/lab1"setwd("/Users/julialewis-borgia/Documents/SDS300/r_labs/lab1/testFolder")
setwd("/Users/julialewis-borgia/Documents/SDS300/r_labs/lab1")
getwd()[1] "/Users/julialewis-borgia/Documents/SDS300/r_labs/lab1"# I went Tools –> Global options –> General and then changed working directory there before changing it back
getwd()[1] "/Users/julialewis-borgia/Documents/SDS300/r_labs/lab1"Go to the course moodle page and find the Labs section. Download the ‘reef_life_survey_habitat.csv’ file. Save it somewhere meaningful (your course rproj directory, perhaps?). Now, read it into R USING CODE (not the ‘import dataset’ GUI). Using read.csv() (base R) or read_csv (tidyverse) is strongly preferred. Have another method? Ask me first!
#Reading in data from a file, naming is, and preparing it as an R object for later use
reef_life_survey <-read.csv('reef_life_survey_habitat.csv')
head(reef_life_survey) FID Key SiteCode Site SiteLat SiteLong Country
1 M3_DATA.1 1 SI16 Eastern Bay Sth Solitary -30.2 153.27 Australia
2 M3_DATA.2 2 SI16 Eastern Bay Sth Solitary -30.2 153.27 Australia
3 M3_DATA.3 3 SI16 Eastern Bay Sth Solitary -30.2 153.27 Australia
4 M3_DATA.4 4 SI16 Eastern Bay Sth Solitary -30.2 153.27 Australia
5 M3_DATA.5 5 SI16 Eastern Bay Sth Solitary -30.2 153.27 Australia
6 M3_DATA.6 6 SI16 Eastern Bay Sth Solitary -30.2 153.27 Australia
Ecoregion Realm SurveyID Depth SurveyDate
1 Tweed-Moreton Temperate Australasia 2000974 9 2009-08-22T14:00:00Z
2 Tweed-Moreton Temperate Australasia 2000974 9 2009-08-22T14:00:00Z
3 Tweed-Moreton Temperate Australasia 2000974 9 2009-08-22T14:00:00Z
4 Tweed-Moreton Temperate Australasia 2000974 9 2009-08-22T14:00:00Z
5 Tweed-Moreton Temperate Australasia 2000974 9 2009-08-22T14:00:00Z
6 Tweed-Moreton Temperate Australasia 2000974 9 2009-08-22T14:00:00Z
Category CategoryDescription MajorCategory NumPoints
1 Bare Bare Rock Other 6
2 Ecr Encrusting corals Coral 1
3 Sand Sand Other 1
4 Bfol Small to medium foliose brown algae Algae 11
5 CCA Crustose coralline algae Algae 14
6 FEA Filamentous epiphytic algae Algae 4
TotalPoints PercentCover geom
1 100 6 POINT (153.27 -30.2)
2 100 1 POINT (153.27 -30.2)
3 100 1 POINT (153.27 -30.2)
4 100 11 POINT (153.27 -30.2)
5 100 14 POINT (153.27 -30.2)
6 100 4 POINT (153.27 -30.2)Follow this link to my github page. Next, find the file ‘belize_coral_survey_data_2016.csv’ and read that file into R using the URL. Need help with this? Try looking here first, then ask me!
#Reading in some coral survey from Justin's github to practice with!
belize_2016 <- read.csv("https://raw.githubusercontent.com/jbaumann3/BIOL234_Biostats_MHC/main/belize_coral_survey_data_2016.csv")
head(belize_2016) 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#select columns
belize_2016_partial <- select(belize_2016, type, lat, life.history, species, percent.of.cover)
head(belize_2016_partial) type lat life.history species percent.of.cover
1 Back Reef 3 Stress Tolerant SSID 0.30
2 Back Reef 3 <NA> MCOM 0.90
3 Back Reef 3 Stress Tolerant SSID 0.80
4 Back Reef 3 Generalist OFAV 0.90
5 Back Reef 3 Weedy UTEN 0.30
6 Back Reef 3 Stress Tolerant SSID 0.85#Filter the data
belize_2016_partial <- belize_2016_partial %>% drop_na(life.history)
head(belize_2016_partial) type lat life.history species percent.of.cover
1 Back Reef 3 Stress Tolerant SSID 0.30
2 Back Reef 3 Stress Tolerant SSID 0.80
3 Back Reef 3 Generalist OFAV 0.90
4 Back Reef 3 Weedy UTEN 0.30
5 Back Reef 3 Stress Tolerant SSID 0.85
6 Back Reef 3 Generalist OFAV 0.80#Add a new column that assigns a name to each latitude
belize_2016_partial <- belize_2016_partial %>% mutate(coastal_town = case_when(lat == 1 ~ "San Pedro", lat == 2 ~ "Belize City", lat == 3 ~ "Dangriga", lat == 4 ~ "Placencia", lat == 5 ~ "Punta Gorda"))
head(belize_2016_partial) type lat life.history species percent.of.cover coastal_town
1 Back Reef 3 Stress Tolerant SSID 0.30 Dangriga
2 Back Reef 3 Stress Tolerant SSID 0.80 Dangriga
3 Back Reef 3 Generalist OFAV 0.90 Dangriga
4 Back Reef 3 Weedy UTEN 0.30 Dangriga
5 Back Reef 3 Stress Tolerant SSID 0.85 Dangriga
6 Back Reef 3 Generalist OFAV 0.80 DangrigaMake 3 exploratory plots in ggplot. You can manipulate data anyway you’d like. I strongly recommend avoiding bar graphs.
# plot 1
# find center and spread (box plot,
# anova or regression would be used as a stats test on this data (multiple (more than two) categorical variables, one numerical variable)
boxplot <- ggplot(belize_2016, aes(x=type, y=percent.of.cover)) +
geom_boxplot(outlier.shape = NA) +
coord_cartesian(ylim = c(0, 1)) +
labs(x = "Coral Reef Type", y = "Percent of Cover")
boxplotWarning: Removed 2286 rows containing non-finite values (stat_boxplot).
# plot 2
boxplot2 <- ggplot(belize_2016, aes(x=life.history, y=percent.of.cover)) +
geom_boxplot(outlier.shape = NA) +
coord_cartesian(ylim = c(0, 1)) +
labs(x = "Life History", y = "Percent of Cover")
boxplot2Warning: Removed 2286 rows containing non-finite values (stat_boxplot).
# plot 3
scatterplot <- ggplot(belize_2016, aes(x=new, y=percent.of.cover)) + geom_point(outlier.shape = NA) +
coord_cartesian(ylim = c(0, 2)) +
labs(x = "New", y = "Percent of Cover")Warning: Ignoring unknown parameters: outlier.shapescatterplotWarning: Removed 2287 rows containing missing values (geom_point).
#save your dataframe to file
write.csv(belize_2016_partial,"C:\\Users\\julialewis-borgia\\Documents\\SDS300\\r_labs\\lab1", row.names=FALSE)You can do this however works for you. You should be able to turn in a link to your html file (if you publish on Rpubs or github, for exmaple) or you can turn in your actual html file. Ensure that is works though! IF I can’t load it you will get a U (and need to revise and resubmit)