Answer the following questions and/or complete the exercises in
RMarkdown. Please embed all of your code and push the final work to your
repository. Your report should be organized, clean, and run free from
errors. Remember, you must remove the # for any included
code chunks to run.
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 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.4
## ── 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 errorsoptions(scipen=999) # turn off scientific notationFor the homework, we will use data about vertebrate home range sizes. The data are in the class folder and the reference is below.
Database of vertebrate home range sizes.. Reference:
Tamburello N, Cote IM, Dulvy NK (2015) Energy and the scaling of animal
space use. The American Naturalist 186(2):196-211. http://dx.doi.org/10.1086/682070.
Data: http://datadryad.org/resource/doi:10.5061/dryad.q5j65/1
1. Load the data into a new object called
homerange.
homerange <- read_csv("data/Tamburelloetal_HomeRangeDatabase.csv")## Rows: 569 Columns: 24
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (16): taxon, common.name, class, order, family, genus, species, primarym...
## dbl (8): mean.mass.g, log10.mass, mean.hra.m2, log10.hra, dimension, preyma...
##
## 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.2. What are the dimensions of the dataframe?
dim(homerange)## [1] 569 243. Are there any NA’s in the dataframe? Try using summary to determine which variables have more or less NA’s.
anyNA(homerange)## [1] TRUE4. What are the names of the columns in the dataframe?
names(homerange)## [1] "taxon" "common.name"
## [3] "class" "order"
## [5] "family" "genus"
## [7] "species" "primarymethod"
## [9] "N" "mean.mass.g"
## [11] "log10.mass" "alternative.mass.reference"
## [13] "mean.hra.m2" "log10.hra"
## [15] "hra.reference" "realm"
## [17] "thermoregulation" "locomotion"
## [19] "trophic.guild" "dimension"
## [21] "preymass" "log10.preymass"
## [23] "PPMR" "prey.size.reference"5. Based on the summary output, do you see anything in the data that looks strange? Think like a biologist and consider the variables.
Some of the variables have stange values, like -999.
6. The min and max functions can be
used to find the minimum and maximum values in a vector. Use these
functions to find the minimum and maximum values for the variable
mean.mass.g.
min(homerange$mean.mass.g)## [1] 0.22max(homerange$mean.mass.g)## [1] 40000007. Change the class of the variables taxon and
order to factors and display their levels.
homerange$taxon <- as.factor(homerange$taxon)homerange$order <- as.factor(homerange$order)8. Use select to pull out the variables taxon
and common.name.
select(homerange, taxon, common.name)## # A tibble: 569 × 2
## taxon common.name
## <fct> <chr>
## 1 lake fishes american eel
## 2 river fishes blacktail redhorse
## 3 river fishes central stoneroller
## 4 river fishes rosyside dace
## 5 river fishes longnose dace
## 6 river fishes muskellunge
## 7 marine fishes pollack
## 8 marine fishes saithe
## 9 marine fishes lined surgeonfish
## 10 marine fishes orangespine unicornfish
## # ℹ 559 more rows9. Use filter to pull out all mammals from the
data.
filter(homerange, taxon == "mammals")## # A tibble: 238 × 24
## taxon common.name class order family genus species primarymethod N
## <fct> <chr> <chr> <fct> <chr> <chr> <chr> <chr> <chr>
## 1 mammals giant golden mo… mamm… afro… chrys… chry… trevel… telemetry* <NA>
## 2 mammals Grant's golden … mamm… afro… chrys… erem… granti telemetry* <NA>
## 3 mammals pronghorn mamm… arti… antil… anti… americ… telemetry* <NA>
## 4 mammals impala mamm… arti… bovid… aepy… melamp… telemetry* <NA>
## 5 mammals hartebeest mamm… arti… bovid… alce… busela… telemetry* <NA>
## 6 mammals barbary sheep mamm… arti… bovid… ammo… lervia telemetry* <NA>
## 7 mammals American bison mamm… arti… bovid… bison bison telemetry* <NA>
## 8 mammals European bison mamm… arti… bovid… bison bonasus telemetry* <NA>
## 9 mammals goat mamm… arti… bovid… capra hircus telemetry* <NA>
## 10 mammals Spanish ibex mamm… arti… bovid… capra pyrena… telemetry* <NA>
## # ℹ 228 more rows
## # ℹ 15 more variables: mean.mass.g <dbl>, log10.mass <dbl>,
## # alternative.mass.reference <chr>, mean.hra.m2 <dbl>, log10.hra <dbl>,
## # hra.reference <chr>, realm <chr>, thermoregulation <chr>, locomotion <chr>,
## # trophic.guild <chr>, dimension <dbl>, preymass <dbl>, log10.preymass <dbl>,
## # PPMR <dbl>, prey.size.reference <chr>10. What is the largest mean.hra.m2 for mammals?
This is a very large number! Which animal has this homerange? Look them
up online and see if you can figure out why this number is so
large.
max(filter(homerange, taxon == "mammals")$mean.hra.m2)## [1] 3550830977Please knit your work as a .pdf or .html file and upload to Canvas. Homework is due before the start of the next lab. No late work is accepted. Make sure to use the formatting conventions of RMarkdown to make your report neat and clean!