#Enter your getwd() code here
getwd()[1] "/home/lohr22m/BIOL234"#Enter your setwd() code here
setwd("~/BIOL234/")Lab 1 Assignment
Complete ALL of the essentials below correctly to earn an ‘S’ on the lab.
Complete the Depth portion successful to earn credit toward a depth boost (every 2 lab depth assignments completed earns a 1/3 letter grade boost to your final grade)
Render your document as a .pdf or .html and submit it to the google folder on Moodle for grading.
…Essentials…
1.) Opening this document confirms that you have a version of RStudio that is working! Try looking at the ‘visual’ tab to see what this document looks like when rendered and then render it and open the resulting file. This pipeline, which we call ‘render checking’ is really important! Render early, render often. When in doubt, render. Make sure your Quarto document is working after you make changes. /
2.) Set the working directory Use getwd() to find the working directory. Then use setwd() and the GUI to set the working directoy. Reminder: It is useful to have a folder for the course that you can use as your starting working directory when you load the RStudio Project for this class. Enter your getwd() and setwd() code in code chunk below
Reminder: To insert a code chunk into Quarto you can use ctrl+alt+I (windows) or cmd+alt+I (Mac) OR click ‘+C’ in the top bar.
3.) Make an RStudio project for our class Name it whatever you’d like. I recommend “Biol234_Biostats” or something similar. Once it is done, take a screen shot of your RStudio screen and embed the image into below using the example code I have provided. It may also be helpful to make a folder on your server or computer for our class and use it as your working directory. /
4.) Load packages without error. Load tidyverse (our favorite and most versatile package) and palmerpenguins (which is just fun data) in the code chunk below
#load packages
library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.0 ✔ purrr 1.0.1
✔ tibble 3.1.8 ✔ dplyr 1.0.10
✔ tidyr 1.3.0 ✔ stringr 1.5.0
✔ readr 2.1.3 ✔ forcats 0.5.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()library(palmerpenguins)Question 5
5.) Basic data viewing - use the dataset mtcars as I did in the lab explanation. Use head(), tail(), str(), nrow(), ncol(), and then change a column from a number to a factor and from a factor back to a number. Confirm that each one of these actions works! Insert a code chunk below to begin.
Getting some information about the dataset
mtcars mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2mtcars2 <- mtcars
#top 6 rows#
head(mtcars2) mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1#last 6 rows#
tail(mtcars2) mpg cyl disp hp drat wt qsec vs am gear carb
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.7 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.5 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.5 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.6 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.6 1 1 4 2#column attributes#
str(mtcars2)'data.frame': 32 obs. of 11 variables:
$ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
$ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
$ disp: num 160 160 108 258 360 ...
$ hp : num 110 110 93 110 175 105 245 62 95 123 ...
$ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
$ wt : num 2.62 2.88 2.32 3.21 3.44 ...
$ qsec: num 16.5 17 18.6 19.4 17 ...
$ vs : num 0 0 1 1 0 1 0 1 1 1 ...
$ am : num 1 1 1 0 0 0 0 0 0 0 ...
$ gear: num 4 4 4 3 3 3 3 4 4 4 ...
$ carb: num 4 4 1 1 2 1 4 2 2 4 ...#number of rows#
nrow(mtcars2)[1] 32#number of columns#
ncol(mtcars2)[1] 11Manipulating the data
#change mpg from num to factor#
mtcars2$mpg=as.factor(mtcars2$mpg)
str(mtcars2$mpg) Factor w/ 25 levels "10.4","13.3",..: 16 16 19 17 13 12 3 20 19 14 ...Changing the data back to how it was
#change mpg back to num attempt 1 did not work#
mtcars2$mpg=as.numeric(mtcars2$mpg)
str(mtcars2$mpg) num [1:32] 16 16 19 17 13 12 3 20 19 14 ...#Attempt 2 worked better, thank you Google#
mtcars2$mpg <- as.numeric(as.character(mtcars2$mpg))
str(mtcars2$mpg) num [1:32] 16 16 19 17 13 12 3 20 19 14 ...Depth
1.) Make a directory (folder) for our class, set is as the wd for your project and then add the following folders within the directory: data, code, labs. Screenshot this folder and insert the image into your Quarto report below 
2.) Do basic data viewing on the penguins dataset from the palmerpenguins package. This includes head(), tail(), str(), nrow(), ncol(), and changing column attributes (as in Essentials #5). CONFIRM that all work! Insert a code chunk below to begin
# double set of :: to open specific dataset from a library?#
palmerpenguins::penguins# A tibble: 344 × 8
species island bill_length_mm bill_depth_mm flipper_…¹ body_…² sex year
<fct> <fct> <dbl> <dbl> <int> <int> <fct> <int>
1 Adelie Torgersen 39.1 18.7 181 3750 male 2007
2 Adelie Torgersen 39.5 17.4 186 3800 fema… 2007
3 Adelie Torgersen 40.3 18 195 3250 fema… 2007
4 Adelie Torgersen NA NA NA NA <NA> 2007
5 Adelie Torgersen 36.7 19.3 193 3450 fema… 2007
6 Adelie Torgersen 39.3 20.6 190 3650 male 2007
7 Adelie Torgersen 38.9 17.8 181 3625 fema… 2007
8 Adelie Torgersen 39.2 19.6 195 4675 male 2007
9 Adelie Torgersen 34.1 18.1 193 3475 <NA> 2007
10 Adelie Torgersen 42 20.2 190 4250 <NA> 2007
# … with 334 more rows, and abbreviated variable names ¹flipper_length_mm,
# ²body_mass_gdata(penguins)
penguindepth <- penguins
head(penguindepth)# A tibble: 6 × 8
species island bill_length_mm bill_depth_mm flipper_l…¹ body_…² sex year
<fct> <fct> <dbl> <dbl> <int> <int> <fct> <int>
1 Adelie Torgersen 39.1 18.7 181 3750 male 2007
2 Adelie Torgersen 39.5 17.4 186 3800 fema… 2007
3 Adelie Torgersen 40.3 18 195 3250 fema… 2007
4 Adelie Torgersen NA NA NA NA <NA> 2007
5 Adelie Torgersen 36.7 19.3 193 3450 fema… 2007
6 Adelie Torgersen 39.3 20.6 190 3650 male 2007
# … with abbreviated variable names ¹flipper_length_mm, ²body_mass_gtail(penguindepth)# A tibble: 6 × 8
species island bill_length_mm bill_depth_mm flipper_le…¹ body_…² sex year
<fct> <fct> <dbl> <dbl> <int> <int> <fct> <int>
1 Chinstrap Dream 45.7 17 195 3650 fema… 2009
2 Chinstrap Dream 55.8 19.8 207 4000 male 2009
3 Chinstrap Dream 43.5 18.1 202 3400 fema… 2009
4 Chinstrap Dream 49.6 18.2 193 3775 male 2009
5 Chinstrap Dream 50.8 19 210 4100 male 2009
6 Chinstrap Dream 50.2 18.7 198 3775 fema… 2009
# … with abbreviated variable names ¹flipper_length_mm, ²body_mass_gstr(penguindepth)tibble [344 × 8] (S3: tbl_df/tbl/data.frame)
$ species : Factor w/ 3 levels "Adelie","Chinstrap",..: 1 1 1 1 1 1 1 1 1 1 ...
$ island : Factor w/ 3 levels "Biscoe","Dream",..: 3 3 3 3 3 3 3 3 3 3 ...
$ bill_length_mm : num [1:344] 39.1 39.5 40.3 NA 36.7 39.3 38.9 39.2 34.1 42 ...
$ bill_depth_mm : num [1:344] 18.7 17.4 18 NA 19.3 20.6 17.8 19.6 18.1 20.2 ...
$ flipper_length_mm: int [1:344] 181 186 195 NA 193 190 181 195 193 190 ...
$ body_mass_g : int [1:344] 3750 3800 3250 NA 3450 3650 3625 4675 3475 4250 ...
$ sex : Factor w/ 2 levels "female","male": 2 1 1 NA 1 2 1 2 NA NA ...
$ year : int [1:344] 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ...nrow(penguindepth)[1] 344ncol(penguindepth)[1] 8penguindepth$bill_depth_mm<-as.factor(penguindepth$bill_depth_mm)
str(penguindepth$bill_depth_mm) Factor w/ 80 levels "13.1","13.2",..: 57 44 50 NA 63 75 48 66 51 72 ...penguindepth$bill_depth_mm<-as.numeric(as.character(penguindepth$bill_depth_mm))
str(penguindepth$bill_depth_mm) num [1:344] 18.7 17.4 18 NA 19.3 20.6 17.8 19.6 18.1 20.2 ...3.) Reformat this document to add headers (heading level 1 and 2 at least!), change formatting of text in other ways. Make at least 3 types of change and document them below (you can just type our text of what you did). The formatting changes should show up as visual differences in your final report! When you are done, you can turn in your assignment on Moodle (using the google form)
\ I put ellipsis around the first header for fun hehe. I made the Depth header Header 2 rather than Header 1 in size by adding an extra pound sign. Last, I put some smaller headers in italics on question 5 to organize it a bit.