#Enter your getwd() code here
getwd()[1] "/Users/kathleenstrachota/Desktop/Mount Holyoke/BIOSTATISTICS/Lab 1"#Enter your setwd() code here
setwd("~/Desktop/Mount Holyoke/BIOSTATISTICS/Lab 1/")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 directory. 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 3.3.5 ✔ purrr 1.0.1
✔ tibble 3.1.6 ✔ dplyr 1.0.10
✔ tidyr 1.3.0 ✔ stringr 1.5.0
✔ readr 2.1.3 ✔ forcats 0.5.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()library(palmerpenguins)Problem 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
#full dataset
mtcars_2 <-mtcars
head(mtcars_2) 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 1tail(mtcars_2) 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 2nrow(mtcars_2)[1] 32ncol(mtcars_2)[1] 11str(mtcars_2) #shows column attributes'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 ...Adjusting columns try 1
Change cylinder to a factor
mtcars_2$cyl = as.factor(mtcars_2$cyl) #make cylinder a factor
str(mtcars_2)'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 : Factor w/ 3 levels "4","6","8": 2 2 1 2 3 2 3 1 1 2 ...
$ 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 ...Change cylinder back to a number
mtcars_2$cyl = as.numeric(mtcars_2$cyl) #make cylinder numeric again
str(mtcars_2)'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 2 2 1 2 3 2 3 1 1 2 ...
$ 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 ...Adjusting columns try 2
Change cylinder to a factor
mtcars_3 <- mtcars
mtcars_3$cyl = as.factor(mtcars_3$cyl) #make cylinder a factor
str(mtcars_3)'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 : Factor w/ 3 levels "4","6","8": 2 2 1 2 3 2 3 1 1 2 ...
$ 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 ...Change cylinder back to a number
mtcars_3$cyl = as.numeric(as.character(mtcars_3$cyl))
str(mtcars_3)'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 ...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
penguins2 <- penguins
head(penguins2)# A tibble: 6 × 8
species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g sex
<fct> <fct> <dbl> <dbl> <int> <int> <fct>
1 Adelie Torge… 39.1 18.7 181 3750 male
2 Adelie Torge… 39.5 17.4 186 3800 fema…
3 Adelie Torge… 40.3 18 195 3250 fema…
4 Adelie Torge… NA NA NA NA <NA>
5 Adelie Torge… 36.7 19.3 193 3450 fema…
6 Adelie Torge… 39.3 20.6 190 3650 male
# … with 1 more variable: year <int>tail(penguins2)# A tibble: 6 × 8
species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g sex
<fct> <fct> <dbl> <dbl> <int> <int> <fct>
1 Chinst… Dream 45.7 17 195 3650 fema…
2 Chinst… Dream 55.8 19.8 207 4000 male
3 Chinst… Dream 43.5 18.1 202 3400 fema…
4 Chinst… Dream 49.6 18.2 193 3775 male
5 Chinst… Dream 50.8 19 210 4100 male
6 Chinst… Dream 50.2 18.7 198 3775 fema…
# … with 1 more variable: year <int>nrow(penguins2)[1] 344ncol(penguins2)[1] 8str(penguins2)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 ...Adjusting bill_length_mm
penguins2$bill_length_mm <- as.factor(penguins2$bill_length_mm)
str(penguins2)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 : Factor w/ 164 levels "32.1","33.1",..: 43 46 52 NA 23 45 41 44 5 66 ...
$ 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 ...penguins2$bill_length_mm = as.numeric(as.character(penguins2$bill_length_mm))
str(penguins2)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 ...#In this chunk, I broke it and tried to figure out how to fix it but was not successful.
penguins2$sex <- as.numeric(penguins2$sex)
penguins2$sex <- as.factor(as.character(penguins2$sex))
str(penguins2)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 "1","2": 2 1 1 NA 1 2 1 2 NA NA ...
$ year : int [1:344] 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ...I found an answer of how to do this on stack overflow, but it used mutate(), pipe operators, and creating multiple new dataframes so I was not certain that I was actually changing sex from a factor to a number and back. Also when I tried to replicate it, I kept getting errors and then it quickly became trip down the rabbit hole of figuring out errors.
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!
I changed all of problem 5’s heading formatting. I made a Problem 5 header in heading level 1 and then created 2 different sections in heading level 2 and then 2 subsections for each of those sections with heading level 3.
When you are done, you can turn in your assignment on Moodle (using the google form)