Welcome! 👋

This is your worksheet — for explanations, analogies, and worked examples, read lab_workbook.html first. Come back here to actually do each module. Work through this over ~4 days, 1-2 modules per day.

Whenever you see <write your code here>, replace it with real code. Whenever you see a question, answer it in a sentence or two right below.

Day 1

Module 1: Meet RStudio 🧭

📖 Read Module 1 in the Workbook first.

Task 1.1: In the Console (not in a chunk), type print("Hello, R!") and press Enter.

Task 1.2: Run the chunk below (green play button, or Cmd/Ctrl + Enter).

print("Hello, R!")

## [1] "Hello, R!"

Question 1.2.1: What’s the difference between typing code in the Console versus typing it in a chunk here?

Typing in the console allows you to run code immediately, but code typed in the source as a chunk is now a part of the doc that can be rerun and saved in the knitted report

Task 1.3: Check the Environment pane (should be empty). Run the chunk below, then check again.

my_first_variable <- 42

Question 1.3.1: What showed up in the Environment pane?

my_first_variable under values and 42 next to it

Task 1.4: Add a comment above the chunk in Task 1.2 explaining, in your own words, what that chunk does.

print("Hello, R!") #this gives the value of a simple string of text

## [1] "Hello, R!"

Knitting Checkpoint 📖

Task 1.5: Click Knit right now, before you’ve filled in anything below. Confirm an HTML file opens showing this document so far, unfinished parts and all.

Task 1.6: In the Console (not in a chunk), run console_only_variable <- 99. Now, in the chunk below, write print(console_only_variable) and try knitting the whole document.

Question 1.6.1: What happened when you knitted? Why, given what the Workbook said about fresh sessions?


Error:
! object 'console_only_variable' not found
Backtrace:
    ▆
 1. └─base::print(console_only_variable)

Quitting from lab_0.Rmd:77-79 [unnamed-chunk-4]
Execution halted

When you knit before typing in the source it is saved, but when you knit after only typing in the console it is not going to store that information.

Task 1.7: Comment out the line you just wrote in Task 1.6, then knit again to confirm the document succeeds cleanly.

Module 2: Variables & Basic Data Types 🔢

📖 Read Module 2 in the Workbook first.

cups_of_coffee <- 3

Task 2.1: Create three variables: one numeric, one character, one logical.

brownies_baked <-10
bakery_name <- "something sweet"
orders_received <- TRUE

Task 2.2: Use class() to check the type of each variable you just made.

class(brownies_baked)

## [1] "numeric"

class(bakery_name)

## [1] "character"

class(orders_received)

## [1] "logical"

cups_yesterday <- 5
cups_today <- 8
total_cups <- cups_yesterday + cups_today
total_cups

## [1] 13

Task 2.4: Reassign cups_today to be 3 more than its current value (same “variable equals itself plus something” pattern above). Print it to confirm.

cups_yesterday <- 5
cups_today <- 8
cups_today <- cups_today + 3
cups_today

## [1] 11

Question 2.4.1: What happens if you add a numeric and a character variable, like cups_today + "hello"? Try it in the Console (not here — it would break the knit!) and describe what R tells you.

Error in cups_today + "hello" : non-numeric argument to binary operator

Trying to add a text value and a numeric value is nonsensical

Day 2

Module 3: Vectors 📦

📖 Read Module 3 in the Workbook first.

daily_sales <- c(12, 18, 9, 22, 15)
daily_sales

## [1] 12 18  9 22 15

Task 3.1: Create a vector of at least 5 numbers of something you like counting.

vegetables_sold <- c(2, 7, 19, 4, 22)
vegetables_sold

## [1]  2  7 19  4 22

daily_sales[1]

## [1] 12

daily_sales[3]

## [1] 9

daily_sales[-1]

## [1] 18  9 22 15

Task 3.2: Pull the 2nd and 4th values from your Task 3.1 vector. Then, separately, pull everything except the last value.

vegetables_sold[2]

## [1] 7

vegetables_sold[4]

## [1] 4

vegetables_sold[-length(vegetables_sold)]

## [1]  2  7 19  4

vegetables_sold * 2

## [1]  4 14 38  8 44

vegetables_sold + 10

## [1] 12 17 29 14 32

Task 3.3: Create a new vector that adds 5 to every value in your Task 3.1 vector. Then check how many items are in your original vector with length().

vegetables_sold_plus_5 <- vegetables_sold + 5
vegetables_sold_plus_5

## [1]  7 12 24  9 27

length(vegetables_sold)

## [1] 5

Question 3.3.1: What do you think happens if you combine a number and text in one vector, like c(1, "two", 3)? Try it — what type does R make the whole vector?

the whole vector now becomes text, or character values

Module 4: Functions & Getting Help 🛠️

📖 Read Module 4 in the Workbook first.

sum(daily_sales)

## [1] 76

mean(daily_sales)

## [1] 15.2

max(daily_sales)

## [1] 22

min(daily_sales)

## [1] 9

sort(daily_sales)

## [1]  9 12 15 18 22

round(mean(daily_sales), 1)

## [1] 15.2

Task 4.1: Find the sum, mean, and max of your Task 3.1 vector.

sum(vegetables_sold)

## [1] 54

mean(vegetables_sold)

## [1] 10.8

max(vegetables_sold)

## [1] 22

min(vegetables_sold)

## [1] 2

sort(vegetables_sold)

## [1]  2  4  7 19 22

round(mean(vegetables_sold), 1)

## [1] 10.8

Task 4.2: Try sort() on your vector, then separately try sort(your_vector, decreasing = TRUE). What’s different?

sort(vegetables_sold)

## [1]  2  4  7 19 22

sort(vegetables_sold, decreasing = TRUE)

## [1] 22 19  7  4  2

Task 4.3: Look up the help page for round() (?round in the Console). What does the digits argument do with a negative number, like -1? (Try round(1234, -1).)

the digits number indicates the number of decimal places (round) or significant digits (signif) to be used. Negative values are allowed.

Day 3

Module 5: Logical Comparisons & Filtering 🔍

📖 Read Module 5 in the Workbook first.

daily_sales > 15

## [1] FALSE  TRUE FALSE  TRUE FALSE

daily_sales > 10 & daily_sales < 20

## [1]  TRUE  TRUE FALSE FALSE  TRUE

daily_sales[daily_sales > 15]

## [1] 18 22

Task 5.1: Keep only the values in your Task 3.1 vector that are greater than its own average.

vegetables_sold > 15

## [1] FALSE FALSE  TRUE FALSE  TRUE

vegetables_sold > 10 & vegetables_sold < 20

## [1] FALSE FALSE  TRUE FALSE FALSE

vegetables_sold[vegetables_sold > 15]

## [1] 19 22

vegetables_sold[vegetables_sold > mean(vegetables_sold)]

## [1] 19 22

Task 5.2: Filter your vector to values between two numbers of your choosing, using &.

vegetables_sold[vegetables_sold > 12 & vegetables_sold < 20]

## [1] 19

Task 5.3: Count how many values are above the average, using sum() on a logical comparison.

sum(vegetables_sold > mean(vegetables_sold))

## [1] 2

Question 5.3.1: In plain words, what does daily_sales[daily_sales > 15] actually do, step by step?

Each value in my vector is compared to 15 (output is TRUE/FALSE) and then leaves behind all that are greater not greater than 15 (FALSE)

Module 6: Packages & Libraries 📚

📖 Read Module 6 in the Workbook first.

# Run these once in the Console if you don't have them yet:
install.packages("readr")
install.packages("dplyr")

library(readr)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

Task 6.1: Run the chunk above. Red text mentioning “masked” objects is normal; an actual error means you need to run install.packages() first.

Task 6.2: Run installed.packages()[, 1] in the Console. Do you see readr and dplyr in the list?

Yes

Question 6.2.1: Why do you think R separates “install” (once) from “load” (every session)?

Loading a package with library() makes its functions available in your current session aftyer it is already installed on your computer

Day 4

Module 7: Understanding File Paths & Your Working Directory 🗂️

📖 Read Module 7 in the Workbook first.

getwd()

## [1] "/Users/vinishabrowne/Desktop/ENIA-5120/labs/lab0"

Task 7.1: Does the printed path match ENIA-5120 itself, or somewhere inside it? (Hint: read the Workbook’s note on why getwd() in the Console can differ from where code runs inside this .Rmd.)

It is the absolute path starting from my computer to user to ENIA-5120 folder

example_path <- file.path("..", "..", "data", "hoya_grounds_sales.csv")
example_path

## [1] "../../data/hoya_grounds_sales.csv"

Task 7.2: lab_template.Rmd lives in ENIA-5120/labs/lab0. Using file.path(), build the relative path from here to a file called practice.csv that lives directly in ENIA-5120/data — how many .. do you need, and why?

ex_path <- file.path("..", "..", "data", "practice.csv")
ex_path

## [1] "../../data/practice.csv"

list.files()

## [1] "lab_0.html"        "lab_0.Rmd"         "lab_answers.Rmd"  
## [4] "lab_workbook.html"

Task 7.3: Run list.files() above — what folder’s contents does it show? Then try list.files("../../data") and compare.

list.files("../../data")

## [1] "climate_stations.csv"   "hoya_grounds_sales.csv"

Question 7.3.1: What’s the difference between an absolute path and a relative path? Why does the working directory inside an .Rmd file default to the .Rmd’s own folder, rather than your R Project’s root?

An absolute path gives lengthy directions from the root of the computer and always leads to the same file regardless of where R origin is (tied to specific user/name). A relative path gives directions from wherever R currently is — and inside an .Rmd, that's always the folder the .Rmd itself lives in, not the R project root, even though the Console's getwd() shows the Project root. This works the same on every
our classmates cpus long as everyone's project folder is set up the same way.

Module 8: Data Frames — Your First Table 📊

📖 Read Module 8 in the Workbook first.

names <- c("Alex", "Jordan", "Sam")
cups <- c(2, 4, 1)
likes_espresso <- c(TRUE, FALSE, TRUE)

coffee_habits <- data.frame(names, cups, likes_espresso)
coffee_habits

##    names cups likes_espresso
## 1   Alex    2           TRUE
## 2 Jordan    4          FALSE
## 3    Sam    1           TRUE

dim(coffee_habits)

## [1] 3 3

coffee_habits$cups

## [1] 2 4 1

mean(coffee_habits$cups)

## [1] 2.333333

Task 8.1: Build your own small data frame with at least 3 columns and 3 rows, about any topic you like.

cookie_names <- c("choc chip", "snickerdoodle", "shortbread")
cookie_counts <- c(12, 24, 36)
good_with_espresso <- c(TRUE, TRUE, FALSE)

bakery_bites <- data.frame(cookie_names, cookie_counts, good_with_espresso)
bakery_bites

##    cookie_names cookie_counts good_with_espresso
## 1     choc chip            12               TRUE
## 2 snickerdoodle            24               TRUE
## 3    shortbread            36              FALSE

Task 8.2: Check its dim(), then pull out one numeric column with $ and find its mean().

dim(bakery_bites)

## [1] 3 3

bakery_bites$cookie_counts

## [1] 12 24 36

mean(bakery_bites$cookie_counts)

## [1] 24

Question 8.2.1: What’s the actual difference between a data frame column and a standalone vector? Why is it useful to bundle several vectors together into one table?

A data frame column and a standalone vector are the same thing but when you combine them into table/data frame each vakue is aligned row by row, so that one
"observation" value stays aligned with its multiple variables and patterns can be identified

coffee_habits[1, ]

##   names cups likes_espresso
## 1  Alex    2           TRUE

coffee_habits[, 2]

## [1] 2 4 1

coffee_habits[coffee_habits$cups > 1, ]

##    names cups likes_espresso
## 1   Alex    2           TRUE
## 2 Jordan    4          FALSE

Task 8.3: Using your data frame from Task 8.1, pull out just the first row, then just the first column, using [row, column] indexing.

bakery_bites[1, ]

##   cookie_names cookie_counts good_with_espresso
## 1    choc chip            12               TRUE

bakery_bites[, 1]

## [1] "choc chip"     "snickerdoodle" "shortbread"

Task 8.4: Filter your data frame down to just the rows where one of your numeric columns is above its own average, using the df[condition, ] pattern.

bakery_bites[bakery_bites$cookie_counts > mean(bakery_bites$cookie_counts), ]

##   cookie_names cookie_counts good_with_espresso
## 3   shortbread            36              FALSE

Question 8.4.1: What do you think coffee_habits[2, ] returns, compared to coffee_habits[, 2]? Try both and describe the difference.

coffee_habits[2, ] returns the entire second row —
and all y values for that observation
coffee_habits[, 2] returns the entire second column — all x values for that variable

Module 9: Sneak Peek – Reading Your First CSV 👀

📖 Read Module 9 in the Workbook first.

sales_path <- file.path("..", "..", "data", "hoya_grounds_sales.csv")
sales <- read_csv(sales_path)

## Rows: 14 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): day_of_week, promo_ran
## dbl (3): day_number, cups_sold, temp_f
## lgl (1): is_weekend
## 
## 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.

head(sales)

## # A tibble: 6 × 6
##   day_number day_of_week cups_sold temp_f is_weekend promo_ran
##        <dbl> <chr>           <dbl>  <dbl> <lgl>      <chr>    
## 1          1 Mon                55     85 FALSE      yes      
## 2          2 Tue                60     62 FALSE      yes      
## 3          3 Wed                54     53 FALSE      yes      
## 4          4 Thu                41     82 FALSE      no       
## 5          5 Fri                57     46 FALSE      yes      
## 6          6 Sat                83     59 TRUE       yes

Task 9.1: Run the chunk above. Confirm you see the coffee cart sales data — that path is the same “up two, into data” pattern from Module 7.

Task 9.2: Use nrow() and ncol() to check how many rows and columns are in sales.

nrow(sales)

## [1] 14

ncol(sales)

## [1] 6

Task 9.3: Pull out the cups_sold column using sales$cups_sold, then find its mean().

mean(sales$cups_sold)

## [1] 60

Task 9.4: Filter sales to just the rows where cups_sold is greater than the column’s average — same df[condition, ] pattern you practiced on coffee_habits in Module 8, just applied to a real dataset.

sales[sales$cups_sold > mean(sales$cups_sold), ]

## # A tibble: 5 × 6
##   day_number day_of_week cups_sold temp_f is_weekend promo_ran
##        <dbl> <chr>           <dbl>  <dbl> <lgl>      <chr>    
## 1          6 Sat                83     59 TRUE       yes      
## 2          7 Sun                71     79 TRUE       no       
## 3         12 Fri                64     69 FALSE      yes      
## 4         13 Sat                64     67 TRUE       no       
## 5         14 Sun                91     74 TRUE       yes

Question 9.4.1: Walk through, in order, everything that happened between running read_csv(sales_path) and being able to run mean(sales$cups_sold). What did the file path do? What did read_csv() turn the file into? What did $ do?

df[condition, ] allows us to ference a sataset or dataframe that is already existing versus establishing all of the vectors one by one and then linking them

Final Reflection 🌡️

Question F.1: Which module felt the most confusing? What specifically tripped you up?

7, understanding the pathways is sometimes visually confusing with all of the "." and "/"

Question F.2: Which module clicked the fastest for you?

mean/median/mode and the other math functions because I am comfortable with excel and its easy to visualize when I think of a graph

Question F.3: On a scale of 1-5, how ready do you feel for Session 1?

Kind of, 6

Lab 00 – R & RStudio Bootcamp (Worksheet)

Your Name