Much of the work in this class will be done via R Markdown documents. R Markdown documents are documents that combine text, R code, and R output, including figures. They are a great way to produce self-contained and documented statistical analyses.

In this first worksheet, you will learn how to do some basic markdown editing. After you have made a change to the document, press “Knit HTML” in R Studio and see what kind of a result you get.

Edit only below this line.

1. Basic Markdown editing

Try out basic R Markdown features, as described here: https://rmarkdown.rstudio.com/authoring_basics.html. Write some text that is bold, and some that is in italics. Make a numbered list and a bulleted list. Make a nested list. Try the block-quote feature.

Name: Mais Alraee

Position: Math instructor

Hobbies:

  1. Sudoku
  2. Playing Piano
  3. Reading

Favorait colors

  • purple
  • red
  • teal

Nested list:

list_1 <- list(1:6, letters[1:5])
list_2 <- list(5:11, letters[6:11])
my_nested_list <- list(list_1, list_2)

my_nested_list
## [[1]]
## [[1]][[1]]
## [1] 1 2 3 4 5 6
## 
## [[1]][[2]]
## [1] "a" "b" "c" "d" "e"
## 
## 
## [[2]]
## [[2]][[1]]
## [1]  5  6  7  8  9 10 11
## 
## [[2]][[2]]
## [1] "f" "g" "h" "i" "j" "k"

Block-quote

Talented data scientists leverage data that everybody sees; visionary data scientists leverage data that nobody sees.-Vincent Granville.

“Data that is loved tends to survive.” - Kurt Bollacker

2. Embedding R code

R code embedded in R chunks will be executed and the output will be shown.

# R code goes here
x <- rnorm(100)  # random sample from normal distribution
dens <- density(x)    # calculate density
dens
## 
## Call:
##  density.default(x = x)
## 
## Data: x (100 obs.);  Bandwidth 'bw' = 0.3699
## 
##        x                 y            
##  Min.   :-4.1207   Min.   :0.0001272  
##  1st Qu.:-2.1514   1st Qu.:0.0164938  
##  Median :-0.1822   Median :0.0539411  
##  Mean   :-0.1822   Mean   :0.1268244  
##  3rd Qu.: 1.7871   3rd Qu.:0.2685626  
##  Max.   : 3.7563   Max.   :0.3542984
plot(dens)            # plot density

Now you try it. For example, take the built-in data set cars, which lists speed and stopping distance for cars from the 1920. Plot speed vs. distance, and/or perform a correlation analysis. Then write a few sentences describing what you see.

library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.1.3
## -- Attaching packages --------------------------------------- tidyverse 1.3.2 --
## v ggplot2 3.3.6     v purrr   0.3.4
## v tibble  3.1.7     v dplyr   1.0.9
## v tidyr   1.2.0     v stringr 1.4.0
## v readr   2.1.2     v forcats 0.5.1
## Warning: package 'ggplot2' was built under R version 4.1.3
## Warning: package 'tibble' was built under R version 4.1.3
## Warning: package 'tidyr' was built under R version 4.1.3
## Warning: package 'readr' was built under R version 4.1.3
## Warning: package 'purrr' was built under R version 4.1.3
## Warning: package 'dplyr' was built under R version 4.1.3
## Warning: package 'stringr' was built under R version 4.1.3
## Warning: package 'forcats' was built under R version 4.1.3
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
cars <- cars
ggplot(data = cars) +
  ggtitle("Speed Vs.Distance") +
  geom_point(mapping = aes(x = speed, y = dist))

One can see a positive association between speed and stopping distance. There are a couple of outliers, but a statistical analysis needs to be done to get a more accurate picture.
Thank you!