Foundations of Business Analytics

What does <- do in R?

assigns the value on the right to the object name on the left

What is a data frame

table with rows and columns

What is a function in R

a command that takes inputs and returns an output

What is a code chunk in a Quarto .qmd file

a block where you write R code that Quarto runs when rendering, and it shows the results (numbers/tables/plots) in the output.

What does Render do in Quarto?

runs all code chunks and converts the .qmd into an output document (e.g., HTML/PDF) with the results inserted

What is a package in R?

a collection of functions, data, and documentation that extends the basic capabilites of R

How business decision-making has evolved

1. Gut instinct (hippo era)

2. Simple data support

3. Advanced analytics

4. Experimentation and causal learning

Types of business analytics

Descriptive

predictive

causual/prescriptive

What does ```{r} mean in Quarto?

starts an R code chunk

What does #| eval: false do?

tells Quarto not to run this code chunk when rendering

What does library(readr) do?

• It loads the readr package so you can use functions from it (including read_csv())

What does class() do

Tells you what type of object something is

Use it as quick sanity check right after importing data, to confirm R read it in the way you expected.

What does names() do

Lists the column/variable names of a data frame

What does head() do

hows just the first few rows of a data frame (6 rows, by default)

preview your data without flooding the screen, handy for datasets with hundreds or thousands of rows

nrow()

Counts how many rows a data frame has

ncol()

Counts how many columns a data frame has

any()

Checks a condition across a whole column of values and returns a single TRUE if at least one value meets it, or FALSE if none do

sum()

dds up every value in a numeric column to give a single total.

ggplot()

Starts a new plot and tells R which dataset to build it from

aes()

Maps columns in your data to visual features of the plot

labs()

Adds or changes the text labels on a plot, such as the axis titles and the main title.

bins

how many bins to split the data into (for histogram)

facet_wrap()

Splits one plot into a grid of smaller plots, one panel per category.

geom_point()

Draws a scatter plot, one dot for every row of data

geom_smooth()

Adds a fitted trend line through your data.

method

the type of line to fit for trendline, for example "lm" for a straight regression line.

se

whether to show a shaded confidence band around the trendline. Set to FALSE to hide it.

alpha

the clarity of the points on scatter plot

scale_x_continuous()

Controls how the x-axis of a plot is drawn

trans

a transformation to apply to the axis, for example "log10"

labels

how to format the axis numbers, for example

scale_fill_okabe_ito()

Applies a colorblind-friendly set of colors to whatever is mapped to fill in your plot

theme_bw()

Switches the plot to a clean black-and-white theme

fct_infreq()

Reorders the categories of a variable by how often they occur, from most common to least common

after_stat()

Lets you use a value that ggplot2 calculates internally while it builds the plot, such as a count, in a further calculation of your own

dplyr

A package for wrangling data

filter()

Keeps only the rows that meet a condition you set, and drops the rest

mutate()

Creates a new column in your data, or changes an existing one, based on a calculation you specify

Build new variables

select()

Keeps only the columns you name, and drops the rest.

arrange()

Sorts the rows of your data by one or more columns

slice_max()

Keeps only the rows with the highest values of a column you choose

group_by()

Splits your data into groups based on the values of one or more columns, without changing what the data looks like on screen.

summarise()

Collapses many rows into a single summary row, using calculations you choose, such as an average

slice_head()

Shows the first few rows of your data, in the order they currently appear

n()

Counts how many rows are in the current group

desc()

Flags a column so that arrange() sorts it from highest to lowest instead of lowest to highest.

pivot_longer()

lengthen “short and wide” datasets by stacking values into a single column

pivot_wider()

shorten “long and narrow” datasets by spreading values into multiple columns

What makes data tidy?

Every tidy dataset has the same structure

Makes common R operations easier

!is.na

Checks for values that are not missing

$

Selects 1 variable in a dataset

==

boolean: ensures values on left = values on right

c()

combines arguments into a vector

!

The not operator