Basics of Data Wrangling
Fundamentals of Data Science
MTH-391A | Spring 2025 | University of Portland
January 27, 2025
Loading Packages
How to load R Packages?
The function library() loads any installed package. In
this case library("tidyverse") loads the
tidyverse package specifically.
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
\(\dagger\) Try the above code
sequence in your console. Then, load the package
plotly.
Variables
How are variables created and stored in R?
\(\dagger\) Create a variable named
ria and initially define it as a number. Then, redefine
ria as a vector. What happens to the original value of
ria (the number) after it is redefined as a vector?
\(\star\) Knowing and keeping track
of what variables are defined is key to understanding why some errors
occur.
Data Frame Key Characteristics
Base R: These data frames are data structures that
comes with base R.
Tibbles: Tibbles are special kinds of data frames
using the tibble package.
\(\star\) A base R data frame can be
converted to a tibble data frame using the tibble()
function. The iris data set is a built-in data in the
datasets packages, which comes with base R.
R Data Frames vs Tibbles, An Example
Sub-setting Columns
- R Data Frames returns a vector.
glimpse(iris[,"Sepal.Width"])
## num [1:150] 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
glimpse(tibble(iris)[,"Sepal.Width"])
## Rows: 150
## Columns: 1
## $ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4…
\(\dagger\) Try the above code
examples using a different column on your console!
\(\star\) Here, the
glimpse() function is used to simplify the output of the
subsets.
Example: Filter One Table Using |>
Define Data Frame as a Tibble
iris_tibble <- tibble(iris)
Simple Example. The following code sequence filters
the iris data frame (in tibble form) to include only the
“setosa” species.
iris_tibble |>
filter(Species == "setosa")
\(\star\) Notice that the first line
is the data frame itself, then the next line is the verb without putting
it directly into the first argument of the filter() verb.
This is a common practice of organizing verbs in a pipeline.
In-Class Demonstration
Case example: Using the iris dataset,
we want to determine the number of rows (or observations) for each
species where the length ratio exceeds \(0.80\) and the width ratio exceeds \(0.50\). The length ratio is defined as
Petal.Length divided by Sepal.Length, and the
width ratio is defined as Petal.Width divided by
Sepal.Width.
The original number of rows pre-transformation
## # A tibble: 3 × 2
## Species n
## <fct> <int>
## 1 setosa 50
## 2 versicolor 50
## 3 virginica 50
Post-transformation results
## # A tibble: 2 × 2
## Species n
## <fct> <int>
## 1 versicolor 2
## 2 virginica 40
\(\dagger\) The goal of the
demonstration is to replicate the shown data frames using the
dplyr verbs.
