Section 7.2 Loading Our Data
We start by importing the necessary packages, loading our data from the
reproresearchR package ([D.1.24]), and quickly doing an overview of the data. For a quick review on loading data into R, refer back to Section 1.7.
library(tidyverse)
library(reproresearchR)
examData <- exam_data
library(skimr)
skim(examData)
── Data Summary ────────────────────────
Values
Name examData
Number of rows 100
Number of columns 5
_______________________
Column type frequency:
character 2
numeric 3
________________________
Group variables None
── Variable type: character ────────────────────────────────────────────────────
skim_variable n_missing complete_rate min max empty
1 Student Name 0 1 3 9 0
2 First Generation College Student 0 1 2 3 0
n_unique whitespace
1 99 0
2 2 0
── Variable type: numeric ──────────────────────────────────────────────────────
skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100
1 Studying Hours 0 1 20.2 18.3 0 8 15 24.2 98
2 Exam Score 0 1 57.4 25.7 5 40 61.5 80 100
3 Anxiety Score 0 1 74.1 17.3 0.0560 69.4 78.2 84.7 97.6
hist
1 ▇▃▁▁▁
2 ▃▃▅▇▅
3 ▁▁▁▆▇
Our data has 5 columns:
-
Student Name: The name of the student
-
Studying Hours: The number of hours they studied for the exam
-
Exam Score: The score they earned on their exam
-
Anxiety Score: The measure of their anxiety levels before the exam
-
First Generation College Student: If the student is or is not a first generation college student (like me)
While we have all of the data needed to begin answering our question, there is one flaw: there are spaces in our column headers.
