P421 Lab - Basic Statistics HW
Maggie Rockwell
2025-09-24
Basic Statistics
Load Libraries
# if you haven't used a given package before, you'll need to download it first
# after download is finished, insert a "#" before the install function so that the file will Knit later
# then run the library function calling that package
#install.packages("expss")
library(psych) # for the describe() command
library(expss) # for the cross_cases() command## Loading required package: maditr##
## Use magrittr pipe '%>%' to chain several operations:
## mtcars %>%
## let(mpg_hp = mpg/hp) %>%
## take(mean(mpg_hp), by = am)
## Import & Examine Data
# Import the "fakedata_2025.csv" file
d2 <- read.csv("Data/projectdata.csv")
str(d2)## 'data.frame': 3148 obs. of 7 variables:
## $ ResponseID: chr "R_BJN3bQqi1zUMid3" "R_2TGbiBXmAtxywsD" "R_12G7bIqN2wB2N65" "R_39pldNoon8CePfP" ...
## $ gender : chr "f" "m" "m" "f" ...
## $ income : chr "1 low" "1 low" "rather not say" "rather not say" ...
## $ stress : num 3.3 3.3 4 3.2 3.1 3.5 3.3 2.4 2.9 2.7 ...
## $ efficacy : num 3.4 3.4 2.2 2.8 3 2.4 2.3 3 3 3.7 ...
## $ belong : num 2.8 4.2 3.6 4 3.4 4.2 3.9 3.6 2.9 2.5 ...
## $ support : num 6 6.75 5.17 5.58 6 ...# Note: for the HW, you will import "projectdata.csv" that you created and exported in the Data Prep LabUnivariate Plots: Histograms & Tables
Tables are used to visualize individual categorical variables. Histograms are used to visualize individual continuous variables.
# use tables to visualize categorical data (2 variables)
table(d2$income)##
## 1 low 2 middle 3 high rather not say
## 877 881 535 855table(d2$gender)##
## f m nb
## 2306 789 53# use histograms to visualize continuous data (4 variables)
hist(d2$support)
hist(d2$efficacy)
hist(d2$belong)
hist(d2$stress)
Univariate Normality for Continuous Variables
describe(d2)## vars n mean sd median trimmed mad min max range
## ResponseID* 1 3148 1574.50 908.89 1574.50 1574.50 1166.81 1.0 3148.0 3147.0
## gender* 2 3148 1.28 0.49 1.00 1.21 0.00 1.0 3.0 2.0
## income* 3 3148 2.43 1.16 2.00 2.42 1.48 1.0 4.0 3.0
## stress 4 3148 3.05 0.60 3.00 3.05 0.59 1.3 4.7 3.4
## efficacy 5 3148 3.13 0.45 3.10 3.13 0.44 1.1 4.0 2.9
## belong 6 3148 3.23 0.60 3.30 3.25 0.59 1.3 5.0 3.7
## support 7 3148 5.54 1.13 5.75 5.66 0.99 0.0 7.0 7.0
## skew kurtosis se
## ResponseID* 0.00 -1.20 16.20
## gender* 1.39 0.85 0.01
## income* 0.15 -1.43 0.02
## stress 0.03 -0.16 0.01
## efficacy -0.23 0.44 0.01
## belong -0.26 -0.12 0.01
## support -1.10 1.43 0.02## For the required write-up below, choose one of these options to paste and edit below based on your output.
## OPTION 1
# We analyzed the skew and kurtosis of our continuous variables and all were within the accepted range (-2/+2).
## OPTION 2
# We analyzed the skew and kurtosis of our continuous variables and (#) were within the accepted range (-2/+2). However, (#) variables (list variable name(s) here) were outside of the accepted range. For this analysis, we will use them anyway, but outside of this class this is bad practice.Write-up of Normality
We analyzed the skew and kurtosis of our continuous variables and all were within the accepted range (-2/+2).
Bivariate Plots
Crosstabs
Crosstabs are used to visualize combinations of two categorical variables.
cross_cases(d2, gender, income)|  income | ||||
|---|---|---|---|---|
|  1 low |  2 middle |  3 high |  rather not say | |
|  gender | ||||
|    f | 642 | 664 | 375 | 625 |
|    m | 218 | 205 | 156 | 210 |
|    nb | 17 | 12 | 4 | 20 |
|    #Total cases | 877 | 881 | 535 | 855 |
## Some students may have issues with this function working. If this happens to you, please try these 2 options:
## Option 1: install the "maditr" package and then call in its library.
## Option 2: If Option 1 doesn't work, then you will use xtabs() instead. Fill in the code below and remove the "#" to run. Then hashtag out the cross_cases() line.
# xtabs(~ + , data=)
# Note: for HW, replace the two lab variables with your project ones)Scatterplots
Scatterplots are used to visualize combinations of two continuous variables.
plot(d2$efficacy, d2$stress,
main= "Scatterplot of Self Efficacy and Stress",
xlab = "Self Efficacy",
ylab = "Stress")
plot(d2$efficacy, d2$support,
main= "Scatterplot of Self Efficacy and Social Support",
xlab = "Self Efficacy",
ylab = "Social Support")
# Note: for HW, you will choose to plot 2 combos of your 4 continuous variables, based on your potential hypotheses. You may repeat 1 variable to see its association with 2 others. You will need replace the variable names on the first line of the function as well as the 'main' (aka plot title), 'xlab' and 'ylab' lines to correctly label the graphs -- remember to use the actual construct names, NOT their R abbrev or full scales, so someone reading your plots can understand them.Boxplots
Boxplots are used to visualize combinations of one categorical and one continuous variable.
# ORDER MATTERS HERE: 'continuous variable' ~ 'categorical variable'
boxplot(data=d2, efficacy~income,
main="Boxplot Self Efficacy by Income",
xlab = "Income",
ylab = "Self Efficacy")
boxplot(data=d2, support~gender,
main="Boxplot of Social Support by Gender",
xlab = "Gender",
ylab = "Social Support")
# Note: for HW, you will choose to plot 2 combos of any of your 4 continuous variables with either of your 2 categorical variables, based on your potential hypotheses. You may repeat 1 variable to see its association with others. Again, will need replace the variable names on the first line of the function as well as the 'main' (aka plot title), 'xlab' and 'ylab' lines to correctly label the graphs -- remember to use the actual construct names, NOT their R abbrev or full scales, so someone reading your plots can understand them.That’s it!!