P421 Lab - Basic Statistics Homework
Caroline
2026-02-15
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("psych")
#install.packages("expss") #new package
library(psych) # for the describe() command
library(expss) # for the cross_cases() command## Loading required package: maditr##
## To modify variables or add new variables:
## let(mtcars, new_var = 42, new_var2 = new_var*hp) %>% head()Import & Examine Data
# Import the "projectdata.csv" file
d2 <- read.csv("projectdata.csv")
describe(d2)## vars n mean sd median trimmed mad min max
## ResponseID* 1 3092 1546.50 892.73 1546.50 1546.50 1146.05 1.0 3092
## gender* 2 3092 1.28 0.49 1.00 1.21 0.00 1.0 3
## sibling* 3 3092 1.10 0.30 1.00 1.00 0.00 1.0 2
## moa_independence 4 3092 3.54 0.46 3.67 3.61 0.49 1.0 4
## swb 5 3092 4.47 1.32 4.67 4.53 1.48 1.0 7
## belong 6 3092 3.23 0.61 3.30 3.25 0.59 1.3 5
## socmeduse 7 3092 34.42 8.60 35.00 34.70 7.41 11.0 55
## range skew kurtosis se
## ResponseID* 3091.0 0.00 -1.20 16.05
## gender* 2.0 1.40 0.90 0.01
## sibling* 1.0 2.73 5.44 0.01
## moa_independence 3.0 -1.44 2.52 0.01
## swb 6.0 -0.37 -0.45 0.02
## belong 3.7 -0.26 -0.13 0.01
## socmeduse 44.0 -0.31 0.26 0.15# 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$gender)##
## f m nb
## 2270 769 53table(d2$sibling)##
## at least one sibling only child
## 2793 299# use histograms to visualize continuous data (4 variables)
hist(d2$moa_independence)
hist(d2$swb)
hist(d2$belong)
hist(d2$socmeduse)
Univariate Normality for Continuous Variables
describe(d2)## vars n mean sd median trimmed mad min max
## ResponseID* 1 3092 1546.50 892.73 1546.50 1546.50 1146.05 1.0 3092
## gender* 2 3092 1.28 0.49 1.00 1.21 0.00 1.0 3
## sibling* 3 3092 1.10 0.30 1.00 1.00 0.00 1.0 2
## moa_independence 4 3092 3.54 0.46 3.67 3.61 0.49 1.0 4
## swb 5 3092 4.47 1.32 4.67 4.53 1.48 1.0 7
## belong 6 3092 3.23 0.61 3.30 3.25 0.59 1.3 5
## socmeduse 7 3092 34.42 8.60 35.00 34.70 7.41 11.0 55
## range skew kurtosis se
## ResponseID* 3091.0 0.00 -1.20 16.05
## gender* 2.0 1.40 0.90 0.01
## sibling* 1.0 2.73 5.44 0.01
## moa_independence 3.0 -1.44 2.52 0.01
## swb 6.0 -0.37 -0.45 0.02
## belong 3.7 -0.26 -0.13 0.01
## socmeduse 44.0 -0.31 0.26 0.15## 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 satisfaction with life, belonging, and social media use were within the accepted range (-2/+2). However, the independence variable was outside of the accepted range. For this analysis, we will use it anyway, but outside of this class this is bad practice
Bivariate Plots
Crosstabs
Crosstabs are used to visualize combinations of two categorical variables.
cross_cases(d2, gender, sibling)|  sibling | ||
|---|---|---|
|  at least one sibling |  only child | |
|  gender | ||
|    f | 2056 | 214 |
|    m | 689 | 80 |
|    nb | 48 | 5 |
|    #Total cases | 2793 | 299 |
## 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$moa_independence, d2$swb,
main="Scatterplot of Independence and Satisfaction with Life",
xlab = "Independence",
ylab = "Satisfaction with Life")
plot(d2$belong, d2$socmeduse,
main="Scatterplot of Belonging and Social Media Use",
xlab = "Belonging",
ylab = "Social Media Use")
# 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, socmeduse~gender,
main="Boxplot of Social Media Use by Gender",
xlab = "Gender",
ylab = "Social Media Use")
boxplot(data=d2, moa_independence~sibling,
main="Boxplot of Independence by Sibling Status",
xlab = "Sibling Status",
ylab = "Independence")
# 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!!