P421 - Basic Statistics Homework
Cecilia Meneghini
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##
## To aggregate several columns with one summary: take(mtcars, mpg, hp, fun = mean, by = am)Import & Examine Data
# Import the "fakedata_2025.csv" file
d2 <- read.csv("Data/eammi2_data_final_SP25-1.csv")
str(d2)## 'data.frame': 3182 obs. of 27 variables:
## $ ResponseID : chr "R_BJN3bQqi1zUMid3" "R_2TGbiBXmAtxywsD" "R_12G7bIqN2wB2N65" "R_39pldNoon8CePfP" ...
## $ gender : chr "f" "m" "m" "f" ...
## $ race_rc : chr "white" "white" "white" "other" ...
## $ age : chr "1 between 18 and 25" "1 between 18 and 25" "1 between 18 and 25" "1 between 18 and 25" ...
## $ income : chr "1 low" "1 low" "rather not say" "rather not say" ...
## $ edu : chr "2 Currently in college" "5 Completed Bachelors Degree" "2 Currently in college" "2 Currently in college" ...
## $ sibling : chr "at least one sibling" "at least one sibling" "at least one sibling" "at least one sibling" ...
## $ party_rc : chr "democrat" "independent" "apolitical" "apolitical" ...
## $ disability : chr NA NA "psychiatric" NA ...
## $ marriage5 : chr "are currently divorced from one another" "are currently married to one another" "are currently married to one another" "are currently married to one another" ...
## $ phys_sym : chr "high number of symptoms" "high number of symptoms" "high number of symptoms" "high number of symptoms" ...
## $ pipwd : num NA NA 2.33 NA NA ...
## $ moa_independence: num 3.67 3.67 3.5 3 3.83 ...
## $ moa_role : num 3 2.67 2.5 2 2.67 ...
## $ moa_safety : num 2.75 3.25 3 1.25 2.25 2.5 4 3.25 2.75 3.5 ...
## $ moa_maturity : num 3.67 3.33 3.67 3 3.67 ...
## $ idea : num 3.75 3.88 3.75 3.75 3.5 ...
## $ swb : num 4.33 4.17 1.83 5.17 3.67 ...
## $ mindful : num 2.4 1.8 2.2 2.2 3.2 ...
## $ belong : num 2.8 4.2 3.6 4 3.4 4.2 3.9 3.6 2.9 2.5 ...
## $ efficacy : num 3.4 3.4 2.2 2.8 3 2.4 2.3 3 3 3.7 ...
## $ support : num 6 6.75 5.17 5.58 6 ...
## $ socmeduse : int 47 23 34 35 37 13 37 43 37 29 ...
## $ usdream : chr "american dream is important and achievable for me" "american dream is important and achievable for me" "american dream is not important and maybe not achievable for me" "american dream is not important and maybe not achievable for me" ...
## $ npi : num 0.6923 0.1538 0.0769 0.0769 0.7692 ...
## $ exploit : num 2 3.67 4.33 1.67 4 ...
## $ stress : num 3.3 3.3 4 3.2 3.1 3.5 3.3 2.4 2.9 2.7 ...# 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$gender)##
## f m nb
## 2332 792 54table(d2$marriage5)##
## are currently divorced from one another
## 736
## are currently married to one another
## 2137
## never married each other and are not together
## 251
## never married each other but are currently together
## 48# use histograms to visualize continuous data (4 variables)
hist(d2$efficacy)
hist(d2$belong)
hist(d2$npi)
hist(d2$exploit)
Univariate Normality for Continuous Variables
describe(d2)## vars n mean sd median trimmed mad min max
## ResponseID* 1 3182 1591.50 918.71 1591.50 1591.50 1179.41 1.00 3182.0
## gender* 2 3178 1.28 0.49 1.00 1.21 0.00 1.00 3.0
## race_rc* 3 3173 5.53 2.13 7.00 5.88 0.00 1.00 7.0
## age* 4 2169 1.11 0.43 1.00 1.00 0.00 1.00 4.0
## income* 5 3157 2.44 1.16 2.00 2.42 1.48 1.00 4.0
## edu* 6 3174 2.51 1.25 2.00 2.18 0.00 1.00 7.0
## sibling* 7 3182 1.10 0.29 1.00 1.00 0.00 1.00 2.0
## party_rc* 8 3165 2.46 1.01 2.00 2.45 0.00 1.00 4.0
## disability* 9 864 3.71 1.70 5.00 3.78 1.48 1.00 6.0
## marriage5* 10 3172 1.88 0.60 2.00 1.83 0.00 1.00 4.0
## phys_sym* 11 3174 2.26 0.86 3.00 2.32 0.00 1.00 3.0
## pipwd 12 1624 2.93 0.56 3.00 2.93 0.40 1.13 5.0
## moa_independence 13 3107 3.54 0.47 3.67 3.61 0.49 1.00 4.0
## moa_role 14 3111 2.97 0.72 3.00 3.00 0.74 1.00 4.0
## moa_safety 15 3123 3.20 0.64 3.25 3.26 0.74 1.00 4.0
## moa_maturity 16 3146 3.59 0.43 3.67 3.65 0.49 1.00 4.0
## idea 17 3177 3.57 0.38 3.62 3.62 0.37 1.00 4.0
## swb 18 3178 4.47 1.32 4.67 4.53 1.48 1.00 7.0
## mindful 19 3173 3.71 0.84 3.73 3.71 0.79 1.13 6.0
## belong 20 3175 3.23 0.60 3.30 3.25 0.59 1.30 5.0
## efficacy 21 3176 3.13 0.45 3.10 3.13 0.44 1.00 4.0
## support 22 3182 5.53 1.14 5.75 5.65 0.99 0.00 7.0
## socmeduse 23 3175 34.45 8.58 35.00 34.72 7.41 11.00 55.0
## usdream* 24 3171 2.39 1.55 2.00 2.24 1.48 1.00 5.0
## npi 25 3167 0.28 0.31 0.15 0.24 0.23 0.00 1.0
## exploit 26 3177 2.39 1.37 2.00 2.21 1.48 1.00 7.0
## stress 27 3176 3.05 0.60 3.00 3.05 0.59 1.30 4.7
## range skew kurtosis se
## ResponseID* 3181.00 0.00 -1.20 16.29
## gender* 2.00 1.40 0.88 0.01
## race_rc* 6.00 -0.98 -0.68 0.04
## age* 3.00 4.42 21.17 0.01
## income* 3.00 0.14 -1.44 0.02
## edu* 6.00 2.18 3.66 0.02
## sibling* 1.00 2.74 5.53 0.01
## party_rc* 3.00 0.42 -1.04 0.02
## disability* 5.00 -0.44 -1.35 0.06
## marriage5* 3.00 0.47 1.48 0.01
## phys_sym* 2.00 -0.52 -1.46 0.02
## pipwd 3.87 0.12 1.34 0.01
## moa_independence 3.00 -1.44 2.53 0.01
## moa_role 3.00 -0.33 -0.84 0.01
## moa_safety 3.00 -0.71 0.03 0.01
## moa_maturity 3.00 -1.20 1.87 0.01
## idea 3.00 -1.54 4.42 0.01
## swb 6.00 -0.36 -0.46 0.02
## mindful 4.87 -0.06 -0.13 0.01
## belong 3.70 -0.26 -0.12 0.01
## efficacy 3.00 -0.29 0.63 0.01
## support 7.00 -1.14 1.61 0.02
## socmeduse 44.00 -0.31 0.26 0.15
## usdream* 4.00 0.62 -1.13 0.03
## npi 1.00 0.94 -0.69 0.01
## exploit 6.00 0.95 0.37 0.02
## stress 3.40 0.04 -0.17 0.01## 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, marriage5)| Â marriage5Â | ||||
|---|---|---|---|---|
|  are currently divorced from one another |  are currently married to one another |  never married each other and are not together |  never married each other but are currently together | |
|  gender | ||||
|    f | 547 | 1556 | 186 | 35 |
|    m | 175 | 544 | 58 | 13 |
|    nb | 14 | 34 | 6 | |
|    #Total cases | 736 | 2134 | 250 | 48 |
## 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$belong, d2$npi,
main="Scatterplot of Needingness to Belong and Narcissistic Personality Inventory",
xlab = "Needingness to Belong",
ylab = "Narcissistic Personality Inventory")
plot(d2$exploit, d2$npi,
main="Scatterplot of Exploitativeness and Narcissitic Personality Inventory",
xlab = "Exploitativeness",
ylab = "Narcissistic Personality Inventory")
# 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, npi~marriage5,
main="Boxplot of Narcissistic Personality Inventory by Parental Marital Status",
xlab = "Parent Marital Status",
ylab = "Narcissistic Personality Inventory")
boxplot(data=d2, npi~gender,
main="Boxplot of Narcissistic Personality Inventory by Gender",
xlab = "Gender",
ylab = "Narcissistic Personality Inventory")
# 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!!