P421 Lab - Basic Statistics Lab
Audrey Itin
2025-05-26
Basic Statistics
Load Libraries
# if you haven't used a given package before, you'll need to download it first
# delete the "#" before the install function and run it to download
# re-insert the "#" 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")
library(psych) # for the describe() command
library(expss) # for the cross_cases() command## Loading required package: maditr##
## To aggregate all non-grouping columns: take_all(mtcars, mean, by = am)##Import Data
# Import the "fakedata.csv" file
d2 <- read.csv("Data/projectdata.csv")
# 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
table(d2$ethnicity)##
## Asian/Asian British - Indian, Pakistani, Bangladeshi, other
## 35
## Black/Black British - Caribbean, African, other
## 12
## Chinese/Chinese British
## 3
## Middle Eastern/Middle Eastern British - Arab, Turkish, other
## 4
## Mixed race - other
## 18
## Mixed race - White and Black/Black British
## 10
## Other ethnic group
## 3
## Prefer not to say
## 10
## White - British, Irish, other
## 322table(d2$gender)##
## female I use another term male Prefer not to say
## 325 25 59 8# use histograms to visualize continuous data
hist(d2$gad)
hist(d2$rse)
hist(d2$brs)
hist(d2$pss)
Univariate Normality for Continuous Variables (individually)
describe(d2)## vars n mean sd median trimmed mad min max range skew
## X 1 417 7555.63 742.18 7488.00 7549.74 937.00 6291 8867 2576 0.07
## ethnicity* 2 417 7.75 2.59 9.00 8.41 0.00 1 9 8 -1.85
## gender* 3 417 1.40 0.80 1.00 1.23 0.00 1 4 3 1.72
## gad 4 417 2.54 0.93 2.57 2.55 1.27 1 4 3 -0.04
## rse 5 417 2.24 0.70 2.20 2.22 0.74 1 4 3 0.33
## brs 6 417 2.67 0.88 2.67 2.67 0.99 1 5 4 0.10
## pss 7 417 3.42 0.92 3.50 3.46 1.11 1 5 4 -0.41
## kurtosis se
## X -1.20 36.34
## ethnicity* 1.82 0.13
## gender* 1.47 0.04
## gad -1.19 0.05
## rse -0.65 0.03
## brs -0.68 0.04
## pss -0.58 0.04## 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.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, ethnicity)|  ethnicity | |||||||||
|---|---|---|---|---|---|---|---|---|---|
|  Asian/Asian British - Indian, Pakistani, Bangladeshi, other |  Black/Black British - Caribbean, African, other |  Chinese/Chinese British |  Middle Eastern/Middle Eastern British - Arab, Turkish, other |  Mixed race - other |  Mixed race - White and Black/Black British |  Other ethnic group |  Prefer not to say |  White - British, Irish, other | |
|  gender | |||||||||
|    I use another term | 2 | 1 | 2 | 20 | |||||
|    Prefer not to say | 1 | 3 | 4 | ||||||
|    female | 28 | 11 | 3 | 3 | 12 | 10 | 3 | 7 | 248 |
|    male | 5 | 1 | 3 | 50 | |||||
|    #Total cases | 35 | 12 | 3 | 4 | 18 | 10 | 3 | 10 | 322 |
# 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$rse, d2$pss,
main="Scatterplot of rse and pss",
xlab = "rse",
ylab = "pss")
plot(d2$gad, d2$brs,
main="Scatterplot of gad and brs",
xlab = "gad",
ylab = "brs")
# Note: for HW, you will choose to plot 2 combos of your 4 continuous variables, based on your 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 variable names, not their 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, rse~gender,
main="Boxplot of rse and gender",
xlab = "rse",
ylab = "gender")
boxplot(data=d2, gad~ethnicity,
main="Boxplot of gad and ethnicity",
xlab = "gad",
ylab = "ethnicity")
# 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 hypotheses. You may repeat 1 variable to see its association with others. Again, 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 variable names, not their scales, so someone reading your plots can understand them.We did it!!