Basic Statistics Homework

Load Libraries

# remember, you might need to install packages

library(psych) # for the describe() command
library(expss) # for the cross_cases() command

Load Data

# Will need to update this for the homework, use MYDATA 
d <- read.csv(file="Data/mydata.csv", header=T)
names(d)

[1] "gender"    "age"       "swb"       "mindful"   "belong"    "socmeduse"

Univariate Plots: Histograms & Tables

table(d$gender)

   f    m   nb 
1582  543   31 

table(d$age)

1 between 18 and 25 2 between 26 and 35 3 between 36 and 45           4 over 45 
               1985                 115                  38                  18 

# for the table use categorical and the hist for continuous 
hist(d$swb)

hist(d$mindful)

hist(d$belong)

hist(d$socmeduse)

Univariate Normality

Check skew and kurtosis.

describe(d)

          vars    n  mean   sd median trimmed  mad   min max range  skew
gender*      1 2156  1.28 0.48   1.00    1.21 0.00  1.00   3  2.00  1.36
age*         2 2156  1.11 0.43   1.00    1.00 0.00  1.00   4  3.00  4.42
swb          3 2156  4.43 1.33   4.50    4.49 1.48  1.00   7  6.00 -0.35
mindful      4 2156  3.72 0.84   3.73    3.72 0.79  1.13   6  4.87 -0.04
belong       5 2156  3.21 0.61   3.20    3.23 0.59  1.30   5  3.70 -0.27
socmeduse    6 2156 34.26 8.59  35.00   34.52 7.41 11.00  55 44.00 -0.30
          kurtosis   se
gender*       0.71 0.01
age*         21.12 0.01
swb          -0.50 0.03
mindful      -0.15 0.02
belong       -0.09 0.01
socmeduse     0.20 0.19

# cut offs for skew and kurtosis ranges from -2 to 2 

Bivariate Plots

Crosstabs

cross_cases(d, gender, age)

	age
	1 between 18 and 25	2 between 26 and 35	3 between 36 and 45	4 over 45
gender
f	1473	69	28	12
m	482	46	9	6
nb	30		1
#Total cases	1985	115	38	18

Scatterplots

plot(d$swb, d$mindful,
     main="Scatterplot of Satisfaction with Life and Mindfulness",
     xlab = " Satisfaction with Life",
     ylab = "Mindfulness")

plot(d$swb, d$belong,
     main="Scatterplot of Satisfaction with Life and Need to Belong",
     xlab = " Satisfaction with Life",
     ylab = "Need to Belong")

plot(d$swb, d$socmeduse,
     main="Scatterplot of Satisfaction with Life and Social Media Use",
     xlab = " Satisfaction with Life",
     ylab = "Social Media Use")

plot(d$mindful, d$belong,
     main="Mindfulness and Need to Belong",
     xlab = "Mindfulness",
     ylab = "Need to Belong")

plot(d$mindful, d$socmeduse,
     main="Scatterplot of Mindfulness and Social Media Use ",
     xlab = "Mindfulness",
     ylab = "Social Media Use")

plot(d$belong, d$socmeduse,
     main="Scatterplot of Need to Belong and Social Media Use ",
     xlab = "Need to Belong",
     ylab = "Social Media Use")

#need code for all continuous 1-2,1-3,1-4, 2-3,2-4, 3-4

Boxplots

# remember that continuous variable comes first, CONTINUOUS ~ CATEGORICAL 
boxplot(data=d, socmeduse~gender,
        main="Boxplot of Social Media Use and Gender ",
        xlab = " Gender ",
        ylab = "Social Media Use")

boxplot(data=d, belong~age,
        main="Boxplot of Need to Belong and Age",
        xlab = "Age ",
        ylab = "Need to Belong")

Write-Up

I reviewed plots and descriptive statistics for my six chosen variables; gender, age, satisfaction with life, mindfulness, need to belong, and social media use. All four of my continuous variables; satisfaction with life, mindfulness, need to belong, and social media use had skew and kurtosis within the accepted range (-2/+2).