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 select columns from data: columns(mtcars, mpg, vs:carb)
## 
## Use 'expss_output_viewer()' to display tables in the RStudio Viewer.
##  To return to the console output, use 'expss_output_default()'.

##Import Data

# Import the projectdata.csv file created in the Data Prep Lab
d2 <- read.csv("~/Downloads/Psy P421( Social Capstone)/Research/Final Paper/Data/projectdata.csv")

Univariate 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$gender)
## 
##    f    m   nb 
## 2320  788   54
# use histograms to visualize continuous data
hist(d2$stress)

hist(d2$swb)

hist(d2$belong)

hist(d2$support)

Univariate Normality for Continuous Variables (individually)

describe(d2)
##             vars    n    mean     sd  median trimmed     mad  min    max  range
## ResponseID*    1 3162 1581.50 912.94 1581.50 1581.50 1172.00  1.0 3162.0 3161.0
## gender*        2 3162    1.28   0.49    1.00    1.21    0.00  1.0    3.0    2.0
## socmeduse      3 3162   34.44   8.57   35.00   34.72    7.41 11.0   55.0   44.0
## stress         4 3162    3.05   0.60    3.00    3.05    0.59  1.3    4.7    3.4
## swb            5 3162    4.48   1.32    4.67    4.53    1.48  1.0    7.0    6.0
## belong         6 3162    3.23   0.61    3.30    3.25    0.59  1.3    5.0    3.7
## support        7 3162    5.53   1.13    5.75    5.66    0.99  0.0    7.0    7.0
##              skew kurtosis    se
## ResponseID*  0.00    -1.20 16.24
## gender*      1.40     0.88  0.01
## socmeduse   -0.31     0.26  0.15
## stress       0.03    -0.17  0.01
## swb         -0.36    -0.45  0.02
## 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.

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.

# Note: our dataset has only one categorical variable (gender), so a crosstab is not applicable here.
table(d2$gender)
## 
##    f    m   nb 
## 2320  788   54

Scatterplots

Scatterplots are used to visualize combinations of two continuous variables.

plot(d2$socmeduse, d2$stress,
     main = "Scatterplot of Social Media Use and Stress",
     xlab = "socmeduse",
     ylab = "stress")

plot(d2$socmeduse, d2$swb,
     main = "Scatterplot of Social Media Use and Subjective Well-Being",
     xlab = "socmeduse",
     ylab = "swb")

Boxplots

Boxplots are used to visualize combinations of one categorical and one continuous variable.

boxplot(d2$stress ~ d2$gender,
        main = "Boxplot of Stress by Gender",
        xlab = "gender",
        ylab = "stress")

boxplot(d2$swb ~ d2$gender,
        main = "Boxplot of Subjective Well-Being by Gender",
        xlab = "gender",
        ylab = "swb")

We did it!!