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 modify variables or add new variables:
##              let(mtcars, new_var = 42, new_var2 = new_var*hp) %>% head()

##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 Lab

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$marriage5)
## 
##             are currently divorced from one another 
##                                                 733 
##                are currently married to one another 
##                                                2119 
##       never married each other and are not together 
##                                                 243 
## never married each other but are currently together 
##                                                  46
table(d2$income)
## 
##          1 low       2 middle         3 high rather not say 
##            876            879            534            852
# use histograms to visualize continuous data
hist(d2$stress)

hist(d2$socmeduse)

hist(d2$belong)

hist(d2$swb)

Univariate Normality for Continuous Variables (individually)

describe(d2)
##             vars    n    mean     sd  median trimmed     mad  min    max  range
## ResponseID*    1 3141 1571.00 906.87 1571.00 1571.00 1163.84  1.0 3141.0 3140.0
## marriage5*     2 3141    1.87   0.59    2.00    1.83    0.00  1.0    4.0    3.0
## income*        3 3141    2.43   1.16    2.00    2.42    1.48  1.0    4.0    3.0
## stress         4 3141    3.05   0.60    3.00    3.05    0.59  1.3    4.7    3.4
## socmeduse      5 3141   34.45   8.55   35.00   34.73    7.41 11.0   55.0   44.0
## belong         6 3141    3.23   0.60    3.30    3.25    0.59  1.3    5.0    3.7
## swb            7 3141    4.48   1.32    4.67    4.53    1.48  1.0    7.0    6.0
##              skew kurtosis    se
## ResponseID*  0.00    -1.20 16.18
## marriage5*   0.46     1.49  0.01
## income*      0.15    -1.43  0.02
## stress       0.03    -0.16  0.01
## socmeduse   -0.32     0.27  0.15
## belong      -0.26    -0.13  0.01
## swb         -0.36    -0.45  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.

cross_cases(d2, marriage5, income)
 income 
 1 low   2 middle   3 high   rather not say 
 marriage5 
   are currently divorced from one another  298 198 73 164
   are currently married to one another  458 614 450 597
   never married each other and are not together  105 55 7 76
   never married each other but are currently together  15 12 4 15
   #Total cases  876 879 534 852 
# 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$stress, d2$socmeduse,
     main="Scatterplot of Stress and Social Media Use",
     xlab = "Stress",
     ylab = "Social Media Use")

plot(d2$stress, d2$belong,
     main="Scatterplot of Stress and Need to Belong",
     xlab = "Stress",
     ylab = "Need to Belong")

# 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, stress~marriage5,
        main="Boxplot of Parent's Marital Status and Stress",
        xlab = "Parent's Marital Status",
        ylab = "Stress")

boxplot(data=d2, swb~income,
        main="Boxplot of Income and Satisfaction with Life",
        xlab = "Income",
        ylab = "Satisfaction with Life")

# 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!!