Basic Statistics Lab

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 for homework, use mydata 
d <- read.csv(file="Data/mydata.csv", header=T)
names(d)

[1] "employment" "gender"     "big5_neu"   "big5_agr"   "big5_open" 
[6] "big5_ext"  

Univariate Plots: Histograms & Tables

table(d$employment)

1 high school equivalent     2 college/university               3 employed 
                    1147                       33                      435 
            4 unemployed                5 retired        prefer not to say 
                      79                        4                       24 

table(d$gender)

            female I use another term               male  Prefer not to say 
              1360                 46                287                 29 

hist(d$big5_neu)

hist(d$big5_agr)

hist(d$big5_open)

hist(d$big5_ext)

Univariate Normality

Check skew and kurtosis.

describe(d)

            vars    n mean   sd median trimmed  mad min max range  skew
employment*    1 1722 1.74 1.13   1.00    1.56 0.00   1   6     5  1.33
gender*        2 1722 1.41 0.82   1.00    1.24 0.00   1   4     3  1.64
big5_neu       3 1722 4.40 1.51   4.67    4.46 1.48   1   7     6 -0.31
big5_agr       4 1722 4.98 1.11   5.00    5.02 0.99   1   7     6 -0.39
big5_open      5 1722 5.20 1.14   5.33    5.29 0.99   1   7     6 -0.72
big5_ext       6 1722 4.35 1.45   4.33    4.40 1.48   1   7     6 -0.25
            kurtosis   se
employment*     1.20 0.03
gender*         1.03 0.02
big5_neu       -0.73 0.04
big5_agr       -0.02 0.03
big5_open       0.43 0.03
big5_ext       -0.76 0.03

Bivariate Plots

Crosstabs

cross_cases(d, big5_neu, employment)

	employment
	1 high school equivalent	2 college/university	3 employed	4 unemployed	5 retired	prefer not to say
big5_neu
1	18		10		1	1
1.333333333	7		10	3		1
1.666666667	25	1	17	5
2	37		36	7
2.333333333	29		27	3	2
2.666666667	50		24	6		2
3	37	1	26	3		1
3.333333333	47	1	39	2		1
3.666666667	59	1	33	3		1
4	68	1	45	3		1
4.333333333	81	3	34	9	1	2
4.666666667	99	5	33	7
5	104	6	35	8		4
5.333333333	117	2	19	6		4
5.666666667	103	3	19	4		2
6	75	1	13	2		1
6.333333333	72	2	4	4
6.666666667	80	5	4	1		1
7	39	1	7	3		2
#Total cases	1147	33	435	79	4	24

Scatterplots

 plot(d$big5_ext, d$big5_agr,
      main="Scatterplot of Extroversion and Agreeableness",
      xlab = "Extroversion",
      ylab = "Agreeableness")

 plot(d$big5_neu, d$big5_agr,
      main="Scatterplot of Neuroticism and Agreeableness",
      xlab = "Neuroticism",
      ylab = "Agreeableness")

  plot(d$big5_open, d$big5_agr,
      main="Scatterplot of Openness and Agreeableness",
      xlab = "Openness",
      ylab = "Agreeableness")

  plot(d$big5_neu, d$big5_open,
      main="Scatterplot of Neuroticism and Openness",
      xlab = "Neuroticism",
      ylab = "Openness")

   plot(d$big5_neu, d$big5_ext,
      main="Scatterplot of Neuroticism and Extroversion",
      xlab = "Neuroticism",
      ylab = "Extroversion")

   plot(d$big5_ext, d$big5_open,
      main="Scatterplot of Extroversion and Openness",
      xlab = "Extroversion",
      ylab = "Openness")

Boxplots

# remember that continious varaible comes first, continious~categorical 
boxplot(data=d, big5_ext~gender,
        main="Boxplot of Extroversion and Gender",
        xlab = "Extroversion",
        ylab = "Gender")

boxplot(data=d, big5_neu~employment,
        main="Boxplot of Neuroticism and Employment",
        xlab = "Neuroticism",
        ylab = "Employment")

Write-Up

We reviewed plots and descriptive statistics for our six chosen variables. All four of our continuous variables had skew and kurtosis within the accepted range (-2/+2), with only employment and gender reaching 1.