#Installing “Here” Package

#install.packages("here")
library("here")
## here() starts at /Users/sammyromero/Documents/LaboratoryinPsychology/P421homework/P421Labs

Basic Statistics

Load Libraries

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)
## 
## Attaching package: 'maditr'
## The following object is masked from 'package:base':
## 
##     sort_by

Import Data

d2 <- read.csv(file = "Data/mydata.csv", header = T)

Univariate Plots: Histograms & Tables

#the table command shows us what the levels of the variable are, and how many participants are in each variable
# categorical variables:
table(d2$disability) 
## 
## chronic health       learning          other       physical    psychiatric 
##            147            124             87             49            378 
##        sensory 
##             70
table(d2$income)
## 
##          1 low       2 middle         3 high rather not say 
##            257            218            141            239
#this command creates a histogram of the variable
# continuous variables:
hist(d2$swb) 

hist(d2$idea)

hist(d2$mindful)

hist(d2$belong)

Univariate Normality

I analyzed the skew and kurtosis of the continuous variables and all were within the accepted range (-2/+2).

describe(d2) #we use this to check univariate normality...skew and kurtosis (-2,+2)
##             vars   n mean   sd median trimmed  mad  min  max range  skew
## disability*    1 855 3.70 1.71   5.00    3.77 1.48 1.00 6.00  5.00 -0.43
## income*        2 855 2.42 1.19   2.00    2.40 1.48 1.00 4.00  3.00  0.14
## swb            3 855 4.06 1.39   4.17    4.10 1.48 1.00 7.00  6.00 -0.19
## idea           4 855 3.59 0.34   3.62    3.63 0.37 2.12 4.00  1.88 -1.11
## mindful        5 855 3.48 0.84   3.47    3.48 0.79 1.13 5.67  4.53 -0.04
## belong         6 855 3.30 0.60   3.30    3.32 0.59 1.40 5.00  3.60 -0.23
##             kurtosis   se
## disability*    -1.36 0.06
## income*        -1.49 0.04
## swb            -0.76 0.05
## idea            1.41 0.01
## mindful        -0.19 0.03
## belong         -0.21 0.02

Bivariate Plots

Crosstabs

cross_cases(d2, disability, income) #update variable 2 and variable 3 with your categorical variable names
 income 
 1 low   2 middle   3 high   rather not say 
 disability 
   chronic health  45 35 22 45
   learning  39 28 25 32
   other  29 19 12 27
   physical  17 17 6 9
   psychiatric  112 100 63 103
   sensory  15 19 13 23
   #Total cases  257 218 141 239

Scatterplots

plot(d2$belong , d2$idea,
     main="Scatterplot of belong and idea",
     xlab = "belong",
     ylab = "idea")

plot(d2$mindful , d2$belong,
     main="Scatterplot of Mindful and Belong",
     xlab = "Mindful",
     ylab = "Belong")

Boxplots

# boxplots use one ONE CATEGORICAL and ONE CONTINUOUS variable!!!
# make sure to enter them in the right order 
# categorical variable goes BEFORE the tilde ~
# continuous variable goes AFTER the tilde ~
boxplot(data=d2, swb~income,
        main="Boxplot of swb and income",
        xlab = "swb",
        ylab = "income")

boxplot(data=d2, idea~disability,
        main="Boxplot of idea and disability",
        xlab = "idea",
        ylab = "disability")