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#read in the data. Save in the r environment to 'caregiversData
caregiversData <- read.csv("caregivers.csv")
#view the help file
?read.csv
## starting httpd help server ... done
#summarize the data
summary(caregiversData)
##        ID         Caregiver.Age   Caregiver.Income Duration.of.caregiving
##  Min.   :  1.00   Min.   :23.00   Min.   :  30.0   Min.   :  2.00        
##  1st Qu.: 25.75   1st Qu.:35.75   1st Qu.: 200.0   1st Qu.: 12.00        
##  Median : 50.50   Median :41.00   Median : 255.0   Median : 24.00        
##  Mean   : 50.50   Mean   :41.85   Mean   : 319.1   Mean   : 29.82        
##  3rd Qu.: 75.25   3rd Qu.:48.25   3rd Qu.: 308.6   3rd Qu.: 36.00        
##  Max.   :100.00   Max.   :70.00   Max.   :2000.0   Max.   :120.00        
##      Health         ADL        Memory.behavior.problems Cognitive.impairment
##  Min.   :1.0   Min.   :22.00   Min.   : 3.0             Min.   : 0.00       
##  1st Qu.:2.0   1st Qu.:42.00   1st Qu.:14.0             1st Qu.: 7.00       
##  Median :2.0   Median :56.00   Median :24.0             Median :15.00       
##  Mean   :2.3   Mean   :57.85   Mean   :26.3             Mean   :13.69       
##  3rd Qu.:3.0   3rd Qu.:77.50   3rd Qu.:34.0             3rd Qu.:19.00       
##  Max.   :4.0   Max.   :90.00   Max.   :66.0             Max.   :27.00       
##  Perceived.social.support     Burden        Satisfaction   FCGHEALTH        
##  Min.   : 95.0            Min.   : 28.00   Min.   :26.0   Length:100        
##  1st Qu.:119.0            1st Qu.: 52.75   1st Qu.:38.0   Class :character  
##  Median :133.0            Median : 69.50   Median :45.0   Mode  :character  
##  Mean   :131.6            Mean   : 69.24   Mean   :44.6                     
##  3rd Qu.:143.2            3rd Qu.: 85.50   3rd Qu.:50.0                     
##  Max.   :172.0            Max.   :115.00   Max.   :64.0                     
##      Sex            Relationship        Education        
##  Length:100         Length:100         Length:100        
##  Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character  
##                                                          
##                                                          
## 
#get variables names of the dataset
names(caregiversData)
##  [1] "ID"                       "Caregiver.Age"           
##  [3] "Caregiver.Income"         "Duration.of.caregiving"  
##  [5] "Health"                   "ADL"                     
##  [7] "Memory.behavior.problems" "Cognitive.impairment"    
##  [9] "Perceived.social.support" "Burden"                  
## [11] "Satisfaction"             "FCGHEALTH"               
## [13] "Sex"                      "Relationship"            
## [15] "Education"
#use a single variable using '$'
caregiversData$Caregiver.Age
##   [1] 41 30 41 35 37 42 49 39 49 40 40 70 49 55 27 39 39 44 33 42 52 48 53 40 35
##  [26] 47 33 41 43 25 35 35 45 36 52 41 40 45 48 50 31 33 30 36 45 32 55 50 37 40
##  [51] 40 49 37 47 41 33 28 33 34 40 54 32 44 44 42 44 25 41 28 24 65 50 40 47 44
##  [76] 37 36 55 45 45 23 42 38 41 25 47 35 59 49 51 54 53 49 44 36 64 51 43 54 29
#summarize caregive age
summary(caregiversData$Caregiver.Age)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   23.00   35.75   41.00   41.85   48.25   70.00
#set ID as a categorical variable or factor
caregiversData$ID <- as.factor(caregiversData$ID)
summary(caregiversData$Caregiver.Age)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   23.00   35.75   41.00   41.85   48.25   70.00
#get mean of caregivers age
mean(caregiversData$Caregiver.Age)
## [1] 41.85
?mean
#see Help File for Mean - includes info on defaults and potential arguments
#na.rm = logical value indicating whether NA values should be stripped before computation proceeds - R indicates missing as NA#
# Separate argument with , 
#if you have missing data set na.rm=T
mean(caregiversData$Caregiver.Age, na.rm = TRUE)
## [1] 41.85
#variance of age
var(caregiversData$Caregiver.Age)
## [1] 84.2702
#standard deviation of age
sd(caregiversData$Caregiver.Age)
## [1] 9.17988
#can also calculate by taking square root of variance
#sqrt(var(caregiversData$Caregiver.Age))
#covariance of burden and age
#cov(variable1 , variable2)
cov(caregiversData$Caregiver.Age , caregiversData$Burden)
## [1] 34.00606
#correlation
cor(caregiversData$Caregiver.Age , caregiversData$Burden)
## [1] 0.1846891
#select a subset
#bind variables to a column - variables listed will be saved into a column
threeVar <- cbind(caregiversData$Burden, caregiversData$ADL,
                  caregiversData$Caregiver.Age)
#covariance of the subset
cov(threeVar)
##           [,1]       [,2]      [,3]
## [1,] 402.30545 153.612121 34.006061
## [2,] 153.61212 406.411616  9.007576
## [3,]  34.00606   9.007576 84.270202
#correlation
cor(threeVar)
##           [,1]       [,2]       [,3]
## [1,] 1.0000000 0.37989578 0.18468913
## [2,] 0.3798958 1.00000000 0.04867297
## [3,] 0.1846891 0.04867297 1.00000000
#plot ADL vs burden
plot(caregiversData$ADL, caregiversData$Burden)

#add labels
#add labels to plot 
plot(caregiversData$ADL, caregiversData$Burden, xlab = "Activities of Daily Living", ylab = "Caregiver Burden")

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