Can create a frequency table with the table() function

(tc <- table(mtcars$cyl))
## 
##  4  6  8 
## 11  7 14
nlevels(mtcars$cyl)
## [1] 0

Can create a table with paired observations

(mytable <- table(mtcars$cyl,mtcars$carb))
##    
##     1 2 3 4 6 8
##   4 5 6 0 0 0 0
##   6 2 0 0 4 1 0
##   8 0 4 3 6 0 1
(table.complete <- addmargins(mytable, FUN=sum, quiet=TRUE))
##      
##        1  2  3  4  6  8 sum
##   4    5  6  0  0  0  0  11
##   6    2  0  0  4  1  0   7
##   8    0  4  3  6  0  1  14
##   sum  7 10  3 10  1  1  32

Some useful summary statistics

mean(mtcars$mpg)
## [1] 20.09062
median(mtcars$mpg)
## [1] 19.2
sd(mtcars$mpg) #Standard Deviation
## [1] 6.026948
max(mtcars$mpg)-min(mtcars$mpg) #Range
## [1] 23.5
IQR(mtcars$mpg) #Interquartile
## [1] 7.375
mean(abs(mtcars$mpg-mean(mtcars$mpg))) #Deviation measure, mean absolute difference
## [1] 4.714453
quantile(mtcars$mpg, probs=c(0.1,0.9)) #Quantiles
##   10%   90% 
## 14.34 30.09

Can handle NA’s with the following functions:

is.na(data) #Returns true if there is N/As in the data
na.omit(data) #Removes observations with N/A

Can compute correlation matrix by passing the dataframe/matrix into the corr function

cor(mtcars)
##             mpg        cyl       disp         hp        drat         wt
## mpg   1.0000000 -0.8521620 -0.8475514 -0.7761684  0.68117191 -0.8676594
## cyl  -0.8521620  1.0000000  0.9020329  0.8324475 -0.69993811  0.7824958
## disp -0.8475514  0.9020329  1.0000000  0.7909486 -0.71021393  0.8879799
## hp   -0.7761684  0.8324475  0.7909486  1.0000000 -0.44875912  0.6587479
## drat  0.6811719 -0.6999381 -0.7102139 -0.4487591  1.00000000 -0.7124406
## wt   -0.8676594  0.7824958  0.8879799  0.6587479 -0.71244065  1.0000000
## qsec  0.4186840 -0.5912421 -0.4336979 -0.7082234  0.09120476 -0.1747159
## vs    0.6640389 -0.8108118 -0.7104159 -0.7230967  0.44027846 -0.5549157
## am    0.5998324 -0.5226070 -0.5912270 -0.2432043  0.71271113 -0.6924953
## gear  0.4802848 -0.4926866 -0.5555692 -0.1257043  0.69961013 -0.5832870
## carb -0.5509251  0.5269883  0.3949769  0.7498125 -0.09078980  0.4276059
##             qsec         vs          am       gear        carb
## mpg   0.41868403  0.6640389  0.59983243  0.4802848 -0.55092507
## cyl  -0.59124207 -0.8108118 -0.52260705 -0.4926866  0.52698829
## disp -0.43369788 -0.7104159 -0.59122704 -0.5555692  0.39497686
## hp   -0.70822339 -0.7230967 -0.24320426 -0.1257043  0.74981247
## drat  0.09120476  0.4402785  0.71271113  0.6996101 -0.09078980
## wt   -0.17471588 -0.5549157 -0.69249526 -0.5832870  0.42760594
## qsec  1.00000000  0.7445354 -0.22986086 -0.2126822 -0.65624923
## vs    0.74453544  1.0000000  0.16834512  0.2060233 -0.56960714
## am   -0.22986086  0.1683451  1.00000000  0.7940588  0.05753435
## gear -0.21268223  0.2060233  0.79405876  1.0000000  0.27407284
## carb -0.65624923 -0.5696071  0.05753435  0.2740728  1.00000000

Can get the density function using d—

dnorm(1.96, mean = 0 , sd = 1)
## [1] 0.05844094

Can get the CDF using p—-

pnorm(1.96, mean = 0 , sd = 1)
## [1] 0.9750021

Can get the relative result for a quantile using q—

qnorm(0.975, mean = 0 , sd = 1)
## [1] 1.959964

Can generate a random number using r—-

rnorm(1, mean = 0, sd = 1)
## [1] -0.1578528