class 1, Jan 23, 2019

we had some basic R commands and we explored R as a super calculator

2+3
## [1] 5
2/3
## [1] 0.6666667

Use rnorm to generate a few random numbers ~N(0,1):

rnorm(100)
##   [1]  1.224752839 -0.211509437  0.710438234  0.453516374 -0.572447001
##   [6]  0.421535132  0.536676069 -0.384082941  1.787063892 -0.332658462
##  [11] -0.757946483 -0.159763366  0.442159041  0.779073768  0.839355305
##  [16] -1.215309604  2.253626323 -1.142028534 -0.076112065  1.190816958
##  [21] -1.685490122  0.694750446  1.162587670  2.017359540  0.406634678
##  [26]  1.448881559  2.109212875  1.195003189 -0.579705604 -0.297615897
##  [31]  0.386013534  1.084697106  0.593781491 -0.742106406  1.163684003
##  [36]  0.384338366 -0.457214844 -0.661792572  0.473577578 -1.591513166
##  [41] -0.632006175  1.503667522 -0.460898055  1.351555560  1.240189257
##  [46]  1.385728376 -0.562381028 -0.190938391 -0.160912484 -0.287888040
##  [51]  1.211272695  0.021625276  2.293053686  0.698753668 -0.649749822
##  [56] -0.617965970  0.306648462 -1.110083565  0.087131320 -0.643355254
##  [61] -0.863190464 -0.982348047  1.964438156 -1.757286640 -0.595188664
##  [66]  1.328094872 -0.438485713 -0.400101764 -0.865263033 -0.135142212
##  [71] -0.396984275 -0.227481058 -2.574886386 -0.635752815 -0.381724518
##  [76]  1.348003613 -0.768024520 -0.387453841  0.401234999 -2.044107084
##  [81] -1.940714392  0.556525858 -0.069012604  0.476393913 -0.940777305
##  [86]  0.646157728  1.141335748 -0.550577770  2.035812580 -0.956112527
##  [91]  0.947205153  0.923868657 -1.091924970  2.577934036  0.251314667
##  [96]  0.008938974 -0.022691630 -0.422104619 -0.628083607  0.056449120
plot(rnorm(100))

class 2, Jan 25, 2019

we made the data Student255 Student (excel/csv/txt) file and imported in R.

student255 <- read.delim("~/comp Stats notes/student255.txt")
student255
##   Student Height Drive Study Course Tutoring Sleeping
## 1       1   70.0  0.10     2      5        0      8.5
## 2       2   70.8  1.00     0      5        0      8.0
## 3       3   64.0  1.50     2      5        0      8.0
## 4       4   69.0  0.15     3      5        0      7.5
## 5       5   68.0  3.00     3      5        0      8.0
## 6       6   68.0  0.00     4      5        0      7.0
## 7       7   73.0  0.33     3      5        0      8.0
## 8       8   70.0  3.00     3      5        0      8.0

In case you want to see every single column seperately:

str(student255)
## 'data.frame':    8 obs. of  7 variables:
##  $ Student : int  1 2 3 4 5 6 7 8
##  $ Height  : num  70 70.8 64 69 68 68 73 70
##  $ Drive   : num  0.1 1 1.5 0.15 3 0 0.33 3
##  $ Study   : int  2 0 2 3 3 4 3 3
##  $ Course  : int  5 5 5 5 5 5 5 5
##  $ Tutoring: int  0 0 0 0 0 0 0 0
##  $ Sleeping: num  8.5 8 8 7.5 8 7 8 8

Suppose you want to plot the Student versus Height graph

attach(student255)
plot(Height)

Here we inserted the “include = TRUE” option to make the figure visible.

Here are some different plot types

plot(Height) #plots scatter plot

hist(Height) #creates a histogram

barplot(Height) #creates a barplot 

barplot(Height, main = "Height of the Students in inches", xlab = "Students", ylab = "Heights")

The line types for graph format are below 1. p= point 2. l= line 3. o= over plotted points & lines 4. b= points joined by lines 5. c= empty points joined by lines 6. s= staircase 7. h= vertical lines

class 3, Jan 28,2019