R workshop - morning

gistfile1.txt

print("ok")
a= 5
b=10
print(a+b)


x <- 4L
20 -> y
print(class(x))
print(class(y))

#vectors    collection of same data types   'C' or 'v'  is used to create vector

apple <- c("red", "green", "yellow")
print(apple)
print(apple[1])

#lists -  collection of dissimilar data types variable
l <- list(1,2,3,"abc",12.5, c('x','y','z'))
print(class(l))
print(class(apple))

#matrix
M= matrix( c(2,2,2,3,3,4,4,4,3), nrow=3, ncol = 3, byrow= FALSE)
print(M)

#array 
a <- array(c("green","yellow"), dim= c(3,3,2))  #3D array 3x3x2
print(a)


#data frame
BMI <- data.frame(
    gender= c("male", "female", "male"),
    height= c(102,120,180),
    weight= c(65,82,55),
    age= c(28,32,65)
)

print(BMI)

install.packages("MASS")
library(mass)

Pima.te
Pima.tr

merged.Pima <- merge(x= Pima.te, y= Pima.tr,
                     by.x = c("bp", "bmi"),
                     by.y = c("bp", "bmi")
                     )


# TESt 1


f1 <- data.frame(
  state = c("alabama", "alaska", "arizona", "arkansas", "Califorinia", "colorado","connecticut","delaware"),
  region = c("S","W","w","s","w","w","NE","S")
)
print(f1)

f2 <- data.frame(
   stat = c("alabama", "alaska", "arizona", "arkansas", "Califorinia", "colorado","connecticut"),
   population = c(95200,12000,50000,125000,805000,350000,725000)
)
print(f2)

f3 <- merge(f1, f2) 
print(f3)

nrow(f1)
ncol(f1)

str(f3)

summary(f1)

###############################

install.packages("downloader")
library(downloader)
install.packages("dplyr")
library(dplyr)
url <- "https://raw.githubusercontent.com/genomicsclass/dagdata/master/inst/extdata/msleep_ggplot2.csv"
filename <- "msleep_ggplot2.csv"
if (!file.exists(filename)) download(url,filename)
msleep <- read.csv("msleep_ggplot2.csv")

#head(msleep)

#summary(msleep)

sleepData= select(msleep, name, sleep_total, sleep_rem, awake, brainwt)
sleepData= select(msleep, -name, -vore, -genus)
sleepData= select(msleep, order:sleep_total)
sleepData= select(msleep, starts_with("sleep"))
filter(sleepData, sleep_total>=16)
head(sleepData)


msleep %>% filter(sleep_total >16)
msleep %>% filter(sleep_total >16, sleep_rem >1.8)

https://www.telerik.com/blogs/creating-your-first-cross-platform-native-app-with-nativescript