Iris.R

# Iris

# Getting the Data
data("iris")
head(iris)

# Loading the Libraries
library(ggplot2)
library(dplyr)
library(tidyr)

# Summary of the Data
summary(iris)

head(iris, n = 10)


# Forming to Long Iris
long_iris <- iris%>%
  gather(part,value,Sepal.Length,Sepal.Width,Petal.Length ,Petal.Width)%>%
  separate(part, c('part', 'measure'), sep = '\\.')
head(long_iris, n=10)


sapply(long_iris, class)

fcts <- c('part', 'measure')
long_iris[fcts] <- lapply(long_iris[fcts], as.factor)
sapply(long_iris, class)


Missing_d <- function(x){sum(is.na(x))/length(x)*100}


apply(long_iris, 2, Missing_d)



is_special <- function(x){
  if(is.numeric(x)) !is.finite(x) else is.na(x)
}


sapply(long_iris, is_special)



# Checking for NA Values

sum(is.na(long_iris$value))


## Exploratory Data Analysis
p <- ggplot(long_iris, aes(x = Species, y = value, col = part)) 
p + geom_jitter(alpha = 0.4, size = 0.8) + facet_grid(.~ measure)
p + geom_jitter(alpha = 0.3, size = 0.8) + stat_boxplot(alpha = 0.5) + facet_grid(.~ measure)
p + geom_jitter(alpha = 0.5, size = 0.8) + stat_boxplot(alpha = 0.5) + facet_grid(.~ part)



iris$Flower <- 1:nrow(iris)

#create wide_iris
wide_iris <- iris %>%
  gather(key, value, -Species, -Flower) %>%
  separate(key, c("Part", "Measure"),sep = "\\.") %>%
  spread(Measure, value)

head(wide_iris, n=10)



q <- ggplot(wide_iris, aes(x = Width, y = Length, col = Species))
q + geom_jitter(alpha = 0.4, size = 0.8) + facet_grid(. ~ Species) + 
  stat_smooth(method = 'lm', se = F)
q + geom_jitter(alpha = 0.4, size = 0.8) + facet_grid(. ~ Part)
q + geom_point(alpha = 0.4, size = 0.8)  + stat_smooth(method = 'lm', fullrange = T, size = 0.5)