Applied_Statistics_-_Data_Visualisation_/ASDV LAB 4 R Script.R at main · mananabbasi/Applied_Statistics_-_Data_Visualisation_ · GitHub

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data()
- cars

# Load the dataset
- cars

# View the first few rows of the dataset
head(cars)

# Summary statistics of the dataset
summary(cars)

plot(cars)

hist(cars$speed)
hist(cars$dist)

plot(density(cars$speed),
     main = "Density Plot of Car Speeds",
     xlab = "Speed (mph)",
     ylab = "Density",
     col = "blue")


Thai_tourist <- read.csv("C:\\Users\\rehma\\Desktop\\ASDV R Script\\ASDV LAB 4\\Thaitourism1.csv", header=TRUE)
names(Thai_tourist)
head(Thai_tourist)
tail(Thai_tourist)
str(Thai_tourist)
summary(Thai_tourist)


Thai_tourist_full <- read.csv("C:\\Users\\rehma\\Desktop\\ASDV R Script\\ASDV LAB 4\\Thaitourism2.csv", header= TRUE)
names(Thai_tourist_full)

head(Thai_tourist_full)
tail(Thai_tourist_full)
str(Thai_tourist_full)
summary(Thai_tourist_full)

Thai_2016<-Thai_tourist[Thai_tourist$Year==2016,]
Thai_2016


Thai_UK <- Thai_tourist_full[Thai_tourist_full$nationality == "UnitedKingdom", ]
Thai_UK


barplot(Thai_2016$Tourists_1000s,names.arg=Thai_2016$Region)

barplot(Thai_2016$Tourists_1000s,
        names.arg = Thai_2016$Region,
        col = "#FFCC00")  # Corrected color format
barplot(Thai_2016$Tourists_1000s,
        names.arg = Thai_2016$Region,
        col = rainbow(8),
        main = "Number of Tourists by Region in 2016",
        xlab = "Region",
        ylab = "Tourists (in thousands)")

hist(Thai_UK$tourists)

hist(Thai_UK$tourists,breaks=16)
hist(Thai_UK$tourists, labels = TRUE)
Thai_Europe <-droplevels(subset(Thai_tourist_full, region=="Europe"))
boxplot(Thai_Europe$tourists, data=Thai_Europe)

boxplot(Thai_Europe$tourists,
        main = "Boxplot of Tourists in Europe",
        ylab = "Number of Tourists",
        col = "lightblue")

boxplot(tourists~nationality, data=Thai_Europe,col=rainbow(8))
Thai_Europe

iris

install.packages("ggplot2")
library(ggplot2)

iris_data <- read.csv("C:\\Users\\rehma\\Desktop\\ASDV R Script\\ASDV LAB 4\\iris.csv", header = TRUE)
iris_data

head(iris_data)
tail(iris_data)
summary(iris_data)

ggplot(iris_data, aes(petal_length, petal_width)) +
  geom_point()

ggplot(iris_data, aes(petal_length, petal_width, colour =
                        species)) + geom_point()

ggplot(iris_data, aes(petal_length, petal_width)) +
  geom_point() + geom_smooth(method="lm")


ggplot(iris_data, aes(petal_length)) +  geom_histogram()


ggplot(iris_data, aes(petal_length, fill = Species)) +
  geom_histogram(position = "identity", alpha = 0.5, bins = 30) +
  labs(title = "Histogram of Petal Length by Species",
       x = "Petal_length (cm)",
       y = "Count") +
  theme_minimal()

names(iris_data)


# Check the structure of iris_data
print(head(iris_data))  # This will show the first few rows of the data

# Check the column names to ensure they are correct
print(names(iris_data))

# Create the histogram
# Load the ggplot2 library
library(ggplot2)

# Create the histogram with the correct column names
ggplot(iris_data, aes(x = petal_length, fill = species)) +
  geom_histogram(position = "identity", alpha = 0.5, bins = 30) +
  labs(title = "Histogram of Petal Length by Species",
       x = "Petal Length (cm)",
       y = "Count") +
  theme_minimal()

ggplot(iris_data, aes(petal_length, species, fill = species)) +
  geom_boxplot()