Pranav_Anand
Pranav Anand
Recently Published
Map
library(maps)
library(ggmap)
data("USArrests")
us_map <- map_data("state")
USArrests$region <- tolower(rownames(USArrests))
map_data <- merge(us_map, USArrests, by = "region")
map_plot <- ggplot(map_data, aes(x = long, y = lat, group = group, fill = Murder)) +
geom_polygon(color = "black") +
scale_fill_gradient(low = "white", high = "red") +
labs(title = "Map: Murder Rate by State",
fill = "Murder Rate") +
theme_void()
print(map_plot)
Box Plot
box_plot <- ggplot(diamonds, aes(x = cut, y = price, fill = cut)) +
geom_boxplot() +
labs(title = "Box Plot: Price Distribution by Cut",
x = "Cut",
y = "Price") +
theme_minimal()
print(box_plot)
Waffle chart
library(wordcloud)
library(fmsb)
library(waffle)
library(reshape2)
color_counts <- diamonds %>%
group_by(color) %>%
summarise(count = n())
waffle_chart <- waffle(color_counts$count, rows = 5,
title = "Waffle Chart: Proportion of Diamonds by Color")
print(waffle_chart)
Word Cloud
library(wordcloud)
library(fmsb)
library(waffle)
library(reshape2)
wordcloud(words = unique(diamonds$clarity), freq = table(diamonds$clarity),
colors = brewer.pal(8, "Dark2"),
main = "Word Cloud: Diamond Clarity")
Line Plot
line_plot <- ggplot(diamonds, aes(x = carat, y = price)) +
geom_point(alpha = 0.3) +
geom_smooth(method = "lm", color = "red") +
labs(title = "Line Plot with Regression: Price Trends Over Carat",
x = "Carat",
y = "Price") +
theme_minimal()
print(line_plot)
Scatter Plot
library(ggplot2)
library(dplyr)
scatter_plot <- ggplot(diamonds, aes(x = carat, y = price, color = cut)) +
geom_point(alpha = 0.5) +
labs(title = "Scatter Plot: Carat vs Price",
x = "Carat",
y = "Price") +
theme_minimal()
print(scatter_plot)
Pie chart
library(ggplot2)
library(dplyr)
clarity_counts <- diamonds %>%
group_by(clarity) %>%
summarise(count = n()) %>%
mutate(percentage = count / sum(count) * 100)
pie_chart <- ggplot(clarity_counts, aes(x = "", y = percentage, fill = clarity)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y") +
labs(title = "Pie Chart: Proportion of Diamonds by Clarity") +
theme_void()
print(pie_chart)
Histogram
library(ggplot2)
histogram <- ggplot(diamonds, aes(x = price)) +
geom_histogram(binwidth = 500, fill = "blue", color = "black") +
labs(title = "Histogram: Distribution of Diamond Prices",
x = "Price",
y = "Frequency") +
theme_minimal()
print(histogram)
Bar Plot
library(ggplot2)
data("diamonds")
bar_chart <- ggplot(diamonds, aes(x = cut, fill = cut)) +
geom_bar() +
labs(title = "Bar Chart: Count of Diamonds by Cut",
x = "Cut",
y = "Count") +
theme_minimal()
print(bar_chart)