Program 1
# Create the data for the chartA <- c(17, 32, 8, 53, 1)# Plot the bar chartbarplot(A, horiz = TRUE, xlab = "X-axis", ylab = "Y-axis", main ="Horizontal Bar Chart" )
How to execute:
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program 2
# Create the data for the chartA <- c(17, 2, 8, 13, 1, 22)B <- c("Jan", "feb", "Mar", "Apr", "May", "Jun") # Plot the bar chartbarplot(A, names.arg = B, xlab ="Month", ylab ="Articles", col ="green", main ="GeeksforGeeks-Article chart")
Program 3
# Create the data for the chart
A <- c(17, 2, 8, 13, 1, 22)
B <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun")
# Plot the bar chart with text features
barplot(A, names.arg = B, xlab = "Month",
ylab = "Articles", col = "steelblue",
main = "GeeksforGeeks - Article Chart",
cex.main = 1.5, cex.lab = 1.2, cex.axis = 1.1)
# Add data labels on top of each bar
text(
x = barplot(A, names.arg = B, col = "steelblue", ylim = c(0, max(A) * 1.2)),
y = A + 1, labels = A, pos = 3, cex = 1.2, col = "black"
)
Program 4
colors = c("green", "orange", "brown")months <- c("Mar", "Apr", "May", "Jun", "Jul")regions <- c("East", "West", "North") # Create the matrix of the values.Values <- matrix(c(2, 9, 3, 11, 9, 4, 8, 7, 3, 12, 5, 2, 8, 10, 11), nrow = 3, ncol = 5, byrow = TRUE) # Create the bar chartbarplot(Values, main = "Total Revenue", names.arg = months, xlab = "Month", ylab = "Revenue", col = colors, beside = TRUE) # Add the legend to the chartlegend("topleft", regions, cex = 0.7, fill = colors)
Program 5
colors = c("green", "orange", "brown")months <- c("Mar", "Apr", "May", "Jun", "Jul")regions <- c("East", "West", "North") # Create the matrix of the values.Values <- matrix(c(2, 9, 3, 11, 9, 4, 8, 7, 3, 12, 5, 2, 8, 10, 11), nrow = 3, ncol = 5, byrow = TRUE) # Create the bar chartbarplot(Values, main = "Total Revenue", names.arg = months, xlab = "Month", ylab = "Revenue", col = colors, beside = TRUE) # Add the legend to the chartlegend("topleft", regions, cex = 0.7, fill = colors)
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program 6
colors = c("green", "orange", "brown")months <- c("Mar", "Apr", "May", "Jun", "Jul")regions <- c("East", "West", "North") # Create the matrix of the values.Values <- matrix(c(2, 9, 3, 11, 9, 4, 8, 7, 3, 12, 5, 2, 8, 10, 11), nrow = 3, ncol = 5, byrow = TRUE) # Create the bar chartbarplot(Values, main = "Total Revenue", names.arg = months, xlab = "Month", ylab = "Revenue", col = colors) # Add the legend to the chartlegend("topleft", regions, cex = 0.7, fill = colors)
Program 7
3D Graph
# To illustrate simple right circular cone
cone <- function(x, y){
sqrt(x ^ 2 + y ^ 2)
}
# prepare variables.
x <- y <- seq(-1, 1, length = 30)
z <- outer(x, y, cone)
# plot the 3D surface
persp(x, y, z)
program 8
# Adding Titles and Labeling Axes to Plot
cone <- function(x, y){
sqrt(x ^ 2 + y ^ 2)
}
# prepare variables.
x <- y <- seq(-1, 1, length = 30)
z <- outer(x, y, cone)
# plot the 3D surface
# Adding Titles and Labeling Axes to Plot
persp(x, y, z,
main="Perspective Plot of a Cone",
zlab = "Height",
theta = 30, phi = 15,
col = "orange", shade = 0.4)
Program 9
# Visualizing a simple DEM model
z <- 2 * volcano # Exaggerate the reliefx <- 10 * (1:nrow(z)) # 10 meter spacing (S to N)y <- 10 * (1:ncol(z)) # 10 meter spacing (E to W)
# Don't draw the grid lines : border = NApar(bg = "gray")persp(x, y, z, theta = 135, phi = 30, col = "brown", scale = FALSE, ltheta = -120, shade = 0.75, border = NA, box = FALSE)
Program 10
# Generate data for the plotx <- seq(-5, 5, length.out = 50)y <- seq(-5, 5, length.out = 50)z <- outer(x, y, function(x, y) sin(sqrt(x^2 + y^2))/sqrt(x^2 + y^2))
# Create the plotpersp(x, y, z, theta = 30, phi = 30, expand = 0.5, col = "lightblue")
Program 11
library(plotly)
df = read.csv('https://raw.githubusercontent.com/plotly/datasets/master/streamtube-wind.csv')
fig <- df %>% plot_ly( type = 'streamtube', x = ~x, y = ~y, z = ~z, u = ~u, v = ~v, w = ~w, starts = list( x = rep(80, 16), y = rep(c(20,30,40,50), 4), z = c(rep(0,4),rep(5,4),rep(10,4),rep(15,4)) ), sizeref = 0.3, showscale = F, maxdisplayed = 3000 ) fig <- fig %>% layout( scene = list( aspectratio = list( x = 2, y = 1, z = 0.3 ) ), margin = list( t = 20, b = 20, l = 20, r = 20 ) )
fig
Program 12
library(plotly)
df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/clebsch-cubic.csv')
fig <- plot_ly(
df,
type='isosurface',
x = ~x,
y = ~y,
z = ~z,
value = ~value,
isomin = -10,
isomax = 10,
surface = list(show = TRUE, count = 4, fill = 0.8, pattern = 'odd'),
caps = list(
x = list(show = TRUE),
y = list(show = TRUE),
z = list(show = TRUE)
)
)
fig <- fig %>%
layout(
margin=list(t = 0, l = 0, b = 0),
scene=list(
camera=list(
eye=list(
x = 1.86,
y = 0.61,
z = 0.98
)
)
)
)
fig
Program 13
# Library
library(leaflet)
# load example data (Fiji Earthquakes) + keep only 100 first lines
data(quakes)
quakes = head(quakes, 100)
# Create a color palette with handmade bins.
mybins=seq(4, 6.5, by=0.5)
mypalette = colorBin( palette="YlOrBr", domain=quakes$mag, na.color="transparent", bins=mybins)
# Final Map
leaflet(quakes) %>%
addTiles() %>%
setView( lat=-27, lng=170 , zoom=4) %>%
addProviderTiles("Esri.WorldImagery") %>%
addCircleMarkers(~long, ~lat,
fillColor = ~mypalette(mag), fillOpacity = 0.7, color="white", radius=8, stroke=FALSE
) %>%
addLegend( pal=mypalette, values=~mag, opacity=0.9, title = "Magnitude", position = "bottomright" )
Program 14
# Libraries
library(babynames)
# Load dataset from github
data <- babynames %>%
filter(name %in% c("Ashley", "Amanda", "Jessica", "Patricia", "Linda", "Deborah", "Dorothy", "Betty", "Helen")) %>%
filter(sex=="F")
# line plot = spaghetti chart
data %>%
ggplot( aes(x=year, y=n, group=name, color=name)) +
geom_line() +
ggtitle("Popularity of American names in the previous 30 years")
Program 15
1. Data visualization with dataset
# Horizontal Bar Plot for
# Ozone concentration in air
barplot(airquality$Ozone,
main = 'Ozone Concenteration in air',
xlab = 'ozone levels', horiz = TRUE)
2. Data visualization with dataset
# Vertical Bar Plot for
# Ozone concentration in air
barplot(airquality$Ozone, main = 'Ozone Concenteration in air',
xlab = 'ozone levels', col ='blue', horiz = FALSE)
3. Data visualization with dataset
# Histogram for Maximum Daily Temperature
data(airquality)
hist(airquality$Temp, main ="La Guardia Airport's\
Maximum Temperature(Daily)",
xlab ="Temperature(Fahrenheit)",
xlim = c(50, 125), col ="yellow",
freq = TRUE)
4. Data visualization with dataset
# Box plot for average wind speed
data(airquality)
boxplot(airquality$Wind, main = "Average wind speed\
at La Guardia Airport",
xlab = "Miles per hour", ylab = "Wind",
col = "orange", border = "brown",
horizontal = TRUE, notch = TRUE)
5. Data visualization with dataset
# Multiple Box plots, each representing
# an Air Quality Parameter
boxplot(airquality[, 0:4],
main ='Box Plots for Air Quality Parameters')
6. Data visualization with dataset
# Scatter plot for Ozone Concentration per month
data(airquality)
plot(airquality$Ozone, airquality$Month,
main ="Scatterplot Example",
xlab ="Ozone Concentration in parts per billion",
ylab =" Month of observation ", pch = 19)
7. Data visualization with dataset
# Set seed for reproducibility
# set.seed(110)
# Create example data
data <- matrix(rnorm(50, 0, 5), nrow = 5, ncol = 5)
# Column names
colnames(data) <- paste0("col", 1:5)
rownames(data) <- paste0("row", 1:5)
# Draw a heatmap
heatmap(data)
Program 16
# library
library(tidyverse)
# Create dataset
data <- data.frame(
individual=paste( "Mister ", seq(1,60), sep=""),
value=sample( seq(10,100), 60, replace=T)
)
# Set a number of 'empty bar'
empty_bar <- 10
# Add lines to the initial dataset
to_add <- matrix(NA, empty_bar, ncol(data))
colnames(to_add) <- colnames(data)
data <- rbind(data, to_add)
data$id <- seq(1, nrow(data))
# Get the name and the y position of each label
label_data <- data
number_of_bar <- nrow(label_data)
angle <- 90 - 360 * (label_data$id-0.5) /number_of_bar # I substract 0.5 because the letter must have the angle of the center of the bars. Not extreme right(1) or extreme left (0)
label_data$hjust <- ifelse( angle < -90, 1, 0)
label_data$angle <- ifelse(angle < -90, angle+180, angle)
# Make the plot
p <- ggplot(data, aes(x=as.factor(id), y=value)) + # Note that id is a factor. If x is numeric, there is some space between the first bar
geom_bar(stat="identity", fill=alpha("green", 0.3)) +
ylim(-100,120) +
theme_minimal() +
theme(
axis.text = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank(),
plot.margin = unit(rep(-1,4), "cm")
) +
coord_polar(start = 0) +
geom_text(data=label_data, aes(x=id, y=value+10, label=individual, hjust=hjust), color="black", fontface="bold",alpha=0.6, size=2.5, angle= label_data$angle, inherit.aes = FALSE )
p
Program 17
# Libraries
library(ggplot2)
library(dplyr)
library(hrbrthemes)
library(viridis)
# create a dataset
data <- data.frame(
name=c( rep("A",500), rep("B",500), rep("B",500), rep("C",20), rep('D', 100) ),
value=c( rnorm(500, 10, 5), rnorm(500, 13, 1), rnorm(500, 18, 1), rnorm(20, 25, 4), rnorm(100, 12, 1) )
)
# sample size
sample_size = data %>% group_by(name) %>% summarize(num=n())
# Plot
data %>%
left_join(sample_size) %>%
mutate(myaxis = paste0(name, "\n", "n=", num)) %>%
ggplot( aes(x=myaxis, y=value, fill=name)) +
geom_violin(width=1.4) +
geom_boxplot(width=0.1, color="grey", alpha=0.2) +
scale_fill_viridis(discrete = TRUE) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A Violin wrapping a boxplot") +
xlab("")
Program 18
# Libraries
library(tidyverse)
library(hrbrthemes)
library(babynames)
library(viridis)
# Load dataset from github
data <- read.table("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/3_TwoNumOrdered.csv", header=T)
data$date <- as.Date(data$date)
# Load dataset from github
don <- babynames %>%
filter(name %in% c("Ashley", "Amanda", "Mary", "Deborah", "Dorothy", "Betty", "Helen", "Jennifer", "Shirley")) %>%
filter(sex=="F")
# Plot
don %>%
ggplot( aes(x=year, y=n, group=name, fill=name)) +
geom_area() +
scale_fill_viridis(discrete = TRUE) +
theme(legend.position="none") +
ggtitle("Popularity of American names in the previous 30 years") +
theme_ipsum() +
theme(
legend.position="none",
panel.spacing = unit(0, "lines"),
strip.text.x = element_text(size = 8),
plot.title = element_text(size=13)
) +
facet_wrap(~name, scale="free_y")
program 19 (Animation)
# Get data:
library(gapminder)
# Charge libraries:
library(ggplot2)
library(gganimate)
# Make a ggplot, but add frame=year: one image per year
ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point() +
scale_x_log10() +
theme_bw() +
# gganimate specific bits:
labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
transition_time(year) +
ease_aes('linear')
# Save at gif:
anim_save("271-ggplot2-animated-gif-chart-with-gganimate1.gif")
Program 20
# libraries:
library(ggplot2)
library(gganimate)
# Make 2 basic states and concatenate them:
a <- data.frame(group=c("A","B","C"), values=c(3,2,4), frame=rep('a',3))
b <- data.frame(group=c("A","B","C"), values=c(5,3,7), frame=rep('b',3))
data <- rbind(a,b)
# Basic barplot:
ggplot(a, aes(x=group, y=values, fill=group)) +
geom_bar(stat='identity')
# Make a ggplot, but add frame=year: one image per year
ggplot(data, aes(x=group, y=values, fill=group)) +
geom_bar(stat='identity') +
theme_bw() +
# gganimate specific bits:
transition_states(
frame,
transition_length = 2,
state_length = 1
) +
ease_aes('sine-in-out')
# Save at gif:
anim_save("288-animated-barplot-transition.gif")
Program 21
1. Barplot
# Horizontal Bar Plot for
# Ozone concentration in air
barplot(weatherHistory$Humidity,
main = 'Humidity in air',
xlab = 'Humidity levels', horiz = TRUE)
2. Histogram
# Horizontal Bar Plot for # Ozone concentration in air hist(weatherHistory$Humidity, main = 'Humidity Concenteration in air', xlab = 'Humidity levels', horiz = TRUE)
3. Box plot
# Box plot for average wind speed data(weatherHistory)
boxplot(weatherHistory$`Temperature (C)`, main = "Average Temp", xlab = "in a day", ylab = "windspeed", col = "orange", border = "brown", horizontal = TRUE, notch = TRUE)
4. Plot
# Scatter plot for Ozone Concentration per month data(weatherHistory)
plot(weatherHistory$`Wind Speed (km/h)`, weatherHistory$`Wind Bearing (degrees)`, main ="Scatterplot Example", xlab ="Ozone Concentration in parts per billion", ylab =" Month of observation ", pch = 19)
Program 22
# Install package if not installedif (!require(plotly)) { install.packages("plotly") library(plotly)} else { library(plotly)}
############################## 1️⃣ 3D SCATTER PLOT#############################
# Generate sample dataset.seed(123)df <- data.frame( x = rnorm(200), y = rnorm(200))
df$z <- df$x^2 + df$y^2
# Create interactive 3D scatter plotscatter_plot <- plot_ly( df, x = ~x, y = ~y, z = ~z, type = "scatter3d", mode = "markers", marker = list( size = 4, color = ~z, colorscale = "Viridis" )) %>% layout(title = "3D Scatter Plot")
scatter_plot
############################## 2️⃣ 3D SURFACE PLOT#############################
# Create grid datax <- seq(-10, 10, length.out = 50)y <- seq(-10, 10, length.out = 50)
z <- outer(x, y, function(x, y) { sin(sqrt(x^2 + y^2))})
# Create interactive 3D surface plotsurface_plot <- plot_ly( x = x, y = y, z = z, type = "surface") %>% layout(title = "3D Surface Plot")
surface_plot
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