---
title: "Dashboard-AMH"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: fill #scroll & fill
social: menu
source_code: embed
self_contained: true
theme: spacelab
---
```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(plotly) # Added for interactive plots
```
# Introduction {data-icon="fa-home"}
## Greeting {data-width="500"}
### Greeting From AMH 🥰
Thank you for sharing webinar session. Merry Christmas and Happy New Year.
## About Training {data-width="500"}
### Total Participants
```{r}
flexdashboard::valueBox(
value=100,
caption= "Total Participants",
icon= "fa-users",
color= "darkblue"
)
```
### Training Participant by Gender
```{r}
gender_counts <- c(45, 55)
names(gender_counts) <- c("Male", "Female")
pie(gender_counts,
main = "Gender Distribution", # Title
col = c("lightblue", "pink"), # Colors for Male, Female
labels = paste0(names(gender_counts), "\n", gender_counts, "%") # Labels with names & %
)
legend("topright", legend = names(gender_counts), fill = c("lightblue", "pink"))
```
### Training Progress
```{r}
Training_access =89
gauge(Training_access,
min = 0, max = 100,
label = "Success %",
symbol = "%",
# Color changes based on value
sectors = gaugeSectors(success = c(70, 100), warning = c(50, 69), danger = c(0, 49)))
```
# Cars
## About car1 {data-width="500"}
### Car data
```{r}
head (mtcars)
```
### Hp Vs Mpg by cyl
```{r}
p1<- ggplot(mtcars, aes(x = hp, y = mpg)) +
geom_point(aes(color = factor(cyl), size = wt), alpha = 0.7) +
labs(title = "MPG vs Horsepower",
x = "Horsepower",
y = "Miles Per Gallon",
color = "Cylinders",
size = "Weight") +
theme_minimal()
ggplotly(p1)
```
## About car2 {data-width="500"}
### Box plot
```{r}
p2 <- ggplot(mtcars, aes(x = factor(cyl), y = mpg, fill = factor(cyl))) +
geom_boxplot(alpha = 0.7) +
geom_jitter(width = 0.2, alpha = 0.5) +
labs(title = "MPG Distribution by Cylinder Count",
x = "Number of Cylinders",
y = "Miles Per Gallon") +
theme_classic() +
theme(legend.position = "none")
ggplotly(p2)
```
### Faceted plot
```{r}
p3 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point(aes(color = factor(cyl))) +
geom_smooth(method = "lm", se = FALSE) +
facet_wrap(~ cyl, ncol = 3) +
labs(title = "MPG vs Weight by Cylinder Count",
x = "Weight (1000 lbs)",
y = "Miles Per Gallon") +
theme_bw()
ggplotly(p3)
```
# Iris
## About iris1 {data-width="500"}
### Iris Data
```{r}
head(iris)
```
### Scatter Plot
```{r}
iris1 <- ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
geom_point(size = 3) +
labs(title = "Sepal Length vs Sepal Width",
x = "Sepal Length (cm)",
y = "Sepal Width (cm)") +
theme_minimal()
ggplotly(iris1)
```
## About iris2 {data-width="500"}
### Box plot
```{r}
iris2 <- ggplot(iris, aes(x = Species, y = Petal.Length, fill = Species)) +
geom_boxplot() +
labs(title = "Distribution of Petal Length by Species") +
theme_classic()
ggplotly(iris2)
```
### Histogram for Petal Width
```{r}
iris3 <- ggplot(iris, aes(x = Petal.Width, fill = Species)) +
geom_histogram(binwidth = 0.2, color = "white", alpha = 0.7) +
facet_wrap(~Species) +
labs(title = "Petal Width Frequency by Species")
ggplotly(iris3)
```
