lab10.Rmd

---
title: "Lab 10:Interactive visualisations and dashboards in R"
author: "Minsu Kang"
Date: "2022-07-20"
documentclass: article
geometry: margin=2in
fontsize: 11pt
output:
  html_document: 
    highlight: tango
  pdf_document:
    highlight: tango
  github_document:
    html_preview: false
---

```{r setup, include = FALSE}
# DO NOT ALTER CODE IN THIS CHUNK
# Set knitr options
knitr::opts_chunk$set(echo = TRUE)

# Load required packages
suppressPackageStartupMessages(library(tidyverse, quietly = TRUE))
suppressPackageStartupMessages(library(plotly, quietly = TRUE))
suppressPackageStartupMessages(library(leaflet, quietly = TRUE))
suppressPackageStartupMessages(library(RColorBrewer, quietly = TRUE))
suppressPackageStartupMessages(library(GGally, quietly = TRUE))
suppressPackageStartupMessages(library(flexdashboard, quietly = TRUE))

# Load data
wine <- read_csv("Red_wine_dataset.csv")
map <- read_csv("map_data.csv")
```

* * *

## Lab report

## Exercises

### Exercise 1
```{r}
str(wine)
```
    i) There are 13 variables(columns) and 1599 observations(rows) in the wine quality dataframe.
    ii) Wine Label variable is character type.



### Exercise 2
```{r}
ggplot(data = wine) +
  geom_bar(aes(x = Label),fill="steelblue") +
  # Adding plot title and y-axis label
  labs(title = "Distribution of Red Wines", y = "Count") +
  # Adding a theme to the plot
  theme_minimal()
```

    - 'Average' category shows the highest count in the label variable.

### Exercise 3
```{r}
ggplot(data = wine) +
  geom_bar(aes(x = Label),fill="Coral") +
  # Adding plot title and y-axis label
  labs(title = "Distribution of Red Wines", y = "Count") +
  theme_light()
```


   

### Exericse 4
```{r}
ggcorr(wine[1:12],
       label = TRUE, 
       label_size = 2, 
       label_alpha = TRUE,
       size = 3,
       hjust = 0.3) + labs(title="correlagram of wine quality dataframe")
```

    i) The highest positive correlated variable between quality is Alcohol(0.5), and lowest variable is Volatile_acidity(-0.4).


### Exericse 5

    - We can conclude wine quality is decided by two volatile acidity and alcohol. I can say if someone want higher
    quality of wine, consider higher alcohol and lower volatile acidity.


### Exericse 6
```{r}
wine %>% 
ggplot() +
  geom_point(mapping= (aes(x=Alcohol, y= Volatile_acidity, color = Label))) +
  theme_light() +
  labs(title="Scatterplot of Alcohol and Volatile acidity", x="Alcohol", y="Volatile acidity")
```


### Exericse 7
```{r}
ggplotly(wine %>% 
ggplot() +
  geom_point(mapping= (aes(x=Alcohol, y= Volatile_acidity, color = Label))) +
  theme_light() +
  labs(title="Scatterplot of Alcohol and Volatile acidity", x="Alcohol", y="Volatile acidity"))
```



### Exercise 8
```{r}
map %>%
  leaflet() %>%
  addTiles() %>%
  addMarkers(lat = ~Lat, lng= ~Lon)
```




### Exericse 9
```{r}
map %>%
  leaflet() %>%
  addTiles() %>%
  addCircleMarkers(lat = ~Lat, lng= ~Lon)
```




### Exericse 10
```{r}
map %>%
leaflet() %>%
  addProviderTiles(providers$Wikimedia) %>%
  setView(lng = -100.243683, lat =  34.052235, zoom = 6) %>%
  addCircleMarkers(lat = ~Lat, lng = ~Lon)
```
    i)We increase the number of Zoom parameter, first-shown map is moved to closed-shot map, and lower number moves map to long-shot map.


### Exericse 11
```{r}
map %>%
leaflet() %>%
  addProviderTiles(providers$Esri.WorldStreetMap) %>%
  setView(lng = -100.243683, lat =  34.052235, zoom = 3) %>%
  addCircleMarkers(lat = ~Lat, lng = ~Lon,
             popup = paste("Name:", map$Name,"<br>",
                            "Rank:", map$Rank, "<br>",
                            "Adress", map$Address, "<br>"
                           ))
```