---
title: "Poland"
author: "World Population"

output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(ggplot2)
library(dplyr)
library(rvest)

library(plotly)
library(plyr)


# Make some noisily increasing data
set.seed(955)
dat <- data.frame(cond = rep(c("A", "B"), each=10),
                  xvar = 1:20 + rnorm(20,sd=3),
                  yvar = 1:20 + rnorm(20,sd=3))
```


About
=======================================================================

Row A
-----------------------------------------------------------------------

### About country {data-width=400}

Poland has a population of approximately 38.2 million as of 2021, and is the ninth-most populous country in Europe, as well as the fifth-most populous member state of the European Union.[282] It has a population density of 122 inhabitants per square kilometre (320 inhabitants/sq mi).[283] The total fertility rate was estimated at 1.33 children born to a woman in 2021, which is among the world's lowest.[284] Furthermore, Poland's population is aging significantly, and the country has a median age of 42.2.

### Administrative Divisions 2 {data-width=600}
```{r}
url <- "https://en.wikipedia.org/wiki/Poland"
html_code <- read_html(url)
table_df <-   html_code %>% html_nodes("table") %>% html_table() %>% .[[3]]

knitr::kable(table_df)
```

Row B
-----------------------------------------------------------------------
- Describe your data set
- scrap information from wiki about your country


Country Graphs
=======================================================================

Row
-----------------------------------------------------------------------

### Scatter Chart with geom_point

```{r}
p <- ggplot(dat, aes(x=xvar, y=yvar)) +
            geom_point(shape=1)      # Use hollow circles
ggplotly(p)
```


### geom_smooth Linear Regression

```{r}
p <- ggplot(dat, aes(x=xvar, y=yvar)) +
            geom_point(shape=1) +    # Use hollow circles
            geom_smooth(method=lm)   # Add linear regression line
ggplotly(p)
```

Row
-----------------------------------------------------------------------

### geom_smooth with Loess Smoothed Fit

```{r}
p <- ggplot(dat, aes(x=xvar, y=yvar)) +
            geom_point(shape=1) +    # Use hollow circles
            geom_smooth()            # Add a loess smoothed fit curve with confidence region
ggplotly(p)
```

Data Tables
=======================================================================

Row 1
-----------------------

### Countries in the World by population {data-width=650}

```{r}
url <- "https://www.worldometers.info/world-population/population-by-country/" 

html_code  <- read_html(url)                                  # read the HTML code
table_html <- html_code %>% html_nodes("table") %>% .[[1]]    # extract the table - first on the page
dat   <- table_html %>% html_table()                     # convert HTML table > data frame  

knitr::kable(dat)
```


Row 2
-----------------------


### Country Codes {data-width=300}

```{r}
url <- "https://www.iban.com/country-codes"
html_code <- read_html(url)
dat <- html_code %>% html_nodes("table") %>% html_table() %>% .[[1]]

knitr::kable(dat)
```


### Administrative Divisions {data-width=600}
```{r}
url <- "https://en.wikipedia.org/wiki/Poland#Demographics"
html_code <- read_html(url)
dat <-   html_code %>% html_nodes("table") %>% html_table() %>% .[[3]]

knitr::kable(dat)
```


My country graphs
=======================================================================