---
title: "Untitled"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    horizontal_layout: fill
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(maps) 
library(lubridate)
temp <- read.csv("temp.csv")

temp$DATE1<- mdy(temp$DATE)


```

Column {data-width=650}
-----------------------------------------------------------------------

### Chart A
Data collected from folloiwng location.

```{r}
temp1<- filter(temp, !is.na(Lat))
usa<- map_data("usa")


clines <- geom_polygon(aes(long, lat, group = group),
                       fill = NA, col = "blue", data = usa)
p <- ggplot() + clines + coord_quickmap()+ theme(panel.background = element_rect(fill = "lightblue",colour = "lightblue",
                                  size = 0.5, linetype = "solid"))
tpoints <- geom_text(aes(x = Lon, y = Lat, label = City),
                     color = "red", data = temp1)
p+tpoints

```

Column {data-width=350}
-----------------------------------------------------------------------

### Chart B
Distribution of temperature over time period. More variation is seen at Camden station.
Cleraly the reason for this is Camden station is close to the coast where temperature is more lickly to vary.


```{r}
ggplot(data = temp1, mapping = aes(x=DATE1, y = Tmean)) +
  geom_smooth()+
  facet_wrap(~City, nrow=2) 

```