05-summary.Rmd

# Publication style figures and saving

'ggpubr' package is wondeful to create publication quality figures

```{r, fig.cap='Scatter plot with facets pub quality', out.width='80%', fig.asp=.75, fig.align='center'}

diamonds2<-diamonds
levels(diamonds2$color)
diamonds2$color<- factor(diamonds2$color, levels =c("D" ,"E" ,"F" ,"G", "H", "I", "J"), 
                         labels=c("Red","Blue","Orange","Pink","Indigo","Jade","Orange"))
         ggplot(diamonds2)+
          geom_point(aes(x=carat,y=price))+
  geom_smooth(aes(x=carat,y=price),method='lm')+
  facet_wrap(~color)+
  ggpubr::theme_pubr(base_size=22)+
           
  ylab("Carat")+ xlab("Price")


```


##Correlation plot
```{r}

diamonds2<-diamonds %>% select(depth,table, price,carat)

# calulate the correlations
c <- cor(diamonds2, use="complete.obs")



```

```{r, fig.cap='Correlation plot', out.width='80%', fig.asp=.75, fig.align='center'}
library(ggcorrplot)
ggcorrplot(c,lab=T, color=c("green","black","orange"))

```

## Time series

Data collected in successive time points- dates, years, hours
Let's look at some time series data 

Customize dates
Weekday name:  %a and %A for abbreviated and full weekday name, respectively
Month name:  %b and %B for abbreviated and full month name, respectively
%d: day of the month as decimal number
%U: week of the year as decimal number (00–53)
%Y: Year 
```{r}
#tidyr::population
head(economics_long)


```
```{r, fig.cap='time series plot', out.width='80%', fig.asp=.75, fig.align='center'}

ggplot(economics_long)+
  geom_line(aes(date, value, color=variable))+
 scale_x_date(breaks='10 years',date_labels = "%b-%y")+ 
  xlab("Date")
  

```