Air_Water_loggers_15min_example.csv

XTS_markdown.Rmd

% Ryan Peek on using xts and ggplot for time-series data
% Noam Ross
% 13-02-06 15:44:25

At [Davis R Users' Group](http://www.noamross.net/davis-r-users-group.html) today, [Ryan Peek](http://watershed.ucdavis.edu/people/ryan-peek) gave a presentation on how he takes data from his field instruments and visualizes it in R.  Here are his notes.  The original *.Rmd file and data can be found [here](https://gist.github.com/noamross/4727046)

SHORT HOW-TO ON USING XTS AND GGPLOT FOR TIME SERIES DATA
=========================================================

XTS is a very helpful package when working with time series data. I work with temperature and flow data frequently, so the ability to work with timeseries, and particularly to shift intervals (from 15 min to hourly or daily) can be very handy. Once data has been re-worked, **ggplot2** is a great package to plot data of all types.

First read in some data and format to POSIXlt.  There are 2 types of time, POSIXlt and POSIXct. POSIXct is generally what ggplot uses and is based on seconds, while POSIXlt can be re-classed into individual components quite easily, but year zero is calculated from 1900 (so 2011 would be year 111).

Read in some data
---------------------------------------------
```{r load data}
## READ IN FILE
inputfile <- "Air_Water_loggers_15min_example.csv"
file<-read.csv(inputfile,stringsAsFactors=FALSE)
## FORMAT TIME COLUMN TO POSIXlt
file$time15<-strptime(file$time15,format="%m/%d/%Y %H:%M")
## ROUND TO NEAREST HOUR
file$time15hr<-round(file$time15,"hour")

## TO VIEW TIME CLASSES USE 'str'
str(unclass(file$time15))
```
Notice that the **$time15** column classes exist for all values even though data only exists to minutes.  Also years are "112" and "111" instead of 2011 and 2012.  Month and hour (24 hour clock) start at zero.

Load the XTS package and make into an xts object
-------------------------------------------------------

```{r make xts, message=FALSE}
library(xts)
## MAKE XTS

## make XTS format using time column for the data columns of interest
df.xts <- xts(x=file[,c(2:7)],order.by= file[ ,'time15']) 
head(df.xts) # notice the row names are now the 'time15' column
```
Notice that the row names exist as the time series data from the *time15'* column.

### Then run any function along a given time interval using XTS functions
* apply.daily - makes daily data
* apply.monthly - monthly
* period.apply(data,endpoints(data,on="interval",1)) # Using interval of your choice

When using Rstudio, you can hit tab at any time to view potential options for a given command or function.  Try *apply*+TAB.

```{r xts function}
# MAKE DAILY AVG, MEAN, AND MAX
dAvg <- apply.daily(df.xts, function(x) apply(x,2,mean)) # daily mean
dMin <- apply.daily(df.xts, function(x) apply(x,2,min)) # daily min
dMax <- apply.daily(df.xts, function(x) apply(x,2,max)) # daily max

# OR MAKE HOURLY (OR PREFERRED TIME INTERVAL)
hrAvg <- period.apply(df.xts, endpoints(df.xts, on="hours",1), function(x) apply(x,2,mean)) # avg over 1 hour period

# RENAME COLUMN NAMES TO MAKE RECOMBINING EASIER
names(dAvg) <- sub("_sol$", ".avg", names(dAvg))
names(dAvg) <- sub("_air$", ".avg", names(dAvg))
names(dMax)<-sub("_sol$", ".max", names(dMax))
names(dMax)<-sub("_air$", ".max", names(dMax))
names(dMin)<-sub("_sol$", ".min", names(dMin))
names(dMin)<-sub("_air$", ".min", names(dMin))

Day <- data.frame('datetime' = index(dAvg),dAvg[,c(1:6)],dMax[,c(1:6)], dMin[,c(1:6)],row.names = NULL)

# WRITE NEW FILE TO A CSV
# write.csv(Hour, file="Airwatertemps_hourly_example.csv", row.names=FALSE)
```

When making a dataframe from an xts object, important to note that you must use *index()* instead of row.names to pull the time stamp/date into a new dataframe.

### Make a ggplot of daily temperatures showing a max and min using *geom_ribbon*

```{r load library}
library(scales)
library(ggplot2)
```
This plot is complex, and code is provided to view as you like.  Main components are:
* __ggplot__: this sets the data and x acess
* __geom_line__: this is to plot lines, custom colors are set by including color _within_ the aes call
* __geom_ribbon__: this is to plot the filled max and min ribbon in the background
*__scale_color/fill_manual__: this allows you to define custom colors for each geom, must match exactly with the color="" or fill=""


```{r fig.height=6,fig.width=8}
RUB<-ggplot(Day,aes(x=datetime)) + 
  geom_line(aes(y=NFA_baro.avg,color="AIR"),size=1,linetype=1,alpha=1)+
  geom_ribbon(aes(ymax=NFA_baro.max,ymin=NFA_baro.min,fill="AIR Max/Min"),
              colour="darkorange",linetype=3,alpha=0.4) +
  geom_ribbon(aes(ymax=RUB.max,ymin=RUB.min,fill="RUB Max/Min"),
              colour="darkgreen",linetype=3,alpha=0.7) +
  geom_line(aes(y=RUB.avg,color="RUB-Water"),size=0.9,linetype=1,alpha=1) + 
  geom_ribbon(aes(datetime,ymax=MFA.max,ymin=MFA.min,fill="MFA Max/Min"),
              colour="gray40",linetype=3,alpha=0.5) +
  geom_line(aes(y=MFA.avg,color="MFA-Water"),size=0.9,linetype=1,alpha=1)+
  ylab(expression(paste("Daily Temperature ( ", degree,"C)",sep=""))) + xlab("") +
  scale_x_datetime(limits = c(as.POSIXct("2012-05-10 12:00"),as.POSIXct("2012-05-30 12:00")), 
                   labels = date_format("%b-%d"),breaks=date_breaks("7 days")) +
  scale_y_continuous(breaks=seq(0,42,by=4),limits=c(0,42),labels=seq(0,42,by=4))+ 
  theme_bw() + labs(title="Daily Air and Water Temperature: Rubicon")+
  scale_color_manual("Mean",breaks=c("AIR","RUB-Water","MFA-Water"),
                     values=c("RUB-Water"="darkgreen","MFA-Water"="darkmagenta","AIR"="orange"))+
  scale_fill_manual(" Max/Min",breaks=c("AIR Max/Min","RUB Max/Min","MFA Max/Min"),
                     values=c("AIR Max/Min"="gold","MFA Max/Min"="darkmagenta","RUB Max/Min"="darkolivegreen3"))+
  theme(legend.direction="horizontal",legend.position="bottom",legend.key=element_blank(),
      legend.background=element_rect(fill="white",colour="gray30"))+guides(fill=guide_legend(keywidth=0.9,keyheight=1))

RUB
```