1 Introduction

In Lab 11, we follow Lab 2.2 of Spatio-Temporal Statistics with R. You can download the electronic pdf through https://spacetimewithr.org/.
- In this document, I will only highlight some important points; that is, the visulization of spatio-temporal data.

2 Spatial Plots

First, we load the dataset.
- z: temperature (F)
- id: weather station ID.
- t: the same as the column day
- We do not really used column julian nor proc

library(dplyr)
library(tidyr)
library(ggplot2)


load("datasets/Tmax.Rdata")
head(Tmax)

##   julian year month day   id  z proc   lat       lon       date t
## 1 728050 1993     5   1 3804 82 Tmax 39.35 -81.43333 1993-05-01 1
## 2 728051 1993     5   2 3804 84 Tmax 39.35 -81.43333 1993-05-02 2
## 3 728052 1993     5   3 3804 79 Tmax 39.35 -81.43333 1993-05-03 3
## 4 728053 1993     5   4 3804 72 Tmax 39.35 -81.43333 1993-05-04 4
## 5 728054 1993     5   5 3804 73 Tmax 39.35 -81.43333 1993-05-05 5
## 6 728055 1993     5   6 3804 78 Tmax 39.35 -81.43333 1993-05-06 6

Next, the plot will be produced by package ggplot2. The new function here
- geom_path: add US states map. You can replace these US map with Australia ones from Week 7 Lab.
- facet_grid(~date): for each date (1, 15 and 30), produce a plot.

Tmax_1 = Tmax %>% filter(t %in% c(1, 15, 30))  # extract data

# Spatial Plots
NOAA_plot <- ggplot(Tmax_1) +                              # plot points
  geom_point(aes(x = lon,y = lat,                          # lon and lat
                 colour = z),                              # attribute color
             size = 1.2) +                                 # point size
  scale_colour_gradientn(name = "degF",                    # attach color scale
                        colours = terrain.colors(10)) +            
  xlab("Longitude (deg)") +                                # x-axis label
  ylab("Latitude (deg)") +                                 # y-axis label
  geom_path(data = map_data("state"),                      # add US states map
            aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
  facet_grid(~date) +                                      # facet by time
  coord_fixed(xlim = c(-105, -75),
              ylim = c(25, 50))  +                         # zoom in
  theme_bw()  

NOAA_plot

3 Time-Series Plots

First, we randomly select 10 station for visualization.

UIDs <- unique(Tmax$id)                     # extract IDs
UIDs_sub <- sample(UIDs, 10)                # sample 10 IDs
Tmax_sub <- filter(Tmax, id %in% UIDs_sub)  # subset data
head(Tmax_sub)

##   julian year month day    id  z proc   lat       lon       date t
## 1 728050 1993     5   1 13873 79 Tmax 33.95 -83.31667 1993-05-01 1
## 2 728051 1993     5   2 13873 77 Tmax 33.95 -83.31667 1993-05-02 2
## 3 728052 1993     5   3 13873 69 Tmax 33.95 -83.31667 1993-05-03 3
## 4 728053 1993     5   4 13873 81 Tmax 33.95 -83.31667 1993-05-04 4
## 5 728054 1993     5   5 13873 83 Tmax 33.95 -83.31667 1993-05-05 5
## 6 728055 1993     5   6 13873 86 Tmax 33.95 -83.31667 1993-05-06 6

Next, time-series is plotted.

## ------------------------------------------------------------------------
TmaxTS <- ggplot(Tmax_sub) +
    geom_line(aes(x = t, y = z)) +          # line plot of z against t
    facet_wrap(~id, ncol = 5) +             # facet by station
    xlab("Day number (days)") +             # x label
    ylab("Tmax (degF)") +                   # y label
    theme_bw() +                            # BW theme
    theme(panel.spacing = unit(1, "lines")) # facet spacing: the space between each subplots. 


TmaxTS

Week 11: Visulization of Spatio-Temporal Data (Optional)

1 Introduction

2 Spatial Plots

3 Time-Series Plots