Visualise spatiotemporal spread data

Load libraries

library("readxl")
library("cowplot")
library("ggplot2")
library("ggpubr")
library("grDevices")
library("dplyr")
library("clifro")
library("viridis")
library("showtext")
library("here")

Import data

dat <-
   read_excel(
      system.file("extdata", "SpatioTemporalSpreadData.xlsx",
                  package = "spatiotemporaldynamics"),
      sheet = 1
   )

Examine data

str(dat)

## tibble [1,800 × 18] (S3: tbl_df/tbl/data.frame)
##  $ location         : chr [1:1800] "Billa Billa" "Billa Billa" "Billa Billa" "Billa Billa" ...
##  $ assessment_date  : POSIXct[1:1800], format: "2020-07-02" "2020-07-02" ...
##  $ assessment_number: num [1:1800] 1 1 1 1 1 1 1 1 1 1 ...
##  $ plot_number      : num [1:1800] 1 1 1 1 1 1 1 1 1 1 ...
##  $ distance         : num [1:1800] 0 9 9 9 9 9 9 9 9 6 ...
##  $ quadrat          : chr [1:1800] "F" "N9" "NE9" "E9" ...
##  $ direction        : chr [1:1800] "NA" "North" "NorthEast" "East" ...
##  $ infected_plants  : num [1:1800] 0 0 0 0 0 0 0 0 0 0 ...
##  $ total_plants     : num [1:1800] 36 48 27 57 53 41 39 31 36 54 ...
##  $ incidence        : num [1:1800] 0 0 0 0 0 0 0 0 0 0 ...
##  $ min_temp         : num [1:1800] 3.99 3.99 3.99 3.99 3.99 ...
##  $ max_temp         : num [1:1800] 20 20 20 20 20 ...
##  $ avg_temp         : num [1:1800] 12 12 12 12 12 ...
##  $ avg_wind_speed   : num [1:1800] 1.52 1.52 1.52 1.52 1.52 ...
##  $ total_rain       : num [1:1800] 1 1 1 1 1 1 1 1 1 1 ...
##  $ min_rh           : num [1:1800] 35.2 35.2 35.2 35.2 35.2 ...
##  $ max_rh           : num [1:1800] 82.7 82.7 82.7 82.7 82.7 ...
##  $ avg_rh           : num [1:1800] 58.9 58.9 58.9 58.9 58.9 ...

Convert variables to their correct classes

cols_1 <-
   c(
      "location",
      "distance",
      "plot_number",
      "quadrat",
      "min_temp",
      "max_temp",
      "min_rh",
      "max_rh",
      "avg_wind_speed",
      "avg_rh",
      "avg_temp",
      "assessment_number",
      "total_rain"
   )
dat[cols_1] <- lapply(dat[cols_1], factor)

cols_2 <- c("infected_plants", "total_plants")
dat[cols_2] <- lapply(dat[cols_2], as.integer)
dat$assessment_date <- as.Date(dat$assessment_date)

Re-check class

sapply(dat, class)

##          location   assessment_date assessment_number       plot_number 
##          "factor"            "Date"          "factor"          "factor" 
##          distance           quadrat         direction   infected_plants 
##          "factor"          "factor"       "character"         "integer" 
##      total_plants         incidence          min_temp          max_temp 
##         "integer"         "numeric"          "factor"          "factor" 
##          avg_temp    avg_wind_speed        total_rain            min_rh 
##          "factor"          "factor"          "factor"          "factor" 
##            max_rh            avg_rh 
##          "factor"          "factor"

Visualise data distribution

Density plot

ggplot(dat, aes(incidence)) +
   geom_density() +
   facet_grid(distance ~ location, labeller = label_both) +
   theme_pubclean(base_family = "Arial Unicode MS")

Line plot with median and max/min values

fig_1 <- ggplot(
   data = dat,
   mapping = aes(x = assessment_date, y = incidence)
) +
   geom_pointrange(
      stat = "summary",
      fun.min = min,
      fun.max = max,
      fun = median
   ) +
   stat_summary(
      fun = median,
      geom = "line",
      aes(group = location)
   ) +
   facet_grid(distance ~ location) +
   xlab("Assessment Date") +
   ylab("Disease Incidence") +
   theme_pubclean(base_family = "Arial Unicode MS")

ggsave(
   here("man", "figures/Fig1.png"),
   width = 9,
   height = 9,
   units = "in",
   dpi = 600
)

cairo_ps(here("man", "figures/Fig1.eps"),
         family = "Arial Unicode MS")

fig_1
dev.off()

## quartz_off_screen 
##                 2

fig_1

Scatter plot with smoothed fit lines

ggplot(dat, aes(assessment_date, incidence)) +
   geom_point() +
   geom_smooth() +
   facet_grid(distance ~ location, labeller = label_both) +
   xlab("Assessment Date") +
   ylab("Disease Incidence") +
   theme_pubclean(base_family = "Arial Unicode MS")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Heat map of disease incidence in quadrats

dat <-
   read_excel(
      system.file("extdata", "SpatioTemporalSpreadData.xlsx",
                  package = "spatiotemporaldynamics"),
      sheet = 1
   )

dat <-
  dat %>%
  mutate(
    x = case_when(
      direction == "North" ~ 0,
      direction == "East" ~ distance,
      direction == "South" ~ 0,
      direction == "West" ~ distance * -1,
      
      direction == "NorthEast" ~ distance,
      direction == "NorthWest" ~ distance * -1,
      
      direction == "SouthEast" ~ distance,
      direction == "SouthWest" ~ distance * -1,
      TRUE ~ 0
    )
  ) %>%
  mutate(
    y = case_when(
      direction == "North" ~ distance,
      direction == "East" ~ 0,
      direction == "South" ~ distance * -1,
      direction == "West" ~ 0,
      
      direction == "NorthEast" ~ distance,
      direction == "NorthWest" ~ distance,
      
      direction == "SouthEast" ~ distance * -1,
      direction == "SouthWest" ~ distance * -1,
      TRUE ~ 0
    )
  )

fig_2 <- ggplot(dat, aes(x = x, y = y)) +
  geom_tile(aes(fill = incidence)) +
  scale_fill_viridis_c(option = "cividis") +
  facet_wrap(. ~ assessment_number + location, nrow = 3) +
  labs(x = "metres",
       y = "metres",
       fill = "Incidence (%)") +
  theme_pubclean(base_family = "Arial Unicode MS") +
  theme(plot.margin = unit(c(3, 0, 0, 0), "lines"),
        legend.position = "bottom") +
  coord_cartesian(clip = "off")

fig_2 <- ggdraw(fig_2) +
   draw_label("North \u2191",
              x = 0.55,
              y = 0.95,
              fontfamily = "Arial Unicode MS")
fig_2

ggsave(
   here("man", "figures/Fig2.png"),
   plot = fig_2,
   width = 9,
   height = 9,
   units = "in",
   dpi = 600
)

cairo_ps(
   here("man", "figures/Fig2.eps"),
   family = "Arial Unicode MS"
)
fig_2
dev.off()

## quartz_off_screen 
##                 2

Wind rose

Import wind direction data and covert wind direction from text to degrees

wind_direc_dat <-
   read_excel(
      system.file("extdata", "WindDirectionData.xlsx",
                  package = "spatiotemporaldynamics"),
      sheet = 1
   ) %>%
   mutate(
      wind_direction_degrees = case_when(
         wind_direction == "N" ~ "0",
         wind_direction == "NbE" ~ "11.25",
         wind_direction == "NNE" ~ "22.5",
         wind_direction == "NEbN" ~ "33.75",
         wind_direction == "NE" ~ "45",
         wind_direction == "NEbE" ~ "56.25",
         wind_direction == "ENE" ~ "67.5",
         wind_direction == "EbN" ~ "73.5",
         wind_direction == "E" ~ "90",
         wind_direction == "EbS" ~ "101.2",
         wind_direction == "ESE" ~ "112.5",
         wind_direction == "SEbE" ~ "123.8",
         wind_direction == "SE" ~ "135.1",
         wind_direction == "SEbS" ~ "146.3",
         wind_direction == "SSE" ~ "157.6",
         wind_direction == "SbE" ~ "168.8",
         wind_direction == "S" ~ "180",
         wind_direction == "SbW" ~ "191.2",
         wind_direction == "SSW" ~ "202.5",
         wind_direction == "SWbS" ~ "213.8",
         wind_direction == "SW" ~ "225",
         wind_direction == "SWbW" ~ "236.2",
         wind_direction == "WSW" ~ "247.5",
         wind_direction == "WbS" ~ "258.8",
         wind_direction == "W" ~ "270",
         wind_direction == "WbN" ~ "281.2",
         wind_direction == "WNW" ~ "292.5",
         wind_direction == "NWbW" ~ "303.8",
         wind_direction == "NW" ~ "315",
         wind_direction == "NWbN" ~ "326.2",
         wind_direction == "NNW" ~ "337.5",
         wind_direction == "NbW" ~ "348.8",
         TRUE ~ wind_direction
      )
   ) %>%
   mutate(wind_direction_degrees = as.numeric(wind_direction_degrees)) %>%
   rename(date = Date, location = Location)
save(wind_direc_dat, file = "../data/wind_direc_dat.rda")

## Warning in gzfile(file, "wb"): cannot open compressed file '../data/
## wind_direc_dat.rda', probable reason 'No such file or directory'

## Error in gzfile(file, "wb"): cannot open the connection

Join wind speed and wind direction data

# Import wind speed data
wind_speed_dat <-
   read_excel(
      system.file("extdata", "WindSpeedData.xlsx",
                  package = "spatiotemporaldynamics"),
      sheet = 1
   ) 
   
   ### Join wind speed and wind direction data
   wind_dat <- left_join(wind_speed_dat, wind_direc_dat) %>%
   select(-date, -wind_direction)

## Joining, by = c("date", "location")

Plot wind rose

fig_3 <-
   with(
      wind_dat,
      windrose(
         wind_speed,
         wind_direction_degrees,
         location,
         n_col = 2,
         legend_title = "Wind speed (m/s)"
      )
   )

fig_3 <-
   fig_3 +
   scale_fill_viridis_d(name = "Wind Speed (m/s)", direction = -1) +
   xlab("") +
   theme_pubclean(base_family = "Arial Unicode MS")

fig_3

ggsave(
   here("man", "figures/Fig3.png"),
   width = 9,
   height = 9,
   units = "in",
   dpi = 600
)

cairo_ps(
   here("man", "figures/Fig3.eps"),
   family = "Arial Unicode MS"
)
fig_3
dev.off()

## quartz_off_screen 
##                 2

Adam Sparks and Ihsan Khaliq

2021-02-26

Load libraries

Import data

Examine data

Convert variables to their correct classes

Re-check class

Visualise data distribution

Density plot

Line plot with median and max/min values

Scatter plot with smoothed fit lines

Heat map of disease incidence in quadrats

Wind rose

Import wind direction data and covert wind direction from text to degrees

Join wind speed and wind direction data

Plot wind rose