Data visualisation

2021-07-14

Import data

# event data includes dates and time out and removed, exposure hours and 
# assessment date

event_data <- read_csv(system.file(
  "extdata",
  "exposure_events.csv",
  package = "rainy",
  mustWork = TRUE
)) %>%
  mutate(
    date_time_out = dmy_hm(date_time_out),
    date_time_removed = dmy_hm(date_time_removed),
    exposed = interval(date_time_out, date_time_removed)
  )

lesion_data <- read_csv(system.file(
  "extdata",
  "lesion_data.csv",
  package = "rainy",
  mustWork = TRUE
)) %>%
  pivot_longer(cols = plant_1:plant_3,
               names_to = "plant",
               values_to = "lesions") %>% 
  group_by(rainfall_event, pot) %>% 
  summarise(total_lesions = sum(lesions, na.rm = TRUE),
         mean_lesions = round(mean(lesions), 1))

Create object for analysis

Lastly we import the weather data and then:

  • filter for only the dates/times with plants deployed,
  • summarise by rainfall event,
  • join with the lesion data, and then
  • create a new column with the total_lesions outliers filtered to use in the box plot, where outliers appear as a part of the geom_boxplot() but not in the geom_jitter().
dat <- read_csv(system.file(
  "extdata",
  "weather_data.csv",
  package = "rainy",
  mustWork = TRUE
)) %>%
  mutate(
    Date_time = round_date(dmy_hms(paste(Date, Time)), "minute"),
    rain_duration = as.integer(`TOTAL Rain Gauge mm` > 0),
    rainfall_event = case_when(
      Date_time %within% event_data[1, "exposed"] ~ 1,
      Date_time %within% event_data[2, "exposed"] ~ 2,
      Date_time %within% event_data[3, "exposed"] ~ 3,
      Date_time %within% event_data[4, "exposed"] ~ 4,
      Date_time %within% event_data[5, "exposed"] ~ 5,
      Date_time %within% event_data[6, "exposed"] ~ 6,
      Date_time %within% event_data[7, "exposed"] ~ 7
    )
  ) %>%
  filter(
    Date_time %within% event_data[1, "exposed"] |
      Date_time %within% event_data[2, "exposed"] |
      Date_time %within% event_data[3, "exposed"] |
      Date_time %within% event_data[4, "exposed"] |
      Date_time %within% event_data[5, "exposed"] |
      Date_time %within% event_data[6, "exposed"] |
      Date_time %within% event_data[7, "exposed"]
  ) %>%
  group_by(rainfall_event) %>%
  summarise(
    wind_direction = round(circular.averaging(`AVERAGE Wind Direction Degs`), 1),
    total_rain = sum(`TOTAL Rain Gauge mm`),
    rain_duration = round(sum(rain_duration / 6), 1),
    temp = round(mean(`AVERAGE Air Temperature DegC`), 1),
    relative_humidity = round(mean(`AVERAGE Relative Humidity %`), 1),
    wind_speed = round(mean(`AVERAGE Wind Speed km/h`) * 0.277778, 1) # convert to m/s
  ) %>%
  left_join(lesion_data, by = "rainfall_event") %>%
  mutate(
    rainfall_event = as.factor(rainfall_event))
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   Date = col_character(),
##   Time = col_time(format = ""),
##   `MAXIMUM Wind Speed km/h` = col_double(),
##   `AVERAGE Wind Speed km/h` = col_double(),
##   `STDEV Wind Speed km/h` = col_double(),
##   `AVERAGE Wind Direction Degs` = col_double(),
##   `S THETA Wind Direction Degs` = col_double(),
##   `MAXIMUM Peak Wind Gust km/h` = col_double(),
##   `VWSP Vector Wind Spd km/h` = col_double(),
##   `VWDIR Vector Wind Dir Degs` = col_double(),
##   `AVERAGE Relative Humidity %` = col_double(),
##   `AVERAGE Air Temperature DegC` = col_double(),
##   `TOTAL Rain Gauge mm` = col_double(),
##   `AVERAGE Solar Radiation Watts/m2` = col_double(),
##   `AVERAGE Barometric Pressure hPa` = col_double(),
##   `AVERAGE Soil Temperature 1 DegC` = col_double(),
##   `AVERAGE Soil Temperature 2 DegC` = col_double(),
##   `AVERAGE Soil Temperature 3 DegC` = col_double()
## )

Check the data.

kable(dat,
      format = "html",
      table.attr = "class='table table-hover'")
rainfall_event wind_direction total_rain rain_duration temp relative_humidity wind_speed pot total_lesions mean_lesions
1 93.7 1.4 0.8 19.6 89.6 4.2 1 0 0.0
1 93.7 1.4 0.8 19.6 89.6 4.2 2 1 0.3
1 93.7 1.4 0.8 19.6 89.6 4.2 3 0 0.0
1 93.7 1.4 0.8 19.6 89.6 4.2 4 1 0.3
1 93.7 1.4 0.8 19.6 89.6 4.2 5 0 0.0
1 93.7 1.4 0.8 19.6 89.6 4.2 6 0 0.0
1 93.7 1.4 0.8 19.6 89.6 4.2 7 0 0.0
1 93.7 1.4 0.8 19.6 89.6 4.2 8 0 0.0
1 93.7 1.4 0.8 19.6 89.6 4.2 9 0 0.0
1 93.7 1.4 0.8 19.6 89.6 4.2 10 0 0.0
1 93.7 1.4 0.8 19.6 89.6 4.2 11 0 0.0
1 93.7 1.4 0.8 19.6 89.6 4.2 12 0 0.0
2 87.1 2.8 0.7 21.0 83.2 3.7 1 5 1.7
2 87.1 2.8 0.7 21.0 83.2 3.7 2 7 2.3
2 87.1 2.8 0.7 21.0 83.2 3.7 3 7 2.3
2 87.1 2.8 0.7 21.0 83.2 3.7 4 0 0.0
2 87.1 2.8 0.7 21.0 83.2 3.7 5 1 0.3
2 87.1 2.8 0.7 21.0 83.2 3.7 6 10 3.3
2 87.1 2.8 0.7 21.0 83.2 3.7 7 0 0.0
2 87.1 2.8 0.7 21.0 83.2 3.7 8 1 0.3
2 87.1 2.8 0.7 21.0 83.2 3.7 9 1 0.3
2 87.1 2.8 0.7 21.0 83.2 3.7 10 0 0.0
2 87.1 2.8 0.7 21.0 83.2 3.7 11 0 0.0
2 87.1 2.8 0.7 21.0 83.2 3.7 12 0 0.0
3 95.4 3.4 1.7 20.3 90.1 4.5 1 0 0.0
3 95.4 3.4 1.7 20.3 90.1 4.5 2 0 0.0
3 95.4 3.4 1.7 20.3 90.1 4.5 3 3 1.0
3 95.4 3.4 1.7 20.3 90.1 4.5 4 4 1.3
3 95.4 3.4 1.7 20.3 90.1 4.5 5 0 0.0
3 95.4 3.4 1.7 20.3 90.1 4.5 6 1 0.3
3 95.4 3.4 1.7 20.3 90.1 4.5 7 1 0.3
3 95.4 3.4 1.7 20.3 90.1 4.5 8 1 0.3
3 95.4 3.4 1.7 20.3 90.1 4.5 9 0 0.0
3 95.4 3.4 1.7 20.3 90.1 4.5 10 1 0.3
3 95.4 3.4 1.7 20.3 90.1 4.5 11 2 0.7
3 95.4 3.4 1.7 20.3 90.1 4.5 12 2 0.7
4 198.1 13.4 7.0 10.5 76.8 2.9 1 41 NA
4 198.1 13.4 7.0 10.5 76.8 2.9 2 58 NA
4 198.1 13.4 7.0 10.5 76.8 2.9 3 121 40.3
4 198.1 13.4 7.0 10.5 76.8 2.9 4 46 15.3
4 198.1 13.4 7.0 10.5 76.8 2.9 5 12 NA
4 198.1 13.4 7.0 10.5 76.8 2.9 6 15 NA
4 198.1 13.4 7.0 10.5 76.8 2.9 7 30 10.0
4 198.1 13.4 7.0 10.5 76.8 2.9 8 22 7.3
4 198.1 13.4 7.0 10.5 76.8 2.9 9 65 NA
4 198.1 13.4 7.0 10.5 76.8 2.9 10 48 16.0
4 198.1 13.4 7.0 10.5 76.8 2.9 11 67 NA
4 198.1 13.4 7.0 10.5 76.8 2.9 12 72 NA
5 236.3 13.4 3.3 10.6 82.9 3.7 1 55 18.3
5 236.3 13.4 3.3 10.6 82.9 3.7 2 41 13.7
5 236.3 13.4 3.3 10.6 82.9 3.7 3 83 27.7
5 236.3 13.4 3.3 10.6 82.9 3.7 4 17 5.7
5 236.3 13.4 3.3 10.6 82.9 3.7 5 29 9.7
5 236.3 13.4 3.3 10.6 82.9 3.7 6 41 13.7
5 236.3 13.4 3.3 10.6 82.9 3.7 7 75 25.0
5 236.3 13.4 3.3 10.6 82.9 3.7 8 15 5.0
5 236.3 13.4 3.3 10.6 82.9 3.7 9 20 6.7
5 236.3 13.4 3.3 10.6 82.9 3.7 10 51 17.0
5 236.3 13.4 3.3 10.6 82.9 3.7 11 72 24.0
5 236.3 13.4 3.3 10.6 82.9 3.7 12 69 23.0
6 249.3 11.8 2.7 10.3 83.0 3.9 1 6 2.0
6 249.3 11.8 2.7 10.3 83.0 3.9 2 12 4.0
6 249.3 11.8 2.7 10.3 83.0 3.9 3 0 0.0
6 249.3 11.8 2.7 10.3 83.0 3.9 4 0 0.0
6 249.3 11.8 2.7 10.3 83.0 3.9 5 0 0.0
6 249.3 11.8 2.7 10.3 83.0 3.9 6 6 2.0
6 249.3 11.8 2.7 10.3 83.0 3.9 7 39 13.0
6 249.3 11.8 2.7 10.3 83.0 3.9 8 1 0.3
6 249.3 11.8 2.7 10.3 83.0 3.9 9 17 5.7
6 249.3 11.8 2.7 10.3 83.0 3.9 10 0 0.0
6 249.3 11.8 2.7 10.3 83.0 3.9 11 0 0.0
6 249.3 11.8 2.7 10.3 83.0 3.9 12 0 0.0
7 83.1 1.4 0.7 21.2 70.3 4.6 1 0 0.0
7 83.1 1.4 0.7 21.2 70.3 4.6 2 0 0.0
7 83.1 1.4 0.7 21.2 70.3 4.6 3 0 0.0
7 83.1 1.4 0.7 21.2 70.3 4.6 4 0 0.0
7 83.1 1.4 0.7 21.2 70.3 4.6 5 0 0.0
7 83.1 1.4 0.7 21.2 70.3 4.6 6 0 0.0
7 83.1 1.4 0.7 21.2 70.3 4.6 7 5 1.7
7 83.1 1.4 0.7 21.2 70.3 4.6 8 0 0.0
7 83.1 1.4 0.7 21.2 70.3 4.6 9 0 0.0
7 83.1 1.4 0.7 21.2 70.3 4.6 10 0 0.0
7 83.1 1.4 0.7 21.2 70.3 4.6 11 0 0.0
7 83.1 1.4 0.7 21.2 70.3 4.6 12 0 0.0

Kernel density plot

ggplot(dat, aes(x = mean_lesions)) +
  geom_density(fill = "steelblue", alpha = 0.7) +
  geom_rug(alpha = 0.4) +
  xlab("Total lesions per pot during each rainfall event")
## Warning: Removed 7 rows containing non-finite values (stat_density).

Kernel density plots showing the shape of data distribution. A strong peak at fewer than 25 lesions per pot was observed.

Scatter plot

Taking the existing dat object, group by rainfall_event again and calculate the total number of lesions in 12 pots. Plot the total_rain on the x-axis, wind_speed on the y-axis, the total_lesions per rainfall_event as a factor using colour and rain_duration as the point size.

dat %>%
  group_by(rainfall_event) %>%
  mutate(total_lesions = sum(total_lesions, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(aes(
    x = total_rain,
    y = wind_speed,
    size = rain_duration,
    colour = as.factor(total_lesions)
  )) +
  geom_point() +
  scale_size_continuous(range = c(3, 10),
                        breaks = sort(unique(dat$rain_duration),
                                      decreasing = FALSE)) +
  scale_colour_viridis_d(direction = -1) +
  guides(
    size = guide_legend(
      title = "Rainfall duration (hour)",
      title.position = "top",
      title.hjust = 0.5
    ),
    colour = guide_legend(
      title = "Total lesions per rainfall event",
      override.aes = list(size = 8),
      title.position = "top",
      title.hjust = 0.5
    )
  ) +
  scale_x_continuous(breaks = seq(from = 0, to = 14, by = 1),
                     limits = c(0, 14)) +
  scale_y_continuous(breaks = seq(from = 2, to = 5, by = 0.25),
                     limits = c(2.75, 4.75)) +
  labs(y = "Wind speed (m/s)",
       x = "Total rainfall (mm)") +
  theme(legend.key = element_blank(),
        plot.margin = margin(25, 25, 10, 25)) +
  coord_cartesian(clip = "off",
                  expand = FALSE)

Boxplot

Box plot of the total lesions for each rainfall event with total rainfall as the colour.

ggplot(
  dat,
  aes(
    x = rainfall_event,
    y = total_lesions,
    group = rainfall_event,
    colour = total_rain,
    fill = total_rain
  )
) +
  geom_boxplot(alpha = 0.4,
               outlier.size = 0) +
  geom_point(size = 5,
             position = position_jitterdodge()) +
  scale_colour_viridis_c(direction = -1,
                         name = "Total rainfall (mm)") +
  scale_fill_viridis_c(direction = -1,
                       name = "Total rainfall (mm)") +
  scale_y_continuous(breaks = seq(from = 0, to = 130, by = 10),
                     limits = c(0, 125)) +
  labs(x = "Rainfall event",
       y = "Total lesions per pot") +
  guides(color = guide_colorbar(
    title.position = "top",
    title.hjust = 0.5,
    barwidth = unit(20, "lines"),
    barheight = unit(0.5, "lines")
  )) +
  theme(legend.key = element_blank(),
        plot.margin = margin(25, 25, 10, 25)) +
  coord_cartesian(clip = "off",
                  expand = FALSE)

Wind rose

Import wind speed and wind direction data.

wind_dat <-
  read_excel(system.file("extdata", "wind_data.xlsx", package = "rainy")) %>%
  mutate(wind_speed = wind_speed / 3.6) %>%
  mutate(wind_direction = as.numeric(wind_direction)) %>%
  mutate(wind_speed = as.numeric(wind_speed)) %>%
  mutate(rainfall_event = as.factor(rainfall_event))

Plot wind rose

fig_3 <-
  with(
    wind_dat,
    windrose(
      wind_speed,
      wind_direction,
      rainfall_event,
      n_col = 3,
      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", base_size = 15)

fig_3

Colophon

## R version 4.1.0 (2021-05-18)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.6
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] kableExtra_1.3.4  readxl_1.3.1      rainy_0.0.0.9000  extrafont_0.17   
##  [5] ggpubr_0.4.0      here_1.0.1        showtext_0.9-2    showtextdb_3.0   
##  [9] sysfonts_0.8.3    viridis_0.6.1     viridisLite_0.4.0 clifro_3.2-5     
## [13] forcats_0.5.1     stringr_1.4.0     dplyr_1.0.7       purrr_0.3.4      
## [17] readr_1.4.0       tidyr_1.1.3       tibble_3.1.2      ggplot2_3.3.5    
## [21] tidyverse_1.3.1   lubridate_1.7.10 
## 
## loaded via a namespace (and not attached):
##  [1] fs_1.5.0           webshot_0.5.2      RColorBrewer_1.1-2 httr_1.4.2        
##  [5] rprojroot_2.0.2    tools_4.1.0        backports_1.2.1    bslib_0.2.5.1     
##  [9] utf8_1.2.1         R6_2.5.0           DBI_1.1.1          colorspace_2.0-2  
## [13] withr_2.4.2        tidyselect_1.1.1   gridExtra_2.3      extrafontdb_1.0   
## [17] curl_4.3.2         compiler_4.1.0     textshaping_0.3.5  cli_3.0.0         
## [21] rvest_1.0.0        xml2_1.3.2         desc_1.3.0         labeling_0.4.2    
## [25] sass_0.4.0         scales_1.1.1       pkgdown_1.6.1      systemfonts_1.0.2 
## [29] digest_0.6.27      foreign_0.8-81     svglite_2.0.0      rmarkdown_2.9     
## [33] rio_0.5.27         pkgconfig_2.0.3    htmltools_0.5.1.1  highr_0.9         
## [37] dbplyr_2.1.1       fastmap_1.1.0      rlang_0.4.11       rstudioapi_0.13   
## [41] farver_2.1.0       jquerylib_0.1.4    generics_0.1.0     jsonlite_1.7.2    
## [45] zip_2.2.0          car_3.0-11         magrittr_2.0.1     Rcpp_1.0.6        
## [49] munsell_0.5.0      fansi_0.5.0        abind_1.4-5        lifecycle_1.0.0   
## [53] stringi_1.6.2      yaml_2.2.1         carData_3.0-4      plyr_1.8.6        
## [57] grid_4.1.0         crayon_1.4.1       haven_2.4.1        hms_1.1.0         
## [61] knitr_1.33         pillar_1.6.1       ggsignif_0.6.2     reshape2_1.4.4    
## [65] reprex_2.0.0       glue_1.4.2         evaluate_0.14      data.table_1.14.0 
## [69] modelr_0.1.8       vctrs_0.3.8        Rttf2pt1_1.3.8     cellranger_1.1.0  
## [73] gtable_0.3.0       assertthat_0.2.1   cachem_1.0.5       openxlsx_4.2.4    
## [77] xfun_0.24          broom_0.7.8        rstatix_0.7.0      ragg_1.1.3        
## [81] memoise_2.0.0      ellipsis_0.3.2