# Data wrangling and visualisation

    library(bagyo)
    library(dplyr)
    library(tidyr)
    library(ggplot2)
    library(lubridate)

## Data wrangling

Following are some examples of how the `bagyo` dataset can be used to demonstrate various data wrangling approaches, particularly those using the `tidyverse` packages.

### Creating summaries

    ## Get number of cyclone categories per year ----
    bagyo |>
      group_by(year, category_name) |>
      count() |>
      group_by(year) |>
      complete(category_name) |>
      ungroup()
    #> # A tibble: 20 × 3
    #>     year category_name             n
    #>    <dbl> <fct>                 <int>
    #>  1  2017 Tropical Depression       5
    #>  2  2017 Tropical Storm            9
    #>  3  2017 Severe Tropical Storm     5
    #>  4  2017 Typhoon                   3
    #>  5  2017 Super Typhoon            NA
    #>  6  2018 Tropical Depression       4
    #>  7  2018 Tropical Storm            7
    #>  8  2018 Severe Tropical Storm     4
    #>  9  2018 Typhoon                   6
    #> 10  2018 Super Typhoon            NA
    #> 11  2019 Tropical Depression       8
    #> 12  2019 Tropical Storm            2
    #> 13  2019 Severe Tropical Storm     3
    #> 14  2019 Typhoon                   8
    #> 15  2019 Super Typhoon            NA
    #> 16  2020 Tropical Depression       6
    #> 17  2020 Tropical Storm            7
    #> 18  2020 Severe Tropical Storm     3
    #> 19  2020 Typhoon                   4
    #> 20  2020 Super Typhoon             2

    ## Get yearly mean cyclone pressure and speed ----
    bagyo |>
      group_by(year) |>
      summarise(mean_pressure = mean(pressure), mean_speed = mean(speed))
    #> # A tibble: 4 × 3
    #>    year mean_pressure mean_speed
    #>   <dbl>         <dbl>      <dbl>
    #> 1  2017          986.       88.0
    #> 2  2018          961.       66.7
    #> 3  2019          976.       59.0
    #> 4  2020          973.       62.0

    ## Get cyclone category mean pressure and speed ----
    bagyo |>
      group_by(category_name) |>
      summarise(
        n = n(),
        mean_pressure = mean(pressure),
        mean_speed = mean(speed)
      )
    #> # A tibble: 5 × 4
    #>   category_name             n mean_pressure mean_speed
    #>   <fct>                 <int>         <dbl>      <dbl>
    #> 1 Tropical Depression      23          996.       39.8
    #> 2 Tropical Storm           25          986.       61.6
    #> 3 Severe Tropical Storm    15          978.       75
    #> 4 Typhoon                  21          941.      102.
    #> 5 Super Typhoon             2          908.      112.

### Working with date and time data

    ## Get cyclone category mean duration (in hours) ----
    bagyo |>
      mutate(duration = end - start) |>
      group_by(category_name) |>
      summarise(mean_duration = mean(duration))
    #> # A tibble: 5 × 2
    #>   category_name         mean_duration
    #>   <fct>                 <drtn>
    #> 1 Tropical Depression    46.69565 hours
    #> 2 Tropical Storm         57.48000 hours
    #> 3 Severe Tropical Storm  79.13333 hours
    #> 4 Typhoon               106.66667 hours
    #> 5 Super Typhoon          77.50000 hours

    ## Get number of cyclones per month by year ----
    bagyo |>
      mutate(month = month(start, label = TRUE)) |>
      group_by(month, year) |>
      count() |>
      ungroup() |>
      complete(month, year, fill = list(n = 0)) |>
      arrange(year, month)
    #> # A tibble: 48 × 3
    #>    month  year     n
    #>    <ord> <dbl> <int>
    #>  1 Jan    2017     1
    #>  2 Feb    2017     1
    #>  3 Mar    2017     0
    #>  4 Apr    2017     2
    #>  5 May    2017     0
    #>  6 Jun    2017     0
    #>  7 Jul    2017     4
    #>  8 Aug    2017     2
    #>  9 Sep    2017     4
    #> 10 Oct    2017     3
    #> # ℹ 38 more rows

## Data visualisation

Following are some examples of how the `bagyo` dataset can be used to demonstrate various data visualisation approaches, particularly those using the `tidyverse` and `ggplot2` packages.

### Bar plots

    ## Get cyclone category mean duration (in hours) ----
    bagyo |>
      mutate(duration = end - start) |>
      group_by(category_name) |>
      summarise(mean_duration = mean(duration)) |>
      ggplot(mapping = aes(x = mean_duration, y = category_name)) +
      geom_col(colour = "#4b876e", fill = "#4b876e", alpha = 0.5) +
      labs(
        title = "Mean duration of cyclones",
        subtitle = "By cyclone categories",
        x = "mean duration (hours)",
        y = NULL
      ) +
      theme_minimal() +
      theme(
        panel.grid.minor.x = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank()
      )

### Scatter plots

    ## Cyclone speed by presssure ----
    bagyo |>
      dplyr::mutate(year = factor(year)) |>
      ggplot(mapping = aes(x = speed, y = pressure)) +
      geom_point(mapping = aes(colour = category_name), size = 3, alpha = 0.5) +
      scale_colour_manual(
        name = NULL,
        values = c("#9c5e60", "#4b876e", "#465b92", "#e5be72", "#5d0505")
      ) +
      labs(
        title = "Cyclone maximum sustained wind speed and maximum central pressure",
        subtitle = "By cyclone categories and year",
        x = "wind speed (km/h)",
        y = "central pressure (hPa)"
      ) +
      facet_wrap(. ~ year, ncol = 4) +
      theme_bw() +
      theme(
        legend.position = "top",
        strip.background = element_rect(
          fill = alpha("#465b92", 0.7), colour = "#465b92"
        ),
        panel.border = element_rect(colour = "#465b92"),
        panel.grid.minor = element_blank()
      )

    bagyo |>
      mutate(
        year = factor(year),
        duration = as.numeric(end - start)
      ) |>
      ggplot(mapping = aes(x = speed, y = duration)) +
      geom_point(
        mapping = aes(colour = year, shape = year), size = 3, alpha = 0.5
      ) +
      geom_smooth(
        mapping = aes(colour = year), method = "lm", se = FALSE, linewidth = 0.75
      ) +
      scale_colour_manual(
        values = c("#9c5e60", "#4b876e", "#465b92", "#e5be72")
      ) +
      scale_shape_manual(values = 15:18) +
      labs(
        title = "Maximum sustained wind speed by duration of cyclones",
        subtitle = "2017-2020",
        x = "speed (km/h)", y = "duration (hours)",
        colour = "Year", shape = "Year"
      ) +
      theme_minimal() +
      theme(legend.position = "top")

### Time series

    ## Get number of cyclones per month by year and plot ----
    bagyo |>
      mutate(month = month(start, label = TRUE)) |>
      group_by(month, year) |>
      count() |>
      ungroup() |>
      complete(month, year, fill = list(n = 0)) |>
      arrange(year, month) |>
      ggplot(mapping = aes(x = month, y = n)) +
      geom_col(colour = "#4b876e", fill = "#4b876e", alpha = 0.5) +
      scale_y_continuous(breaks = seq(from = 0, to = 6, by = 1)) +
      labs(
        title = "Number of cyclones over time",
        subtitle = "2017-2020",
        x = NULL,
        y = "n"
      ) +
      facet_wrap(. ~ year, ncol = 4) +
      theme_bw() +
      theme(
        strip.background = element_rect(
          fill = alpha("#465b92", 0.7), colour = "#465b92"
        ),
        panel.border = element_rect(colour = "#465b92"),
        panel.grid.minor.y = element_blank(),
        panel.grid.major.x = element_blank(),
        axis.text.x = element_text(size = 10, angle = 90, hjust = 1, vjust = 0.5)
      )

### Distribution plots

    bagyo |>
      mutate(year = factor(year)) |>
      ggplot(mapping = aes(x = year, y = speed)) +
      geom_boxplot(colour = "#4b876e", fill = "#4b876e", alpha = 0.5) +
      labs(
        title = "Distribution of tropical cyclone maximum sustained wind speed",
        subtitle = "2017-2022",
        x = NULL, y = "speed (km/h)"
      ) +
      theme_minimal() +
      theme(panel.grid.major.x = element_blank())

    bagyo |>
      mutate(year = factor(year)) |>
      ggplot(mapping = aes(x = year, y = speed)) +
      geom_boxplot(colour = "#4b876e") +
      geom_jitter(
        colour = "#4b876e", fill = "#4b876e", alpha = 0.5,
        shape = 21, size = 2, width = 0.2
      ) +
      labs(
        title = "Distribution of tropical cyclone maximum sustained wind speed",
        subtitle = "2017-2022",
        x = NULL, y = "speed (km/h)"
      ) +
      theme_minimal() +
      theme(panel.grid.major.x = element_blank())

    bagyo |>
      mutate(year = factor(year)) |>
      ggplot(mapping = aes(x = year, y = speed)) +
      geom_violin(colour = "#4b876e", fill = "#4b876e", alpha = 0.5) +
      geom_jitter(colour = "#4b876e", size = 3, width = 0.2) +
      labs(
        title = "Distribution of tropical cyclone maximum sustained wind speed",
        subtitle = "2017-2022",
        x = NULL, y = "speed (km/h)"
      ) +
      theme_minimal() +
      theme(panel.grid.major.x = element_blank())