Presentation

Tidy Friday

Team Tornado

Citation: Tidy Tuesday: https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-05-16/readme.md

Load libraries

Load data

Should you be afraid of Tornados??!

Occurrences of Tornados in the USA since 1950 - 2022:

tornados %>% 
    ggplot(aes(x=as.factor(mag), fill = as.factor(mag))) +
    geom_histogram(stat = "count", show.legend = FALSE) +
    labs(
        title = "Number of Tornados by Magnitude in the USA \n(1950-2020)",
        x = "Magnitude",
        y = "Number of \nTornados",
        fill = ""
    ) +
    theme_bw()

## Warning in geom_histogram(stat = "count", show.legend = FALSE): Ignoring
## unknown parameters: `binwidth`, `bins`, and `pad`

Where do Tornados occur?

#create the unique ID for each tornado
data <- tornados %>% 
        select(om,yr,date,st,slat,slon,mag) %>% 
        mutate(id = paste(date,om,sep = "-")) %>% 
        select(id,yr,st,slat,slon,mag) 

# count the total number of tornados for each states since 1950
freq_st <- data %>% 
            group_by(st) %>% 
            summarise(total_tornados = n()) %>% 
            arrange(desc(total_tornados))

### Map

# Load the usastates data
usastates <- map_data("state")

# Starting location of the tornados 
tornados_points <- data.frame(
    lon = data$slon,
    lat = data$slat,
    Magnitude = data$mag
)
# Map
ggplot() + 
    geom_polygon(data = usastates, aes(x = long, y = lat, group = group), fill = "white", color = "black") +
    geom_point(data = tornados_points, aes(x = lon, y = lat, colour = Magnitude),  size = 0.05) +
    scale_colour_gradient(low = "lightblue", high = "darkblue") +
    labs(title = "US Map with Tornados occurence", subtitle = "Tornados' starting location ") +
    theme_minimal()

Frequencies of Tornadoes pr State in the USA:

# Convert the state name
usastates <- usastates %>% 
    mutate(state = state.abb[match(str_to_title((usastates$region)),state.name,)])
# Merge the data
# merged_data <- merge(usastates, freq_st, by.x = "state", by.y = "st",all.x = TRUE)
# sometimes it will change the order of rows
# left join
merged_data2 <-left_join(usastates, freq_st, by =c("state"= "st"))
usastates <- usastates %>% 
    mutate(id=row_number())


# Calculate state centers loctions for text display
state_centers <- merged_data2 %>%
    select(state,long,lat,total_tornados)%>%
    group_by(state,total_tornados) %>%
    summarize(center_long = mean(long), center_lat = mean(lat)) %>% 
    select(state,center_long,center_lat,total_tornados)

## `summarise()` has grouped output by 'state'. You can override using the
## `.groups` argument.

# Map

ggplot() +
    geom_polygon(data = merged_data2, aes(x = long, y = lat, group = group), fill = "white", color = "black") +
    geom_polygon(data = merged_data2, aes(x = long, y = lat, group = group, fill = total_tornados), color = "black") +
    scale_fill_gradient(low = "lightblue", high = "darkblue") +
    labs(title = "US Map", subtitle = "Color shades represent the frequency of tornados") +
    theme_minimal() +
    geom_text(data = state_centers, aes(x = center_long, y = center_lat - 0.8, label = total_tornados), hjust = 0.7, color = "yellow") +
    geom_text(data = state_centers, aes(x = center_long, y = center_lat , label = state, fontface = "bold"), hjust = 0.7, color = "yellow")    

## Warning: Removed 1 rows containing missing values (`geom_text()`).
## Removed 1 rows containing missing values (`geom_text()`).

Where do the most severe Tornados occur:

#create the unique ID for each tornado

data <- tornados %>% 
        select(om,yr,date,st,slat,slon,mag) %>% 
        mutate(id = paste(date,om,sep = "-")) %>% 
        select(id,yr,st,slat,slon,mag) 
# count the total number of tornados for each states since 1950

freq_st <- data %>% 
    group_by(st, mag = as.factor(mag), .drop = F) %>% 
    summarise(total_tornados = n(), .groups = "drop") %>% 
    arrange(desc(total_tornados))


### Map

# Load the usastates data
usastates <- map_data("state")


# Starting location of the tornados 
tornados_points <- data.frame(
    lon = data$slon,
    lat = data$slat,
    Magnitude = data$mag
)

# Convert the state name
usastates <- usastates %>% 
    mutate(state = state.abb[match(str_to_title((usastates$region)),state.name,)])
# Merge the data
# merged_data <- merge(usastates, freq_st, by.x = "state", by.y = "st",all.x = TRUE)
# sometimes it will change the order of rows
# left join
merged_data2 <-left_join(usastates, freq_st, by =c("state"= "st"))

## Warning in left_join(usastates, freq_st, by = c(state = "st")): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 21 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.

usastates <- usastates %>% 
    mutate(id=row_number())


# Calculate state centers loctions for text display
state_centers <- merged_data2 %>%
    filter(!is.na(mag)) %>%
    select(state,long,lat,total_tornados, mag)%>%
    group_by(state, mag, total_tornados) %>%
    mutate(center_long = mean(long), center_lat = mean(lat))

# Map ----
merged_data2 %>% 
    filter(!is.na(mag)) %>%
    ggplot() +
    geom_polygon(aes(x = long, y = lat, group = group), fill = "white", color = "black") +
    geom_polygon(aes(x = long, y = lat, group = group, fill = total_tornados), color = "black") +
    scale_fill_gradient(low = "lightblue", high = "darkblue", na.value = "#D2D7D9", trans = "log") +
    labs(title = "US Map", subtitle = "Color shades represent the frequency of tornados") +
    theme_minimal() +
    theme(legend.position = "none")+
    facet_wrap(~as.factor(mag))+
    geom_text(data = state_centers, aes(x = center_long, y = center_lat - 0.8, label = total_tornados), hjust = 0.7, color = "yellow")

## Warning: Transformation introduced infinite values in discrete y-axis