Last updated: 2019-03-03

Checks: 6 0

Knit directory: ncdc_storm_events/

This reproducible R Markdown analysis was created with workflowr (version 1.2.0). The Report tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

The command set.seed(20181114) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .Rproj.user/

Untracked files:
    Untracked:  analysis/figure/
    Untracked:  docs/

Unstaged changes:
    Modified:   .gitignore

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
Rmd 94e33c4 Tim Trice 2019-03-03 Add Tornadoes

library(kableExtra)
library(tidyverse)
details <- 
  read_csv(
    file = here::here("./output/details.csv"), 
    col_types = cols(
      .default = col_character(),
      EPISODE_ID = col_integer(), 
      EVENT_ID = col_integer(),
      STATE_FIPS = col_integer(),
      CZ_FIPS = col_integer(),
      BEGIN_DATE_TIME = col_datetime(format = ""),
      END_DATE_TIME = col_datetime(format = ""),
      INJURIES_DIRECT = col_integer(),
      INJURIES_INDIRECT = col_integer(),
      DEATHS_DIRECT = col_integer(),
      DEATHS_INDIRECT = col_integer(),
      DAMAGE_PROPERTY = col_number(),
      DAMAGE_CROPS = col_number(),
      MAGNITUDE = col_double(),
      TOR_LENGTH = col_double(),
      TOR_WIDTH = col_double(),
      BEGIN_RANGE = col_integer(),
      END_RANGE = col_integer(),
      BEGIN_LAT = col_double(),
      BEGIN_LON = col_double(),
      END_LAT = col_double(),
      END_LON = col_double()
    )
  )

bad_lon <- 
  filter(details, !between(BEGIN_LON, -180, 180) | !between(END_LON, -180, 180))

Fujita Scales

tornado_scales <- 
  tibble(
    EF_scale = c("EF0", "EF1", "EF2", "EF3", "EF4", "EF5"), 
    EF_wind = c("65-85", "86-110", "111-135", "136-165", "166-200", ">200"), 
    F_scale = c("F0", "F1", "F2", "F3", "F4", "F5"), 
    F_wind = c("40-72", "73-112", "113-157", "158-206", "207-260", "261-318")
  )

tornado_scales %>% 
  kable() %>% 
  kable_styling(c("striped", "bordered")) %>% 
  add_header_above(c("Enhanced Fujita Wind Scale" = 2L, "Fujita Scale" = 2L))
Enhanced Fujita Wind Scale
Fujita Scale
EF_scale EF_wind F_scale F_wind
EF0 65-85 F0 40-72
EF1 86-110 F1 73-112
EF2 111-135 F2 113-157
EF3 136-165 F3 158-206
EF4 166-200 F4 207-260
EF5 >200 F5 261-318
enhanced_fujita <- 
  tibble(
    Wind = seq(65, 320, by = 1), 
    Scale = case_when(
      between(Wind, 65, 85)   ~ "EF0", 
      between(Wind, 86, 110)  ~ "EF1", 
      between(Wind, 111, 135) ~ "EF2", 
      between(Wind, 136, 165) ~ "EF3", 
      between(Wind, 166, 200) ~ "EF4", 
      between(Wind, 200, 320) ~ "EF5", 
    )
  )

fujita <- 
  tibble(
    Wind = seq(40, 318, by = 1), 
    Scale = case_when(
      between(Wind, 40, 72)   ~ "F0", 
      between(Wind, 73, 112)  ~ "F1", 
      between(Wind, 113, 157) ~ "F2", 
      between(Wind, 158, 206) ~ "F3", 
      between(Wind, 207, 260) ~ "F4", 
      between(Wind, 261, 318) ~ "F5", 
    )
  )
enhanced_fujita %>% 
  ggplot() + 
  aes(x = Wind, y = Scale, color = Scale) + 
  geom_line(size = 10) + 
  scale_x_continuous(
    limits = c(0, 320), 
    breaks = seq(0, 320, by = 20), 
    minor_breaks = seq(0, 320, by = 10), 
    expand = c(0, 0)
  ) + 
  theme(legend.position = "bottom") + 
  guides(color = guide_legend(title = "Enhanced Fujita Scale", nrow = 1L))

fujita %>% 
  ggplot() + 
  aes(x = Wind, y = Scale, color = Scale) + 
  geom_line(size = 10) + 
  scale_x_continuous(
    limits = c(0, 320), 
    breaks = seq(0, 320, by = 20), 
    minor_breaks = seq(0, 320, by = 10), 
    expand = c(0, 0)
  ) + 
  theme(legend.position = "bottom") + 
  guides(color = guide_legend(title = "Fujita Scale", nrow = 1L))

Invalid TOR_F_SCALE Values

details %>%
  filter(EVENT_TYPE == "Tornado") %>% 
  #' Remove bad latitude/longitude values
  group_by(TOR_F_SCALE) %>% 
  summarise(n = n())
# A tibble: 14 x 2
   TOR_F_SCALE     n
   <chr>       <int>
 1 <NA>         1970
 2 EF0          8428
 3 EF1          5317
 4 EF2          1546
 5 EF3           444
 6 EF4           103
 7 EF5            14
 8 EFU           108
 9 F0          20769
10 F1          16922
11 F2           9022
12 F3           2897
13 F4            999
14 F5            124
details %>% 
  filter(
    EVENT_TYPE == "Tornado", 
    is.na(TOR_F_SCALE) | TOR_F_SCALE == "EFU"
  ) %>% 
  select(EPISODE_ID, EVENT_ID, EVENT_TYPE, TOR_F_SCALE, MAGNITUDE, MAGNITUDE_TYPE) %>% 
  group_by(TOR_F_SCALE, MAGNITUDE, MAGNITUDE_TYPE) %>% 
  summarise(n = n()) %>% 
  arrange(TOR_F_SCALE, MAGNITUDE, MAGNITUDE_TYPE, n) %>% 
  kable() %>% 
  kable_styling("striped")
TOR_F_SCALE MAGNITUDE MAGNITUDE_TYPE n
EFU NA NA 108
NA 0 NA 1966
NA 1 NA 2
NA 2 NA 2

Where TOR_F_SCALE is NA or equals “EFU”, there is no valuable data to correct this. The variable MAGNITUDE is used to mark the measured extent of wind speeds (or hail, in some cases) and MAGNITUDE_TYPE identifies how that observation was obtained. Both of these values are NA for “EFU” observations, and MAGNITUDE does not reflect valid wind speed values for a tornado where TOR_F_SCALE is NA.

With this, can make TOR_F_SCALE a NA_character_ value where TOR_F_SCALE is “EFU”.

details$TOR_F_SCALE[details$TOR_F_SCALE == "EFU"] <- NA_character_
details %>%
  filter(
    EVENT_TYPE == "Tornado", 
    !is.na(TOR_F_SCALE)
  ) %>% 
  #' Remove observations with bad latitude/longitude values
  setdiff(bad_lon) %>% 
  select(EVENT_TYPE, TOR_F_SCALE, BEGIN_LAT, BEGIN_LON) %>% 
  extract(
    col = TOR_F_SCALE, 
    into = "SCALE", 
    regex = "^E*F([[:digit:]])$", 
    convert = TRUE, 
    remove = FALSE
  ) %>% 
  mutate(SCALE = fct_reorder(.f = as.character(SCALE), .x = SCALE)) %>% 
  ggplot() + 
  aes(
    x = BEGIN_LON, 
    y = BEGIN_LAT, 
    color = SCALE
  ) +
  geom_polygon(
    data = rnaturalearth::countries110, 
    aes(x = long, y = lat, group = group), 
    fill = "white", 
    color = "black", 
    size = 0.1
  ) + 
  geom_point() + 
  coord_cartesian() + 
  guides(color = guide_legend(nrow = 1L)) +
  viridis::scale_color_viridis(discrete = TRUE) +
  # scale_color_brewer(palette = "Set1") +
  theme_void() + 
  theme(legend.position = "bottom")
Warning: Removed 1013 rows containing missing values (geom_point).

Where TOR_F_SCALE is “F*“, do we have valid MAGNITUDE values?

details %>% 
  filter(grepl("^F[[:digit:]]$", TOR_F_SCALE)) %>% 
  select(TOR_F_SCALE, MAGNITUDE) %>% 
  group_by(TOR_F_SCALE) %>% 
  summarise(
    Min = min(MAGNITUDE), 
    Max = max(MAGNITUDE)
  )
# A tibble: 6 x 3
  TOR_F_SCALE   Min   Max
  <chr>       <dbl> <dbl>
1 F0             NA    NA
2 F1             NA    NA
3 F2             NA    NA
4 F3             NA    NA
5 F4             NA    NA
6 F5             NA    NA

…and “EF*“?

details %>% 
  filter(grepl("^EF[[:digit:]]$", TOR_F_SCALE)) %>% 
  select(TOR_F_SCALE, MAGNITUDE) %>% 
  group_by(TOR_F_SCALE) %>% 
  summarise(
    Min = min(MAGNITUDE), 
    Max = max(MAGNITUDE)
  )
# A tibble: 6 x 3
  TOR_F_SCALE   Min   Max
  <chr>       <dbl> <dbl>
1 EF0            NA    NA
2 EF1            NA    NA
3 EF2            NA    NA
4 EF3            NA    NA
5 EF4            NA    NA
6 EF5            NA    NA

So, at least one record for each scale has a NA value.

Severe Tornadoes

Under the Enhanced Fujita Scale, tornadoes rated EF4 or higher (winds > 166mph) produce devastating damage. Corresponding to the Fujita Scale, this would be at least a F3.

Plot tornadic events produced by at least a F3 or EF4 tornado.

details %>% 
  filter(TOR_F_SCALE %in% c("F3", "F4", "F5", "EF4", "EF5")) %>% 
  setdiff(bad_lon) %>% 
  select(EVENT_TYPE, TOR_F_SCALE, BEGIN_LAT, BEGIN_LON) %>% 
  extract(
    col = TOR_F_SCALE, 
    into = "SCALE", 
    regex = "^E*F([[:digit:]])$", 
    convert = TRUE, 
    remove = FALSE
  ) %>% 
  mutate(SCALE = fct_reorder(.f = as.character(SCALE), .x = SCALE)) %>% 
  ggplot() + 
  aes(
    x = BEGIN_LON, 
    y = BEGIN_LAT, 
    color = SCALE
  ) +
  borders("state") +
  geom_point() +
  coord_map() +
  theme_void() + 
  labs(
    title = "Tornadic events of at least EF4 or F3", 
    subtitle = "No events exist outside of the continental United"
  )
Warning: Removed 42 rows containing missing values (geom_point).

Tornado Width

details %>% 
  filter(!is.na(TOR_WIDTH), !is.na(TOR_F_SCALE)) %>% 
  select(TOR_F_SCALE, TOR_WIDTH) %>% 
  ggplot() + 
  aes(x = TOR_F_SCALE, y = TOR_WIDTH, fill = TOR_F_SCALE) + 
  geom_boxplot() + 
  coord_flip() + 
  scale_y_continuous(
    labels = scales::comma
  ) + 
  theme(legend.position = "none") + 
  labs(
    title = "Tornado Width by Scale", 
    x = "(Enhanced) Fujita Scale",
    y = "Tornado Width (feet)"
  )

Tornado Length (Distance Travelled)

details %>% 
  filter(!is.na(TOR_LENGTH), !is.na(TOR_F_SCALE)) %>% 
  select(TOR_F_SCALE, TOR_LENGTH) %>% 
  ggplot() + 
  aes(x = TOR_F_SCALE, y = TOR_LENGTH, fill = TOR_F_SCALE) + 
  geom_boxplot() + 
  coord_flip() + 
  theme(legend.position = "none") + 
  labs(
    title = "Tornado Length by Scale", 
    x = "(Enhanced) Fujita Scale",
    y = "Tornado Length (miles)"
  )

details %>% 
  filter(
    !is.na(TOR_LENGTH), 
    !is.na(TOR_F_SCALE), 
    TOR_LENGTH <= 100
  ) %>% 
  select(TOR_F_SCALE, TOR_LENGTH) %>% 
  ggplot() + 
  aes(x = TOR_F_SCALE, y = TOR_LENGTH, fill = TOR_F_SCALE) + 
  geom_boxplot() + 
  coord_flip() + 
  theme(legend.position = "none") + 
  labs(
    title = "Tornado Length by Scale", 
    subtitle = "Tornadoes with a length <= 100 miles", 
    x = "(Enhanced) Fujita Scale",
    y = "Tornado Length (miles)"
  )



devtools::session_info()
─ Session info ──────────────────────────────────────────────────────────
 setting  value                       
 version  R version 3.5.2 (2018-12-20)
 os       Ubuntu 18.04.2 LTS          
 system   x86_64, linux-gnu           
 ui       X11                         
 language (EN)                        
 collate  en_US.UTF-8                 
 ctype    en_US.UTF-8                 
 tz       America/Chicago             
 date     2019-03-03                  

─ Packages ──────────────────────────────────────────────────────────────
 package       * version date       lib source        
 assertthat      0.2.0   2017-04-11 [1] CRAN (R 3.5.2)
 backports       1.1.3   2018-12-14 [1] CRAN (R 3.5.2)
 broom           0.5.1   2018-12-05 [1] CRAN (R 3.5.2)
 callr           3.1.1   2018-12-21 [1] CRAN (R 3.5.2)
 cellranger      1.1.0   2016-07-27 [1] CRAN (R 3.5.2)
 class           7.3-15  2019-01-01 [1] CRAN (R 3.5.2)
 classInt        0.3-1   2018-12-18 [1] CRAN (R 3.5.2)
 cli             1.0.1   2018-09-25 [1] CRAN (R 3.5.2)
 colorspace      1.4-0   2019-01-13 [1] CRAN (R 3.5.2)
 crayon          1.3.4   2017-09-16 [1] CRAN (R 3.5.2)
 DBI             1.0.0   2018-05-02 [1] CRAN (R 3.5.2)
 desc            1.2.0   2018-05-01 [1] CRAN (R 3.5.2)
 devtools        2.0.1   2018-10-26 [1] CRAN (R 3.5.2)
 digest          0.6.18  2018-10-10 [1] CRAN (R 3.5.2)
 dplyr         * 0.8.0.1 2019-02-15 [1] CRAN (R 3.5.2)
 e1071           1.7-0.1 2019-01-21 [1] CRAN (R 3.5.2)
 evaluate        0.12    2018-10-09 [1] CRAN (R 3.5.2)
 fansi           0.4.0   2018-10-05 [1] CRAN (R 3.5.2)
 forcats       * 0.3.0   2018-02-19 [1] CRAN (R 3.5.2)
 fs              1.2.6   2018-08-23 [1] CRAN (R 3.5.2)
 generics        0.0.2   2018-11-29 [1] CRAN (R 3.5.2)
 ggplot2       * 3.1.0   2018-10-25 [1] CRAN (R 3.5.2)
 git2r           0.24.0  2019-01-07 [1] CRAN (R 3.5.2)
 glue            1.3.0   2018-07-17 [1] CRAN (R 3.5.2)
 gridExtra       2.3     2017-09-09 [1] CRAN (R 3.5.2)
 gtable          0.2.0   2016-02-26 [1] CRAN (R 3.5.2)
 haven           2.0.0   2018-11-22 [1] CRAN (R 3.5.2)
 here            0.1     2017-05-28 [1] CRAN (R 3.5.2)
 highr           0.7     2018-06-09 [1] CRAN (R 3.5.2)
 hms             0.4.2   2018-03-10 [1] CRAN (R 3.5.2)
 htmltools       0.3.6   2017-04-28 [1] CRAN (R 3.5.2)
 httr            1.4.0   2018-12-11 [1] CRAN (R 3.5.2)
 jsonlite        1.6     2018-12-07 [1] CRAN (R 3.5.2)
 kableExtra    * 1.0.1   2019-01-22 [1] CRAN (R 3.5.2)
 knitr           1.21    2018-12-10 [1] CRAN (R 3.5.2)
 labeling        0.3     2014-08-23 [1] CRAN (R 3.5.2)
 lattice         0.20-38 2018-11-04 [1] CRAN (R 3.5.2)
 lazyeval        0.2.1   2017-10-29 [1] CRAN (R 3.5.2)
 lubridate       1.7.4   2018-04-11 [1] CRAN (R 3.5.2)
 magrittr        1.5     2014-11-22 [1] CRAN (R 3.5.2)
 mapproj         1.2.6   2018-03-29 [1] CRAN (R 3.5.2)
 maps          * 3.3.0   2018-04-03 [1] CRAN (R 3.5.2)
 memoise         1.1.0   2017-04-21 [1] CRAN (R 3.5.2)
 modelr          0.1.2   2018-05-11 [1] CRAN (R 3.5.2)
 munsell         0.5.0   2018-06-12 [1] CRAN (R 3.5.2)
 nlme            3.1-137 2018-04-07 [1] CRAN (R 3.5.2)
 pillar          1.3.1   2018-12-15 [1] CRAN (R 3.5.2)
 pkgbuild        1.0.2   2018-10-16 [1] CRAN (R 3.5.2)
 pkgconfig       2.0.2   2018-08-16 [1] CRAN (R 3.5.2)
 pkgload         1.0.2   2018-10-29 [1] CRAN (R 3.5.2)
 plyr            1.8.4   2016-06-08 [1] CRAN (R 3.5.2)
 prettyunits     1.0.2   2015-07-13 [1] CRAN (R 3.5.2)
 processx        3.2.1   2018-12-05 [1] CRAN (R 3.5.2)
 ps              1.3.0   2018-12-21 [1] CRAN (R 3.5.2)
 purrr         * 0.2.5   2018-05-29 [1] CRAN (R 3.5.2)
 R6              2.3.0   2018-10-04 [1] CRAN (R 3.5.2)
 Rcpp            1.0.0   2018-11-07 [1] CRAN (R 3.5.2)
 readr         * 1.3.1   2018-12-21 [1] CRAN (R 3.5.2)
 readxl          1.2.0   2018-12-19 [1] CRAN (R 3.5.2)
 remotes         2.0.2   2018-10-30 [1] CRAN (R 3.5.2)
 rlang           0.3.1   2019-01-08 [1] CRAN (R 3.5.2)
 rmarkdown       1.11    2018-12-08 [1] CRAN (R 3.5.2)
 rnaturalearth   0.1.0   2017-03-21 [1] CRAN (R 3.5.2)
 rprojroot       1.3-2   2018-01-03 [1] CRAN (R 3.5.2)
 rstudioapi      0.9.0   2019-01-09 [1] CRAN (R 3.5.2)
 rvest           0.3.2   2016-06-17 [1] CRAN (R 3.5.2)
 scales          1.0.0   2018-08-09 [1] CRAN (R 3.5.2)
 sessioninfo     1.1.1   2018-11-05 [1] CRAN (R 3.5.2)
 sf              0.7-3   2019-02-21 [1] CRAN (R 3.5.2)
 sp              1.3-1   2018-06-05 [1] CRAN (R 3.5.2)
 stringi         1.2.4   2018-07-20 [1] CRAN (R 3.5.2)
 stringr       * 1.3.1   2018-05-10 [1] CRAN (R 3.5.2)
 tibble        * 2.0.1   2019-01-12 [1] CRAN (R 3.5.2)
 tidyr         * 0.8.2   2018-10-28 [1] CRAN (R 3.5.2)
 tidyselect      0.2.5   2018-10-11 [1] CRAN (R 3.5.2)
 tidyverse     * 1.2.1   2017-11-14 [1] CRAN (R 3.5.2)
 units           0.6-2   2018-12-05 [1] CRAN (R 3.5.2)
 usethis         1.4.0   2018-08-14 [1] CRAN (R 3.5.2)
 utf8            1.1.4   2018-05-24 [1] CRAN (R 3.5.2)
 viridis         0.5.1   2018-03-29 [1] CRAN (R 3.5.2)
 viridisLite     0.3.0   2018-02-01 [1] CRAN (R 3.5.2)
 webshot         0.5.1   2018-09-28 [1] CRAN (R 3.5.2)
 whisker         0.3-2   2013-04-28 [1] CRAN (R 3.5.2)
 withr           2.1.2   2018-03-15 [1] CRAN (R 3.5.2)
 workflowr       1.2.0   2019-02-14 [1] CRAN (R 3.5.2)
 xfun            0.4     2018-10-23 [1] CRAN (R 3.5.2)
 xml2            1.2.0   2018-01-24 [1] CRAN (R 3.5.2)
 yaml            2.2.0   2018-07-25 [1] CRAN (R 3.5.2)

[1] /usr/local/lib/R/site-library
[2] /usr/lib/R/site-library
[3] /usr/lib/R/library