Last updated: 2019-10-25

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Knit directory: wflow-tornadoes/

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Rmd ebe2481 Peter Carbonetto 2019-10-26 Quickstart commit from wflow_quickstart()

This is my analysis of tornado data downloaded from the NOAA website.

Load packages

Load the packages used for the analysis.

library(readr)
library(ggplot2)
library(cowplot)

Load tornado data

Load the 2018 storm event data from the CSV file. The output should be a data frame with 62,339 rows and 51 columns.

storms <- read_csv("StormEvents_details-ftp_v1.0_d2018_c20190817.csv.gz")
class(storms) <- "data.frame"
nrow(storms)
ncol(storms)
# [1] 62339
# [1] 51

Here we focus on events classified as tornadoes. There should be 1,250 weather events that are tornadoes.

tornadoes <- subset(storms,EVENT_TYPE == "Tornado")
nrow(tornadoes)
# [1] 1250

The tornadoes are categorized by a severity scale, with “EF3” being the most severe. Only a small number (12) of the tornadoes 2018 were classified as EF3.

tornadoes <- transform(tornadoes,TOR_F_SCALE = factor(TOR_F_SCALE))
summary(tornadoes$TOR_F_SCALE)
# EF0 EF1 EF2 EF3 EFU 
# 681 446  93  12  18

Plot geographic distribution of tornadoes

Using the geographic data provided (the latitudes and longitudes), create a map showing the location of all tornado events that occurred in 2018. Color the tornadoes by severity to highlight the most severe tornadoes (shown in orange and magenta).

rows      <- order(tornadoes$TOR_F_SCALE)
tornadoes <- tornadoes[rows,]
ggplot(tornadoes,aes(x = BEGIN_LON,y = BEGIN_LAT,color = TOR_F_SCALE)) +
  geom_path(data = map_data("state"),aes(x = long,y = lat,group = group),
            color = "black") +
  geom_point(shape = 20,size = 4) +
  scale_color_manual(values = c("lightskyblue","skyblue","darkorange",
                                "magenta","gray")) +
  labs(x = "longitude",y = "latitude")

As expected, the greatest concentration of severe tornadoes is in Midwestern states such as Kansas and Mississippi. There is also a quite large cluster of severe tornadoes in and around upper Louisiana. Indeed, there was a “tornado outbreak” that occurred in Louisiana in April 2018.


sessionInfo()
# R version 3.4.3 (2017-11-30)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS High Sierra 10.13.6
# 
# Matrix products: default
# BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
# LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
# 
# locale:
# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
# 
# attached base packages:
# [1] stats     graphics  grDevices utils     datasets  methods   base     
# 
# other attached packages:
# [1] cowplot_0.9.4 ggplot2_3.2.0 readr_1.3.1  
# 
# loaded via a namespace (and not attached):
#  [1] Rcpp_1.0.1           pillar_1.3.1         compiler_3.4.3      
#  [4] git2r_0.26.1.9001    plyr_1.8.4           workflowr_1.4.0.9001
#  [7] tools_3.4.3          digest_0.6.18        evaluate_0.13       
# [10] tibble_2.1.1         gtable_0.2.0         pkgconfig_2.0.2     
# [13] rlang_0.3.1          yaml_2.2.0           xfun_0.7            
# [16] withr_2.1.2.9000     stringr_1.4.0        dplyr_0.8.0.1       
# [19] knitr_1.23           fs_1.2.7             hms_0.4.2           
# [22] maps_3.3.0           rprojroot_1.3-2      grid_3.4.3          
# [25] tidyselect_0.2.5     glue_1.3.1           R6_2.4.0            
# [28] rmarkdown_1.16       purrr_0.2.5          magrittr_1.5        
# [31] whisker_0.3-2        backports_1.1.2      scales_0.5.0        
# [34] htmltools_0.3.6      assertthat_0.2.1     colorspace_1.4-0    
# [37] labeling_0.3         stringi_1.4.3        lazyeval_0.2.1      
# [40] munsell_0.4.3        crayon_1.3.4