Weather Damage Report

The basic goal of this project is to explore the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database, seeking to answer some questions about severe weather events. The main focus of this study is to allow full-research reproducibility, using literate programming for each iteration.

Tools Used

R language compiler
R base graphic devices
Tidyverse library packages
Cowplot library package
RMarkdown library package
Knitr library package

Files

Raw data set: Storm Database
CODEBOOK: step-by-step book explaining the code processing.
Figures: the plotted images
report.Rmd: the script to compile the project from source

Build from Source

Inside the project root folder, execute the following

R
library(rmarkdown)
rmarkdown::render("report.Rmd", "html_document")

CODEBOOK

Data Processing

Setup

Loading libraries

## ── Attaching packages ───────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.0     ✓ purrr   0.3.4
## ✓ tibble  3.0.1     ✓ dplyr   0.8.5
## ✓ tidyr   1.0.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0

## ── Conflicts ──────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

## 
## ********************************************************

## Note: As of version 1.0.0, cowplot does not change the

##   default ggplot2 theme anymore. To recover the previous

##   behavior, execute:
##   theme_set(theme_cowplot())

## ********************************************************

Setting seed for reproducibility

set.seed(144)

Showing current environment

sessionInfo()

## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
## 
## locale:
##  [1] LC_CTYPE=pt_BR.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=pt_BR.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=pt_BR.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=pt_BR.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] cowplot_1.0.0   forcats_0.5.0   stringr_1.4.0   dplyr_0.8.5    
##  [5] purrr_0.3.4     readr_1.3.1     tidyr_1.0.2     tibble_3.0.1   
##  [9] ggplot2_3.3.0   tidyverse_1.3.0
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.0.0 xfun_0.13        haven_2.2.0      lattice_0.20-41 
##  [5] colorspace_1.4-1 vctrs_0.2.4      generics_0.0.2   htmltools_0.4.0 
##  [9] yaml_2.2.1       rlang_0.4.6      pillar_1.4.3     glue_1.4.0      
## [13] withr_2.2.0      DBI_1.1.0        dbplyr_1.4.3     modelr_0.1.7    
## [17] readxl_1.3.1     lifecycle_0.2.0  munsell_0.5.0    gtable_0.3.0    
## [21] cellranger_1.1.0 rvest_0.3.5      codetools_0.2-16 evaluate_0.14   
## [25] knitr_1.28       fansi_0.4.1      broom_0.5.6      Rcpp_1.0.4.6    
## [29] scales_1.1.0     backports_1.1.6  jsonlite_1.6.1   fs_1.4.1        
## [33] hms_0.5.3        digest_0.6.25    stringi_1.4.6    grid_3.6.3      
## [37] cli_2.0.2        tools_3.6.3      magrittr_1.5     crayon_1.3.4    
## [41] pkgconfig_2.0.3  ellipsis_0.3.0   xml2_1.3.2       reprex_0.3.0    
## [45] lubridate_1.7.8  assertthat_0.2.1 rmarkdown_2.1    httr_1.4.1      
## [49] rstudioapi_0.11  R6_2.4.1         nlme_3.1-147     compiler_3.6.3

Downloading the dataset

download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2", "stormdata.csv.bz2")

Read dataset into variable removing blank spaces and transform into tidyverse compatible

storm_data <- as_tibble(read.csv(file = "stormdata.csv.bz2", na.strings = ""))

Get a glimpse of the data

glimpse(storm_data)

## Rows: 902,297
## Columns: 37
## $ STATE__    <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ BGN_DATE   <fct> 4/18/1950 0:00:00, 4/18/1950 0:00:00, 2/20/1951 0:00:00, 6…
## $ BGN_TIME   <fct> 0130, 0145, 1600, 0900, 1500, 2000, 0100, 0900, 2000, 2000…
## $ TIME_ZONE  <fct> CST, CST, CST, CST, CST, CST, CST, CST, CST, CST, CST, CST…
## $ COUNTY     <dbl> 97, 3, 57, 89, 43, 77, 9, 123, 125, 57, 43, 9, 73, 49, 107…
## $ COUNTYNAME <fct> MOBILE, BALDWIN, FAYETTE, MADISON, CULLMAN, LAUDERDALE, BL…
## $ STATE      <fct> AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL…
## $ EVTYPE     <fct> TORNADO, TORNADO, TORNADO, TORNADO, TORNADO, TORNADO, TORN…
## $ BGN_RANGE  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ BGN_AZI    <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ BGN_LOCATI <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ END_DATE   <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ END_TIME   <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ COUNTY_END <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ COUNTYENDN <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ END_RANGE  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ END_AZI    <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ END_LOCATI <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ LENGTH     <dbl> 14.0, 2.0, 0.1, 0.0, 0.0, 1.5, 1.5, 0.0, 3.3, 2.3, 1.3, 4.…
## $ WIDTH      <dbl> 100, 150, 123, 100, 150, 177, 33, 33, 100, 100, 400, 400, …
## $ F          <int> 3, 2, 2, 2, 2, 2, 2, 1, 3, 3, 1, 1, 3, 3, 3, 4, 1, 1, 1, 1…
## $ MAG        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ FATALITIES <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 4, 0, 0, 0, 0…
## $ INJURIES   <dbl> 15, 0, 2, 2, 2, 6, 1, 0, 14, 0, 3, 3, 26, 12, 6, 50, 2, 0,…
## $ PROPDMG    <dbl> 25.0, 2.5, 25.0, 2.5, 2.5, 2.5, 2.5, 2.5, 25.0, 25.0, 2.5,…
## $ PROPDMGEXP <fct> K, K, K, K, K, K, K, K, K, K, M, M, K, K, K, K, K, K, K, K…
## $ CROPDMG    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ CROPDMGEXP <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ WFO        <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ STATEOFFIC <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ZONENAMES  <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ LATITUDE   <dbl> 3040, 3042, 3340, 3458, 3412, 3450, 3405, 3255, 3334, 3336…
## $ LONGITUDE  <dbl> 8812, 8755, 8742, 8626, 8642, 8748, 8631, 8558, 8740, 8738…
## $ LATITUDE_E <dbl> 3051, 0, 0, 0, 0, 0, 0, 0, 3336, 3337, 3402, 3404, 0, 3432…
## $ LONGITUDE_ <dbl> 8806, 0, 0, 0, 0, 0, 0, 0, 8738, 8737, 8644, 8640, 0, 8540…
## $ REMARKS    <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ REFNUM     <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,…

Count NAs

sum_na <- storm_data %>%
    summarize_all(funs(sum(is.na(.))))

## Warning: funs() is soft deprecated as of dplyr 0.8.0
## Please use a list of either functions or lambdas: 
## 
##   # Simple named list: 
##   list(mean = mean, median = median)
## 
##   # Auto named with `tibble::lst()`: 
##   tibble::lst(mean, median)
## 
##   # Using lambdas
##   list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once per session.

glimpse(sum_na)

## Rows: 1
## Columns: 37
## $ STATE__    <int> 0
## $ BGN_DATE   <int> 0
## $ BGN_TIME   <int> 0
## $ TIME_ZONE  <int> 0
## $ COUNTY     <int> 0
## $ COUNTYNAME <int> 1589
## $ STATE      <int> 0
## $ EVTYPE     <int> 0
## $ BGN_RANGE  <int> 0
## $ BGN_AZI    <int> 547332
## $ BGN_LOCATI <int> 287743
## $ END_DATE   <int> 243411
## $ END_TIME   <int> 238978
## $ COUNTY_END <int> 0
## $ COUNTYENDN <int> 902297
## $ END_RANGE  <int> 0
## $ END_AZI    <int> 724837
## $ END_LOCATI <int> 499225
## $ LENGTH     <int> 0
## $ WIDTH      <int> 0
## $ F          <int> 843563
## $ MAG        <int> 0
## $ FATALITIES <int> 0
## $ INJURIES   <int> 0
## $ PROPDMG    <int> 0
## $ PROPDMGEXP <int> 465934
## $ CROPDMG    <int> 0
## $ CROPDMGEXP <int> 618413
## $ WFO        <int> 142069
## $ STATEOFFIC <int> 248769
## $ ZONENAMES  <int> 594029
## $ LATITUDE   <int> 47
## $ LONGITUDE  <int> 0
## $ LATITUDE_E <int> 40
## $ LONGITUDE_ <int> 0
## $ REMARKS    <int> 287433
## $ REFNUM     <int> 0

Select only needed data from the processed dataset

filter_data <- storm_data %>% 
    select(STATE, EVTYPE, FATALITIES, INJURIES, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP)

glimpse(filter_data)

## Rows: 902,297
## Columns: 8
## $ STATE      <fct> AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL…
## $ EVTYPE     <fct> TORNADO, TORNADO, TORNADO, TORNADO, TORNADO, TORNADO, TORN…
## $ FATALITIES <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 4, 0, 0, 0, 0…
## $ INJURIES   <dbl> 15, 0, 2, 2, 2, 6, 1, 0, 14, 0, 3, 3, 26, 12, 6, 50, 2, 0,…
## $ PROPDMG    <dbl> 25.0, 2.5, 25.0, 2.5, 2.5, 2.5, 2.5, 2.5, 25.0, 25.0, 2.5,…
## $ PROPDMGEXP <fct> K, K, K, K, K, K, K, K, K, K, M, M, K, K, K, K, K, K, K, K…
## $ CROPDMG    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ CROPDMGEXP <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…

Impute 0 to NAs

tidy_storm <- filter_data %>%
    replace(is.na(.), "0"
    ) %>% 
    mutate(PROPDMGEXP = as.character(PROPDMGEXP),
           CROPDMGEXP = as.character(CROPDMGEXP))
glimpse(tidy_storm)

## Rows: 902,297
## Columns: 8
## $ STATE      <fct> AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL…
## $ EVTYPE     <fct> TORNADO, TORNADO, TORNADO, TORNADO, TORNADO, TORNADO, TORN…
## $ FATALITIES <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 4, 0, 0, 0, 0…
## $ INJURIES   <dbl> 15, 0, 2, 2, 2, 6, 1, 0, 14, 0, 3, 3, 26, 12, 6, 50, 2, 0,…
## $ PROPDMG    <dbl> 25.0, 2.5, 25.0, 2.5, 2.5, 2.5, 2.5, 2.5, 25.0, 25.0, 2.5,…
## $ PROPDMGEXP <chr> "K", "K", "K", "K", "K", "K", "K", "K", "K", "K", "M", "M"…
## $ CROPDMG    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ CROPDMGEXP <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0"…

Find unique values in prop and crop

unique_prop <- unique(tidy_storm$PROPDMGEXP)
unique_crop <- unique(tidy_storm$CROPDMGEXP)

unique_prop

##  [1] "K" "M" "0" "B" "m" "+" "5" "6" "?" "4" "2" "3" "h" "7" "H" "-" "1" "8"

unique_crop

## [1] "0" "M" "K" "m" "B" "?" "k" "2"

Create dictionary for the unique values

rep_prop <- c(1e3, 1e6, 0, 1e9, 1e6, 0, 1e5, 1e6, 0, 1e4, 1e2, 1e3, 1e2, 1e7, 1e2, 0, 1e1, 1e8)
rep_crop <- c(0, 1e6, 1e3, 1e6, 1e9, 0, 1e3, 1e2)

prop_dictionary <- cbind(unique_prop, rep_prop)
crop_dictionary <- cbind(unique_crop, rep_crop)
prop_dictionary

##       unique_prop rep_prop
##  [1,] "K"         "1000"  
##  [2,] "M"         "1e+06" 
##  [3,] "0"         "0"     
##  [4,] "B"         "1e+09" 
##  [5,] "m"         "1e+06" 
##  [6,] "+"         "0"     
##  [7,] "5"         "1e+05" 
##  [8,] "6"         "1e+06" 
##  [9,] "?"         "0"     
## [10,] "4"         "10000" 
## [11,] "2"         "100"   
## [12,] "3"         "1000"  
## [13,] "h"         "100"   
## [14,] "7"         "1e+07" 
## [15,] "H"         "100"   
## [16,] "-"         "0"     
## [17,] "1"         "10"    
## [18,] "8"         "1e+08"

crop_dictionary

##      unique_crop rep_crop
## [1,] "0"         "0"     
## [2,] "M"         "1e+06" 
## [3,] "K"         "1000"  
## [4,] "m"         "1e+06" 
## [5,] "B"         "1e+09" 
## [6,] "?"         "0"     
## [7,] "k"         "1000"  
## [8,] "2"         "100"

Remap values back into the tidy data set using plyr

tidy_storm$PROPDMGEXP <- plyr::mapvalues(tidy_storm$PROPDMGEXP, from = unique_prop, to = rep_prop)
tidy_storm$CROPDMGEXP <- plyr::mapvalues(tidy_storm$CROPDMGEXP, from = unique_crop, to = rep_crop)

tidy_storm <- tidy_storm %>% 
    mutate(PROPDMGEXP = as.double(PROPDMGEXP),
           CROPDMGEXP = as.double(CROPDMGEXP))

glimpse(tidy_storm)

## Rows: 902,297
## Columns: 8
## $ STATE      <fct> AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL…
## $ EVTYPE     <fct> TORNADO, TORNADO, TORNADO, TORNADO, TORNADO, TORNADO, TORN…
## $ FATALITIES <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 4, 0, 0, 0, 0…
## $ INJURIES   <dbl> 15, 0, 2, 2, 2, 6, 1, 0, 14, 0, 3, 3, 26, 12, 6, 50, 2, 0,…
## $ PROPDMG    <dbl> 25.0, 2.5, 25.0, 2.5, 2.5, 2.5, 2.5, 2.5, 25.0, 25.0, 2.5,…
## $ PROPDMGEXP <dbl> 1e+03, 1e+03, 1e+03, 1e+03, 1e+03, 1e+03, 1e+03, 1e+03, 1e…
## $ CROPDMG    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ CROPDMGEXP <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…

Questions

1. Across the United States, which types of events (as indicated in the EVTYPE variable are most harmful with respect to population health?

Rank fatalities and injuries according to EVTYPE

sum_events_global <- tidy_storm %>%
    select(-STATE) %>% 
    group_by(EVTYPE) %>%
    summarize_all(
        list(sum)
    )

sum_fatalities_global <- sum_events_global %>% 
    select(EVTYPE, FATALITIES) %>% 
    arrange(desc(FATALITIES))

sum_fatalities_top10 <- slice(sum_fatalities_global, 1:10)

sum_injuries_global <- sum_events_global %>% 
    select(EVTYPE, INJURIES) %>% 
    arrange(desc(INJURIES))

sum_injuries_top10 <- slice(sum_injuries_global, 1:10)

Plot results

plot1 <- ggplot(sum_fatalities_top10, aes(reorder(EVTYPE, -FATALITIES), FATALITIES, group = EVTYPE, alpha = EVTYPE)) +
    geom_bar(stat = "identity") +
    theme_minimal() +
    theme(
        axis.text.x = element_text(angle = 90),
        legend.position = "none"
          ) +
    labs(
        title = "Major Causes of Fatality",
        x = "",
        y = "Fatalities"
    )

plot2 <- ggplot(sum_injuries_top10, aes(reorder(EVTYPE, -INJURIES), INJURIES, group = EVTYPE, alpha = EVTYPE, fill = "red")) +
    geom_bar(stat = "identity") +
    theme_minimal() +
    theme(
        axis.text.x = element_text(angle = 90),
        legend.position = "none"
          ) +
    labs(
        title = "Major Causes of Injuries",
        x = "",
        y = "Injuries"
    )
plot_grid(plot1, plot2)

2. Across the United States, which types of events have the greatest economic consequences?

Prepare vars for measured and expected damage

# Measured damage

propdmg_global <- sum_events_global %>% 
    select(EVTYPE, PROPDMG) %>% 
    arrange(desc(PROPDMG))

propdmg_top10 <- slice(propdmg_global, 1:10)

cropdmg_global <- sum_events_global %>% 
    select(EVTYPE, CROPDMG) %>% 
    arrange(desc(CROPDMG))

cropdmg_top10 <- slice(cropdmg_global, 1:10)

# Expected damage
exp_propdmg_global <- sum_events_global %>% 
    select(EVTYPE, PROPDMGEXP) %>% 
    arrange(desc(PROPDMGEXP))

exp_propdmg_top10 <- slice(exp_propdmg_global, 1:10)

exp_cropdmg_global <- sum_events_global %>% 
    select(EVTYPE, CROPDMGEXP) %>% 
    arrange(desc(CROPDMGEXP))

exp_cropdmg_top10 <- slice(exp_cropdmg_global, 1:10)

Plot the results

plot3 <- ggplot(propdmg_top10, aes(reorder(EVTYPE, -PROPDMG), PROPDMG, group = EVTYPE, alpha = EVTYPE)) +
    geom_bar(stat = "identity", fill = "rosybrown") +
    theme_minimal() +
    theme(
        axis.text.x = element_text(angle = 90),
        legend.position = "none"
          ) +
    labs(
        title = "Major Causes of Prop Damage",
        x = "",
        y = "Gross cost ($)"
    )

plot4 <- ggplot(cropdmg_top10, aes(reorder(EVTYPE, -CROPDMG), CROPDMG, group = EVTYPE, alpha = EVTYPE)) +
    geom_bar(stat = "identity", fill = "navyblue") +
    theme_minimal() +
    theme(
        axis.text.x = element_text(angle = 90),
        legend.position = "none"
          ) +
    labs(
        title = "Major Causes of Crop Damage",
        x = "",
        y = "Gross cost ($)"
    )
plot_grid(plot3, plot4)

plot5 <- ggplot(exp_propdmg_top10, aes(reorder(EVTYPE, -PROPDMGEXP), PROPDMGEXP, group = EVTYPE, alpha = EVTYPE)) +
    geom_bar(stat = "identity", fill = "firebrick") +
    theme_minimal() +
    theme(
        axis.text.x = element_text(angle = 90),
        legend.position = "none"
          ) +
    labs(
        title = "Expected Causes of Prop Damage",
        x = "",
        y = "Gross cost ($)"
    )

plot6 <- ggplot(exp_cropdmg_top10, aes(reorder(EVTYPE, -CROPDMGEXP), CROPDMGEXP, group = EVTYPE, alpha = EVTYPE)) +
    geom_bar(stat = "identity", fill = "slateblue3") +
    theme_minimal() +
    theme(
        axis.text.x = element_text(angle = 90),
        legend.position = "none"
          ) +
    labs(
        title = "Expected Causes of Crop Damage",
        x = "",
        y = "Gross cost ($)"
    )
plot_grid(plot5, plot6, align = "hv")

Results

The extreme events that took most lives and caused most injuries in the United States are tornado, heat, and flash flood with tornado being about 2/3 of the total reported cases.

The events that had the most economic damage in the US are hurricanes, tornadoes, flood and drought.

Name		Name	Last commit message	Last commit date
Latest commit History 16 Commits
report_files		report_files
.gitignore		.gitignore
README.md		README.md
report.Rmd		report.Rmd
report.html		report.html

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Weather Damage Report

Tools Used

Files

Build from Source

CODEBOOK

Data Processing

Setup

Loading libraries

Setting seed for reproducibility

Showing current environment

Downloading the dataset

Read dataset into variable removing blank spaces and transform into tidyverse compatible

Get a glimpse of the data

Count NAs

Select only needed data from the processed dataset

Impute 0 to NAs

Find unique values in prop and crop

Create dictionary for the unique values

Remap values back into the tidy data set using plyr

Questions

1. Across the United States, which types of events (as indicated in the EVTYPE variable are most harmful with respect to population health?

Rank fatalities and injuries according to EVTYPE

Plot results

2. Across the United States, which types of events have the greatest economic consequences?

Prepare vars for measured and expected damage

Plot the results

Results

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Weather Damage Report

Tools Used

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CODEBOOK

Data Processing

Setup

Loading libraries

Setting seed for reproducibility

Showing current environment

Downloading the dataset

Read dataset into variable removing blank spaces and transform into tidyverse compatible

Get a glimpse of the data

Count NAs

Select only needed data from the processed dataset

Impute 0 to NAs

Find unique values in prop and crop

Create dictionary for the unique values

Remap values back into the tidy data set using plyr

Questions

1. Across the United States, which types of events (as indicated in the EVTYPE variable are most harmful with respect to population health?

Rank fatalities and injuries according to EVTYPE

Plot results

2. Across the United States, which types of events have the greatest economic consequences?

Prepare vars for measured and expected damage

Plot the results

Results

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