Learning R Markdown
Janish Parikh
Introduction to R Markdown
R Markdown is a tool within RStudio that allows you to write documents, presentations, or webpages that combine written text with code. They are the like Jupyter Notebooks in Python.
- The text in the document can be fully formatted in a report style (e.g., with headers, bolded text, hyperlinks, etc.)
- Your code runs when the document is created
- You can choose to make your code visible or not
- Documents can be output as PDFs, Word Documents, HTML, and other formats
Why use R Markdown ?
- It helps you put your code and all the text/insights in place and saves you from the hassle of making a report.
- The strongest argument for using R Markdown is reproducibility; all of your analyses and text describing those analyses are in one place.
- With R Markdown, you don’t get a final report unless the code runs perfectly throughout the document.
- The document will serve as a record for how you arrived at the results you include in your papers
- You can pass on your code to readers in addition to the report content
- Dynamic documents can change as data are updated
- Documents can also be used for future data releases and/or different subsets of data
- There are dozens of different use cases listed in the R Markdown Definitive Guide
Getting Started
Steps to create a new R Markdown document:
Open RStudio
Select File > New File > R Markdown…
Enter Title, Author, and Output Format (this can be changed later)
Select OK
Let’s breakdown a R Markdown
There are four key ingredients to any R Markdown document:
- YAML Header
- Formatted Text
- Code Chunks
- Inline Text
YAML Header
- YAML stands for “Yet Another Markup Language”
- This is where you set options for your overall document
- You can find it at the top of any R Markdown
- YAML can also be used to store document metadata (author, date published, etc.)
- Through the YAML header you can set:
- font (size and style)
- default figure options (height, width, etc.)
- reference custom CSS (Cascading Style Sheets) Code
---
title: "Data101"
author: "Janish Parikh"
date: "Nov 29, 2021"
output: html_document
---Formatted Text
For any markdown syntax, there is equivalent HTML syntax. You can use either version in R Markdown documents that you intend to output to HTML. HTML syntax will not be interpreted correctly if you output to PDF.
| Format | Markdown | Formatted Text |
|---|---|---|
| italics | *italics* OR _italics_ |
italics |
| bold | **bold text** OR __bold text__ |
bold text |
| strikethrough | ~~strikethrough~~ |
|
| superscript | superscript^text^ |
superscripttext |
| subscript | subscript~text~ |
subscripttext |
| hyperlink | [Prof. Marian](https://amelie-marian.cs.rutgers.edu) |
Prof. Marian |
| highlight code | 'highlighted code' |
Note: ' is a backtick | highlighted code|
Section Headings
Header 1
Header 2
Header 3
Header 4
Lists
Unordered List
- Item
- Sub-item
- Item
- Item
Ordered List
- Item Number 1
- Item Number 2
- Sub-item
- Sub-item
- Item Number 3
Line Breaks
Simple line breaks can be somewhat confusing in R Markdown. In order to to create a line break, you have to end a line with two spaces.
If you want more than one line between paragraphs, you need to use
<br>which is HTML code for a manual line break.
Code Chunks
Code chunks are where the magic happens in R Markdown. Code chunks can contain any code that you would would use in R. The code you enter gets executed and the results are shown in the document.
The syntax for code chunks looks like this:
'''{r}
SOME CODE GOES HERE
'''By default, you will see the grey box with code highlighting. You will not see the code chunk options. You can run code chunks individually within R Studio or execute the code line-by-line as you would in a normal R Script.
Points to Note
- Code chunks are run in order so you need to keep straight what variables need to be created in which order. This seems obvious, but can be problematic if you run code line-by-line and have variables loaded in the global environment.
- You can run the code chunks individually.
Tables
We can use the code chunks to print tables
head(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosaWe can also output more nicely formatted tables using knitr.
knitr::kable(iris[1:5,],caption = "Iris Dataset")| Sepal.Length | Sepal.Width | Petal.Length | Petal.Width | Species |
|---|---|---|---|---|
| 5.1 | 3.5 | 1.4 | 0.2 | setosa |
| 4.9 | 3.0 | 1.4 | 0.2 | setosa |
| 4.7 | 3.2 | 1.3 | 0.2 | setosa |
| 4.6 | 3.1 | 1.5 | 0.2 | setosa |
| 5.0 | 3.6 | 1.4 | 0.2 | setosa |
Plots and Figures
if (!require("pacman")) install.packages("pacman")
pacman::p_load(ggplot2)We can also use code chunks to print figures
gp2 <- ggplot(data = mtcars,
aes(x = mpg, fill= as.factor(cyl)))+
geom_histogram(
aes(y=..density..),
binwidth = 1) +
geom_density(alpha=.2) #Add density curve
gp2<-gp2+ scale_fill_discrete(name = "No. of Cylinders")+ theme_minimal()
gp2
You can refer ggplot further here
Code Chunk Arguments
Code chunks accept optional arguments
'''{r name, eval=FALSE, warning=FALSE, message=FALSE, fig.height=6, fig.width=4, dpi=300, fig.align='center'}
# some r code
'''- name - This is not mandatory, however a good practice to label your code chunks. Two figures cannot have the same name
- echo - Whether to display the code chunk or just show the results. If you want the code embedded in your document but don’t want the reader of the document to see it, you can set echo=FALSE (Default:
echo=True) - eval - Whether to run the code in the code chunk. ‘eval=FALSE’ can be used if you want to display the code but not have it run (Default:
eval=TRUE) - warning - Whether to display warning messages in the document (Default:
warning=TRUE) - message - Whether to display code messages in the document (Default:
message=TRUE) - results - Whether and how to display the computation of the results
- cache - Saves output of the code chunk for use when you run the R Markdown file the next time (within the same session). This is useful if you have a code chunk that takes a long time to run. (Default:
cache=FALSE) - fig.height, fig.width - Specify the height and width of the figure to make it fit into the space you have available.
- dpi - Specifies the pixels per inch. This effectively controls the size of the objects (text, lines, etc.) in your figure. Too low and your figure can look pixelated.
- fig.align - Specify whether your figure appears right, left, or center aligned.
- fig.cap - Enter text that will be included underneath your plot as a caption.
Here is a code chunk with warning = TRUE and message = TRUE (i.e., the defaults for code chunks):
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionThese are things you don’t want in your report, but luckily you can turn them off.
And with warning = FALSE and message = FALSE:
library(dplyr)Inline Code
Inline code allows you to create dynamic fills in your documents that update as the data are updated. If you have values that could potentially change when you add new data or make changes, you should consider inline code. Some examples:
- Numeric values or percentages
- The outcome of a statistical test (e.g., F-value, Degrees of Freedom, p-value)
- Text references to numeric values (e.g., whether a value increased, decreased, or stayed the same).
- Time-related variables (e.g., month that the report refers to)
The syntax for inline code starts with a 'r and ends with ' (backticks).
Some text 'r CODE GOES HERE' some more text.Let’s say, for example, you want to write a sentence that updates when new data are collected each month. You first provide the data:
lastmon <- 4.7
lmon <- "May"
thismon <- 4.9
tmon <- "June"And then type the inline code:
- The unemployment rate in ‘r tmon’ was ‘r paste0(thismon, “%”)’, ‘r ifelse(thismon < lastmon, paste0(" down “,paste0(abs(thismon-lastmon),”% from “)) ,ifelse(thismon > lastmon, paste0(” up “,paste0(abs(thismon-lastmon),”% from “)),” unchanged from "))’ ‘r paste0(lastmon, “%”)’ in ‘r lmon’
And it would look like this:
- The unemployment rate in June was 4.9%, up 0.2% from 4.7% in May.
How to Add Images?
 
If you get an error like: File ~/Downloads/test_image.jpeg not found in resource path Error: pandoc document conversion failed with error 99 Execution halted
If the image can be moved, put it in the same directory as your .Rmd file. If you can’t move it to the file directory and if you don’t speak UNIX, you can get the path by:
- Finding the file with the finder
- Right click on it hold the option key on your keyboard and you will see the menu change from Copy “HW.jpg” to say Copy “HW.jpeg” as pathname
- Paste that path into your code.
Let’s look at an example R Markdown
Exploring the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database - Health and Economic Impacts
Synopsis
Storms and other severe weather events can cause both public health and economic problems for communities and municipalities. Many severe events can result in fatalities, injuries, and property damage, and preventing such outcomes to the extent possible is a key concern.
This project involves exploring the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. This database tracks characteristics of major storms and weather events in the United States, including when and where they occur, as well as estimates of any fatalities, injuries, and property damage.
The analysis of the data shows that tornadoes, by far, have the greatest health impact as measured by the number of injuries and fatalities The analysis also shows that floods cause the greatest economic impact as measured by property damage and crop damage.
Loading the packages and prepare the R environment
I used the following libraries in my analysis
library('dplyr')
library('ggplot2')Data Description
The data for this assignment come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size. You can download the file from the course web site: Storm Data
There is also some documentation of the database available. Here you will find how some of the variables are constructed/defined.
National Weather Service Storm Data Documentation
The events in the database start in the year 1950 and end in November 2011. In the earlier years of the database there are generally fewer events recorded, most likely due to a lack of good records. More recent years should be considered more complete.
Goal
The basic goal of this analysis is to explore the NOAA Storm Database and answer the following basic questions about severe weather events.
- Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
- Across the United States, which types of events have the greatest economic consequences?
Loading the Data
The data was downloaded from the link above and saved on local computer (in setwd command one can replace local file path with path of folder where the data was downloaded). Then it was loaded on the R using the read.csv command.
storm_data<-as.data.frame(read.csv("~/Downloads/repdata-data-StormData.bz2", header = T,comment.char = ""))Examining the Storm Data Loaded
dim(storm_data)## [1] 902297 37summary(storm_data)## STATE__ BGN_DATE BGN_TIME TIME_ZONE
## Min. : 1.0 Length:902297 Length:902297 Length:902297
## 1st Qu.:19.0 Class :character Class :character Class :character
## Median :30.0 Mode :character Mode :character Mode :character
## Mean :31.2
## 3rd Qu.:45.0
## Max. :95.0
##
## COUNTY COUNTYNAME STATE EVTYPE
## Min. : 0.0 Length:902297 Length:902297 Length:902297
## 1st Qu.: 31.0 Class :character Class :character Class :character
## Median : 75.0 Mode :character Mode :character Mode :character
## Mean :100.6
## 3rd Qu.:131.0
## Max. :873.0
##
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE
## Min. : 0.000 Length:902297 Length:902297 Length:902297
## 1st Qu.: 0.000 Class :character Class :character Class :character
## Median : 0.000 Mode :character Mode :character Mode :character
## Mean : 1.484
## 3rd Qu.: 1.000
## Max. :3749.000
##
## END_TIME COUNTY_END COUNTYENDN END_RANGE
## Length:902297 Min. :0 Mode:logical Min. : 0.0000
## Class :character 1st Qu.:0 NA's:902297 1st Qu.: 0.0000
## Mode :character Median :0 Median : 0.0000
## Mean :0 Mean : 0.9862
## 3rd Qu.:0 3rd Qu.: 0.0000
## Max. :0 Max. :925.0000
##
## END_AZI END_LOCATI LENGTH WIDTH
## Length:902297 Length:902297 Min. : 0.0000 Min. : 0.000
## Class :character Class :character 1st Qu.: 0.0000 1st Qu.: 0.000
## Mode :character Mode :character Median : 0.0000 Median : 0.000
## Mean : 0.2301 Mean : 7.503
## 3rd Qu.: 0.0000 3rd Qu.: 0.000
## Max. :2315.0000 Max. :4400.000
##
## F MAG FATALITIES INJURIES
## Min. :0.0 Min. : 0.0 Min. : 0.0000 Min. : 0.0000
## 1st Qu.:0.0 1st Qu.: 0.0 1st Qu.: 0.0000 1st Qu.: 0.0000
## Median :1.0 Median : 50.0 Median : 0.0000 Median : 0.0000
## Mean :0.9 Mean : 46.9 Mean : 0.0168 Mean : 0.1557
## 3rd Qu.:1.0 3rd Qu.: 75.0 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. :5.0 Max. :22000.0 Max. :583.0000 Max. :1700.0000
## NA's :843563
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## Min. : 0.00 Length:902297 Min. : 0.000 Length:902297
## 1st Qu.: 0.00 Class :character 1st Qu.: 0.000 Class :character
## Median : 0.00 Mode :character Median : 0.000 Mode :character
## Mean : 12.06 Mean : 1.527
## 3rd Qu.: 0.50 3rd Qu.: 0.000
## Max. :5000.00 Max. :990.000
##
## WFO STATEOFFIC ZONENAMES LATITUDE
## Length:902297 Length:902297 Length:902297 Min. : 0
## Class :character Class :character Class :character 1st Qu.:2802
## Mode :character Mode :character Mode :character Median :3540
## Mean :2875
## 3rd Qu.:4019
## Max. :9706
## NA's :47
## LONGITUDE LATITUDE_E LONGITUDE_ REMARKS
## Min. :-14451 Min. : 0 Min. :-14455 Length:902297
## 1st Qu.: 7247 1st Qu.: 0 1st Qu.: 0 Class :character
## Median : 8707 Median : 0 Median : 0 Mode :character
## Mean : 6940 Mean :1452 Mean : 3509
## 3rd Qu.: 9605 3rd Qu.:3549 3rd Qu.: 8735
## Max. : 17124 Max. :9706 Max. :106220
## NA's :40
## REFNUM
## Min. : 1
## 1st Qu.:225575
## Median :451149
## Mean :451149
## 3rd Qu.:676723
## Max. :902297
## Extracting variables of interest for analysis of weather impact on health and economy
From a list of variables in storm_data, these are columns of interest:
Health variables:
- FATALITIES: approx. number of mortalities
- INJURIES: approx. number of injuries
Economic variables:
-PROPDMG: approx. property damage -PROPDMGEXP: the units for property damage value -CROPDMG: approx. crop damages -CROPDMGEXP: the units for crop damage value
Events - target variable:
EVTYPE: weather event (Tornados, Wind, Snow, Flood, etc..)
Extract variables of interest from original data set:
target_variables<-c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")
target_data<-storm_data[,target_variables]
#Renaming the columns of target_data for better understanding and readibility
colnames(target_data)<-c("event_type","fatalities","injuries","property_damage","property_damage_exponent","crop_damage","crop_damage_exponent")summary(target_data)## event_type fatalities injuries property_damage
## Length:902297 Min. : 0.0000 Min. : 0.0000 Min. : 0.00
## Class :character 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.00
## Mode :character Median : 0.0000 Median : 0.0000 Median : 0.00
## Mean : 0.0168 Mean : 0.1557 Mean : 12.06
## 3rd Qu.: 0.0000 3rd Qu.: 0.0000 3rd Qu.: 0.50
## Max. :583.0000 Max. :1700.0000 Max. :5000.00
## property_damage_exponent crop_damage crop_damage_exponent
## Length:902297 Min. : 0.000 Length:902297
## Class :character 1st Qu.: 0.000 Class :character
## Mode :character Median : 0.000 Mode :character
## Mean : 1.527
## 3rd Qu.: 0.000
## Max. :990.000Checking if any of the target variables has any NA or NAN values:
for (i in 1:7){
print(paste("No of NA/NAN Values in", names(target_data)[i], "are", sum(is.na(target_data[,i])) ))
}## [1] "No of NA/NAN Values in event_type are 0"
## [1] "No of NA/NAN Values in fatalities are 0"
## [1] "No of NA/NAN Values in injuries are 0"
## [1] "No of NA/NAN Values in property_damage are 0"
## [1] "No of NA/NAN Values in property_damage_exponent are 0"
## [1] "No of NA/NAN Values in crop_damage are 0"
## [1] "No of NA/NAN Values in crop_damage_exponent are 0"Transforming Extracted Variables
sort(table(target_data$event_type),decreasing = T)[1:10]##
## HAIL TSTM WIND THUNDERSTORM WIND TORNADO
## 288661 219940 82563 60652
## FLASH FLOOD FLOOD THUNDERSTORM WINDS HIGH WIND
## 54277 25326 20843 20212
## LIGHTNING HEAVY SNOW
## 15754 15708The Event Type can be grouped into few broad categories
There are many cases where event type are just Upper and Lower Cases of word like Winter, WINTER, winter. We will group events like TUNDERSTORM WIND, TUNDERSTORM WINDS, HIGH WIND, etc. by containing the keyword ‘WIND’ as one event WIND. We will transform other types of events in a similar way. New variable event_category is the transform variable of event_type that have 10 different types of events: HEAT, FLOOD, etc., and type MISC for events in which name the keyword is not found.
target_data$event_category<-"MISC"
target_data$event_category[grep("HAIL", target_data$event_type, ignore.case = T )]<-"hail"
target_data$event_category[grep("STORM", target_data$event_type, ignore.case = T )]<-"storm"
target_data$event_category[grep("THUNDERSTORM", target_data$event_type, ignore.case = T )]<-"thunderstorm"
target_data$event_category[grep("HEAT", target_data$event_type, ignore.case = T )]<-"heat"
target_data$event_category[grep("drought", target_data$event_type, ignore.case = T )]<-"drought"
target_data$event_category[grep("TORNADO", target_data$event_type, ignore.case = T )]<-"tornado"
target_data$event_category[grep("FlOOD", target_data$event_type, ignore.case = T )]<-"flood"
target_data$event_category[grep("FLD", target_data$event_type, ignore.case = T )]<-"flood" #floods in streams, urban area
target_data$event_category[grep("WIND", target_data$event_type, ignore.case = T )]<-"wind"
target_data$event_category[grep("SNOW", target_data$event_type, ignore.case = T )]<-"snow"
target_data$event_category[grep("WINTER", target_data$event_type, ignore.case = T )]<-"winter"
target_data$event_category[grep("FREEZE", target_data$event_type, ignore.case = T )]<-"winter"
target_data$event_category[grep("ICE", target_data$event_type, ignore.case = T )]<-"ice"
target_data$event_category[grep("RAIN", target_data$event_type, ignore.case = T )]<-"rain"
target_data$event_category[grep("WILDFIRE", target_data$event_type, ignore.case = T )]<-"wildfire"
target_data$event_category[grep("FIRE", target_data$event_type, ignore.case = T )]<-"fire"
target_data$event_category[grep("THUNDERSTORM", target_data$event_type, ignore.case = T )]<-"thunderstorm"
target_data$event_category[grep("LIGHTNING", target_data$event_type, ignore.case = T )]<-"lightning"
target_data$event_category[grep("FIRE", target_data$event_type, ignore.case = T )]<-"fire"
target_data$event_category[grep("BLIZZARD", target_data$event_type, ignore.case = T )]<-"blizzard"
target_data$event_category[grep("FUNNEL", target_data$event_type, ignore.case = T)] <-"tornado" #funnel clouds lead to tornadoes on land
target_data$event_category[grep("WATERSPOUT", target_data$event_type, ignore.case = T )]<-"tornado" #Basically they are tornadoes on water
table(target_data$event_category)##
## blizzard drought fire flood hail heat
## 2745 2505 4240 86071 289269 2631
## ice lightning MISC rain snow storm
## 2194 15775 7996 12235 17572 1563
## thunderstorm tornado wind winter
## 109558 71536 255361 21046Checking the values for variables that represent units of dollars:
table(target_data$property_damage_exponent)##
## - ? + 0 1 2 3 4 5 6
## 465934 1 8 5 216 25 13 4 4 28 4
## 7 8 B h H K m M
## 5 1 40 1 6 424665 7 11330table(target_data$crop_damage_exponent)##
## ? 0 2 B k K m M
## 618413 7 19 1 9 21 281832 1 1994We can see there are incorrect units: 1. - k/K stand for thousands dollars (10^3) 2. - m/M stand for Million dollars (10^6) 3. - b/B stand for Billion dolllars (10^9) 4. - All the rest would be considered as dollars including NA’s
target_data$property_damage_exponent <- toupper(target_data$property_damage_exponent)
target_data$crop_damage_exponent <- toupper(target_data$crop_damage_exponent)
target_data$crop_damage_factor[(target_data$crop_damage_exponent == "")] <- 10^0
target_data$crop_damage_factor[(target_data$crop_damage_exponent == "?")] <- 10^0
target_data$crop_damage_factor[(target_data$crop_damage_exponent == "0")] <- 10^0
target_data$crop_damage_factor[(target_data$crop_damage_exponent == "2")] <- 10^2
target_data$crop_damage_factor[(target_data$crop_damage_exponent == "K")] <- 10^3
target_data$crop_damage_factor[(target_data$crop_damage_exponent == "M")] <- 10^6
target_data$crop_damage_factor[(target_data$crop_damage_exponent == "B")] <- 10^9
target_data$total_crop_damage<- target_data$crop_damage * target_data$crop_damage_factor
target_data$prop_damage_factor[(target_data$property_damage_exponent == "")] <- 10^0
target_data$prop_damage_factor[(target_data$property_damage_exponent == "-")] <- 10^0
target_data$prop_damage_factor[(target_data$property_damage_exponent == "?")] <- 10^0
target_data$prop_damage_factor[(target_data$property_damage_exponent == "+")] <- 10^0
target_data$prop_damage_factor[(target_data$property_damage_exponent == "0")] <- 10^0
target_data$prop_damage_factor[(target_data$property_damage_exponent == "1")] <- 10^1
target_data$prop_damage_factor[(target_data$property_damage_exponent == "2")] <- 10^2
target_data$prop_damage_factor[(target_data$property_damage_exponent == "3")] <- 10^3
target_data$prop_damage_factor[(target_data$property_damage_exponent == "4")] <- 10^4
target_data$prop_damage_factor[(target_data$property_damage_exponent == "5")] <- 10^5
target_data$prop_damage_factor[(target_data$property_damage_exponent == "6")] <- 10^6
target_data$prop_damage_factor[(target_data$property_damage_exponent == "7")] <- 10^7
target_data$prop_damage_factor[(target_data$property_damage_exponent == "8")] <- 10^8
target_data$prop_damage_factor[(target_data$property_damage_exponent == "H")] <- 10^2
target_data$prop_damage_factor[(target_data$property_damage_exponent == "K")] <- 10^3
target_data$prop_damage_factor[(target_data$property_damage_exponent == "M")] <- 10^6
target_data$prop_damage_factor[(target_data$property_damage_exponent == "B")] <- 10^9
target_data$total_property_damage<- target_data$property_damage * target_data$prop_damage_factor
target_data[which.max(target_data$total_property_damage),]## event_type fatalities injuries property_damage property_damage_exponent
## 605953 FLOOD 0 0 115 B
## crop_damage crop_damage_exponent event_category crop_damage_factor
## 605953 32.5 M flood 1e+06
## total_crop_damage prop_damage_factor total_property_damage
## 605953 32500000 1e+09 1.15e+11target_data[which.max(target_data$total_crop_damage),]## event_type fatalities injuries property_damage property_damage_exponent
## 198389 RIVER FLOOD 0 0 5 B
## crop_damage crop_damage_exponent event_category crop_damage_factor
## 198389 5 B flood 1e+09
## total_crop_damage prop_damage_factor total_property_damage
## 198389 5e+09 1e+09 5e+09Finding out the event that caused maximum loss to property and crop We can see that both the events belong to the category ‘flood’
target_data[which.max(target_data$total_property_damage),c(1,4,5,8,12)]## event_type property_damage property_damage_exponent event_category
## 605953 FLOOD 115 B flood
## total_property_damage
## 605953 1.15e+11target_data[which.max(target_data$total_crop_damage),c(1,7,8,9,10)]## event_type crop_damage_exponent event_category crop_damage_factor
## 198389 RIVER FLOOD B flood 1e+09
## total_crop_damage
## 198389 5e+09Analysis
Aggregating events for economic variables
#We have the property damage and crop damage caused by individual event.
#Now we take a sum of the damages grouped by the event category
# Aggregate property damage by event_category
property_damage_byevents <- ddply(target_data, .(event_category), summarize, prop_damage = sum(total_property_damage, na.rm = TRUE))
property_damage_byevents$id1 <- "property"
crop_damage_byevents <- ddply(target_data, .(event_category), summarize, crop_damage = sum(total_crop_damage, na.rm = TRUE))
crop_damage_byevents$id2 <- "crop"
economy_loss<- join(property_damage_byevents, crop_damage_byevents, by ="event_category", type ='inner')
economy_loss$total_damage<-economy_loss$prop_damage+economy_loss$crop_damage
economy_loss## event_category prop_damage id1 crop_damage id2 total_damage
## 1 blizzard 664913950 property 112060000 crop 776973950
## 2 drought 1046306000 property 13972621780 crop 15018927780
## 3 fire 8501628500 property 403281630 crop 8904910130
## 4 flood 168237211482 property 12270394200 crop 180507605682
## 5 hail 15736043513 property 3046837473 crop 18782880986
## 6 heat 20125750 property 904413500 crop 924539250
## 7 ice 3974061410 property 5027114300 crop 9001175710
## 8 lightning 935727930 property 12097090 crop 947825020
## 9 MISC 86010643990 property 7191331250 crop 93201975240
## 10 rain 3265197192 property 919315800 crop 4184512992
## 11 snow 1012999700 property 134683100 crop 1147682800
## 12 storm 55925713550 property 698851000 crop 56624564550
## 13 thunderstorm 6641014705 property 653000388 crop 7294015093
## 14 tornado 57013082226 property 414961520 crop 57428043746
## 15 wind 12444218618 property 1406724150 crop 13850942768
## 16 winter 6795980251 property 1936505000 crop 8732485251Aggregating events for public health variables
#We have the number of fatalities and injuries caused by individual event.
#Now we take a sum of the life damages grouped by the event category
# Aggregate property damage by event_category
fatalities_byevents <- ddply(target_data, .(event_category), summarize, total_deaths = sum(fatalities, na.rm = TRUE))
property_damage_byevents$id <- "property"
injuries_byevents <- ddply(target_data, .(event_category), summarize, total_injuries = sum(injuries, na.rm = TRUE))
crop_damage_byevents$id <- "crop"
public_health_loss<- join(fatalities_byevents, injuries_byevents, by ="event_category", type ='inner')
public_health_loss$total_damage<-public_health_loss$total_deaths+public_health_loss$total_injuries
public_health_loss## event_category total_deaths total_injuries total_damage
## 1 blizzard 101 806 907
## 2 drought 6 19 25
## 3 fire 90 1608 1698
## 4 flood 1552 8681 10233
## 5 hail 15 1371 1386
## 6 heat 3132 9209 12341
## 7 ice 102 2166 2268
## 8 lightning 817 5232 6049
## 9 MISC 1578 4303 5881
## 10 rain 114 305 419
## 11 snow 159 1151 1310
## 12 storm 116 868 984
## 13 thunderstorm 210 2478 2688
## 14 tornado 5639 91439 97078
## 15 wind 1235 9001 10236
## 16 winter 279 1891 2170Finding out the event that caused maximum loss to public health We can see that Tornado on land are responsible for maximum no of loss to human health Whether we consider only no of death or injuries, tornadoes prove to be the most deadly
public_health_loss[which.max(public_health_loss$total_injuries),]## event_category total_deaths total_injuries total_damage
## 14 tornado 5639 91439 97078public_health_loss[which.max(public_health_loss$total_deaths),]## event_category total_deaths total_injuries total_damage
## 14 tornado 5639 91439 97078public_health_loss[which.max(public_health_loss$total_injuries),]## event_category total_deaths total_injuries total_damage
## 14 tornado 5639 91439 97078Results
Across the United States, which types of events have the greatest economic consequences?
economy_loss$event_catogery<-as.factor(economy_loss$event_category)
ggplot() + geom_bar(data = economy_loss,
aes(x = event_catogery, y = total_damage,
fill = interaction(total_damage, event_category)),
stat = "identity", show.legend = F) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab("Event Type") + ylab("Total Damage to the Economy")
Across the United States, which types of events are most harmful with respect to population health?
economy_loss$event_category<-as.factor(economy_loss$event_category)
ggplot() + geom_bar(data = public_health_loss,
aes(x = event_category, y = total_damage,
fill = interaction(total_damage, event_category)),
stat = "identity", show.legend = F) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab("Event Type") + ylab("Total Damage to Population Health")
Finishing Up
- You can “knit” the document to some format.
- HTML is available right away.
- In order to knit to PDF or Word you need to install a LaTeX package.
Additional Resources
These websites can help fill in some of the gaps left by this document.