library (readr)
library(knitr)
library(tidyverse)
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library (ggplot2)
library(dplyr)
library(hrbrthemes)
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##       Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and
##       if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow
library(plotly)
## 
## Attaching package: 'plotly'
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Data Pull

healthcare <- read.csv("Downloads/phe_healthcare.csv")


healthcare2 <- head(healthcare, 25)


options(knitr.kable.NA = "**")
knitr::kable(NA)
x
** 
result = FALSE
knitr::kable(healthcare2, align = "lccrr")
date area_name healthcare_type patients
2020-03-19 London Patients in mechanical ventilation beds **
2020-03-19 London Patients in Hospital **
2020-03-19 London Patients admitted to Hospital 240
2020-03-19 Rest of England Patients in mechanical ventilation beds **
2020-03-19 Rest of England Patients in Hospital **
2020-03-19 Rest of England Patients admitted to Hospital 346
2020-03-20 London Patients in mechanical ventilation beds **
2020-03-20 London Patients in Hospital 841
2020-03-20 London Patients admitted to Hospital 272
2020-03-20 Rest of England Patients in mechanical ventilation beds **
2020-03-20 Rest of England Patients in Hospital 739
2020-03-20 Rest of England Patients admitted to Hospital 419
2020-03-21 London Patients in mechanical ventilation beds **
2020-03-21 London Patients in Hospital 1081
2020-03-21 London Patients admitted to Hospital 311
2020-03-21 Rest of England Patients in mechanical ventilation beds **
2020-03-21 Rest of England Patients in Hospital 1071
2020-03-21 Rest of England Patients admitted to Hospital 466
2020-03-22 London Patients in mechanical ventilation beds **
2020-03-22 London Patients in Hospital 1266
2020-03-22 London Patients admitted to Hospital 335
2020-03-22 Rest of England Patients in mechanical ventilation beds **
2020-03-22 Rest of England Patients in Hospital 1404
2020-03-22 Rest of England Patients admitted to Hospital 524
2020-03-23 London Patients in mechanical ventilation beds **

Organized Table

This data set is located on the government website, London.gov.uk, and provides the user with count of patients with Covid-19, and is categorized by “healthcare type”, “currently hospitalized”, “on a ventilator”, or “admitted to hospital”. The data is also available by date, and filtered by location. Please see the link below for the full data set.

healthcare2 %>% 
  kable(booktabs = TRUE, col.names = c("Date", "Area Name","Healthcare Type","Patients"),caption = "Datatable for London Covid Cases - https://data.london.gov.uk/dataset/coronavirus--covid-19--cases")
Datatable for London Covid Cases - https://data.london.gov.uk/dataset/coronavirus--covid-19--cases
Date Area Name Healthcare Type Patients
2020-03-19 London Patients in mechanical ventilation beds **
2020-03-19 London Patients in Hospital **
2020-03-19 London Patients admitted to Hospital 240
2020-03-19 Rest of England Patients in mechanical ventilation beds **
2020-03-19 Rest of England Patients in Hospital **
2020-03-19 Rest of England Patients admitted to Hospital 346
2020-03-20 London Patients in mechanical ventilation beds **
2020-03-20 London Patients in Hospital 841
2020-03-20 London Patients admitted to Hospital 272
2020-03-20 Rest of England Patients in mechanical ventilation beds **
2020-03-20 Rest of England Patients in Hospital 739
2020-03-20 Rest of England Patients admitted to Hospital 419
2020-03-21 London Patients in mechanical ventilation beds **
2020-03-21 London Patients in Hospital 1081
2020-03-21 London Patients admitted to Hospital 311
2020-03-21 Rest of England Patients in mechanical ventilation beds **
2020-03-21 Rest of England Patients in Hospital 1071
2020-03-21 Rest of England Patients admitted to Hospital 466
2020-03-22 London Patients in mechanical ventilation beds **
2020-03-22 London Patients in Hospital 1266
2020-03-22 London Patients admitted to Hospital 335
2020-03-22 Rest of England Patients in mechanical ventilation beds **
2020-03-22 Rest of England Patients in Hospital 1404
2020-03-22 Rest of England Patients admitted to Hospital 524
2020-03-23 London Patients in mechanical ventilation beds ** 
healthcare3 <- head(healthcare, 35)

Associated Graphs

p <- healthcare %>%
  ggplot( aes(x=date, y=patients,fill = area_name)) + labs(title = "Interactive Graph For Patients By Area") +
    geom_area(fill="#69b3a2",alpha=0.5) +
    geom_line(color="#69b3a2") +
    ylab("patients") +
    theme_ipsum() 
ggplotly(p)
## Warning: Removed 34 rows containing missing values (position_stack).
p <- healthcare3 %>%
  ggplot( aes(x=date, y=patients,fill = area_name)) + labs(title = "'Zoomed In' Patients By Area") +
    geom_area(fill="#69b3a2",alpha=0.5) +
    geom_line(color="#69b3a2") +
    ylab("patients") +
    theme_ipsum() 
ggplotly(p)
## Warning: Removed 14 rows containing missing values (position_stack).
data <- ggplot(healthcare3,aes(x = date, y = patients, fill=area_name)) + labs(title = "Total Cases By Day") + geom_bar(stat = "identity", position = "dodge")
data
## Warning: Removed 14 rows containing missing values (geom_bar).

Its interesting to view the historical data during a time in which was unpredictable. As stated previously, within a matter of days, March 20th to 24th, cases have doubled, admitting more individuals in the hospital. Looking back on this sort of data, allows for an analyst to describe the events, given variables in the environment, which also allows for security and risk-protection measures for a potential event like this in the future. This of course may be for the sake of financials or health. Not one individual could predict the extent in which the pandemic affected many people, and the tolls it took on our economy. Having reliable and readily data and visualizations, will allow a decision maker to spot trends and hopefully prevent as much as possible in the next event.

The difficulties were limited when preparing the data. The author has done a fantastic job at classifying patient according to regions, and a specific healthcare type. The graph that allows you to interact by zooming in and out was sort of difficult, because it requires a new library and an unfamiliar set of tools to construct the graph.