DEM 7273 - Example 1 - Introduction to R
Ying Huang
August 26, 2020
## Warning: package 'kableExtra' was built under R version 4.0.2## Warning: package 'dplyr' was built under R version 4.0.2Installation
Mac users
To Install R
- Open an internet browser and go to www.r-project.org.
- Click the “download R” link in the middle of the page under “Getting Started.”
- Select a CRAN location (a mirror site) and click the corresponding link.
- Click on the “Download R for (Mac) OS X” link at the top of the page.
- Click on the file containing the latest version of R under “Files.”
- Save the .pkg file, double-click it to open, and follow the installation instructions.
- Now that R is installed, you need to download and install RStudio.
To Install RStudio
- Go to www.rstudio.com and click on the “Download RStudio” button.
- Click on “Download RStudio Desktop.”
- Click on the version recommended for your system, or the latest Mac version, save the .dmg file on your computer, double-click it to open, and then drag and drop it to your applications folder.
Windows Users
To Install R:
Open an internet browser and go to www.r-project.org.
Click the “download R” link in the middle of the page under “Getting Started.”
Select a CRAN location (a mirror site) and click the corresponding link.
Click on the “Download R for Windows” link at the top of the page.
Click on the “install R for the first time” link at the top of the page.
Click “Download R for Windows” and save the executable file somewhere on your computer. Run the .exe file and follow the installation instructions.
Now that R is installed, you need to download and install RStudio.
To Install RStudio
Go to www.rstudio.com and click on the “Download RStudio” button.
Click on “Download RStudio Desktop.”
Click on the version recommended for your system, or the latest Windows version, and save the executable file. Run the .exe file and follow the installation instructions.
Rstudio Introduction
R file types
.R files R uses a basic script file with the .R extension. This type of file is useful if you’re going to write a function or do some analysis and don’t want to have formatted output or text.
.Rmd files Rstudio uses a form of the markdown formatting language, called Rmarkdown, for creating formatted documents that include code chunks, tables, figures and statistical output. This entire example is written in Rmarkdown!
Rmarkdown is nice for lots of reasons, such as the ability to insert latex equations into documents
\[y_i \sim Normal (x` \beta, \sigma_2)\] or to include output tables directly into a document:
## Warning: package 'broom' was built under R version 4.0.2## Warning: package 'pander' was built under R version 4.0.2Quitting from lines 81-88 (REX1_Rintro.Rmd) Error: No tidy method for objects of class summary.lm
without having to make tables in Word or some other program. You can basically do your entire analysis and slideshow or paper write up, including bibliography in Rstudio.
R basics
Libraries and packages
R uses libraries to do different types of analysis, so we will need to install lots of different libraries to do different things. These need to be downloaded from the internet, using the install.packages() command. You only need to install a package once. E.g.
install.packages("car") will install the lme4 library. To use the functions within it, type
library(car)
Now you have access to those functions.
I strongly recommend you install several packages prior to us beginning. Dr. Corey Sparks has written a short script on Github you can use it by running:
source("https://raw.githubusercontent.com/coreysparks/Rcode/master/install_first_short.R")This will install a few dozen R packages that are commonly used for social science analysis.
Below we will go through a simple R session where we introduce some concepts that are important for R.
Basic R functions
1) Set the working directory
#setwd("~")
getwd() # Shows the working directory (wd)## [1] "/Users/samo/Downloads"### Select the working directory interactively (SESSION)2) R is a calculator
#addition and subtraction
3+7
3-7
#multiplication and division
3*7
3/7
#powers
3^2
3^3
#functions
log(3/7)
exp(3/7)
sin(3/7)
3) Variables and objects
In R we assign values to objects (object-oriented programming). These can generally have any name, but some names are reserved for R. For instance you probably wouldn’t want to call something ‘mean’ because there’s a ‘mean()’ function already in R. For instance:
x<-(-3)
y<-7
x+y## [1] 4x*y## [1] -21log(x*y)## Warning in log(x * y): NaNs produced## [1] NaN4) Vectors
R thinks everything is a matrix, or a vector, meaning a row or column of numbers, or characters. One of R’s big selling points is that much of it is completely vectorized. Meaning, I can apply an operation along all elements of a vector without having to write a loop. For example, if I want to multiply a vector of numbers by a constant. I can just do:
x<-c(3, 4, 5, 6, 7)
#c() makes a vector
y<-7
x*y## [1] 21 28 35 42 49R is also very good about using vectors, let’s say I wanted to find the third element of x:
x[3]## [1] 5#or if I want to test if this element is 10
x[3]==10## [1] FALSEx[3]!=10## [1] TRUE#of is it larger than another number:
x[3]>3## [1] TRUE#or is any element of the whole vector greater than 3
x>3## [1] FALSE TRUE TRUE TRUE TRUEIf you want to see what’s in an object, use str(), for structure
str(x)## num [1:5] 3 4 5 6 7and we see that x is numeric, and has those values.
We can also see different characteristics of x
#how long is x?
length(x)## [1] 5#is x numeric?
is.numeric(x)## [1] TRUEis.character(x)## [1] FALSE#is any element of x missing?
is.na(x)## [1] FALSE FALSE FALSE FALSE FALSE#now i'll modify x
x<-c(x, NA) #combine x and a missing value ==NA
x## [1] 3 4 5 6 7 NAprint(x)## [1] 3 4 5 6 7 NAis.na(x)## [1] FALSE FALSE FALSE FALSE FALSE TRUENote: NA stands for Not Available, which R uses to represent missing values.
5) Replacing elements of vectors
Above, we had a missing value in X, let’s say we want to replace it with another value:
x<-ifelse(test = is.na(x)==T, yes = sqrt(7.2), no = x)
x## [1] 3.000000 4.000000 5.000000 6.000000 7.000000 2.683282Exercise
Let’s create a character variable. Using R jargon, we would say we are going to create a character vector, or a vector whose mode is character. These are the genders of our hypothetical students:
gender<-c("f","f","f", NA, "m","m","m","m")
gender## [1] "f" "f" "f" NA "m" "m" "m" "m"print(gender)## [1] "f" "f" "f" NA "m" "m" "m" "m"Data frames
Traditionally, R organizes variables into data frames, these are like a spreadsheet. In R terminology, the columns are called vectors, variables, or just columns. R calls the rows observations, cases, or just rows. The columns can have names, and the dataframe itself can have data of different types. Here we make a short data frame with three columns, two numeric and one character:
mydat<- data.frame(
x=c(1,2,3,4,5),
y=c(10, 20, 35, 57, 37),
group=c("A", "A" ,"A", "B", "B")
)
#See the size of the dataframe
dim(mydat)## [1] 5 3length(mydat$x)## [1] 5#Open the dataframe in a viewer and just print it
#View(mydat)
print(mydat)## x y group
## 1 1 10 A
## 2 2 20 A
## 3 3 35 A
## 4 4 57 B
## 5 5 37 BReal data
note, please make a folder on your computer so you can store things for this class in a single location!!!! Organization is Key to Success in Graduate School
install.packages(“foreign”) # need to install package –foreign– first. (You do this only once) *The haven orreadr library can read files from other statistical packages easily, so if you have data in Stata, SAS or SPSS (barf!), you can read it into R using those functions, for example, the read_dta() function reads stata files.
Loading a Stata data file
#library(haven)
#bexar_covid <- read_dta("users/pnl830/Dropbox/covid/bexar-covid.dta")
#View(bexar_covid)Don’t know what a function’s called use ??
??stata ??csv
and Rstudio will show you a list of functions that have these strings in them.
Extracting data directly from packages
#install.packages("covid19.analytics")
library(covid19.analytics)## Warning: package 'covid19.analytics' was built under R version 4.0.2#covid19.genomic.data()
data<-covid19.data("ts-confirmed")## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv## Data retrieved on 2020-09-02 21:05:47 || Range of dates on data: 2020-01-22--2020-09-01 | Nbr of records: 266## -------------------------------------------------------------------------------- # specify a location
report.summary(geo.loc="NorthAmerica")## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv## Data retrieved on 2020-09-02 21:05:48 || Range of dates on data: 2020-01-22--2020-09-01 | Nbr of records: 266## --------------------------------------------------------------------------------## >>> checking data integrity...## No critical issues have been found.## >>> checking data consistency...## Warning in consistency.check(data, n0, nf, datasetName, details): Inconsistency
## of type.II in ts-confirmed data detected -- 47 records (out of 266) show
## inconsistencies in the data...## [1] "NORTHAMERICA"
## [1] "CANADA" "US" "MEXICO"## Warning in if (!(toupper(geo.ind) %in% provinces.states) & !(toupper(geo.ind)
## %in% : the condition has length > 1 and only the first element will be used## ################################################################################
## ##### TS-CONFIRMED Cases -- Data dated: 2020-09-01 :: 2020-09-02 21:05:48
## ################################################################################
## Number of Countries/Regions reported: 3
## Number of Cities/Provinces reported: 15
## Unique number of distinct geographical locations combined: 16
## --------------------------------------------------------------------------------
## For selected locations ts-confirmed Totals: 6811298
## --------------------------------------------------------------------------------
## Country.Region Province.State Totals RelPerc GlobalPerc LastDayChange t-2 t-3 t-7 t-14 t-30
## 1 US 6073840 89.17 23.59 43253 34156 35337 44109 46436 45368
## 2 Mexico 606036 8.90 2.35 6476 3719 4129 5267 5792 4767
## 3 Canada Quebec 62614 0.92 0.24 122 140 120 142 64 123
## 4 Canada Ontario 44418 0.65 0.17 139 136 98 117 90 58
## 5 Canada Alberta 14066 0.21 0.05 164 426 0 127 82 0
## 6 Canada British Columbia 5848 0.09 0.02 58 294 0 62 68 0
## 7 Canada Saskatchewan 1622 0.02 0.01 3 4 0 3 4 17
## 8 Canada Manitoba 1232 0.02 0.00 18 28 31 25 15 7
## 9 Canada Nova Scotia 1085 0.02 0.00 0 2 0 1 1 0
## 10 Canada Newfoundland and Labrador 269 0.00 0.00 0 0 0 0 0 0
## --------------------------------------------------------------------------------
## Global Perc. Average: 1.65 (sd: 5.88)
## Global Perc. Average in top 10 : 2.64 (sd: 7.4)
## --------------------------------------------------------------------------------
## ================================================================================## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv## Data retrieved on 2020-09-02 21:05:49 || Range of dates on data: 2020-01-22--2020-09-01 | Nbr of records: 266## --------------------------------------------------------------------------------## >>> checking data integrity...## No critical issues have been found.## >>> checking data consistency...## Warning in consistency.check(data, n0, nf, datasetName, details): Inconsistency
## of type.II in ts-deaths data detected -- 36 records (out of 266) show
## inconsistencies in the data...
## [1] "CANADA"
## [1] "US"
## [1] "MEXICO"
## ################################################################################
## ##### TS-DEATHS Cases -- Data dated: 2020-09-01 :: 2020-09-02 21:05:50
## ################################################################################
## Number of Countries/Regions reported: 3
## Number of Cities/Provinces reported: 15
## Unique number of distinct geographical locations combined: 16
## --------------------------------------------------------------------------------
## For selected locations ts-deaths Totals: 259084
## --------------------------------------------------------------------------------
## Country.Region Province.State Totals Perc LastDayChange t-2 t-3 t-7 t-14 t-30
## 1 US 184664 3.04 1067 573 310 1225 1353 530
## 2 Mexico 65241 10.77 827 256 339 626 707 266
## 3 Canada Quebec 5762 9.20 2 2 3 1 2 2
## 4 Canada Ontario 2858 6.43 1 1 0 3 0 3
## 5 Canada Alberta 241 1.71 2 2 0 0 2 0
## 6 Canada British Columbia 209 3.57 1 4 0 0 0 0
## 7 Canada Nova Scotia 65 5.99 0 0 0 0 0 0
## 8 Canada Saskatchewan 24 1.48 0 0 0 1 0 0
## 9 Canada Manitoba 14 1.14 0 0 0 0 1 0
## 10 Canada Newfoundland and Labrador 3 1.12 0 0 0 0 0 0
## --------------------------------------------------------------------------------
## --------------------------------------------------------------------------------
## ================================================================================## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv## Data retrieved on 2020-09-02 21:05:50 || Range of dates on data: 2020-01-22--2020-09-01 | Nbr of records: 253## --------------------------------------------------------------------------------## >>> checking data integrity...## No critical issues have been found.## >>> checking data consistency...## Warning in consistency.check(data, n0, nf, datasetName, details): Inconsistency
## of type.II in ts-recovered data detected -- 70 records (out of 253) show
## inconsistencies in the data...
## [1] "CANADA"
## [1] "US"
## [1] "MEXICO"
## ################################################################################
## ##### TS-RECOVERED Cases -- Data dated: 2020-09-01 :: 2020-09-02 21:05:51
## ################################################################################
## Number of Countries/Regions reported: 3
## Number of Cities/Provinces reported: 1
## Unique number of distinct geographical locations combined: 3
## --------------------------------------------------------------------------------
## For selected locations ts-recovered Totals: 2820817
## --------------------------------------------------------------------------------
## Country.Region Province.State Totals LastDayChange t-2 t-3 t-7 t-14 t-30
## 1 US 2202663 17838 30886 13325 30766 26890 44757
## 2 Mexico 501722 5500 6498 5441 2991 4566 10915
## 3 Canada 116432 412 560 166 411 467 83
## --------------------------------------------------------------------------------
## --------------------------------------------------------------------------------
## ================================================================================## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-01-2020.csv
## [1] "CANADA"
## [1] "US"
## [1] "MEXICO"
## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY CONFIRMED Cases -- Data dated: 2020-09-02 :: 2020-09-02 21:05:52
## ############################################################################################################################################
## Number of Countries/Regions reported: 3
## Number of Cities/Provinces reported: 102
## Unique number of distinct geographical locations combined: 3316
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Los Angeles, California, US 242521 0.94 5829 2.40 0 0.00 236692 97.60
## 2 Miami-Dade, Florida, US 159059 0.62 2537 1.60 0 0.00 156522 98.40
## 3 Maricopa, Arizona, US 134004 0.52 2976 2.22 0 0.00 131028 97.78
## 4 Cook, Illinois, US 126992 0.49 5065 3.99 0 0.00 121927 96.01
## 5 Harris, Texas, US 107490 0.42 2208 2.05 0 0.00 105282 97.95
## 6 Ciudad de Mexico, Mexico 99564 0.39 10591 10.64 82245 82.61 6728 6.76
## 7 Dallas, Texas, US 72252 0.28 959 1.33 0 0.00 71293 98.67
## 8 Broward, Florida, US 72245 0.28 1187 1.64 0 0.00 71058 98.36
## 9 Queens, New York, US 70288 0.27 7224 10.28 0 0.00 63064 89.72
## 10 Mexico, Mexico 68516 0.27 8082 11.80 57137 83.39 3297 4.81
## ============================================================================================================================================## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY DEATHS Cases -- Data dated: 2020-09-02 :: 2020-09-02 21:05:52
## ############################################################################################################################################
## Number of Countries/Regions reported: 3
## Number of Cities/Provinces reported: 102
## Unique number of distinct geographical locations combined: 3316
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Ciudad de Mexico, Mexico 99564 0.39 10591 10.64 82245 82.61 6728 6.76
## 2 Mexico, Mexico 68516 0.27 8082 11.80 57137 83.39 3297 4.81
## 3 Kings, New York, US 65118 0.25 7290 11.20 0 0.00 57828 88.80
## 4 Queens, New York, US 70288 0.27 7224 10.28 0 0.00 63064 89.72
## 5 Los Angeles, California, US 242521 0.94 5829 2.40 0 0.00 236692 97.60
## 6 Quebec, Canada 62614 0.24 5762 9.20 55438 88.54 1414 2.26
## 7 Cook, Illinois, US 126992 0.49 5065 3.99 0 0.00 121927 96.01
## 8 Bronx, New York, US 51663 0.20 4912 9.51 0 0.00 46751 90.49
## 9 Veracruz, Mexico 28581 0.11 3702 12.95 23486 82.17 1393 4.87
## 10 Puebla, Mexico 27070 0.11 3575 13.21 22217 82.07 1278 4.72
## ============================================================================================================================================## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY RECOVERED Cases -- Data dated: 2020-09-02 :: 2020-09-02 21:05:52
## ############################################################################################################################################
## Number of Countries/Regions reported: 3
## Number of Cities/Provinces reported: 102
## Unique number of distinct geographical locations combined: 3316
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Recovered, US 0 0.00 0 NaN 2202663 Inf -2202663 -Inf
## 2 Ciudad de Mexico, Mexico 99564 0.39 10591 10.64 82245 82.61 6728 6.76
## 3 Mexico, Mexico 68516 0.27 8082 11.80 57137 83.39 3297 4.81
## 4 Quebec, Canada 62614 0.24 5762 9.20 55438 88.54 1414 2.26
## 5 Ontario, Canada 44418 0.17 2858 6.43 40192 90.49 1368 3.08
## 6 Guanajuato, Mexico 31998 0.12 2150 6.72 27062 84.57 2786 8.71
## 7 Tabasco, Mexico 28471 0.11 2589 9.09 24704 86.77 1178 4.14
## 8 Nuevo Leon, Mexico 29524 0.11 2285 7.74 24150 81.80 3089 10.46
## 9 Veracruz, Mexico 28581 0.11 3702 12.95 23486 82.17 1393 4.87
## 10 Puebla, Mexico 27070 0.11 3575 13.21 22217 82.07 1278 4.72
## ============================================================================================================================================## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY ACTIVE Cases -- Data dated: 2020-09-02 :: 2020-09-02 21:05:52
## ############################################################################################################################################
## Number of Countries/Regions reported: 3
## Number of Cities/Provinces reported: 102
## Unique number of distinct geographical locations combined: 3316
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Los Angeles, California, US 242521 0.94 5829 2.40 0 0 236692 97.60
## 2 Miami-Dade, Florida, US 159059 0.62 2537 1.60 0 0 156522 98.40
## 3 Maricopa, Arizona, US 134004 0.52 2976 2.22 0 0 131028 97.78
## 4 Cook, Illinois, US 126992 0.49 5065 3.99 0 0 121927 96.01
## 5 Harris, Texas, US 107490 0.42 2208 2.05 0 0 105282 97.95
## 6 Dallas, Texas, US 72252 0.28 959 1.33 0 0 71293 98.67
## 7 Broward, Florida, US 72245 0.28 1187 1.64 0 0 71058 98.36
## 8 Queens, New York, US 70288 0.27 7224 10.28 0 0 63064 89.72
## 9 Clark, Nevada, US 59716 0.23 1134 1.90 0 0 58582 98.10
## 10 Kings, New York, US 65118 0.25 7290 11.20 0 0 57828 88.80
## ============================================================================================================================================## Confirmed Deaths Recovered Active
## Totals
## 6811298 259084 2820817 NA
## Average
## 2054.07 78.13 850.67 NA
## Standard Deviation
## 8443.85 430.23 38329.52 NA
##
##
## * Statistical estimators computed considering 3316 independent reported entries## >>> checking data integrity...## No critical issues have been found.
##
##
## ********************************************************************************
## ******************************** OVERALL SUMMARY********************************
## ********************************************************************************
## **** Time Series CANADA,US,MEXICO TOTS ****
## ts-confirmed ts-deaths ts-recovered
## 6811298 259084 2820817
## 3.8% 41.41%
## **** Time Series CANADA,US,MEXICO AVGS ****
## ts-confirmed ts-deaths ts-recovered
## 425706.12 16192.75 940272.33
## 3.8% 220.87%
## **** Time Series CANADA,US,MEXICO SDS ****
## ts-confirmed ts-deaths ts-recovered
## 1513612.27 47751.7 1110105.73
## 3.15% 73.34%
##
##
## * Statistical estimators computed considering 16/16/3 independent reported entries per case-type
## ********************************************************************************report.summary(geo.loc="US")## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv## Data retrieved on 2020-09-02 21:05:52 || Range of dates on data: 2020-01-22--2020-09-01 | Nbr of records: 266## --------------------------------------------------------------------------------## >>> checking data integrity...## No critical issues have been found.## >>> checking data consistency...## Warning in consistency.check(data, n0, nf, datasetName, details): Inconsistency
## of type.II in ts-confirmed data detected -- 47 records (out of 266) show
## inconsistencies in the data...## [1] "US"
## ################################################################################
## ##### TS-CONFIRMED Cases -- Data dated: 2020-09-01 :: 2020-09-02 21:05:53
## ################################################################################
## Number of Countries/Regions reported: 1
## Number of Cities/Provinces reported: 1
## Unique number of distinct geographical locations combined: 1
## --------------------------------------------------------------------------------
## For selected locations ts-confirmed Totals: 6073840
## --------------------------------------------------------------------------------
## Country.Region Province.State Totals RelPerc GlobalPerc LastDayChange t-2 t-3 t-7 t-14 t-30
## 1 US 6073840 100 23.59 43253 34156 35337 44109 46436 45368
## --------------------------------------------------------------------------------
## Global Perc. Average: 23.59 (sd: NA)
## Global Perc. Average in top 1 : 23.59 (sd: NA)
## --------------------------------------------------------------------------------
## ================================================================================## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv## Data retrieved on 2020-09-02 21:05:54 || Range of dates on data: 2020-01-22--2020-09-01 | Nbr of records: 266## --------------------------------------------------------------------------------## >>> checking data integrity...## No critical issues have been found.## >>> checking data consistency...## Warning in consistency.check(data, n0, nf, datasetName, details): Inconsistency
## of type.II in ts-deaths data detected -- 36 records (out of 266) show
## inconsistencies in the data...
## [1] "US"
## ################################################################################
## ##### TS-DEATHS Cases -- Data dated: 2020-09-01 :: 2020-09-02 21:05:55
## ################################################################################
## Number of Countries/Regions reported: 1
## Number of Cities/Provinces reported: 1
## Unique number of distinct geographical locations combined: 1
## --------------------------------------------------------------------------------
## For selected locations ts-deaths Totals: 184664
## --------------------------------------------------------------------------------
## Country.Region Province.State Totals Perc LastDayChange t-2 t-3 t-7 t-14 t-30
## 1 US 184664 3.04 1067 573 310 1225 1353 530
## --------------------------------------------------------------------------------
## --------------------------------------------------------------------------------
## ================================================================================## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv## Data retrieved on 2020-09-02 21:05:55 || Range of dates on data: 2020-01-22--2020-09-01 | Nbr of records: 253## --------------------------------------------------------------------------------## >>> checking data integrity...## No critical issues have been found.## >>> checking data consistency...## Warning in consistency.check(data, n0, nf, datasetName, details): Inconsistency
## of type.II in ts-recovered data detected -- 70 records (out of 253) show
## inconsistencies in the data...
## [1] "US"
## ################################################################################
## ##### TS-RECOVERED Cases -- Data dated: 2020-09-01 :: 2020-09-02 21:05:56
## ################################################################################
## Number of Countries/Regions reported: 1
## Number of Cities/Provinces reported: 1
## Unique number of distinct geographical locations combined: 1
## --------------------------------------------------------------------------------
## For selected locations ts-recovered Totals: 2202663
## --------------------------------------------------------------------------------
## Country.Region Province.State Totals Perc LastDayChange t-2 t-3 t-7 t-14 t-30
## 1 US 2202663 36.26 17838 30886 13325 30766 26890 44757
## --------------------------------------------------------------------------------
## --------------------------------------------------------------------------------
## ================================================================================## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-01-2020.csv
## [1] "US"
## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY CONFIRMED Cases -- Data dated: 2020-09-02 :: 2020-09-02 21:05:56
## ############################################################################################################################################
## Number of Countries/Regions reported: 1
## Number of Cities/Provinces reported: 58
## Unique number of distinct geographical locations combined: 3270
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Los Angeles, California, US 242521 0.94 5829 2.40 0 0 236692 97.60
## 2 Miami-Dade, Florida, US 159059 0.62 2537 1.60 0 0 156522 98.40
## 3 Maricopa, Arizona, US 134004 0.52 2976 2.22 0 0 131028 97.78
## 4 Cook, Illinois, US 126992 0.49 5065 3.99 0 0 121927 96.01
## 5 Harris, Texas, US 107490 0.42 2208 2.05 0 0 105282 97.95
## 6 Dallas, Texas, US 72252 0.28 959 1.33 0 0 71293 98.67
## 7 Broward, Florida, US 72245 0.28 1187 1.64 0 0 71058 98.36
## 8 Queens, New York, US 70288 0.27 7224 10.28 0 0 63064 89.72
## 9 Kings, New York, US 65118 0.25 7290 11.20 0 0 57828 88.80
## 10 Clark, Nevada, US 59716 0.23 1134 1.90 0 0 58582 98.10
## ============================================================================================================================================## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY DEATHS Cases -- Data dated: 2020-09-02 :: 2020-09-02 21:05:56
## ############################################################################################################################################
## Number of Countries/Regions reported: 1
## Number of Cities/Provinces reported: 58
## Unique number of distinct geographical locations combined: 3270
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Kings, New York, US 65118 0.25 7290 11.20 0 0 57828 88.80
## 2 Queens, New York, US 70288 0.27 7224 10.28 0 0 63064 89.72
## 3 Los Angeles, California, US 242521 0.94 5829 2.40 0 0 236692 97.60
## 4 Cook, Illinois, US 126992 0.49 5065 3.99 0 0 121927 96.01
## 5 Bronx, New York, US 51663 0.20 4912 9.51 0 0 46751 90.49
## 6 New York, New York, US 32165 0.12 3170 9.86 0 0 28995 90.14
## 7 Maricopa, Arizona, US 134004 0.52 2976 2.22 0 0 131028 97.78
## 8 Wayne, Michigan, US 31357 0.12 2889 9.21 0 0 28468 90.79
## 9 Miami-Dade, Florida, US 159059 0.62 2537 1.60 0 0 156522 98.40
## 10 Harris, Texas, US 107490 0.42 2208 2.05 0 0 105282 97.95
## ============================================================================================================================================## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY RECOVERED Cases -- Data dated: 2020-09-02 :: 2020-09-02 21:05:56
## ############################################################################################################################################
## Number of Countries/Regions reported: 1
## Number of Cities/Provinces reported: 58
## Unique number of distinct geographical locations combined: 3270
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Recovered, US 0 0.00 0 NaN 2202663 Inf -2202663 -Inf
## 2 Autauga, Alabama, US 1354 0.01 23 1.70 0 0 1331 98.30
## 3 Baldwin, Alabama, US 4445 0.02 38 0.85 0 0 4407 99.15
## 4 Barbour, Alabama, US 629 0.00 7 1.11 0 0 622 98.89
## 5 Bibb, Alabama, US 538 0.00 7 1.30 0 0 531 98.70
## 6 Blount, Alabama, US 1045 0.00 11 1.05 0 0 1034 98.95
## 7 Bullock, Alabama, US 541 0.00 13 2.40 0 0 528 97.60
## 8 Butler, Alabama, US 840 0.00 36 4.29 0 0 804 95.71
## 9 Calhoun, Alabama, US 2411 0.01 32 1.33 0 0 2379 98.67
## 10 Chambers, Alabama, US 874 0.00 39 4.46 0 0 835 95.54
## ============================================================================================================================================## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY ACTIVE Cases -- Data dated: 2020-09-02 :: 2020-09-02 21:05:56
## ############################################################################################################################################
## Number of Countries/Regions reported: 1
## Number of Cities/Provinces reported: 58
## Unique number of distinct geographical locations combined: 3270
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Los Angeles, California, US 242521 0.94 5829 2.40 0 0 236692 97.60
## 2 Miami-Dade, Florida, US 159059 0.62 2537 1.60 0 0 156522 98.40
## 3 Maricopa, Arizona, US 134004 0.52 2976 2.22 0 0 131028 97.78
## 4 Cook, Illinois, US 126992 0.49 5065 3.99 0 0 121927 96.01
## 5 Harris, Texas, US 107490 0.42 2208 2.05 0 0 105282 97.95
## 6 Dallas, Texas, US 72252 0.28 959 1.33 0 0 71293 98.67
## 7 Broward, Florida, US 72245 0.28 1187 1.64 0 0 71058 98.36
## 8 Queens, New York, US 70288 0.27 7224 10.28 0 0 63064 89.72
## 9 Clark, Nevada, US 59716 0.23 1134 1.90 0 0 58582 98.10
## 10 Kings, New York, US 65118 0.25 7290 11.20 0 0 57828 88.80
## ============================================================================================================================================## Confirmed Deaths Recovered Active
## Totals
## 6073840 184664 2202663 NA
## Average
## 1857.44 56.47 673.6 NA
## Standard Deviation
## 8008.52 305.46 38518.93 NA
##
##
## * Statistical estimators computed considering 3270 independent reported entries## >>> checking data integrity...## No critical issues have been found.
##
##
## ********************************************************************************
## ******************************** OVERALL SUMMARY********************************
## ********************************************************************************
## **** Time Series US TOTS ****
## ts-confirmed ts-deaths ts-recovered
## 6073840 184664 2202663
## 3.04% 36.26%
## **** Time Series US AVGS ****
## ts-confirmed ts-deaths ts-recovered
## 6073840 184664 2202663
## 3.04% 36.26%
## **** Time Series US SDS ****
## ts-confirmed ts-deaths ts-recovered
## NA NA NA
## NA% NA%
##
##
## * Statistical estimators computed considering 1/1/1 independent reported entries per case-type
## ********************************************************************************report.summary(geo.loc="China")## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv## Data retrieved on 2020-09-02 21:05:57 || Range of dates on data: 2020-01-22--2020-09-01 | Nbr of records: 266## --------------------------------------------------------------------------------## >>> checking data integrity...## No critical issues have been found.## >>> checking data consistency...## Warning in consistency.check(data, n0, nf, datasetName, details): Inconsistency
## of type.II in ts-confirmed data detected -- 47 records (out of 266) show
## inconsistencies in the data...## [1] "CHINA"
## ################################################################################
## ##### TS-CONFIRMED Cases -- Data dated: 2020-09-01 :: 2020-09-02 21:05:58
## ################################################################################
## Number of Countries/Regions reported: 1
## Number of Cities/Provinces reported: 33
## Unique number of distinct geographical locations combined: 33
## --------------------------------------------------------------------------------
## For selected locations ts-confirmed Totals: 89933
## --------------------------------------------------------------------------------
## Country.Region Province.State Totals RelPerc GlobalPerc LastDayChange t-2 t-3 t-7 t-14 t-30
## 1 China Hubei 68139 75.77 0.26 0 0 0 0 0 0
## 2 China Hong Kong 4822 5.36 0.02 12 9 15 24 26 78
## 3 China Guangdong 1742 1.94 0.01 2 1 1 0 0 4
## 4 China Zhejiang 1278 1.42 0.00 0 0 1 0 0 0
## 5 China Henan 1276 1.42 0.00 0 0 0 0 0 0
## 6 China Hunan 1019 1.13 0.00 0 0 0 0 0 0
## 7 China Anhui 991 1.10 0.00 0 0 0 0 0 0
## 8 China Heilongjiang 948 1.05 0.00 0 0 0 0 0 0
## 9 China Beijing 935 1.04 0.00 0 0 0 0 0 0
## 10 China Jiangxi 935 1.04 0.00 0 0 0 0 3 0
## --------------------------------------------------------------------------------
## Global Perc. Average: 0.01 (sd: 0.05)
## Global Perc. Average in top 10 : 0.03 (sd: 0.08)
## --------------------------------------------------------------------------------
## ================================================================================## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv## Data retrieved on 2020-09-02 21:05:58 || Range of dates on data: 2020-01-22--2020-09-01 | Nbr of records: 266## --------------------------------------------------------------------------------## >>> checking data integrity...## No critical issues have been found.## >>> checking data consistency...## Warning in consistency.check(data, n0, nf, datasetName, details): Inconsistency
## of type.II in ts-deaths data detected -- 36 records (out of 266) show
## inconsistencies in the data...
## [1] "CHINA"
## ################################################################################
## ##### TS-DEATHS Cases -- Data dated: 2020-09-01 :: 2020-09-02 21:05:59
## ################################################################################
## Number of Countries/Regions reported: 1
## Number of Cities/Provinces reported: 33
## Unique number of distinct geographical locations combined: 33
## --------------------------------------------------------------------------------
## For selected locations ts-deaths Totals: 4724
## --------------------------------------------------------------------------------
## Country.Region Province.State Totals Perc LastDayChange t-2 t-3 t-7 t-14 t-30
## 1 China Hubei 4512 6.62 0 0 0 0 0 0
## 2 China Hong Kong 90 1.87 1 1 1 1 1 3
## 3 China Henan 22 1.72 0 0 0 0 0 0
## 4 China Heilongjiang 13 1.37 0 0 0 0 0 0
## 5 China Beijing 9 0.96 0 0 0 0 0 0
## 6 China Guangdong 8 0.46 0 0 0 0 0 0
## 7 China Shandong 7 0.84 0 0 0 0 0 0
## 8 China Shanghai 7 0.77 0 0 0 0 0 0
## 9 China Anhui 6 0.61 0 0 0 0 0 0
## 10 China Chongqing 6 1.03 0 0 0 0 0 0
## --------------------------------------------------------------------------------
## --------------------------------------------------------------------------------
## ================================================================================## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv## Data retrieved on 2020-09-02 21:05:59 || Range of dates on data: 2020-01-22--2020-09-01 | Nbr of records: 253## --------------------------------------------------------------------------------## >>> checking data integrity...## No critical issues have been found.## >>> checking data consistency...## Warning in consistency.check(data, n0, nf, datasetName, details): Inconsistency
## of type.II in ts-recovered data detected -- 70 records (out of 253) show
## inconsistencies in the data...
## [1] "CHINA"
## ################################################################################
## ##### TS-RECOVERED Cases -- Data dated: 2020-09-01 :: 2020-09-02 21:06:00
## ################################################################################
## Number of Countries/Regions reported: 1
## Number of Cities/Provinces reported: 33
## Unique number of distinct geographical locations combined: 33
## --------------------------------------------------------------------------------
## For selected locations ts-recovered Totals: 84652
## --------------------------------------------------------------------------------
## Country.Region Province.State Totals Perc LastDayChange t-2 t-3 t-7 t-14 t-30
## 1 China Hubei 63627 93.38 0 0 1 0 0 0
## 2 China Hong Kong 4380 90.83 38 22 33 53 102 78
## 3 China Guangdong 1721 98.79 0 1 2 2 2 2
## 4 China Zhejiang 1268 99.22 0 0 0 0 0 0
## 5 China Henan 1254 98.28 0 0 0 0 0 0
## 6 China Hunan 1015 99.61 0 0 0 0 0 0
## 7 China Anhui 985 99.39 0 0 0 0 0 0
## 8 China Heilongjiang 935 98.63 0 0 1 0 0 0
## 9 China Jiangxi 934 99.89 0 0 0 1 0 0
## 10 China Beijing 926 99.04 0 0 0 0 0 1
## --------------------------------------------------------------------------------
## --------------------------------------------------------------------------------
## ================================================================================## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-01-2020.csv
## [1] "CHINA"
## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY CONFIRMED Cases -- Data dated: 2020-09-02 :: 2020-09-02 21:06:01
## ############################################################################################################################################
## Number of Countries/Regions reported: 1
## Number of Cities/Provinces reported: 33
## Unique number of distinct geographical locations combined: 33
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Hubei, China 68139 0.26 4512 6.62 63627 93.38 0 0.00
## 2 Hong Kong, China 4822 0.02 90 1.87 4380 90.83 352 7.30
## 3 Guangdong, China 1742 0.01 8 0.46 1721 98.79 13 0.75
## 4 Zhejiang, China 1278 0.00 1 0.08 1268 99.22 9 0.70
## 5 Henan, China 1276 0.00 22 1.72 1254 98.28 0 0.00
## 6 Hunan, China 1019 0.00 4 0.39 1015 99.61 0 0.00
## 7 Anhui, China 991 0.00 6 0.61 985 99.39 0 0.00
## 8 Heilongjiang, China 948 0.00 13 1.37 935 98.63 0 0.00
## 9 Beijing, China 935 0.00 9 0.96 926 99.04 0 0.00
## 10 Jiangxi, China 935 0.00 1 0.11 934 99.89 0 0.00
## ============================================================================================================================================## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY DEATHS Cases -- Data dated: 2020-09-02 :: 2020-09-02 21:06:01
## ############################################################################################################################################
## Number of Countries/Regions reported: 1
## Number of Cities/Provinces reported: 33
## Unique number of distinct geographical locations combined: 33
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Hubei, China 68139 0.26 4512 6.62 63627 93.38 0 0.00
## 2 Hong Kong, China 4822 0.02 90 1.87 4380 90.83 352 7.30
## 3 Henan, China 1276 0.00 22 1.72 1254 98.28 0 0.00
## 4 Heilongjiang, China 948 0.00 13 1.37 935 98.63 0 0.00
## 5 Beijing, China 935 0.00 9 0.96 926 99.04 0 0.00
## 6 Guangdong, China 1742 0.01 8 0.46 1721 98.79 13 0.75
## 7 Shandong, China 831 0.00 7 0.84 809 97.35 15 1.81
## 8 Shanghai, China 908 0.00 7 0.77 842 92.73 59 6.50
## 9 Anhui, China 991 0.00 6 0.61 985 99.39 0 0.00
## 10 Chongqing, China 583 0.00 6 1.03 577 98.97 0 0.00
## ============================================================================================================================================## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY RECOVERED Cases -- Data dated: 2020-09-02 :: 2020-09-02 21:06:01
## ############################################################################################################################################
## Number of Countries/Regions reported: 1
## Number of Cities/Provinces reported: 33
## Unique number of distinct geographical locations combined: 33
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Hubei, China 68139 0.26 4512 6.62 63627 93.38 0 0.00
## 2 Hong Kong, China 4822 0.02 90 1.87 4380 90.83 352 7.30
## 3 Guangdong, China 1742 0.01 8 0.46 1721 98.79 13 0.75
## 4 Zhejiang, China 1278 0.00 1 0.08 1268 99.22 9 0.70
## 5 Henan, China 1276 0.00 22 1.72 1254 98.28 0 0.00
## 6 Hunan, China 1019 0.00 4 0.39 1015 99.61 0 0.00
## 7 Anhui, China 991 0.00 6 0.61 985 99.39 0 0.00
## 8 Heilongjiang, China 948 0.00 13 1.37 935 98.63 0 0.00
## 9 Jiangxi, China 935 0.00 1 0.11 934 99.89 0 0.00
## 10 Beijing, China 935 0.00 9 0.96 926 99.04 0 0.00
## ============================================================================================================================================## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY ACTIVE Cases -- Data dated: 2020-09-02 :: 2020-09-02 21:06:01
## ############################################################################################################################################
## Number of Countries/Regions reported: 1
## Number of Cities/Provinces reported: 33
## Unique number of distinct geographical locations combined: 33
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Hong Kong, China 4822 0.02 90 1.87 4380 90.83 352 7.30
## 2 Shanghai, China 908 0.00 7 0.77 842 92.73 59 6.50
## 3 Sichuan, China 653 0.00 3 0.46 626 95.87 24 3.68
## 4 Xinjiang, China 902 0.00 3 0.33 877 97.23 22 2.44
## 5 Shandong, China 831 0.00 7 0.84 809 97.35 15 1.81
## 6 Fujian, China 383 0.00 1 0.26 369 96.34 13 3.39
## 7 Guangdong, China 1742 0.01 8 0.46 1721 98.79 13 0.75
## 8 Hebei, China 365 0.00 6 1.64 346 94.79 13 3.56
## 9 Tianjin, China 229 0.00 3 1.31 213 93.01 13 5.68
## 10 Zhejiang, China 1278 0.00 1 0.08 1268 99.22 9 0.70
## ============================================================================================================================================## Confirmed Deaths Recovered Active
## Totals
## 89933 4724 84652 557
## Average
## 2725.24 143.15 2565.21 16.88
## Standard Deviation
## 11774.5 784.44 10990.45 61.28
##
##
## * Statistical estimators computed considering 33 independent reported entries## >>> checking data integrity...## No critical issues have been found.
##
##
## ********************************************************************************
## ******************************** OVERALL SUMMARY********************************
## ********************************************************************************
## **** Time Series CHINA TOTS ****
## ts-confirmed ts-deaths ts-recovered
## 89933 4724 84652
## 5.25% 94.13%
## **** Time Series CHINA AVGS ****
## ts-confirmed ts-deaths ts-recovered
## 2725.24 143.15 2565.21
## 5.25% 94.13%
## **** Time Series CHINA SDS ****
## ts-confirmed ts-deaths ts-recovered
## 11774.5 784.44 10990.45
## 6.66% 93.34%
##
##
## * Statistical estimators computed considering 33/33/33 independent reported entries per case-type
## ********************************************************************************# displaying top 10s
report.summary()## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv## Data retrieved on 2020-09-02 21:06:01 || Range of dates on data: 2020-01-22--2020-09-01 | Nbr of records: 266## --------------------------------------------------------------------------------## >>> checking data integrity...## No critical issues have been found.## >>> checking data consistency...## Warning in consistency.check(data, n0, nf, datasetName, details): Inconsistency
## of type.II in ts-confirmed data detected -- 47 records (out of 266) show
## inconsistencies in the data...## ################################################################################
## ##### TS-CONFIRMED Cases -- Data dated: 2020-09-01 :: 2020-09-02 21:06:02
## ################################################################################
## Number of Countries/Regions reported: 188
## Number of Cities/Provinces reported: 82
## Unique number of distinct geographical locations combined: 266
## --------------------------------------------------------------------------------
## Worldwide ts-confirmed Totals: 25749642
## --------------------------------------------------------------------------------
## Country.Region Province.State Totals GlobalPerc LastDayChange t-2 t-3 t-7 t-14 t-30
## 1 US 6073840 23.59 43253 34156 35337 44109 46436 45368
## 2 Brazil 3950931 15.34 42659 45961 16158 47161 49298 16641
## 3 India 3769523 14.64 78357 69921 78512 85687 69672 52050
## 4 Russia 997072 3.87 4670 4932 4897 4642 4790 5364
## 5 Peru 652037 2.53 4871 7731 9474 6944 7828 4250
## 6 South Africa 628259 2.44 1218 1985 2505 2684 3916 5377
## 7 Colombia 624026 2.42 8932 7190 8020 10130 13056 10199
## 8 Mexico 606036 2.35 6476 3719 4129 5267 5792 4767
## 9 Spain 470973 1.83 8115 23572 0 7296 6671 8532
## 10 Argentina 428239 1.66 10504 9309 7187 10550 6693 4824
## --------------------------------------------------------------------------------
## Global Perc. Average: 0.38 (sd: 1.98)
## Global Perc. Average in top 10 : 7.07 (sd: 7.83)
## --------------------------------------------------------------------------------
## ================================================================================## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv## Data retrieved on 2020-09-02 21:06:02 || Range of dates on data: 2020-01-22--2020-09-01 | Nbr of records: 266## --------------------------------------------------------------------------------## >>> checking data integrity...## No critical issues have been found.## >>> checking data consistency...## Warning in consistency.check(data, n0, nf, datasetName, details): Inconsistency
## of type.II in ts-deaths data detected -- 36 records (out of 266) show
## inconsistencies in the data...
## ################################################################################
## ##### TS-DEATHS Cases -- Data dated: 2020-09-01 :: 2020-09-02 21:06:03
## ################################################################################
## Number of Countries/Regions reported: 188
## Number of Cities/Provinces reported: 82
## Unique number of distinct geographical locations combined: 266
## --------------------------------------------------------------------------------
## Worldwide ts-deaths Totals: 857015
## --------------------------------------------------------------------------------
## Country.Region Province.State Totals Perc LastDayChange t-2 t-3 t-7 t-14 t-30
## 1 US 184664 3.04 1067 573 310 1225 1353 530
## 2 Brazil 122596 3.10 1215 553 566 1085 1212 561
## 3 India 66333 1.76 1045 819 971 1115 978 803
## 4 Mexico 65241 10.77 827 256 339 626 707 266
## 5 United Kingdom 41504 12.31 3 2 1 16 16 1
## 6 Italy 35491 13.14 8 6 4 13 7 12
## 7 France 30518 9.94 23 25 10 0 -1 -1
## 8 Spain 29152 6.19 58 83 0 47 127 27
## 9 Peru 28944 4.44 156 181 136 188 177 197
## 10 Iran 21672 5.75 101 109 103 119 153 215
## --------------------------------------------------------------------------------
## --------------------------------------------------------------------------------
## ================================================================================## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv## Data retrieved on 2020-09-02 21:06:04 || Range of dates on data: 2020-01-22--2020-09-01 | Nbr of records: 253## --------------------------------------------------------------------------------## >>> checking data integrity...## No critical issues have been found.## >>> checking data consistency...## Warning in consistency.check(data, n0, nf, datasetName, details): Inconsistency
## of type.II in ts-recovered data detected -- 70 records (out of 253) show
## inconsistencies in the data...
## ################################################################################
## ##### TS-RECOVERED Cases -- Data dated: 2020-09-01 :: 2020-09-02 21:06:04
## ################################################################################
## Number of Countries/Regions reported: 188
## Number of Cities/Provinces reported: 68
## Unique number of distinct geographical locations combined: 253
## --------------------------------------------------------------------------------
## Worldwide ts-recovered Totals: 17073236
## --------------------------------------------------------------------------------
## Country.Region Province.State Totals LastDayChange t-2 t-3 t-7 t-14 t-30
## 1 Brazil 3345240 76649 30976 35430 49896 50685 36100
## 2 India 2901908 62026 65081 60868 65432 58848 44306
## 3 US 2202663 17838 30886 13325 30766 26890 44757
## 4 Russia 813603 6264 2398 2576 6317 6757 3411
## 5 South Africa 549993 9070 2319 1910 4861 5973 10810
## 6 Mexico 501722 5500 6498 5441 2991 4566 10915
## 7 Peru 471599 16142 8782 8658 0 3434 3904
## 8 Colombia 469552 10092 8851 10047 11651 13975 6488
## 9 Chile 385790 1911 1295 1401 1805 1845 1565
## 10 Iran 325124 1891 1812 1574 1812 1647 2126
## --------------------------------------------------------------------------------
## --------------------------------------------------------------------------------
## ================================================================================## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-01-2020.csv
## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY CONFIRMED Cases -- Data dated: 2020-09-02 :: 2020-09-02 21:06:05
## ############################################################################################################################################
## Number of Countries/Regions reported: 188
## Number of Cities/Provinces reported: 563
## Unique number of distinct geographical locations combined: 3954
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Sao Paulo, Brazil 814375 3.16 30375 3.73 655747 80.52 128253 15.75
## 2 Maharashtra, India 808306 3.14 24903 3.08 584537 72.32 198866 24.60
## 3 South Africa 628259 2.44 14263 2.27 549993 87.54 64003 10.19
## 4 Andhra Pradesh, India 445139 1.73 4053 0.91 339876 76.35 101210 22.74
## 5 Tamil Nadu, India 433969 1.69 7418 1.71 374172 86.22 52379 12.07
## 6 Argentina 428239 1.66 8919 2.08 308376 72.01 110944 25.91
## 7 Iran 376894 1.46 21672 5.75 325124 86.26 30098 7.99
## 8 Karnataka, India 351481 1.36 5837 1.66 254626 72.44 91018 25.90
## 9 Saudi Arabia 316670 1.23 3929 1.24 291514 92.06 21227 6.70
## 10 Bangladesh 314946 1.22 4316 1.37 208177 66.10 102453 32.53
## ============================================================================================================================================## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY DEATHS Cases -- Data dated: 2020-09-02 :: 2020-09-02 21:06:05
## ############################################################################################################################################
## Number of Countries/Regions reported: 188
## Number of Cities/Provinces reported: 563
## Unique number of distinct geographical locations combined: 3954
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 England, United Kingdom 291179 1.13 36854 12.66 0 0.00 254325 87.34
## 2 France 306951 1.19 30518 9.94 73727 24.02 202706 66.04
## 3 Sao Paulo, Brazil 814375 3.16 30375 3.73 655747 80.52 128253 15.75
## 4 Maharashtra, India 808306 3.14 24903 3.08 584537 72.32 198866 24.60
## 5 Iran 376894 1.46 21672 5.75 325124 86.26 30098 7.99
## 6 Lombardia, Italy 100317 0.39 16867 16.81 76368 76.13 7082 7.06
## 7 Rio de Janeiro, Brazil 226800 0.88 16217 7.15 204845 90.32 5738 2.53
## 8 South Africa 628259 2.44 14263 2.27 549993 87.54 64003 10.19
## 9 Lima, Peru 309162 1.20 12832 4.15 0 0.00 296330 95.85
## 10 Ciudad de Mexico, Mexico 99564 0.39 10591 10.64 82245 82.61 6728 6.76
## ============================================================================================================================================## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY RECOVERED Cases -- Data dated: 2020-09-02 :: 2020-09-02 21:06:05
## ############################################################################################################################################
## Number of Countries/Regions reported: 188
## Number of Cities/Provinces reported: 563
## Unique number of distinct geographical locations combined: 3954
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Recovered, US 0 0.00 0 NaN 2202663 Inf -2202663 -Inf
## 2 Sao Paulo, Brazil 814375 3.16 30375 3.73 655747 80.52 128253 15.75
## 3 Maharashtra, India 808306 3.14 24903 3.08 584537 72.32 198866 24.60
## 4 South Africa 628259 2.44 14263 2.27 549993 87.54 64003 10.19
## 5 Unknown, Peru 0 0.00 0 NaN 471599 Inf -471599 -Inf
## 6 Tamil Nadu, India 433969 1.69 7418 1.71 374172 86.22 52379 12.07
## 7 Andhra Pradesh, India 445139 1.73 4053 0.91 339876 76.35 101210 22.74
## 8 Iran 376894 1.46 21672 5.75 325124 86.26 30098 7.99
## 9 Argentina 428239 1.66 8919 2.08 308376 72.01 110944 25.91
## 10 Saudi Arabia 316670 1.23 3929 1.24 291514 92.06 21227 6.70
## ============================================================================================================================================## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY ACTIVE Cases -- Data dated: 2020-09-02 :: 2020-09-02 21:06:05
## ############################################################################################################################################
## Number of Countries/Regions reported: 188
## Number of Cities/Provinces reported: 563
## Unique number of distinct geographical locations combined: 3954
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Lima, Peru 309162 1.20 12832 4.15 0 0.00 296330 95.85
## 2 England, United Kingdom 291179 1.13 36854 12.66 0 0.00 254325 87.34
## 3 Los Angeles, California, US 242521 0.94 5829 2.40 0 0.00 236692 97.60
## 4 France 306951 1.19 30518 9.94 73727 24.02 202706 66.04
## 5 Maharashtra, India 808306 3.14 24903 3.08 584537 72.32 198866 24.60
## 6 Miami-Dade, Florida, US 159059 0.62 2537 1.60 0 0.00 156522 98.40
## 7 Maricopa, Arizona, US 134004 0.52 2976 2.22 0 0.00 131028 97.78
## 8 Sao Paulo, Brazil 814375 3.16 30375 3.73 655747 80.52 128253 15.75
## 9 Cook, Illinois, US 126992 0.49 5065 3.99 0 0.00 121927 96.01
## 10 Argentina 428239 1.66 8919 2.08 308376 72.01 110944 25.91
## ============================================================================================================================================## Confirmed Deaths Recovered Active
## Totals
## 25749642 857015 17074386 NA
## Average
## 6512.3 216.75 4318.26 NA
## Standard Deviation
## 33882.64 1344.45 44218.98 NA
##
##
## * Statistical estimators computed considering 3954 independent reported entries## >>> checking data integrity...## No critical issues have been found.
##
##
## ********************************************************************************
## ******************************** OVERALL SUMMARY********************************
## ********************************************************************************
## **** Time Series Worldwide TOTS ****
## ts-confirmed ts-deaths ts-recovered
## 25749642 857015 17073236
## 3.33% 66.3%
## **** Time Series Worldwide AVGS ****
## ts-confirmed ts-deaths ts-recovered
## 96803.17 3221.86 67483.15
## 3.33% 69.71%
## **** Time Series Worldwide SDS ****
## ts-confirmed ts-deaths ts-recovered
## 508760.74 15411.2 320756.79
## 3.03% 63.05%
##
##
## * Statistical estimators computed considering 266/266/253 independent reported entries per case-type
## ********************************************************************************# get the top 20
report.summary(Nentries=20,graphical.output=FALSE)## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv## Data retrieved on 2020-09-02 21:06:06 || Range of dates on data: 2020-01-22--2020-09-01 | Nbr of records: 266## --------------------------------------------------------------------------------## >>> checking data integrity...## No critical issues have been found.## >>> checking data consistency...## Warning in consistency.check(data, n0, nf, datasetName, details): Inconsistency
## of type.II in ts-confirmed data detected -- 47 records (out of 266) show
## inconsistencies in the data...## ################################################################################
## ##### TS-CONFIRMED Cases -- Data dated: 2020-09-01 :: 2020-09-02 21:06:06
## ################################################################################
## Number of Countries/Regions reported: 188
## Number of Cities/Provinces reported: 82
## Unique number of distinct geographical locations combined: 266
## --------------------------------------------------------------------------------
## Worldwide ts-confirmed Totals: 25749642
## --------------------------------------------------------------------------------
## Country.Region Province.State Totals GlobalPerc LastDayChange t-2 t-3 t-7 t-14 t-30
## 1 US 6073840 23.59 43253 34156 35337 44109 46436 45368
## 2 Brazil 3950931 15.34 42659 45961 16158 47161 49298 16641
## 3 India 3769523 14.64 78357 69921 78512 85687 69672 52050
## 4 Russia 997072 3.87 4670 4932 4897 4642 4790 5364
## 5 Peru 652037 2.53 4871 7731 9474 6944 7828 4250
## 6 South Africa 628259 2.44 1218 1985 2505 2684 3916 5377
## 7 Colombia 624026 2.42 8932 7190 8020 10130 13056 10199
## 8 Mexico 606036 2.35 6476 3719 4129 5267 5792 4767
## 9 Spain 470973 1.83 8115 23572 0 7296 6671 8532
## 10 Argentina 428239 1.66 10504 9309 7187 10550 6693 4824
## 11 Chile 413145 1.60 1419 1752 1965 1380 1182 1762
## 12 Iran 376894 1.46 1682 1642 1754 2243 2444 2598
## 13 United Kingdom 337168 1.31 1295 1406 1715 1048 812 938
## 14 Saudi Arabia 316670 1.23 898 951 910 1068 1363 1258
## 15 Bangladesh 314946 1.22 1950 2174 1897 2519 2747 1356
## 16 France 306951 1.19 4776 2855 10789 5185 -54 -144
## 17 Pakistan 296149 1.15 300 213 264 482 613 762
## 18 Turkey 271705 1.06 1572 1587 1482 1313 1303 995
## 19 Italy 270189 1.05 975 996 1365 1366 642 159
## 20 Germany 246015 0.96 1213 1497 470 1427 1586 891
## --------------------------------------------------------------------------------
## Global Perc. Average: 0.38 (sd: 1.98)
## Global Perc. Average in top 20 : 4.14 (sd: 6.17)
## --------------------------------------------------------------------------------
## ================================================================================## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv## Data retrieved on 2020-09-02 21:06:07 || Range of dates on data: 2020-01-22--2020-09-01 | Nbr of records: 266## --------------------------------------------------------------------------------## >>> checking data integrity...## No critical issues have been found.## >>> checking data consistency...## Warning in consistency.check(data, n0, nf, datasetName, details): Inconsistency
## of type.II in ts-deaths data detected -- 36 records (out of 266) show
## inconsistencies in the data...## ################################################################################
## ##### TS-DEATHS Cases -- Data dated: 2020-09-01 :: 2020-09-02 21:06:07
## ################################################################################
## Number of Countries/Regions reported: 188
## Number of Cities/Provinces reported: 82
## Unique number of distinct geographical locations combined: 266
## --------------------------------------------------------------------------------
## Worldwide ts-deaths Totals: 857015
## --------------------------------------------------------------------------------
## Country.Region Province.State Totals Perc LastDayChange t-2 t-3 t-7 t-14 t-30
## 1 US 184664 3.04 1067 573 310 1225 1353 530
## 2 Brazil 122596 3.10 1215 553 566 1085 1212 561
## 3 India 66333 1.76 1045 819 971 1115 978 803
## 4 Mexico 65241 10.77 827 256 339 626 707 266
## 5 United Kingdom 41504 12.31 3 2 1 16 16 1
## 6 Italy 35491 13.14 8 6 4 13 7 12
## 7 France 30518 9.94 23 25 10 0 -1 -1
## 8 Spain 29152 6.19 58 83 0 47 127 27
## 9 Peru 28944 4.44 156 181 136 188 177 197
## 10 Iran 21672 5.75 101 109 103 119 153 215
## 11 Colombia 20050 3.21 388 299 300 295 360 367
## 12 Russia 17250 1.73 122 83 68 114 115 79
## 13 South Africa 14263 2.27 114 121 47 194 159 173
## 14 Chile 11321 2.74 32 45 63 32 32 99
## 15 Belgium 9897 11.58 2 1 3 -117 10 5
## 16 Germany 9307 3.78 4 3 1 4 8 0
## 17 Argentina 8919 2.08 259 203 104 276 282 165
## 18 Indonesia 7505 4.23 88 74 82 86 69 66
## 19 Iraq 7123 2.99 81 83 68 72 85 66
## 20 Ecuador 6571 5.75 15 1 18 42 41 31
## --------------------------------------------------------------------------------
## --------------------------------------------------------------------------------
## ================================================================================## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv## Data retrieved on 2020-09-02 21:06:08 || Range of dates on data: 2020-01-22--2020-09-01 | Nbr of records: 253## --------------------------------------------------------------------------------## >>> checking data integrity...## No critical issues have been found.## >>> checking data consistency...## Warning in consistency.check(data, n0, nf, datasetName, details): Inconsistency
## of type.II in ts-recovered data detected -- 70 records (out of 253) show
## inconsistencies in the data...## ################################################################################
## ##### TS-RECOVERED Cases -- Data dated: 2020-09-01 :: 2020-09-02 21:06:09
## ################################################################################
## Number of Countries/Regions reported: 188
## Number of Cities/Provinces reported: 68
## Unique number of distinct geographical locations combined: 253
## --------------------------------------------------------------------------------
## Worldwide ts-recovered Totals: 17073236
## --------------------------------------------------------------------------------
## Country.Region Province.State Totals LastDayChange t-2 t-3 t-7 t-14 t-30
## 1 Brazil 3345240 76649 30976 35430 49896 50685 36100
## 2 India 2901908 62026 65081 60868 65432 58848 44306
## 3 US 2202663 17838 30886 13325 30766 26890 44757
## 4 Russia 813603 6264 2398 2576 6317 6757 3411
## 5 South Africa 549993 9070 2319 1910 4861 5973 10810
## 6 Mexico 501722 5500 6498 5441 2991 4566 10915
## 7 Peru 471599 16142 8782 8658 0 3434 3904
## 8 Colombia 469552 10092 8851 10047 11651 13975 6488
## 9 Chile 385790 1911 1295 1401 1805 1845 1565
## 10 Iran 325124 1891 1812 1574 1812 1647 2126
## 11 Argentina 308376 7181 7188 6787 5599 5194 2276
## 12 Saudi Arabia 291514 718 1129 1226 1013 1180 1974
## 13 Pakistan 280970 288 135 207 514 2139 820
## 14 Turkey 245929 1003 1087 1027 1002 1002 1003
## 15 Germany 218403 1608 1512 493 1358 777 686
## 16 Bangladesh 208177 3290 2980 3044 3427 2913 1066
## 17 Italy 207944 291 -883 312 314 364 129
## 18 Iraq 180473 3871 3722 3860 3454 2529 2225
## 19 Philippines 158012 450 159 22302 1064 620 264
## 20 Spain 150376 0 0 0 0 0 0
## --------------------------------------------------------------------------------
## --------------------------------------------------------------------------------
## ================================================================================## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-01-2020.csv## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY CONFIRMED Cases -- Data dated: 2020-09-02 :: 2020-09-02 21:06:09
## ############################################################################################################################################
## Number of Countries/Regions reported: 188
## Number of Cities/Provinces reported: 563
## Unique number of distinct geographical locations combined: 3954
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Sao Paulo, Brazil 814375 3.16 30375 3.73 655747 80.52 128253 15.75
## 2 Maharashtra, India 808306 3.14 24903 3.08 584537 72.32 198866 24.60
## 3 South Africa 628259 2.44 14263 2.27 549993 87.54 64003 10.19
## 4 Andhra Pradesh, India 445139 1.73 4053 0.91 339876 76.35 101210 22.74
## 5 Tamil Nadu, India 433969 1.69 7418 1.71 374172 86.22 52379 12.07
## 6 Argentina 428239 1.66 8919 2.08 308376 72.01 110944 25.91
## 7 Iran 376894 1.46 21672 5.75 325124 86.26 30098 7.99
## 8 Karnataka, India 351481 1.36 5837 1.66 254626 72.44 91018 25.90
## 9 Saudi Arabia 316670 1.23 3929 1.24 291514 92.06 21227 6.70
## 10 Bangladesh 314946 1.22 4316 1.37 208177 66.10 102453 32.53
## 11 Lima, Peru 309162 1.20 12832 4.15 0 0.00 296330 95.85
## 12 France 306951 1.19 30518 9.94 73727 24.02 202706 66.04
## 13 England, United Kingdom 291179 1.13 36854 12.66 0 0.00 254325 87.34
## 14 Metropolitana, Chile 274145 1.06 8559 3.12 260827 95.14 4759 1.74
## 15 Turkey 271705 1.06 6417 2.36 245929 90.51 19359 7.13
## 16 Moscow, Russia 263059 1.02 4832 1.84 215383 81.88 42844 16.29
## 17 Bahia, Brazil 259418 1.01 5448 2.10 243876 94.01 10094 3.89
## 18 Los Angeles, California, US 242521 0.94 5829 2.40 0 0.00 236692 97.60
## 19 Iraq 238338 0.93 7123 2.99 180473 75.72 50742 21.29
## 20 Uttar Pradesh, India 235757 0.92 3542 1.50 176677 74.94 55538 23.56
## ============================================================================================================================================
## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY DEATHS Cases -- Data dated: 2020-09-02 :: 2020-09-02 21:06:09
## ############################################################################################################################################
## Number of Countries/Regions reported: 188
## Number of Cities/Provinces reported: 563
## Unique number of distinct geographical locations combined: 3954
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 England, United Kingdom 291179 1.13 36854 12.66 0 0.00 254325 87.34
## 2 France 306951 1.19 30518 9.94 73727 24.02 202706 66.04
## 3 Sao Paulo, Brazil 814375 3.16 30375 3.73 655747 80.52 128253 15.75
## 4 Maharashtra, India 808306 3.14 24903 3.08 584537 72.32 198866 24.60
## 5 Iran 376894 1.46 21672 5.75 325124 86.26 30098 7.99
## 6 Lombardia, Italy 100317 0.39 16867 16.81 76368 76.13 7082 7.06
## 7 Rio de Janeiro, Brazil 226800 0.88 16217 7.15 204845 90.32 5738 2.53
## 8 South Africa 628259 2.44 14263 2.27 549993 87.54 64003 10.19
## 9 Lima, Peru 309162 1.20 12832 4.15 0 0.00 296330 95.85
## 10 Ciudad de Mexico, Mexico 99564 0.39 10591 10.64 82245 82.61 6728 6.76
## 11 Belgium 85487 0.33 9897 11.58 18457 21.59 57133 66.83
## 12 Argentina 428239 1.66 8919 2.08 308376 72.01 110944 25.91
## 13 Madrid, Spain 128178 0.50 8662 6.76 40736 31.78 78780 61.46
## 14 Metropolitana, Chile 274145 1.06 8559 3.12 260827 95.14 4759 1.74
## 15 Ceara, Brazil 216333 0.84 8447 3.90 191511 88.53 16375 7.57
## 16 Mexico, Mexico 68516 0.27 8082 11.80 57137 83.39 3297 4.81
## 17 Pernambuco, Brazil 127287 0.49 7614 5.98 110583 86.88 9090 7.14
## 18 Indonesia 177571 0.69 7505 4.23 128057 72.12 42009 23.66
## 19 Tamil Nadu, India 433969 1.69 7418 1.71 374172 86.22 52379 12.07
## 20 Kings, New York, US 65118 0.25 7290 11.20 0 0.00 57828 88.80
## ============================================================================================================================================
## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY RECOVERED Cases -- Data dated: 2020-09-02 :: 2020-09-02 21:06:09
## ############################################################################################################################################
## Number of Countries/Regions reported: 188
## Number of Cities/Provinces reported: 563
## Unique number of distinct geographical locations combined: 3954
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Recovered, US 0 0.00 0 NaN 2202663 Inf -2202663 -Inf
## 2 Sao Paulo, Brazil 814375 3.16 30375 3.73 655747 80.52 128253 15.75
## 3 Maharashtra, India 808306 3.14 24903 3.08 584537 72.32 198866 24.60
## 4 South Africa 628259 2.44 14263 2.27 549993 87.54 64003 10.19
## 5 Unknown, Peru 0 0.00 0 NaN 471599 Inf -471599 -Inf
## 6 Tamil Nadu, India 433969 1.69 7418 1.71 374172 86.22 52379 12.07
## 7 Andhra Pradesh, India 445139 1.73 4053 0.91 339876 76.35 101210 22.74
## 8 Iran 376894 1.46 21672 5.75 325124 86.26 30098 7.99
## 9 Argentina 428239 1.66 8919 2.08 308376 72.01 110944 25.91
## 10 Saudi Arabia 316670 1.23 3929 1.24 291514 92.06 21227 6.70
## 11 Metropolitana, Chile 274145 1.06 8559 3.12 260827 95.14 4759 1.74
## 12 Karnataka, India 351481 1.36 5837 1.66 254626 72.44 91018 25.90
## 13 Turkey 271705 1.06 6417 2.36 245929 90.51 19359 7.13
## 14 Bahia, Brazil 259418 1.01 5448 2.10 243876 94.01 10094 3.89
## 15 Moscow, Russia 263059 1.02 4832 1.84 215383 81.88 42844 16.29
## 16 Bangladesh 314946 1.22 4316 1.37 208177 66.10 102453 32.53
## 17 Rio de Janeiro, Brazil 226800 0.88 16217 7.15 204845 90.32 5738 2.53
## 18 Ceara, Brazil 216333 0.84 8447 3.90 191511 88.53 16375 7.57
## 19 Para, Brazil 200985 0.78 6176 3.07 186253 92.67 8556 4.26
## 20 Minas Gerais, Brazil 218781 0.85 5364 2.45 181888 83.14 31529 14.41
## ============================================================================================================================================
## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY ACTIVE Cases -- Data dated: 2020-09-02 :: 2020-09-02 21:06:09
## ############################################################################################################################################
## Number of Countries/Regions reported: 188
## Number of Cities/Provinces reported: 563
## Unique number of distinct geographical locations combined: 3954
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Lima, Peru 309162 1.20 12832 4.15 0 0.00 296330 95.85
## 2 England, United Kingdom 291179 1.13 36854 12.66 0 0.00 254325 87.34
## 3 Los Angeles, California, US 242521 0.94 5829 2.40 0 0.00 236692 97.60
## 4 France 306951 1.19 30518 9.94 73727 24.02 202706 66.04
## 5 Maharashtra, India 808306 3.14 24903 3.08 584537 72.32 198866 24.60
## 6 Miami-Dade, Florida, US 159059 0.62 2537 1.60 0 0.00 156522 98.40
## 7 Maricopa, Arizona, US 134004 0.52 2976 2.22 0 0.00 131028 97.78
## 8 Sao Paulo, Brazil 814375 3.16 30375 3.73 655747 80.52 128253 15.75
## 9 Cook, Illinois, US 126992 0.49 5065 3.99 0 0.00 121927 96.01
## 10 Argentina 428239 1.66 8919 2.08 308376 72.01 110944 25.91
## 11 Harris, Texas, US 107490 0.42 2208 2.05 0 0.00 105282 97.95
## 12 Bangladesh 314946 1.22 4316 1.37 208177 66.10 102453 32.53
## 13 Andhra Pradesh, India 445139 1.73 4053 0.91 339876 76.35 101210 22.74
## 14 Karnataka, India 351481 1.36 5837 1.66 254626 72.44 91018 25.90
## 15 Madrid, Spain 128178 0.50 8662 6.76 40736 31.78 78780 61.46
## 16 Catalonia, Spain 110667 0.43 5749 5.19 26203 23.68 78715 71.13
## 17 Dallas, Texas, US 72252 0.28 959 1.33 0 0.00 71293 98.67
## 18 Broward, Florida, US 72245 0.28 1187 1.64 0 0.00 71058 98.36
## 19 South Africa 628259 2.44 14263 2.27 549993 87.54 64003 10.19
## 20 Queens, New York, US 70288 0.27 7224 10.28 0 0.00 63064 89.72
## ============================================================================================================================================
## Confirmed Deaths Recovered Active
## Totals
## 25749642 857015 17074386 NA
## Average
## 6512.3 216.75 4318.26 NA
## Standard Deviation
## 33882.64 1344.45 44218.98 NA
##
##
## * Statistical estimators computed considering 3954 independent reported entries## >>> checking data integrity...## No critical issues have been found.##
##
## ********************************************************************************
## ******************************** OVERALL SUMMARY********************************
## ********************************************************************************
## **** Time Series Worldwide TOTS ****
## ts-confirmed ts-deaths ts-recovered
## 25749642 857015 17073236
## 3.33% 66.3%
## **** Time Series Worldwide AVGS ****
## ts-confirmed ts-deaths ts-recovered
## 96803.17 3221.86 67483.15
## 3.33% 69.71%
## **** Time Series Worldwide SDS ****
## ts-confirmed ts-deaths ts-recovered
## 508760.74 15411.2 320756.79
## 3.03% 63.05%
##
##
## * Statistical estimators computed considering 266/266/253 independent reported entries per case-type
## ********************************************************************************More next week!!