Prediction of Covid19 spread and death in India over next 3 months and comparison of its current situation to its neighboring countries
Mirza Wasim Ahmed
23/11/2020
Project Overviews:
Drawing live covid19 time series data with the help of ‘covid19.analytics’ package.Interactive world map with covid19 information for every country/ territory/ region.
Filter out and prepare data for further analysis.
Display covid19 spread in India.
Prediction for next 3 months in India with the help of ‘prophet’ package.
Evaluation of model performance.
Prediction of number of deaths.
Comparison of its present situation to its neighboring countries of India.
#######################
#### Load Packages ####
#######################
my_packages <- c("lubridate", "ggplot2", "dplyr", "prophet", "covid19.analytics", "pheatmap")
lapply(my_packages, require, character.only= T)## [[1]]
## [1] TRUE
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## [1] TRUE
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## [1] TRUE
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## [1] TRUE
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## [1] TRUE
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## [1] TRUECovid19 data is available for 269 countries and territories all over the world from 22nd January 2020.
#############################
#### Scrap Covid19 data #####
#### and data preparation####
#############################
world_covid <- covid19.data(case= "ts-confirmed")## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## --------------------------------------------------------------------------------View(world_covid)
live.map(world_covid)#########################
#### Data preparation####
#########################
ind_covid <- world_covid %>% filter(Country.Region=='India')
View(ind_covid)
# Transpose
ind_covid <- data.frame(t(ind_covid))
str(ind_covid)## 'data.frame': 310 obs. of 1 variable:
## $ t.ind_covid.: chr "" "India" "20.59368" "78.96288" ...ind_covid <- cbind(rownames(ind_covid), data.frame(ind_covid, row.names = NULL))
View(ind_covid)
# Rename the columns
colnames(ind_covid)<- c('Date', 'Number_of_cases')
# Remove unwanted rows
ind_covid <- slice(ind_covid, -c(1:4))
str(ind_covid)## 'data.frame': 306 obs. of 2 variables:
## $ Date : chr "2020-01-22" "2020-01-23" "2020-01-24" "2020-01-25" ...
## $ Number_of_cases: chr "0" "0" "0" "0" ...ind_covid$Date <- ymd(ind_covid$Date)
ind_covid$Number_of_cases <- as.numeric(ind_covid$Number_of_cases)################################################
#### Visualization Covid19 Spread in India #####
################################################
attach(ind_covid)
qplot(Date, Number_of_cases, xlab = '', ylab= 'Number of cases',
main= 'Covid19 Spread in India')
Prediction of covid19 in India for next 3 months with the help of package called ‘prophet’.
################################################
#### Prediction of Covid19 Spread in India #####
################################################
attach(ind_covid)## The following objects are masked from ind_covid (pos = 3):
##
## Date, Number_of_casesds <- Date
y <- Number_of_cases
mydf <- data.frame(ds,y)
d<- prophet(mydf)## Disabling yearly seasonality. Run prophet with yearly.seasonality=TRUE to override this.## Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.pred <- make_future_dataframe(d, periods = 90)
View(tail(pred))
forecast <- predict(d,pred)
dyplot.prophet(d,forecast,xlab= '', ylab= 'Number of cases',
main= 'Covid19 prediction in India')## Warning: `select_()` is deprecated as of dplyr 0.7.0.
## Please use `select()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.prophet_plot_components(d,forecast)
By 19th February number of confirmed cases may reach up to 14.6 millions. These predictions are valid assuming that current situation continues. Now the number of cases being reported is more than 50,000.
Weekly trend shows a drop in number of cases on every Tuesdays and a spike on Wednesday which is not supposed to be real but artificial may be because of data entry,testing and reporting.And definitely it does not mean that the risk of being infected is lower on Wednesdays and higher saturdays.
# Model accuracy.#
#######################
#### Model Accuracy####
#######################
projected <- forecast$yhat[1:306]
real_value <- d$history$y
plot(projected, real_value, xlab = 'Predicted Value', ylab = 'True Value',
main= 'True Values vs Predicted Values')
abline(lm(projected~real_value), lwd=2)
summary(lm(projected~real_value))##
## Call:
## lm(formula = projected ~ real_value)
##
## Residuals:
## Min 1Q Median 3Q Max
## -193415 -3928 -1117 1832 250807
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.530e+03 4.779e+03 0.32 0.749
## real_value 9.993e-01 1.258e-03 794.58 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 66660 on 304 degrees of freedom
## Multiple R-squared: 0.9995, Adjusted R-squared: 0.9995
## F-statistic: 6.314e+05 on 1 and 304 DF, p-value: < 2.2e-16A clear linear pattern can be seen. The linear model fits well. Neither much under estimation nor much over estimation can be seen in the model fitting. Also p-value is lower than 2.2e-16 which indicates that the model is statistically significant.
Pediction of number of deaths in India in next 3 months
########################################
#### Time series data for casualties ###
########################################
covid19.TS.deaths <- covid19.data("ts-deaths")## 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 2020-11-24 00:35:07 || Range of dates on data: 2020-01-22--2020-11-22 | Nbr of records: 269## --------------------------------------------------------------------------------# Number of deaths in India #
ind_covid.death <- covid19.TS.deaths %>% filter(Country.Region=='India')
df.ind.death <- data.frame(t(ind_covid.death))
df.ind.death <- cbind(rownames(df.ind.death),
data.frame(df.ind.death,row.names = NULL))
str(df.ind.death)## 'data.frame': 310 obs. of 2 variables:
## $ rownames(df.ind.death): chr "Province.State" "Country.Region" "Lat" "Long" ...
## $ t.ind_covid.death. : chr "" "India" "20.59368" "78.96288" ...# Rename the columns
colnames(df.ind.death)<- c('Date', 'Number_of_deaths')
# Remove unwanted rows
df.ind.death <- slice(df.ind.death, -c(1:4))
df.ind.death$Date <- ymd(df.ind.death$Date)
df.ind.death$Number_of_deaths <- as.numeric(df.ind.death$Number_of_deaths)
str(df.ind.death)## 'data.frame': 306 obs. of 2 variables:
## $ Date : Date, format: "2020-01-22" "2020-01-23" ...
## $ Number_of_deaths: num 0 0 0 0 0 0 0 0 0 0 ...attach(df.ind.death)
ds <- Date
y <- Number_of_deaths
new.df <- data.frame(ds,y)
d.death<- prophet(new.df)
pred.death <- make_future_dataframe(d.death, periods = 90)
View(tail(pred.death))
forecast1 <- predict(d.death,pred.death)
dyplot.prophet(d.death,forecast1,xlab= '', ylab= 'Number of deaths',
main= 'Prediction of death by Covid19 in India')Comparison of India’s situation with neighbor countries
Growth rate of covid19 India is much higher than its neighboring countries. Whereas number daily new cases reported in India is much higher than its neighbor countries. Countries like Sri Lanka, Bangladesh and Pakistan are doing better than India in terms of fighting with covid19.
####################
#### Comparison ####
####################
#compute changes and growth rates per location for 'India'
growth.rate(world_covid,geo.loc=c("India","Bangladesh",
"Pakistan","Sri Lanka"))## [1] "INDIA"
## [1] "BANGLADESH"
## [1] "PAKISTAN"
## [1] "SRILANKA"
## Processing... INDIA## Processing... BANGLADESH## Processing... PAKISTAN## Processing... SRI LANKA
## $Changes
## geo.loc 2020-01-23 2020-01-24 2020-01-25 2020-01-26 2020-01-27 2020-01-28
## 1 INDIA 0 0 0 0 0 0
## 2 BANGLADESH 0 0 0 0 0 0
## 3 PAKISTAN 0 0 0 0 0 0
## 4 SRI LANKA 0 0 0 0 1 0
## 2020-01-29 2020-01-30 2020-01-31 2020-02-01 2020-02-02 2020-02-03 2020-02-04
## 1 0 1 0 0 1 1 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09 2020-02-10 2020-02-11
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16 2020-02-17 2020-02-18
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24 2020-02-25
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 2
## 4 0 0 0 0 0 0 0
## 2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02 2020-03-03
## 1 0 0 0 0 0 2 0
## 2 0 0 0 0 0 0 0
## 3 0 0 2 0 0 1 0
## 4 0 0 0 0 0 0 0
## 2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09 2020-03-10
## 1 23 2 1 3 5 4 13
## 2 0 0 0 0 3 0 0
## 3 0 1 0 0 0 10 3
## 4 0 0 0 0 0 0 0
## 2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16 2020-03-17
## 1 6 11 9 20 11 6 23
## 2 0 0 0 0 2 3 2
## 3 1 8 3 22 83 100 63
## 4 1 0 4 4 8 10 16
## 2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22 2020-03-23 2020-03-24
## 1 14 38 50 86 66 103 37
## 2 4 3 3 5 2 6 6
## 3 155 47 229 46 99 97 91
## 4 7 9 13 4 5 15 5
## 2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30 2020-03-31
## 1 121 70 160 100 37 227 146
## 2 0 5 4 0 0 1 2
## 3 138 172 122 102 120 221 180
## 4 0 4 0 7 4 5 21
## 2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06 2020-04-07
## 1 601 545 24 515 506 1190 533
## 2 3 2 5 9 18 35 41
## 3 303 265 132 339 609 269 228
## 4 3 5 8 7 10 2 7
## 2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12 2020-04-13 2020-04-14
## 1 605 809 873 848 759 1248 1034
## 2 54 112 94 58 139 182 209
## 3 226 206 316 219 266 341 546
## 4 4 1 0 8 12 7 16
## 2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19 2020-04-20 2020-04-21
## 1 835 1108 922 1370 1893 924 1541
## 2 219 341 266 306 312 492 434
## 3 536 106 613 710 70 1147 511
## 4 5 0 6 10 17 33 6
## 2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26 2020-04-27 2020-04-28
## 1 1290 1707 1453 1753 1607 1561 1873
## 2 390 414 503 309 418 497 549
## 3 1079 785 783 605 587 697 913
## 4 20 38 52 40 63 65 31
## 2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03 2020-05-04 2020-05-05
## 1 1738 1801 2394 2442 2806 3932 2963
## 2 641 564 571 552 665 688 786
## 3 1292 1297 989 981 857 1108 2024
## 4 30 14 27 15 13 33 20
## 2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10 2020-05-11 2020-05-12
## 1 3587 3364 3344 3113 4353 3607 3524
## 2 790 706 709 636 887 1034 969
## 3 571 1791 2301 1598 1747 2255 962
## 4 26 27 11 12 16 6 20
## 2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17 2020-05-18 2020-05-19
## 1 3763 3942 3787 4864 5050 4630 6147
## 2 1162 1041 1202 930 1273 1602 1251
## 3 490 3011 0 1352 1974 1841 1932
## 4 26 10 10 25 21 11 35
## 2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24 2020-05-25 2020-05-26
## 1 5553 6198 6568 6629 7113 6414 5843
## 2 1617 1773 1694 1873 1532 1975 1166
## 3 2193 2603 1743 2164 1748 1356 1446
## 4 1 27 13 21 52 41 137
## 2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31 2020-06-01 2020-06-02
## 1 7293 7300 8105 8336 8782 7761 8821
## 2 1541 2029 2523 1764 2545 2381 2911
## 3 2076 2801 2429 3039 2964 3938 4065
## 4 150 61 28 62 13 10 40
## 2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07 2020-06-08 2020-06-09
## 1 9633 9889 9471 10438 10864 8442 10218
## 2 2695 2423 2828 2635 2743 2735 3171
## 3 4801 3985 4734 4960 4728 4646 5385
## 4 66 48 4 13 21 22 2
## 2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14 2020-06-15 2020-06-16
## 1 10459 10930 11458 11929 11502 10667 10974
## 2 3190 3187 3471 2856 3141 3099 3862
## 3 5834 6397 0 6472 12073 4443 5839
## 4 10 8 3 4 5 16 10
## 2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21 2020-06-22 2020-06-23
## 1 12881 13586 14516 15403 14831 14933 15968
## 2 4008 3803 3243 3240 3531 3480 3412
## 3 5358 4944 6604 4951 4471 3946 3892
## 4 9 23 3 0 0 1 40
## 2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28 2020-06-29 2020-06-30
## 1 16922 17296 18552 19906 19459 18522 18641
## 2 3462 3946 3868 3504 3809 4014 3682
## 3 4044 2775 3138 4072 3557 2825 4133
## 4 10 9 4 19 4 2 8
## 2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05 2020-07-06 2020-07-07
## 1 19160 20903 22771 24850 24248 22251 22753
## 2 3775 4019 3114 3288 2738 3201 3027
## 3 4339 4087 0 3387 6535 2691 2980
## 4 7 12 3 5 2 1 4
## 2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12 2020-07-13 2020-07-14
## 1 24879 26506 27114 28606 28732 28498 29429
## 2 3489 3360 2949 2686 2666 3099 3163
## 3 3359 2751 2752 2521 2753 1979 2165
## 4 13 60 300 57 106 29 19
## 2020-07-15 2020-07-16 2020-07-17 2020-07-18 2020-07-19 2020-07-20 2020-07-21
## 1 32676 34975 35252 38697 40425 37132 37740
## 2 3533 2733 3034 2709 2459 2928 3057
## 3 2145 0 4003 1579 1587 1013 1332
## 4 6 16 10 6 21 6 0
## 2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26 2020-07-27 2020-07-28
## 1 45720 49310 48916 48611 49981 44457 51596
## 2 2744 2856 2548 2520 2275 2772 2960
## 3 1763 1209 1487 1226 0 1176 936
## 4 22 1 11 6 12 23 5
## 2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02 2020-08-03 2020-08-04
## 1 50294 52783 61242 54735 52972 52050 52509
## 2 3009 2695 2772 2199 886 1356 1918
## 3 1063 1114 903 0 1394 762 0
## 4 0 4 1 0 8 5 6
## 2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09 2020-08-10 2020-08-11
## 1 56282 62538 61537 64399 62064 53601 60963
## 2 2654 2977 2851 2611 2487 2907 2996
## 3 675 727 782 842 634 539 531
## 4 5 0 0 2 3 27 9
## 2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16 2020-08-17 2020-08-18
## 1 66999 64553 64732 64030 57711 55018 64572
## 2 2995 2617 2766 2644 2024 2595 3200
## 3 730 753 626 747 1168 0 617
## 4 1 1 4 4 3 7 2
## 2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23 2020-08-24 2020-08-25
## 1 69672 68900 69876 69239 61408 60975 57224
## 2 2747 2868 2401 2265 1973 2485 2545
## 3 613 513 630 586 1087 0 450
## 4 0 16 23 6 6 6 12
## 2020-08-26 2020-08-27 2020-08-28 2020-08-29 2020-08-30 2020-08-31 2020-09-01
## 1 85687 77266 76472 78761 78512 69921 78357
## 2 2519 2436 2211 2131 1897 2174 1950
## 3 482 445 415 319 264 213 300
## 4 13 2 3 6 17 37 43
## 2020-09-02 2020-09-03 2020-09-04 2020-09-05 2020-09-06 2020-09-07 2020-09-08
## 1 83883 83341 86432 90632 90802 75809 89706
## 2 2582 2158 1929 1950 1592 2202 1892
## 3 865 498 513 0 878 330 426
## 4 9 10 4 6 2 0 17
## 2020-09-09 2020-09-10 2020-09-11 2020-09-12 2020-09-13 2020-09-14 2020-09-15
## 1 95735 96551 97570 94372 92071 83809 90123
## 2 1827 1892 1792 1282 1476 1812 1724
## 3 371 341 584 526 539 404 665
## 4 7 8 14 26 39 28 9
## 2020-09-16 2020-09-17 2020-09-18 2020-09-19 2020-09-20 2020-09-21 2020-09-22
## 1 97894 96424 93337 92605 86961 75083 83347
## 2 1615 1593 1541 1567 1546 1703 1557
## 3 545 752 645 640 633 582 532
## 4 0 5 5 2 4 12 14
## 2020-09-23 2020-09-24 2020-09-25 2020-09-26 2020-09-27 2020-09-28 2020-09-29
## 1 86508 86052 85362 88600 82170 70589 80472
## 2 1666 1540 1383 1106 1275 1407 1488
## 3 799 798 566 694 566 675 747
## 4 11 9 12 4 11 3 11
## 2020-09-30 2020-10-01 2020-10-02 2020-10-03 2020-10-04 2020-10-05 2020-10-06
## 1 86821 81484 79476 75829 74442 61267 72049
## 2 1436 1508 1396 1182 1125 1442 1499
## 3 543 625 553 632 0 1111 624
## 4 6 2 6 7 7 111 739
## 2020-10-07 2020-10-08 2020-10-09 2020-10-10 2020-10-11 2020-10-12 2020-10-13
## 1 78524 70496 73272 74383 66732 55342 63509
## 2 1520 1441 1278 1203 1193 1472 1537
## 3 583 661 671 666 385 531 615
## 4 207 29 35 105 124 92 194
## 2020-10-14 2020-10-15 2020-10-16 2020-10-17 2020-10-18 2020-10-19 2020-10-20
## 1 67708 63371 62212 61871 55722 46790 54044
## 2 1684 1600 1527 1209 1274 1637 1380
## 3 755 659 575 567 433 582 710
## 4 132 74 110 121 63 87 186
## 2020-10-21 2020-10-22 2020-10-23 2020-10-24 2020-10-25 2020-10-26 2020-10-27
## 1 55839 54366 53370 50129 45148 36470 43893
## 2 1545 1696 1586 1094 1308 1436 1335
## 3 736 736 847 832 707 773 825
## 4 167 309 866 368 351 541 457
## 2020-10-28 2020-10-29 2020-10-30 2020-10-31 2020-11-01 2020-11-02 2020-11-03
## 1 49881 48648 48268 46963 45231 38310 46253
## 2 1493 1681 1604 1320 1568 1736 1659
## 3 908 1078 807 977 1123 1167 1313
## 4 335 586 633 239 397 275 409
## 2020-11-04 2020-11-05 2020-11-06 2020-11-07 2020-11-08 2020-11-09 2020-11-10
## 1 50210 47638 50356 45674 45903 38073 44281
## 2 1517 1842 1469 1289 1474 1683 1699
## 3 1302 1376 1502 1436 1650 1637 1708
## 4 443 383 400 449 510 356 430
## 2020-11-11 2020-11-12 2020-11-13 2020-11-14 2020-11-15 2020-11-16 2020-11-17
## 1 47905 44879 44684 41100 30548 29163 38617
## 2 1733 1845 1767 1531 1837 2139 2212
## 3 1808 2304 2165 2443 2128 2050 2298
## 4 635 373 468 392 704 387 401
## 2020-11-18 2020-11-19 2020-11-20 2020-11-21 2020-11-22
## 1 45576 45882 46232 45209 44059
## 2 2111 2364 2275 1847 2060
## 3 2547 2738 2843 2665 2756
## 4 327 439 439 491 400
##
## $Growth.Rate
## geo.loc 2020-01-24 2020-01-25 2020-01-26 2020-01-27 2020-01-28 2020-01-29
## 1 INDIA NaN NaN NaN NaN NaN NaN
## 2 BANGLADESH NaN NaN NaN NaN NaN NaN
## 3 PAKISTAN NaN NaN NaN NaN NaN NaN
## 4 SRI LANKA NaN NaN NaN NA 0 NaN
## 2020-01-30 2020-01-31 2020-02-01 2020-02-02 2020-02-03 2020-02-04 2020-02-05
## 1 NA 0 NaN NA 1 0 NaN
## 2 NaN NaN NaN NaN NaN NaN NaN
## 3 NaN NaN NaN NaN NaN NaN NaN
## 4 NaN NaN NaN NaN NaN NaN NaN
## 2020-02-06 2020-02-07 2020-02-08 2020-02-09 2020-02-10 2020-02-11 2020-02-12
## 1 NaN NaN NaN NaN NaN NaN NaN
## 2 NaN NaN NaN NaN NaN NaN NaN
## 3 NaN NaN NaN NaN NaN NaN NaN
## 4 NaN NaN NaN NaN NaN NaN NaN
## 2020-02-13 2020-02-14 2020-02-15 2020-02-16 2020-02-17 2020-02-18 2020-02-19
## 1 NaN NaN NaN NaN NaN NaN NaN
## 2 NaN NaN NaN NaN NaN NaN NaN
## 3 NaN NaN NaN NaN NaN NaN NaN
## 4 NaN NaN NaN NaN NaN NaN NaN
## 2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24 2020-02-25 2020-02-26
## 1 NaN NaN NaN NaN NaN NaN NaN
## 2 NaN NaN NaN NaN NaN NaN NaN
## 3 NaN NaN NaN NaN NaN NA 0
## 4 NaN NaN NaN NaN NaN NaN NaN
## 2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02 2020-03-03 2020-03-04
## 1 NaN NaN NaN NaN NA 0 NA
## 2 NaN NaN NaN NaN NaN NaN NaN
## 3 NaN NA 0 NaN NA 0 NaN
## 4 NaN NaN NaN NaN NaN NaN NaN
## 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09 2020-03-10 2020-03-11
## 1 0.08695652 0.5 3 1.666667 0.8 3.25 0.4615385
## 2 NaN NaN NaN NA 0.0 NaN NaN
## 3 NA 0.0 NaN NaN NA 0.30 0.3333333
## 4 NaN NaN NaN NaN NaN NaN NA
## 2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16 2020-03-17 2020-03-18
## 1 1.833333 0.8181818 2.222222 0.550000 0.5454545 3.8333333 0.6086957
## 2 NaN NaN NaN NA 1.5000000 0.6666667 2.0000000
## 3 8.000000 0.3750000 7.333333 3.772727 1.2048193 0.6300000 2.4603175
## 4 0.000000 NA 1.000000 2.000000 1.2500000 1.6000000 0.4375000
## 2020-03-19 2020-03-20 2020-03-21 2020-03-22 2020-03-23 2020-03-24 2020-03-25
## 1 2.7142857 1.315789 1.7200000 0.7674419 1.560606 0.3592233 3.270270
## 2 0.7500000 1.000000 1.6666667 0.4000000 3.000000 1.0000000 0.000000
## 3 0.3032258 4.872340 0.2008734 2.1521739 0.979798 0.9381443 1.516484
## 4 1.2857143 1.444444 0.3076923 1.2500000 3.000000 0.3333333 0.000000
## 2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30 2020-03-31 2020-04-01
## 1 0.5785124 2.2857143 0.6250000 0.3700000 6.135135 0.6431718 4.1164384
## 2 NA 0.8000000 0.0000000 NaN NA 2.0000000 1.5000000
## 3 1.2463768 0.7093023 0.8360656 1.1764706 1.841667 0.8144796 1.6833333
## 4 NA 0.0000000 NA 0.5714286 1.250000 4.2000000 0.1428571
## 2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06 2020-04-07 2020-04-08
## 1 0.9068220 0.0440367 21.458333 0.9825243 2.3517787 0.4478992 1.1350844
## 2 0.6666667 2.5000000 1.800000 2.0000000 1.9444444 1.1714286 1.3170732
## 3 0.8745875 0.4981132 2.568182 1.7964602 0.4417077 0.8475836 0.9912281
## 4 1.6666667 1.6000000 0.875000 1.4285714 0.2000000 3.5000000 0.5714286
## 2020-04-09 2020-04-10 2020-04-11 2020-04-12 2020-04-13 2020-04-14 2020-04-15
## 1 1.3371901 1.0791100 0.9713631 0.8950472 1.6442688 0.8285256 0.8075435
## 2 2.0740741 0.8392857 0.6170213 2.3965517 1.3093525 1.1483516 1.0478469
## 3 0.9115044 1.5339806 0.6930380 1.2146119 1.2819549 1.6011730 0.9816850
## 4 0.2500000 0.0000000 NA 1.5000000 0.5833333 2.2857143 0.3125000
## 2020-04-16 2020-04-17 2020-04-18 2020-04-19 2020-04-20 2020-04-21 2020-04-22
## 1 1.3269461 0.8321300 1.485900 1.38175182 0.4881141 1.6677489 0.8371188
## 2 1.5570776 0.7800587 1.150376 1.01960784 1.5769231 0.8821138 0.8986175
## 3 0.1977612 5.7830189 1.158238 0.09859155 16.3857143 0.4455100 2.1115460
## 4 0.0000000 NA 1.666667 1.70000000 1.9411765 0.1818182 3.3333333
## 2020-04-23 2020-04-24 2020-04-25 2020-04-26 2020-04-27 2020-04-28 2020-04-29
## 1 1.3232558 0.8512009 1.2064694 0.9167142 0.9713752 1.1998719 0.9279231
## 2 1.0615385 1.2149758 0.6143141 1.3527508 1.1889952 1.1046278 1.1675774
## 3 0.7275255 0.9974522 0.7726692 0.9702479 1.1873935 1.3098996 1.4151150
## 4 1.9000000 1.3684211 0.7692308 1.5750000 1.0317460 0.4769231 0.9677419
## 2020-04-30 2020-05-01 2020-05-02 2020-05-03 2020-05-04 2020-05-05 2020-05-06
## 1 1.0362486 1.3292615 1.0200501 1.1490581 1.401283 0.7535605 1.2105974
## 2 0.8798752 1.0124113 0.9667250 1.2047101 1.034586 1.1424419 1.0050891
## 3 1.0038700 0.7625289 0.9919110 0.8735984 1.292882 1.8267148 0.2821146
## 4 0.4666667 1.9285714 0.5555556 0.8666667 2.538462 0.6060606 1.3000000
## 2020-05-07 2020-05-08 2020-05-09 2020-05-10 2020-05-11 2020-05-12 2020-05-13
## 1 0.9378311 0.9940547 0.9309211 1.398330 0.8286239 0.9769892 1.0678207
## 2 0.8936709 1.0042493 0.8970381 1.394654 1.1657272 0.9371373 1.1991744
## 3 3.1366025 1.2847571 0.6944807 1.093242 1.2907842 0.4266075 0.5093555
## 4 1.0384615 0.4074074 1.0909091 1.333333 0.3750000 3.3333333 1.3000000
## 2020-05-14 2020-05-15 2020-05-16 2020-05-17 2020-05-18 2020-05-19 2020-05-20
## 1 1.0475684 0.9606799 1.2843940 1.038240 0.9168317 1.3276458 0.90336750
## 2 0.8958692 1.1546590 0.7737105 1.368817 1.2584446 0.7808989 1.29256595
## 3 6.1448980 0.0000000 NA 1.460059 0.9326241 1.0494297 1.13509317
## 4 0.3846154 1.0000000 2.5000000 0.840000 0.5238095 3.1818182 0.02857143
## 2020-05-21 2020-05-22 2020-05-23 2020-05-24 2020-05-25 2020-05-26 2020-05-27
## 1 1.116153 1.0596967 1.009287 1.0730125 0.9017292 0.9109760 1.248160
## 2 1.096475 0.9554428 1.105667 0.8179391 1.2891645 0.5903797 1.321612
## 3 1.186959 0.6696120 1.241538 0.8077634 0.7757437 1.0663717 1.435685
## 4 27.000000 0.4814815 1.615385 2.4761905 0.7884615 3.3414634 1.094891
## 2020-05-28 2020-05-29 2020-05-30 2020-05-31 2020-06-01 2020-06-02 2020-06-03
## 1 1.0009598 1.1102740 1.0285009 1.0535029 0.8837395 1.136580 1.0920531
## 2 1.3166775 1.2434697 0.6991677 1.4427438 0.9355599 1.222596 0.9257987
## 3 1.3492293 0.8671903 1.2511322 0.9753208 1.3286100 1.032250 1.1810578
## 4 0.4066667 0.4590164 2.2142857 0.2096774 0.7692308 4.000000 1.6500000
## 2020-06-04 2020-06-05 2020-06-06 2020-06-07 2020-06-08 2020-06-09 2020-06-10
## 1 1.0265753 0.95773081 1.1021012 1.0408124 0.7770619 1.21037669 1.023586
## 2 0.8990724 1.16714816 0.9317539 1.0409867 0.9970835 1.15941499 1.005992
## 3 0.8300354 1.18795483 1.0477398 0.9532258 0.9826565 1.15906156 1.083380
## 4 0.7272727 0.08333333 3.2500000 1.6153846 1.0476190 0.09090909 5.000000
## 2020-06-11 2020-06-12 2020-06-13 2020-06-14 2020-06-15 2020-06-16 2020-06-17
## 1 1.0450330 1.048307 1.0411067 0.9642049 0.9274039 1.028780 1.1737744
## 2 0.9990596 1.089112 0.8228176 1.0997899 0.9866285 1.246208 1.0378042
## 3 1.0965033 0.000000 NA 1.8654203 0.3680113 1.314202 0.9176229
## 4 0.8000000 0.375000 1.3333333 1.2500000 3.2000000 0.625000 0.9000000
## 2020-06-18 2020-06-19 2020-06-20 2020-06-21 2020-06-22 2020-06-23 2020-06-24
## 1 1.0547318 1.0684528 1.0611050 0.9628644 1.0068775 1.0693096 1.059744
## 2 0.9488523 0.8527478 0.9990749 1.0898148 0.9855565 0.9804598 1.014654
## 3 0.9227324 1.3357605 0.7496972 0.9030499 0.8825766 0.9863153 1.039054
## 4 2.5555556 0.1304348 0.0000000 NaN NA 40.0000000 0.250000
## 2020-06-25 2020-06-26 2020-06-27 2020-06-28 2020-06-29 2020-06-30 2020-07-01
## 1 1.0221014 1.0726179 1.0729840 0.9775445 0.9518475 1.0064248 1.027842
## 2 1.1398036 0.9802331 0.9058945 1.0870434 1.0538199 0.9172895 1.025258
## 3 0.6862018 1.1308108 1.2976418 0.8735265 0.7942086 1.4630088 1.049843
## 4 0.9000000 0.4444444 4.7500000 0.2105263 0.5000000 4.0000000 0.875000
## 2020-07-02 2020-07-03 2020-07-04 2020-07-05 2020-07-06 2020-07-07 2020-07-08
## 1 1.0909708 1.0893652 1.091300 0.9757746 0.9176427 1.022561 1.093438
## 2 1.0646358 0.7748196 1.055877 0.8327251 1.1691015 0.945642 1.152626
## 3 0.9419221 0.0000000 NA 1.9294361 0.4117827 1.107395 1.127181
## 4 1.7142857 0.2500000 1.666667 0.4000000 0.5000000 4.000000 3.250000
## 2020-07-09 2020-07-10 2020-07-11 2020-07-12 2020-07-13 2020-07-14 2020-07-15
## 1 1.0653965 1.0229382 1.0550269 1.004405 0.9918558 1.0326690 1.1103333
## 2 0.9630267 0.8776786 0.9108172 0.992554 1.1624156 1.0206518 1.1169776
## 3 0.8189937 1.0003635 0.9160610 1.092027 0.7188522 1.0939869 0.9907621
## 4 4.6153846 5.0000000 0.1900000 1.859649 0.2735849 0.6551724 0.3157895
## 2020-07-16 2020-07-17 2020-07-18 2020-07-19 2020-07-20 2020-07-21 2020-07-22
## 1 1.0703574 1.007920 1.0977250 1.044655 0.9185405 1.016374 1.211447
## 2 0.7735635 1.110135 0.8928807 0.907715 1.1907279 1.044057 0.897612
## 3 0.0000000 NA 0.3944542 1.005066 0.6383113 1.314906 1.323574
## 4 2.6666667 0.625000 0.6000000 3.500000 0.2857143 0.000000 NA
## 2020-07-23 2020-07-24 2020-07-25 2020-07-26 2020-07-27 2020-07-28 2020-07-29
## 1 1.07852143 0.9920097 0.9937648 1.0281829 0.889478 1.1605821 0.9747655
## 2 1.04081633 0.8921569 0.9890110 0.9027778 1.218462 1.0678211 1.0165541
## 3 0.68576290 1.2299421 0.8244788 0.0000000 NA 0.7959184 1.1356838
## 4 0.04545455 11.0000000 0.5454545 2.0000000 1.916667 0.2173913 0.0000000
## 2020-07-30 2020-07-31 2020-08-01 2020-08-02 2020-08-03 2020-08-04 2020-08-05
## 1 1.0494890 1.1602599 0.8937494 0.9677903 0.9825946 1.008818 1.0718543
## 2 0.8956464 1.0285714 0.7932900 0.4029104 1.5304740 1.414454 1.3837331
## 3 1.0479774 0.8105925 0.0000000 NA 0.5466284 0.000000 NA
## 4 NA 0.2500000 0.0000000 NA 0.6250000 1.200000 0.8333333
## 2020-08-06 2020-08-07 2020-08-08 2020-08-09 2020-08-10 2020-08-11 2020-08-12
## 1 1.111155 0.9839937 1.046509 0.9637417 0.8636408 1.1373482 1.0990109
## 2 1.121703 0.9576755 0.915819 0.9525086 1.1688782 1.0306158 0.9996662
## 3 1.077037 1.0756534 1.076726 0.7529691 0.8501577 0.9851577 1.3747646
## 4 0.000000 NaN NA 1.5000000 9.0000000 0.3333333 0.1111111
## 2020-08-13 2020-08-14 2020-08-15 2020-08-16 2020-08-17 2020-08-18 2020-08-19
## 1 0.9634920 1.0027729 0.9891553 0.9013119 0.9533365 1.1736523 1.0789816
## 2 0.8737896 1.0569354 0.9558930 0.7655068 1.2821146 1.2331407 0.8584375
## 3 1.0315068 0.8313413 1.1932907 1.5635877 0.0000000 NA 0.9935170
## 4 1.0000000 4.0000000 1.0000000 0.7500000 2.3333333 0.2857143 0.0000000
## 2020-08-20 2020-08-21 2020-08-22 2020-08-23 2020-08-24 2020-08-25 2020-08-26
## 1 0.9889195 1.0141655 0.9908839 0.8868990 0.9929488 0.938483 1.4973962
## 2 1.0440481 0.8371688 0.9433569 0.8710817 1.2595033 1.024145 0.9897839
## 3 0.8368679 1.2280702 0.9301587 1.8549488 0.0000000 NA 1.0711111
## 4 NA 1.4375000 0.2608696 1.0000000 1.0000000 2.000000 1.0833333
## 2020-08-27 2020-08-28 2020-08-29 2020-08-30 2020-08-31 2020-09-01 2020-09-02
## 1 0.9017237 0.9897238 1.0299325 0.9968385 0.8905772 1.1206504 1.0705234
## 2 0.9670504 0.9076355 0.9638173 0.8901924 1.1460200 0.8969641 1.3241026
## 3 0.9232365 0.9325843 0.7686747 0.8275862 0.8068182 1.4084507 2.8833333
## 4 0.1538462 1.5000000 2.0000000 2.8333333 2.1764706 1.1621622 0.2093023
## 2020-09-03 2020-09-04 2020-09-05 2020-09-06 2020-09-07 2020-09-08 2020-09-09
## 1 0.9935386 1.0370886 1.048593 1.0018757 0.8348825 1.1833160 1.0672084
## 2 0.8357862 0.8938832 1.010886 0.8164103 1.3831658 0.8592189 0.9656448
## 3 0.5757225 1.0301205 0.000000 NA 0.3758542 1.2909091 0.8708920
## 4 1.1111111 0.4000000 1.500000 0.3333333 0.0000000 NA 0.4117647
## 2020-09-10 2020-09-11 2020-09-12 2020-09-13 2020-09-14 2020-09-15 2020-09-16
## 1 1.0085235 1.0105540 0.9672235 0.9756178 0.9102649 1.0753380 1.0862266
## 2 1.0355774 0.9471459 0.7154018 1.1513261 1.2276423 0.9514349 0.9367749
## 3 0.9191375 1.7126100 0.9006849 1.0247148 0.7495362 1.6460396 0.8195489
## 4 1.1428571 1.7500000 1.8571429 1.5000000 0.7179487 0.3214286 0.0000000
## 2020-09-17 2020-09-18 2020-09-19 2020-09-20 2020-09-21 2020-09-22 2020-09-23
## 1 0.9849838 0.9679851 0.9921575 0.9390530 0.8634100 1.1100649 1.0379258
## 2 0.9863777 0.9673572 1.0168722 0.9865986 1.1015524 0.9142689 1.0700064
## 3 1.3798165 0.8577128 0.9922481 0.9890625 0.9194313 0.9140893 1.5018797
## 4 NA 1.0000000 0.4000000 2.0000000 3.0000000 1.1666667 0.7857143
## 2020-09-24 2020-09-25 2020-09-26 2020-09-27 2020-09-28 2020-09-29 2020-09-30
## 1 0.9947288 0.9919816 1.0379326 0.9274266 0.8590605 1.140008 1.0788970
## 2 0.9243697 0.8980519 0.7997108 1.1528029 1.1035294 1.057569 0.9650538
## 3 0.9987484 0.7092732 1.2261484 0.8155620 1.1925795 1.106667 0.7269076
## 4 0.8181818 1.3333333 0.3333333 2.7500000 0.2727273 3.666667 0.5454545
## 2020-10-01 2020-10-02 2020-10-03 2020-10-04 2020-10-05 2020-10-06 2020-10-07
## 1 0.9385287 0.9753571 0.9541119 0.9817088 0.8230166 1.1759838 1.0898694
## 2 1.0501393 0.9257294 0.8467049 0.9517766 1.2817778 1.0395284 1.0140093
## 3 1.1510129 0.8848000 1.1428571 0.0000000 NA 0.5616562 0.9342949
## 4 0.3333333 3.0000000 1.1666667 1.0000000 15.8571429 6.6576577 0.2801083
## 2020-10-08 2020-10-09 2020-10-10 2020-10-11 2020-10-12 2020-10-13 2020-10-14
## 1 0.8977637 1.0393781 1.0151627 0.8971405 0.8293173 1.147573 1.0661166
## 2 0.9480263 0.8868841 0.9413146 0.9916874 1.2338642 1.044158 1.0956409
## 3 1.1337907 1.0151286 0.9925484 0.5780781 1.3792208 1.158192 1.2276423
## 4 0.1400966 1.2068966 3.0000000 1.1809524 0.7419355 2.108696 0.6804124
## 2020-10-15 2020-10-16 2020-10-17 2020-10-18 2020-10-19 2020-10-20 2020-10-21
## 1 0.9359455 0.9817109 0.9945187 0.9006158 0.8397042 1.1550331 1.0332137
## 2 0.9501188 0.9543750 0.7917485 1.0537634 1.2849294 0.8430055 1.1195652
## 3 0.8728477 0.8725341 0.9860870 0.7636684 1.3441109 1.2199313 1.0366197
## 4 0.5606061 1.4864865 1.1000000 0.5206612 1.3809524 2.1379310 0.8978495
## 2020-10-22 2020-10-23 2020-10-24 2020-10-25 2020-10-26 2020-10-27 2020-10-28
## 1 0.9736206 0.9816797 0.9392730 0.9006364 0.8077877 1.2035372 1.1364227
## 2 1.0977346 0.9351415 0.6897856 1.1956124 1.0978593 0.9296657 1.1183521
## 3 1.0000000 1.1508152 0.9822904 0.8497596 1.0933522 1.0672704 1.1006061
## 4 1.8502994 2.8025890 0.4249423 0.9538043 1.5413105 0.8447320 0.7330416
## 2020-10-29 2020-10-30 2020-10-31 2020-11-01 2020-11-02 2020-11-03 2020-11-04
## 1 0.9752812 0.9921888 0.9729635 0.9631199 0.8469855 1.2073349 1.0855512
## 2 1.1259210 0.9541939 0.8229426 1.1878788 1.1071429 0.9556452 0.9144063
## 3 1.1872247 0.7486085 1.2106568 1.1494371 1.0391808 1.1251071 0.9916222
## 4 1.7492537 1.0802048 0.3775671 1.6610879 0.6926952 1.4872727 1.0831296
## 2020-11-05 2020-11-06 2020-11-07 2020-11-08 2020-11-09 2020-11-10 2020-11-11
## 1 0.9487751 1.0570553 0.9070220 1.005014 0.8294229 1.163055 1.081841
## 2 1.2142386 0.7975027 0.8774677 1.143522 1.1417910 1.009507 1.020012
## 3 1.0568356 1.0915698 0.9560586 1.149025 0.9921212 1.043372 1.058548
## 4 0.8645598 1.0443864 1.1225000 1.135857 0.6980392 1.207865 1.476744
## 2020-11-12 2020-11-13 2020-11-14 2020-11-15 2020-11-16 2020-11-17 2020-11-18
## 1 0.9368333 0.9956550 0.9197923 0.7432603 0.9546615 1.324178 1.1802056
## 2 1.0646278 0.9577236 0.8664403 1.1998694 1.1643985 1.034128 0.9543400
## 3 1.2743363 0.9396701 1.1284065 0.8710602 0.9633459 1.120976 1.1083551
## 4 0.5874016 1.2546917 0.8376068 1.7959184 0.5497159 1.036176 0.8154613
## 2020-11-19 2020-11-20 2020-11-21 2020-11-22 NA
## 1 1.006714 1.0076283 0.9778725 0.9745626 NA
## 2 1.119848 0.9623519 0.8118681 1.1153221 NA
## 3 1.074990 1.0383492 0.9373901 1.0341463 NA
## 4 1.342508 1.0000000 1.1184510 0.8146640 NA