Why does R throw an error on iterative calculation - r

Question

I'm looking at covid-19 data to calculate estimates for the reproductive number R0.
library(ggplot2)
library(dplyr)
library(tidyr)
library(stringr)
library(TTR)
# Get COVID cases, available from:
url <- "https://static.usafacts.org/public/data/covid-19/covid_confirmed_usafacts.csv"
DoubleCOV <- read.csv(url, stringsAsFactors = FALSE)
names(DoubleCOV)[1] <- "countyFIPS"
DoubleCovid <- pivot_longer(DoubleCOV, cols=starts_with("X"),
values_to="cases",
names_to=c("X","date_infected"),
names_sep="X") %>%
mutate(infected = as.Date(date_infected, format="%m.%d.%y"),
countyFIPS = str_pad(as.character(countyFIPS), 5, pad="0"))
#data is by county, summarise for the state of interest
stateData <- DoubleCovid %>% filter(State == "AL") %>% filter(cases != 0) %>%
group_by(infected) %>% summarise(sum(cases)) %>%
mutate(DaysSince = infected - min(infected))
names(stateData)[2] <- "cumCases"
#3 day moving average to smooth a little
stateData <- stateData %>% mutate(MA = runMean(cumCases,3))
#calculate doubling rate (DR) and then R0 infectious period/doubling rate
for(j in 4:nrow(stateData)){
stateData$DR[j] <- log(2)/log(stateData$MA[j]/stateData$MA[j-1])
stateData$R0[j] <- 14/stateData$DR[j]
}
CDplot <- stateData %>%
ggplot(mapping = aes(x = as.numeric(DaysSince), y = R0)) +
geom_line(color = "firebrick")
print(CDplot)
So in the above the state of interest is Alabama, hence filter(State == "AL") and this works.
But if I change the state to "NY" I get
Error in `$<-.data.frame`(`*tmp*`, "DR", value = c(NA, NA, NA, 0.733907206043719 :
replacement has 4 rows, data has 39
head(stateData) yields
infected cumCases DaysSince MA
<date> <int> <drtn> <dbl>
1 2020-03-02 1 0 days NA
2 2020-03-03 2 1 days NA
3 2020-03-04 11 2 days 4.67
4 2020-03-05 23 3 days 12
5 2020-03-06 25 4 days 19.7
6 2020-03-07 77 5 days 41.7
The moving average values in rows 3 and 4 (12 and 4.67) would yield a doubling rate of 0.734 which aligns with the value in the error message value = c(NA, NA, NA, 0.733907206043719 but why does it throw an error after that?
Bonus question: I know loops are frowned upon in R...is there a way to get the moving average and R0 calculation without one?

Answer 1

You have to initialise the new variables before you can access them using the j index. Due to recycling, Alabama, which has 28 rows (divisible by 4), does not return an error, only the warnings about uninitialised columns. New York, however, has 39 rows, which is not divisible by 4 so recycling fails and R returns an error. You shouldn't ignore warnings, sometimes you can, but it's not a good idea.
Try this to see what R (you) is trying to do:
stateData[4]
You should get all rows of the 4th column, not the 4th row.
Solution: initialise your DR and R0 columns first.
stateData$DR <- NA
stateData$R0 <- NA
for(j in 4:nrow(stateData)){
stateData$DR[j] <- log(2)/log(stateData$MA[j]/stateData$MA[j-1])
stateData$R0[j] <- 14/stateData$DR[j]
}
For the bonus question, you can use lag in the same mutate with MA:
stateData <- stateData %>% mutate(MA = runMean(cumCases,3),
DR = log(2)/log(MA/lag(MA)),
R0 = 14 / DR)
stateData
# A tibble: 28 x 6
infected cumCases DaysSince MA DR R0
<date> <int> <drtn> <dbl> <dbl> <dbl>
1 2020-03-13 5 0 days NA NA NA
2 2020-03-14 11 1 days NA NA NA
3 2020-03-15 22 2 days 12.7 NA NA
4 2020-03-16 29 3 days 20.7 1.42 9.89
5 2020-03-17 39 4 days 30 1.86 7.53
6 2020-03-18 51 5 days 39.7 2.48 5.64
7 2020-03-19 78 6 days 56 2.01 6.96
8 2020-03-20 106 7 days 78.3 2.07 6.78
9 2020-03-21 131 8 days 105 2.37 5.92
10 2020-03-22 167 9 days 135. 2.79 5.03
# ... with 18 more rows
I'm using Alabama's data.