Filling missing rows - r

Question

I have a large data set, a sample is given below:
df <- data.frame(stringsAsFactors=FALSE,
Date = c("2015-10-26", "2015-10-26", "2015-10-26", "2015-10-26",
"2015-10-27", "2015-10-27", "2015-10-27"),
Ticker = c("ANZ", "CBA", "NAB", "WBC", "ANZ", "CBA", "WBC"),
Open = c(29.11, 77.89, 32.69, 31.87, 29.05, 77.61, 31.84),
High = c(29.17, 77.93, 32.76, 31.92, 29.08, 78.1, 31.95),
Low = c(28.89, 77.37, 32.42, 31.71, 28.9, 77.54, 31.65),
Close = c(28.9, 77.5, 32.42, 31.84, 28.94, 77.74, 31.77),
Volume = c(6350170L, 2251288L, 3804239L, 5597684L, 5925519L, 2424679L,
5448863L)
)
The problem I am trying to solve is the missing data for NAB on 27-10-2015
I want the last value to repeat itself for the missing dates:
Date Ticker Open High Low Close Volume
2 2015-10-27 NAB 32.69 32.76 32.42 32.42 3804239
Any ideas on how to do this?
I have already unsuccessfully tried gather + spread

Answer 1

What if you tried something like this?
library(zoo)
res <- expand.grid(Date = unique(df$Date), Ticker = unique(df$Ticker))
res2 <- merge(res, df, all.x = TRUE)
res2 <- res2[order(res2$Ticker, res2$Date),]
res3 <- na.locf(res2)
res3[order(res3$Date, res3$Ticker),]
# Date Ticker Open High Low Close Volume
#1 2015-10-26 ANZ 29.11 29.17 28.89 28.90 6350170
#3 2015-10-26 CBA 77.89 77.93 77.37 77.50 2251288
#5 2015-10-26 NAB 32.69 32.76 32.42 32.42 3804239
#6 2015-10-26 WBC 31.87 31.92 31.71 31.84 5597684
#2 2015-10-27 ANZ 29.05 29.08 28.90 28.94 5925519
#4 2015-10-27 CBA 77.61 78.10 77.54 77.74 2424679
#8 2015-10-27 NAB 32.69 32.76 32.42 32.42 3804239
#7 2015-10-27 WBC 31.84 31.95 31.65 31.77 5448863
I'm assuming that if a Ticker/Day combo does not exist, you want to create one and LOCF it. This is what the expand.grid does.

Answer 2

tidyr::complete and tidyr::fill are built just for this situation:
library(tidyverse)
df %>%
complete(Date,Ticker) %>%
arrange(Ticker) %>%
fill(names(.)) %>%
arrange(Date)
#
# # A tibble: 8 x 7
# Date Ticker Open High Low Close Volume
# <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
# 1 2015-10-26 ANZ 29.11 29.17 28.89 28.90 6350170
# 2 2015-10-26 CBA 77.89 77.93 77.37 77.50 2251288
# 3 2015-10-26 NAB 32.69 32.76 32.42 32.42 3804239
# 4 2015-10-26 WBC 31.87 31.92 31.71 31.84 5597684
# 5 2015-10-27 ANZ 29.05 29.08 28.90 28.94 5925519
# 6 2015-10-27 CBA 77.61 78.10 77.54 77.74 2424679
# 7 2015-10-27 NAB 32.69 32.76 32.42 32.42 3804239
# 8 2015-10-27 WBC 31.84 31.95 31.65 31.77 5448863

Answer 3

Another potential solution (Note: I had to convert your date vector to Date format, but this could be reversed in the final output):
library(tidyr)
library(dplyr)
df <- data.frame(stringsAsFactors=FALSE,
Date = as.Date(c("2015-10-26", "2015-10-26", "2015-10-26", "2015-10-26",
"2015-10-27", "2015-10-27", "2015-10-27")),
Ticker = c("ANZ", "CBA", "NAB", "WBC", "ANZ", "CBA", "WBC"),
Open = c(29.11, 77.89, 32.69, 31.87, 29.05, 77.61, 31.84),
High = c(29.17, 77.93, 32.76, 31.92, 29.08, 78.1, 31.95),
Low = c(28.89, 77.37, 32.42, 31.71, 28.9, 77.54, 31.65),
Close = c(28.9, 77.5, 32.42, 31.84, 28.94, 77.74, 31.77),
Volume = c(6350170L, 2251288L, 3804239L, 5597684L, 5925519L, 2424679L,
5448863L))
tickers<- unique(df$Ticker)
dates<- as.Date(df$Date)
possibilities<- as.data.frame(unique(expand.grid(dates,tickers)))
colnames(possibilities)<- c('Date','Ticker')
missing<- anti_join(possibilities,df[,c('Date','Ticker')])
missing_filled<- if(nrow(missing)>0){
replacement<- cbind(missing,filter(df,Date==missing$Date-1,Ticker==missing$Ticker)[,3:7])
}
final<- arrange(rbind(df,replacement),Date)