I have a df with two columns of interest: Date and Quality. Date is a daily time series. There are three options for quality - Good, Estimated, Missing. With one of these options being associated with a given date.
I would like to retrieve two pieces of information: (1) is a list of consecutive stretches an option has over the time series; and (2) the dates associated with those consecutive records.
For example,
1900-01-01 Good
1900-01-02 Good
1900-01-03 Good
1900-01-04 Estimated
1900-01-05 Good
1900-01-06 Good
1900-01-07 Estimated
1900-01-08 Good
So here we for Good we would have a consecutive list of 3,2,1 and I would like to return a date list of 1900-01-01 to 1900-01-03, 1900-01-05 to 1900-01-06 and 1900-01-08 associated with the 3,2,1 list.
You can use rle
Below sections shows the consecutive lengths for Good
encodes <- rle(df$Quality)
encodes$lengths[encodes$values == "Good"]
[1] 3 2 1
Getting the dates can be done directly from the df
df <- read.table(text = "Date Quality
1900-01-01 Good
1900-01-02 Good
1900-01-03 Good
1900-01-04 Estimated
1900-01-05 Good
1900-01-06 Good
1900-01-07 Estimated
1900-01-08 Good", header = T, stringsAsFactors = F)
library(data.table)
setDT(df)
out <-
df[order(Date), .(start = Date[1], end = Date[.N], .N),
by = .(Quality, id = rleid(Quality))][, -'id']
out[Quality == 'Good']
# Quality start end N
# 1: Good 1900-01-01 1900-01-03 3
# 2: Good 1900-01-05 1900-01-06 2
# 3: Good 1900-01-08 1900-01-08 1
Data used
df <- fread('
Date Quality
1900-01-01 Good
1900-01-02 Good
1900-01-03 Good
1900-01-04 Estimated
1900-01-05 Good
1900-01-06 Good
1900-01-07 Estimated
1900-01-08 Good
')
df[, Date := as.Date(Date)]
One dplyr
possibility could be:
df %>%
mutate(rleid = with(rle(V2), rep(seq_along(lengths), lengths)),
V1 = as.Date(V1, format = "%Y-%m-%d")) %>%
group_by(rleid, V2) %>%
summarise(res = paste0(min(V1), ":", max(V1)))
rleid V2 res
<int> <chr> <chr>
1 1 Good 1900-01-01:1900-01-03
2 2 Estimated 1900-01-04:1900-01-04
3 3 Good 1900-01-05:1900-01-06
4 4 Estimated 1900-01-07:1900-01-07
5 5 Good 1900-01-08:1900-01-08
Or:
df %>%
mutate(rleid = with(rle(V2), rep(seq_along(lengths), lengths)),
V1 = as.Date(V1, format = "%Y-%m-%d")) %>%
group_by(rleid, V2) %>%
summarise(res = paste0(min(V1), ":", max(V1))) %>%
group_by(V2) %>%
mutate(rleid = seq_along(rleid)) %>%
arrange(V2, rleid)
rleid V2 res
<int> <chr> <chr>
1 1 Estimated 1900-01-04:1900-01-04
2 2 Estimated 1900-01-07:1900-01-07
3 1 Good 1900-01-01:1900-01-03
4 2 Good 1900-01-05:1900-01-06
5 3 Good 1900-01-08:1900-01-08
Or:
df %>%
mutate(rleid = with(rle(V2), rep(seq_along(lengths), lengths)),
V1 = as.Date(V1, format = "%Y-%m-%d")) %>%
group_by(rleid, V2) %>%
summarise(res = paste0(min(V1), ":", max(V1)),
n = n()) %>%
group_by(V2) %>%
mutate(rleid = seq_along(rleid)) %>%
arrange(V2, rleid)
rleid V2 res n
<int> <chr> <chr> <int>
1 1 Estimated 1900-01-04:1900-01-04 1
2 2 Estimated 1900-01-07:1900-01-07 1
3 1 Good 1900-01-01:1900-01-03 3
4 2 Good 1900-01-05:1900-01-06 2
5 3 Good 1900-01-08:1900-01-08 1
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