I have a dataset that looks like this:
city period_day date
1 barcelona morning 2017-01-15
2 sao_paulo afternoon 2016-12-07
3 sao_paulo morning 2016-11-16
4 barcelona morning 2016-11-06
5 barcelona afternoon 2016-12-31
6 sao_paulo afternoon 2016-11-30
7 barcelona morning 2016-10-15
8 barcelona afternoon 2016-11-30
9 sao_paulo afternoon 2016-12-24
10 sao_paulo afternoon 2017-02-02
For every row, I want to count how many rows have an older date than the date of the row, both for city and period_day. In this case, I want this result:
city period_day date row_count
1 barcelona morning 2017-01-15 2
2 sao_paulo afternoon 2016-12-07 1
3 sao_paulo morning 2016-11-16 0
4 barcelona morning 2016-11-06 1
5 barcelona afternoon 2016-12-31 1
6 sao_paulo afternoon 2016-11-30 0
7 barcelona morning 2016-10-15 0
8 barcelona afternoon 2016-11-30 0
9 sao_paulo afternoon 2016-12-24 2
10 sao_paulo afternoon 2017-02-02 3
When row_count equals to 0, it means that it's the older date.
I came up with a solution, but it took too long with more data. That's the code:
get_count_function <- function(df) {
idx <- 1:nrow(df)
count <- sapply(idx, function(x) {
name_city <-
df %>% select(city) %>% filter(row_number() == x) %>% pull()
name_period <-
df %>% select(period_day) %>% filter(row_number() == x) %>% pull()
date_row <- df %>%
select(date) %>%
filter(row_number() == x) %>%
pull()
date_any_row <- df %>%
filter(dplyr::row_number() != x,
city == name_city,
period_day == name_period) %>%
select(date) %>%
pull()
how_many <- sum(date_row > date_any_row)
return(how_many)
})
return(count)
}
How could I turn this function more efficient?
Try this one:
library(tidyverse)
dat %>%
group_by(city, period_day) %>%
mutate(row_count = order(date) - 1) %>%
ungroup()
When you call order
it returns indices, pointing to the order of the value in a selected group of values ( date
). Subtracting 1
from the indices, you obtain the count of values preceding current value, in a particular group. Eg if it is the min. value in a group, it has index 1
, so nothing preceding it ( 1 - 1 = 0
), if the index is 2
- only one value is preceding it (one older date
before it) etc.
Data:
dat <- read.table(
text = " city period_day date
barcelona morning 2017-01-15
sao_paulo afternoon 2016-12-07
sao_paulo morning 2016-11-16
barcelona morning 2016-11-06
barcelona afternoon 2016-12-31
sao_paulo afternoon 2016-11-30
barcelona morning 2016-10-15
barcelona afternoon 2016-11-30
sao_paulo afternoon 2016-12-24
sao_paulo afternoon 2017-02-02",
header = T,
colClasses = c("character", "character", "Date")
)
This should work if you are willing to use the data.table
package:
library(data.table)
dat <- read.table(header=T, row.names=1, text="
city period_day date
1 barcelona morning 2017-01-15
2 sao_paulo afternoon 2016-12-07
3 sao_paulo morning 2016-11-16
4 barcelona morning 2016-11-06
5 barcelona afternoon 2016-12-31
6 sao_paulo afternoon 2016-11-30
7 barcelona morning 2016-10-15
8 barcelona afternoon 2016-11-30
9 sao_paulo afternoon 2016-12-24
10 sao_paulo afternoon 2017-02-02
")
dat <- as.data.table(dat)
dat[, row_count := (order(as.Date(date)) - 1), by=.(city, period_day)]
# Check
dat
## city period_day date row_count
## 1: barcelona morning 2017-01-15 2
## 2: sao_paulo afternoon 2016-12-07 1
## 3: sao_paulo morning 2016-11-16 0
## 4: barcelona morning 2016-11-06 1
## 5: barcelona afternoon 2016-12-31 1
## 6: sao_paulo afternoon 2016-11-30 0
## 7: barcelona morning 2016-10-15 0
## 8: barcelona afternoon 2016-11-30 0
## 9: sao_paulo afternoon 2016-12-24 2
## 10: sao_paulo afternoon 2017-02-02 3
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