I want to use the Prophet() function in R, but I cannot transform my column "YearWeek" to a as.Date() column.
I have a column "YearWeek" that stores values from 201401 up to 201937 i.e. starting in 2014 week 1 up to 2019 week 37.
I don't know how to declare this column as a date in the form yyyy-ww needed to use the Prophet() function.
Does anyone know how to do this?
Thank you in advance.
One solution could be to append a 01 to the end of your yyyy-ww formatted dates.
Data:
library(tidyverse)
df <- cross2(2014:2019, str_pad(1:52, width = 2, pad = 0)) %>%
map_df(set_names, c("year", "week")) %>%
transmute(date = paste(year, week, sep = "")) %>%
arrange(date)
head(df)
#> # A tibble: 6 x 1
#> date
#> <chr>
#> 1 201401
#> 2 201402
#> 3 201403
#> 4 201404
#> 5 201405
#> 6 201406
Now let's append the 01 and convert to date:
df %>%
mutate(date = paste(date, "01", sep = ""),
new_date = as.Date(date, "%Y%U%w"))
#> # A tibble: 312 x 2
#> date new_date
#> <chr> <date>
#> 1 20140101 2014-01-05
#> 2 20140201 2014-01-12
#> 3 20140301 2014-01-19
#> 4 20140401 2014-01-26
#> 5 20140501 2014-02-02
#> 6 20140601 2014-02-09
#> 7 20140701 2014-02-16
#> 8 20140801 2014-02-23
#> 9 20140901 2014-03-02
#> 10 20141001 2014-03-09
#> # ... with 302 more rows
Created on 2019-10-10 by the reprex package (v0.3.0)
More info about a numeric week of the year can be found here.
Related
I am working with a large list of dataframes that use inconsistent date formats. I would like to conditionally mutate across the list so that any dataframe that contains a string will use one date format, and those that do not contain the string use another format. In other words, I want to distinguish between dataframes launched in year 2019 (which use mdy) and those launched in all others years (which use dmy).
The following code will conditionally mutate rows within a dataframe, but I am unsure how to conditionally mutate across the entire column.
dataframes %>% map(~.x %>%
mutate(date_time = if_else(str_detect(date_time, "/19 "),
mdy_hms(date_time), dmy_hms(date_time)))
Thank you!
edit
Data and code example. There are dataframes that contain a mixture of years.
library(tidyverse)
library(lubridate)
dataframes <- list(
tibble(date_time = c("07/06/19 01:00:00 PM", "07/06/20 01:00:00 PM"), num = 1:2), # July 6th
tibble(date_time = c("06/07/20 01:00:00 PM", "06/07/21 01:00:00 PM"), num = 1:2) # July 6th
)
dataframes %>%
map(~.x %>%
mutate(date_time = if_else(str_detect(date_time, "/19 "),
mdy_hms(date_time), dmy_hms(date_time)),
date = date(date_time),
month = month(date_time),
doy = yday(date_time)))
[[1]]
# A tibble: 2 × 5
date_time num date month doy
<dttm> <int> <date> <dbl> <dbl>
1 2019-07-06 13:00:00 1 2019-07-06 7 187
2 2020-06-07 13:00:00 2 2020-06-07 6 159
[[2]]
# A tibble: 2 × 5
date_time num date month doy
<dttm> <int> <date> <dbl> <dbl>
1 2020-07-06 13:00:00 1 2020-07-06 7 188
2 2021-07-06 13:00:00 2 2021-07-06 7 187
If you are trying to determine the format of the date column for the whole data.frame based on the presence of any date from 2019, then a small tweak of your code should work.
Instead of evaluating each record for the presence of /19 , you set the condition of the if_else() to be any(str_detect(...)) which returns TRUE if any of the values are TRUE. However the result of any() is always of length 1 so you then need to rep() the result to match the length of the whole data.frame using dplyr::n().
library(tidyverse)
library(lubridate)
dataframes <- list(
tibble(date_time = c("07/06/19 01:00:00 PM", "07/06/20 01:00:00 PM"), num = 1:2), # July 6th
tibble(date_time = c("06/07/20 01:00:00 PM", "06/07/21 01:00:00 PM"), num = 1:2) # July 6th
)
dataframes %>%
map( ~ .x %>%
mutate(
date_time = if_else(str_detect(date_time, "/19 ") %>%
any() %>%
rep(n()),
mdy_hms(date_time),
dmy_hms(date_time)),
date = date(date_time),
month = month(date_time),
doy = yday(date_time)
))
#> [[1]]
#> # A tibble: 2 × 5
#> date_time num date month doy
#> <dttm> <int> <date> <dbl> <dbl>
#> 1 2019-07-06 13:00:00 1 2019-07-06 7 187
#> 2 2020-07-06 13:00:00 2 2020-07-06 7 188
#>
#> [[2]]
#> # A tibble: 2 × 5
#> date_time num date month doy
#> <dttm> <int> <date> <dbl> <dbl>
#> 1 2020-07-06 13:00:00 1 2020-07-06 7 188
#> 2 2021-07-06 13:00:00 2 2021-07-06 7 187
Created on 2022-07-20 by the reprex package (v2.0.1)
I have two datasets that I would like to join based on date. One is a survey dataset, and the other is a list of prices at various dates. The dates don't match exactly, so I would like to join on the nearest date in the survey dataset (the price data is weekly).
Here's a brief snippet of what the survey dataset looks like (there are many other variables, but here's the two most relevant):
ID
actual.date
20120377
2012-09-26
2020455822
2020-11-23
20126758
2012-10-26
20124241
2012-10-25
2020426572
2020-11-28
And here's the price dataset (also much larger, but you get the idea):
date
price.var1
price.var2
2017-10-30
2.74733926399869
2.73994826674735
2015-03-16
2.77028200438506
2.74079930272231
2010-10-18
3.4265947805337
3.41591263539176
2012-10-29
4.10095806545397
4.14717556976502
2012-01-09
3.87888859352037
3.93074237884497
What I would like to do is join the price dataset to the survey dataset, joining on the nearest date.
I've tried a number of different things, none of which have worked to my satisfaction.
#reading in sample data
library(data.table)
library(dplyr)
survey <- fread(" ID actual.date
1: 20120377 2012-09-26
2: 2020455822 2020-11-23
3: 20126758 2012-10-26
4: 20124241 2012-10-25
5: 2020426572 2020-11-28
> ") %>% select(-V1)
price <- fread("date price.var1 price.var2
1: 2017-10-30 2.747339 2.739948
2: 2015-03-16 2.770282 2.740799
3: 2010-10-18 3.426595 3.415913
4: 2012-10-29 4.100958 4.147176
5: 2012-01-09 3.878889 3.930742") %>% select(-V1)
#using data.table
setDT(survey)[,DT_DATE := actual.date]
setDT(price)[,DT_DATE := date]
survey_price <- survey[price,on=.(DT_DATE),roll="nearest"]
#This works, and they join, but it drops a ton of observations, which won't work
#using dplyr
library(dplyr)
survey_price <- left_join(survey,price,by=c("actual.date"="date"))
#this joins them without dropping observations, but all of the price variables become NAs
You were almost there.
In the DT[i,on] syntax, i should be survey to join on all its rows
setDT(survey)
setDT(price)
survey_price <- price[survey,on=.(date=actual.date),roll="nearest"]
survey_price
date price.var1 price.var2 ID
<IDat> <num> <num> <int>
1: 2012-09-26 4.100958 4.147176 20120377
2: 2020-11-23 2.747339 2.739948 2020455822
3: 2012-10-26 4.100958 4.147176 20126758
4: 2012-10-25 4.100958 4.147176 20124241
5: 2020-11-28 2.747339 2.739948 2020426572
Convert the dates to numeric and find the closest date from the survey for price with Closest() from DescTools, and take that value.
Example datasets
survey <- tibble(
ID = sample(20000:40000, 9, replace = TRUE),
actual.date = seq(today() %m+% days(5), today() %m+% days(5) %m+% months(2),
"week")
)
price <- tibble(
date = seq(today(), today() %m+% months(2), by = "week"),
price_1 = sample(2:6, 9, replace = TRUE),
price_2 = sample(2:6, 9, replace = TRUE)
)
survey
# A tibble: 9 x 2
ID actual.date
<int> <date>
1 34592 2022-05-07
2 37846 2022-05-14
3 22715 2022-05-21
4 22510 2022-05-28
5 30143 2022-06-04
6 34348 2022-06-11
7 21538 2022-06-18
8 39802 2022-06-25
9 36493 2022-07-02
price
# A tibble: 9 x 3
date price_1 price_2
<date> <int> <int>
1 2022-05-02 6 6
2 2022-05-09 3 2
3 2022-05-16 6 4
4 2022-05-23 6 2
5 2022-05-30 2 6
6 2022-06-06 2 4
7 2022-06-13 2 2
8 2022-06-20 3 5
9 2022-06-27 5 6
library(tidyverse)
library(lubridate)
library(DescTools)
price <- price %>%
mutate(date = Closest(survey$actual.date %>%
as.numeric, date %>%
as.numeric) %>%
as_date())
# A tibble: 9 x 3
date price_1 price_2
<date> <int> <int>
1 2022-05-07 6 6
2 2022-05-14 3 2
3 2022-05-21 6 4
4 2022-05-28 6 2
5 2022-06-04 2 6
6 2022-06-11 2 4
7 2022-06-18 2 2
8 2022-06-25 3 5
9 2022-07-02 5 6
merge(survey, price, by.x = "actual.date", by.y = "date")
actual.date ID price_1 price_2
1 2022-05-07 34592 6 6
2 2022-05-14 37846 3 2
3 2022-05-21 22715 6 4
4 2022-05-28 22510 6 2
5 2022-06-04 30143 2 6
6 2022-06-11 34348 2 4
7 2022-06-18 21538 2 2
8 2022-06-25 39802 3 5
9 2022-07-02 36493 5 6
I'm working with trip ticket data and it includes a column with dates and times. I'm want to group trips according to Morning(05:00 - 10:59), Lunch(11:00-12:59), Afternoon(13:00-17:59), Evening(18:00-23:59), and Dawn/Graveyard(00:00-04:59) and then count the number of trips (by means of counting the unique values in the trip_id column) for each of those categories.
Only I don't know how to group/summarize according to time values. Is this possible in R?
trip_id start_time end_time day_of_week
1 CFA86D4455AA1030 2021-03-16 08:32:30 2021-03-16 08:36:34 Tuesday
2 30D9DC61227D1AF3 2021-03-28 01:26:28 2021-03-28 01:36:55 Sunday
3 846D87A15682A284 2021-03-11 21:17:29 2021-03-11 21:33:53 Thursday
4 994D05AA75A168F2 2021-03-11 13:26:42 2021-03-11 13:55:41 Thursday
5 DF7464FBE92D8308 2021-03-21 09:09:37 2021-03-21 09:27:33 Sunday
Here's a solution with hour() and case_when().
library(tidyverse)
library(lubridate)
#>
#> Attaching package: 'lubridate'
#> The following objects are masked from 'package:base':
#>
#> date, intersect, setdiff, union
trip <- tibble(start_time = mdy_hm("1/1/2022 1:00") + minutes(seq(0, 700, 15)))
trip <- trip %>%
mutate(
hr = hour(start_time),
time_of_day = case_when(
hr >= 5 & hr < 11 ~ "morning",
hr >= 11 & hr < 13 ~ "afternoon",
TRUE ~ "fill in the rest yourself :)"
)
)
print(trip)
#> # A tibble: 47 x 3
#> start_time hr time_of_day
#> <dttm> <int> <chr>
#> 1 2022-01-01 01:00:00 1 fill in the rest yourself :)
#> 2 2022-01-01 01:15:00 1 fill in the rest yourself :)
#> 3 2022-01-01 01:30:00 1 fill in the rest yourself :)
#> 4 2022-01-01 01:45:00 1 fill in the rest yourself :)
#> 5 2022-01-01 02:00:00 2 fill in the rest yourself :)
#> 6 2022-01-01 02:15:00 2 fill in the rest yourself :)
#> 7 2022-01-01 02:30:00 2 fill in the rest yourself :)
#> 8 2022-01-01 02:45:00 2 fill in the rest yourself :)
#> 9 2022-01-01 03:00:00 3 fill in the rest yourself :)
#> 10 2022-01-01 03:15:00 3 fill in the rest yourself :)
#> # ... with 37 more rows
trips <- trip %>%
count(time_of_day)
print(trips)
#> # A tibble: 3 x 2
#> time_of_day n
#> <chr> <int>
#> 1 afternoon 7
#> 2 fill in the rest yourself :) 16
#> 3 morning 24
Created on 2022-03-21 by the reprex package (v2.0.1)
I want to find the highest average of departure delay in time windows of length 1 week in flights dataset of nycflights13 package.
I've used
seq(min(flights:time_hour), max(flights:time_hour), by = "week")
to find the dates with the difference of one week. But I don't know how to group by these dates to find the average departure delay of each period. How can I do this using tidyverse package?
Thank you for your help in advance.
We can use {lubridate} to round each date to the nearest week. Two wrinkles to think about:
To count weeks beginning with Jan 1, you'll need to specify the week_start arg. Otherwise lubridate will count from the previous Sunday, which in this case is 12/30/2012.
You also need to deal with incomplete weeks. In this case, the last week of the year only contains one day. I chose to drop weeks with < 7 days for this demo.
library(tidyverse)
library(lubridate)
library(nycflights13)
data(flights)
# what weekday was the first of the year?
weekdays(min(flights$time_hour))
#> [1] "Tuesday"
# Tuesday = day #2 so we'll pass `2` to `week_start`
flights %>%
group_by(week = floor_date(time_hour, unit = "week", week_start = 2)) %>%
filter(n_distinct(day) == 7) %>% # drop incomplete weeks
summarize(dep_delay_avg = mean(dep_delay, na.rm = TRUE)) %>%
arrange(desc(dep_delay_avg))
#> # A tibble: 52 x 2
#> week dep_delay_avg
#> <dttm> <dbl>
#> 1 2013-06-25 00:00:00 40.6 # week of June 25 had longest delays
#> 2 2013-07-09 00:00:00 24.4
#> 3 2013-12-17 00:00:00 24.0
#> 4 2013-07-23 00:00:00 21.8
#> 5 2013-03-05 00:00:00 21.7
#> 6 2013-04-16 00:00:00 21.6
#> 7 2013-07-16 00:00:00 20.4
#> 8 2013-07-02 00:00:00 20.1
#> 9 2013-12-03 00:00:00 19.9
#> 10 2013-05-21 00:00:00 19.2
#> # ... with 42 more rows
Created on 2022-03-06 by the reprex package (v2.0.1)
Edit: as requested by OP, here is a solution using only core {tidyverse} packages, without {lubridate}:
library(tidyverse)
library(nycflights13)
data(flights)
flights %>%
group_by(week = (as.POSIXlt(time_hour)$yday) %/% 7) %>%
filter(n_distinct(day) == 7) %>%
summarize(
week = as.Date(min(time_hour)),
dep_delay_avg = mean(dep_delay, na.rm = TRUE)
) %>%
arrange(desc(dep_delay_avg))
Suppose I have a daily rain data.frame like this:
df.meteoro = data.frame(Dates = seq(as.Date("2017/1/19"), as.Date("2018/1/18"), "days"),
rain = rnorm(length(seq(as.Date("2017/1/19"), as.Date("2018/1/18"), "days"))))
I'm trying to sum the accumulated rain between a 14 days interval with this code:
library(tidyverse)
library(lubridate)
df.rain <- df.meteoro %>%
mutate(TwoWeeks = round_date(df.meteoro$data, "14 days")) %>%
group_by(TwoWeeks) %>%
summarise(sum_rain = sum(rain))
The problem is that it isn't starting on 2017-01-19 but on 2017-01-15 and I was expecting my output dates to be:
"2017-02-02" "2017-02-16" "2017-03-02" "2017-03-16" "2017-03-30" "2017-04-13"
"2017-04-27" "2017-05-11" "2017-05-25" "2017-06-08" "2017-06-22" "2017-07-06" "2017-07-20"
"2017-08-03" "2017-08-17" "2017-08-31" "2017-09-14" "2017-09-28" "2017-10-12" "2017-10-26"
"2017-11-09" "2017-11-23" "2017-12-07" "2017-12-21" "2018-01-04" "2018-01-18"
TL;DR I have a year long daily rain data.frame and want to sum the accumulate rain for the dates above.
Please help.
Use of round_date in the way you have shown it will not give you 14-day periods as you might expect. I have taken a different approach in this solution and generated a sequence of dates between your first and last dates and grouped these into 14-day periods then joined the dates to your observations.
startdate = min(df.meteoro$Dates)
enddate = max(df.meteoro$Dates)
dateseq =
data.frame(Dates = seq.Date(startdate, enddate, by = 1)) %>%
mutate(group = as.numeric(Dates - startdate) %/% 14) %>%
group_by(group) %>%
mutate(starts = min(Dates))
df.rain <- df.meteoro %>%
right_join(dateseq) %>%
group_by(starts) %>%
summarise(sum_rain = sum(rain))
head(df.rain)
> head(df.rain)
# A tibble: 6 x 2
starts sum_rain
<date> <dbl>
1 2017-01-19 6.09
2 2017-02-02 5.55
3 2017-02-16 -3.40
4 2017-03-02 2.55
5 2017-03-16 -0.12
6 2017-03-30 8.95
Using a right-join to the date sequence is to ensure that if there are missing observation days that spanned a complete time period you'd still get that period listed in the result (though in your case you have a complete year of dates anyway).
round_date rounds to the nearest multiple of unit (here, 14 days) since some epoch (probably the Unix epoch of 1970-01-01 00:00:00), which doesn't line up with your purpose.
To get what you want, you can do the following:
df.rain = df.meteoro %>%
mutate(days_since_start = as.numeric(Dates - as.Date("2017/1/18")),
TwoWeeks = as.Date("2017/1/18") + 14*ceiling(days_since_start/14)) %>%
group_by(TwoWeeks) %>%
summarise(sum_rain = sum(rain))
This computes days_since_start as the days since 2017/1/18 and then manually rounds to the next multiple of two weeks.
Assuming you want to round to the closest date from the ones you have specified I guess the following will work
targetDates<-seq(ymd("2017-02-02"),ymd("2018-01-18"),by='14 days')
df.meteoro$Dates=targetDates[sapply(df.meteoro$Dates,function(x) which.min(abs(interval(targetDates,x))))]
sum_rain=ddply(df.meteoro,.(Dates),summarize,sum_rain=sum(rain,na.rm=T))
as you can see not all dates have the same number of observations. Date "2017-02-02" for instance has all the records between "2017-01-19" until "2017-02-09", which are 22 records. From "2017-02-10" on dates are rounded to "2017-02-16" etc.
This may be a cheat, but assuming each row/observation is a separate day, then why not just group by every 14 rows and sum.
# Assign interval groups, each 14 rows
df.meteoro$my_group <-rep(1:100, each=14, length.out=nrow(df.meteoro))
# Grab Interval Names
my_interval_names <- df.meteoro %>%
select(-rain) %>%
group_by(my_group) %>%
slice(1)
# Summarise
df.meteoro %>%
group_by(my_group) %>%
summarise(rain = sum(rain)) %>%
left_join(., my_interval_names)
#> Joining, by = "my_group"
#> # A tibble: 27 x 3
#> my_group rain Dates
#> <int> <dbl> <date>
#> 1 1 3.86 2017-01-19
#> 2 2 -0.581 2017-02-02
#> 3 3 -0.876 2017-02-16
#> 4 4 1.80 2017-03-02
#> 5 5 3.79 2017-03-16
#> 6 6 -3.50 2017-03-30
#> 7 7 5.31 2017-04-13
#> 8 8 2.57 2017-04-27
#> 9 9 -1.33 2017-05-11
#> 10 10 5.41 2017-05-25
#> # ... with 17 more rows
Created on 2018-03-01 by the reprex package (v0.2.0).