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)
Related
I have a dataframe with a few columns that contain time/ date information. I'm familiar with using lubridate to parse date-time (ie mm/dd/yyyy hh:mm:ss), but this dataframe has date time in reverse order (ie hh:mm:ss mm/dd/yyyy). How do I get this to read as a date/time? The column is currently reading as a character which is useless to me. Below is an example of what my dataset looks like. I can't make the "time_date" column read as a date -time.
df <- tribble(~activity, ~time_date,
"run", "15:06:17 03/08/2016",
"skip", "09:01:00 03/08/2016")
You should first convert it to a date time with right format and after that you can use strftime with the desired format like this:
datetimes <- as.POSIXct(df$time_date, format = "%H:%M:%S %m/%d/%Y")
df$time_date <- strftime(datetimes, format = "%m/%d/%Y %H:%M:%S")
df
#> # A tibble: 2 × 2
#> activity time_date
#> <chr> <chr>
#> 1 run 03/08/2016 15:06:17
#> 2 skip 03/08/2016 09:01:00
Created on 2023-01-04 with reprex v2.0.2
With dplyr and lubridate on character class data.
library(dplyr)
library(lubridate)
df %>%
rowwise() %>%
mutate(dd = strsplit(time_date, " "),
date_time = mdy_hms(paste(unlist(dd)[2], unlist(dd)[1])),
dd = NULL) %>%
ungroup()
# A tibble: 2 × 3
activity time_date date_time
<chr> <chr> <dttm>
1 run 15:06:17 03/08/2016 2016-03-08 15:06:17
2 skip 09:01:00 03/08/2016 2016-03-08 09:01:00
Alternatively using str_extract
df %>%
mutate(date_time = mdy_hms(paste(str_extract(time_date, " \\d+/.+"),
str_extract(time_date, "\\d+:.+ "))))
# A tibble: 2 × 3
activity time_date date_time
<chr> <chr> <dttm>
1 run 15:06:17 03/08/2016 2016-03-08 15:06:17
2 skip 09:01:00 03/08/2016 2016-03-08 09:01:00
You can use lubridate::parse_date_time() and specify the order as "HMS mdy":
library(dplyr)
library(lubridate)
df %>%
mutate(date_time = parse_date_time(time_date, "HMS mdy"))
# A tibble: 2 × 3
activity time_date date_time
<chr> <chr> <dttm>
1 run 15:06:17 03/08/2016 2016-03-08 15:06:17
2 skip 09:01:00 03/08/2016 2016-03-08 09:01:00
For example,
dateIntervals <- as.Date(c("2020-08-10", "2020-11-11", "2021-07-05"))
possibleDates <- seq(as.Date("2020-01-02"), dateIntervals[3], by = "day")
genDF<-function() data.frame(Date = sample(possibleDates, 100), Value = runif(100))
listdf <-replicate(2, genDF(), simplify = FALSE)
Yes, listdf has two dataframe elements(each 100 random dates in possibleDates and values)
and listdf[[1]] is like this
A data.frame: 100 × 2
Date Value
<date> <dbl>
2020-07-24 0.63482411
2020-02-26 0.25989280
2020-10-26 0.21721077
2020-10-11 0.34774192
2020-08-18 0.67758312
2020-02-03 0.22929624
2020-06-10 0.30279353
2020-05-29 0.95549488
...
lapply(listdf, function(x) split(x, findInterval(x$Date, dateIntervals)))
Made listdf as a 2*3 list, splitted by date.
1.$`0`
A data.frame: 43 × 2
Date Value
<date> <dbl>
1 2020-07-24 0.63482411
2 2020-02-26 0.25989280
6 2020-02-03 0.22929624
7 2020-06-10 0.30279353
...
$`1`
A data.frame: 15 × 2
Date Value
<date> <dbl>
3 2020-10-26 0.21721077
4 2020-10-11 0.34774192
5 2020-08-18 0.67758312
31 2020-11-09 0.59149301
...
$`2`
A data.frame: 42 × 2
Date Value
<date> <dbl>
9 2021-06-28 0.10055644
10 2021-05-17 0.63942936
12 2021-04-22 0.63589801
13 2021-02-01 0.70106156
...
2.$`0`
A data.frame: 43 × 2
Date Value
<date> <dbl>
2 2020-07-16 0.81376364
4 2020-07-03 0.05152627
7 2020-01-21 0.98677433
8 2020-03-23 0.13513921
...
$`1`
A data.frame: 18 × 2
Date Value
<date> <dbl>
5 2020-11-01 0.02740125
12 2020-09-04 0.82042568
15 2020-08-12 0.54190868
16 2020-09-19 0.05933666
18 2020-10-05 0.04983061
...
$`2`
A data.frame: 38 × 2
Date Value
<date> <dbl>
1 2021-04-13 0.46199245
3 2021-06-12 0.71461155
6 2021-01-24 0.56527997
9 2021-04-17 0.72634151
13 2021-04-20 0.55489499
...
I want only first two of the splitted ones.($'0' and $'1' for 1. and 2.)
is there any parameter in the split function which does things like this?
(getting only first or last n elements)
I want something like this...
lapply(listdf, function(x) split(x, findInterval(x$Date, dateIntervals), some parameter=2))
yes this "2". Getting only the first two ones. is there a function parameter in split which can do this?
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'm doing something quite simple. Given a dataframe of start dates and end dates for specific periods I want to expand/create a full sequence for each period binned by week (with the factor for each row), then output this in a single large dataframe.
For instance:
library(tidyverse)
library(lubridate)
# Dataset
start_dates = ymd_hms(c("2019-05-08 00:00:00",
"2020-01-17 00:00:00",
"2020-03-03 00:00:00",
"2020-05-28 00:00:00",
"2020-12-10 00:00:00",
"2021-05-07 00:00:00",
"2022-01-04 00:00:00"), tz = "UTC")
end_dates = ymd_hms(c( "2019-10-24 00:00:00",
"2020-03-03 00:00:00",
"2020-05-28 00:00:00",
"2020-12-10 00:00:00",
"2021-05-07 00:00:00",
"2022-01-04 00:00:00",
"2022-01-19 00:00:00"), tz = "UTC")
df1 = data.frame(studying = paste0("period",seq(1:7),sep = ""),start_dates,end_dates)
It was suggested to me to use do(), which currently works fine but I hate it when things are superseded. I also have a way of doing it using map2. But reading the file (https://dplyr.tidyverse.org/reference/do.html) suggests you can use nest_by(), across() and summarise() to do the same job as do(), how would I go about getting same result? I've tried a lot of things but I just can't seem to get it.
# do() way to do it
df1 %>%
group_by(studying) %>%
do(data.frame(week=seq(.$start_dates,.$end_dates,by="1 week")))
# transmute() way to do it
df1 %>%
transmute(weeks = map2(start_dates,end_dates, seq, by = "1 week"), studying)
%>% unnest(cols = c(weeks))
As the documentation of ?do suggests, we can now use summarise and replace the . with across():
library(tidyverse)
library(lubridate)
df1 %>%
group_by(studying) %>%
summarise(week = seq(across()$start_dates,
across()$end_dates,
by = "1 week"))
#> `summarise()` has grouped output by 'studying'. You can override using the
#> `.groups` argument.
#> # A tibble: 134 x 2
#> # Groups: studying [7]
#> studying week
#> <chr> <dttm>
#> 1 period1 2019-05-08 00:00:00
#> 2 period1 2019-05-15 00:00:00
#> 3 period1 2019-05-22 00:00:00
#> 4 period1 2019-05-29 00:00:00
#> 5 period1 2019-06-05 00:00:00
#> 6 period1 2019-06-12 00:00:00
#> 7 period1 2019-06-19 00:00:00
#> 8 period1 2019-06-26 00:00:00
#> 9 period1 2019-07-03 00:00:00
#> 10 period1 2019-07-10 00:00:00
#> # … with 124 more rows
Created on 2022-01-19 by the reprex package (v0.3.0)
You can also use tidyr::complete:
df1 %>%
group_by(studying) %>%
complete(start_dates = seq(from = start_dates, to = end_dates, by = "1 week")) %>%
select(-end_dates, weeks = start_dates)
# A tibble: 134 x 2
# Groups: studying [7]
studying weeks
<chr> <dttm>
1 period1 2019-05-08 00:00:00
2 period1 2019-05-15 00:00:00
3 period1 2019-05-22 00:00:00
4 period1 2019-05-29 00:00:00
5 period1 2019-06-05 00:00:00
6 period1 2019-06-12 00:00:00
7 period1 2019-06-19 00:00:00
8 period1 2019-06-26 00:00:00
9 period1 2019-07-03 00:00:00
10 period1 2019-07-10 00:00:00
# ... with 124 more rows
Although marked Experimental the help file for group_modify does say that
‘group_modify()’ is an evolution of ‘do()’
and, in fact, the code for the example in the question using group_modify is nearly the same as with do.
# with group_modify
df2 <- df1 %>%
group_by(studying) %>%
group_modify(~ data.frame(week = seq(.$start_dates, .$end_dates, by = "1 week")))
# with do
df0 <- df1 %>%
group_by(studying) %>%
do(data.frame(week = seq(.$start_dates, .$end_dates, by = "1 week")))
identical(df2, df0)
## [1] TRUE
Not sure if this exactly what you are looking for, but here is my attempt with rowwise and unnest
df1 %>%
rowwise() %>%
mutate(week = list(seq(start_dates, end_dates, by = "1 week"))) %>%
select(studying, week) %>%
unnest(cols = c(week))
Another approach:
library(tidyverse)
df1 %>%
group_by(studying) %>%
summarise(df = tibble(weeks = seq(start_dates, end_dates, by = 'week'))) %>%
unnest(df)
#> `summarise()` has grouped output by 'studying'. You can override using the `.groups` argument.
#> # A tibble: 134 × 2
#> # Groups: studying [7]
#> studying weeks
#> <chr> <dttm>
#> 1 period1 2019-05-08 00:00:00
#> 2 period1 2019-05-15 00:00:00
#> 3 period1 2019-05-22 00:00:00
#> 4 period1 2019-05-29 00:00:00
#> 5 period1 2019-06-05 00:00:00
#> 6 period1 2019-06-12 00:00:00
#> 7 period1 2019-06-19 00:00:00
#> 8 period1 2019-06-26 00:00:00
#> 9 period1 2019-07-03 00:00:00
#> 10 period1 2019-07-10 00:00:00
#> # … with 124 more rows
Created on 2022-01-20 by the reprex package (v2.0.1)
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.