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I have a database of information pertaining to individuals observed over time. I would like to find a way to obtain the age of these individuals whenever a record was taken. Assuming the BIRTH assigns a value of 0, I would like to obtain the age either in days or months for the visits after. It would also be helpful to obtain a final age (either day or month) for each individual (*not included in the code). For example, for ID (A), the final age would be 10 months. I would like to use the lubridate function as it's in-built date feature makes it easier to work with dates. Any help with this is much appreciated.
date<-c("2000-01-01","2000-01-14","2000-01-25","2000-02-12","2000-02-27","2000-06-05","2000-10-30",
"2001-02-04","2001-06-15","2001-12-26","2002-05-22","2002-06-04",
"2000-01-08","2000-07-11","2000-08-18","2000-11-27")
ID<-c("A","A","A","A","A","A","A",
"B","B","B","B","B",
"C","C","C","C")
status<-c("BIRTH","ETC","ETC","ETC","ETC","ETC","ETC",
"BIRTH","ETC","ETC","ETC","ETC",
"BIRTH","ETC","ETC","ETC")
df1<-data.frame(date,ID,status)
print(df1)
date ID status
1 2000-01-01 A BIRTH
2 2000-01-14 A ETC
3 2000-01-25 A ETC
4 2000-02-12 A ETC
5 2000-02-27 A ETC
6 2000-06-05 A ETC
7 2000-10-30 A ETC
8 2001-02-04 B BIRTH
9 2001-06-15 B ETC
10 2001-12-26 B ETC
11 2002-05-22 B ETC
12 2002-06-04 B ETC
13 2000-01-08 C BIRTH
14 2000-07-11 C ETC
15 2000-08-18 C ETC
16 2000-11-27 C ETC
date.new<-c("2000-01-01","2000-01-14","2000-01-25","2000-02-12","2000-02-27","2000-06-05","2000-10-30",
"2001-02-04","2001-06-15","2001-12-26","2002-05-22","2001-02-04",
"2000-01-08","2000-07-11","2000-08-18","2000-11-27")
ID.new<-c("A","A","A","A","A","A","A",
"B","B","B","B","B",
"C","C","C","C")
status.new<-c("BIRTH","ETC","ETC","ETC","ETC","ETC","ETC",
"BIRTH","ETC","ETC","ETC","ETC",
"BIRTH","ETC","ETC","ETC")
age<-c(0,1,1,2,2,6,10,
0,4,10,15,16,
0,6,7,10)
df2<-data.frame(date.new,ID.new,status.new,age)
print(df2)
date.new ID.new status.new age
1 2000-01-01 A BIRTH 0
2 2000-01-14 A ETC 1
3 2000-01-25 A ETC 1
4 2000-02-12 A ETC 2
5 2000-02-27 A ETC 2
6 2000-06-05 A ETC 6
7 2000-10-30 A ETC 10
8 2001-02-04 B BIRTH 0
9 2001-06-15 B ETC 4
10 2001-12-26 B ETC 10
11 2002-05-22 B ETC 15
12 2001-02-04 B ETC 16
13 2000-01-08 C BIRTH 0
14 2000-07-11 C ETC 6
15 2000-08-18 C ETC 7
16 2000-11-27 C ETC 10
For calculations related to age in years or months, I'd like to encourage you to try the clock package rather than lubridate. lubridate is a great package, but produces some unexpected results with these kinds of calculations if you aren't 100% sure of what you are doing. In clock, the function to do this is date_count_between(). Notice that one of the results is different between clock and lubridate here:
library(clock)
library(lubridate, warn.conflicts = FALSE)
library(dplyr, warn.conflicts = FALSE)
df <- tibble(
date = c("2000-01-01","2000-01-14",
"2000-01-25","2000-02-12","2000-02-27","2000-06-05",
"2000-10-30","2001-02-04","2001-06-15","2001-12-26",
"2002-05-22","2002-06-04","2000-01-08","2000-07-11",
"2000-08-18","2000-11-27"),
ID = c("A","A","A","A","A","A",
"A","B","B","B","B","B","C","C","C","C"),
status = c("BIRTH","ETC","ETC","ETC",
"ETC","ETC","ETC","BIRTH","ETC","ETC","ETC","ETC",
"BIRTH","ETC","ETC","ETC")
)
df %>%
mutate(date = date_parse(date)) %>%
group_by(ID) %>%
mutate(birth_date = date[status == "BIRTH"]) %>%
ungroup() %>%
mutate(
age_clock = date_count_between(birth_date, date, "month"),
age_lubridate = as.period(date - birth_date) %/% months(1))
#> # A tibble: 16 × 6
#> date ID status birth_date age_clock age_lubridate
#> <date> <chr> <chr> <date> <int> <dbl>
#> 1 2000-01-01 A BIRTH 2000-01-01 0 0
#> 2 2000-01-14 A ETC 2000-01-01 0 0
#> 3 2000-01-25 A ETC 2000-01-01 0 0
#> 4 2000-02-12 A ETC 2000-01-01 1 1
#> 5 2000-02-27 A ETC 2000-01-01 1 1
#> 6 2000-06-05 A ETC 2000-01-01 5 5
#> 7 2000-10-30 A ETC 2000-01-01 9 9
#> 8 2001-02-04 B BIRTH 2001-02-04 0 0
#> 9 2001-06-15 B ETC 2001-02-04 4 4
#> 10 2001-12-26 B ETC 2001-02-04 10 10
#> 11 2002-05-22 B ETC 2001-02-04 15 15
#> 12 2002-06-04 B ETC 2001-02-04 16 15
#> 13 2000-01-08 C BIRTH 2000-01-08 0 0
#> 14 2000-07-11 C ETC 2000-01-08 6 6
#> 15 2000-08-18 C ETC 2000-01-08 7 7
#> 16 2000-11-27 C ETC 2000-01-08 10 10
clock says that 2001-02-04 to 2002-06-04 is 16 months, while the lubridate method here only says it is 15 months. This has to do with the fact that the lubridate calculation uses the length of an average month, which doesn't always accurately reflect how we think about months.
Consider this simple example, I think most people would agree that a child born on this date in February is considered "1 month and 1 day" old. But lubridate shows 0 months!
library(clock)
library(lubridate, warn.conflicts = FALSE)
# "1 month and 1 day apart"
feb <- as.Date("2020-02-28")
mar <- as.Date("2020-03-29")
# As expected when thinking about age in months
date_count_between(feb, mar, "month")
#> [1] 1
# Not expected
as.period(mar - feb) %/% months(1)
#> [1] 0
secs_in_day <- 86400
secs_in_month <- as.numeric(months(1))
secs_in_month / secs_in_day
#> [1] 30.4375
# Less than 30.4375 days, so not 1 month
mar - feb
#> Time difference of 30 days
The issue is that lubridate uses the length of an average month in the computation, which is 30.4375 days. But there are only 30 days between these two dates, so it isn't considered a full month.
clock, on the other hand, uses the day component of the starting date to determine if a "full month" has passed or not. In other words, because we have passed the 28th of March, clock decides that 1 month has passed, which is consistent with how we generally think about age.
Using dplyr and lubridate, we can do the following. We first turn the date column into a date. Then we group by ID, find the birth date and calculate the number of months since that date via some lubridate magic (see How do I use the lubridate package to calculate the number of months between two date vectors where one of the vectors has NA values?).
library(dplyr)
library(lubridate)
df1 %>%
mutate(date = as_date(date)) %>%
group_by(ID) %>%
mutate(birth_date = date[status == "BIRTH"],
age = as.period(date - birth_date) %/% months(1)) %>%
ungroup()
Which gives:
date ID status birth_date age
<date> <fct> <fct> <date> <dbl>
1 2000-01-01 A BIRTH 2000-01-01 0
2 2000-01-14 A ETC 2000-01-01 0
3 2000-01-25 A ETC 2000-01-01 0
4 2000-02-12 A ETC 2000-01-01 1
5 2000-02-27 A ETC 2000-01-01 1
6 2000-06-05 A ETC 2000-01-01 5
7 2000-10-30 A ETC 2000-01-01 9
8 2001-02-04 B BIRTH 2001-02-04 0
9 2001-06-15 B ETC 2001-02-04 4
10 2001-12-26 B ETC 2001-02-04 10
11 2002-05-22 B ETC 2001-02-04 15
12 2002-06-04 B ETC 2001-02-04 15
13 2000-01-08 C BIRTH 2000-01-08 0
14 2000-07-11 C ETC 2000-01-08 6
15 2000-08-18 C ETC 2000-01-08 7
16 2000-11-27 C ETC 2000-01-08 10
Which is your expected output except for some rounding differences. See my comment on your question.
I have to calculate the following data Number of frost change days**(NFCD)**** as weekly basis.
That means the number of days in which minimum temperature and maximum temperature cross 0°C.
Let's say I work with years 1957-1980 with hourly temp.
Example data (couple of rows look like):
Date Time (UTC) temperature
1957-07-01 00:00:00 5
1957-07-01 03:00:00 6.2
1957-07-01 05:00:00 9
1957-07-01 06:00:00 10
1957-07-01 07:00:00 10
1957-07-01 08:00:00 14
1957-07-01 09:00:00 13.2
1957-07-01 10:00:00 15
1957-07-01 11:00:00 15
1957-07-01 12:00:00 16.3
1957-07-01 13:00:00 15.8
Expected data:
year month week NFCD
1957 7 1 1
1957 7 2 5
dat <- data.frame(date=c(rep("A",5),rep("B",5)), time=rep(1:5, times=2), temp=c(1:5,-2,1:4))
dat
# date time temp
# 1 A 1 1
# 2 A 2 2
# 3 A 3 3
# 4 A 4 4
# 5 A 5 5
# 6 B 1 -2
# 7 B 2 1
# 8 B 3 2
# 9 B 4 3
# 10 B 5 4
aggregate(temp ~ date, data = dat, FUN = function(z) min(z) <= 0 && max(z) > 0)
# date temp
# 1 A FALSE
# 2 B TRUE
(then rename temp to NFCD)
Using the data from r2evans's answer you can also use tidyverse logic:
library(tidyverse)
dat %>%
group_by(date) %>%
summarize(NFCD = min(temp) < 0 & max(temp) > 0)
which gives:
# A tibble: 2 x 2
date NFCD
<chr> <lgl>
1 A FALSE
2 B TRUE
I have yearly observations of income for a series of geographies, like this:
library(dplyr)
library(lubridate)
date <- c("2004-01-01", "2005-01-01", "2006-01-01",
"2004-01-01", "2005-01-01", "2006-01-01")
geo <- c(1, 1, 1, 2, 2, 2)
inc <- c(10, 12, 14, 32, 34, 50)
data <- tibble(date = ymd(date), geo, inc)
date geo inc
<date> <dbl> <dbl>
1 2004-01-01 1 10
2 2005-01-01 1 12
3 2006-01-01 1 14
4 2004-01-01 2 32
5 2005-01-01 2 34
6 2006-01-01 2 50
I need to insert mid-year values, as averages of the start-of-year and end-of-year observations, so that the data is every 6 months. The outcome would like this:
2004-01-01 1 10
2004-06-01 1 11
2005-01-01 1 12
2004-06-01 1 13
2006-01-01 1 14
2004-01-01 2 32
2004-06-01 2 33
2005-01-01 2 34
2004-06-01 2 42
2006-01-01 2 50
Would appreciate any ideas.
Grouped by 'geoo', add (+) the 'inc' with the next value (lead) and get the average (/2), as well as add 5 months to the 'date', then filter out the NA elements in 'inc', bind the rows with the original data
library(dplyr)
library(lubridate)
data %>%
group_by(geo) %>%
summarise(date = date %m+% months(5),
inc = (inc + lead(inc))/2, .groups = 'drop') %>%
filter(!is.na(inc)) %>%
bind_rows(data, .) %>%
arrange(geo, date)
-output
# A tibble: 10 x 3
# date geo inc
# <date> <dbl> <dbl>
# 1 2004-01-01 1 10
# 2 2004-06-01 1 11
# 3 2005-01-01 1 12
# 4 2005-06-01 1 13
# 5 2006-01-01 1 14
# 6 2004-01-01 2 32
# 7 2004-06-01 2 33
# 8 2005-01-01 2 34
# 9 2005-06-01 2 42
#10 2006-01-01 2 50
You can use complete to create a sequence of dates for 6 months and then use na.approx to fill the NA values with interpolated values.
library(dplyr)
library(lubridate)
data %>%
group_by(geo) %>%
tidyr::complete(date = seq(min(date), max(date), by = '6 months')) %>%
mutate(date = if_else(is.na(inc), date %m-% months(1), date),
inc = zoo::na.approx(inc))
# geo date inc
# <dbl> <date> <dbl>
# 1 1 2004-01-01 10
# 2 1 2004-06-01 11
# 3 1 2005-01-01 12
# 4 1 2005-06-01 13
# 5 1 2006-01-01 14
# 6 2 2004-01-01 32
# 7 2 2004-06-01 33
# 8 2 2005-01-01 34
# 9 2 2005-06-01 42
#10 2 2006-01-01 50
I'm attempting to create a data frame that shows all of the in between months for my data set, by subject. Here is an example of what the data looks like:
dat <- data.frame(c(1, 1, 1, 2, 3, 3, 3, 4, 4, 4), c(rep(30, 2), rep(25, 5), rep(20, 3)), c('2017-01-01', '2017-02-01', '2017-04-01', '2017-02-01', '2017-01-01', '2017-02-01', '2017-03-01', '2017-01-01',
'2017-02-01', '2017-04-01'))
colnames(dat) <- c('id', 'value', 'date')
dat$Out.Of.Study <- c("", "", "Out", "Out", "", "", "Out", "", "", "Out")
dat
id value date Out.Of.Study
1 1 30 2017-01-01
2 1 30 2017-02-01
3 1 25 2017-04-01 Out
4 2 25 2017-02-01 Out
5 3 25 2017-01-01
6 3 25 2017-02-01
7 3 25 2017-03-01 Out
8 4 20 2017-01-01
9 4 20 2017-02-01
10 4 20 2017-04-01 Out
If I want to show the in between months where no data was collected (but the subject was still enrolled in the study) I can use the complete() function. However, the issue is that I get all missing months for each subject id based on the min and max month identified in the data set:
## Add Dates by Group
library(tidyr)
complete(dat, id, date)
id date value Out.Of.Study
1 1 2017-01-01 30
2 1 2017-02-01 30
3 1 2017-03-01 NA <NA>
4 1 2017-04-01 25 Out
5 2 2017-01-01 NA <NA>
6 2 2017-02-01 25 Out
7 2 2017-03-01 NA <NA>
8 2 2017-04-01 NA <NA>
9 3 2017-01-01 25
10 3 2017-02-01 25
11 3 2017-03-01 25 Out
12 3 2017-04-01 NA <NA>
13 4 2017-01-01 20
14 4 2017-02-01 20
15 4 2017-03-01 NA <NA>
16 4 2017-04-01 20 Out
The issue with this is that I don't want the missing months to exceed the subject's final observed month (essentially, I have subjects who are censored and would need to be removed from the study) or show up prior to the month a subject started the study. For example, subject 2 was only a participant in the month '2017-02-01'. There for, I'd like the data to represent that this was the only month they were in there and not have them represented by the extra months after and the extra month before, as shown above. The same is the case with subject 3, who has an extra month, even though they are out of the study.
Perhaps the complete() isn't the best way to go about this?
This can be solved by creating a sequence of months individually for each id and by joining the sequences with dat to complete the missing months.
1. data.table
(The question is tagged with tidyr. But as I am more acquainted with data.table I have tried this first.)
library(data.table)
# coerce date strings to class Date
setDT(dat)[, date := as.Date(date)]
# create sequence of months for each id
sdt <- dat[, .(date = seq(min(date), max(date), "month")), by = id]
# join
dat[sdt, on = .(id, date)]
id value date Out.Of.Study
1: 1 30 2017-01-01
2: 1 30 2017-02-01
3: 1 NA 2017-03-01 <NA>
4: 1 25 2017-04-01 Out
5: 2 25 2017-02-01 Out
6: 3 25 2017-01-01
7: 3 25 2017-02-01
8: 3 25 2017-03-01 Out
9: 4 20 2017-01-01
10: 4 20 2017-02-01
11: 4 NA 2017-03-01 <NA>
12: 4 20 2017-04-01 Out
Note that there is only one row for id == 2 as requested by the OP.
This approach requires to coerce date from factor to class Date to make sure that all missing months will be completed.
This is also safer than to rely on the avialable date factors in the dataset. For illustration, let's assume that id == 4 is Out in month 2017-06-01 (June) instead of 2017-04-01 (April). Then, there would be no month 2017-05-01 (May) in the whole dataset and the final result would be incomplete.
Without creating the temporary variable sdt the code becomes
library(data.table)
setDT(dat)[, date := as.Date(date)][
dat[, .(date = seq(min(date), max(date), "month")), by = id], on = .(id, date)]
2. tidyr / dplyr
library(dplyr)
library(tidyr)
# coerce date strings to class Date
dat <- dat %>%
mutate(date = as.Date(date))
dat %>%
# create sequence of months for each id
group_by(id) %>%
expand(date = seq(min(date), max(date), "month")) %>%
# join to complete the missing month for each id
left_join(dat, by = c("id", "date"))
# A tibble: 12 x 4
# Groups: id [?]
id date value Out.Of.Study
<dbl> <date> <dbl> <chr>
1 1 2017-01-01 30 ""
2 1 2017-02-01 30 ""
3 1 2017-03-01 NA NA
4 1 2017-04-01 25 Out
5 2 2017-02-01 25 Out
6 3 2017-01-01 25 ""
7 3 2017-02-01 25 ""
8 3 2017-03-01 25 Out
9 4 2017-01-01 20 ""
10 4 2017-02-01 20 ""
11 4 2017-03-01 NA NA
12 4 2017-04-01 20 Out
There is a variant which does not update dat:
library(dplyr)
library(tidyr)
dat %>%
mutate(date = as.Date(date)) %>%
right_join(group_by(., id) %>%
expand(date = seq(min(date), max(date), "month")),
by = c("id", "date"))
I would still use complete (probably the right method to use here), but after it would subset rows that exceed row with "Out". You can do this with dplyr::between.
dat %>%
group_by(id) %>%
complete(date) %>%
# Filter rows that are between 1 and the one that has "Out"
filter(between(row_number(), 1, which(Out.Of.Study == "Out")))
id date value Out.Of.Study
<dbl> <fct> <dbl> <chr>
1 1 2017-01-01 30 ""
2 1 2017-02-01 30 ""
3 1 2017-03-01 NA NA
4 1 2017-04-01 25 Out
5 2 2017-01-01 NA NA
6 2 2017-02-01 25 Out
7 3 2017-01-01 25 ""
8 3 2017-02-01 25 ""
9 3 2017-03-01 25 Out
10 4 2017-01-01 20 ""
11 4 2017-02-01 20 ""
12 4 2017-03-01 NA NA
13 4 2017-04-01 20 Out
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I have a data frame in long format, with one observation row per measurement. I want to loop through each unique ID and find the "minimum" date for each unique individual. For example, patient 1 may be measured at three different times, but I want the earliest time. I thought about sorting the dataset by the date (in increasing order) and removing all duplicates, but I'm not sure if this is the best way to go. Any help or suggestions would be greatly appreciated. Thank you!
We can use data.table. Convert the 'data.frame' to 'data.table' (setDT(df1)), grouped by 'ID', order the 'Date' (assuming that it is in Date class or else change to Date class with as.Date with correct format), and get the first observation with head
library(data.table)
setDT(df1)[order(Date), head(.SD, 1), by = ID]
Here is another way using basic R:
earliestDates = aggregate(list(date = df$date), list(ID = df$ID), min)
result = merge(earliestDates,df)
earliestDates is a two column data frame that has the minimum date by ID. The merge will join the values in the other columns.
Example:
set.seed(1)
ID = floor(runif(20,1,5))
day = as.Date(floor(runif(20,1,25)),origin = "2017-1-1")
weight = floor(runif(20,80,95))
df = data.frame(ID = ID, date = day, weight = weight)
> df
ID date weight
1 2 2017-01-24 92
2 2 2017-01-07 89
3 3 2017-01-17 91
4 4 2017-01-05 88
5 1 2017-01-08 87
6 4 2017-01-11 91
7 4 2017-01-02 80
8 3 2017-01-11 87
9 3 2017-01-22 90
10 1 2017-01-10 90
11 1 2017-01-13 87
12 1 2017-01-16 92
13 3 2017-01-13 86
14 2 2017-01-06 83
15 4 2017-01-21 81
16 2 2017-01-18 81
17 3 2017-01-21 84
18 4 2017-01-04 87
19 2 2017-01-19 89
20 4 2017-01-11 86
After the aggregate and merge, the result is:
> result
ID date weight
1 1 2017-01-08 87
2 2 2017-01-06 83
3 3 2017-01-11 87
4 4 2017-01-02 80
Try the following dplyr code:
library(dplyr)
set.seed(12345)
###Create test dataset
tb <- tibble(id = rep(1:10, each = 3),
date = rep(seq(as.Date("2017-07-01"), by=10, len=10), 3),
obs = rnorm(30))
# # A tibble: 30 × 3
# id date obs
# <int> <date> <dbl>
# 1 2017-07-01 0.5855288
# 1 2017-07-11 0.7094660
# 1 2017-07-21 -0.1093033
# 2 2017-07-31 -0.4534972
# 2 2017-08-10 0.6058875
# 2 2017-08-20 -1.8179560
# 3 2017-08-30 0.6300986
# 3 2017-09-09 -0.2761841
# 3 2017-09-19 -0.2841597
# 4 2017-09-29 -0.9193220
# # ... with 20 more rows
###Pipe the dataset through dplyr's 'group_by' and 'filter' commands
tb %>% group_by(id) %>%
filter(date == min(date)) %>%
ungroup() %>%
distinct()
# # A tibble: 10 × 3
# id date obs
# <int> <date> <dbl>
# 1 2017-07-01 0.5855288
# 2 2017-07-31 -0.4534972
# 3 2017-08-30 0.6300986
# 4 2017-07-01 -0.1162478
# 5 2017-07-21 0.3706279
# 6 2017-08-20 0.8168998
# 7 2017-07-01 0.7796219
# 8 2017-07-11 1.4557851
# 9 2017-08-10 -1.5977095
# 10 2017-09-09 0.6203798