I have a dataframe with 3 columns
> str(lagdf)
'data.frame': 2208 obs. of 3 variables:
$ time: POSIXct, format: "2015-10-27 00:00:00" "2015-10-27 00:15:00" "2015-10-27 00:30:00" "2015-10-27 00:45:00" ...
$ site: Factor w/ 23 levels "2001","2002",..: 1 1 1 1 1 1 1 1 1 1 ...
$ lag : int 8 8 8 8 8 8 8 8 7 8 ...
the column lag represent the lag of a particular site for that particular time
> head(lagdf,14)
time site lag
1 2015-10-27 00:00:00 2001 8
2 2015-10-27 00:15:00 2001 8
3 2015-10-27 00:30:00 2001 8
4 2015-10-27 00:45:00 2001 8
5 2015-10-27 01:00:00 2001 8
6 2015-10-27 01:15:00 2001 8
7 2015-10-27 01:30:00 2001 8
8 2015-10-27 01:45:00 2001 8
9 2015-10-27 02:00:00 2001 7
10 2015-10-27 02:15:00 2001 8
11 2015-10-27 02:30:00 2001 9
12 2015-10-27 02:45:00 2001 9
13 2015-10-27 03:00:00 2001 9
14 2015-10-27 03:15:00 2001 8
I'd like to be able to spread lag so that I can have as column a lag for each particular site.
site lag1 lag2 lag3
2001 8 8 8
the time column will not remain
using tidyr didn't help
You can indeed use tidyr in combination with dplyr:
library(tidyr)
library(dplyr)
lagdf %>% group_by(site) %>%
select(-time) %>%
mutate(row = paste0("lag",row_number())) %>%
spread(row, lag)
Source: local data frame [1 x 15]
site lag1 lag10 lag11 lag12 lag13 lag14 lag2 lag3 lag4 lag5 lag6 lag7 lag8 lag9
(int) (int) (int) (int) (int) (int) (int) (int) (int) (int) (int) (int) (int) (int) (int)
1 2001 8 8 9 9 9 8 8 8 8 8 8 8 8 7
Related
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
This is a representation of my dataset
ID<-c(rep(1,10),rep(2,8))
year<-c(2007,2007,2007,2008,2008,2009,2010,2009,2010,2011,
2008,2008,2009,2010,2009,2010,2011,2011)
month<-c(2,7,12,4,11,6,11,1,9,4,3,6,7,4,9,11,2,8)
mydata<-data.frame(ID,year,month)
I want to calculate for each individual the number of months from the initial date. I am using two variables: year and month.
I firstly order years and months:
mydata2<-mydata%>%group_by(ID,year)%>%arrange(year,month,.by_group=T)
Then I created the variable date considering that the day begin with 01:
mydata2$date<-paste("01",mydata2$month,mydata2$year,sep = "-")
then I used lubridate to change this variable in date format
mydata2$date<-dmy(mydata2$date)
But after this, I really don't know what to do, in order to have such a dataset (preferably using dplyr code) below:
ID year month date dif_from_init
1 1 2007 2 01-2-2007 0
2 1 2007 7 01-7-2007 5
3 1 2007 12 01-12-2007 10
4 1 2008 4 01-4-2008 14
5 1 2008 11 01-11-2008 21
6 1 2009 1 01-1-2009 23
7 1 2009 6 01-6-2009 28
8 1 2010 9 01-9-2010 43
9 1 2010 11 01-11-2010 45
10 1 2011 4 01-4-2011 50
11 2 2008 3 01-3-2008 0
12 2 2008 6 01-6-2008 3
13 2 2009 7 01-7-2009 16
14 2 2009 9 01-9-2009 18
15 2 2010 4 01-4-2010 25
16 2 2010 11 01-11-2010 32
17 2 2011 2 01-2-2011 35
18 2 2011 8 01-8-2011 41
One way could be:
mydata %>%
group_by(ID) %>%
mutate(date = as.Date(sprintf('%d-%d-01',year, month)),
diff = as.numeric(round((date - date[1])/365*12)))
# A tibble: 18 x 5
# Groups: ID [2]
ID year month date diff
<dbl> <dbl> <dbl> <date> <dbl>
1 1 2007 2 2007-02-01 0
2 1 2007 7 2007-07-01 5
3 1 2007 12 2007-12-01 10
4 1 2008 4 2008-04-01 14
5 1 2008 11 2008-11-01 21
6 1 2009 6 2009-06-01 28
7 1 2010 11 2010-11-01 45
8 1 2009 1 2009-01-01 23
9 1 2010 9 2010-09-01 43
10 1 2011 4 2011-04-01 50
11 2 2008 3 2008-03-01 0
12 2 2008 6 2008-06-01 3
13 2 2009 7 2009-07-01 16
14 2 2010 4 2010-04-01 25
15 2 2009 9 2009-09-01 18
16 2 2010 11 2010-11-01 32
17 2 2011 2 2011-02-01 35
18 2 2011 8 2011-08-01 41
This question already has answers here:
How to get summary statistics by group
(14 answers)
Closed 2 years ago.
I have the following data for 7 years (2012-2018) and need to calculate the median for every month for all the years. Thank you for any help!
Ano Mes Dia Hora UI Date
<dbl> <dbl> <dbl> <dbl> <dbl> <dttm>
1 2012 1 1 0 37.9 2012-01-01 00:00:00
2 2012 1 1 6 9.18 2012-01-01 06:00:00
3 2012 1 1 12 1.18 2012-01-01 12:00:00
4 2012 1 1 18 27.0 2012-01-01 18:00:00
5 2012 1 2 0 -292. 2012-01-02 00:00:00
6 2012 1 2 6 98.2 2012-01-02 06:00:00
7 2012 1 2 12 95.9 2012-01-02 12:00:00
8 2012 1 2 18 6.19 2012-01-02 18:00:00
9 2012 1 3 0 -4.65 2012-01-03 00:00:00
10 2012 1 3 6 40.1 2012-01-03 06:00:00
# ... with 10,215 more rows
df %>%
group_by(Mes) %>%
summarise(median_ui = median(UI))
I received a set of dates, but it turns out that time is reported in days since 01-01-1960 in this specific data set.
D_INDDTO
1 20758
2 20856
3 21062
4 19740
5 21222
6 21203
The specific date of interest for Patient 1 is 20758 days since 01-01-60
I want to create a new covariate u$date containing the specific date of interest i d%m%y%. I tried
library(tidyverse)
u %>% mutate(date=as.date(D_INDDTO,origin="1960-01-01")
But that did not solve it.
u <- structure(list(D_INDDTO = c(20758, 20856, 21062, 19740, 21222,
21203, 20976, 20895, 18656, 18746)), row.names = c(NA, 10L), class = "data.frame")
Try this:
#Code 1
u %>% mutate(date=as.Date("1960-01-01")+D_INDDTO)
Output:
D_INDDTO date
1 20758 2016-10-31
2 20856 2017-02-06
3 21062 2017-08-31
4 19740 2014-01-17
5 21222 2018-02-07
6 21203 2018-01-19
7 20976 2017-06-06
8 20895 2017-03-17
9 18656 2011-01-29
10 18746 2011-04-29
Or this:
#Code 2
u %>% mutate(date=as.Date(D_INDDTO,origin="1960-01-01"))
Output:
D_INDDTO date
1 20758 2016-10-31
2 20856 2017-02-06
3 21062 2017-08-31
4 19740 2014-01-17
5 21222 2018-02-07
6 21203 2018-01-19
7 20976 2017-06-06
8 20895 2017-03-17
9 18656 2011-01-29
10 18746 2011-04-29
Or this:
#Code 3
u %>% mutate(date=format(as.Date(D_INDDTO,origin="1960-01-01"),'%d%m%y'))
Output:
D_INDDTO date
1 20758 311016
2 20856 060217
3 21062 310817
4 19740 170114
5 21222 070218
6 21203 190118
7 20976 060617
8 20895 170317
9 18656 290111
10 18746 290411
If more customization is required:
#Code 4
u %>% mutate(date=format(as.Date(D_INDDTO,origin="1960-01-01"),'%d-%m-%Y'))
Output:
D_INDDTO date
1 20758 31-10-2016
2 20856 06-02-2017
3 21062 31-08-2017
4 19740 17-01-2014
5 21222 07-02-2018
6 21203 19-01-2018
7 20976 06-06-2017
8 20895 17-03-2017
9 18656 29-01-2011
10 18746 29-04-2011
I have a large df with dates that were accidentally coerced into the wrong format.
Data:
id <- c(1:12)
date <- c("2014-01-03","2001-08-14","2001-08-14","2014-06-02","2006-06-14", "2006-06-14",
"2014-08-08","2014-08-08","2008-04-14","2009-12-13","2010-09-14","2012-09-14")
df <- data.frame(id,date)
Structure:
id date
1 1 2014-01-03
2 2 2001-08-14
3 3 2001-08-14
4 4 2014-06-02
5 5 2006-06-14
6 6 2006-06-14
7 7 2014-08-08
8 8 2014-08-08
9 9 2008-04-14
10 10 2009-12-13
11 11 2010-09-14
12 12 2012-09-14
The data set only includes, or rather should only include the years 2014 and 2013. The dates 2001-08-14 and 2006-06-14 are most likely 2014-08-01 and 2014-06-06, respectively.
Output:
id date
1 1 2014-01-03
2 2 2014-08-01
3 3 2014-08-01
4 4 2014-06-02
5 5 2014-06-06
6 6 2014-06-06
7 7 2014-08-08
8 8 2014-08-08
9 9 2014-04-08
10 10 2013-12-09
11 11 2014-09-10
12 12 2014-09-12
How can I reconcile this mess?
Package lubridate has the convenient function year that will be useful here.
library(lubridate)
# Convert date to proper date class variable
df$date <- as.Date(df$date)
# Isolate problematic indices; when year is not in 2013 or 2014,
# we'll go to and from character representation. We'll trim
# the "20" in front of the "false year" and then specify the
# proper format to read the character back into a Date class.
tmp.indices <- which(!year(df$date) %in% c("2013", "2014"))
df$date[tmp.indices] <- as.Date(substring(as.character(df$date[tmp.indices]),
first = 3), format = "%d-%m-%y")
Result:
id date
1 1 2014-01-03
2 2 2014-08-01
3 3 2014-08-01
4 4 2014-06-02
5 5 2014-06-06
6 6 2014-06-06
7 7 2014-08-08
8 8 2014-08-08
9 9 2014-04-08
10 10 2013-12-09
11 11 2014-09-10
12 12 2014-09-12
We could convert the 'date' column to 'Date' class, extract the 'year' to create a logical index ('indx') for years 2013, 2014).
df$date <- as.Date(df$date)
indx <- !format(df$date, '%Y') %in% 2013:2014
By using lubridate, convert to 'Date' class using dmy after removing the first two characters.
library(lubridate)
df$date[indx] <- dmy(sub('^..', '', df$date[indx]))
df
# id date
#1 1 2014-01-03
#2 2 2014-08-01
#3 3 2014-08-01
#4 4 2014-06-02
#5 5 2014-06-06
#6 6 2014-06-06
#7 7 2014-08-08
#8 8 2014-08-08
#9 9 2014-04-08
#10 10 2013-12-09
#11 11 2014-09-10
#12 12 2014-09-12