Creating unique time measurement ID for each user based on date of measurement (varying per user) - r
I am having a problem with my analyses in R that I am hoping you guys can help me with. So I have a dataset with multiple daily measurements per participant (DiaryEating), all participants have a unique ID (UserID), and all measurements are timestamped with the date (DateVariable). However, these measurements instances differ for participants (i.e. some got measured on the 1st, others on the 3rd of the month). For my analysis, instead of the DateVariable, I need a Time variable that indicates whether this is the 1st, 2nd or 3rd measurement PER participant.
This sounds very simple, but has proven very difficult to me (Yes, I do indeed suck very hard at this).
Code looks something like:
UserID (1,1,1,2,2,2,3,3,3,3)
DateVariable( 2018/10/30, 2018/10/31, 2018/11/01, 2018/10/30, 2018/10/31, 2018/11/03, 2018/10/30, 2018/10/31, 2018/11/02, 2018/11/04)
DiaryEating (3,3,4,2,3,3,4,4,4,4,5)
TimeMeas(WHAT I WANT TO CREATE WOULD BE): 1,2,3,1,2,3,1,2,3,4)
You can use dplyr::group_by as:
library(dplyr)
df %>%
group_by(UserID) %>%
mutate(Time = row_number())
Related
Creating new datasets from unique dates in R
I have a dataset of 2015 with every day of the year. In this dataset, there are actions that happen on any given day. Some days have more actions than others, therefore some days have many more entries than others. I am trying to create a function that will create an individual dataset per day of the year without having to code 365 of these: df <- subset(dataset, date== "2015-01-01") I have looked at dyplyr's group_by(), however I do not want a summary per day, it is important that I get to see the whole observation on any given day for graphing purposes.
How to count the number of days that pass between two dates in a dataset column in R
I am working with a dataset This is the dataset. In the dataset there are 33 unique Ids that are repeated for each day they provided data, within 30 days, from their fitbit. I am trying to count the number of days they input data through the ActivityDay column and group it to the Id, so that I can see how many total days they used their fitbit out of the 30 days. the Activity date data type was originally POSIXct and I converted it to Date type. How can I count the dates as number or days and group it to each indvidual ID? I tried using count within a dplyr::summarise to get the ID and number of days counted while grouping the data to the ID. that failed. I also thought of using a case_when, however, I thought that wouldn't work because it wouldn't count all the way up to the end dates I specify, so anything between the two dates would get the ouputs I specified. I also tried count_date_between(min(user_device_activity), max(user_device_activity), by 'day') but it said that the function doesn't exist and when I tried installing it. It said it didn't exist within R.
library(dplyr) user_device_activity %>% distinct(Id, ActivityDate) %>% # in case duplicates possible in data count(Id, month = lubridate::floor_date(ActivityDate, "month"))
Average after 2 group_by's in R
I am new to R can't find the right syntax for a specific average I need. I have a large fitbit dataset of heartrate per second for 30 people, for a month each. I want an average of heartrate per day per person to make the data easier to manage and join with other fitbit data. First few lines of Data The columns I have are Id (person Id#), Time (Date-Time), and Value (Heartrate). I already separated Time into two columns, one for date and one for time only. My idea is to group the information by person, then by date and get one average number per person per day. But, my code is not doing that. hr_avg <- hr_per_second %>% group_by(Id) %>% group_by(Date) %>% summarize(mean(Value)) As a result I get an average by date only. I can't do this manually because the dataset is so big, Excel can't open it. And I can't upload it to BigQuery either, the database I learned to use during my data analysis course. Thanks.
R - how to create lagged variables by id, day, assessment nr and specifying the interval
Maybe someone here can help me out! What I need to do in R is: create lags for multiple variables considering id, day and day_nr (as I have multiple assessments for each participants on each day, and no lags should be created overnight, meaning no lag for the first assessment in the morning by the last observation on the former day) I tried several options, for example this, but didnt manage to put in more than id: library(data.table) data[, lag.value:=c(NA, value[-.N]), by=id] Furthermore, I now included the specific day time of the assessment and lags should only be created for obsersavtions with an interval <3hours between them, as number of assessment per day are irregular. Any idea how i could do this in R? Thanks a lot!! Tine
Different age calculation for different rows
I'm an absolute R beginner here working on a Master's project.
I have a data.frame that contains information on trotting horses (their wins, earnings, time records and such). The data is organised in a way that every row contains information for a specific year the horse competed and including a first row for each horse of "Total", so there's a summary for every variable for it's total competing life. It looks like this:
I created a new variable with their age using the age_calc function in the eeptools package:
travdata$Age<-age_calc(as.Date(travdata$Birth.date), enddate=as.Date("2016-12-31"),
units="years")
With no problems. What I'm trying to figure out is if there is any way I can calculate the age of the horses for each specific year I have info on them-that is, the "Total" row would have their age up until 2016-12-31, for the year 2015 it would have their age at that time and so on. I've been trying to include if statements in age_calc but it won't work and I'm really at a loss on how best to do this.
Any literature or help you could point me to would be much, much appreciated.
MWE
travdata <- data.frame(
"Id.Number"=c(rep("1938-98",3),rep("1803-97",7),rep("1221-03",4)),
"Name"=c(rep("Muuttuva",3),rep("Pelson Poika",7),rep("Muusan Muisto",4)),
"Sex"=c(rep("Mare",3),rep("Gelding",7),rep("Gelding",4)),
"Birth.year"=c(rep(1998,3),rep(1997,7),rep(2003,4)),
"Birth.date"=c(rep("1998-07-01",3),rep("1997-07-14",7),rep("2003-05-07",4)),
"Competition.year" = c("Total",2005,2004,"Total",2003,2004,2006,2005,2002,2001,2008,2010,"Total",2009),
"starts"=c(20,11,9,44,21,6,7,5,3,2,1,1,4,2),
"X1st.placements"=c(0,0,0,3,3,0,0,0,0,0,0,0,0,0),
"X2nd.placements"=c(2,2,0,1,0,1,0,0,0,0,0,0,0,0),
"X3rd.placements"=c(2,2,0,1,1,0,0,0,0,0,0,0,0,0),
"Earnings.euro"=c(1525,1425,100,2078,1498,580,0,0,0,0,0,0,10,10)
)
The trick is to filter out the "Total" rows and specify a format for the as.Date() function
library(eeptools)
travdata <- data.frame(
"Id.Number"=c(rep("1938-98",3),rep("1803-97",7),rep("1221-03",4)),
"Name"=c(rep("Muuttuva",3),rep("Pelson Poika",7),rep("Muusan Muisto",4)),
"Sex"=c(rep("Mare",3),rep("Gelding",7),rep("Gelding",4)),
"Birth.year"=c(rep(1998,3),rep(1997,7),rep(2003,4)),
"Birth.date"=c(rep("1998-07-01",3),rep("1997-07-14",7),rep("2003-05-07",4)),
"Competition.year" = c("Total",2005,2004,"Total",2003,2004,2006,2005,2002,2001,2008,2010,"Total",2009),
"starts"=c(20,11,9,44,21,6,7,5,3,2,1,1,4,2),
"X1st.placements"=c(0,0,0,3,3,0,0,0,0,0,0,0,0,0),
"X2nd.placements"=c(2,2,0,1,0,1,0,0,0,0,0,0,0,0),
"X3rd.placements"=c(2,2,0,1,1,0,0,0,0,0,0,0,0,0),
"Earnings.euro"=c(1525,1425,100,2078,1498,580,0,0,0,0,0,0,10,10)
)
travdata$Age<-age_calc(as.Date(travdata$Birth.date),
enddate=as.Date("2016-12-31"), units="years")
competitions <- travdata[travdata$Competition.year!="Total",]
competitions$Competition.age<-age_calc(
as.Date(competitions$Birth.date),
enddate=as.Date(competitions$Competition.year, format="%Y"),
units="years",F)