I have a workspace where I have study of the weather of every hour past one year (temperature, CO2 and stuff).
What I need to do is split whole workspace depending on date (cause I have several 2009-01-01 etc) and in next step summarize the data for each day separetly (I'm looking for summary of every variable for every day separetly).
I was searching for some kind of function and have one, which is almoust good. Separating day works quite good, but summary is really bad.
df <- data.frame(date=rep(seq.POSIXt(as.POSIXct("2009-01-01"), by="day", length.out=31), each=1))
summary(split(df, as.Date(df$date),AM19))
Related
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.
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.
I'm very new to R so I'm sorry if some of the terminology I use is incorrect. I have a large .csv file for daily visits, with the columns including the date (in D/M/Y format) and the number of visits that day in a seperate column. The date starts on 05/01/20 and ends on 06/11/20. I've plotted a daily time series of this data, and now I'm trying to plot a weekly time series, with the total sum of daily visits totaled together to get a weekly total, starting Monday and ending Sunday. I have looked through other similar questions on this site, and came across this code:
Week <- as.Date(cut(DF$Date, "week"))
aggregate(Frequency ~ Week, DF, sum)
However, I can't seem to get it to work. I would prefer to keep it as simple as possible. I also have the forecast package and zoo package installed if that helps.
I have a question regarding declaring my dataset as a time series. I have weekly historical data of demand of a certain product. Every week is labeled as 201401, 201402, up to the current month 201937.
The problem arises once I declare the set as ts <- ts(data, start=2014, frequency=52). Because every year consists of 365.25 days, in my set I have a week 201553. So from week 201601, every week is basically a week later, which causes problem when finding seasonality patterns.
Do I have to delete week 201553 or what is the appropriate continuation?
If you set frequency=365.25/7, things should work out fine.
References
https://otexts.com/fpp2/ts-objects.html
https://www.rdocumentation.org/packages/stats/versions/3.6.1/topics/ts (interestingly, the trick with frequency=52.17857 is not mentioned here)
http://manishbarnwal.com/blog/2017/05/03/time_series_and_forecasting_using_R/
I have a daily rainfall data for 36 years. I want to analyze the time series, but my data is still in the form of frame data, how I change the frame data into time series. My data is a variable, how to unify the year number with the date and month, so the data is only in one column
You could use a time series package for that, such as fpp i.e. install.packages('fpp'). Since you don't give an example code, I can't really help you properly with it but it's quite easy.
ts(your_data, start =, frequency = ) At start = you put the year or month where you'd start and at frequency = you'd put e.g. 36 since you talk about 36 years.
You might want to check out https://robjhyndman.com/. He has an online (free) book available that walks you through the use of his package as well as providing useful information with respect to time series analysis.
Hope this helps.