I have a dataframe DF in which I have numerous of columns, one is with Dates and an other is the Hour.
My point is that I need to find the PRICE (dame datafra 36 hours before. All my days don't have 24 hours so I can't just shift my data set.
My idea was to look for the day before in my dataset & 12 hours before.
This is what I wrote but this is not working:
for (i in 38:nrow(DF)){
RefDay=as.Date(DF$Date[i])
HourRef=DF$Hour[i]
DF$P24[i]=DF[which(DF$Date == (RefDay-1))& which(DF$Hour == (HourRef-36)),"PRICE"]
}
Here is my DF:
'data.frame': 20895 obs. of 45 variables:
$ Hour : Factor w/ 24 levels "0","1","2","3",..: 1 2 3 4 5 6 7 8 9 10 ...
$ Date : POSIXct, format: "2016-07-01" "2016-07-01" "2016-07-01" "2016-07-01" ...
$ PRICE : num 29.4 24.7 23.4 21.9 20.2 ...
Here is a sample of my data:
DF.Hour DF.Date DF.PRICE
1 0 2016-07-01 29.36
2 1 2016-07-01 24.69
3 2 2016-07-01 23.42
4 3 2016-07-01 21.91
5 4 2016-07-01 20.19
6 5 2016-07-01 22.44
Try to fill the data.frame with full days. You can do it with complete in tidyr. It will fill the not existing values with NA.
If you have any NAs in your full data.frame you can go for the 36th element before with for example lag(price, 36).
DF <- complete(DF, Hour, Date) %>% arrange(Date)
DF$Price[is.na(DF$Price)] <- lag(Price, 36)
Related
I am having a data frame with a specific date range for each row.
stuID stdID roleStart roleEnd
1 1 7 2010-11-18 2020-06-14
2 2 2 2012-08-13 2014-04-01
3 2 4 2014-04-01 2015-10-01
4 2 3 2015-10-01 2018-10-01
5 2 6 2018-10-01 2020-06-14
6 3 4 2014-03-03 2015-10-01
I need to generate the rows based on the weeks of the date. To be precise, I need to populate the rows based on week between two dates in the given data frame.
I tried to achieve this using the following piece of code
extendedData <- reshape2::melt(setNames(lapply(1:nrow(df), function(x) seq.Date(df[x, "roleStart"],
df[x, "roleEnd"], by = "1 week")),df$stuID))
But when I execute this, I am getting the error message
Error in seq.int(0, to0 - from, by) : wrong sign in 'by' argument
This is the structure of the dataframe
'data.frame': 350 obs. of 4 variables:
$ stuID : int 1 2 2 2 2 3 3 3 4 4 ...
$ stdID : int 7 2 4 3 6 4 3 6 1 2 ...
$ roleStart: Date, format: "2010-11-18" "2012-08-13" "2014-04-01" "2015-10-01" ...
$ roleEnd : Date, format: "2020-06-14" "2014-04-01" "2015-10-01" "2018-10-01" ...
Can anyone say what's wrong with the code?
Thanks in advance!!
Here's a way to do this using tidyverse functions :
library(dplyr)
df %>%
mutate(date = purrr::map2(roleStart, roleEnd, seq, by = 'week')) %>%
tidyr::unnest(date)
As far as your code is concerned it works fine till this step i.e generating weekly dates
lapply(1:nrow(df), function(x)
seq.Date(df[x, "roleStart"], df[x, "roleEnd"], by = "1 week"))
I am not sure what you are trying to do with setNames and melt functions there.
I am working on a monthly-based time-series data set:
> head(data, n=10)
# A tibble: 10 x 2
Month Inflation
<dttm> <dbl>
1 1979-01-01 00:00:00 0.0258
2 1979-02-01 00:00:00 0.0234
3 1979-03-01 00:00:00 0.0055
4 1979-04-01 00:00:00 0.0302
5 1979-05-01 00:00:00 0.0305
6 1979-06-01 00:00:00 0.0232
7 1979-07-01 00:00:00 0.025
8 1979-08-01 00:00:00 0.0234
9 1979-09-01 00:00:00 0.0074
10 1979-10-01 00:00:00 0.0089
Although it appears that the data is yet to be recognized as a time-series data as it shows the following structure:
> str(data)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 479 obs. of 2 variables:
$ Month : POSIXct, format: "1979-01-01" "1979-02-01" "1979-03-01" "1979-04-01" ...
$ Inflation: num 0.0258 0.0234 0.0055 0.0302 0.0305 0.0232 0.025 0.0234 0.0074 0.0089 ...
When I tried to convert it using xts function, it gave me this error:
> inflation <- xts(data[,-1], order.by=as.Date(data[,1], "%m/%d/%Y"))
Error in as.Date.default(data[, 1], "%m/%d/%Y") :
do not know how to convert 'data[, 1]' to class “Date”
Please help me with the most appropriate way of data conversion.
Thanks
# You have something like:
data <- data.frame(
Month = as.Date(as.Date("1979-01-01"):as.Date("2000-01-01"), origin="1970-01-01"),
Inflation = rnorm(7671)) # same number of obs
Create TS
choose start and end dates appropriatelly
tseries <- ts(data$Inflation, start = c(1979,1), end = c(2000,1), frequency = 12)
plot(tseries)
[enter image description here][1][enter image description here][2]I have a data frame "RH", with hourly data and I want to convert it to daily maximum and minimum data. This code was very useful [question]:Aggregating hourly data into daily aggregates
RH$Date <- strptime(RH$Date,format="%y/%m/%d)
RH$day <- trunc(RH$Date,"day")
require(plyr)
x <- ddply(RH,.(Date),
summarize,
aveRH=mean(RH),
maxRH=max(RH),
minRH=min(RH)
)
But my first 5 years data are 3 hours data not hourly. so no results for those years. Any suggestion? Thank you in advance.
'data.frame': 201600 obs. of 3 variables:
$ Date: chr "1985/01/01" "1985/01/01" "1985/01/01" "1985/01/01" ...
$ Hour: int 1 2 3 4 5 6 7 8 9 10 ...
$ RH : int NA NA 93 NA NA NA NA NA 79 NA ...
The link you provided is an old one. The code is still perfectly good and would work, but here's a more modern version using dplyr and lubridate
df <- read.table(text='date_time value
"01/01/2000 01:00" 30
"01/01/2000 02:00" 31
"01/01/2000 03:00" 33
"12/31/2000 23:00" 25',header=TRUE,stringsAsFactors=FALSE)
library(dplyr);library(lubridate)
df %>%
mutate(date_time=as.POSIXct(date_time,format="%m/%d/%Y %H:%M")) %>%
group_by(date(date_time)) %>%
summarise(mean=mean(value,na.rm=TRUE),max=max(value,na.rm=TRUE),
min=min(value,na.rm=TRUE))
`date(date_time)` mean max min
<date> <dbl> <dbl> <dbl>
1 2000-01-01 31.33333 33 30
2 2000-12-31 25.00000 25 25
EDIT
Since there's already a date column, this should work:
RH %>%
group_by(Date) %>%
summarise(mean=mean(RH,na.rm=TRUE),max=max(RH,na.rm=TRUE),
min=min(RH,na.rm=TRUE))
I know this have been asked several times but I could not find the right way to get around my problem. I have a very simple CSV file that I upload, looking like:
27.07.2015,100
28.07.2015,100.1504
29.07.2015,100.1957
30.07.2015,100.5044
31.07.2015,100.7661
03.08.2015,100.9308
04.08.2015,100.8114
05.08.2015,100.6927
06.08.2015,100.7501
07.08.2015,100.7194
10.08.2015,100.8197
11.08.2015,100.8133
Now I need to convert my data.frame into xts so I can use the PerformanceAnalytics package. My data.frame has the structure:
> str(mpey)
'data.frame': 243 obs. of 2 variables:
$ V1: Factor w/ 243 levels "01.01.2016","01.02.2016",..: 210 218 228 234 241 21 30 38 45 52 ...
- attr(*, "names")= chr "5" "6" "7" "8" ...
$ V2: Factor w/ 242 levels "100","100.0062",..: 1 4 5 10 16 20 17 13 15 14 ...
- attr(*, "names")= chr "5" "6" "7" "8" ...
I tried different things with as.xts function but could make it work.
Could you please help me get over this?
Here's a solution using the tidyquant package, which contains as_xts() for coercing data frames to xts objects and as_tibble() for coercing time series objects such as xts to tibbles ("tidy" data frames).
Recreate your data
> data_df
# A tibble: 12 × 2
date value
<fctr> <fctr>
1 27.07.2015 100
2 28.07.2015 100.1504
3 29.07.2015 100.1957
4 30.07.2015 100.5044
5 31.07.2015 100.7661
6 03.08.2015 100.9308
7 04.08.2015 100.8114
8 05.08.2015 100.6927
9 06.08.2015 100.7501
10 07.08.2015 100.7194
11 10.08.2015 100.8197
12 11.08.2015 100.8133
First, we need to reformat your data frame. The dates and values are both stored as factors and they need to be in a date and double class, respectively. We'll load tidyquant and reformat the data frame. Note that tidyquant loads the tidyverse and financial packages so you don't need to load anything else. The date can be converted with lubridate::dmy which converts characters in a day-month-year format to date. The value needs to go from factor to character then from character to double, and this is done by nesting as.numeric and as.character.
> library(tidyquant)
> data_tib <- data_df %>%
mutate(date = dmy(date),
value = as.numeric(as.character(value)))
> data_tib
# A tibble: 12 × 2
date value
<date> <dbl>
1 2015-07-27 100.0000
2 2015-07-28 100.1504
3 2015-07-29 100.1957
4 2015-07-30 100.5044
5 2015-07-31 100.7661
6 2015-08-03 100.9308
7 2015-08-04 100.8114
8 2015-08-05 100.6927
9 2015-08-06 100.7501
10 2015-08-07 100.7194
11 2015-08-10 100.8197
12 2015-08-11 100.8133
Now, we can coerce to xts using the tidyquant::as_xts() function. Just specify date_col = date.
> data_xts <- data_tib %>%
as_xts(date_col = date)
> data_xts
value
2015-07-27 100.0000
2015-07-28 100.1504
2015-07-29 100.1957
2015-07-30 100.5044
2015-07-31 100.7661
2015-08-03 100.9308
2015-08-04 100.8114
2015-08-05 100.6927
2015-08-06 100.7501
2015-08-07 100.7194
2015-08-10 100.8197
2015-08-11 100.8133
Here, i have a data set with Start date and End Date and the usages. I have calculated the number of Days between these two days and got the daily usages. (I am okay with one flat usages for each day for now).
Now, what i want to achieve is the sum of the usage for each day in those TIME-FRAME FOR month of June. For example, the first case will be just the Daily_usage
START_DATE END_DATE x DAYS DAILY_USAGE
1 2015-05-01 2015-06-01 261605.00 32 8175.156250
And, for 2nd, i want to the add the Usage 3905 to June 1st, and also to June 2nd because it spans in both June 1st and June 2nd.
2015-05-04 2015-06-02 117159.00 30 3905.3000000
I want to continue doing this for all 387 rows and at the end get the sum of Usages for each day. And,I do not know how to do this for hundreds of records.
This is what my datasets looks right now:
str(YYY)
'data.frame': 387 obs. of 5 variables:
$ START_DATE : Date, format: "2015-05-01" "2015-05-04" "2015-05-11" "2015- 05-13" ...
$ END_DATE : Date, format: "2015-06-01" "2015-06-01" "2015-06-01" "2015-06-01" ...
$ x : num 261605 1380796 183 103 489 ...
$ DAYS : num 32 29 22 20 19 12 1 34 30 29 ...
$ DAILY_USAGE: num 8175.16 47613.66 8.32 5.13 25.74 ...
Also, the header.
START_DATE END_DATE x DAYS DAILY_USAGE
1 2015-05-01 2015-06-01 261605.00 32 8175.1562500
2 2015-05-04 2015-06-01 1380796.00 29 47613.6551724
6 2015-05-21 2015-06-01 1392.00 12 116.0000000
7 2015-06-01 2015-06-01 2503.00 1 2503.0000000
8 2015-04-30 2015-06-02 0.00 34 0.0000000
9 2015-05-04 2015-06-02 117159.00 30 3905.3000000
10 2015-05-05 2015-06-02 193334.00 29 6666.6896552
13 2015-05-04 2015-06-03 630.00 31 20.3225806
and so on........
Example of data sets and Results
I will call this data set. EXAMPLE1 (For 3 days, mocked up data)
START_DATE END_DATE x DAYS DAILY_USAGE
5/1/2015 6/1/2015 261605 32 8175.15625
5/4/2015 6/1/2015 1380796 29 47613.65517
5/11/2015 6/1/2015 183 22 8.318181818
4/30/2015 6/2/2015 0 34 0
5/20/2015 6/2/2015 70 14 5
6/1/2015 6/2/2015 569 2 284.5
6/1/2015 6/3/2015 582 3 194
6/2/2015 6/3/2015 6 2 3
For the above examples, answer should be like this
DAY USAGE
6/1/2015 56280.6296
6/2/2015 486.5
6/3/2015 197
HOW?
In Example 1, for June 1st, i have added all the rows of usages except the last row usage because the last row doesn't include the the date 06/01 in time-frame. It starts in 06/02 and ends in 06/03.
To get June 2nd, i have added all the usages from Row 4 to 8 because June 2nd is between all of those start and end dates.
For June 3rd, i have only added, Last two rows to get 197.
So, where to sum, depends on the time-frame of Start & End_date.
Hope this helps!
There might be a easy trick to do this than to write 400 lines of If else statement.
Thank you again for your time!!
-Gyve
library(lubridate)
indx <- lapply(unique(mdy(df[,2])), '%within%', interval(mdy(df[,1]), mdy(df[,2])))
cbind.data.frame(DAY=unique(df$END_DATE),
USAGE=unlist(lapply(indx, function(x) sum(df$DAILY_USAGE[x]))))
# DAY USAGE
# 1 6/1/2015 56280.63
# 2 6/2/2015 486.50
# 3 6/3/2015 197.00
Explanation
We can expand it to explain what is happening:
indx <- lapply(unique(mdy(df[,2])), '%within%', interval(mdy(df[,1]), mdy(df[,2])))
The unique end dates are tested to be within the range days in the first and second columns. mdy is a quick way to convert to POSIXct with lubridate. The operator %within% tests a date against an interval. We created intervals with interval('col1', 'col2'). This creates an index that we can subset the data by.
In our final data frame,
cbind.data.frame(DAY=unique(df$END_DATE),
creates the first column of dates.
And,
USAGE=unlist(lapply(indx, function(x) sum(df$DAILY_USAGE[x])))
takes the sum of df$DAILY_USAGE by the index that we created.