[R] join/merge two data frames
Bert Gunter
bgunter@4567 @end|ng |rom gm@||@com
Thu Apr 3 02:39:12 CEST 2025
You might do better posting here:
https://stat.ethz.ch/mailman/listinfo/r-sig-finance
Cheers,
Bert
"An educated person is one who can entertain new ideas, entertain others,
and entertain herself."
On Wed, Apr 2, 2025 at 4:39 PM Arnaud Gaboury <arnaud.gaboury using gmail.com>
wrote:
> I will try to be more precised. Here is the very final table I want:
> portfolio_valuation <- structure(list(time = structure(c(1735153199,
> 1735239599), tzone = "", class = c("POSIXct", "POSIXt")),
> BTCUSDC = c(107000, 106000), SUIUSDC = c(4.7, 4.5), USDT = c(0,
> 320000), BTCUSDC_qty = c(5, 2), SUIUSDC_qty = c(200, 250),
> valuation = c(535940, 533125), return = c(NA_real_, -0.525)), row.names
> = c(NA, -2L), class = c("tbl_df", "tbl", "data.frame"))
>
> portfolio_valuation
> # A tibble: 2 × 8
> time BTCUSDC SUIUSDC USDT BTCUSDC_qty SUIUSDC_qty
> valuation return
> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
> <dbl> <dbl>
> 1 2024-12-25 18:59:59 107000 4.7 0 5 200
> 535940 NA
> 2 2024-12-26 18:59:59 106000 4.5 320000 2 250
> 533125 -0.525
>
> As you can see, I will keep only a few things. I want one line per day,
> @ 18:59:59. The line will show:
> - the token I have in my portfolio (can be zero)
> - which quantity
> - their closing price for the day
> - valuation (sum of token_qty x token_price + USDT). Note: USDT is
> numeric dollar (cash).
> - return: (diff ptf val day 1 - diff ptf val day 0)/ptf val day 0
>
> The two tibbles I start with are:
> - token_close_sample: daily closing price for each token. The token
> list is > the token in my portfolio. I will fetch a regularly refreshed
> list of token I usually trade. USDT value is 1
> - all_trade_sample: a tibble with all my trades. One trade per line
> with its timestamp, and some trade info (price, quantity etc).
>
> I hope this details will help to clarify.
>
> On Wed, 2025-04-02 at 15:41 -0400, avi.e.gross using gmail.com wrote:
> > Arnaud,
> >
> > I won't comment on other aspects but want to ask how sure you are
> > that your data is guaranteed to have a single row reflecting a
> > closing price at 18:59:59 exactly?
> >
> > It may be true for your data source. I note that markets technically
> > close at 4:00 PM, New York time, but many have after-hours trading,
> > and there are days it closes early (such as 1 PM) and times when
> > trading is halted.
> >
> > Generally, you can get closing prices (or other data) from other
> > reliable sources and you could choose to merge data from such a
> > source in rather than calculating them from your data. If you do want
> > to use your data, one suggestion is to use the LAST record in each
> > grouping for a day. I find that easy to do in dplyr by having a
> > column containing the date info except for the time, and another
> > containing the time in a sortable format. You can then sort the
> > data.frame by the date and then time and then group your data.frame
> > by the date and and select only last record in each group and you
> > have the last time, whatever that may be.
> >
> > Again, this may not apply in your case. As you note, you are planning
> > on doing many things, one step at a time, and early stages can set up
> > your data.frames in ways that make later stages easier to do. As one
> > example, you could create a column in early stages that marks if the
> > current row is a closing row or not.
> >
> > -----Original Message-----
> > From: R-help <r-help-bounces using r-project.org> On Behalf Of Arnaud
> > Gaboury
> > Sent: Wednesday, April 2, 2025 2:10 PM
> > To: Ebert,Timothy Aaron <tebert using ufl.edu>; r-help using r-project.org
> > Subject: Re: [R] join/merge two data frames
> >
> >
> >
> > On Wed, 2025-04-02 at 16:55 +0000, Ebert,Timothy Aaron wrote:
> > > Your result data frame example makes no sense to me. The price and
> > > executed_qty are the same for all symbols?
> > >
> > > To get it all into one data frame you need a common variable that
> > > is
> > > used to join the data frames.
> > > My guess is that all_trade_sample$symbol has equivalents to the
> > > variables in token_close_sample.
> > >
> > > You need to pivot one of the tables, and then full join them.
> > > Please look at the join functions. In base R it is "merge()" and in
> > > dyply it is full_join(), left_join() and similar.
> > >
> > > I will pivot all_trade_sample to make all of the elements in
> > > "symbol"
> > > into variables.
> > >
> > > I will do this in tidyverse. There are some parts of this that I
> > > really like. However, it can as easily be done using base R.
> > > Library(tidyr)
> > > Library(dplyr)
> > > token_close_long <- token_close_sample %>%
> > > pivot_longer(cols = -time, names_to = "symbol", values_to =
> > > "close_price")
> > > #I rename time in token_close_long so that it will be preserved
> > > colnames(token_close_long) <- c("time_close", "symbol",
> > > "close_price")
> > > combined <- full_join(all_trade_sample,token_close_long,
> > > by="symbol")
> > >
> > > This generates errors. This is ok, but I need to know more about
> > > all_trade_sample. Each symbol appears multiple times in
> > > all_trade_sample. Each instance of "AAVEUSIX" has a different time
> > > stamp. So maybe filter to eliminate all but the first (earliest)
> > > time?
> > >
> > > The program looks something like this:
> > > token_close_long <- token_close_sample %>%
> > > pivot_longer(cols = -time, names_to = "symbol", values_to =
> > > "close_price")
> > >
> > > #I will rename time in token_close_long so that it will be
> > > preserved
> > > colnames(token_close_long) <- c("time_close", "symbol",
> > > "close_price")
> > > combined <- full_join(all_trade_sample,token_close_long,
> > > by="symbol")
> > >
> > > filtered_trades <- all_trade_sample |>
> > > group_by(symbol) |>
> > > slice_min(time, n = 1)|>
> > > ungroup()
> > >
> > > combined <- full_join(filtered_trades,token_close_long,
> > > by="symbol")
> > >
> > > I did not do something right, as there are four closing prices for
> > > each symbol. However, the general approach should work even if it
> > > needs a little modification to give the correct result. Your
> > > closing
> > > price is only relevant based on some other price (probably buying
> > > price, but could be opening price).
> > >
> > > Tim
> >
> > Thank you Tim for your answer. I will have a close look at it later
> > today. But in short:
> > - I will do some work on my data later in the script. The tibble I
> > want
> > is far from being the result.
> > - Time of closing price is everyday the same: 18:59:59. I will
> > compute
> > returns, volatility, correlations etc so I need a common reference.
> > Prices for the trades change and are given by the exchange. I don't
> > need them, won't do anything.
> > - Yes, symbols may be the common variable.
> > - I feel comfortable with tibbles and tidyverse
> > - All_trade_sample has the goal to fetch price (price of asset when
> > the
> > trade is done) and executed_qty. btc_price is needed at one point of
> > my
> > calculation, but will not be shown in the very final table.
> > - Multiple lines for one only symbol means I made multiple trades of
> > this asset during the same day.
> >
> > I will test your suggestions.
> > Thank you again.
> >
> >
> > >
> > > -----Original Message-----
> > > From: R-help <r-help-bounces using r-project.org> On Behalf Of Arnaud
> > > Gaboury
> > > Sent: Wednesday, April 2, 2025 6:20 AM
> > > To: r-help using r-project.org
> > > Subject: [R] join/merge two data frames
> > >
> > > [External Email]
> > >
> > > I work on a trading journal for a portfolio of crypto currencies.
> > > The
> > > goal is to fetch from my account (binance exchange) the trades I
> > > have
> > > done and daily closing prices of my assets.
> > > The first part (getting the data from exchange) are in two parts.
> > >
> > > 1- get the daily closing prices of my assets. Here is a sample of
> > > my
> > > data frame:
> > > token_close_sample <- structure(list(time =
> > > structure(c(1734371999.999, 1734458399.999, 1734544799.999,
> > > 1734631199.999), tzone = "", class = c("POSIXct", "POSIXt")),
> > > BTCUSDC
> > > = c(107112.36, 107517.25, 104630.49, 98692.01 ), SUIUSDC =
> > > c(4.7252,
> > > 4.6923, 4.7017, 4.2422), ENAUSDC = c(1.1862, 1.1412, 1.0928,
> > > 1.0256),
> > > AAVEUSDC = c(388, 365.68, 373.15, 316.69 ), ETHUSDC = c(4034.74,
> > > 3975.39, 3879.06, 3474.91), FTMUSDC = c(1.381, 1.3596, 1.2222,
> > > 1.0445)), row.names = c(NA, -4L), class = c("tbl_df", "tbl",
> > > "data.frame"))
> > >
> > > the tibble looks like this:
> > > time BTCUSDC SUIUSDC ENAUSDC AAVEUSDC ETHUSDC
> > > FTMUSDC
> > > <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
> > > <dbl>
> > > 1 2024-12-16 18:59:59 107112. 4.73 1.19 388 4035.
> > > 1.38
> > > 2 2024-12-17 18:59:59 107517. 4.69 1.14 366. 3975.
> > > 1.36
> > > 3 2024-12-18 18:59:59 104630. 4.70 1.09 373. 3879.
> > > 1.22
> > > 4 2024-12-19 18:59:59 98692. 4.24 1.03 317. 3475.
> > > 1.04
> > >
> > > 2- get my trades. Here is a sample:
> > > all_trade_sample <- structure(list(time =
> > > structure(c(1737335082.949,
> > > 1737336735.697, 1738059550.671, 1738142709.422, 1738142709.422,
> > > 1738169351.788 ), tzone = "UTC", class = c("POSIXct", "POSIXt")),
> > > symbol = c("AAVEUSDC", "AAVEUSDC", "SUIUSDC", "AAVEUSDC",
> > > "AAVEUSDC",
> > > "ETHUSDC"), executed_qty = c(866.666, -834.998, 67649.3, -0.393, -
> > > 0.393, 36.1158), price = c(0.003005, 0.003131, 0.000038,
> > > 294.738321,
> > > 294.738321, 0.03027), cummulative_quote_qty = c(262699.317950113, -
> > > 263696.723173419, 263987.20719179, -115.83216, -115.83216,
> > > 111456.491386979
> > > ), day = structure(c(20108, 20108, 20116, 20117, 20117, 20117 ),
> > > class = "Date")), row.names = c(NA, -6L), class = c("tbl_df",
> > > "tbl",
> > > "data.frame"))
> > >
> > > the tibble looks like this:
> > > time symbol executed_qty price
> > > cummulative_quote_qty day
> > > <dttm> <chr> <dbl> <dbl>
> > > <dbl> <date>
> > > 1 2025-01-20 01:04:42 AAVEUSDC 867. 0.00300
> > > 262699. 2025-01-20
> > > 2 2025-01-20 01:32:15 AAVEUSDC -835. 0.00313
> > > -
> > > 263697. 2025-01-20
> > > 3 2025-01-28 10:19:10 SUIUSDC 67649. 0.000038
> > > 263987. 2025-01-28
> > > 4 2025-01-29 09:25:09 AAVEUSDC -0.393 295.
> > > -116. 2025-01-29
> > > 5 2025-01-29 09:25:09 AAVEUSDC -0.393 295.
> > > -116. 2025-01-29
> > > 6 2025-01-29 16:49:11 ETHUSDC 36.1 0.0303
> > > 111456. 2025-01-29
> > >
> > > Now, to finalize, I want to get all the info in one data frame so I
> > > can compute daily valuation (with potential trades, or in/out of
> > > asset).
> > > The finalized tibble should look something like this:
> > > result <- structure(list(time = structure(c(1734371999.999,
> > > 1734458399.999, 1734544799.999, 1734631199.999, 1737335082.949,
> > > 1737336735.697, 1738059550.671, 1734721199, 1734807599, 1734893999,
> > > 1734980399, 1735066799, 1735153199, 1735239599, 1735325999,
> > > 1738142709.422, 1735412399, 1738142709.422, 1738169351.788,
> > > 1735498799), tzone = "", class = c("POSIXct", "POSIXt")), BTCUSDC =
> > > c(107112.36, 107517.25, 104630.49, 98692.01,NA_real_, NA_real_,
> > > 102000, 101500, 101700,100300,100400,102300,102300,103100,
> > > NA_real_,
> > > 99800, NA_real_, NA_real_,NA_real_, 99900 ), SUIUSDC = c(4.7252,
> > > 4.6923, 4.7017, 4.2422, NA_real_, NA_real_, 4.25, 4.26, 4.7, 4.65,
> > > 4.52, 4.23, 4.17, 4.34, NA_real_, 4.52, NA_real_,
> > > NA_real_,NA_real_,
> > > 4.44), ENAUSDC = c(1.1862, 1.1412, 1.0928, 1.0256, NA_real_,
> > > NA_real_, 1.176, 1.16, 1.163, 1.183, 1.196, 1.165, 1.158, 1.142,
> > > NA_real_, 1.196, NA_real_, NA_real_,NA_real_, 1.113), AAVEUSDC =
> > > c(388, 365.68, 373.15, 316.69, NA_real_, NA_real_, 102000, 101500,
> > > 101700,100300,100400,102300,102300,103100, NA_real_, 99800,
> > > NA_real_,
> > > NA_real_,NA_real_, 99900 ), ETHUSDC = c(4034.74, 3975.39, 3879.06,
> > > 3474.91, NA_real_,NA_real_, 3420, 3410, 3412, 3367, 3388, 3355,
> > > 3374,
> > > 3392, NA_real_, 3401, NA_real_, NA_real_,NA_real_, 3411), FTMUSDC =
> > > c(1.381,1.3596, 1.2222, 1.0445, NA_real_,NA_real_, 1.36, 1.368,
> > > 1.342, 1.339, 1.436, 1.562, 1.53, 1.62, NA_real_, 1.31, NA_real_,
> > > NA_real_,NA_real_, 1.58), executed_qty = c(NA_real_, NA_real_,
> > > NA_real_, NA_real_, 230, 559, NA_real_, NA_real_,NA_real_,
> > > NA_real_,
> > > NA_real_,NA_real_, NA_real_, NA_real_, 200, NA_real_, 700, 200,
> > > 350,
> > > NA_real_), price = c( NA_real_, NA_real_, NA_real_, NA_real_, 2, 3,
> > > NA_real_, NA_real_,NA_real_, NA_real_, NA_real_,NA_real_, NA_real_,
> > > NA_real_, 10, NA_real_, 3, 4, 5, NA_real_) ), row.names = c(NA, -
> > > 20L), class = c("tbl_df", "tbl", "data.frame"))
> > >
> > > The idea is to have:
> > > - one line each day with closing price of my portfolio assets at
> > > 18:59:59
> > > - one line for each trade where I shall write 'asset', 'price' and
> > > 'executed_qty'.
> > >
> > > I spent quite some time trying to figure out how to do it, but
> > > couldn't. I don't even know if it is possible. Of course, I don't
> > > want to add anything (price, date, asset...) by hand.
> > > Thank you for any help.
> > >
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