conditionally aggregate columns using tidyverse for large time series dataset - r

After looking at a few other asked questions, and reading a few guides, I'm not able to find a suitable solution to my specific problem. Here's an example of the data to begin:
data <- data.frame(
Date = sample(c("1993-07-05", "1993-07-05", "1993-07-05", "1993-08-30", "1993-08-30", "1993-08-30", "1993-08-30", "1993-09-04", "1993-09-04")),
Site = sample(c("1", "1", "1", "1", "1", "1", "1", "1", "1")),
Station = sample(c("1", "2", "3", "1", "2", "3", "4", "1", "2")),
Oxygen = sample(c("0.9", "0.4", "4.2", "5.6", "7.3", "4.3", "9.5", "5.3", "0.3")))
I want to average all the oxygen values for the stations that are nested within a site that corresponds to a date. My dataset has a couple of thousand rows, and like in the example, there are an uneven number of stations, and the dates are uneven in length.
The output I'm looking for are columns like, "Date -> Site -> Average Oxygen", foregoing the need for a station column altogether in the new version of the time series.
Any help would be greatly appreciated!


After grouping by 'Site', 'Date', get the mean of 'Oxygen' (after converting it to numeric - it is factor column)
library(tidyverse)
data %>%
group_by(Site, Date) %>%
summarise(AverageOxygen = mean(as.numeric(as.character(Oxygen))))
# A tibble: 3 x 3
# Groups: Site [1]
# Site Date AverageOxygen
# <fct> <fct> <dbl>
#1 1 1993-07-05 3.97
#2 1 1993-08-30 5.2
#3 1 1993-09-04 2.55


Try:
library(hablar)
library(tidyverse)
data %>%
retype() %>%
group_by(Site, Date) %>%
summarize(AverageOxygen = mean(Oxygen))
which gives you:
# A tibble: 3 x 3
# Groups: Site [?]
Site Date AverageOxygen
<int> <date> <dbl>
1 1 1993-07-05 4.7
2 1 1993-08-30 3.55
3 1 1993-09-04 4.75

Related

Calculate average real variability for successive multiple measurements in R

I have a long-form dataframe, with a column (B) including the absolute successive differences between values in column (A), for each individual's ID separately.
ID = c("1", "1", "1", "1", "1", "1", "1", "2", "2", "2", "2", "2", "2")
A = c("120", "115", "125", "119", "128", "129", "130", "140", "142", "143", "145", "144", "148")
B = c("NA", "5", "10", "6", "9", "1", "1", "NA", "2", "1", "2", "1", "4")
DF <- data.frame(ID, A, B)
I would like to create a new column (C), that is the sum of the absolute differences before and including each value, divided by (the number of measurements used to calculate it minus 1).
This is what I would like the data to look like:
I hope this makes sense, any help greatly appreciated!

Here's a tidyverse solution. You can first group_by the ID, then divide the cumulative sum (cumsum) of B by the row_number minus one. You can only do this after omitting the first row of each group and replacing it with NA
Note also that in your example, the 'numeric' columns are actually character vectors, so have to be coerced to numeric first.
library(tidyverse)
DF %>%
mutate(across(A:B, \(x) suppressWarnings(as.numeric(x)))) %>%
group_by(ID) %>%
mutate(C = c(NA, cumsum(B[-1])/(row_number() - 1)[-1]))
#> # A tibble: 13 x 4
#> # Groups: ID [2]
#> ID A B C
#> <chr> <dbl> <dbl> <dbl>
#> 1 1 120 NA NA
#> 2 1 115 5 5
#> 3 1 125 10 7.5
#> 4 1 119 6 7
#> 5 1 128 9 7.5
#> 6 1 129 1 6.2
#> 7 1 130 1 5.33
#> 8 2 140 NA NA
#> 9 2 142 2 2
#> 10 2 143 1 1.5
#> 11 2 145 2 1.67
#> 12 2 144 1 1.5
#> 13 2 148 4 2
Created on 2022-11-11 with reprex v2.0.2

Dynamic calculations with panel data in R

I have a panel data (individuals observed in different time periods) like df, with much more individuals in my dataset:
id <- c("1", "1", "1", "2", "2", "2")
t <- c("1", "2", "3", "1", "2", "3")
w <- c("0.17", "NA", "NA", "0.23", "NA", "NA")
alpha <- c("0.15", "0.15", "0.15", "0.15", "0.15", "0.15")
rho <- c("0.10", "0.21", "0.32", "0.12", "0.2", "0.08")
df <- data.frame(id, t, w, alpha, rho)
I would like to fill w following these mathematical dynamics: w_id_t = w_id_t-1 * alpha + rho_id_t. However, I do not know how to move the outcome in such a way that appears in the following line, so I can continue with the dynamic calculations. The outcome should look like df_dynamics:
w_new <- c("0.17", "0.2355", "0.345", "0.23", "0.2345", "0.115")
df_dynamics <- data.frame(id, t, w_new, alpha, rho)
Any clue?

The columns are all character class. We may need to convert to numeric first and then do a group by operation
library(dplyr)
type.convert(df, as.is = TRUE) %>%
group_by(id) %>%
mutate(w = coalesce(w, lag(first(w) * first(alpha) + lead(rho)))) %>%
ungroup
# A tibble: 6 × 5
id t w alpha rho
<int> <int> <dbl> <dbl> <dbl>
1 1 1 0.17 0.15 0.1
2 1 2 0.236 0.15 0.21
3 1 3 0.346 0.15 0.32
4 2 1 0.23 0.15 0.12
5 2 2 0.234 0.15 0.2
6 2 3 0.114 0.15 0.08

How can I reshape multiple columns of long data to wide?

structure(tibble(c("top", "jng", "mid", "bot", "sup"), c("369", "Karsa", "knight", "JackeyLove", "yuyanjia"),
c("Malphite", "Rek'Sai", "Zoe", "Aphelios", "Braum"), c("1", "1", "1", "1", "1"), c("7", "5", "7", "5", "0"),
c("6079-7578", "6079-7578", "6079-7578", "6079-7578", "6079-7578")), .Names = c("position", "player", "champion", "result", "kills", "gameid"))
Output:
# A tibble: 5 x 6
position player champion result kills gameid
* <chr> <chr> <chr> <chr> <chr> <chr>
1 top 369 Malphite 1 7 6079-7578
2 jng Karsa Rek'Sai 1 5 6079-7578
3 mid knight Zoe 1 7 6079-7578
4 bot JackeyLove Aphelios 1 5 6079-7578
5 sup yuyanjia Braum 1 0 6079-7578
My desired output would be:
structure(list(gameid = "6079-7578", result = "1", player_top = "369",
player_jng = "Karsa", player_mid = "knight", player_bot = "JackeyLove",
player_sup = "yuyanjia", champion_top = "Malphite", champion_jng = "Rek'Sai",
champion_mid = "Zoe", champion_bot = "Aphelios", champion_sup = "Braum",
kills_top = "7", kills_jng = "5", kills_mid = "7", kills_bot = "5",
kills_sup = "0"), row.names = c(NA, -1L), class = c("tbl_df",
"tbl", "data.frame"))
which looks like this:
gameid result player_top player_jng player_mid player_bot player_sup champion_top champion_jng champion_mid champion_bot champion_sup
1 6079-7578 1 369 Karsa knight JackeyLove yuyanjia Malphite RekSai Zoe Aphelios Braum
kills_top kills_jng kills_mid kills_bot kills_sup
1 7 5 7 5 0
I know I should use pivot_wider() and something like drop_na, but I don't know how to do pivot_wider() with mutiple columns and collapse the rows at the same time. Any help would be appreciated.

You can use pivot_wider() for this, defining the "position" variable as the variable that the new column names come from in names_from and the three variables with values you want to use to fill those columns with as values_from.
By default the multiple values_from variables are pasted on to the front of new columns names. This can be changed, but in this case that matches the naming structure you want.
All other variables in the original dataset will be used as the id_cols in the order that they appear.
library(tidyr)
pivot_wider(dat,
names_from = "position",
values_from = c("player", "champion", "kills"))
#> result gameid player_top player_jng player_mid player_bot player_sup
#> 1 1 6079-7578 369 Karsa knight JackeyLove yuyanjia
#> champion_top champion_jng champion_mid champion_bot champion_sup kills_top
#> 1 Malphite Rek'Sai Zoe Aphelios Braum 7
#> kills_jng kills_mid kills_bot kills_sup
#> 1 5 7 5 0
You can control the order of your id columns in the output by explicitly writing them out via id_cols. Here's an example, matching your desired output.
pivot_wider(dat, id_cols = c("gameid", "result"),
names_from = "position",
values_from = c("player", "champion", "kills"))
#> gameid result player_top player_jng player_mid player_bot player_sup
#> 1 6079-7578 1 369 Karsa knight JackeyLove yuyanjia
#> champion_top champion_jng champion_mid champion_bot champion_sup kills_top
#> 1 Malphite Rek'Sai Zoe Aphelios Braum 7
#> kills_jng kills_mid kills_bot kills_sup
#> 1 5 7 5 0
Created on 2021-06-24 by the reprex package (v2.0.0)

Using data.table might help here. In dcast() each row will be identified by a unique combo of gameid and result, the columns will be spread by position, and filled with values from the variables listed in value.var.
library(data.table)
library(dplyr)
df <- structure(tibble(c("top", "jng", "mid", "bot", "sup"), c("369", "Karsa", "knight", "JackeyLove", "yuyanjia"),
c("Malphite", "Rek'Sai", "Zoe", "Aphelios", "Braum"), c("1", "1", "1", "1", "1"), c("7", "5", "7", "5", "0"),
c("6079-7578", "6079-7578", "6079-7578", "6079-7578", "6079-7578")), .Names = c("position", "player", "champion", "result", "kills", "gameid"))
df2 <- dcast(setDT(df), gameid + result~position, value.var = list('player','champion','kills'))

How to use the Spread function using only one value of the target column?

We have the following data frame a with something like this:
> a
google_prod Value
1 categoria ML
2 google 120
3 youtube 24
4 categoria AO
5 google 2
6 youtube 0
7 categoria ML
8 google 27
9 youtube 0
10 categoria AO
11 google 5
12 youtube 0
We would like to get this:
categoria google_prod Value
1 ML google 120
2 ML youtube 24
3 AO google 2
4 AO youtube 0
5 ML google 27
6 ML youtube 0
7 AO google 5
8 AO youtube 0
In other words, perform a type of application of the Spread or similar function, where only one value is taken from the google_prod column to apply it, in this case it would be the 'categoria' value.

library(tidyverse)
# getting the data
category <- rep(c("categoria", "google", "youtube"), 4)
value <- c("ML", "120", "24", "AO", "2", "0", "ML", "27", "0", "AO", "5", "0")
df <- tibble(category, value)
df %>%
mutate(helper = rep(1:(nrow(df)/3), each = 3)) %>%
pivot_wider(names_from = category, values_from = value) %>%
select(-helper) %>%
pivot_longer(names_to = "google_prod", values_to = "values", -1)
# # A tibble: 8 x 3
# categoria google_prod values
# <chr> <chr> <chr>
# 1 ML google 120
# 2 ML youtube 24
# 3 AO google 2
# 4 AO youtube 0
# 5 ML google 27
# 6 ML youtube 0
# 7 AO google 5
# 8 AO youtube 0

Here is another idea with creating a group with cumsum and extracting the first element
library(dplyr)
mydf %>%
group_by(grp = cumsum(google_prod == 'categoria')) %>%
mutate(categoria = first(Value)) %>%
slice(-1) %>%
ungroup %>%
select(-grp) %>%
type.convert(as.is = TRUE)
# A tibble: 8 x 3
# google_prod Value categoria
# <chr> <int> <chr>
#1 google 120 ML
#2 youtube 24 ML
#3 google 2 AO
#4 youtube 0 AO
#5 google 27 ML
#6 youtube 0 ML
#7 google 5 AO
#8 youtube 0 AO
data
mydf <- structure(list(google_prod = c("categoria", "google", "youtube",
"categoria", "google", "youtube", "categoria", "google", "youtube",
"categoria", "google", "youtube"), Value = c("ML", "120", "24",
"AO", "2", "0", "ML", "27", "0", "AO", "5", "0")),
class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12"))

One idea would be the following. As far as I see the pattern, you are targeting values that contain two capital letters in Value. I searched where they are using grep() and obtained indice. Using this information, I created a group variable using findIntervals(). For each group, I aggregated data; I extracted and put the capital-letter value in categoria. In similar ways, I created two more columns. They are lists. So I used unnest() in the end to get the output.
library(tidyverse)
ind <- grep(x = mydf$Value, pattern = "[A-Z]+")
group_by(mydf, group = findInterval(x = 1:n(), vec = ind)) %>%
summarize(categoria = Value[google_prod == "categoria"],
Google_prod = list(google_prod[google_prod != "categoria"]),
Value = list(Value[google_prod != "categoria"])) %>%
unnest(cols = Google_prod:Value)
group categoria Google_prod Value
<int> <chr> <chr> <chr>
1 1 ML google 120
2 1 ML youtube 24
3 2 AO google 2
4 2 AO youtube 0
5 3 ML google 27
6 3 ML youtube 0
7 4 AO google 5
8 4 AO youtube 0
DATA
mydf <- structure(list(google_prod = c("categoria", "google", "youtube",
"categoria", "google", "youtube", "categoria", "google", "youtube",
"categoria", "google", "youtube"), Value = c("ML", "120", "24",
"AO", "2", "0", "ML", "27", "0", "AO", "5", "0")), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12"))

Transposing data frames [duplicate]

This question already has answers here:
Transposing a dataframe maintaining the first column as heading
(5 answers)
Closed 1 year ago.
Happy Weekends.
I've been trying to replicate the results from this blog post in R. I am looking for a method of transposing the data without using t, preferably using tidyr or reshape. In example below, metadata is obtained by transposing data.
metadata <- data.frame(colnames(data), t(data[1:4, ]) )
colnames(metadata) <- t(metadata[1,])
metadata <- metadata[-1,]
metadata$Multiplier <- as.numeric(metadata$Multiplier)
Though it achieves what I want, I find it little unskillful. Is there any efficient workflow to transpose the data frame?
dput of data
data <- structure(list(Series.Description = c("Unit:", "Multiplier:",
"Currency:", "Unique Identifier: "), Nominal.Broad.Dollar.Index. = c("Index:_1997_Jan_100",
"1", NA, "H10/H10/JRXWTFB_N.M"), Nominal.Major.Currencies.Dollar.Index. = c("Index:_1973_Mar_100",
"1", NA, "H10/H10/JRXWTFN_N.M"), Nominal.Other.Important.Trading.Partners.Dollar.Index. = c("Index:_1997_Jan_100",
"1", NA, "H10/H10/JRXWTFO_N.M"), AUSTRALIA....SPOT.EXCHANGE.RATE..US..AUSTRALIAN...RECIPROCAL.OF.RXI_N.M.AL. = c("Currency:_Per_AUD",
"1", "USD", "H10/H10/RXI$US_N.M.AL"), SPOT.EXCHANGE.RATE...EURO.AREA. = c("Currency:_Per_EUR",
"1", "USD", "H10/H10/RXI$US_N.M.EU"), NEW.ZEALAND....SPOT.EXCHANGE.RATE..US..NZ...RECIPROCAL.OF.RXI_N.M.NZ.. = c("Currency:_Per_NZD",
"1", "USD", "H10/H10/RXI$US_N.M.NZ"), United.Kingdom....Spot.Exchange.Rate..US..Pound.Sterling.Reciprocal.of.rxi_n.m.uk = c("Currency:_Per_GBP",
"0.01", "USD", "H10/H10/RXI$US_N.M.UK"), BRAZIL....SPOT.EXCHANGE.RATE..REAIS.US.. = c("Currency:_Per_USD",
"1", "BRL", "H10/H10/RXI_N.M.BZ"), CANADA....SPOT.EXCHANGE.RATE..CANADIAN...US.. = c("Currency:_Per_USD",
"1", "CAD", "H10/H10/RXI_N.M.CA"), CHINA....SPOT.EXCHANGE.RATE..YUAN.US.. = c("Currency:_Per_USD",
"1", "CNY", "H10/H10/RXI_N.M.CH"), DENMARK....SPOT.EXCHANGE.RATE..KRONER.US.. = c("Currency:_Per_USD",
"1", "DKK", "H10/H10/RXI_N.M.DN"), HONG.KONG....SPOT.EXCHANGE.RATE..HK..US.. = c("Currency:_Per_USD",
"1", "HKD", "H10/H10/RXI_N.M.HK"), INDIA....SPOT.EXCHANGE.RATE..RUPEES.US. = c("Currency:_Per_USD",
"1", "INR", "H10/H10/RXI_N.M.IN"), JAPAN....SPOT.EXCHANGE.RATE..YEA.US.. = c("Currency:_Per_USD",
"1", "JPY", "H10/H10/RXI_N.M.JA"), KOREA....SPOT.EXCHANGE.RATE..WON.US.. = c("Currency:_Per_USD",
"1", "KRW", "H10/H10/RXI_N.M.KO"), Malaysia...Spot.Exchange.Rate..Ringgit.US.. = c("Currency:_Per_USD",
"1", "MYR", "H10/H10/RXI_N.M.MA"), MEXICO....SPOT.EXCHANGE.RATE..PESOS.US.. = c("Currency:_Per_USD",
"1", "MXN", "H10/H10/RXI_N.M.MX"), NORWAY....SPOT.EXCHANGE.RATE..KRONER.US.. = c("Currency:_Per_USD",
"1", "NOK", "H10/H10/RXI_N.M.NO"), SWEDEN....SPOT.EXCHANGE.RATE..KRONOR.US.. = c("Currency:_Per_USD",
"1", "SEK", "H10/H10/RXI_N.M.SD"), SOUTH.AFRICA....SPOT.EXCHANGE.RATE..RAND.US.. = c("Currency:_Per_USD",
"1", "ZAR", "H10/H10/RXI_N.M.SF"), Singapore...SPOT.EXCHANGE.RATE..SINGAPORE...US.. = c("Currency:_Per_USD",
"1", "SGD", "H10/H10/RXI_N.M.SI"), SRI.LANKA....SPOT.EXCHANGE.RATE..RUPEES.US.. = c("Currency:_Per_USD",
"1", "LKR", "H10/H10/RXI_N.M.SL"), SWITZERLAND....SPOT.EXCHANGE.RATE..FRANCS.US.. = c("Currency:_Per_USD",
"1", "CHF", "H10/H10/RXI_N.M.SZ"), TAIWAN....SPOT.EXCHANGE.RATE..NT..US.. = c("Currency:_Per_USD",
"1", "TWD", "H10/H10/RXI_N.M.TA"), THAILAND....SPOT.EXCHANGE.RATE....THAILAND. = c("Currency:_Per_USD",
"1", "THB", "H10/H10/RXI_N.M.TH"), VENEZUELA....SPOT.EXCHANGE.RATE..BOLIVARES.US.. = c("Currency:_Per_USD",
"1", "VEB", "H10/H10/RXI_N.M.VE")), .Names = c("Series.Description",
"Nominal.Broad.Dollar.Index.", "Nominal.Major.Currencies.Dollar.Index.",
"Nominal.Other.Important.Trading.Partners.Dollar.Index.", "AUSTRALIA....SPOT.EXCHANGE.RATE..US..AUSTRALIAN...RECIPROCAL.OF.RXI_N.M.AL.",
"SPOT.EXCHANGE.RATE...EURO.AREA.", "NEW.ZEALAND....SPOT.EXCHANGE.RATE..US..NZ...RECIPROCAL.OF.RXI_N.M.NZ..",
"United.Kingdom....Spot.Exchange.Rate..US..Pound.Sterling.Reciprocal.of.rxi_n.m.uk",
"BRAZIL....SPOT.EXCHANGE.RATE..REAIS.US..", "CANADA....SPOT.EXCHANGE.RATE..CANADIAN...US..",
"CHINA....SPOT.EXCHANGE.RATE..YUAN.US..", "DENMARK....SPOT.EXCHANGE.RATE..KRONER.US..",
"HONG.KONG....SPOT.EXCHANGE.RATE..HK..US..", "INDIA....SPOT.EXCHANGE.RATE..RUPEES.US.",
"JAPAN....SPOT.EXCHANGE.RATE..YEA.US..", "KOREA....SPOT.EXCHANGE.RATE..WON.US..",
"Malaysia...Spot.Exchange.Rate..Ringgit.US..", "MEXICO....SPOT.EXCHANGE.RATE..PESOS.US..",
"NORWAY....SPOT.EXCHANGE.RATE..KRONER.US..", "SWEDEN....SPOT.EXCHANGE.RATE..KRONOR.US..",
"SOUTH.AFRICA....SPOT.EXCHANGE.RATE..RAND.US..", "Singapore...SPOT.EXCHANGE.RATE..SINGAPORE...US..",
"SRI.LANKA....SPOT.EXCHANGE.RATE..RUPEES.US..", "SWITZERLAND....SPOT.EXCHANGE.RATE..FRANCS.US..",
"TAIWAN....SPOT.EXCHANGE.RATE..NT..US..", "THAILAND....SPOT.EXCHANGE.RATE....THAILAND.",
"VENEZUELA....SPOT.EXCHANGE.RATE..BOLIVARES.US.."), row.names = c(NA,
4L), class = "data.frame")

Using tidyr, you gather all the columns except the first, and then you spread the gathered columns.
Try:
library(dplyr)
library(tidyr)
data %>%
gather(var, val, 2:ncol(data)) %>%
spread(Series.Description, val)

library(dplyr)
# Omitted data <- structure part ...
Here is something that replicates what's in the main answer, but more generically (e.g., works where Series.Description is not the first column of the result) and using the newer pivot_wider/pivot_longer verbs.
df_transpose <- function(df) {
df %>%
tidyr::pivot_longer(-1) %>%
tidyr::pivot_wider(names_from = 1, values_from = value)
}
df_transpose(data)
#> # A tibble: 26 x 5
#> name `Unit:` `Multiplier:` `Currency:` `Unique Identifi…
#> <chr> <chr> <chr> <chr> <chr>
#> 1 Nominal.Broad.Dollar.… Index:_19… 1 <NA> H10/H10/JRXWTFB_…
#> 2 Nominal.Major.Currenc… Index:_19… 1 <NA> H10/H10/JRXWTFN_…
#> 3 Nominal.Other.Importa… Index:_19… 1 <NA> H10/H10/JRXWTFO_…
#> 4 AUSTRALIA....SPOT.EXC… Currency:… 1 USD H10/H10/RXI$US_N…
#> 5 SPOT.EXCHANGE.RATE...… Currency:… 1 USD H10/H10/RXI$US_N…
#> 6 NEW.ZEALAND....SPOT.E… Currency:… 1 USD H10/H10/RXI$US_N…
#> 7 United.Kingdom....Spo… Currency:… 0.01 USD H10/H10/RXI$US_N…
#> 8 BRAZIL....SPOT.EXCHAN… Currency:… 1 BRL H10/H10/RXI_N.M.…
#> 9 CANADA....SPOT.EXCHAN… Currency:… 1 CAD H10/H10/RXI_N.M.…
#> 10 CHINA....SPOT.EXCHANG… Currency:… 1 CNY H10/H10/RXI_N.M.…
#> # … with 16 more rows
But note that (like the answer above) the name of the first column is lost. The following retains this (as, I guess does the spread_(names(data)[1], "val") approach proposed by #jbkunst above).
df_transpose <- function(df) {
first_name <- colnames(df)[1]
temp <-
df %>%
tidyr::pivot_longer(-1) %>%
tidyr::pivot_wider(names_from = 1, values_from = value)
colnames(temp)[1] <- first_name
temp
}
df_transpose(data)
#> # A tibble: 26 x 5
#> Series.Description `Unit:` `Multiplier:` `Currency:` `Unique Identif…
#> <chr> <chr> <chr> <chr> <chr>
#> 1 Nominal.Broad.Dollar.In… Index:_1… 1 <NA> H10/H10/JRXWTFB…
#> 2 Nominal.Major.Currencie… Index:_1… 1 <NA> H10/H10/JRXWTFN…
#> 3 Nominal.Other.Important… Index:_1… 1 <NA> H10/H10/JRXWTFO…
#> 4 AUSTRALIA....SPOT.EXCHA… Currency… 1 USD H10/H10/RXI$US_…
#> 5 SPOT.EXCHANGE.RATE...EU… Currency… 1 USD H10/H10/RXI$US_…
#> 6 NEW.ZEALAND....SPOT.EXC… Currency… 1 USD H10/H10/RXI$US_…
#> 7 United.Kingdom....Spot.… Currency… 0.01 USD H10/H10/RXI$US_…
#> 8 BRAZIL....SPOT.EXCHANGE… Currency… 1 BRL H10/H10/RXI_N.M…
#> 9 CANADA....SPOT.EXCHANGE… Currency… 1 CAD H10/H10/RXI_N.M…
#> 10 CHINA....SPOT.EXCHANGE.… Currency… 1 CNY H10/H10/RXI_N.M…
#> # … with 16 more rows
Created on 2021-05-30 by the reprex package (v2.0.0)

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