Year A B C D E F
1993-Q1 15.3 5.77 437.02 487.68 97 86.9
1993-Q2 13.5 5.74 455.2 504.5 94.7 85.4
1993-Q3 12.9 5.79 469.42 523.37 92.4 82.9
:::
2021-Q1 18.3 6.48 35680.82 29495.92 182.2 220.4
2021-Q2 7.9 6.46 36940.3 30562.03 180.4 218
Dataset1 <- read.csv('C:/Users/s/Desktop/R/intro/data/Dataset1.csv')
class(Dataset1)
[1] "data.frame"
time_series <- ts(Dataset1, start=1993, frequency = 4)
class(time_series)
[1] "mts" "ts" "matrix"
I don't know how to proceed from there to read my Year column as dates (quaterly) instead of numbers!
Date class does not work well with ts class. It is better to use year and quarter. Using the input shown reproducibly in the Note at the end use read.csv.zoo with yearqtr class and then convert it to ts. The strip.white is probably not needed but we added it just in case.
library(zoo)
z <- read.csv.zoo("Dataset1.csv", FUN = as.yearqtr, format = "%Y-Q%q",
strip.white = TRUE)
tt <- as.ts(z)
tt
## A B C D E F
## 1993 Q1 15.3 5.77 437.02 487.68 97.0 86.9
## 1993 Q2 13.5 5.74 455.20 504.50 94.7 85.4
## 1993 Q3 12.9 5.79 469.42 523.37 92.4 82.9
class(tt)
## [1] "mts" "ts" "matrix"
as.integer(time(tt)) # years
## [1] 1993 1993 1993
cycle(tt) # quarters
## Qtr1 Qtr2 Qtr3
## 1993 1 2 3
as.numeric(time(tt)) # time in years
## [1] 1993.00 1993.25 1993.50
If you did want to use Date class it would be better to use a zoo (or xts) series.
zd <- aggregate(z, as.Date, c)
zd
## A B C D E F
## 1993-01-01 15.3 5.77 437.02 487.68 97.0 86.9
## 1993-04-01 13.5 5.74 455.20 504.50 94.7 85.4
## 1993-07-01 12.9 5.79 469.42 523.37 92.4 82.9
If you want a data frame or xts object then fortify.zoo(z), fortify.zoo(zd), as.xts(z) or as.xts(zd) can be used depending on which one you want.
Note
Lines <- "Year,A,B,C,D,E,F
1993-Q1,15.3,5.77,437.02,487.68,97,86.9
1993-Q2,13.5,5.74,455.2,504.5,94.7,85.4
1993-Q3,12.9,5.79,469.42,523.37,92.4,82.9
"
cat(Lines, file = "Dataset1.csv")
lubridate has really nice year-quarter function yq to convert year quarters to dates.
Dataset1<-structure(list(Year = c("1993-Q1", "1993-Q2", "1993-Q3", "1993-Q4", "1994-Q1", "1994-Q2"), ChinaGDP = c(15.3, 13.5, 12.9, 14.1, 14.1, 13.3), Yuan = c(5.77, 5.74, 5.79, 5.81, 8.72, 8.7), totalcredit = c(437.02, 455.2, 469.42, 521.68, 363.42, 389.01), bankcredit = c(487.68, 504.5, 523.37, 581.83, 403.48, 431.06), creditpercGDP = c(97, 94.7, 92.4, 95.6, 91.9, 90), creditGDPratio = c(86.9, 85.4, 82.9, 85.7, 82.8, 81.2)), row.names = c(NA, 6L), class = "data.frame")
library(lubridate)
library(dplyr)
df_quarter <- Dataset1 %>%
mutate(date=yq(Year)) %>%
relocate(date, .after=Year)
df_quarter
#> Year date ChinaGDP Yuan totalcredit bankcredit creditpercGDP
#> 1 1993-Q1 1993-01-01 15.3 5.77 437.02 487.68 97.0
#> 2 1993-Q2 1993-04-01 13.5 5.74 455.20 504.50 94.7
#> 3 1993-Q3 1993-07-01 12.9 5.79 469.42 523.37 92.4
#> 4 1993-Q4 1993-10-01 14.1 5.81 521.68 581.83 95.6
#> 5 1994-Q1 1994-01-01 14.1 8.72 363.42 403.48 91.9
#> 6 1994-Q2 1994-04-01 13.3 8.70 389.01 431.06 90.0
#> creditGDPratio
#> 1 86.9
#> 2 85.4
#> 3 82.9
#> 4 85.7
#> 5 82.8
#> 6 81.2
Created on 2022-01-15 by the reprex package (v2.0.1)
Related
I seem to have some trouble converting my data frame data into a time series. I have a typical data set consisting of date, export quantity, GDP, FDI etc.
# A tibble: 252 x 10
Date `Maize Exports (m/t)` `Rainfall (mm)` `Temperature ©` `Exchange rate (R/$)` `Maize price (R)` `FDI (Million R)` GDP (Million~1 Oil p~2 Infla~3
<dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2000-05-01 00:00:00 21000 30.8 14.4 0.144 678. 4337 9056 192. 5.1
2 2000-06-01 00:00:00 54000 14.9 14.0 0.147 583. -4229 9056 205. 5.1
3 2000-07-01 00:00:00 134000 11.1 12.6 0.144 518. -4229 8841 196. 5.9
4 2000-08-01 00:00:00 213000 6.1 15.3 0.143 526. -4229 8841 205. 6.8
5 2000-09-01 00:00:00 123000 38.5 17.8 0.138 576. 6315 8841 234. 6.8
6 2000-10-01 00:00:00 94000 61.9 20.1 0.132 636. 6315 4487 231. 7.1
7 2000-11-01 00:00:00 192000 93.9 19.9 0.129 685. 6315 4487 250. 7.1
8 2000-12-01 00:00:00 134000 85.6 22.3 0.132 747. -2143 4487 192. 7
9 2001-01-01 00:00:00 133000 92.4 23.4 0.0875 1066. -5651 7365 226. 5
10 2001-02-01 00:00:00 168000 51 22.0 0.0879 1042. -5651 7365 233. 5.9
I've installed the right packages (readxl), I've used the as.Date function so ensure my Date is recognized as such, and I've used the as.ts function to convert the dataset. However, after using the as.ts function, the date column is all muddled up into a random number and not a date anymore. What am I doing wrong? Please help!
Date Maize Exports (m/t) Rainfall (mm) Temperature © Exchange rate (R/$) Maize price (R) FDI (Million R) GDP (Million R) Oil prices (R/barrel)
[1,] 957139200 21000 30.8 14.36 0.1435235 677.88 4337 9056 192.35
[2,] 959817600 54000 14.9 13.96 0.1474926 583.48 -4229 9056 205.36
[3,] 962409600 134000 11.1 12.61 0.1437298 518.10 -4229 8841 196.38
[4,] 965088000 213000 6.1 15.27 0.1433075 525.59 -4229 8841 204.66
[5,] 967766400 123000 38.5 17.83 0.1382170 576.08 6315 8841 233.64
[6,] 970358400 94000 61.9 20.10 0.1322751 635.79 6315 4487 231.27
In short nothing is wrong - and while this response should really be a comment, I wanted to use a full answer to have a bit more space to explain.
Behind each date is a numeric value tethered to an origin, so this is just R's way of handling it. And since you imported from excel originally, those origins may not line up if you tried to cross check it (see below).
You didn't make your question reproducible, but I put some similar data together to demonstrate what's going on:
Data
df <- data.frame(date = as.Date(c("2000-05-01",
"2000-06-01",
"2000-07-01",
"2000-08-01",
"2000-09-01",
"2000-10-01",
"2000-11-01")),
maize = c(21, 54, 132, 213, 123, 94, 192) * 1000,
rainfall = c(30, 14, 11, 6, 38, 61, 93))
tb <- tidyr::as_tibble(df)
Turning this into a time series object using as.ts()
tb_ts <- as.ts(tb)
# Time Series:
# Start = 1
# End = 7
# Frequency = 1
# date maize rainfall
# 1 11078 21000 30
# 2 11109 54000 14
# 3 11139 132000 11
# 4 11170 213000 6
# 5 11201 123000 38
# 6 11231 94000 61
# 7 11262 192000 93
Since I created these data in R, the "origin" is January 1, 1970, and we can see this in numerical dates from the time series object and convert them back into date formats:
as.Date(tb_ts[1:7], origin = '1970-01-01')
# [1] "2000-05-01" "2000-06-01" "2000-07-01" "2000-08-01"
# [5] "2000-09-01" "2000-10-01" "2000-11-01"
Note that if you import data from Excel, Excel's origin is December 30th, 1899 (i.e., as.Date(xx, origin = "1899-12-30")), so if you tried that you get the wrong dates:
as.Date(tb_ts[1:7], origin = "1899-12-30")
# [1] "1930-04-30" "1930-05-31" "1930-06-30" "1930-07-31"
# [5] "1930-08-31" "1930-09-30" "1930-10-31
The function worked as it's supposed to. Keeping the date format you're familiar with isn't practical for execution, so it converts the dates to a different value, usually something like the number of days (or minutes or seconds) since a certain year, usually Jan. 1 1970. For example, here is a little set to make the point:
# a test vector of dates
> del1 <- seq(as.Date("2012-04-01"), length.out=4, by=30)
# looks like
> del1
[1] "2012-04-01" "2012-05-01" "2012-05-31" "2012-06-30"
# use the as.ts
> as.ts(del1)
Time Series:
Start = 1
End = 4
Frequency = 1
[1] 15431 15461 15491 15521
So you can see the dates, which are 30 days apart, are converted to a series of values that are 30 integers apart.
I have a data set that will be used for time series. the date column is currently structured as follows:
> head(cam_shiller)
div stock dates
1 0.495 7.09 1933m1
2 0.490 6.25 1933m2
3 0.485 6.23 1933m3
4 0.480 6.89 1933m4
5 0.475 8.87 1933m5
6 0.470 10.39 1933m6
If I'm not mistaken, monthly data for time series should look like this: yyyy-mm. So I'm trying to make my date column look like this:
div stock dates
1 0.495 7.09 1933-01
2 0.490 6.25 1933-02
3 0.485 6.23 1933-03
4 0.480 6.89 1933-04
5 0.475 8.87 1933-05
6 0.470 10.39 1933-06
However, using the as.yearmo function produces a column full of NAs. I tried removing the 'm' and replacing it with a dash, and then running as.yearmo again. Now the results look like this:
div stock dates
1 0.495 7.09 Jan 1933
2 0.490 6.25 Feb 1933
3 0.485 6.23 Mar 1933
4 0.480 6.89 Apr 1933
5 0.475 8.87 May 1933
6 0.470 10.39 Jun 1933
How do I change the dates into the yyyy-mm format?
library(zoo)
cam_shiller = read.csv('https://raw.githubusercontent.com/bandcar/Examples/main/cam_shiller.csv')
cam_shiller$dates = gsub('m', '-', cam_shiller$dates)
cam_shiller$dates = as.yearmon(cam_shiller$dates)
Actually, in ts you just need to specify start= and frequency.
res <- ts(cam_shiller[, -3], start=1933, frequency=12)
res
# div stock
# Jan 1933 0.4950 7.09
# Feb 1933 0.4900 6.25
# Mar 1933 0.4850 6.23
# Apr 1933 0.4800 6.89
# May 1933 0.4750 8.87
# Jun 1933 0.4700 10.39
# Jul 1933 0.4650 11.23
# Aug 1933 0.4600 10.67
# Sep 1933 0.4550 10.58
# Oct 1933 0.4500 9.55
# Nov 1933 0.4450 9.78
# Dec 1933 0.4400 9.97
# Jan 1934 0.4408 10.54
# Feb 1934 0.4417 11.32
# Mar 1934 0.4425 10.74
# Apr 1934 0.4433 10.92
# May 1934 0.4442 9.81
# Jun 1934 0.4450 9.94
# Jul 1934 0.4458 9.47
# Aug 1934 0.4467 9.10
# Sep 1934 0.4475 8.88
# Oct 1934 0.4483 8.95
# Nov 1934 0.4492 9.20
# Dec 1934 0.4500 9.26
# ...
Or
ts(cam_shiller$stock, start=c(1933, 1), frequency=12)
# Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
# 1933 7.09 6.25 6.23 6.89 8.87 10.39 11.23 10.67 10.58 9.55 9.78 9.97
# 1934 10.54 11.32 10.74 10.92 9.81 9.94 9.47 9.10 8.88 8.95 9.20 9.26
# 1935 9.26 8.98 8.41 9.04 9.75 10.12 10.65 11.37 11.61 11.92 13.04 13.04
# ...
It may be wise to check beforehand that there are no gaps in the data by evaluating the column and row variances of years and month matrices:
test <- do.call(rbind, strsplit(cam_shiller$dates, 'm')) |>
type.convert(as.is=TRUE)
matrixStats::colVars(matrix(test[, 1], 12))
# [1] 0 0 ...
matrixStats::rowVars(matrix(test[, 2], 12))
# [1] 0 0 0 0 0 0 0 0 0 0 0 0
If you use the xts::xts, it's rather picky since it wants a time-based class such as "Date" or "POSIXct". So you need whole dates, i.e. paste a 01 as pseudo date.
res <- transform(cam_shiller, dates=strptime(paste(dates, '01'), format='%Ym%m %d')) |>
{\(.) xts::as.xts(.[1:2], .$dates)}()
head(res)
# div stock
# 1933-01-01 0.495 7.09
# 1933-02-01 0.490 6.25
# 1933-03-01 0.485 6.23
# 1933-04-01 0.480 6.89
# 1933-05-01 0.475 8.87
# 1933-06-01 0.470 10.39
class(res)
# [1] "xts" "zoo"
Data:
cam_shiller <- structure(list(div = c(0.495, 0.49, 0.485, 0.48, 0.475, 0.47,
0.465, 0.46, 0.455, 0.45, 0.445, 0.44, 0.4408, 0.4417, 0.4425,
0.4433, 0.4442, 0.445, 0.4458, 0.4467, 0.4475, 0.4483, 0.4492,
0.45), stock = c(7.09, 6.25, 6.23, 6.89, 8.87, 10.39, 11.23,
10.67, 10.58, 9.55, 9.78, 9.97, 10.54, 11.32, 10.74, 10.92, 9.81,
9.94, 9.47, 9.1, 8.88, 8.95, 9.2, 9.26), dates = c("1933m1",
"1933m2", "1933m3", "1933m4", "1933m5", "1933m6", "1933m7", "1933m8",
"1933m9", "1933m10", "1933m11", "1933m12", "1934m1", "1934m2",
"1934m3", "1934m4", "1934m5", "1934m6", "1934m7", "1934m8", "1934m9",
"1934m10", "1934m11", "1934m12")), row.names = c(NA, 24L), class = "data.frame")
Try lubridate::ym to change dates to yyyy-mm format
library(tidyverse)
cam_shiller = read.csv('https://raw.githubusercontent.com/bandcar/Examples/main/cam_shiller.csv')
cam_shiller %>%
mutate(
date = lubridate::ym(dates),
date = strftime(date, "%Y-%m")
) %>%
head()
#> div stock dates date
#> 1 0.495 7.09 1933m1 1933-01
#> 2 0.490 6.25 1933m2 1933-02
#> 3 0.485 6.23 1933m3 1933-03
#> 4 0.480 6.89 1933m4 1933-04
#> 5 0.475 8.87 1933m5 1933-05
#> 6 0.470 10.39 1933m6 1933-06
Created on 2022-10-01 with reprex v2.0.2
The form in the question is already correct. It is not true
that you need to change it. It renders as Jan 1933, etc. but internally it is represented as year+(month-1)/12 (where month is a number 1, 2, ..., 12) which is exactly what you need for analysis. You do not want a character string of the form yyyy-mm for analysis.
If by "time series" you mean a zoo series then using u defined in the Note at the end, z below gives that with a yearmon index. The index argument to read.csv.zoo gives the column number or name of the index, the FUN argument tells it how to convert it and the format argument tells it the precise form of the dates.
If what you mean by time series is that you want a ts series then tt below gives that.
If what you mean is a data frame with a yearmon column then DF below gives that.
With either a zoo series or a ts series one could perform a variety of analyses. For example, acf(z) or acf(tt) would give the autocorrelation function.
For more information see ?read.csv.zoo . There is also an entire vignette on read.zoo and its variants. The vignettes are linked to on the CRAN home page for zoo. Also see ?strptime for the percent codes.
library(zoo)
# zoo series with yearmon column
z <- read.csv.zoo(u, index = 3, FUN = as.yearmon, format = "%Ym%m")
# ts series
tt <- as.ts(z)
# data frame with yearmon column
DF <- u |>
read.csv() |>
transform(dates = as.yearmon(dates, "%Ym%m"))
A character string of the form yyyy-mm is not a suitable form for most analyses but if you really did want that anyways then
# zoo series with yyyy-mm character string index
z2 <- aggregate(z, format(index(z), "%Y-%m"), c)
# data.frame with yyyy-mm character string column
DF2 <- transform(DF, dates = format(dates, "%Y-%m"))
Note
u <- "https://raw.githubusercontent.com/bandcar/Examples/main/cam_shiller.csv"
I created these lines (function) to modify a specific column of a data frame, I want to use this function to run it for different column and data frame, but the function does not work, I got a error code message.
change.date <- function(df_date,col_nb,first.year, second.year){
df_date$col_nb <- gsub(first.year, second.year, df_date$col_nb)
df_date$col_nb <- as.Date(df_date$col_nb)
df_date$col_nb <- as.numeric(df_date$col_nb)
}
change.date(df_2020,df_2020[1], "2020","2020")
Error in $<-.data.frame`(*tmp*`, "col_nb", value = character(0)):
replacement table has 0 rows, replaced table has 7265
my reproducible data are:
df_2020 <- dput(test_qst)
structure(list(Date = structure(c(1588809600, 1588809600, 1588809600,
1588809600, 1588809600, 1588809600, 1588809600, 1588809600, 1588809600,
1588809600, 1588809600, 1588809600, 1588809600, 1588809600), class = c("POSIXct",
"POSIXt"), tzone = "UTC"), Depth = c(1.72, 3.07, 3.65, 4.58,
5.39, 6.31, 7.27, 8.57, 9.73, 10.78, 11.71, 12.81, 13.79, 14.96
), salinity = c(34.7299999999999, 34.79, 34.76, 34.78, 34.77,
34.79, 34.76, 34.71, 34.78, 34.78, 34.7999999999999, 34.86, 34.7999999999999,
34.83)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-14L))
You may try
change.date <- function(df_date,col_nb,first.year, second.year){
df_date[[col_nb]] <- gsub(first.year, second.year, df_date[[col_nb]])
df_date[[col_nb]] <- as.Date(df_date[[col_nb]])
df_date[[col_nb]] <- as.numeric(df_date[[col_nb]])
df_date
}
change.date(df_2020, "Date", "2020","2020")
Date Depth salinity
<dbl> <dbl> <dbl>
1 18389 1.72 34.7
2 18389 3.07 34.8
3 18389 3.65 34.8
4 18389 4.58 34.8
5 18389 5.39 34.8
6 18389 6.31 34.8
7 18389 7.27 34.8
8 18389 8.57 34.7
9 18389 9.73 34.8
10 18389 10.8 34.8
11 18389 11.7 34.8
12 18389 12.8 34.9
13 18389 13.8 34.8
14 18389 15.0 34.8
One issue you may find when using gsub is that you lose the dates. Unless you need a numerical timescale, then it may be better to keep dates for plotting and analysis.
Using dplyr, this extracts the years, changes them, and then creates dates again, (even if they are the same year):
library(dplyr)
change.date <- function(df_date, col_nb = "Date", first.year, second.year) {
col_nb <- which(colnames(df_date) %in% col_nb)
df_date %>%
mutate(year = lubridate::year(.[[col_nb]])) %>%
mutate(year = ifelse(year == first.year, second.year, year)) %>%
mutate(Date = lubridate::make_date(year, lubridate::month(.[[col_nb]]), lubridate::day(.[[col_nb]]))) %>%
select(-year)
}
change.date(df_2020, "Date", 2020, 2020)
# A tibble: 14 x 3
Date Depth salinity
<date> <dbl> <dbl>
1 2020-05-07 1.72 34.7
2 2020-05-07 3.07 34.8
3 2020-05-07 3.65 34.8
4 2020-05-07 4.58 34.8
5 2020-05-07 5.39 34.8
6 2020-05-07 6.31 34.8
7 2020-05-07 7.27 34.8
8 2020-05-07 8.57 34.7
9 2020-05-07 9.73 34.8
10 2020-05-07 10.8 34.8
11 2020-05-07 11.7 34.8
12 2020-05-07 12.8 34.9
13 2020-05-07 13.8 34.8
14 2020-05-07 15.0 34.8
If you do want numerical dates, then use this instead of the second last line:
mutate(Date = as.numeric(lubridate::make_date(year, lubridate::month(.[[col_nb]]), lubridate::day(.[[col_nb]])))) %>%
One comment on your function is to be consistent on the case. Camel case, snake case or, less so, dot case are all acceptable, but using a combination makes it harder to keep track of variables, e.g. df_date versus first.year.
I have the following dataset of weather conditions in 5 different sites observed in 15-minute intervals over a year, and am developing a shiny app based on it.
site_id date_time latitude longitude ambient_air_tem~ relative_humidy barometric_pres~ average_wind_sp~ particulate_den~
<chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 arc1046 2019-11-15 09:15:00 -37.8 145. 14.4 65.4 1007. 7.45 3.9
2 arc1048 2019-11-15 09:15:00 -37.8 145. 14.0 65.5 1006. 6.95 4.4
3 arc1045 2019-11-15 09:15:00 -37.8 145. 14.8 60 1007. 4.93 3.9
4 arc1047 2019-11-15 09:15:00 -37.8 145. 14.4 66.1 1008. 7.85 4.5
5 arc1050 2019-11-15 09:15:00 -37.8 145. 14.1 64.7 1007. 5.8 3.9
6 arc1045 2019-11-15 09:30:00 -37.8 145. 15.4 57.1 1007. 4.43 3.8
7 arc1046 2019-11-15 09:30:00 -37.8 145. 14.8 63.2 1007. 7.6 4.5
8 arc1047 2019-11-15 09:30:00 -37.8 145. 15.2 62.7 1008 7.13 3.6
9 arc1048 2019-11-15 09:30:00 -37.8 145. 14.6 62.2 1007. 7.09 4.7
10 arc1050 2019-11-15 09:30:00 -37.8 145. 14.6 62.5 1007 5.94 3.5
I mapped the 5 sites using leaflet.
leaflet(quarter_hour_readings) %>%
addTiles() %>%
addCircleMarkers(
layerId = ~site_id,
label = ~site_id)
And now want to include radial(spider) plots on each of the markers on the map, upon selecting a single date. For now I have filtered out the data values at a single date, for the following radial plot.
library(fmsb)
dat <- rbind(c(85.00,100.00,2000.00,160.00,999.9,1999.9),
c(-40.00,0.00,10.00,0.00,0.00,0.00),
quarter_hour_readings %>%
filter(date_time == as.POSIXct("2019-11-15 09:15:00",tz="UTC")) %>%
column_to_rownames(var="site_id") %>%
select(c("ambient_air_temperature","relative_humidy","barometric_pressure", "average_wind_speed", "particulate_density_2.5", "particulate_density_10")))
radarchart(dat)
I am however unsure how to include these raidal plots on the respective markers on the map and if there was an easier way to handle this. Although I found this package to insert minicharts on leaflet maps, I wasn't able to find how to add radar plots on a map.
Note. Since you did not provide a reproducible dataset, I take some fake data.
You can follow the approach described here:
m <- leaflet() %>% addTiles()
rand_lng <- function(n = 5) rnorm(n, -93.65, .01)
rand_lat <- function(n = 5) rnorm(n, 42.0285, .01)
rdr_dat <- structure(list(total = c(5, 1, 2.15031008049846, 4.15322054177523,
2.6359076872468),
phys = c(15, 3, 12.3804132539814, 6.6208886719424,
12.4789917719968),
psycho = c(3, 0, 0.5, NA, 3),
social = c(5, 1, 2.82645894121379,
4.82733338139951, 2.81333662476391),
env = c(5, 1, 5, 2.5, 4)),
row.names = c(NA, -5L), class = "data.frame")
makePlotURI <- function(expr, width, height, ...) {
pngFile <- plotPNG(function() { expr }, width = width, height = height, ...)
on.exit(unlink(pngFile))
base64 <- httpuv::rawToBase64(readBin(pngFile, raw(1), file.size(pngFile)))
paste0("data:image/png;base64,", base64)
}
set.seed(1)
plots <- data.frame(lat = rand_lat(),
lng = rand_lng(),
radar = rep(makePlotURI({radarchart(rdr_dat)}, 200, 200, bg = "white"), 5))
m %>% addMarkers(icon = ~ icons(radar), data = plots)
I like to use the advanted of BatchgetSymbols.
Any advice how I can best manipulate the output to receive the format below?
symbols_RP <- c('VDNR.L','VEUD.L','VDEM.L','IDTL.L','IEMB.L','GLRE.L','IGLN.L')
#Setting price download date range
from_date <- as.Date('2019-01-01')
to_date <- as.Date(Sys.Date())
get.symbol.adjclose <- function(ticker) {
l.out <- BatchGetSymbols(symbols_RP, first.date = from_date, last.date = to_date, do.cache=TRUE, freq.data = "daily", do.complete.data = TRUE, do.fill.missing.prices = TRUE, be.quiet = FALSE)
return(l.out$df.tickers)
}
prices <- get.symbol.adjclose(symbols_RP)
Output Batchgetsymbols
$df.tickers
price.open price.high price.low price.close volume price.adjusted ref.date ticker ret.adjusted.prices ret.closing.prices
1 60.6000 61.7950 60.4000 61.5475 4717 60.59111 2019-01-02 VDNR.L NA NA
2 60.7200 60.9000 60.5500 60.6650 22015 59.72233 2019-01-03 VDNR.L -1.433838e-02 -1.433852e-02
3 60.9050 60.9500 60.9050 61.8875 1010 60.92583 2019-01-04 VDNR.L 2.015164e-02 2.015165e-02
4 62.3450 62.7850 62.3400 62.7300 820 61.75524 2019-01-07 VDNR.L 1.361339e-02 1.361340e-02
Desired output below:
VTI PUTW VEA VWO TLT VNQI GLD EMB UST FTAL
2019-01-02 124.6962 25.18981 35.72355 36.92347 118.6449 48.25209 121.33 97.70655 55.18464 45.76
2019-01-03 121.8065 25.05184 35.43429 36.34457 119.9950 48.32627 122.43 98.12026 56.01122 45.54
2019-01-04 125.8384 25.39677 36.52383 37.49271 118.6061 49.38329 121.44 98.86311 55.10592 46.63
2019-01-07 127.1075 25.57416 36.63954 37.56989 118.2564 49.67072 121.86 99.28625 54.81071 46.54
2019-01-08 128.4157 25.61358 36.89987 37.78215 117.9456 50.06015 121.53 99.21103 54.54502 47.05
2019-01-09 129.0210 25.56431 37.35305 38.33209 117.7610 50.39395 122.31 99.38966 54.56470 47.29
as I know from other languages, I could use for loop, but I know there is faster ways in r.
Maybe one could hint me the r-way?
Improved version:
get.symbol.adjclose <- function(ticker) {
l.out <- BatchGetSymbols(symbols_RP, first.date = from_date, last.date = to_date, do.cache=TRUE, freq.data = "daily", do.complete.data = TRUE, do.fill.missing.prices = TRUE, be.quiet = FALSE)
return(as.data.frame(l.out$df.tickers[c("ticker","ref.date","price.open","price.high","price.low","price.close","volume","price.adjusted")]))
}
Using dplyr and tidyr. I'm selecting price.adjusted, but you can use any of the prices you need.
library(dplyr)
library(tidyr)
prices %>%
select(ref.date, ticker, price.adjusted) %>% # select columns before pivot_wider
pivot_wider(names_from = ticker, values_from = price.adjusted)
# A tibble: 352 x 7
ref.date GLRE.L IDTL.L IGLN.L VDEM.L VDNR.L VEUD.L
<date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2019-01-02 NA NA 25.2 51.0 60.6 30.2
2 2019-01-03 32.2 4.50 25.3 50.3 59.7 30.1
3 2019-01-04 32.6 4.47 25.2 51.7 60.9 30.9
4 2019-01-07 32.8 4.47 25.3 51.8 61.8 31.0
5 2019-01-08 32.8 4.44 25.2 51.9 62.0 31.3
6 2019-01-09 33.3 4.43 25.3 53.0 62.7 31.7
7 2019-01-10 33.5 4.41 25.3 53.2 62.7 31.7
8 2019-01-11 33.8 4.40 25.3 53.1 62.8 31.6
9 2019-01-14 33.8 4.41 25.3 52.7 62.7 31.4
10 2019-01-15 34.0 4.41 25.3 53.1 63.1 31.4
# ... with 342 more rows
Note from BatchGetSymbols :
IEMB.L OUT: not enough data (thresh.bad.data = 75%)