data converted into time series format but not able to perform analysis

[zeeshan0112]

the data frame gdp should be in time series for running BBQ function of BCDating package. the class is showing ts but i am geting this error.
Please help.
Thanks in advance

code

df<- read.csv("gdp_us.csv")
tseries <- read.zoo(df)
tseries_ts <- as.ts(tseries)
gdp <- na.remove(tseries_ts)

dat <- BBQ(gdp, name="Dating Business Cycles of USA")
show(dat)
summary(dat)
plot(dat)
plot(dat,gdp_ts)

output

> tseries <- read.zoo(df)
> tseries_ts <- as.ts(tseries)
> gdp <- na.remove(tseries_ts)
> dat <- BBQ(gdp, name="Dating Business Cycles of USA")
Error in BCDating.peakstroughs(start = start(y), end = end(y), freq = frequency(y),  : 
  frequency must be 12 (monthly dates) or 4 (quarterly dates)
> class(gdp)
[1] "ts"

sample data

|date|gdp|
|---|---|
|01-01-1947|1.24|
|01-04-1947|1.14|
|01-07-1947|1.45|
|01-10-1947|3.91|
|01-01-1948|2.26|
|01-04-1948|2.5|
|01-07-1948|2.37|
|01-10-1948|0.42|
|01-01-1949|-1.94|
|01-04-1949|-1.36|
|01-07-1949|0.56|
|01-10-1949|-0.84|
|01-01-1950|3.63|
|01-04-1950|3.29|
|01-07-1950|5.77|
|01-10-1950|3.69|
|01-01-1951|4.78|
|01-04-1951|2.35|
|01-07-1951|2.08|
|01-10-1951|1.35|
|01-01-1952|1.01|
|01-04-1952|0.34|
|01-07-1952|1.81|
|01-10-1952|3.44|
|01-01-1953|1.85|
|01-04-1953|0.96|
|01-07-1953|-0.15|
|01-10-1953|-1.35|
|01-01-1954|-0.16|
|01-04-1954|0.2|
|01-07-1954|1.25|
|01-10-1954|2.19|
|01-01-1955|3.23|
|01-04-1955|2.01|
|01-07-1955|2.02|
|01-10-1955|1.57|
|01-01-1956|0.6|
|01-04-1956|1.4|
|01-07-1956|1.15|
|01-10-1956|2.01|
|01-01-1957|1.98|
|01-04-1957|0.48|
|01-07-1957|1.56|
|01-10-1957|-0.97|
|01-01-1958|-1.57|
|01-04-1958|0.94|
|01-07-1958|2.85|
|01-10-1958|2.75|
|01-01-1959|2.11|
|01-04-1959|2.36|
|01-07-1959|0.45|
|01-10-1959|0.67|
|01-01-1960|2.59|
|01-04-1960|-0.29|
|01-07-1960|0.83|
|01-10-1960|-1|
|01-01-1961|0.88|

[technocrat]

The error message says it all& dash;dail or weekly data is not acceptable to the BBQ() function. To proceed re-think about what output you are trying to derive from the input or refactor the input to aggregate. If the later use ts() and work from there.

[zeeshan0112]

dat <- BBQ(gdp_ts, name="Dating Business Cycles of USA")
show(dat)
Peaks Troughs Duration
1 1948Q4 1949Q4 4
2 1953Q2 1954Q1 3
3 1957Q3 1958Q1 2
4 2008Q3 2009Q2 3
5 2019Q4 2020Q2 2
summary(dat)
Phase ]Start ;End] Duration LevStart LevEnd Amplitude
1 Expansion 1948Q4 NA NA 280 NA
2 Recession 1948Q4 1949Q4 4 280 271 9.7
3 Expansion 1949Q4 1953Q2 14 271 392 121.1
4 Recession 1953Q2 1954Q1 3 392 385 6.4
5 Expansion 1954Q1 1957Q3 14 385 479 94.1
6 Recession 1957Q3 1958Q1 2 479 468 11.9
7 Expansion 1958Q1 2008Q3 202 468 14899 14431.5
8 Recession 2008Q3 2009Q2 3 14899 14381 517.8
9 Expansion 2009Q2 2019Q4 42 14381 21694 7313.2
10 Recession 2019Q4 2020Q2 2 21694 19477 2217.0
11 Expansion 2020Q2 NA 19477 NA NA

      Amplitude Duration

Exp=]T;P] 5490.0 68.0
Rec=]P;T] 552.6 2.8

> dat <- BBQ(gdp_ts, name="Dating Business Cycles of USA")
> show(dat)
   Peaks Troughs Duration
1 1948Q4  1949Q4        4
2 1953Q2  1954Q1        3
3 1957Q3  1958Q1        2
4 2008Q3  2009Q2        3
5 2019Q4  2020Q2        2
> summary(dat)
       Phase ]Start  ;End] Duration LevStart LevEnd Amplitude
1  Expansion   <NA> 1948Q4       NA       NA    280        NA
2  Recession 1948Q4 1949Q4        4      280    271       9.7
3  Expansion 1949Q4 1953Q2       14      271    392     121.1
4  Recession 1953Q2 1954Q1        3      392    385       6.4
5  Expansion 1954Q1 1957Q3       14      385    479      94.1
6  Recession 1957Q3 1958Q1        2      479    468      11.9
7  Expansion 1958Q1 2008Q3      202      468  14899   14431.5
8  Recession 2008Q3 2009Q2        3    14899  14381     517.8
9  Expansion 2009Q2 2019Q4       42    14381  21694    7313.2
10 Recession 2019Q4 2020Q2        2    21694  19477    2217.0
11 Expansion 2020Q2   <NA>       NA    19477     NA        NA

          Amplitude Duration
Exp=]T;P]    5490.0     68.0
Rec=]P;T]     552.6      2.8

[technocrat]

@zeeshan0112

gdp_ts

by

d <- data.frame(date = c(
  "01-01-1947", "01-04-1947", "01-07-1947",
  "01-10-1947", "01-01-1948", "01-04-1948", "01-07-1948", "01-10-1948",
  "01-01-1949", "01-04-1949", "01-07-1949", "01-10-1949", "01-01-1950",
  "01-04-1950", "01-07-1950", "01-10-1950", "01-01-1951", "01-04-1951",
  "01-07-1951", "01-10-1951", "01-01-1952", "01-04-1952", "01-07-1952",
  "01-10-1952", "01-01-1953", "01-04-1953", "01-07-1953", "01-10-1953",
  "01-01-1954", "01-04-1954", "01-07-1954", "01-10-1954", "01-01-1955",
  "01-04-1955", "01-07-1955", "01-10-1955", "01-01-1956", "01-04-1956",
  "01-07-1956", "01-10-1956", "01-01-1957", "01-04-1957", "01-07-1957",
  "01-10-1957", "01-01-1958", "01-04-1958", "01-07-1958", "01-10-1958",
  "01-01-1959", "01-04-1959", "01-07-1959", "01-10-1959", "01-01-1960",
  "01-04-1960", "01-07-1960", "01-10-1960", "01-01-1961"
), gdp = c(
  1.24,
  1.14, 1.45, 3.91, 2.26, 2.5, 2.37, 0.42, -1.94,
  -1.36, 0.56, -0.84, 3.63, 3.29, 5.77, 3.69, 4.78,
  2.35, 2.08, 1.35, 1.01, 0.34, 1.81, 3.44, 1.85,
  0.96, -0.15, -1.35, -0.16, 0.2, 1.25, 2.19, 3.23,
  2.01, 2.02, 1.57, 0.6, 1.4, 1.15, 2.01, 1.98,
  0.48, 1.56, -0.97, -1.57, 0.94, 2.85, 2.75, 2.11,
  2.36, 0.45, 0.67, 2.59, -0.29, 0.83, -1, 0.88
))


ind <- c(rep(4, floor(dim(d)[1]/4)), floor(dim(d)[1]%%4))
ind <- rep(1:length(ind), times = ind)

head(apply(d[2], 2, function(x) tapply(x, ind, sum))) |>
  ts(start = c(1947,1), frequency =  4) 
#>       Qtr1  Qtr2  Qtr3  Qtr4
#> 1947  7.74  7.55 -3.58 16.38
#> 1948 10.56  6.60

^{Created on 2023-02-19 with reprex v2.0.2}