README.md
In mitsuoxv/tqr: Add tq_xx functions to tidyquant package to facilitate economic analyses
Introduction to tqr package
Mitsuo Shiota
2019-07-19
- tqr: add-on to tsibble, inspired by
tidyquant
- Installation
- Libraries
- Transform a tsibble into lower
frequency
- Time-wise functions to transform a numeric vector are useful
and
flexible
- cal_time_wise: function factory to produce a function to
transform a
tsibble
- tq_diff: calculate
differences
- “order_by” argument in time-wise functions to transform a
numeric
vector
- moving_average and tq_ma: calculate moving
averages
- growth_rate and tq_gr: calculate growth
rates
- season_adjust and tq_sa: calculate seasonally adjusted
values
Updated: 2021-09-04
tqr: add-on to tsibble, inspired by tidyquant
When I utilize tidyquant
package
for economic analyses, I often find myself writing similar functions
repeatedly. I also find the idea of time aware dataframe of tsibble
package may
help me deal with time series data more confidently. So I have decided
to build this package to facilitate my work.
As of version 0.0.0.9011, I have added time-wise functions to transform
a numeric vector, and a function factory using those time-wise
functions, rewritten functions to transform a tsibble using that
function factory, and deprecated old function factories.
-
Time-wise functions to transform a numeric vector
-
moving_average: transform into moving averages
- growth_rate: transform into growth rates
-
season_adjust: transform into seasonally adjusted values
-
A function factory to produce functions to transform a tsibble
-
cal_time_wise: produce functions using time_wise vector functions
-
Functions to transform a tsibble
-
tq_diff: transform numeric columns into differences
- tq_ma: transform numeric columns into moving averages
- tq_gr: transform numeric columns into growth rates
-
tq_sa: transform numeric columns into seasonally adjusted values
-
Deprecated function factories
-
cal_factory: produce functions
- cal_factory_zoo: produce functions utilizing zoo package
- cal_factory_ts: produce functions utilizing ts class
- cal_factory_xts: produce functions utilizing xts package
Installation
This package is only for my own use for now. “You” means “future me”.
You can install the development version with:
remotes::install_github("mitsuoxv/tqr")
Libraries
As I read Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting:
principles and practice, 3rd edition, OTexts: Melbourne, Australia.
OTexts.com/fpp3., I use fpp3 meta package
here.
library(fpp3)
library(tqr)
Transform a tsibble into lower frequency
If you are familiar with tsibble functions, like index_by and
group_by_key, you can convert to lower frequency.
aus_livestock
#> # A tsibble: 29,364 x 4 [1M]
#> # Key: Animal, State [54]
#> Month Animal State Count
#> <mth> <fct> <fct> <dbl>
#> 1 1976 Jul Bulls, bullocks and steers Australian Capital Territory 2300
#> 2 1976 Aug Bulls, bullocks and steers Australian Capital Territory 2100
#> 3 1976 Sep Bulls, bullocks and steers Australian Capital Territory 2100
#> 4 1976 Oct Bulls, bullocks and steers Australian Capital Territory 1900
#> 5 1976 Nov Bulls, bullocks and steers Australian Capital Territory 2100
#> 6 1976 Dec Bulls, bullocks and steers Australian Capital Territory 1800
#> 7 1977 Jan Bulls, bullocks and steers Australian Capital Territory 1800
#> 8 1977 Feb Bulls, bullocks and steers Australian Capital Territory 1900
#> 9 1977 Mar Bulls, bullocks and steers Australian Capital Territory 2700
#> 10 1977 Apr Bulls, bullocks and steers Australian Capital Territory 2300
#> # … with 29,354 more rows
aus_livestock %>%
index_by(Quarter = yearquarter(Month)) %>%
group_by_key() %>%
summarize(Count = sum(Count))
#> # A tsibble: 9,788 x 4 [1Q]
#> # Key: Animal, State [54]
#> # Groups: Animal [7]
#> Animal State Quarter Count
#> <fct> <fct> <qtr> <dbl>
#> 1 Bulls, bullocks and steers Australian Capital Territory 1976 Q3 6500
#> 2 Bulls, bullocks and steers Australian Capital Territory 1976 Q4 5800
#> 3 Bulls, bullocks and steers Australian Capital Territory 1977 Q1 6400
#> 4 Bulls, bullocks and steers Australian Capital Territory 1977 Q2 7700
#> 5 Bulls, bullocks and steers Australian Capital Territory 1977 Q3 7000
#> 6 Bulls, bullocks and steers Australian Capital Territory 1977 Q4 6900
#> 7 Bulls, bullocks and steers Australian Capital Territory 1978 Q1 7800
#> 8 Bulls, bullocks and steers Australian Capital Territory 1978 Q2 8500
#> 9 Bulls, bullocks and steers Australian Capital Territory 1978 Q3 7900
#> 10 Bulls, bullocks and steers Australian Capital Territory 1978 Q4 7900
#> # … with 9,778 more rows
If you are familiar with dplyr and tidyselect functions, you can convert
multiple variables at once.
aus_production
#> # A tsibble: 218 x 7 [1Q]
#> Quarter Beer Tobacco Bricks Cement Electricity Gas
#> <qtr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1956 Q1 284 5225 189 465 3923 5
#> 2 1956 Q2 213 5178 204 532 4436 6
#> 3 1956 Q3 227 5297 208 561 4806 7
#> 4 1956 Q4 308 5681 197 570 4418 6
#> 5 1957 Q1 262 5577 187 529 4339 5
#> 6 1957 Q2 228 5651 214 604 4811 7
#> 7 1957 Q3 236 5317 227 603 5259 7
#> 8 1957 Q4 320 6152 222 582 4735 6
#> 9 1958 Q1 272 5758 199 554 4608 5
#> 10 1958 Q2 233 5641 229 620 5196 7
#> # … with 208 more rows
aus_production %>%
index_by(Year = year(Quarter)) %>%
summarize(across(!Quarter, sum))
#> # A tsibble: 55 x 7 [1Y]
#> Year Beer Tobacco Bricks Cement Electricity Gas
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1956 1032 21381 798 2128 17583 24
#> 2 1957 1046 22697 850 2318 19144 25
#> 3 1958 1055 23466 911 2457 20390 26
#> 4 1959 1052 23919 981 2617 22176 26
#> 5 1960 1084 24328 1077 2800 24240 29
#> 6 1961 1124 25088 995 2859 25204 27
#> 7 1962 1145 25117 1027 2934 27659 29
#> 8 1963 1190 25541 1115 3121 30639 30
#> 9 1964 1256 26396 1310 3625 34226 30
#> 10 1965 1311 26346 1364 3812 37056 30
#> # … with 45 more rows
Time-wise functions to transform a numeric vector are useful and flexible
In the above examples, sum function transforms a numeric vector.
tsibble package provides difference function, which also transforms a
time-wise numeric vector. Using it, you can create a new variable
“diff”.
aus_arrivals %>%
group_by_key() %>%
mutate(diff = difference(Arrivals)) %>%
ungroup()
#> # A tsibble: 508 x 4 [1Q]
#> # Key: Origin [4]
#> Quarter Origin Arrivals diff
#> <qtr> <chr> <int> <int>
#> 1 1981 Q1 Japan 14763 NA
#> 2 1981 Q2 Japan 9321 -5442
#> 3 1981 Q3 Japan 10166 845
#> 4 1981 Q4 Japan 19509 9343
#> 5 1982 Q1 Japan 17117 -2392
#> 6 1982 Q2 Japan 10617 -6500
#> 7 1982 Q3 Japan 11737 1120
#> 8 1982 Q4 Japan 20961 9224
#> 9 1983 Q1 Japan 20671 -290
#> 10 1983 Q2 Japan 12235 -8436
#> # … with 498 more rows
Or you can transform a existing variable “Arrivals”. You can use
difference function flexibly.
aus_arrivals %>%
group_by_key() %>%
mutate(Arrivals = difference(Arrivals)) %>%
ungroup()
#> # A tsibble: 508 x 3 [1Q]
#> # Key: Origin [4]
#> Quarter Origin Arrivals
#> <qtr> <chr> <int>
#> 1 1981 Q1 Japan NA
#> 2 1981 Q2 Japan -5442
#> 3 1981 Q3 Japan 845
#> 4 1981 Q4 Japan 9343
#> 5 1982 Q1 Japan -2392
#> 6 1982 Q2 Japan -6500
#> 7 1982 Q3 Japan 1120
#> 8 1982 Q4 Japan 9224
#> 9 1983 Q1 Japan -290
#> 10 1983 Q2 Japan -8436
#> # … with 498 more rows
cal_time_wise: function factory to produce a function to transform a tsibble
cal_time_wise function is functionized from the last example, and is a
function factory. It receives a time-wise function to transform a
numeric vector as its first argument, and it returns a function to
transform a tsibble.
tq_diff: calculate differences
tq_diff function is created like below. tq_ma, tq_gr and tq_sa
functions are also created by this function factory.
tq_diff <- cal_time_wise(
tsibble::difference
)
The variables, which these created functions transform, are all numeric
variables which are not key or index variables.
In the below example, “Month” is the index variable, and “State” and
“Industry” are the key variables. “Series ID” is a character vector. So
tq_diff transforms just “Turnover”, a numeric variable.
aus_retail %>%
tq_diff()
#> # A tsibble: 64,532 x 5 [1M]
#> # Key: State, Industry [152]
#> State Industry `Series ID` Month Turnover
#> <chr> <chr> <chr> <mth> <dbl>
#> 1 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Apr NA
#> 2 Australian Capital Territory Cafes, restaurant… A3349849A 1982 May -1
#> 3 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Jun 0.200
#> 4 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Jul 0.4
#> 5 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Aug -0.4
#> 6 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Sep 0.6
#> 7 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Oct 0.600
#> 8 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Nov 0.600
#> 9 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Dec 1.5
#> 10 Australian Capital Territory Cafes, restaurant… A3349849A 1983 Jan -3.1
#> # … with 64,522 more rows
For arguments other than the first one, a tsibble, of tq_diff
function, look at tsibble::difference document. Note that n in the
previous version of tq_diff is changed to lag.
“order_by” argument in time-wise functions to transform a numeric vector
You can find “order_by” argument in tsibble::difference document. It
is optional in difference function.
tsbl <- tsibble(year = 2000:2005, value = (0:5)^2, index = year)
scrambled <- tsbl %>% slice(sample(nrow(tsbl)))
#> Warning: Current temporal ordering may yield unexpected results.
#> ℹ Suggest to sort by ``, `year` first.
wrong <- mutate(scrambled, diff = difference(value))
#> Warning: Current temporal ordering may yield unexpected results.
#> ℹ Suggest to sort by ``, `year` first.
arrange(wrong, year)
#> # A tsibble: 6 x 3 [1Y]
#> year value diff
#> <int> <dbl> <dbl>
#> 1 2000 0 -4
#> 2 2001 1 1
#> 3 2002 4 -12
#> 4 2003 9 8
#> 5 2004 16 NA
#> 6 2005 25 16
right <- mutate(scrambled, diff = difference(value, order_by = year))
#> Warning: Current temporal ordering may yield unexpected results.
#> ℹ Suggest to sort by ``, `year` first.
arrange(right, year)
#> # A tsibble: 6 x 3 [1Y]
#> year value diff
#> <int> <dbl> <dbl>
#> 1 2000 0 NA
#> 2 2001 1 1
#> 3 2002 4 3
#> 4 2003 9 5
#> 5 2004 16 7
#> 6 2005 25 9
cal_time_wise function assigns the index variable in a tsibble to
“order_by” argument of difference function. As a result, tq_diff
function returns right even if the rows are reshuffled.
tsbl %>%
tq_diff()
#> # A tsibble: 6 x 2 [1Y]
#> year value
#> <int> <dbl>
#> 1 2000 NA
#> 2 2001 1
#> 3 2002 3
#> 4 2003 5
#> 5 2004 7
#> 6 2005 9
scrambled %>%
tq_diff() %>%
arrange(year)
#> Warning: Current temporal ordering may yield unexpected results.
#> ℹ Suggest to sort by ``, `year` first.
#> # A tsibble: 6 x 2 [1Y]
#> year value
#> <int> <dbl>
#> 1 2000 NA
#> 2 2001 1
#> 3 2002 3
#> 4 2003 5
#> 5 2004 7
#> 6 2005 9
moving_average and tq_ma: calculate moving averages
moving_average function calculates moving averages using slider
package. “n” specifies the window width. “order_by” is optional, and
works in the same way as in difference function. For other arguments,
refer to its document.
aus_livestock %>%
mutate(Count = if_else(row_number() == 4, NA_real_, Count)) %>%
group_by_key() %>%
mutate(
ma3 = moving_average(Count, n = 3),
ma3_na.rm = moving_average(Count, n = 3, na.rm = TRUE),
ma3_left = moving_average(Count, n = 3, .align = "left")
) %>%
ungroup()
#> # A tsibble: 29,364 x 7 [1M]
#> # Key: Animal, State [54]
#> Month Animal State Count ma3 ma3_na.rm ma3_left
#> <mth> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 1976 Jul Bulls, bullocks and steers Australia… 2300 NA NA 2167.
#> 2 1976 Aug Bulls, bullocks and steers Australia… 2100 NA NA NA
#> 3 1976 Sep Bulls, bullocks and steers Australia… 2100 2167. 2167. NA
#> 4 1976 Oct Bulls, bullocks and steers Australia… NA NA 2100 NA
#> 5 1976 Nov Bulls, bullocks and steers Australia… 2100 NA 2100 1900
#> 6 1976 Dec Bulls, bullocks and steers Australia… 1800 NA 1950 1833.
#> 7 1977 Jan Bulls, bullocks and steers Australia… 1800 1900 1900 2133.
#> 8 1977 Feb Bulls, bullocks and steers Australia… 1900 1833. 1833. 2300
#> 9 1977 Mar Bulls, bullocks and steers Australia… 2700 2133. 2133. 2500
#> 10 1977 Apr Bulls, bullocks and steers Australia… 2300 2300 2300 2567.
#> # … with 29,354 more rows
tq_ma function transforms “Count” variable. For arguments other than
the first one, a tsibble, look at moving_average document.
aus_livestock %>%
tq_ma(n = 3)
#> # A tsibble: 29,364 x 4 [1M]
#> # Key: Animal, State [54]
#> Month Animal State Count
#> <mth> <fct> <fct> <dbl>
#> 1 1976 Jul Bulls, bullocks and steers Australian Capital Territory NA
#> 2 1976 Aug Bulls, bullocks and steers Australian Capital Territory NA
#> 3 1976 Sep Bulls, bullocks and steers Australian Capital Territory 2167.
#> 4 1976 Oct Bulls, bullocks and steers Australian Capital Territory 2033.
#> 5 1976 Nov Bulls, bullocks and steers Australian Capital Territory 2033.
#> 6 1976 Dec Bulls, bullocks and steers Australian Capital Territory 1933.
#> 7 1977 Jan Bulls, bullocks and steers Australian Capital Territory 1900
#> 8 1977 Feb Bulls, bullocks and steers Australian Capital Territory 1833.
#> 9 1977 Mar Bulls, bullocks and steers Australian Capital Territory 2133.
#> 10 1977 Apr Bulls, bullocks and steers Australian Capital Territory 2300
#> # … with 29,354 more rows
growth_rate and tq_gr: calculate growth rates
growth_rate function calculates growth rates. “n” specifies the lag.
“order_by” is optional, and works in the same way as in difference
function. For other arguments, refer to its document.
aus_arrivals %>%
group_by_key() %>%
mutate(
gr_yoy = growth_rate(Arrivals, n = 4),
gr_annualized = growth_rate(Arrivals, n = 1, annualize = 4),
gr_yoy_not_pct = growth_rate(Arrivals, n = 4, pct = FALSE)
)
#> # A tsibble: 508 x 6 [1Q]
#> # Key: Origin [4]
#> # Groups: Origin [4]
#> Quarter Origin Arrivals gr_yoy gr_annualized gr_yoy_not_pct
#> <qtr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 1981 Q1 Japan 14763 NA NA NA
#> 2 1981 Q2 Japan 9321 NA -84.1 NA
#> 3 1981 Q3 Japan 10166 NA 41.5 NA
#> 4 1981 Q4 Japan 19509 NA 1256. NA
#> 5 1982 Q1 Japan 17117 15.9 -40.7 0.159
#> 6 1982 Q2 Japan 10617 13.9 -85.2 0.139
#> 7 1982 Q3 Japan 11737 15.5 49.4 0.155
#> 8 1982 Q4 Japan 20961 7.44 917. 0.0744
#> 9 1983 Q1 Japan 20671 20.8 -5.42 0.208
#> 10 1983 Q2 Japan 12235 15.2 -87.7 0.152
#> # … with 498 more rows
tq_gr function transforms “Arrivals” variable in this example. For
arguments other than the first one, a tsibble, look at growth_rate
document.
aus_arrivals %>%
tq_gr(n = 4)
#> # A tsibble: 508 x 3 [1Q]
#> # Key: Origin [4]
#> Quarter Origin Arrivals
#> <qtr> <chr> <dbl>
#> 1 1981 Q1 Japan NA
#> 2 1981 Q2 Japan NA
#> 3 1981 Q3 Japan NA
#> 4 1981 Q4 Japan NA
#> 5 1982 Q1 Japan 15.9
#> 6 1982 Q2 Japan 13.9
#> 7 1982 Q3 Japan 15.5
#> 8 1982 Q4 Japan 7.44
#> 9 1983 Q1 Japan 20.8
#> 10 1983 Q2 Japan 15.2
#> # … with 498 more rows
season_adjust and tq_sa: calculate seasonally adjusted values
season_adjust function calculates seasonally adjusted values using
season package. It requires not just “x” argument, a numeric vector,
but also “order_by” argument, a time vector. For other arguments, refer
to season document.
aus_arrivals %>%
group_by_key() %>%
mutate(sa = season_adjust(Arrivals, Quarter)) %>%
ungroup()
#> # A tsibble: 508 x 4 [1Q]
#> # Key: Origin [4]
#> Quarter Origin Arrivals sa
#> <qtr> <chr> <int> <dbl>
#> 1 1981 Q1 Japan 14763 12179.
#> 2 1981 Q2 Japan 9321 12742.
#> 3 1981 Q3 Japan 10166 13107.
#> 4 1981 Q4 Japan 19509 14065.
#> 5 1982 Q1 Japan 17117 14004.
#> 6 1982 Q2 Japan 10617 14564.
#> 7 1982 Q3 Japan 11737 14909.
#> 8 1982 Q4 Japan 20961 15542.
#> 9 1983 Q1 Japan 20671 16868.
#> 10 1983 Q2 Japan 12235 16584.
#> # … with 498 more rows
tq_sa function transforms “Arrivals” variable in this example.
aus_arrivals %>%
tq_sa()
#> # A tsibble: 508 x 3 [1Q]
#> # Key: Origin [4]
#> Quarter Origin Arrivals
#> <qtr> <chr> <dbl>
#> 1 1981 Q1 Japan 12179.
#> 2 1981 Q2 Japan 12742.
#> 3 1981 Q3 Japan 13107.
#> 4 1981 Q4 Japan 14065.
#> 5 1982 Q1 Japan 14004.
#> 6 1982 Q2 Japan 14564.
#> 7 1982 Q3 Japan 14909.
#> 8 1982 Q4 Japan 15542.
#> 9 1983 Q1 Japan 16868.
#> 10 1983 Q2 Japan 16584.
#> # … with 498 more rows
tq_sa function returns right even if the rows are reshuffled, as
tq_diff, tq_ma and tq_gr functions do.
aus_arrivals %>%
slice(sample(nrow(aus_arrivals))) %>%
tq_sa() %>%
arrange(Origin, Quarter)
#> Warning: Current temporal ordering may yield unexpected results.
#> ℹ Suggest to sort by `Origin`, `Quarter` first.
#> Warning: Current temporal ordering may yield unexpected results.
#> ℹ Suggest to sort by `Origin`, `Quarter` first.
#> Warning: Current temporal ordering may yield unexpected results.
#> ℹ Suggest to sort by `Origin`, `Quarter` first.
#> Warning: Current temporal ordering may yield unexpected results.
#> ℹ Suggest to sort by `Origin`, `Quarter` first.
#> # A tsibble: 508 x 3 [1Q]
#> # Key: Origin [4]
#> Quarter Origin Arrivals
#> <qtr> <chr> <dbl>
#> 1 1981 Q1 Japan 12179.
#> 2 1981 Q2 Japan 12742.
#> 3 1981 Q3 Japan 13107.
#> 4 1981 Q4 Japan 14065.
#> 5 1982 Q1 Japan 14004.
#> 6 1982 Q2 Japan 14564.
#> 7 1982 Q3 Japan 14909.
#> 8 1982 Q4 Japan 15542.
#> 9 1983 Q1 Japan 16868.
#> 10 1983 Q2 Japan 16584.
#> # … with 498 more rows
EOL
mitsuoxv/tqr documentation built on Nov. 14, 2021, 7:18 p.m.
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Introduction to tqr package
Mitsuo Shiota 2019-07-19
- tqr: add-on to tsibble, inspired by
tidyquant
- Installation
- Libraries
- Transform a tsibble into lower frequency
- Time-wise functions to transform a numeric vector are useful and flexible
- cal_time_wise: function factory to produce a function to transform a tsibble
- tq_diff: calculate differences
- “order_by” argument in time-wise functions to transform a numeric vector
- moving_average and tq_ma: calculate moving averages
- growth_rate and tq_gr: calculate growth rates
- season_adjust and tq_sa: calculate seasonally adjusted values
Updated: 2021-09-04
tqr: add-on to tsibble, inspired by tidyquant
When I utilize tidyquant
package
for economic analyses, I often find myself writing similar functions
repeatedly. I also find the idea of time aware dataframe of tsibble
package may
help me deal with time series data more confidently. So I have decided
to build this package to facilitate my work.
As of version 0.0.0.9011, I have added time-wise functions to transform a numeric vector, and a function factory using those time-wise functions, rewritten functions to transform a tsibble using that function factory, and deprecated old function factories.
-
Time-wise functions to transform a numeric vector
-
moving_average: transform into moving averages
- growth_rate: transform into growth rates
-
season_adjust: transform into seasonally adjusted values
-
A function factory to produce functions to transform a tsibble
-
cal_time_wise: produce functions using time_wise vector functions
-
Functions to transform a tsibble
-
tq_diff: transform numeric columns into differences
- tq_ma: transform numeric columns into moving averages
- tq_gr: transform numeric columns into growth rates
-
tq_sa: transform numeric columns into seasonally adjusted values
-
Deprecated function factories
-
cal_factory: produce functions
- cal_factory_zoo: produce functions utilizing zoo package
- cal_factory_ts: produce functions utilizing ts class
- cal_factory_xts: produce functions utilizing xts package
Installation
This package is only for my own use for now. “You” means “future me”.
You can install the development version with:
remotes::install_github("mitsuoxv/tqr")
Libraries
As I read Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting:
principles and practice, 3rd edition, OTexts: Melbourne, Australia.
OTexts.com/fpp3., I use fpp3 meta package
here.
library(fpp3)
library(tqr)
Transform a tsibble into lower frequency
If you are familiar with tsibble functions, like index_by and
group_by_key, you can convert to lower frequency.
aus_livestock
#> # A tsibble: 29,364 x 4 [1M]
#> # Key: Animal, State [54]
#> Month Animal State Count
#> <mth> <fct> <fct> <dbl>
#> 1 1976 Jul Bulls, bullocks and steers Australian Capital Territory 2300
#> 2 1976 Aug Bulls, bullocks and steers Australian Capital Territory 2100
#> 3 1976 Sep Bulls, bullocks and steers Australian Capital Territory 2100
#> 4 1976 Oct Bulls, bullocks and steers Australian Capital Territory 1900
#> 5 1976 Nov Bulls, bullocks and steers Australian Capital Territory 2100
#> 6 1976 Dec Bulls, bullocks and steers Australian Capital Territory 1800
#> 7 1977 Jan Bulls, bullocks and steers Australian Capital Territory 1800
#> 8 1977 Feb Bulls, bullocks and steers Australian Capital Territory 1900
#> 9 1977 Mar Bulls, bullocks and steers Australian Capital Territory 2700
#> 10 1977 Apr Bulls, bullocks and steers Australian Capital Territory 2300
#> # … with 29,354 more rows
aus_livestock %>%
index_by(Quarter = yearquarter(Month)) %>%
group_by_key() %>%
summarize(Count = sum(Count))
#> # A tsibble: 9,788 x 4 [1Q]
#> # Key: Animal, State [54]
#> # Groups: Animal [7]
#> Animal State Quarter Count
#> <fct> <fct> <qtr> <dbl>
#> 1 Bulls, bullocks and steers Australian Capital Territory 1976 Q3 6500
#> 2 Bulls, bullocks and steers Australian Capital Territory 1976 Q4 5800
#> 3 Bulls, bullocks and steers Australian Capital Territory 1977 Q1 6400
#> 4 Bulls, bullocks and steers Australian Capital Territory 1977 Q2 7700
#> 5 Bulls, bullocks and steers Australian Capital Territory 1977 Q3 7000
#> 6 Bulls, bullocks and steers Australian Capital Territory 1977 Q4 6900
#> 7 Bulls, bullocks and steers Australian Capital Territory 1978 Q1 7800
#> 8 Bulls, bullocks and steers Australian Capital Territory 1978 Q2 8500
#> 9 Bulls, bullocks and steers Australian Capital Territory 1978 Q3 7900
#> 10 Bulls, bullocks and steers Australian Capital Territory 1978 Q4 7900
#> # … with 9,778 more rows
If you are familiar with dplyr and tidyselect functions, you can convert multiple variables at once.
aus_production
#> # A tsibble: 218 x 7 [1Q]
#> Quarter Beer Tobacco Bricks Cement Electricity Gas
#> <qtr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1956 Q1 284 5225 189 465 3923 5
#> 2 1956 Q2 213 5178 204 532 4436 6
#> 3 1956 Q3 227 5297 208 561 4806 7
#> 4 1956 Q4 308 5681 197 570 4418 6
#> 5 1957 Q1 262 5577 187 529 4339 5
#> 6 1957 Q2 228 5651 214 604 4811 7
#> 7 1957 Q3 236 5317 227 603 5259 7
#> 8 1957 Q4 320 6152 222 582 4735 6
#> 9 1958 Q1 272 5758 199 554 4608 5
#> 10 1958 Q2 233 5641 229 620 5196 7
#> # … with 208 more rows
aus_production %>%
index_by(Year = year(Quarter)) %>%
summarize(across(!Quarter, sum))
#> # A tsibble: 55 x 7 [1Y]
#> Year Beer Tobacco Bricks Cement Electricity Gas
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1956 1032 21381 798 2128 17583 24
#> 2 1957 1046 22697 850 2318 19144 25
#> 3 1958 1055 23466 911 2457 20390 26
#> 4 1959 1052 23919 981 2617 22176 26
#> 5 1960 1084 24328 1077 2800 24240 29
#> 6 1961 1124 25088 995 2859 25204 27
#> 7 1962 1145 25117 1027 2934 27659 29
#> 8 1963 1190 25541 1115 3121 30639 30
#> 9 1964 1256 26396 1310 3625 34226 30
#> 10 1965 1311 26346 1364 3812 37056 30
#> # … with 45 more rows
Time-wise functions to transform a numeric vector are useful and flexible
In the above examples, sum function transforms a numeric vector.
tsibble package provides difference function, which also transforms a
time-wise numeric vector. Using it, you can create a new variable
“diff”.
aus_arrivals %>%
group_by_key() %>%
mutate(diff = difference(Arrivals)) %>%
ungroup()
#> # A tsibble: 508 x 4 [1Q]
#> # Key: Origin [4]
#> Quarter Origin Arrivals diff
#> <qtr> <chr> <int> <int>
#> 1 1981 Q1 Japan 14763 NA
#> 2 1981 Q2 Japan 9321 -5442
#> 3 1981 Q3 Japan 10166 845
#> 4 1981 Q4 Japan 19509 9343
#> 5 1982 Q1 Japan 17117 -2392
#> 6 1982 Q2 Japan 10617 -6500
#> 7 1982 Q3 Japan 11737 1120
#> 8 1982 Q4 Japan 20961 9224
#> 9 1983 Q1 Japan 20671 -290
#> 10 1983 Q2 Japan 12235 -8436
#> # … with 498 more rows
Or you can transform a existing variable “Arrivals”. You can use
difference function flexibly.
aus_arrivals %>%
group_by_key() %>%
mutate(Arrivals = difference(Arrivals)) %>%
ungroup()
#> # A tsibble: 508 x 3 [1Q]
#> # Key: Origin [4]
#> Quarter Origin Arrivals
#> <qtr> <chr> <int>
#> 1 1981 Q1 Japan NA
#> 2 1981 Q2 Japan -5442
#> 3 1981 Q3 Japan 845
#> 4 1981 Q4 Japan 9343
#> 5 1982 Q1 Japan -2392
#> 6 1982 Q2 Japan -6500
#> 7 1982 Q3 Japan 1120
#> 8 1982 Q4 Japan 9224
#> 9 1983 Q1 Japan -290
#> 10 1983 Q2 Japan -8436
#> # … with 498 more rows
cal_time_wise: function factory to produce a function to transform a tsibble
cal_time_wise function is functionized from the last example, and is a
function factory. It receives a time-wise function to transform a
numeric vector as its first argument, and it returns a function to
transform a tsibble.
tq_diff: calculate differences
tq_diff function is created like below. tq_ma, tq_gr and tq_sa
functions are also created by this function factory.
tq_diff <- cal_time_wise(
tsibble::difference
)
The variables, which these created functions transform, are all numeric variables which are not key or index variables.
In the below example, “Month” is the index variable, and “State” and
“Industry” are the key variables. “Series ID” is a character vector. So
tq_diff transforms just “Turnover”, a numeric variable.
aus_retail %>%
tq_diff()
#> # A tsibble: 64,532 x 5 [1M]
#> # Key: State, Industry [152]
#> State Industry `Series ID` Month Turnover
#> <chr> <chr> <chr> <mth> <dbl>
#> 1 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Apr NA
#> 2 Australian Capital Territory Cafes, restaurant… A3349849A 1982 May -1
#> 3 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Jun 0.200
#> 4 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Jul 0.4
#> 5 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Aug -0.4
#> 6 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Sep 0.6
#> 7 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Oct 0.600
#> 8 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Nov 0.600
#> 9 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Dec 1.5
#> 10 Australian Capital Territory Cafes, restaurant… A3349849A 1983 Jan -3.1
#> # … with 64,522 more rows
For arguments other than the first one, a tsibble, of tq_diff
function, look at tsibble::difference document. Note that n in the
previous version of tq_diff is changed to lag.
“order_by” argument in time-wise functions to transform a numeric vector
You can find “order_by” argument in tsibble::difference document. It
is optional in difference function.
tsbl <- tsibble(year = 2000:2005, value = (0:5)^2, index = year)
scrambled <- tsbl %>% slice(sample(nrow(tsbl)))
#> Warning: Current temporal ordering may yield unexpected results.
#> ℹ Suggest to sort by ``, `year` first.
wrong <- mutate(scrambled, diff = difference(value))
#> Warning: Current temporal ordering may yield unexpected results.
#> ℹ Suggest to sort by ``, `year` first.
arrange(wrong, year)
#> # A tsibble: 6 x 3 [1Y]
#> year value diff
#> <int> <dbl> <dbl>
#> 1 2000 0 -4
#> 2 2001 1 1
#> 3 2002 4 -12
#> 4 2003 9 8
#> 5 2004 16 NA
#> 6 2005 25 16
right <- mutate(scrambled, diff = difference(value, order_by = year))
#> Warning: Current temporal ordering may yield unexpected results.
#> ℹ Suggest to sort by ``, `year` first.
arrange(right, year)
#> # A tsibble: 6 x 3 [1Y]
#> year value diff
#> <int> <dbl> <dbl>
#> 1 2000 0 NA
#> 2 2001 1 1
#> 3 2002 4 3
#> 4 2003 9 5
#> 5 2004 16 7
#> 6 2005 25 9
cal_time_wise function assigns the index variable in a tsibble to
“order_by” argument of difference function. As a result, tq_diff
function returns right even if the rows are reshuffled.
tsbl %>%
tq_diff()
#> # A tsibble: 6 x 2 [1Y]
#> year value
#> <int> <dbl>
#> 1 2000 NA
#> 2 2001 1
#> 3 2002 3
#> 4 2003 5
#> 5 2004 7
#> 6 2005 9
scrambled %>%
tq_diff() %>%
arrange(year)
#> Warning: Current temporal ordering may yield unexpected results.
#> ℹ Suggest to sort by ``, `year` first.
#> # A tsibble: 6 x 2 [1Y]
#> year value
#> <int> <dbl>
#> 1 2000 NA
#> 2 2001 1
#> 3 2002 3
#> 4 2003 5
#> 5 2004 7
#> 6 2005 9
moving_average and tq_ma: calculate moving averages
moving_average function calculates moving averages using slider
package. “n” specifies the window width. “order_by” is optional, and
works in the same way as in difference function. For other arguments,
refer to its document.
aus_livestock %>%
mutate(Count = if_else(row_number() == 4, NA_real_, Count)) %>%
group_by_key() %>%
mutate(
ma3 = moving_average(Count, n = 3),
ma3_na.rm = moving_average(Count, n = 3, na.rm = TRUE),
ma3_left = moving_average(Count, n = 3, .align = "left")
) %>%
ungroup()
#> # A tsibble: 29,364 x 7 [1M]
#> # Key: Animal, State [54]
#> Month Animal State Count ma3 ma3_na.rm ma3_left
#> <mth> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 1976 Jul Bulls, bullocks and steers Australia… 2300 NA NA 2167.
#> 2 1976 Aug Bulls, bullocks and steers Australia… 2100 NA NA NA
#> 3 1976 Sep Bulls, bullocks and steers Australia… 2100 2167. 2167. NA
#> 4 1976 Oct Bulls, bullocks and steers Australia… NA NA 2100 NA
#> 5 1976 Nov Bulls, bullocks and steers Australia… 2100 NA 2100 1900
#> 6 1976 Dec Bulls, bullocks and steers Australia… 1800 NA 1950 1833.
#> 7 1977 Jan Bulls, bullocks and steers Australia… 1800 1900 1900 2133.
#> 8 1977 Feb Bulls, bullocks and steers Australia… 1900 1833. 1833. 2300
#> 9 1977 Mar Bulls, bullocks and steers Australia… 2700 2133. 2133. 2500
#> 10 1977 Apr Bulls, bullocks and steers Australia… 2300 2300 2300 2567.
#> # … with 29,354 more rows
tq_ma function transforms “Count” variable. For arguments other than
the first one, a tsibble, look at moving_average document.
aus_livestock %>%
tq_ma(n = 3)
#> # A tsibble: 29,364 x 4 [1M]
#> # Key: Animal, State [54]
#> Month Animal State Count
#> <mth> <fct> <fct> <dbl>
#> 1 1976 Jul Bulls, bullocks and steers Australian Capital Territory NA
#> 2 1976 Aug Bulls, bullocks and steers Australian Capital Territory NA
#> 3 1976 Sep Bulls, bullocks and steers Australian Capital Territory 2167.
#> 4 1976 Oct Bulls, bullocks and steers Australian Capital Territory 2033.
#> 5 1976 Nov Bulls, bullocks and steers Australian Capital Territory 2033.
#> 6 1976 Dec Bulls, bullocks and steers Australian Capital Territory 1933.
#> 7 1977 Jan Bulls, bullocks and steers Australian Capital Territory 1900
#> 8 1977 Feb Bulls, bullocks and steers Australian Capital Territory 1833.
#> 9 1977 Mar Bulls, bullocks and steers Australian Capital Territory 2133.
#> 10 1977 Apr Bulls, bullocks and steers Australian Capital Territory 2300
#> # … with 29,354 more rows
growth_rate and tq_gr: calculate growth rates
growth_rate function calculates growth rates. “n” specifies the lag.
“order_by” is optional, and works in the same way as in difference
function. For other arguments, refer to its document.
aus_arrivals %>%
group_by_key() %>%
mutate(
gr_yoy = growth_rate(Arrivals, n = 4),
gr_annualized = growth_rate(Arrivals, n = 1, annualize = 4),
gr_yoy_not_pct = growth_rate(Arrivals, n = 4, pct = FALSE)
)
#> # A tsibble: 508 x 6 [1Q]
#> # Key: Origin [4]
#> # Groups: Origin [4]
#> Quarter Origin Arrivals gr_yoy gr_annualized gr_yoy_not_pct
#> <qtr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 1981 Q1 Japan 14763 NA NA NA
#> 2 1981 Q2 Japan 9321 NA -84.1 NA
#> 3 1981 Q3 Japan 10166 NA 41.5 NA
#> 4 1981 Q4 Japan 19509 NA 1256. NA
#> 5 1982 Q1 Japan 17117 15.9 -40.7 0.159
#> 6 1982 Q2 Japan 10617 13.9 -85.2 0.139
#> 7 1982 Q3 Japan 11737 15.5 49.4 0.155
#> 8 1982 Q4 Japan 20961 7.44 917. 0.0744
#> 9 1983 Q1 Japan 20671 20.8 -5.42 0.208
#> 10 1983 Q2 Japan 12235 15.2 -87.7 0.152
#> # … with 498 more rows
tq_gr function transforms “Arrivals” variable in this example. For
arguments other than the first one, a tsibble, look at growth_rate
document.
aus_arrivals %>%
tq_gr(n = 4)
#> # A tsibble: 508 x 3 [1Q]
#> # Key: Origin [4]
#> Quarter Origin Arrivals
#> <qtr> <chr> <dbl>
#> 1 1981 Q1 Japan NA
#> 2 1981 Q2 Japan NA
#> 3 1981 Q3 Japan NA
#> 4 1981 Q4 Japan NA
#> 5 1982 Q1 Japan 15.9
#> 6 1982 Q2 Japan 13.9
#> 7 1982 Q3 Japan 15.5
#> 8 1982 Q4 Japan 7.44
#> 9 1983 Q1 Japan 20.8
#> 10 1983 Q2 Japan 15.2
#> # … with 498 more rows
season_adjust and tq_sa: calculate seasonally adjusted values
season_adjust function calculates seasonally adjusted values using
season package. It requires not just “x” argument, a numeric vector,
but also “order_by” argument, a time vector. For other arguments, refer
to season document.
aus_arrivals %>%
group_by_key() %>%
mutate(sa = season_adjust(Arrivals, Quarter)) %>%
ungroup()
#> # A tsibble: 508 x 4 [1Q]
#> # Key: Origin [4]
#> Quarter Origin Arrivals sa
#> <qtr> <chr> <int> <dbl>
#> 1 1981 Q1 Japan 14763 12179.
#> 2 1981 Q2 Japan 9321 12742.
#> 3 1981 Q3 Japan 10166 13107.
#> 4 1981 Q4 Japan 19509 14065.
#> 5 1982 Q1 Japan 17117 14004.
#> 6 1982 Q2 Japan 10617 14564.
#> 7 1982 Q3 Japan 11737 14909.
#> 8 1982 Q4 Japan 20961 15542.
#> 9 1983 Q1 Japan 20671 16868.
#> 10 1983 Q2 Japan 12235 16584.
#> # … with 498 more rows
tq_sa function transforms “Arrivals” variable in this example.
aus_arrivals %>%
tq_sa()
#> # A tsibble: 508 x 3 [1Q]
#> # Key: Origin [4]
#> Quarter Origin Arrivals
#> <qtr> <chr> <dbl>
#> 1 1981 Q1 Japan 12179.
#> 2 1981 Q2 Japan 12742.
#> 3 1981 Q3 Japan 13107.
#> 4 1981 Q4 Japan 14065.
#> 5 1982 Q1 Japan 14004.
#> 6 1982 Q2 Japan 14564.
#> 7 1982 Q3 Japan 14909.
#> 8 1982 Q4 Japan 15542.
#> 9 1983 Q1 Japan 16868.
#> 10 1983 Q2 Japan 16584.
#> # … with 498 more rows
tq_sa function returns right even if the rows are reshuffled, as
tq_diff, tq_ma and tq_gr functions do.
aus_arrivals %>%
slice(sample(nrow(aus_arrivals))) %>%
tq_sa() %>%
arrange(Origin, Quarter)
#> Warning: Current temporal ordering may yield unexpected results.
#> ℹ Suggest to sort by `Origin`, `Quarter` first.
#> Warning: Current temporal ordering may yield unexpected results.
#> ℹ Suggest to sort by `Origin`, `Quarter` first.
#> Warning: Current temporal ordering may yield unexpected results.
#> ℹ Suggest to sort by `Origin`, `Quarter` first.
#> Warning: Current temporal ordering may yield unexpected results.
#> ℹ Suggest to sort by `Origin`, `Quarter` first.
#> # A tsibble: 508 x 3 [1Q]
#> # Key: Origin [4]
#> Quarter Origin Arrivals
#> <qtr> <chr> <dbl>
#> 1 1981 Q1 Japan 12179.
#> 2 1981 Q2 Japan 12742.
#> 3 1981 Q3 Japan 13107.
#> 4 1981 Q4 Japan 14065.
#> 5 1982 Q1 Japan 14004.
#> 6 1982 Q2 Japan 14564.
#> 7 1982 Q3 Japan 14909.
#> 8 1982 Q4 Japan 15542.
#> 9 1983 Q1 Japan 16868.
#> 10 1983 Q2 Japan 16584.
#> # … with 498 more rows
EOL
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