README.md
In MjStansfi/GEKS_package: Calculate GEKS Indexes with Various Splice Options
GEKS
This package is a modification of the FEWS
package. Where the
individual fixed effects calculation occurs, it has been replaced by a
(modified to include imputed-tornqvist and jevons) GEKS calculation from
IndexNumR: A Package for Index Number
Calculation.
-
To see the original FEWS package written by Donal Lynch go
here
-
To see the complete IndexNumR package written by Graham White go
here
From the IndexNumR package it uses:
-
GEKS_w - modified
-
GEKSIndex - modified
-
utils
-
fisher_t
-
fixed_t
-
tornqvist_t
-
jevons_t
-
ITRYGEKS_t - newly introduced
Installation
GEKS is still in development. For now it can be installed from GitHub
using the following code
devtools::install_github("MjStansfi/GEKS_package")
#Other suggestions
# devtools::install_github("MjStansfi/FEWS_package")
# devtools::install_github("MjStansfi/TPDdecomp")
# devtools::install_github("grahamjwhite/IndexNumR")
# Once installed, the package can be loaded as usual
library(GEKS)
library(dplyr)
library(ggplot2)
Usage
The primary function provided by the GEKS package is the GEKS()
function. Running ?GEKS() should give all the required information on
how using the function. An example of running the GEKS() the function
is shown below.
Example
As part of the package, a couple of datasets are provided, including the
Turvery dataset as found in the Consumer Price Index
Manual.
ggplot(turvey, aes(x = month, y = price)) +
geom_line(aes(color = commodity)) +
geom_point(aes(color = commodity))+
theme_bw() +
ggtitle("Artificial prices of seasonal products Data created by R. Turvey")
The GEKS is calculated below with a mean splice and a window length of
13 months. Note the difference being the FEWS function takes ‘weights’
while the GEKS function takes quantities.
turvey_GEKS <- GEKS(times = turvey$month,
price = turvey$price,
quantity = turvey$quantity,
id = turvey$commodity,
window_length = 13,
splice_pos = "mean",
index_method = "tornqvist",
num_cores = NULL)
The resulting index is displayed below
ggplot(turvey_GEKS$geks, aes(x = price_date, y = fe_indexes)) +
geom_line() +
theme_bw() +
ggtitle("GEKS with mean splice for Turvey data")+
ylab("Price Index") +
xlab("Date")
We can compare the FEWS and GEKS as follows
turvey_FEWS <-FEWS(times = turvey$month,
logprice = log(turvey$price),
id = turvey$commodity,
window_length = 13,
weight = turvey$price * turvey$quantity,
splice_pos = "geomean",
num_cores = NULL)
The resulting index is displayed below
ggplot(turvey_FEWS$fews, aes(x = price_date, y = fe_indexes, colour = "FEWS")) +
geom_line() +
geom_line(aes(x =turvey_GEKS$geks$price_date, y= turvey_GEKS$geks$fe_indexes, colour = "GEKS-tornqvist"))+
theme_bw() +
ggtitle("Different multilateral, with mean splice for Turvey data")+
ylab("Price Index") +
xlab("Date")
Imputation Törnqvist rolling year GEKS (ITRYGEKS)
Key functions in this package have been modified to calculate a
Imputation Törnqvist rolling year GEKS (ITRYGEKS) index.
For this to work you need to employ the ‘features’ parameter of the GEKS
function. Below we use this feature to calculate an index based on
synthetic GfK data.
str(synthetic_gfk)
#> 'data.frame': 5509 obs. of 15 variables:
#> $ month_num : int 0 1 2 3 4 5 6 7 8 9 ...
#> $ char11 : Factor w/ 10 levels "brand_a","brand_b",..: 1 1 1 1 1 1 1 1 1 1 ...
#> $ char1 : num 10.6 10.6 10.6 10.6 10.6 10.6 10.6 10.6 10.6 10.6 ...
#> $ char2 : int 16006 16006 16006 16006 16006 16006 16006 16006 16006 16006 ...
#> $ char3 : Factor w/ 19 levels "val_a","val_b",..: 18 18 18 18 18 18 18 18 18 18 ...
#> $ char4 : Factor w/ 4 levels "val_a","val_b",..: 1 1 1 1 1 1 1 1 1 1 ...
#> $ char5 : Factor w/ 5 levels "val_a","val_b",..: 1 1 1 1 1 1 1 1 1 1 ...
#> $ char6 : Factor w/ 88 levels "FBOK6552","GR3000",..: 49 49 49 49 49 49 49 49 49 49 ...
#> $ char7 : Factor w/ 3 levels "AAA","BBB","CCC": 3 3 3 3 3 3 3 3 3 3 ...
#> $ char8 : Factor w/ 2 levels "100M","150D": 2 2 2 2 2 2 2 2 2 2 ...
#> $ char9 : Factor w/ 4 levels "A100","A450",..: 3 3 3 3 3 3 3 3 3 3 ...
#> $ char10 : Factor w/ 5 levels "abb","bbb","bhy",..: 5 5 5 5 5 5 5 5 5 5 ...
#> $ prodid_num: int 3 3 3 3 3 3 3 3 3 3 ...
#> $ quantity : int 280 126 148 56 69 43 22 21 9 17 ...
#> $ value : int 196420 85312 95920 38552 47397 28303 14812 13701 6304 10651 ...
Now we wrangle this into the necessary format
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
#Ensure that for each period there is only 1 observation of a given product
#ie sum all quantity and value for given month/product
synthetic_gfk <- synthetic_gfk%>%
group_by(month_num,prodid_num)%>%
mutate(quantity = sum(quantity),
value = sum(value))%>%
ungroup()%>%
unique
#Calculate the unit value (price)
synthetic_gfk$uv <- synthetic_gfk$value/synthetic_gfk$quantity
#Extract data.frame containing features of interest
features <-synthetic_gfk[,grepl("char",colnames(synthetic_gfk))]
Once the dataframe is in the correct format we can run it through the
main function with index_method set to ‘impute-tornqvist’.
ITRYGEKS_index <- GEKS(times = synthetic_gfk$month_num,
price = synthetic_gfk$uv,
quantity = synthetic_gfk$quantity,
id = synthetic_gfk$prodid_num,
features = features,
window_length = 13,
splice_pos = "mean",
index_method = "impute-tornqvist",
num_cores = NULL)
#> Number of windows: 14
#> | | | 0% | |===== | 7% | |========= | 14% | |============== | 21% | |=================== | 29% | |======================= | 36% | |============================ | 43% | |================================ | 50% | |===================================== | 57% | |========================================== | 64% | |============================================== | 71% | |=================================================== | 79% | |======================================================== | 86% | |============================================================ | 93% | |=================================================================| 100%
#>
#> FE model complete. Splicing results together
#>
#> Finished. It took 34.79 seconds
including_first_window <- c(ITRYGEKS_index$fixed_effects$fe_indexes[1:12], #Take first 12 observations
ITRYGEKS_index$geks$fe_indexes*ITRYGEKS_index$fixed_effects$fe_indexes[13]) #Manually splice on at position 13(window length)
Lets run a standard tornqvist GEKS and FEWS to compare
GEKS_index <- GEKS(times = synthetic_gfk$month_num,
price = synthetic_gfk$uv,
quantity = synthetic_gfk$quantity,
id = synthetic_gfk$prodid_num,
features = NULL,
window_length = 13,
splice_pos = "geomean",
index_method = "tornqvist",
num_cores = NULL)
#> Number of windows: 14
#> | | | 0% | |===== | 7% | |========= | 14% | |============== | 21% | |=================== | 29% | |======================= | 36% | |============================ | 43% | |================================ | 50% | |===================================== | 57% | |========================================== | 64% | |============================================== | 71% | |=================================================== | 79% | |======================================================== | 86% | |============================================================ | 93% | |=================================================================| 100%
#>
#> FE model complete. Splicing results together
#>
#> Finished. It took 0.8 seconds
FEWS_index <- FEWS(times = synthetic_gfk$month_num,
logprice = log(synthetic_gfk$uv),
id = synthetic_gfk$prodid_num,
window_length = 13,
weight = synthetic_gfk$value,
splice_pos = "geomean",
num_cores = NULL)
#> Number of windows: 14
#> | | | 0% | |===== | 7% | |========= | 14% | |============== | 21% | |=================== | 29% | |======================= | 36% | |============================ | 43% | |================================ | 50% | |===================================== | 57% | |========================================== | 64% | |============================================== | 71% | |=================================================== | 79% | |======================================================== | 86% | |============================================================ | 93% | |=================================================================| 100%
#>
#> FE model complete. Splicing results together
#>
#> Finished. It took 0.65 seconds
ggplot(ITRYGEKS_index$geks, aes(x =ITRYGEKS_index$geks$price_date, y = ITRYGEKS_index$geks$fe_indexes, colour = "ITRYGEKS")) +
geom_line() +
geom_line(aes(x =GEKS_index$geks$price_date, y= GEKS_index$geks$fe_indexes, colour = "GEKS-tornqvist"))+
geom_line(aes(x =FEWS_index$fews$price_date, y= FEWS_index$fews$fe_indexes, colour = "FEWS"))+
theme_bw() +
ggtitle("Different multilateral, with mean splice for synthetic Gfk data")+
ylab("Price Index") +
xlab("Date")
MjStansfi/GEKS_package documentation built on May 12, 2021, 8:44 p.m.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
GEKS
This package is a modification of the FEWS package. Where the individual fixed effects calculation occurs, it has been replaced by a (modified to include imputed-tornqvist and jevons) GEKS calculation from IndexNumR: A Package for Index Number Calculation.
-
To see the original FEWS package written by Donal Lynch go here
-
To see the complete IndexNumR package written by Graham White go here
From the IndexNumR package it uses:
-
GEKS_w - modified
-
GEKSIndex - modified
-
utils
-
fisher_t
-
fixed_t
-
tornqvist_t
-
jevons_t
-
ITRYGEKS_t - newly introduced
Installation
GEKS is still in development. For now it can be installed from GitHub using the following code
devtools::install_github("MjStansfi/GEKS_package")
#Other suggestions
# devtools::install_github("MjStansfi/FEWS_package")
# devtools::install_github("MjStansfi/TPDdecomp")
# devtools::install_github("grahamjwhite/IndexNumR")
# Once installed, the package can be loaded as usual
library(GEKS)
library(dplyr)
library(ggplot2)
Usage
The primary function provided by the GEKS package is the GEKS()
function. Running ?GEKS() should give all the required information on
how using the function. An example of running the GEKS() the function
is shown below.
Example
As part of the package, a couple of datasets are provided, including the Turvery dataset as found in the Consumer Price Index Manual.
ggplot(turvey, aes(x = month, y = price)) +
geom_line(aes(color = commodity)) +
geom_point(aes(color = commodity))+
theme_bw() +
ggtitle("Artificial prices of seasonal products Data created by R. Turvey")
The GEKS is calculated below with a mean splice and a window length of 13 months. Note the difference being the FEWS function takes ‘weights’ while the GEKS function takes quantities.
turvey_GEKS <- GEKS(times = turvey$month,
price = turvey$price,
quantity = turvey$quantity,
id = turvey$commodity,
window_length = 13,
splice_pos = "mean",
index_method = "tornqvist",
num_cores = NULL)
The resulting index is displayed below
ggplot(turvey_GEKS$geks, aes(x = price_date, y = fe_indexes)) +
geom_line() +
theme_bw() +
ggtitle("GEKS with mean splice for Turvey data")+
ylab("Price Index") +
xlab("Date")
We can compare the FEWS and GEKS as follows
turvey_FEWS <-FEWS(times = turvey$month,
logprice = log(turvey$price),
id = turvey$commodity,
window_length = 13,
weight = turvey$price * turvey$quantity,
splice_pos = "geomean",
num_cores = NULL)
The resulting index is displayed below
ggplot(turvey_FEWS$fews, aes(x = price_date, y = fe_indexes, colour = "FEWS")) +
geom_line() +
geom_line(aes(x =turvey_GEKS$geks$price_date, y= turvey_GEKS$geks$fe_indexes, colour = "GEKS-tornqvist"))+
theme_bw() +
ggtitle("Different multilateral, with mean splice for Turvey data")+
ylab("Price Index") +
xlab("Date")
Imputation Törnqvist rolling year GEKS (ITRYGEKS)
Key functions in this package have been modified to calculate a Imputation Törnqvist rolling year GEKS (ITRYGEKS) index.
For this to work you need to employ the ‘features’ parameter of the GEKS function. Below we use this feature to calculate an index based on synthetic GfK data.
str(synthetic_gfk)
#> 'data.frame': 5509 obs. of 15 variables:
#> $ month_num : int 0 1 2 3 4 5 6 7 8 9 ...
#> $ char11 : Factor w/ 10 levels "brand_a","brand_b",..: 1 1 1 1 1 1 1 1 1 1 ...
#> $ char1 : num 10.6 10.6 10.6 10.6 10.6 10.6 10.6 10.6 10.6 10.6 ...
#> $ char2 : int 16006 16006 16006 16006 16006 16006 16006 16006 16006 16006 ...
#> $ char3 : Factor w/ 19 levels "val_a","val_b",..: 18 18 18 18 18 18 18 18 18 18 ...
#> $ char4 : Factor w/ 4 levels "val_a","val_b",..: 1 1 1 1 1 1 1 1 1 1 ...
#> $ char5 : Factor w/ 5 levels "val_a","val_b",..: 1 1 1 1 1 1 1 1 1 1 ...
#> $ char6 : Factor w/ 88 levels "FBOK6552","GR3000",..: 49 49 49 49 49 49 49 49 49 49 ...
#> $ char7 : Factor w/ 3 levels "AAA","BBB","CCC": 3 3 3 3 3 3 3 3 3 3 ...
#> $ char8 : Factor w/ 2 levels "100M","150D": 2 2 2 2 2 2 2 2 2 2 ...
#> $ char9 : Factor w/ 4 levels "A100","A450",..: 3 3 3 3 3 3 3 3 3 3 ...
#> $ char10 : Factor w/ 5 levels "abb","bbb","bhy",..: 5 5 5 5 5 5 5 5 5 5 ...
#> $ prodid_num: int 3 3 3 3 3 3 3 3 3 3 ...
#> $ quantity : int 280 126 148 56 69 43 22 21 9 17 ...
#> $ value : int 196420 85312 95920 38552 47397 28303 14812 13701 6304 10651 ...
Now we wrangle this into the necessary format
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
#Ensure that for each period there is only 1 observation of a given product
#ie sum all quantity and value for given month/product
synthetic_gfk <- synthetic_gfk%>%
group_by(month_num,prodid_num)%>%
mutate(quantity = sum(quantity),
value = sum(value))%>%
ungroup()%>%
unique
#Calculate the unit value (price)
synthetic_gfk$uv <- synthetic_gfk$value/synthetic_gfk$quantity
#Extract data.frame containing features of interest
features <-synthetic_gfk[,grepl("char",colnames(synthetic_gfk))]
Once the dataframe is in the correct format we can run it through the main function with index_method set to ‘impute-tornqvist’.
ITRYGEKS_index <- GEKS(times = synthetic_gfk$month_num,
price = synthetic_gfk$uv,
quantity = synthetic_gfk$quantity,
id = synthetic_gfk$prodid_num,
features = features,
window_length = 13,
splice_pos = "mean",
index_method = "impute-tornqvist",
num_cores = NULL)
#> Number of windows: 14
#> | | | 0% | |===== | 7% | |========= | 14% | |============== | 21% | |=================== | 29% | |======================= | 36% | |============================ | 43% | |================================ | 50% | |===================================== | 57% | |========================================== | 64% | |============================================== | 71% | |=================================================== | 79% | |======================================================== | 86% | |============================================================ | 93% | |=================================================================| 100%
#>
#> FE model complete. Splicing results together
#>
#> Finished. It took 34.79 seconds
including_first_window <- c(ITRYGEKS_index$fixed_effects$fe_indexes[1:12], #Take first 12 observations
ITRYGEKS_index$geks$fe_indexes*ITRYGEKS_index$fixed_effects$fe_indexes[13]) #Manually splice on at position 13(window length)
Lets run a standard tornqvist GEKS and FEWS to compare
GEKS_index <- GEKS(times = synthetic_gfk$month_num,
price = synthetic_gfk$uv,
quantity = synthetic_gfk$quantity,
id = synthetic_gfk$prodid_num,
features = NULL,
window_length = 13,
splice_pos = "geomean",
index_method = "tornqvist",
num_cores = NULL)
#> Number of windows: 14
#> | | | 0% | |===== | 7% | |========= | 14% | |============== | 21% | |=================== | 29% | |======================= | 36% | |============================ | 43% | |================================ | 50% | |===================================== | 57% | |========================================== | 64% | |============================================== | 71% | |=================================================== | 79% | |======================================================== | 86% | |============================================================ | 93% | |=================================================================| 100%
#>
#> FE model complete. Splicing results together
#>
#> Finished. It took 0.8 seconds
FEWS_index <- FEWS(times = synthetic_gfk$month_num,
logprice = log(synthetic_gfk$uv),
id = synthetic_gfk$prodid_num,
window_length = 13,
weight = synthetic_gfk$value,
splice_pos = "geomean",
num_cores = NULL)
#> Number of windows: 14
#> | | | 0% | |===== | 7% | |========= | 14% | |============== | 21% | |=================== | 29% | |======================= | 36% | |============================ | 43% | |================================ | 50% | |===================================== | 57% | |========================================== | 64% | |============================================== | 71% | |=================================================== | 79% | |======================================================== | 86% | |============================================================ | 93% | |=================================================================| 100%
#>
#> FE model complete. Splicing results together
#>
#> Finished. It took 0.65 seconds
ggplot(ITRYGEKS_index$geks, aes(x =ITRYGEKS_index$geks$price_date, y = ITRYGEKS_index$geks$fe_indexes, colour = "ITRYGEKS")) +
geom_line() +
geom_line(aes(x =GEKS_index$geks$price_date, y= GEKS_index$geks$fe_indexes, colour = "GEKS-tornqvist"))+
geom_line(aes(x =FEWS_index$fews$price_date, y= FEWS_index$fews$fe_indexes, colour = "FEWS"))+
theme_bw() +
ggtitle("Different multilateral, with mean splice for synthetic Gfk data")+
ylab("Price Index") +
xlab("Date")
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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