Package 'panelWranglR'

Wrapper for find correlations

Description

Just a helper function for correl_panel.

Usage

corr_finder(df, corr_cutoff)

Arguments

df	The dataframe to use.
corr_cutoff	The correlation cutoff to pass to findCorrelations

Examples

X_1 <- rnorm(1000)
X_2 <- rnorm(1000) + 0.6 * X_1
X_3 <- rnorm(1000) - 0.4 * X_1

data_fm <- do.call( cbind, list( X_1,
                                 X_2,
                                 X_3 ))

corr_finder( df = data_fm,
             corr_cutoff = 0.3 )

---

Collect a panel, from wide to long

Description

Transforms cross sectional/time dummies to unified variables

Usage

panel_collect(data, cross.section = NULL, cross.section.columns = NULL,
  time.variable = NULL, time.variable.columns = NULL)

Arguments

data	The panel to transform
cross.section	The name of the transformed cross sectional variable supply as chracter.
cross.section.columns	The names of the columns indicating cross sections to collect.
time.variable	The name of the transformed time variable supply as character.
time.variable.columns	The names of the columns indicating time variables to collect.

Details

For time variables named like "Time_Var_i" with arbitrary i, the program will check that all time variables are named using this convention, and strip this convention

Value

A collected data.table, with new columns constructed by collecting from the wide format.

Examples

x_1 <- rnorm( 10 )
cross_levels <- c( "AT", "DE" )
time <- seq(1:5)
time <- rep(time, 2)
geo_list <- list()
for(i in 1:length(cross_levels))
{
  geo <- rep( cross_levels[i],
                100 )
                  geo_list[[i]] <- geo
                  }
                  geo <- unlist(geo_list)
                  geo <- as.data.frame(geo)

 example_data <- cbind( time,
                       x_1 )
 example_data <- as.data.frame(example_data)

 example_data <- cbind( geo,
                       example_data)
 names(example_data) <- c("geo", "time", "x_1")

# generate dummies using panel_dummify()
 test_dummies <- panel_dummify( data = example_data,
                                cross.section = "geo",
                                time.variable = "time")
panel_collect( data = test_dummies,
               cross.section = "geo",
               cross.section.columns = c( "AT", "DE"))

---

Panel linear combinations

Description

A function to find highly correlated variables in a panel of data, both by cross sections and by time dummies.

Usage

panel_correl(data, cross.section = NULL, time.variable = NULL,
  corr.threshold = 0.7, autocorr.threshold = 0.5,
  cross.threshold = 0.7, select.cross.sections = NULL,
  select.time.periods = NULL)

Arguments

data	The data to use, a data.frame or a data.table.
cross.section	The name of the cross sectional variable.
time.variable	The name of the time variable.
corr.threshold	The correlation threshold for finding significant correlations in the base specification, disregarding time or cross sectional dependencies.
autocorr.threshold	The correlation threshold for autocorrelation (splitting the pooled panel into cross sections).
cross.threshold	The correlation threshold for finding significant correlations in the cross sections.
select.cross.sections	An optional subset of cross sectional units.
select.time.periods	An optional subset of time periods

Examples

x_1 <- rnorm( 100 )
   x_2 <- rnorm( 100 ) + 0.5 * x_1
   cross_levels <- c( "AT", "DE")
   time <- seq(1:50)
   time <- rep(time, 2)
   geo_list <- list()
   for(i in 1:length(cross_levels))
   {  geo <- rep( cross_levels[i], 50 )
      geo_list[[i]] <- geo }
   geo <- unlist(geo_list)
   geo <- as.data.frame(geo)

   example_data <-  do.call ( cbind, list( time, x_1, x_2))
   example_data <- as.data.frame(example_data)
   example_data <- cbind( geo,
                         example_data)

                         names(example_data) <- c("geo", "time", "x_1",
                                                 "x_2")

   panel_correl( data = example_data,
                 cross.section = "geo",
                 time.variable = "time",
                 corr.threshold = 0.2,
                 autocorr.threshold = 0.5,
                 cross.threshold = 0.1)

---

Tidy panel differencing

Description

Efficient, tidy panel differencing

Usage

panel_diff(data, cross.section, time.variable = NULL, diff.order = 1,
  lags = 1, variables.selected = NULL, keep.original = FALSE)

Arguments

data	The data input, anything coercible to a data.table.
cross.section	The cross section argument, see examples.
time.variable	The variable to indicate time in your panel. Defaults to NULL, though it is recommended to have a time variable.
diff.order	The number of applications of the difference operator to use in panel differencing. Defaults to 1.
lags	The number of lags to use for differences. Defaults to 1.
variables.selected	A variable selection for variables to difference, defaults to NULL and differences ALL variables.
keep.original	Whether to keep the original undifferenced data, defaults to FALSE.

Details

Works on a full data.table backend for maximum speed wherever possible.

Value

The differenced data.table which contains either only the differenced variables, or also the original variables.

Examples

X <- matrix(rnorm(4000),800,5)
tim <- seq(1:400)
geo_AT <- rep(c("AT"), length = 400)
geo_NO <- rep(c("NO"), length = 400)
both_vec_1 <- cbind(tim,geo_NO)
both_vec_2 <- cbind(tim,geo_AT)
both <- rbind(both_vec_1,both_vec_2)
names(both[,"geo_NO"]) <- "geo"
X <- cbind(both,X)

panel_diff(data = X,
           cross.section = "geo_NO",
           time.variable = "tim",
           diff.order = 1,
           lags = 1,
           variables.selected = c("V3","V4"),
           keep.original = TRUE)

---

Tidy time/variable dummies for panel data

Description

A simple function to dummify cross sections or time variables in panel data.

Usage

panel_dummify(data, cross.section = NULL, time.variable = NULL)

Arguments

data	The panel to dummify
cross.section	The cross section variable in the panel. Defaults to NULL.
time.variable	The variable to indicate time in your panel. Defaults to NULL.

Details

The encoding is binary, whether this is more appropriate than using a factor variable is up to the user.

Value

A new data.table, with the original variables to dummify removed, and new dummy columns included.

Examples

x_1 <- rnorm( 10 )
cross_levels <- c( "AT", "DE" )
time <- seq(1:5)
time <- rep(time, 2)
geo_list <- list()
for(i in 1:length(cross_levels))
{
  geo <- rep( cross_levels[i],
                100 )
                  geo_list[[i]] <- geo
                  }
                  geo <- unlist(geo_list)
                  geo <- as.data.frame(geo)

 example_data <- cbind( time,
                        x_1 )
 example_data <- as.data.frame(example_data)

 example_data <- cbind( geo,
                        example_data)
 names(example_data) <- c("geo", "time", "x_1")

 test_dummies <- panel_dummify( data = example_data,
                                cross.section = "geo",
                                time.variable = "time")

---

Tidy panel lagging

Description

Efficient, tidy panel lagging

Usage

panel_lag(data, cross.section, time.variable = NULL, lags = 1,
  variables.selected = NULL, keep.original = TRUE)

Arguments

data	The data input, anything coercible to a data.table.
cross.section	The cross section argument, see examples.
time.variable	The variable to indicate time in your panel. Defaults to NULL, though it is recommended to have a time variable.
lags	The lags to use in panel lagging.
variables.selected	A variable selection for variables to lag, defaults to NULL and lags ALL variables.
keep.original	Whether to keep the original unlagged data, defaults to TRUE.

Details

Works on a full data.table backend for maximum speed wherever possible.

Value

The lagged data.table which contains either only the lagged variables, or also the original variables.

Examples

X <- matrix(rnorm(4000),800,5)
tim <- seq(1:400)
geo_AT <- rep(c("AT"), length = 400)
geo_NO <- rep(c("NO"), length = 400)
both_vec_1 <- cbind(tim,geo_NO)
both_vec_2 <- cbind(tim,geo_AT)
both <- rbind(both_vec_1,both_vec_2)
names(both[,"geo_NO"]) <- "geo"
X <- cbind(both,X)

panel_lag(data = X,
          cross.section = "geo_NO",
          time.variable = "tim",
          lags = 5,
          variables.selected = c("V5","tim", "V7"),
          keep.original = TRUE)

---

Tidy panel leading

Description

Efficient, tidy panel leading

Usage

panel_lead(data, cross.section, time.variable = NULL, leads = 1,
  variables.selected = NULL, keep.original = TRUE)

Arguments

data	The data input, anything coercible to a data.table.
cross.section	The cross section argument, see examples.
time.variable	The variable to indicate time in your panel. Defaults to NULL, though it is recommended to have a time variable.
leads	The leads to use in panel leading.
variables.selected	A variable selection for variables to lead, defaults to NULL and leads ALL variables.
keep.original	Whether to keep the original unleadged data, defaults to TRUE.

Details

Works on a full data.table backend for maximum speed wherever possible.

Value

The leading data.table which contains either only the leading variables, or also the original variables.

Examples

X <- matrix(rnorm(4000),800,5)
tim <- seq(1:400)
geo_AT <- rep(c("AT"), length = 400)
geo_NO <- rep(c("NO"), length = 400)
both_vec_1 <- cbind(tim,geo_NO)
both_vec_2 <- cbind(tim,geo_AT)
both <- rbind(both_vec_1,both_vec_2)
names(both[,"geo_NO"]) <- "geo"
X <- cbind(both,X)

panel_lead(data = X,
          cross.section = "geo_NO",
          time.variable = "tim",
          leads = 5,
          variables.selected = c("V5","tim", "V7"),
          keep.original = TRUE)

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