pp_clean | R Documentation |
Clean a policy portfolio dataset into a tidy object.
pp_clean( d, Sector = NULL, Country.name = "Country", Year.name = "Year", Instrument.name = "Instrument", Target.name = "Target", coding.category.name = "Coding category", coding.category = 2, Direction.name = "Direction", directions = c(0, 1, -1), associated.vars = NULL, date = FALSE, debug = FALSE )
d |
Data frame in an uncleaned and untidy structure containing data from a policy portfolio. |
Sector |
Character vector with the Sector of the dataset. |
Country.name |
Character vector of length one with the name of the variable that contains the country name. |
Year.name |
Character vector of length one with the name of the variable that contains the year. |
Instrument.name |
Character vector of length one with the name of the variable that contains the instruments. |
Target.name |
Character vector of length one with the name of the variable that contains the targets. |
coding.category.name |
Character vector of length one with the name of the variable that contains the coding category. |
coding.category |
Numerical value with the level of the category that captures the combination of instrument and target. |
Direction.name |
Character vector of length one with the name of the variable that contains the direction of the policy change. |
directions |
Numerical vector with the numeric values of the direction of the policy changes, namely "Status quo", "Expansion" and "Dismantling". Defaults to, 0, 1 and -1, respectively. |
associated.vars |
Character vector indicating variables that contain characteristics of the policy space. |
date |
By default, return Year as the only time indicator. If TRUE, return the full date with dd-mm-YYYY. |
debug |
Logical value. When TRUE, print more verbose information about the cleaning process. |
D Data frame in a tidy format with the following columns: "Country", "Sector", "Year", "Instrument", "Target" and "covered". "covered" is a binary identificator of whether the portfolio space is covered by policy intervention (1) or not (0). The remaining columns identify the case. Notice that "Year" is a numeric value, while the remaining 4 case identifiers are factors.
## Not run: X <- read.table("raw_data.csv", header = TRUE) D <- pp_clean(X, Sector = "Education") # Now 'D' is a tidy dataset suitable for being used in the context of the 'PolicyPortfolio' package. ## End(Not run)
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