Nothing
R/get_cspp_data.R
In cspp: A Tool for the Correlates of State Policy Project Data
Defines functions
get_cspp_data
Documented in
get_cspp_data
#' Load CSPP data into the R environment
#'
#' \code{get_cspp_data} loads either a full or subsetted version of the full
#' CSPP dataset into the R environment as a dataframe.
#'
#'@name get_cspp_data
#'
#' @param vars Default is NULL. If left blank, returns all variables within the
#' dataset. Takes a string or vector of strings. See
#' \code{\link{get_var_info}} for pulling variable names and
#' \code{\link{get_cites}} for citations of specific variables and datasets.
#' Names of variables must be exact matches to variables in the dataset.
#' @param var_category Default is NA. If left blank, returns all datasets. Takes
#' a string or vector of strings.
#'
#' Options are one of, or a combination of: "demographics", "economic-fiscal",
#' "government", "elections", "policy_ideology", "criminal justice",
#' "education", "healthcare", "welfare", "rights", "environment",
#' "drug-alcohol", "gun control", "labor", "transportation", "misc.
#' regulation"
#' @param states Default is NULL. If left blank, returns all states. Takes a
#' string or vector of strings of state abbreviations. Use \code{state.abb} to
#' load state abbreviations into the R environment.
#' @param years Default is NULL. If left blank, returns all years. Coverage
#' begins at 1900 and runs to 2019. However, coverage depends on the specific
#' variable -- see \code{get_var_info}.
#'
#' Input can be a vector of years (or a singular year), such as c(2000, 2001,
#' 2002, 2012) or seq(2000, 2012).
#' @param core Default is FALSE. If TRUE, merge the core CSPP data (approximately 70 common and important variables) with the search result.
#' @param output Default is NULL. One of "csv", "dta", "rdata". Optional
#' parameter for writing the resulting dataframe to a file.
#' @param path The directory to write the file to. Default is blank, so writes to
#' working directory. Exclude final slash: e.g., \code{path = "dir1/dir2"}
#'
#' @seealso \code{\link{get_var_info}}, \code{\link{get_cites}}, \code{\link{generate_map}}
#'
#' @importFrom dplyr "%>%" filter arrange left_join full_join bind_rows group_by
#' if_else mutate distinct rename n summarize
#' @importFrom tidyselect all_of any_of
#'
#' @export
#'
#' @examples
#'
#' ## returns full dataset
#' data <- get_cspp_data()
#'
#' ## use variable names from get_var_info
#' data <- get_cspp_data(vars = get_var_info(var_names="pctpop")$variable)
#'
#' ## return subsets
#' # note: this returns the specific variables listed as well as those in the
#' # var_category argument
#' data <- get_cspp_data(vars = c("sess_length", "hou_majority", "term_length"),
#' var_category = "demographics",
#' states = c("NC", "VA", "GA"),
#' years = seq(1995, 2004))
#'
#'
get_cspp_data <- function(vars = NULL,
var_category = NULL,
states = NULL,
years = NULL,
core = FALSE,
output = NULL,
path = ""){
correlates <- csppData::correlates
codebook <- csppData::codebook
data <- correlates
#---- vars ----
if(!is.null(vars) & is.null(var_category)) {
# check user input
if(!all(vars %in% codebook$variable)) {
bad <- vars[!(vars %in% codebook$variable)]
message(paste("Bad variable(s): ", paste(bad, collapse=", "), sep=" "))
stop("Invalid variable name. Use get_var_info to select variables.")
} else {
data <- data %>%
dplyr::select(tidyselect::all_of(c("st","stateno","state","state_fips",
"state_icpsr","year")),
tidyselect::all_of(vars))
}
}
#---- category ----
if(!is.null(var_category) & is.null(vars)) {
# check user input
if(!(all(var_category %in% unique(codebook$category)))) {
message(paste("Bad category: ", paste(var_category[!(var_category %in% unique(codebook$category))], collapse=", "), sep=" "))
stop("Invalid category name.")
} else {
var_cat <- codebook %>%
dplyr::filter(category %in% var_category)
data <- data %>%
dplyr::select(tidyselect::all_of(c("st","stateno","state","state_fips",
"state_icpsr","year")),
tidyselect::all_of(var_cat$variable))
}
}
#---- when both category and vars take values ----
# need this check so as not
# to double filter functionally, this means the returned dataframe can result
# in variables NOT in the category the user specified
if(!is.null(var_category) & !is.null(vars)) {
# check user input
if(!(all(var_category %in% unique(codebook$category)))) {
message(paste("Bad category: ", paste(var_category[!(var_category %in% unique(codebook$category))], collapse=", "), sep=" "))
stop("Invalid category name.")
}
if(!all(vars %in% codebook$variable)) {
bad <- vars[!(vars %in% codebook$variable)]
message(paste("Bad variable(s): ", paste(bad, collapse=", "), sep=" "))
stop("Invalid variable name. Use get_var_info to select variables.")
}
var_cat <- codebook %>%
dplyr::filter(category %in% var_category)
var_cat <- c(var_cat$variable, vars)
data <- data %>%
dplyr::select(tidyselect::all_of(c("st","stateno","state","state_fips",
"state_icpsr","year")),
tidyselect::all_of(var_cat))
}
#---- states ----
if(!is.null(states)) {
states <- stringr::str_to_upper(states)
# check user input:
if(!all((states %in% state.abb))) {
message(paste("Bad state(s): ", paste(states[!(states %in% unique(correlates$st))], collapse=", "), sep=" "))
stop("Invalid state abbreviation(s).")
} else {
data <- data %>%
dplyr::filter(st %in% states) %>%
dplyr::arrange(state)
}
}
#---- years ----
if(!is.null(years)) {
# check user input
if(!all(years %in% seq(1900, 2020))) {
stop("Years must be within seq(1900, 2020)")
} else {
data <- data %>%
dplyr::filter(year %in% years)
}
}
# stop if empty dataframe
if(nrow(data) == 0) {
stop("Your request returned an empty dataframe.")
} else {
if(is.null(output) & path != "") {
message("File not written: output cannot be empty.")
}
if(!is.null(output)) {
if(path != "") {
path <- paste(path, "/", sep="")
}
if(!(output %in% c("dta", "csv", "rdata"))) {
stop("Value must be one of 'dta', 'csv', 'rdata'.")
}
if(output == "dta") {
names(data) <- gsub("-|\\.|\\/|'|\\[|\\]", "", names(data))
names(data) <- gsub(" ", "_", names(data))
haven::write_dta(data, paste(path, "cspp_data.dta", sep=""))
message("File saved to ", paste(path, "cspp_data.dta", sep=""))
}
if(output == "csv") {
readr::write_csv(data, paste(path, "cspp_data.csv", sep=""))
message("File saved to ", paste(path, "cspp_data.dta", sep=""))
}
if(output == "rdata") {
save(data, file = paste(path, "cspp_data.RData", sep=""))
message("File saved to ", paste(path, "cspp_data.dta", sep=""))
}
}
# check if any of the variables have additional footnotes in the codebook:
note_vars <- c("bfh_cpi_multiplier", "gov_fin_fy", "housing_prices_quar",
"noofvotes", "cartheftrate", "carthefttotal",
"murderrate", "murdertotal", "propcrimerate",
"propcrimetotal", "raperate", "rapetotal",
"bus_energy_consum", "bus_energy_consum_pc")
if(length(names(data)[names(data) %in% note_vars]) > 0) {
message("Note: the following variables have additional footnotes in the codebook (https://ippsr.msu.edu/sites/default/files/CorrelatesCodebook.pdf):\n",
paste(names(data)[names(data) %in% note_vars], collapse = ", "))
}
if(core){
core_data_vars <- c("year"
,"st"
,"stateno"
,"state"
,"state_fips"
,"state_icpsr"
,"poptotal"
,"evangelical_pop"
,"nonwhite"
,"gini_coef"
,"region"
,"total_expenditure"
,"total_revenue"
,"gsppcap"
,"incomepcap"
,"unemployment"
,"povrate"
,"earned_income_taxcredit"
,"x_sales_taxes"
,"x_tax_burden"
,"x_top_corporateincometaxrate"
,"prez_election_year"
,"hou_chamber"
,"sen_chamber"
,"h_diffs"
,"s_diffs"
,"inst6014_nom"
,"bowen_legprof_firstdim"
,"bowen_legprof_seconddim"
,"speaker_power"
,"pctfemaleleg"
,"efna_index"
,"vepvotingrate"
,"ranney4_control"
,"overall_fin_reg"
,"oelecsc"
,"opartsc"
,"nonindiv_contrib"
,"union_density"
,"pollib_median"
,"innovatescore_boehmkeskinner"
,"soc_capital_ma"
,"citi6013"
,"wpid"
,"mood"
,"anti_environment"
,"anti_aid"
,"anti_defense"
,"anti_education"
,"anti_welfare"
,"anti_race"
,"death_penalty"
,"guncontrol_stand_your_ground"
,"vcrimerate"
,"z_education_expenditures_per_pup"
,"mathscore4th"
,"readscore4th"
,"sqli"
,"health_rank"
,"genderrights_state_eras"
,"infconsent"
,"w_ec_access"
,"solaw"
,"vaaban"
,"w_environment_solar_taxcredit"
,"frps"
,"drugs_medical_marijuana"
,"gambling_lottery_adoption"
,"guncontrol_assaultweapon_ban"
,"guncontrol_opencarry"
,"labor_minwage_abovefed"
,"labor_right_to_work"
)
core <- correlates[, colnames(correlates) %in% core_data_vars]
# core <- core %>%
# dplyr::rename(st.abb = st)
return(left_join(data, core,
# use 'by' to suppress the dplyr joining message
by = intersect(colnames(data),
colnames(core)
)
)
)
} else {
return(data)
}
}
}
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Defines functions get_cspp_data
Documented in get_cspp_data
#' Load CSPP data into the R environment
#'
#' \code{get_cspp_data} loads either a full or subsetted version of the full
#' CSPP dataset into the R environment as a dataframe.
#'
#'@name get_cspp_data
#'
#' @param vars Default is NULL. If left blank, returns all variables within the
#' dataset. Takes a string or vector of strings. See
#' \code{\link{get_var_info}} for pulling variable names and
#' \code{\link{get_cites}} for citations of specific variables and datasets.
#' Names of variables must be exact matches to variables in the dataset.
#' @param var_category Default is NA. If left blank, returns all datasets. Takes
#' a string or vector of strings.
#'
#' Options are one of, or a combination of: "demographics", "economic-fiscal",
#' "government", "elections", "policy_ideology", "criminal justice",
#' "education", "healthcare", "welfare", "rights", "environment",
#' "drug-alcohol", "gun control", "labor", "transportation", "misc.
#' regulation"
#' @param states Default is NULL. If left blank, returns all states. Takes a
#' string or vector of strings of state abbreviations. Use \code{state.abb} to
#' load state abbreviations into the R environment.
#' @param years Default is NULL. If left blank, returns all years. Coverage
#' begins at 1900 and runs to 2019. However, coverage depends on the specific
#' variable -- see \code{get_var_info}.
#'
#' Input can be a vector of years (or a singular year), such as c(2000, 2001,
#' 2002, 2012) or seq(2000, 2012).
#' @param core Default is FALSE. If TRUE, merge the core CSPP data (approximately 70 common and important variables) with the search result.
#' @param output Default is NULL. One of "csv", "dta", "rdata". Optional
#' parameter for writing the resulting dataframe to a file.
#' @param path The directory to write the file to. Default is blank, so writes to
#' working directory. Exclude final slash: e.g., \code{path = "dir1/dir2"}
#'
#' @seealso \code{\link{get_var_info}}, \code{\link{get_cites}}, \code{\link{generate_map}}
#'
#' @importFrom dplyr "%>%" filter arrange left_join full_join bind_rows group_by
#' if_else mutate distinct rename n summarize
#' @importFrom tidyselect all_of any_of
#'
#' @export
#'
#' @examples
#'
#' ## returns full dataset
#' data <- get_cspp_data()
#'
#' ## use variable names from get_var_info
#' data <- get_cspp_data(vars = get_var_info(var_names="pctpop")$variable)
#'
#' ## return subsets
#' # note: this returns the specific variables listed as well as those in the
#' # var_category argument
#' data <- get_cspp_data(vars = c("sess_length", "hou_majority", "term_length"),
#' var_category = "demographics",
#' states = c("NC", "VA", "GA"),
#' years = seq(1995, 2004))
#'
#'
get_cspp_data <- function(vars = NULL,
var_category = NULL,
states = NULL,
years = NULL,
core = FALSE,
output = NULL,
path = ""){
correlates <- csppData::correlates
codebook <- csppData::codebook
data <- correlates
#---- vars ----
if(!is.null(vars) & is.null(var_category)) {
# check user input
if(!all(vars %in% codebook$variable)) {
bad <- vars[!(vars %in% codebook$variable)]
message(paste("Bad variable(s): ", paste(bad, collapse=", "), sep=" "))
stop("Invalid variable name. Use get_var_info to select variables.")
} else {
data <- data %>%
dplyr::select(tidyselect::all_of(c("st","stateno","state","state_fips",
"state_icpsr","year")),
tidyselect::all_of(vars))
}
}
#---- category ----
if(!is.null(var_category) & is.null(vars)) {
# check user input
if(!(all(var_category %in% unique(codebook$category)))) {
message(paste("Bad category: ", paste(var_category[!(var_category %in% unique(codebook$category))], collapse=", "), sep=" "))
stop("Invalid category name.")
} else {
var_cat <- codebook %>%
dplyr::filter(category %in% var_category)
data <- data %>%
dplyr::select(tidyselect::all_of(c("st","stateno","state","state_fips",
"state_icpsr","year")),
tidyselect::all_of(var_cat$variable))
}
}
#---- when both category and vars take values ----
# need this check so as not
# to double filter functionally, this means the returned dataframe can result
# in variables NOT in the category the user specified
if(!is.null(var_category) & !is.null(vars)) {
# check user input
if(!(all(var_category %in% unique(codebook$category)))) {
message(paste("Bad category: ", paste(var_category[!(var_category %in% unique(codebook$category))], collapse=", "), sep=" "))
stop("Invalid category name.")
}
if(!all(vars %in% codebook$variable)) {
bad <- vars[!(vars %in% codebook$variable)]
message(paste("Bad variable(s): ", paste(bad, collapse=", "), sep=" "))
stop("Invalid variable name. Use get_var_info to select variables.")
}
var_cat <- codebook %>%
dplyr::filter(category %in% var_category)
var_cat <- c(var_cat$variable, vars)
data <- data %>%
dplyr::select(tidyselect::all_of(c("st","stateno","state","state_fips",
"state_icpsr","year")),
tidyselect::all_of(var_cat))
}
#---- states ----
if(!is.null(states)) {
states <- stringr::str_to_upper(states)
# check user input:
if(!all((states %in% state.abb))) {
message(paste("Bad state(s): ", paste(states[!(states %in% unique(correlates$st))], collapse=", "), sep=" "))
stop("Invalid state abbreviation(s).")
} else {
data <- data %>%
dplyr::filter(st %in% states) %>%
dplyr::arrange(state)
}
}
#---- years ----
if(!is.null(years)) {
# check user input
if(!all(years %in% seq(1900, 2020))) {
stop("Years must be within seq(1900, 2020)")
} else {
data <- data %>%
dplyr::filter(year %in% years)
}
}
# stop if empty dataframe
if(nrow(data) == 0) {
stop("Your request returned an empty dataframe.")
} else {
if(is.null(output) & path != "") {
message("File not written: output cannot be empty.")
}
if(!is.null(output)) {
if(path != "") {
path <- paste(path, "/", sep="")
}
if(!(output %in% c("dta", "csv", "rdata"))) {
stop("Value must be one of 'dta', 'csv', 'rdata'.")
}
if(output == "dta") {
names(data) <- gsub("-|\\.|\\/|'|\\[|\\]", "", names(data))
names(data) <- gsub(" ", "_", names(data))
haven::write_dta(data, paste(path, "cspp_data.dta", sep=""))
message("File saved to ", paste(path, "cspp_data.dta", sep=""))
}
if(output == "csv") {
readr::write_csv(data, paste(path, "cspp_data.csv", sep=""))
message("File saved to ", paste(path, "cspp_data.dta", sep=""))
}
if(output == "rdata") {
save(data, file = paste(path, "cspp_data.RData", sep=""))
message("File saved to ", paste(path, "cspp_data.dta", sep=""))
}
}
# check if any of the variables have additional footnotes in the codebook:
note_vars <- c("bfh_cpi_multiplier", "gov_fin_fy", "housing_prices_quar",
"noofvotes", "cartheftrate", "carthefttotal",
"murderrate", "murdertotal", "propcrimerate",
"propcrimetotal", "raperate", "rapetotal",
"bus_energy_consum", "bus_energy_consum_pc")
if(length(names(data)[names(data) %in% note_vars]) > 0) {
message("Note: the following variables have additional footnotes in the codebook (https://ippsr.msu.edu/sites/default/files/CorrelatesCodebook.pdf):\n",
paste(names(data)[names(data) %in% note_vars], collapse = ", "))
}
if(core){
core_data_vars <- c("year"
,"st"
,"stateno"
,"state"
,"state_fips"
,"state_icpsr"
,"poptotal"
,"evangelical_pop"
,"nonwhite"
,"gini_coef"
,"region"
,"total_expenditure"
,"total_revenue"
,"gsppcap"
,"incomepcap"
,"unemployment"
,"povrate"
,"earned_income_taxcredit"
,"x_sales_taxes"
,"x_tax_burden"
,"x_top_corporateincometaxrate"
,"prez_election_year"
,"hou_chamber"
,"sen_chamber"
,"h_diffs"
,"s_diffs"
,"inst6014_nom"
,"bowen_legprof_firstdim"
,"bowen_legprof_seconddim"
,"speaker_power"
,"pctfemaleleg"
,"efna_index"
,"vepvotingrate"
,"ranney4_control"
,"overall_fin_reg"
,"oelecsc"
,"opartsc"
,"nonindiv_contrib"
,"union_density"
,"pollib_median"
,"innovatescore_boehmkeskinner"
,"soc_capital_ma"
,"citi6013"
,"wpid"
,"mood"
,"anti_environment"
,"anti_aid"
,"anti_defense"
,"anti_education"
,"anti_welfare"
,"anti_race"
,"death_penalty"
,"guncontrol_stand_your_ground"
,"vcrimerate"
,"z_education_expenditures_per_pup"
,"mathscore4th"
,"readscore4th"
,"sqli"
,"health_rank"
,"genderrights_state_eras"
,"infconsent"
,"w_ec_access"
,"solaw"
,"vaaban"
,"w_environment_solar_taxcredit"
,"frps"
,"drugs_medical_marijuana"
,"gambling_lottery_adoption"
,"guncontrol_assaultweapon_ban"
,"guncontrol_opencarry"
,"labor_minwage_abovefed"
,"labor_right_to_work"
)
core <- correlates[, colnames(correlates) %in% core_data_vars]
# core <- core %>%
# dplyr::rename(st.abb = st)
return(left_join(data, core,
# use 'by' to suppress the dplyr joining message
by = intersect(colnames(data),
colnames(core)
)
)
)
} else {
return(data)
}
}
}
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