## ----eval=F------------------------------------------------------------------- # # For latest developmental verison: # library(devtools) # install_github("correlatesstatepolicy/cspp") # # # For CRAN version: # install.packages("cspp") ## ----message = FALSE---------------------------------------------------------- # Load the package library(cspp) # Find variables based on a category demo_variables <- get_var_info(categories = "demographics") # Use these variables to get a full or subsetted version of the data cspp_data <- get_cspp_data(vars = demo_variables$variable, years = seq(2000, 2010)) ## ----------------------------------------------------------------------------- library(dplyr) glimpse(cspp_data[1:15],) ## ----------------------------------------------------------------------------- # All variables all_variables <- get_var_info() # Full dataset all_data <- get_cspp_data() ## ----------------------------------------------------------------------------- # Search for variables by name get_var_info(var_names = c("pop","femal")) %>% dplyr::glimpse() ## ----------------------------------------------------------------------------- # Search by name and description: get_var_info(related_to = c("pop", "femal")) %>% dplyr::glimpse() ## ----------------------------------------------------------------------------- # See variable categories: unique(get_var_info()$category) ## ----------------------------------------------------------------------------- # Find variables by category: var_cats <- get_var_info(categories = c("gun control", "labor")) ## ----eval = F----------------------------------------------------------------- # # Get subsetted data and save to dataframe # data <- get_cspp_data(vars = c("sess_length", "hou_majority", "term_length"), # var_category = "demographics", # states = c("NC", "VA", "GA"), # years = seq(1995, 2004)) ## ----------------------------------------------------------------------------- # Use get_var_info to generate variable vector inline get_cspp_data(vars = get_var_info(related_to = "concealed carry")$variable, states = "NC", years = 1999) ## ----------------------------------------------------------------------------- # Simple dataframe for one variable get_cites(var_names = "poptotal") %>% dplyr::glimpse() # Using get_var_info to return variable citations cite_ex <- get_cites(var_names = get_var_info(related_to = "concealed carry")$variable) cite_ex$plaintext_cite[3:4] ## ----eval=F------------------------------------------------------------------- # get_cites(var_names = "poptotal", # write_out = TRUE, # file_path = "~/path/to/file.csv", # format = "csv") ## ----out.width='60%'---------------------------------------------------------- library(ggplot2) # optional, but needed to remove legend # Generates a map of the percentage of the population over 65 generate_map(get_cspp_data(var_category = "demographics"), var_name = "pctpopover65") + ggplot2::theme(legend.position = "none") ## ----out.width='60%'---------------------------------------------------------- library(dplyr) generate_map(get_cspp_data(var_category = "demographics") %>% dplyr::filter(st %in% c("NC", "VA", "SC")), var_name = "pctpopover65", poly_args = list(color = "black"), drop_NA_states = TRUE) + ggplot2::theme(legend.position = "none") ## ----out.width='60%'---------------------------------------------------------- generate_map(get_cspp_data(var_category = "demographics") %>% dplyr::filter(st %in% c("NC", "VA", "SC", "TN", "GA", "WV", "MS", "AL", "KY")), var_name = "pctpopover65", poly_args = list(color = "black"), drop_NA_states = TRUE) + ggplot2::scale_fill_gradient(low = "white", high = "red") + ggplot2::theme(legend.position = "none") + ggplot2::ggtitle("% Population Over 65") ## ----out.width="100%", dpi=180------------------------------------------------ # panel of all states' adoption of medical marijuana laws cspp <- get_cspp_data(vars = "drugs_medical_marijuana") # visualize panel: plot_panel(cspp) ## ---- out.width="100%", dpi=180----------------------------------------------- plot_panel(cspp_data = get_cspp_data(vars = "pollib_median"), colors = c("firebrick4", "steelblue2", "gray"), years = seq(1960, 2010)) + ggplot2::ggtitle("Policy liberalism") ## ----------------------------------------------------------------------------- # Returns dataframe of state dyads get_network_data() %>% dplyr::glimpse() ## ----------------------------------------------------------------------------- network.df <- get_network_data(category = c("Economic", "Political")) names(network.df) ## ----------------------------------------------------------------------------- cspp_data <- get_cspp_data(vars = c("sess_length", "hou_majority"), years = seq(1999, 2000)) network.df <- get_network_data(category = "Distance Travel Migration", merge_data = cspp_data) names(network.df) library(dplyr) head(cspp_data %>% arrange(st)) # the merged value of Alaska's hou_majority value will be mean(c(-0.129, -0.115)) ## ----message=F, warning=F, dpi=180-------------------------------------------- library(ggraph) library(igraph) network.df <- select(network.df, from = st.abb1, to = st.abb2, ACS_Migration) network.df %>% filter(from %in% c("NC", "VA", "SC", "GA")) %>% graph_from_data_frame() %>% ggraph(layout="fr") + geom_edge_link(aes(edge_alpha = ACS_Migration), edge_color = "royalblue") + geom_node_point() + geom_node_text(aes(label = name), repel = TRUE, point.padding = unit(0.2, "lines")) + theme_void() + theme(legend.position = "none") ## ----message=F, warning=F, dpi=180-------------------------------------------- network.df %>% filter(from %in% c("NC")) %>% graph_from_data_frame() %>% ggraph(layout="linear") + geom_edge_arc(aes(edge_alpha = ACS_Migration), edge_color = "royalblue") + geom_node_text(aes(label = name), size = 2) + theme_void() + theme(legend.position = "none")