Moving closer to 0.5.1

DESCRIPTION

@@ -22,7 +22,7 @@ LazyData: true
 Imports:
     tidyr, dplyr, ggplot2, corrplot, lfe, multiwayvcov, lmtest,
     stargazer, scales, shiny, DT, openssl, tictoc, shinycssloaders, kableExtra, 
-    rio, zip
+    rio, zip, rlang
 RoxygenNote: 6.1.1
 Suggests:
     gapminder, rmarkdown, htmltools, knitr, devtools, tidyquant, wbstats

LICENSE

@@ -1,2 +1,2 @@
-YEAR: 2019
+YEAR: 2018-2020
 COPYRIGHT HOLDER: Joachim Gassen

NAMESPACE

@@ -21,6 +21,7 @@ importFrom(shinycssloaders, withSpinner)
 importFrom(tictoc, tic)
 importFrom(rio, import)
 importFrom(zip, zip)
+importFrom(rlang, syms)
 
 # Normal export stuff follows
 

NEWS.md

@@ -6,9 +6,16 @@ Extensions:
 resulting notebook
 
 
+Bug fixes:
+
+* Removed bug that stopped displays from rendering on user uploaded data
+
+
 Minor Issues: 
 
-* Allowed `ExPanD()` to be called with `ts_id = NULL` and `cs_id != NULL` so
+* Updated World Bank data
+
+* Allowed `ExPanD()` to be called with `ts_id == NULL` and `cs_id != NULL` so
 that data frame variables can be specified as cross-sectional identifiers
 in the cross-sectional mode
 
@@ -19,6 +26,9 @@ time series indentifiers were still provided as variable options in the
 * Included manual zip package import in `NAMESPACE` to make `devtoools::check()` 
 happy on various CRAN systems
 
+* Changed shinyapps.io URLs in vignettes to plain HTTP as winbuilder seems to
+have an issue with the SSL handshake of shinyapps.io
+
 
 # ExPanDaR 0.5.0
 

R/prepapre_correlation_graph.R

@@ -8,10 +8,12 @@ cor_mat <- function(x, ...) {
 
   for (i in 1:(n - 1)) {
     for (j in (i + 1):n) {
-      tmp <- stats::cor.test(mat[, i], mat[, j], ...)
-      out_r[i, j] <- out_r[j, i] <- tmp$estimate
-      out_p[i, j] <- out_p[j, i] <- tmp$p.value
-      out_n[i, j] <- out_n[j, i] <- sum(!is.na(mat[, i]) & !is.na(mat[, j]))
+      out_n[i, j] <- out_n[j, i] <- sum(is.finite(mat[, i]) & is.finite(mat[, j]))
+      if (out_n[i, j] > 2) {
+        tmp <- stats::cor.test(mat[, i], mat[, j], ...)
+        out_r[i, j] <- out_r[j, i] <- tmp$estimate
+        out_p[i, j] <- out_p[j, i] <- tmp$p.value
+      }
     }
   }
   colnames(out_r) <- rownames(out_r) <- colnames(mat)

data-raw/worldbank_data.R

@@ -1,10 +1,7 @@
-# (C) Joachim Gassen 2018, gassen@wiwi.hu-berlin.de, see LICENSE file for details
+# (C) Joachim Gassen 2020, gassen@wiwi.hu-berlin.de, see LICENSE file for details
 #
 # This code generates the Worldbank dataset.
 
-# clear workspace
-rm(list=ls())
-
 library(dplyr)
 library(tidyr)
 library(wbstats)
@@ -15,18 +12,15 @@ refresh <- TRUE
 pull_worldbank_data <- function() {
   new_cache <- wbcache()
   all_vars <- as.character(unique(new_cache$indicators$indicatorID))
-  vars <- c("SP.POP.TOTL", "NY.GDP.MKTP.KD", "SP.DYN.LE00.IN",
-            "SI.POV.GINI", "SH.XPD.PUBL.ZS", "SH.XPD.PRIV.ZS",
-            "SH.DYN.MORT", "SL.UEM.TOTL.ZS", "SH.MED.PHYS.ZS",
-            "SE.XPD.TOTL.GD.ZS", "SE.ADT.LITR.ZS", "NY.GDP.PCAP.KD",
-            "NY.GDP.PCAP.KD.ZG", "NY.GNS.ICTR.ZS", "NY.GNP.PCAP.KD",
-            "NE.EXP.GNFS.ZS", "NE.DAB.TOTL.ZS", "NE.CON.PETC.ZS",
-            "NE.CON.GOVT.ZS", "NE.TRD.GNFS.ZS", "NE.CON.TETC.ZS",
-            "NY.GDS.TOTL.ZS", "NE.IMP.GNFS.ZS", "EN.ATM.CO2E.PC",
-            "DT.INT.DECT.GN.ZS", "DT.DOD.DECT.GN.ZS", "BX.KLT.DINV.WD.GD.ZS",
-            "DT.TDS.DPPG.GN.ZS", "MS.MIL.XPND.GD.ZS", "GB.XPD.RSDV.GD.ZS")
+  vars <- c("SP.POP.TOTL", "NY.GDP.MKTP.KD", "NY.GDP.PCAP.KD",
+            "DT.DOD.DECT.GN.ZS", "DT.TDS.DPPG.GN.ZS", "NY.GDS.TOTL.ZS",
+            "NE.EXP.GNFS.ZS", "NE.IMP.GNFS.ZS", "GB.XPD.RSDV.GD.ZS",
+            "SE.XPD.TOTL.GD.ZS", "SH.XPD.CHEX.GD.ZS", "MS.MIL.XPND.GD.ZS",
+            "EN.ATM.CO2E.PC", "SL.UEM.TOTL.ZS", "SI.POV.GINI",
+            "SE.ADT.LITR.ZS", "SH.DYN.MORT", "SP.DYN.LE00.IN")
 
-  data_wide <- wb(indicator = vars, mrv = 70, return_wide = TRUE)
+  data_wide <- wb(indicator = vars, mrv = 70,
+                  return_wide = TRUE, cache = new_cache)
 
   new_cache$indicators[new_cache$indicators[,"indicatorID"] %in% vars, ] %>%
     rename(var_name = indicatorID) %>%
@@ -66,16 +60,16 @@ if (refresh) {
   wb_list <- pull_worldbank_data()
   worldbank <- wb_list[[1]]
   worldbank_data_def <- wb_list[[2]]
-  save(worldbank, file = "data/worldbank.RData")
-  save(worldbank_data_def, file = "data/worldbank_data_def.RData")
+  save(worldbank, file = "data/worldbank.RData", version = 2)
+  save(worldbank_data_def, file = "data/worldbank_data_def.RData", version = 2)
 } else {
   load("data/worldbank.RData")
   load("data/worldbank_data_def.RData")
 }
 
 if (refresh) {
   worldbank_var_def <- read.csv("data-raw/worldbank_var_def.csv", stringsAsFactors = FALSE)
-  save(worldbank_var_def, file = "data/worldbank_var_def.RData")
+  save(worldbank_var_def, file = "data/worldbank_var_def.RData", version = 2)
 } else load("data/worldbank_var_def.RData")
 
 load("data/ExPanD_config_worldbank.RData")

data-raw/worldbank_var_def.csv

@@ -10,13 +10,13 @@ gdp_capita,NY.GDP.PCAP.KD,numeric,0
 extdebt_gni,DT.DOD.DECT.GN.ZS,numeric,1
 debtservice_gni,DT.TDS.DPPG.GN.ZS,numeric,1
 grossdomsavings_gdp,NY.GDS.TOTL.ZS,numeric,1
-privconsumption_gdp,NE.CON.PETC.ZS,numeric,1
-govconsumption_gdp,NE.CON.GOVT.ZS,numeric,1
 exports_gdp,NE.EXP.GNFS.ZS,numeric,1
 imports_gdp,NE.IMP.GNFS.ZS,numeric,1
 resdevelop_gdp,GB.XPD.RSDV.GD.ZS,numeric,1
 govtspendeducation_gdp,SE.XPD.TOTL.GD.ZS,numeric,1
-pubspendhealth_gdp,SH.XPD.PUBL.ZS,numeric,1
+healthexpenditure_gdp,SH.XPD.CHEX.GD.ZS,numeric,1
+militaryexpenditure_gdp,MS.MIL.XPND.GD.ZS,numeric,1
+c02_emissions_capita,EN.ATM.CO2E.PC,numeric,1
 unemployment,SL.UEM.TOTL.ZS,numeric,1
 giniindex,SI.POV.GINI,numeric,1
 literacy_rate,SE.ADT.LITR.ZS,numeric,1