typos in the vignettes corrected.

worldbank · Dec 7, 2024 · e94f4d8 · e94f4d8
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diff --git a/vignettes/articles/get-cp-example.Rmd b/vignettes/articles/get-cp-example.Rmd
@@ -1,8 +1,8 @@
 ---
-title: "get-cp-example"
+title: "Get Country Profiles"
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{pipr}
+  %\VignetteIndexEntry{get-cp-example}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---
@@ -16,6 +16,10 @@ knitr::opts_chunk$set(
 )
 ```
 
+```{r}
+library(pipr)
+```
+
 ## Overview
 
 The `get_cp()` and `get_cp_ki()` functions provide **country profile data**, also available in [PIP's Country Profile page](https://pip.worldbank.org/country-profiles/IDN). This dataset comes from the [Global Monitoring Indicators (GMI)](https://datacatalog.worldbank.org/int/search/dataset/0065517/global-monitoring-indicators--gmi-), a set of harmonized indicators produced from the Global Monitoring Database (GMD) [^1]

diff --git a/vignettes/articles/get-gd-example.Rmd b/vignettes/articles/get-gd-example.Rmd
@@ -1,8 +1,8 @@
 ---
-title: "get-gd-example"
+title: "Get Grouped Data Parameters"
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{pipr}
+  %\VignetteIndexEntry{get-gd-example}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---
@@ -159,12 +159,12 @@ ggplot(poverty_stats, aes(x = poverty_line)) +
 
 ### Lorenz Curve Data
 ```{r, warning=FALSE, message=FALSE}
-lorenz_points_lq <- get_gd(datt_data$L, datt_data$p, estimate = "lorenz", lorenz = "lq", server = "dev")
-lorenz_points_lq_10 <- get_gd(datt_data$L, datt_data$p, estimate = "lorenz", lorenz = "lq", n_bins = 10, server = "dev")
+lorenz_points_lq <- get_gd(datt_rural$L, datt_rural$p, estimate = "lorenz", lorenz = "lq")
+lorenz_points_lq_10 <- get_gd(datt_rural$L, datt_rural$p, estimate = "lorenz", lorenz = "lq", n_bins = 10)
 
 ggplot() +
   geom_bar(data = lorenz_points_lq_10, aes(x = weight, y = welfare), stat = "identity", fill = "darkorange", alpha = 0.3) +
-  geom_point(data = datt_data, aes(x = p, y = L), color = "darkorange", size = 2) +
+  geom_point(data = datt_rural, aes(x = p, y = L), color = "darkorange", size = 2) +
   geom_point(data = lorenz_points_lq, aes(x = weight, y = welfare), color = "darkorange", size = 0.5) +
   geom_abline(intercept = 0, slope = 1, color = "black") +
   labs(
@@ -174,10 +174,12 @@ ggplot() +
   ) +
   theme_minimal()
 ```
+
+
 ### Rural vs Urban
 ```{r, warning=FALSE, message=FALSE}
-lorenz_rural <- get_gd(datt_rural$L, datt_rural$p, estimate = "lorenz", lorenz = "lq", server = "dev", n_bins = 1000)
-lorenz_urban <- get_gd(datt_urban$L, datt_urban$p, estimate = "lorenz", lorenz = "lq", server = "dev", n_bins = 1000)
+lorenz_rural <- get_gd(datt_rural$L, datt_rural$p, estimate = "lorenz", lorenz = "lq", n_bins = 1000)
+lorenz_urban <- get_gd(datt_urban$L, datt_urban$p, estimate = "lorenz", lorenz = "lq", n_bins = 1000)
 
 ggplot() +
   geom_point(data = lorenz_rural, aes(x = weight, y = welfare), color = "darkorange", size = 0.5) +