[API] spiR: Application

Learn how to retrieve data from spiR and produce a visual.

This course will teach you how to use the spiR package and create impactful visualizations with the Social Progress Index data.

Loading Data

library(spiR)
library(kableExtra)
library(ggplot2)
library(dplyr)

spi_2014 <- spi_data(country = c("USA", "FRA", "BRA", "CHN", "ZAF", "CAN"), 
                       year = c("2014","2015","2016", "2017", "2018", "2019"), 
                       indicators = "SPI")

Data Wrangling

spi_2014$value <- as.numeric(spi_2014$value)

Visualizing Data

kable(spi_2014)%>%
scroll_box(width = "100%", height = "200px")

countryName	var_code	var_year	var_indicator	value
Brazil	BRA	2014	SPI	73.59
Canada	CAN	2014	SPI	86.97
China	CHN	2014	SPI	61.58
France	FRA	2014	SPI	87.10
South Africa	ZAF	2014	SPI	64.65
United States	USA	2014	SPI	84.74
Brazil	BRA	2015	SPI	73.45
Canada	CAN	2015	SPI	87.17
China	CHN	2015	SPI	62.38
France	FRA	2015	SPI	87.19
South Africa	ZAF	2015	SPI	65.38
United States	USA	2015	SPI	84.71
Brazil	BRA	2016	SPI	74.12
Canada	CAN	2016	SPI	87.25
China	CHN	2016	SPI	62.89
France	FRA	2016	SPI	87.48
South Africa	ZAF	2016	SPI	66.19
United States	USA	2016	SPI	85.09
Brazil	BRA	2017	SPI	72.80
Canada	CAN	2017	SPI	87.79
China	CHN	2017	SPI	63.73
France	FRA	2017	SPI	87.60
South Africa	ZAF	2017	SPI	66.74
United States	USA	2017	SPI	84.18
Brazil	BRA	2018	SPI	72.66
Canada	CAN	2018	SPI	88.60
China	CHN	2018	SPI	64.16
France	FRA	2018	SPI	87.69
South Africa	ZAF	2018	SPI	66.56
United States	USA	2018	SPI	83.85
Brazil	BRA	2019	SPI	72.87
Canada	CAN	2019	SPI	88.81
China	CHN	2019	SPI	64.54
France	FRA	2019	SPI	87.79
South Africa	ZAF	2019	SPI	67.44
United States	USA	2019	SPI	83.62

ggplot(data = spi_2014, aes(x = var_year, y = value, color = countryName)) + 
  geom_line() +
  theme_bw() +
  theme(plot.title = element_text(hjust = 0.5)) + 
  labs(title = "Social Progress Index of different countries since 2014",
       x = "Years",
       y = "Social Progress Index Score",
       colour = "Countries",
       caption = "Source: Nüance-R")

Another example

spi_2019 <- spi_data(country = c("USA", "FRA", "BRA", "CHN", "ZAF", "CAN"),
                   year = c("2019"),
                   indicators = "SPI")

spi_2019$value <- as.numeric(spi_2019$value)

kable(spi_2019)%>%
scroll_box(width = "100%", height = "200px")

countryName	var_code	var_year	var_indicator	value
Brazil	BRA	2019	SPI	72.87
Canada	CAN	2019	SPI	88.81
China	CHN	2019	SPI	64.54
France	FRA	2019	SPI	87.79
South Africa	ZAF	2019	SPI	67.44
United States	USA	2019	SPI	83.62

ggplot(data = spi_2019, aes(x = countryName, y = value, fill = countryName)) + 
  geom_col() +
  theme_bw() +
  theme(plot.title = element_text(hjust = 0.5)) +
  labs(title = "Social Progress Index of different countries in 2019",
       x = "Countries",
       y = "Social Progress Index Score",
       colour = "Countries",
       caption = "Source: Nüance-R")