[API] spiR: Application

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

Thierry Warin https://warin.ca/aboutme.html (HEC Montréal and CIRANO (Canada))https://www.hec.ca/en/profs/thierry.warin.html
01-24-2020

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

Social Progress Index of different countries since 2014


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

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

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

kable(myData)%>%
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 = myData, 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")

Social Progress Index of different countries in 2019


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

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

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

kable(myData)%>%
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 = myData, 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")

Citation

For attribution, please cite this work as

Warin (2020, Jan. 24). Thierry Warin: [API] spiR: Application. Retrieved from https://warin.ca/posts/api-spir-application/

BibTeX citation

@misc{warin2020[api],
  author = {Warin, Thierry},
  title = {Thierry Warin: [API] spiR: Application},
  url = {https://warin.ca/posts/api-spir-application/},
  year = {2020}
}