HW9 APIs

Task 1: Use world bank data to analyze something

library(wbstats)   
str(wb_cachelist, max.level = 1) # Finding available data

## List of 8
##  $ countries    : tibble [304 × 18] (S3: tbl_df/tbl/data.frame)
##  $ indicators   : tibble [16,649 × 8] (S3: tbl_df/tbl/data.frame)
##  $ sources      : tibble [63 × 9] (S3: tbl_df/tbl/data.frame)
##  $ topics       : tibble [21 × 3] (S3: tbl_df/tbl/data.frame)
##  $ regions      : tibble [48 × 4] (S3: tbl_df/tbl/data.frame)
##  $ income_levels: tibble [7 × 3] (S3: tbl_df/tbl/data.frame)
##  $ lending_types: tibble [4 × 3] (S3: tbl_df/tbl/data.frame)
##  $ languages    : tibble [23 × 3] (S3: tbl_df/tbl/data.frame)

wbsearch("gdp growth")

##                indicatorID                                       indicator
## 676         5.51.01.10.gdp                           Per capita GDP growth
## 679         6.0.GDP_growth                           GDP growth (annual %)
## 7128            GFDD.OI.19 Banking crisis dummy (1=banking crisis, 0=none)
## 9961        NV.AGR.TOTL.ZG          Real agricultural GDP growth rates (%)
## 10162    NY.GDP.MKTP.KD.ZG                           GDP growth (annual %)
## 10165 NY.GDP.MKTP.KN.87.ZG                           GDP growth (annual %)

#choose NY.GDP.MKTP.KD.ZG

library(wbstats)
library(ggplot2)
library(dplyr)
library(sf)

# Get gross domestic product per capita data for all countries from 1960 to 2022
gdp <- wb(country = "all", indicator = "NY.GDP.MKTP.KD.ZG", startdate = 2016, enddate = 2022)

# world country polygons 'medium' scale 
world_geo <- rnaturalearth::ne_countries(scale = 50, returnclass = "sf")
  
gdp_geo <- left_join(world_geo, gdp, by = c("iso_a2" = "iso2c"))

library(ggplot2)
library(viridis)
library(gridExtra)

year <- seq(min(gdp$date),max(gdp$date))

map <- list()

for (i in year) {
p <- ggplot(subset(gdp_geo,date == i)) +
    geom_sf(aes(fill = value)) +
    scale_fill_viridis("value") +
    labs(title = paste("Annual GDP Growth in", i))

 t <- which(year == i)
 # add the plot to the list
  map[[t]]<- p
}  
grid.arrange(grobs = map)

From the GDP growth rate map between 2018 and 2020, it is evident that the COVID-19 pandemic had a significant impact on the global economy. The map clearly shows that the GDP growth rate suffered a substantial decrease during this period, as a result of the pandemic’s widespread impact on various sectors. However, the economy appears to have started its recovery from the year 2021.

Task 2: Use Google trends data to analyze something

library(gtrendsR)
library(reshape2)
TikTok = gtrends(c("TikTok hearing"), gprop = "web",geo = c("US"), time = "today 3-m")[[1]]
TikTok = dcast(TikTok, date ~ keyword + geo, value.var = "hits")
#plot
library(ggplot2)
library(ggthemes)
g <- ggplot(data= TikTok) + 
  geom_line(aes(x=date, y=`TikTok hearing_US`)) + 
  ylim(0, max(TikTok$`TikTok hearing_US`))+
  labs(titles = "Google Trends for TikTok hearing hearing past 90 days in the US")+ ylab("Searches") + xlab("date")
g

The Google Trends data analysis reveals an interesting trend regarding the TikTok hearing that took place on March 23. Starting from the beginning of March, the search volume for TikTok began to rise gradually, leading up to the date of the hearing. However, after March 23, there was a significant and rapid increase in the search volume for TikTok. This indicates that the hearing may have had a substantial impact on public interest and awareness of TikTok, resulting in a surge of online searches for the platform.