HW8-APIs
Acacia
2022-03-22
1. World Bank Data: Remittance and South asian economy
South Asia is one of the largest remittance recipient region.South Asian countries send out a significant number of migrant workers annually and remittances sent by migrant workers become a significant source of funds for economic development of the countries. Here I am looking into the trends of remittance (% of GDP) from 2000 to 2020 for four South Asian countries that have significantly larger number of outgoing migrant workers. As a share of gross domestic product, Nepal received the largest formal remittance inflows from 2003 - 2020. The contribution of remittance is almost one-fourth of the total GDP of Nepal.
library(wbstats)
new_wb_cache <- wb_search(pattern = "remittances")
remittance_SA <- wb_data(indicator = "BX.TRF.PWKR.DT.GD.ZS", start_date = 2000, end_date = 2021, country = c("Bangladesh", "India", "Nepal", "Sri Lanka"))
value <- remittance_SA$BX.TRF.PWKR.DT.GD.ZS
mh <- theme(legend.position = "right",
legend.direction = "vertical",
legend.justification = "right",
legend.background = element_rect(fill = "gray95", size = 0.5),
legend.title = element_blank()) +
theme(text = element_text(face = "bold", size = 9))
remi_plot <- ggplot(remittance_SA, aes(x = date, y = value, color = country)) +
geom_line(size = 1.25) +
ggtitle ("Contribution of remittance to GDP (%)\n") +
xlab("") +
ylab("Remittance (% of GDP)\n") +
scale_x_continuous(breaks = seq(2000,2021,3),expand = expansion(mult = c(0, 0),
add = c(0, 2)), limits = c(2000,2021)) +
scale_y_continuous(breaks = seq(0.00,28,4), expand = c(0, 0),
limits = c(0.00, 28)) +
theme(plot.title = element_text(size = 12, face = "bold", hjust = 0.5)) +
theme(axis.title.y = element_text(size = 7, face = "bold", vjust = 0.5)) +
theme(axis.text = element_text(size = 8)) + theme_classic() + mh
remi_plot
2. Google trends: The rise of “Near Me” searches
People searching online for local services is now more than ever before. As advancement in technology continue, so do the users, as seen in their searches. Adding “near me” to the end of a search query shows the intent of users to take immediate action on search results. Here, I am looking into the trend for the term “near me”. The plot shows that the increase in “near me” search queries to find a specific thing in a specific area and in a specific period of time are rising considerably. There was a decrease in search during 2020 when COVID-19 was at its peak as more people stayed inside. With COVID gradually decreasing, search trend for “near me” is increasing.
library(gtrendsR)
search <- gtrends(keyword = c("near me"), time= "2015-01-01 2021-12-31")
time_trend <- search$interest_over_time %>%
dplyr::mutate(hits = ifelse(hits == "<1",0.5,as.numeric(hits)),
date = as.Date(date))
ggplot(time_trend, aes(x = date, y = hits, colour = keyword)) +
geom_line(size = 1.5) +
ggtitle ("The rise of Near Me searches\n") +
xlab("") +
ylab("Hits\n") +
theme_classic() +
theme(legend.position = "right",
legend.direction = "vertical",
legend.justification = "right",
legend.background = element_rect(fill = "gray95", size = 0.5),
legend.title = element_blank()) +
theme(text = element_text(face = "bold", size = 9))
2a. Use of “Near Me” across the world
Now, let us look in which country “near me” is searched more.
search$interest_by_country %>%
arrange(desc(hits)) %>%
head(10)## location hits keyword geo gprop
## 1 United States 100 near me world web
## 2 United Kingdom 37 near me world web
## 3 Australia 36 near me world web
## 4 Canada 30 near me world web
## 5 South Africa 22 near me world web
## 6 India 19 near me world web
## 7 United Arab Emirates 17 near me world web
## 8 New Zealand 17 near me world web
## 9 Malaysia 17 near me world web
## 10 Ireland 12 near me world web# plot in world map
world <- map_data("world")
# change the region names to match the region names returned by Google Trends
world %>%
mutate(region = replace(region, region=="USA", "United States")) %>%
mutate(region = replace(region, region=="UK", "United Kingdom")) -> world
# create data frame for plotting
search$interest_by_country %>%
filter(location %in% world$region, hits > 0) %>%
mutate(region = location, hits = as.numeric(hits)) %>%
select(region, hits) -> my_df
ggplot() +
geom_map(data = world,
map = world,
aes(x = long, y = lat, map_id = region),
fill="#ffffff", color="#ffffff", size=0.15) +
geom_map(data = my_df,
map = world,
aes(fill = hits, map_id = region),
color="#ffffff", size=0.15) +
scale_fill_continuous(low = 'grey', high = 'red') +
theme(axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title = element_blank())
As expected the “near me” is more searched in the United States followed by Australia, United Kingdom, Canada and South Africa. One of the common thing among these five countries is that they all are English speaking countries.
2b. “Near me” accross the United States
As “near me” is more searched in the United States. Let us see which state is more interested in “near me”.
search_US <- gtrends("near me", geo = "US", time = "all")
state <- map_data("state")
search_US$interest_by_region %>%
mutate(region = tolower(location)) %>%
filter(region %in% state$region) %>%
select(region, hits) -> my_df
ggplot() +
geom_map(data = state,
map = state,
aes(x = long, y = lat, map_id = region),
fill="#ffffff", color="#ffffff", size=0.15) +
geom_map(data = my_df,
map = state,
aes(fill = hits, map_id = region),
color="#ffffff", size=0.15) +
scale_fill_continuous(low = 'grey', high = 'red') +
theme(panel.background = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title = element_blank())
Florida, North Carolina and Delaware have the highest hits. As all three states are the travel destinations of the US, I think the result is of no surprise. States like Montana, North Dakota and South Dakota have least number of searches.