R Markdown
# Load the package
library(wbstats)
# Extract the metadeta
meta <- wb_cache()
# Show the names of the sublists within the given meta list
names(meta)
## [1] "countries" "indicators" "sources" "topics"
## [5] "regions" "income_levels" "lending_types" "languages"
# Show one of the sublist: topics
meta$topics
## # A tibble: 21 × 3
## topic_id topic topic_desc
## <dbl> <chr> <chr>
## 1 1 Agriculture & Rural Development "For the 70 percent of the world's …
## 2 2 Aid Effectiveness "Aid effectiveness is the impact th…
## 3 3 Economy & Growth "Economic growth is central to econ…
## 4 4 Education "Education is one of the most power…
## 5 5 Energy & Mining "The world economy needs ever-incre…
## 6 6 Environment "Natural and man-made environmental…
## 7 7 Financial Sector "An economy's financial markets are…
## 8 8 Health "Improving health is central to the…
## 9 9 Infrastructure "Infrastructure helps determine the…
## 10 10 Social Protection & Labor "The supply of labor available in a…
## # ℹ 11 more rows
# Show the top 6 rows for the indicators that contains the characters "gdp"
gdp_vars <- wb_search("gdp")
head(gdp_vars)
## # A tibble: 6 × 3
## indicator_id indicator indicator_desc
## <chr> <chr> <chr>
## 1 5.51.01.10.gdp Per capita GDP growth GDP per capit…
## 2 6.0.GDP_current GDP (current $) GDP is the su…
## 3 6.0.GDP_growth GDP growth (annual %) Annual percen…
## 4 6.0.GDP_usd GDP (constant 2005 $) GDP is the su…
## 5 6.0.GDPpc_constant GDP per capita, PPP (constant 2011 internat… GDP per capit…
## 6 BG.GSR.NFSV.GD.ZS Trade in services (% of GDP) Trade in serv…
# Extract data for the above indicator "6.0.GDP_current"
wb_data("6.0.GDP_current")
## # A tibble: 270 × 9
## iso2c iso3c country date `6.0.GDP_current` unit obs_status footnote
## <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
## 1 AR ARG Argentina 2000 284203750000 <NA> <NA> <NA>
## 2 AR ARG Argentina 2001 268696750000 <NA> <NA> <NA>
## 3 AR ARG Argentina 2002 97724513456. <NA> <NA> <NA>
## 4 AR ARG Argentina 2003 127586251761. <NA> <NA> <NA>
## 5 AR ARG Argentina 2004 181873056797. <NA> <NA> <NA>
## 6 AR ARG Argentina 2005 220813784300. <NA> <NA> <NA>
## 7 AR ARG Argentina 2006 262666517347. <NA> <NA> <NA>
## 8 AR ARG Argentina 2007 329317513143. <NA> <NA> <NA>
## 9 AR ARG Argentina 2008 403781994528. <NA> <NA> <NA>
## 10 AR ARG Argentina 2009 376627876888. <NA> <NA> <NA>
## # ℹ 260 more rows
## # ℹ 1 more variable: last_updated <date>
# Extract the total population and current US$ GDP for US and Norway
pop_gdp <- wb_data(indicator = c("SP.POP.TOTL", "NY.GDP.MKTP.CD"),
country = c("US", "NO"),
start_date = 2020, end_date = 2024)
pop_gdp
## # A tibble: 10 × 6
## iso2c iso3c country date NY.GDP.MKTP.CD SP.POP.TOTL
## <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 NO NOR Norway 2020 3.68e11 5379475
## 2 NO NOR Norway 2021 5.03e11 5408320
## 3 NO NOR Norway 2022 5.96e11 5457127
## 4 NO NOR Norway 2023 4.83e11 5519594
## 5 NO NOR Norway 2024 4.84e11 5572272
## 6 US USA United States 2020 2.14e13 331577720
## 7 US USA United States 2021 2.37e13 332099760
## 8 US USA United States 2022 2.60e13 334017321
## 9 US USA United States 2023 2.77e13 336806231
## 10 US USA United States 2024 2.92e13 340110988
# Load the package
library(quantmod)
## Loading required package: xts
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
## Loading required package: TTR
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
getSymbols(c("NVDA", "TSLA"), source = "yahoo")
## [1] "NVDA" "TSLA"
# Find the last 10 data points for NVDA
tail(NVDA, 10)
## NVDA.Open NVDA.High NVDA.Low NVDA.Close NVDA.Volume NVDA.Adjusted
## 2025-09-08 167.55 170.96 167.35 168.31 163769100 168.3005
## 2025-09-09 169.09 170.98 166.74 170.76 157548400 170.7504
## 2025-09-10 176.64 179.29 175.47 177.33 226852000 177.3200
## 2025-09-11 179.68 180.28 176.48 177.17 151159300 177.1700
## 2025-09-12 177.77 178.60 176.45 177.82 124911000 177.8200
## 2025-09-15 175.67 178.85 174.51 177.75 147061600 177.7500
## 2025-09-16 177.00 177.50 174.38 174.88 140737800 174.8800
## 2025-09-17 172.64 173.20 168.41 170.29 211843800 170.2900
## 2025-09-18 173.98 177.10 172.96 176.24 191763300 176.2400
## 2025-09-19 175.77 178.08 175.18 176.67 236658800 176.6700
# Question 6, Option A
getSymbols("MSFT", src="yahoo",from = "2024-10-01", to = "2025-01-31")
## [1] "MSFT"
tail(MSFT)
## MSFT.Open MSFT.High MSFT.Low MSFT.Close MSFT.Volume MSFT.Adjusted
## 2025-01-23 442.00 446.75 441.50 446.71 18389300 444.2688
## 2025-01-24 445.16 446.65 441.40 444.06 15549500 441.6333
## 2025-01-27 424.01 435.20 423.50 434.56 35647800 432.1852
## 2025-01-28 434.60 448.38 431.38 447.20 23491700 444.7561
## 2025-01-29 446.69 446.88 440.40 442.33 23581400 439.9127
## 2025-01-30 418.77 422.86 413.16 414.99 54586300 412.7221
# Question 6, Option B
getSymbols("MSFT", src="yahoo",from = "2024-10-01", to = "2025-02-01")
## [1] "MSFT"
tail(MSFT)
## MSFT.Open MSFT.High MSFT.Low MSFT.Close MSFT.Volume MSFT.Adjusted
## 2025-01-24 445.16 446.65 441.40 444.06 15549500 441.6333
## 2025-01-27 424.01 435.20 423.50 434.56 35647800 432.1852
## 2025-01-28 434.60 448.38 431.38 447.20 23491700 444.7561
## 2025-01-29 446.69 446.88 440.40 442.33 23581400 439.9127
## 2025-01-30 418.77 422.86 413.16 414.99 54586300 412.7221
## 2025-01-31 418.98 420.69 414.91 415.06 34161900 412.7917
# Question 8
plot(MSFT$MSFT.Open)
# FRED data pull
getSymbols(c("FPCPITOTLZGCHN", "FPCPITOTLZGEGY", "FPCPITOTLZGUSA"),
src = "FRED")
## [1] "FPCPITOTLZGCHN" "FPCPITOTLZGEGY" "FPCPITOTLZGUSA"
tail(FPCPITOTLZGUSA)
## FPCPITOTLZGUSA
## 2019-01-01 1.812210
## 2020-01-01 1.233584
## 2021-01-01 4.697859
## 2022-01-01 8.002800
## 2023-01-01 4.116338
## 2024-01-01 2.949525
# Use pacman to install/load necessary packages
pacman:: p_load(httr, jsonlite)