DataSources

World Bank

library(wbstats)

The WB can be search with a single word or multiple words. For example searching for unemployment information.

# Search  WB API
unemployment_search <- wbsearch(pattern = "unemployment")

datatable(data = unemployment_search, 
          options = list(scrollX = T), # Gives acces to scroll on the X axis.
          caption = "Unemployment")

This is how to search the WB with multiple criteria.

# Multiple search words 
multiple_search <- wbsearch(pattern = "poverty|unemployment|employment") 

datatable(data = multiple_search , 
          options = list(scrollX = T), # Gives acces to scroll on the X axis. 
          caption = "multiple_search")

World Bank II

There are two different ways of getting the World Bank data. The second way is using the WDI package.

library(WDI)

Create a New Cache

new_wb_cache <- wbcache()

Search World Bank Indicators

WDIsearch('gdp') 

# For multiple searches. 
WDIsearch('gdp.*capital.*constant')

wb_dat <- wb(indicator = c("NY.GDP.PCAP.KD", "SP.DYN.LE00.IN", "SP.DYN.IMRT.IN")) 

unemploy <- wbsearch(pattern = "unemployment", # Word to be match
                     fields = c("indicator", "indicatorDesc"), # Column names through which to search, i.e. 
                     extra = FALSE)

education <- wbsearch(pattern = "education")

world_geo <- rnaturalearth::ne_countries(scale = 50, returnclass = "sf")
popa <- wb(country = "MX", 
               indicator = "ccx_yaurr_pop_tot", 
               mrv = 20)

pop_geo <- left_join(world_geo, pop_data, by = c("iso_a2" = "iso2c"))

v<- wb(indicator = "NY.GDP.MKTP.CD", startdate = 2000, enddate = 2019)

B<- wb(country = "all", # You can select specific country, or countries by c(USA, MXN, etc.)
       indicator = "NY.GDP.MKTP.CD", # You need to know indicator name.
       startdate = 2000, # 2016M01 for  months
       enddate = 2018,  # mrv can be use for the last 20 years, mrv = 20
       return_wide = FALSE,
       gapfill, 
       freq, 
       cache, 
       lang = c("en", "es", "fr", "ar", "zh"),
       removeNA = TRUE, 
       POSIXct = FALSE, 
       include_dec = FALSE,
       include_unit = FALSE,
       include_obsStatus = FALSE,
       include_lastUpdated = FALSE)

OECD

OECD has a R library to get data straight from OECD data bases into your R consul.

library(OECD)

The first thing to do is to get the data list. This would create the data set of the OECD

# Code for getting data OECD data list.  
datalist <- get_datasets()

# Creates Data table
datatable(data = datalist, 
          options = list(scrollX = T), # Gives acces to scroll on X Axis
          caption = "DATALIST")

Search the data set you just created with get_datasets

# Search codes and discriptions of available OECD series. 
unemployment = search_dataset("unemployment", data = datalist)
unemployment

## # A tibble: 7 x 2
##   id              title                                                         
##   <fct>           <fct>                                                         
## 1 DUR_I           Incidence of unemployment by duration                         
## 2 DUR_D           Unemployment by duration                                      
## 3 AVD_DUR         Average duration of unemployment                              
## 4 AEO2012_CH6_FI~ Figure 4: Youth and adult unemployment                        
## 5 AEO2012_CH6_FI~ Figure 29: Youth employment and unemployment by education and~
## 6 AEO2012_CH6_FI~ Figure 19: The trade off between vulnerable employment and un~
## 7 NRR             Net Replacement Rates in unemployment

The main function in the OECD package in r is get_dataset.

df <- get_dataset("EO78_TRADE", # a string with the code for the desired data set 
             filter = list("DEU", "FRA"),
             start_time = 2008, end_time = 2015)

get_data_structure() # Get the data structure of a data set.

browse_metadata() # Browse the metadata related to a series.

get_dataset() # Download OECD data sets.

US Census

library(tidycensus) # you need api key and setting it up.

For the US Census you will need to get a API key. To get an API key go to the US census website.

# Get the data from the US Census
LA <- get_acs(geography = "tract", 
              variables = "B25077_001", #Median value (dollars). HOUSEHOLDS
              state = "CA",
              county = "Los Angeles County",
              geometry = TRUE,
              cache_table = TRUE) # For faster results.

# Plots the data
ggplot(LA, aes(fill = estimate)) + geom_sf() #This will map varibles.

The Code below would gather different

# Many Variables. This is whats running the program. 
racevars <- c(White = "P005003", #Total Population
              Black = "P005004", # Total Population
              Asian = "P005006",# Total Population
              Hispanic = "P004003") # Total Population
# Second Part. Use the above recevars value. 
LosAngeles <- get_decennial(geography = "tract", 
                        variables = racevars, # This is where to add the variables created. 
                        state = "CA", 
                        county = "Los Angeles", 
                        geometry = TRUE,
                        summary_var = "P001001",
                        cache_table = TRUE) # For faster results.) 

There are different ways of mapping in R. One way is using the code below.

# Will Map it
LosAngeles %>%
  mutate(pct = 100 * (value / summary_value)) %>%
  ggplot(aes(fill = pct)) +
  facet_wrap(~variable) +
  geom_sf(color = NA) +
  coord_sf(crs = 26915) + 
  scale_fill_viridis_c()

Quandl

library(Quandl) # For Financial data gathering. 

oil <- Quandl('NSE/OIL', type = "xts")

datatable(data = oil, 
          options = list(scrollX = T), # Gives acces to scroll on the X axis.
          caption = "Oil")

plot(oil$Open)

Other places

library(gapminder)
library(quantmod)
library(acs)