An R Package to query TCdata360 and Govdata360 data, metadata, and more

This R package makes it easy for our users to engage with the TCdata360 API and Govdata360 API. Functionalities include easier download of datasets, metadata, and related information, as well as searching based on user-inputted query.

Interested to see more advanced use cases? Take a look at our data360r Use Cases section below!

For an overview of the benefits of using data360r, read our blog here: To see the actual package, go to


Introduction to data360r

How to Install

This package can be easily installed by typing this in the R console:


For users who are installing this package in their office work stations which use a corporate proxy or network (e.g., World Bank users), please use the following installation code instead:

httr::set_config( config( ssl_verifypeer = 0L ) )

Usage and Examples

This version of the package supports the following functionalities. For more information, use the built-in help() function and ? help operator in R to access the detailed documentation pages for each function.

get_data360: Download TC/Govdata360 data by country, indicator, dataset, timeframe, or a combination of these

This function downloads the requested data by using the TC/Govdata360 API and transforms it into a dataframe.

Output: data frame (wide or long, depending on user input) containing requested data.

Some examples of its usage:

#get data for dataset ID 51 in TCdata360
df <- get_data360(dataset_id = 51)

#get data for countries USA, PHL in Govdata360
df2 <- get_data360(site = 'gov', country_iso3 = c('USA', 'PHL'))

#get data for indicator IDs 944, 972 in TCdata360
df3 <- get_data360(indicator_id = c(944, 972))

#get data for indicator IDs 944, 972 in 2011-2013 in long format in TCdata360
df4 <- get_data360(indicator_id = c(944, 972),
timeframes = c(2011, 2012, 2013), output_type = 'long')

get_metadata360: Download TC/Govdata360 metadata

This function downloads the requested TC/Govdata360 metadata, such as:

Output: wide dataframe containing requested metadata.

Some examples of its usage:

#get all indicator metadata in Govdata360
df_indicators <- get_metadata360(site="gov", metadata_type = "indicators")

#get all country metadata in TCdata360
df_countries <- get_metadata360(metadata_type = 'countries')

#get all dataset metadata in TCdata360
df_datasets <- get_metadata360(metadata_type = 'datasets')

search_360: Search TC/Govdata360 indicators, countries, categories, and dataset lists

Don't know what codes to write as inputs for the above two functions? This helpful function searches TC/Govdata360 indicators, countries, categories, and dataset lists based on a user-inputted search query.

Output: dataframe containing top search results

Some examples of its usage:

#search a country's code in TCdata360
search_360('Philippines', search_type = 'country')

#search for top 10 relevant indicator codes in TCdata360
search_360('GDP', search_type = 'indicator', limit_results = 10)

#search for top 10 indicators of a database in TCdata360
search_360('World Development Indicators', search_type = 'indicator', limit_results = 10)

#search for top 10 indicators of a data provider in TCdata360
search_360('WEF', search_type = 'indicator', limit_results = 10)

#search for top 10 relevant categories in Govdata360
search_360('Governance', site='gov', search_type = 'category', limit_results = 10)

get_resources360: Download TC/Govdata360 resource information

This function downloads the requested TC/Govdata360 resource information such as:

Output: wide dataframe containing requested resource information.

Some examples of its usage:

#get all indicator metadata in Govdata360
df_indicators <- get_metadata360(site="gov", metadata_type = "indicators")

#get all country metadata in TCdata360
df_countries <- get_metadata360(metadata_type = 'countries')

#get all dataset metadata in TCdata360
df_datasets <- get_metadata360(metadata_type = 'datasets')

Diving in data360r: Package Use Cases in just 3+ lines of R code

This section covers the following use cases, which caters to R users ranging from the beginner to advanced levels:

Look out for friendly tips when using the data360r package, which can be found in specially-formatted boxes such as this one:

TIP: If you want to see more use cases that aren’t covered here or provide feedback on the data360r package, feel free to drop us a message at!

Use Case #1: Downloading relevant indicator data for a specific country

For most users, it’s important to quickly find and download data you need for a report. For example, what if we need to download data related to “woman business” for the United States?

Step 1. Search for indicator IDs of indicators related to “woman business”. For simplicity, let’s search for the top 5 indicators related to “woman business”. Note that 7 results are returned since some indicators have two IDs (one for TCdata360, the other for Govdata360).

df_usecase1 <- search_360("woman business", search_type="indicator", limit_results = 5)

TIP: We can easily get the array of indicator IDs of the top 5 related indicators using df_usecase1$id.

TIP: For the user’s convenience, search_360 brings back results in decreasing order of relevance (represented by the “score” column). However, note also that search_360 returns the union of all search results for each individual term. For better search results, try to keep the search string query specific but concise.

Step 2. Search for Country ISO3 for United States. Let’s search for the ISO3 of all countries related to “United States”. We take note of the slug “USA” of the first result (which is a perfect match with score = 1.0) which is the country ISO3 we need.

> search_360("United States", search_type="country")
### Output:
	id	name	slug	type	score	redirect	dataset
1	NA	United States	USA	country	1.00000000	FALSE	NA

TIP: For country-type results, the “slug” column provides the Country ISO3 ID.

Step 3. Get indicator data related to “woman business” for USA as a dataframe. Putting it altogether, we use the results of previous Steps 1 & 2 to get a wide dataframe containing the data we need.

> df_usecase1_result <- get_data360(indicator_id=df_usecase1$id, country_iso3="USA")

[Optional] Step 4. Export R dataframe as CSV. What if we want to export the dataframe so that we can use it in Excel? We can use the write.csv function (via utils package, which the data360r package already installs for you) to do this.

> write.csv(df_usecase1_result, ‘df_usecase1_result.csv')

Here’s the output using Excel: Excel Output

Use Case #2: Comparing and visualizing indicators with ggplot2

For intermediate R users who are comfortable with using R for data visualization, dataframes usually need to be in a long format especially when used with the ggplot2 R package. So how do we use data360r together with ggplot2?

Step 1. Search IDs of indicators for comparison. For this example, let’s get the indicator IDs for the “What is the legal age of marriage for boys and for girls?” indicators. We note that these are indicator IDs 204 and 205, respectively.

> search_360("marriage", search_type="indicator")
### Output:
	id	name	slug	type	score	dataset	redirect
1	204	What is the legal age of marriage for boys?	age.marr.male	indicator	0.1111111	Women, Business and the Law	FALSE
2	205	What is the legal age of marriage for girls?	age.marr.fem	indicator	0.1111111	Women, Business and the Law	FALSE

TIP: data360r package functions are compatible with the tidyverse R package, so you can use these with together with “pipes” %>%. For example, to remove duplicates you can run: search_360("men", search_type="indicator") %>% distinct(name,.keep_all=TRUE)

Step 2. Get indicator data for all countries for year 2016. How do we get the indicator data in long format using get_data360? Simply add the parameter output_type="long" in the function call, and voila! For simplicity, we limit the indicator data to year 2016 only by adding the parameter timeframes = c(2016).

> df_usecase2_result <- get_data360(indicator_id = c(204, 205), timeframes = c(2016), output_type = 'long')

TIP: The default output_type for getdata_360 is a wide dataframe. For getdata_360 outputs with output_type = ‘long’, the column for the timeframes is always called “Period” whereas the column for the indicator values is always called “Observation”. Knowing this is helpful especially when making reusable code snippets with data360r functions.

Step 3. Plot indicator data using ggplot2. Since the resulting dataframe from get_data360 is in a long dataframe format, it’s fairly straightforward to generate plots using these. For example, let’s generate overlapping histograms to quickly compare the two indicators.

> library(ggplot2)
> ggplot(df_usecase2_result, aes(x=Observation, cond=Indicator,fill=Indicator)) +
 	geom_histogram(binwidth=.75, alpha=.25, position="identity")

Here's how the plot looks like: Simple ggplot2 plot

Step 4 [Optional]. Generate a more advanced ggplot2 plot with data360r. To show its versatility, let’s generate a more complex plot with data360r. First, we query the indicator data using getdata_360 and merge this with the countries’ region metadata using get_metadata360. We remove countries under the region “NAC” for simplicity.

> library(tidyverse)
> df_usecase2_result <- get_data360(indicator_id = c(204, 205), output_type = 'long') %>% 
	merge(select(get_metadata360(),iso3,region), by.x="Country ISO3", by.y="iso3") %>% 
	filter(!(region == "NAC"))

We then use facet_wrap to generate multiple kernel density estimator (KDE) plots comparing the two indicators, by geographic region.

> ggplot(df_usecase2_result, aes(x=Observation, cond=Indicator, fill=Indicator)) +
	geom_density(alpha=.5) +
	facet_wrap(~region) +
	theme(legend.position="right") +
	scale_fill_manual(name="Gender",values=c("blue","red"), labels=c("boys","girls")) +
	ggtitle("Country-level Density of Legal Age for Marriage, by gender and region (WBL 2016)")

Here's how the resulting plot looks like: Advanced ggplot2 plot

Use Case #3: Running regression on the WEF Global Competitiveness Index dataset

What if we want to focus on a single dataset and conduct a quick regression analysis on this?

Step 1. Get the dataset ID of the desired dataset. Let’s look through the dataset metadata and identify a dataset we want to use. For this use case, let’s focus on the WEF Global Competitiveness Index (GCI) dataset with dataset id == 53.

> df_usecase3_datasets <- get_metadata360(metadata_type = "datasets")

Step 2. Get the indicator data for WEF GCI 2016-2017. For simplicity, we get all WEF GCI data from the timeframe 2016-2017 in a long dataframe format.

> library(tidyverse)
> df_usecase3_result <- get_data360(dataset_id=c(53), output_type = 'long') %>%

Step 3.a. Preprocessing WEF GCI data for linear regression. For simplicity, we only keep all WEF GCI indicators with Subindicator type == ‘Value’. We then reshape the resulting dataframe such that the indicators are the column names using reshape::acast. This makes it easier to fit the indicator data to regression models.

> df2 <- filter(df_usecase3_result, df_usecase3_result$"Subindicator Type" == "Value", !
> df3 <-, df2$"Country ISO3" ~ df2$Indicator, value.var="Observation"))

Step 3.b. Regression on “Innovation” and “Technological Readiness” indicators. Since the dataframe has been preprocessed appropriately, it’s straightforward to implement regression on WEF GCI 2016-2017 indicators. Before fitting the data to a regression model, let’s first generate a scatterplot for selected WEF GCI indicators. For simplicity, let’s focus on the “Innovation” and “Technological Readiness” indicators.

> qplot(df3$"Innovation", df3$"Technological Readiness", data = df3)

Here's how the scatterplot looks like: Scatterplot

The scatterplot suggests that Innovation increases quadratically with Technological Readiness. Let’s fit a quadratic regression model to these data points. Based on the summary results, the model is a good fit. We also generate the supplementary model plots to see if the results make sense.

> mod_usecase3_quad <- lm(df3$"Innovation" ~ poly(df3$"Technological Readiness", 2))
> summary(mod_usecase3_quad)

lm(formula = df3$Innovation ~ poly(df3$"Technological Readiness", 

     Min       1Q   Median       3Q      Max 
-0.89642 -0.32320 -0.00312  0.24694  1.19196 

                                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)                              3.55455    0.03764  94.441  < 2e-16 ***
poly(df3$"Technological Readiness", 2)1  7.83017    0.44054  17.774  < 2e-16 ***
poly(df3$"Technological Readiness", 2)2  2.99204    0.44054   6.792 3.29e-10 ***
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4405 on 134 degrees of freedom
Multiple R-squared:  0.7299,	Adjusted R-squared:  0.7258 
F-statistic:   181 on 2 and 134 DF,  p-value: < 2.2e-16

> par(mfrow = c(2, 2))
> plot(mod_usecase3_quad)

Here's how the resulting plots looks like: Regression supporting plots