In unhcr-americas/IatiTidy: Tidy Data from IATI Data Store & FTS API

To understand how the UNOCHA Financial Tracking Service is working, you can wathc this intro video

OCHA has looked at building interoperrability with IATI through a pilot project in 2019/2020, resulting in Lessons from connecting IATI And FTS and the following report: Improving humanitarian transparency with the International Aid Transparency Initiative (IATI)and the UN OCHA Financial Tracking Service (FTS)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(IatiTidy)
library(unhcRstyle)
library(tidyverse)

First we can pull data from the API with the get_FTS function. here we pull data for the last 3 years.

data <- get_FTS(boundary = "year=2018,2019,2020")

The result of the query is a list with multiple outputs - We can look first at destination

Destination

First we subset the output on Countries in South America

South_America <- c("Argentina", "Bolivia, Plurinational State of" , "Brazil", "Chile" , "Colombia" ,
  "Ecuador" , "Falkland Islands (Malvinas)" , "Guyana" , "Paraguay" , "Peru" ,
  "Suriname", "Uruguay"  , "Venezuela, Bolivarian Republic of")


## list of flowid linked to South America

flowidsouthamerica <- as.character(data$flows.destination[data$flows.destination$name %in% South_America, c("flowid") ])

#Now subset the resulting
flow2SoutAhmerica <- data$flows.destination[data$flows.destination$flowid %in% flowidsouthamerica,  ]

let's look now at some tabulation - first check by flow-type

#knitr::kable(head(ftsflow2020, 10))

year <- as.data.frame(table(flow2SoutAhmerica[ flow2SoutAhmerica$type == "UsageYear", c("name")],
                                      flow2SoutAhmerica[ flow2SoutAhmerica$type == "UsageYear", c("status")],
                                      useNA = "ifany"))
ggplot(year) +
 aes(x = Var1, fill = Var2, weight = Freq) +
 geom_bar() +
 scale_fill_viridis_d(option = "plasma") +
 labs(x = "Year", y = "# of Flow", 
      title = "Flow per year and type in South America", 
      caption = "Based on FTS Data", 
      fill = "Flow Type") +
 unhcr_theme()+
 theme(legend.position = "top") 

Let's filter only on the paid type in 2020

#knitr::kable(head(ftsflow2020, 10))

dataplot <- flow2SoutAhmerica %>%
  ## Filter flow id that were paid in 2020  
  filter(flowid %in% as.character(flow2SoutAhmerica[flow2SoutAhmerica$name %in% c("2020"), c("flowid") ])) %>% 
  # Also to avoid double count, we need to filter out the flows with Children   
  filter(status %in% "paid") %>%
  # Also to avoid double count, we need to filter out the flows with Children
  filter(haschild %in% "nochild") %>%
  # Also to avoid double count, we need to filter only rows representing countries
  filter(type %in% "Location") %>%
  select(name, amountUSD) %>%
  group_by(name) %>%
  summarise(amountUSD = sum(amountUSD))%>%
  # Redefine the levels of the `relig` factor variable
  mutate(name = fct_reorder(name, amountUSD))  

#levels(dataplot$name)

ggplot(dataplot ) +
  aes(x = fct_reorder(name, amountUSD),  weight = amountUSD) +
  geom_bar(fill = unhcr_blue) +
 # facet_wrap(vars(method)) +
  scale_fill_brewer(palette = "Accent") +
  labs(x = "Country", y = "Amount in USD", 
       title = "Total Amount per Country", 
       fill = "Delivery Method") +
  unhcr_theme()+
 # theme_minimal() +
  theme(legend.position = "top") +
  coord_flip()+ 
  scale_y_continuous(labels = format_si())

#knitr::kable(head(ftsflow2020, 10))


dataplot <- flow2SoutAhmerica %>%
 ## Filter flow id that were paid in 2020  
 filter(flowid %in% as.character(flow2SoutAhmerica[flow2SoutAhmerica$name %in% c("2020"), c("flowid") ])) %>% 
 # Also to avoid double count, we need to filter out the flows with Children   
 filter(status %in% "paid") %>%
 # Also to avoid double count, we need to filter out the flows with Children
 filter(haschild %in% "nochild") %>%
 # Also to avoid double count, we need to filter only rows representing countries
 filter(type %in% "Location") %>%
 select(name,  method, amountUSD) %>%
 group_by(name,  method) %>%
 summarise(amountUSD = sum(amountUSD))%>%
 # Redefine the levels of the `relig` factor variable
 mutate(name = fct_reorder(name, amountUSD))  

levels(dataplot$name)

 ggplot(dataplot ) +
 aes(x = fct_reorder(name, amountUSD),  weight = amountUSD) +
 geom_bar(fill = unhcr_blue) +
 facet_wrap(vars(method)) +
 scale_fill_brewer(palette = "Accent") +
 labs(x = "Country", y = "Amount in USD", 
      title = "Total Amount per Country", 
      fill = "Delivery Method") +
  unhcr_theme()+
 # theme_minimal() +
  theme(legend.position = "top") +
  coord_flip()+ 
  scale_y_continuous(labels = format_si())

#knitr::kable(head(ftsflow2020, 10))


flow2SoutAhmerica.paid.unique <- flow2SoutAhmerica.paid[ flow2SoutAhmerica.paid$childFlowIds == "NULL", ]

## How many record per countries..

unhcr-americas/IatiTidy documentation built on June 6, 2021, 12:24 a.m.