In bedatadriven/activityinfo-R: R interface to ActivityInfo.org, an information management software for humanitarian and development operations

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 4
)

R is known to be a very powerful language for the analysis and visualization. For visualizing data, there are better existing solutions that provide quite bit of interactivity with the ActivityInfo data e.g. ActivityInfo's built-in visualization tools, Power BI, Tableau etc.

However, there are number of things one can only do with R like advanced analysis such as prediction, statistical analysis, text mining and so on.

R is a batteries-included language that has many built-in calls for many statistical analysis. Besides, it has external package system that one of the best ones is ggplot that provides powerful graphics to the users.

library(activityinfo)

Build a regression model

We have an example fake dataset illustrating the building maintainance of the high schools in the Netherlands.

We first pull the data from ActivityInfo and we then use R calls to perform analyses. The schools table below is the saved & cleaned version of the data pulled via the ActivityInfo API R client's queryTable() call.

pat <- system.file("extdata", "schools.csv", package = "activityinfo")
schools <- read.csv(pat, stringsAsFactors = FALSE)
head(schools)

As an advanced analysis, we try to predict the building value based on the square meters of the building. For this analysis we use a linear regression model. On the one column, we have a high school that it has the square meters of the building and on the other column, we have a building value information.

library(ggplot2)

ggplot(schools, aes(square_meters_of_school, building_value)) +
  geom_point(aes(color = as.factor(is_painted))) +
  geom_smooth(method = "lm", se = FALSE) +
  scale_y_continuous(labels = scales::dollar_format(
    scale = 1 / 1e6,
    prefix = "\u20ac",
    suffix = "M"
  )) +
  scale_color_discrete(
    name = "The building painted",
    breaks = c(1, 0),
    labels = c("Yes", "No")
  ) +
  labs(
    title = "Building value vs. sqm of building",
    x = "Square meters of building",
    y = "Building value"
  ) +
  theme_minimal()

Text analysis

We count the number of words in the description field, which is a multi-line narrative field in the ActivityInfo, and produce a bar chart.

library(quanteda)

n_tokens <- ntoken(char_tolower(schools$situation_description), remove_punct = TRUE)
n_tokens <- as.integer(n_tokens)
df <- data.frame(
  school = schools$school_name,
  n_tokens = n_tokens,
  stringsAsFactors = FALSE
)
df

ggplot(df, aes(x = reorder(school, n_tokens), y = n_tokens)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  coord_flip() +
  theme_minimal()

For deeper information how the textual data stored in the ActivityInfo is analyzed, please check the QualMiner project.

bedatadriven/activityinfo-R documentation built on Dec. 21, 2024, 8:23 a.m.