1. Check data that we have already gathered at the first stage from this spreadsheet.
2. Look for data for potential measurements that mentioned in this spreadsheet for 2015, 2019, 2021, 2022 years .
3. Make a table describing the data already available and those that should be collected. Proposed measurements: short description, who made this data, source, columns description, timeframe, unit of observation, link to data on disk, etc.

Modify the script ./manipulation/ellis-ua-admin.R so that its product ./data-private/derived/ua-admin-map.rds includes rada as one of the levels of administrative units.
Specifically, create columns rada_code and rada_name in the object ds_admin

@Tytser , you will need to procure the data table where settlements are mapped onto radas, but if I remember correctly from the last time we spoke you have it handy somewhere.

Objective

Design the following frequency distribution graph :

X - number of radas in hromada's composition
Y - number of hromadas (frequency of occurce of hromadas with this number of radas)
Facet_wrap - oblast, free Y scaling, fixed X scaling
Color/FIll - region (e.g. West, East)

The oblasts arranged on 5 x 5 grid in integer sequence starting with 1 in the top left and increasing by 1 from letf to right, continuing each row with the leftmost cell. The order is controlled via column map_position in Kodificator tab oblasti and assigned as factor levels before passing to ggplot()

Notes

1. Develop ./analysis/regions-and-distributions/regions-and-distributions.R (which is an exact copy of ./scripts/templates/report-isolated.R) to host the script for producing this graph in chunk graph-1
2. Please work in ./analysis/regions-and-distribution/regions-and-distribution-yourname.R scripts on the main branch. I will be pulling consolidated solutions into the main script ./analysis/regions-and-distributions/regions-and-distributions.R
3. Do not commit graphs.

Looking forward

Prepare to develop this graph in a graphing function which would work with both discrete values on the X-scale (e.g. counts of radas) and continuous values (e.g. tax revenue)

Modifications

• add median lines for oblast, region, and country
• print total number of hromadas in the top right corner of the cell
• create display label for the facet that includes region name
• connect Rmd to R and kick-off a dynamic report

Glance ahead at the solution

For a given data set ds, defined as

ds_in <- 
  tibble::tribble(
  ~id, ~codes_1, ~date_1, ~codes_2, ~date_2,
  1, "22"   , "2015-01-01", "22,33"   , "2021-01-01",
  2, "44,55", "2015-01-01", "44,55,66", "2021-01-01",
  3, "77,88", "2015-01-01", "77,99"   , "2021-01-01",
  4, "100"  , "2015-01-01", "100"     , "2021-01-01",
 )

where id identifies hromadas (ATC) and codes_* contain the list of radas that comprise the hromada at that time (date_*)

> ds_in
# A tibble: 4 x 5
     id codes_1 date_1     codes_2  date_2    
  <dbl> <chr>   <chr>      <chr>    <chr>     
1     1 22      2015-01-01 22,33    2021-01-01
2     2 44,55   2015-01-01 44,55,66 2021-01-01
3     3 77,88   2015-01-01 77,99    2021-01-01
4     4 100     2015-01-01 100      2021-01-01

please develop a solution that would transform the data into the following form:

> ds_out
# A tibble: 14 x 3
      id date        code
   <dbl> <chr>      <dbl>
 1     1 2015-01-01    22
 2     1 2021-01-01    22
 3     1 2021-01-01    33
 4     2 2015-01-01    44
 5     2 2015-01-01    55
 6     2 2021-01-01    44
 7     2 2021-01-01    55
 8     2 2021-01-01    66
 9     3 2015-01-01    77
10     3 2015-01-01    88
11     3 2021-01-01    77
12     3 2021-01-01    99
13     4 2015-01-01   100
14     4 2021-01-01   100

Notes & Thoughts

1. Let's try to stay in the tidyverse, if possible.
2. I think purrr package might be useful here, but not sure
3. Use pivot_longer for wide-to-long transformation

Data tables have been provisioned by @velgaks and placed in the shared folder data-private/raw. The names of the file follow the pattern "revenue-NN.xlsx"

Use ./manipulation/ellis-budget.R to organize the code for wrangling this data into analysis-ready form.

Source: BOOST-analysis from OpenBudget. Data comes from the Ministry of Finance

Develop a function that would intake hromada's code and output a graph that shows how this hromada was accumulating radas as its members.

Wrangle the economic data for hromadas.

Compose a collection of data tables and programming scripts to represent the hierarchy of administrative levels of Ukraine.

Objectives

• describe relationship among oblast (24+1+2), raion (140), hormada (1,470), and rada (11,250)
• create relational database to accommodate accumulating measures in the future (research center)
• organize shape files for each unit of each level (working map)
• Latinize the names of relevant administrative nomenclature

@izasimovych could you please help me group oblasts into 5–6 larger regions? E.g. west, center, northeast, east, south, crimea. Or some thing that makes more sense to you in the context of our analysis.

Problem

Please see "./analysis/task/02-compare-rada-sources" for detailed description of the problem and fuller data context.

# We have two data files mapping radas to hromadas
# The first dataset we called  `rada_local` comes from the file
# "Центр суспільних даних. Місцеві ради 2014"  https://docs.google.com/spreadsheets/d/1iEbUsZSDGbJUzl_6wC3vgoVJ7GzOlc9f/edit?usp=sharing&ouid=106674411047619625756&rtpof=true&sd=trueентр суспільних даних. Місцеві ради 2014
rada_local
#  this data set stores information on N = _______ radas
rada_local %>% pull(rada_code) %>% unique() %>% length() %>% scales::comma()

#  the SECOND dataset we called `rada_united` comes from the file
# Центр суспільних даних. Обєдання громад - https://docs.google.com/spreadsheets/d/1xAFUDx8nf2oaIezWSBLaqitdxwEiQaOw/edit?usp=sharing&ouid=106674411047619625756&rtpof=true&sd=true
rada_united
# the list of radas in this file counts N = ________ radas
rada_united %>% pull(rada_code) %>% unique() %>% length() %>% scales::comma()
# this file records what radas makes up hromadas at the end of the amalgamation (2021)
# TODO:
# Explore the discrepancy between these two files
# Using the labels in the dataset `ds_admin`,  describe what radas/hromadas are
# missing from each file and speculate/expolain why. 

# Answer the following question:
# If we disregard the "Local" source and use only rada_united, will we miss anything relevant to our project? 
# In other words, if we need to rely on the mapping between radas and hromadas,
# are we safe to use the mapping derived from the "United" source? ( I think yes,
# but we need the proof)

# Notes:
# 1. Occupied territories is the most likely culprit, but there might be something else 
# 2. The report should compile into an html document
# 3. Please use the "main" branch, but create a separate script with your solution
# and call it "./analysis/tasks/02-compare-rada-sources-yourname.R"

@izasimovych , please follow the pattern for populating vector metric_name_label on line 40 of ./manipulation/ellis-economics.R to create descriptive labels for economic measured of hromadas stored in
App 3. Показники фінансвої спроможності тергромад 1 кв 2022 (в ІАС)

Notes:

• Try to keep it as short as possible. Ideally we don't want to be longer that 50-70 characters. The shorter the better.
• Please use the main branch and the same file to contribute, but DO NOT commit any other lines outside of the specified in this issue.

• 1. Застосувати алгоритм перетворення з тестових даних до даних датасету громад (привести до вигляду "id-date-code")
• 2. Створіть графік зміни кількості рад в громадах з 2015 по 2020
• 3. Створіть скрипт у ./analysis/temporal-composition/tc.R у якому буде створений цей графік, використовуючи дані, створені ./manipulation/1-ellis.R

When replicating, the script ./manipulate/ellis-demography.R generates the following error, seemingly from cause by line 132:

Error in `mutate()`:
! Problem while computing `hromada_name = str_replace(hromada_name, "i", "і")`.
Caused by error in `stri_replace_first_regex()`:
! object 'hromada_name' not found
Run `rlang::last_error()` to see where the error occurred.

Task

Develop a series of graphs that describe the available data assets that deal with economic indicators. Specifically, work with the product of the ./manipulation/ellis-economics.R (which digests App 3. Показники фінансвої спроможності тергромад 1 кв 2022 (в ІАС ) We don't know yet what those graphs will look like, but we want to:

• look at univariate distribution of each indicator
• look at bivariate distributions among indicators
• look at how indicators change with time
• how oblasts and regions vary among each other

Notes:

1. Use ./analysis/economic-outcomes/economic-outcomes.R as the starting point.
2. Store work in ./analysis/economic-outcomes/economic-outcomes-yourname.R script, I will integrate successful compositions in the main script ./analysis/economic-outcomes/economic-outcomes.R .
3. Do not commit graphs
4. See this commit for an example.
5. Contain each graph in a dedicated chunk (e.g. graph-2)

When performing string manipulations on Ukrainian characters, it sometimes does not recognize and/or transforms the matched characters:

Sys.setlocale("LC_CTYPE", "russian")
Sys.setlocale("LC_CTYPE", "ukr")
d <- tibble::tribble(
  ~a , ~ b,
  "громада", "область"
)
d %>% mutate(a = str_remove(a,"гр"))

Please investigate this behavior and report possible solutions

Explore population of Ukraine in 2022 using the official statistics from the State Statistics Service.

Serhii @Tytser is the dedicated driver of the analysis. This means that only he should be editing the script "./analysis/ua-pop-2022.R`. Other analysts, who want to contribute to the efforts, need to create a named version of the script (e.g. "./analysis/ua-pop-2022/ua-pop-2022-andriy.R" and alert the driver of the proposed changed

@izasimovych , the data table you have provisioned( App 3. Показники фінансвої спроможності тергромад 1 кв 2022 (в ІАС)) contains the measure entitled "ПДФО (грн/1 особу)" in column Z. It's not clear to what time point this measure captures. Please modify the cell Z2 to clarify.

Also, please double-check the accuracy and appropriateness of latinized labels in row 3.