project_start.qmd

# Project start

In this chapter, we are going to work together on a very simple project. This
project will stay with us until the end of the book. As we will go deeper in the
book together, you will rewrite that project by implementing the techniques I
will teach you. By the end of the book you will have built a reproducible
analytical pipeline. To get things going, we are going to keep it simple; our
goal here is to get an analysis done, that's it. We won't focus on
reproducibility. We are going to download some data, and analyse it, that's it.

## Housing in Luxembourg

We are going to download data about house prices in Luxembourg. Luxembourg is a
little Western European country the author hails from that looks like a shoe and
is about the size of .98 Rhode Islands. Did you know that Luxembourg is a
constitutional monarchy, and not a kingdom like Belgium, but a Grand-Duchy, and
actually the last Grand-Duchy in the World? Also, what you should know to
understand what we will be doing is that the country of Luxembourg is divided
into Cantons, and each Cantons into Communes. If Luxembourg was the USA, Cantons
would be States and Communes would be Counties (or Parishes or Boroughs). What’s
confusing is that “Luxembourg” is also the name of a Canton, and of a Commune,
which also has the status of a city and is the capital of the country. So
Luxembourg the country, is divided into Cantons, one of which is called
Luxembourg as well, cantons are divided into communes, and inside the canton of
Luxembourg there's the commune of Luxembourg which is also the city of
Luxembourg, sometimes called Luxembourg-City, which is the capital of the
country.

::: {.content-hidden when-format="pdf"}
<figure>
    <img src="images/lux_rhode_island.png"
         alt="Luxembourg is about as big as the US State of Rhode Island."></img>
    <figcaption>Luxembourg is about as big as the US State of Rhode Island.</figcaption>
</figure>
:::

::: {.content-visible when-format="pdf"}
```{r, echo = F, out.width="400px"}
#| fig-cap: "Luxembourg is about as big as the US State of Rhode Island."
knitr::include_graphics("images/lux_rhode_island.png")
```
:::

What you should also know is that the population is about 645,000 as of writing
(January 2023), half of which are foreigners. Around 400,000 persons work in
Luxembourg, of which half do not live in Luxembourg; so every morning from
Monday to Friday, 200,000 people enter the country to work and then leave in the
evening to go back to either Belgium, France or Germany, the neighbouring
countries. As you can imagine, this puts enormous pressure on the transportation
system and on the roads, but also on the housing market; everyone wants to live
in Luxembourg to avoid the horrible daily commute, and everyone wants to live
either in the capital city, or in the second largest urban area in the south, in
a city called Esch-sur-Alzette.

The plot below shows the value of the House Price Index through time for
Luxembourg and the European Union:

```{r, echo = F}
#https://ec.europa.eu/eurostat/databrowser/bookmark/21530d9a-c423-4ab7-998a-7170326136cd?lang=en
housing_lu_eu <- read.csv("datasets/prc_hpi_a__custom_4705395_page_linear.csv.gz")

withr::with_package("ggplot2",
  {
    ggplot(data = housing_lu_eu) +
      geom_line(aes(y = OBS_VALUE, x = TIME_PERIOD, group = geo, colour = geo),
                size = 1.5) +
      labs(title = "House price and sales index (2010 = 100)",
           caption = "Source: Eurostat") +
      theme_minimal() +
      theme(legend.position = "bottom")
  }
  )

```

If you want to download the data, click
[here](https://github.com/b-rodrigues/rap4all/raw/master/datasets/prc_hpi_a__custom_4705395_page_linear.csv.gz)^[https://is.gd/AET0ir].

Let us paste the definition of the HPI in here (taken from the HPI's
[metadata](https://archive.is/OrQwA)^[https://archive.is/OrQwA, archived link for posterity.] page):

*The House Price Index (HPI) measures inflation in the residential property market. The HPI
captures price changes of all types of dwellings purchased by households (flats, detached houses,
terraced houses, etc.). Only transacted dwellings are considered, self-build dwellings are
excluded. The land component of the dwelling is included.*

So from the plot, we can see that the price of dwellings more than doubled
between 2010 and 2021; the value of the index is 214.81 in 2021 for Luxembourg,
and 138.92 for the European Union as a whole.

There is a lot of heterogeneity though; the capital and the communes right next
to the capital are much more expensive than communes from the less densely
populated north, for example. The south of the country is also more expensive
than the north, but not as much as the capital and surrounding communes. Not
only is price driven by demand, but also by scarcity; in 2021, 0.5% of residents
owned 50% of the buildable land for housing purposes (Source: *Observatoire de
l'Habitat, Note 29*, [archived download
link](https://archive.org/download/note-29/note-29.pdf)^[https://archive.org/download/note-29/note-29.pdf]).

Our project will be quite simple; we are going to download some data, supplied
as an Excel file, compiled by the Housing Observatory (*Observatoire de
l'Habitat*, a service from the Ministry of Housing, which monitors the evolution
of prices in the housing market, among other useful services like the
identification of vacant lots). The advantage of their data when compared to
Eurostat's data is that the data is disaggregated by commune. The disadvantage
is that they only supply nominal prices, and no index (and the data is trapped
inside Excel and not ready for analysis with R). Nominal prices are the prices
that you read on price tags in shops. The problem with nominal prices is that it
is difficult to compare them through time. Ask yourself the following question:
would you prefer to have had 500€ (or USDs) in 2003 or in 2023? You probably
would have preferred them in 2003, as you could purchase a lot more with $500
then than now. In fact, according to a random inflation calculator I googled, to
match the purchasing power of $500 in 2003, you'd need to have $793 in 2023 (and
I'd say that we find very similar values for €). But it doesn't really matter if
that calculation is 100% correct: what matters is that the value of money
changes, and comparisons through time are difficult, hence why an index is quite
useful. So we are going to convert these nominal prices to real prices. Real
prices take inflation into account and so allow us to compare prices through
time.

So to summarise; our goal is to:

- Get data trapped inside an Excel file into a neat data frame;
- Convert nominal to real prices using a simple method;
- Make some tables and plots and call it a day (for now).

We are going to start in the most basic way possible; we are simply going to
write a script and deal with each step separately.

## Saving trapped data from Excel

Getting data from Excel into a tidy data frame can be very tricky. This is
because very often, Excel is used as some kind of dashboard or presentation
tool. So data is made human-readable, in contrast to machine readable. Let us
quickly discuss this topic as it is essential to grasp the difference between
the two (and in our experience, a lot of collective pain inflicted to
statisticians and researchers could have been avoided if this concept was more
well-known). The picture below shows an Excel file made for human consumption:

::: {.content-hidden when-format="pdf"}
<figure>
    <img src="images/obs_hab_xlsx_overview.png"
         alt="An Excel file meant for human eyes."></img>
    <figcaption>An Excel file meant for human eyes.</figcaption>
</figure>
:::

::: {.content-visible when-format="pdf"}
```{r, echo = F}
#| fig-cap: "An Excel file meant for human eyes."
knitr::include_graphics("images/obs_hab_xlsx_overview.png")
```
:::

So why is this file not machine-readable? Here are some issues:

- The table does not start in the top-left corner of the spreadsheet, which is where most importing tools expect it to be;
- The spreadsheet starts with a header that contains an image and some text;
- Numbers are text and use "," as the thousands separator;
- You don't see it in the screenshot, but each year is in a separate sheet.

That being said, this Excel file is still very tame, and going from this Excel
to a tidy data frame will not be too difficult. In fact, we suspect that whoever
made this Excel file is well aware of the contradicting requirements of human
and machine readable formatting of data, and strove to find a compromise.
Because more often than not, getting human readable data into a machine readable
format is a nightmare. We could call data like this *machine-friendly* data.

If you want to follow along, you can download the Excel file
[here](https://github.com/b-rodrigues/rap4all/raw/master/datasets/vente-maison-2010-2021.xlsx)^[https://is.gd/1vvBAc]
(downloaded on January 2023 from the [luxembourguish open data
portal](https://data.public.lu/en/datasets/prix-annonces-des-logements-par-commune/)^[https://data.public.lu/en/datasets/prix-annonces-des-logements-par-commune/]).
But you don’t need to follow along with code, because I will link the completed
scripts for you to download later.

Each sheet contains a dataset with the following columns:

- *Commune*: the commune (the smallest administrative division of territory);
- *Nombre d'offres*: the total number of selling offers;
- *Prix moyen annoncé en Euros courants*: Average selling price in nominal Euros;
- *Prix moyen annoncé au m2 en Euros courants*: Average selling price in square meters in nominal Euros.

For ease of presentation, I'm going to show you each step of the analysis here
separately, but I'll be putting everything together in a single script once
I'm done explaining each step. So first, let's load some packages:

```{r, messages = FALSE}
library(dplyr)
library(purrr)
library(readxl)
library(stringr)
library(janitor)
```

Next, the code below downloads the data, and puts it in a data frame:

```{r}
# The url below points to an Excel file
# hosted on the book’s github repository
url <- "https://is.gd/1vvBAc"

raw_data <- tempfile(fileext = ".xlsx")

download.file(url, raw_data, method = "auto", mode = "wb")

sheets <- excel_sheets(raw_data)

read_clean <- function(..., sheet){
  read_excel(..., sheet = sheet) |>
    mutate(year = sheet)
}

raw_data <- map(
  sheets,
  ~read_clean(raw_data,
              skip = 10,
              sheet = .)
                   ) |>
  bind_rows() |>
  clean_names()

raw_data <- raw_data |>
  rename(
    locality = commune,
    n_offers = nombre_doffres,
    average_price_nominal_euros = prix_moyen_annonce_en_courant,
    average_price_m2_nominal_euros = prix_moyen_annonce_au_m2_en_courant,
    average_price_m2_nominal_euros = prix_moyen_annonce_au_m2_en_courant
  ) |>
  mutate(locality = str_trim(locality)) |>
  select(year, locality, n_offers, starts_with("average"))
```

If you are familiar with the `{tidyverse}` the above code should be quite easy
to follow. We start by downloading the raw Excel file and save the sheet names
into a variable. We then use a function called `read_clean()`, which takes the
path to the Excel file and the sheet names as an argument to read the required
sheet into a data frame. We use `skip = 10` to skip the first 10 lines in each
Excel sheet because the first 10 lines contain a header. The last thing this
function does is add a new column called `year` which contains the year of the
data. We’re lucky, because the sheet names are the years: "2010", "2011" and so
on. We then map this function to the list of sheet names, thus reading in all
the data from all the sheets into one list of data frames. We then use
`bind_rows()`, to bind each data frame into a single data frame, by row.
Finally, we rename the columns (by translating their names from French to
English) and only select the required columns. If you don’t understand each step
of what is going on, don’t worry too much about it; this book is not about
learning how to use R.

Running this code results in a neat data set:

```{r}
raw_data
```


But there’s a problem: columns that should be of type numeric are of type
character instead (`average_price_nominal_euros` and
`average_price_m2_nominal_euros`). There's also another issue, which you would
eventually catch as you’ll explore the data: naming of the communes is
not consistent. Let's take a look:

```{r}
raw_data |>
  filter(grepl("Luxembourg", locality)) |>
  count(locality)

```

We can see that the city of Luxembourg is spelled in two different ways. It's
the same with another commune, Pétange:

```{r}
raw_data |>
  filter(grepl("P.tange", locality)) |>
  count(locality)

```

So sometimes it is spelled correctly, with an "é", sometimes not. Let's write
some code to correct both these issues:

```{r}
raw_data <- raw_data |>
  mutate(locality = ifelse(grepl("Luxembourg-Ville", locality),
                           "Luxembourg",
                           locality),
         locality = ifelse(grepl("P.tange", locality),
                           "Pétange",
                           locality)
         ) |>
  mutate(across(starts_with("average"), as.numeric))

```

Now this is interesting -- converting the `average` columns to numeric resulted in some `NA` values.
Let's see what happened:

```{r}
raw_data |>
  filter(is.na(average_price_nominal_euros))
```

It turns out that there are no prices for certain communes, but that we also
have some rows with garbage in there. Let's go back to the raw data to see what
this is about:

::: {.content-hidden when-format="pdf"}
<figure>
    <img src="images/obs_hab_xlsx_missing.png"
         alt="Always look at your data."></img>
    <figcaption>Always look at your data.</figcaption>
</figure>
:::

::: {.content-visible when-format="pdf"}
```{r, echo = F}
#| fig-cap: "Always look at your data."
knitr::include_graphics("images/obs_hab_xlsx_missing.png")
```
:::

So it turns out that there are some rows that we need to remove. We can start by
removing rows where `locality` is missing. Then we have a row where `locality`
is equal to "Total d'offres". This is simply the total of every offer from every
commune. We could keep that in a separate data frame, or even remove it. Finally
there's a row, the last one, that states the source of the data, which we can
remove.

In the screenshot above, we see another row that we don't see in our filtered
data frame: one where `n_offers` is missing. This row gives the national average
for columns `average_prince_nominal_euros` and `average_price_m2_nominal_euros`.
What we are going to do is create two datasets: one with data on communes, and
the other on national prices. Let's first remove the rows stating the sources:

```{r}
raw_data <- raw_data |>
  filter(!grepl("Source", locality))
```

Let's now only keep the communes in our data:

```{r}
commune_level_data <- raw_data |>
    filter(!grepl("nationale|offres", locality),
           !is.na(locality))
```

And let's create a dataset with the national data as well:

```{r}
country_level <- raw_data |>
  filter(grepl("nationale", locality)) |>
  select(-n_offers)

offers_country <- raw_data |>
  filter(grepl("Total d.offres", locality)) |>
  select(year, n_offers)

country_level_data <- full_join(country_level, offers_country) |>
  select(year, locality, n_offers, everything()) |>
  mutate(locality = "Grand-Duchy of Luxembourg")

```

Now the data looks clean, and we can start the actual analysis... or can we?
Before proceeding, it would be nice to make sure that we got every commune in
there. For this, we need a list of communes from Luxembourg. [Thankfully,
Wikipedia has such a
list.](https://en.wikipedia.org/wiki/List_of_communes_of_Luxembourg)^[https://en.wikipedia.org/wiki/List_of_communes_of_Luxembourg]

Let's scrape and save this list:

```{r}
current_communes <-
  "https://en.wikipedia.org/wiki/List_of_communes_of_Luxembourg" |>
  rvest::read_html() |>
  rvest::html_table() |>
  purrr::pluck(1) |>
  janitor::clean_names()

```

We scrape the table from the Wikipedia page using `{rvest}`.
`rvest::html_table()` returns a list of tables from the Wikipedia table, and
then we use `purrr::pluck()` to keep the first table from the website, which is
what we need (I made the calls to the packages explicit, because you might not
be familiar with these packages). `janitor::clean_names()` transforms column
names written for human eyes into machine friendly names (for example `Growth
rate in %` would be transformed to `growth_rate_in_percent`).

Let’s see if we have all the communes in our data:

```{r}
setdiff(unique(commune_level_data$locality), current_communes$commune)
```

We see many communes that are in our `commune_level_data`, but not in
`current_communes`. There’s one obvious reason: differences in spelling, for
example, "Kaerjeng" in our data, but "Käerjeng" in the table from Wikipedia. But
there’s also a less obvious reason; since 2010, several communes have merged
into new ones. So there are communes that are in our data in 2010 and
2011, but disappear from 2012 onwards. So we need to do several things: first,
get a list of all existing communes from 2010 onwards, and then, harmonise
spelling. Here again, we can use a list from Wikipedia:

```{r}
former_communes <-
  "https://en.wikipedia.org/wiki/Communes_of_Luxembourg#Former_communes" |>  
  rvest::read_html() |>
  rvest::html_table() |>
  purrr::pluck(3) |>
  janitor::clean_names() |>
  dplyr::filter(year_dissolved > 2009)

former_communes

```

As you can see, since 2010 many communes have merged to form new ones. We can
now combine the list of current and former communes, as well as harmonise their
names:

```{r}
communes <- unique(c(former_communes$name, current_communes$commune))
# we need to rename some communes

# Different spelling of these communes between wikipedia and the data

communes[which(communes == "Clemency")] <- "Clémency"
communes[which(communes == "Redange")] <- "Redange-sur-Attert"
communes[which(communes == "Erpeldange-sur-Sûre")] <- "Erpeldange"
communes[which(communes == "Luxembourg-City")] <- "Luxembourg"
communes[which(communes == "Käerjeng")] <- "Kaerjeng"
communes[which(communes == "Petange")] <- "Pétange"
```

Let’s run our test again:

```{r}
setdiff(unique(commune_level_data$locality), communes)
```

Great! When we compare the communes that are in our data with every commune that
has existed since 2010, we don’t have any commune that is unaccounted for. So
are we done with cleaning the data? Yes, we can now start with analysing the
data. Take a look
[here](https://raw.githubusercontent.com/b-rodrigues/rap4all/master/scripts/save_data.R)^[https://is.gd/7PhUjd]
to see the finalised script. Also read some of the comments that I’ve added. This
is a typical R script, and at first glance, one might wonder what is wrong with
it. Actually, not much, but the problem if you leave this script as it is, is
that it is very likely that we will have problems rerunning it in the future. As
it turns out, this script is not reproducible. But we will discuss this in much
more detail later on. For now, let’s analyse our cleaned data.

## Analysing the data

We are now going to analyse the data. The first thing we are going to do is
compute a Laspeyeres price index. This price index allows us to make comparisons
through time; for example, the index at year 2012 measures how much more
expensive (or cheaper) housing became relative to the base year (2010). However,
since we only have one 'good' (housing), this index becomes quite simple to compute: it is
nothing but the prices at year *t* divided by the prices in 2010 (if we had a
basket of goods, we would need to use the Laspeyeres index formula to compute
the index at all periods).

For this section, I will perform a rather simple analysis. I will immediately
show you the R script: take a look at it
[here](https://raw.githubusercontent.com/b-rodrigues/rap4all/master/scripts/analysis.R)^[https://is.gd/qCJEbi].
For the analysis I selected 5 communes and plotted the evolution of prices
compared to the national average.

This analysis might seem trivially simple, but it contains all the needed
ingredients to illustrate everything else that I’m going to teach you in this
book.

Most analyses would stop here: after all, we have what we need; our goal was to
get the plots for the 5 communes of Luxemourg, Esch-sur-Alzette, Mamer, Schengen
(which gave its name to the [Schengen
Area](https://en.wikipedia.org/wiki/Schengen_Area)^[https://en.wikipedia.org/wiki/Schengen_Area])
and Wincrange. However, let’s ask ourselves the following important questions:

- How easy would it be for someone else to rerun the analysis?
- How easy would it be to update the analysis once new data gets published?
- How easy would it be to reuse this code for other projects?
- What guarantee do we have that if the scripts get run in 5 years, with the same input data, we get the same output?

Let’s answer these questions one by one.

## Your project is not done

### How easy would it be for someone else to rerun the analysis?

The analysis is composed of two R scripts, one to prepare the data, another to
actually run the analysis proper. Performing the analysis might seem quite easy,
because each script contains comments as to what is going on, and the code is
not that complicated. However, we are missing any project-level documentation
that would provide clear instructions as to how to run the analysis. This might
seem simple for us who wrote these scripts, but we are familiar with R, and this
is still fresh in our brains. Should someone less familiar with R have to run
the script, there is no clue for them as to how they should do it. And of
course, should the analysis be more complex (suppose it’s composed of a dozens
scripts), this gets even worse. It might not even be easy for you to remember
how to run this in 5 months!

And what about the required dependencies? Many packages were used in the
analysis. How should these get installed? Ideally, the same versions of the
packages you used and the same version of R should get used by that person to
rerun the analysis.

All of this still needs to be documented, but listing the packages that were
used for an analysis and their versions takes quite some time. Thankfully, in
part 2, we will learn about the `{renv}` package to deal with this in a couple
lines of code.

### How easy would it be to update the project?

If new data gets published, all the points discussed previously are still valid,
plus you need to make sure that the updated data is still close enough to the
previous data such that it can pass through the data cleaning steps you wrote.
You should also make sure that the update did not introduce a mistake in past
data, or at least alert you if that is the case. Sometimes, when new years get
added, data for previous years also get corrected, so it would be nice to make
sure that you know this. Also, in the specific case of our data, communes might
get fused into a new one, or maybe even divided into smaller communes (even
though this has not happened in a long time, it is not entirely out of the
question).

In summary, what is missing from the current project are enough tests to make
sure that an update to the data can happen smoothly.

### How easy would it be to reuse this code for another project?

Said plainly, not very easy. With code in this state you have no choice but to
copy and paste it into a new script and change it adequately. For re-usability,
nothing beats structuring your code into functions and ideally you would even
package them. We are going to learn just that in future chapters of this book.

But sometimes you might not be interested in reusing code for another project:
however, even if that’s the case, structuring your code into functions and
packaging them makes it easy to reuse code even inside the same project. Look at
the last part of the `analysis.R` script: we copied and pasted the same code 5
times and only slightly changed it. We are going to learn how not to repeat
ourselves by using functions and you will immediately see the benefits of
writing functions, even when simply to reuse them inside the same project.

### What guarantee do we have that the output is stable through time?

Now this might seem weird: after all, if we start from the same dataset, does it
matter *when* we run the scripts? We should be getting the same result if we
build the project today, in 5 months or in 5 years. Well, not necessarily. While
it is true that R is quite stable, this cannot necessarily be said of the
packages that we use. There is no guarantee that the authors of the packages
will not change the package’s functions to work differently, or take arguments
in a different order, or even that the packages will all be available at all in
5 years. And even if the packages are still available and function the same,
bugs in the packages might get corrected that could alter the result. This might
seem like a non-problem; after all, if bugs get corrected, shouldn’t you be
happy to update your results as well? But this depends on what it is we’re
talking about. Sometimes it is necessary to reproduce results exactly as they
were, even if they were wrong, for example in the context of an audit.

So we also need a way to somehow snapshot and freeze the computational
environment that was used to create the project originally.

## Conclusion

We now have a basic analysis that has all we need to get started. In the coming
chapters, we are going to learn about topics that will make it easy to write
code that is more robust, better documented and tested, and most importantly
easy to rerun (and thus to reproduce the results). The first step will actually
not involve having to start rewriting our scripts though; next we are going to
learn about Git, a tool that will make our life easier by versioning our code.