Data_Wrangling_Part1_(data_cleaning).Rmd

---
title: ""
output: 
  html_document: 
    df_print: paged
    code_folding: hide
---

Data wrangling is a set of procedures aimed at "cleaning" a data frame, removing inconsistencies, typos and mistakes, to prepare a solid ground for future analysis.

The mindset that will guide us here will be to remove rows that don't pertain to real transactions between the retailer and the customers.

Building up from our previous document, we will start by checking, for every column, if the definitions stated in the source are respected.

```{r, include = FALSE}
library(readxl)
df <- read_excel(here::here("online_retail_II.xlsx"))
library(dplyr)
df <- df %>%
  mutate(CustomerID = as.character(`Customer ID`), .keep = "unused", .after = Price)
```

<br>

# - *Invoice*

The first definition determines that, if an invoice starts with `C`, like `C489449`, it means that it has been cancelled.

But we can also find values that start with a letter different that `C`.

```{r}
library(stringr)
df %>%
  filter(str_length(Invoice) != 6 &
           !str_starts(Invoice, "C"))
```

Invoices that don't seem to be actual transactions with a customer, so we will remove them.

<br>

# - *StockCode*

About the stock codes, not all of them are "5-digit integral number".

```{r}
df %>%
  filter(str_length(StockCode) != 5)
```

Among those, values like `79323P` and `79323W` are actual transactions and must be kept.

From the remaining `77` values,

```{r}
df %>%
  filter(str_length(StockCode) != 5 &
           !str_detect(StockCode, "^\\d{5}[a-zA-Z]{1,2}$")) %>%
  count(StockCode, Description, sort = TRUE, name = "Number of Occurrences")
```

we will keep `PADS` and the values starting with `DCGS`, `SP` or `gift`, leaving us with `2900` rows to remove from the data frame.

```{r}
df %>%
  filter(str_length(StockCode) != 5 &
           !str_detect(StockCode, "^\\d{5}[a-zA-Z]{1,2}$") &
           !str_detect(StockCode, "PADS|DCGS|SP|gift"))
```

<br>

## - *D for Discount*

About `D`, that stands for `Discount`, we follow the common understanding that, under certain conditions, a discount is applied to an invoice to lower its total amount, and already we notice that none of the `Discount` stock codes have a negative value, as one should expect, in the `Price` column.

```{r}
library(knitr)
kable(df %>%
        filter(StockCode == "D" &
                 Price < 0) %>%
        tally(name = "Number of `Discount` Stock Codes with a Negative Price"), align = "l")
```

It could be the case though that the discount is applied through the negative value in the `Quantity` column (multiplying `Quantity` per `Price`), but there are as well `5` purchases with a positive value in it.

```{r}
df %>% 
  filter(StockCode == "D") %>%
  count("Negative Quantity" = if_else(Quantity < 0, "Yes", "No"))
```

The negative values pertain to cancelled invoices while the positive ones to confirmed ones.

```{r}
df %>% 
  filter(StockCode == "D") %>%
  count("Negative Quantity" = if_else(Quantity < 0, "Yes", "No"),
        Status = if_else(str_starts(Invoice, "C"), "Cancelled", "Confirmed")
        , name = "Number of Invoices")
```

<br>

Overall there are `90` invoices, `85` cancelled and `5` confirmed, with at least one row with `StockCode` equal to `D`,

```{r}
df %>% 
  group_by(Invoice) %>%
  filter(any(StockCode == "D")) %>%
  summarise(n = n()) %>%
  count(Status = if_else(str_starts(Invoice, "C"), "Cancelled", "Confirmed"), name = "Number of Invoices")
```

<br>

for a total of `131` rows.

```{r}
df %>% 
  group_by(Invoice) %>%
  filter(any(StockCode == "D")) %>%
  ungroup()
```

<br>

`81` of those are single line invoices, so we don't understand what they should have discounted.

```{r}
df %>% 
  group_by(Invoice) %>%
  filter(any(StockCode == "D")) %>%
  summarise("Number of Items" = n()) %>%
  count(`Number of Items`, sort = TRUE, name = "Number of Occurrences")
```

Maybe it's a discount on a previous invoice, but we wouldn't know how to connect them together, plus `76` out of those `81` single line invoices have been cancelled as well.

```{r}
df %>% 
  group_by(Invoice) %>%
  filter(any(StockCode == "D") &
           n() == 1) %>%
  ungroup() %>%
  count("Status of Single Line Invoices" = if_else(str_starts(Invoice, "C"), "Cancelled", "Confirmed"), name = "Number of Invoices")
```

<br>

The `5` that have not been cancelled have, like all the others, a positive value in the `Price` column and that, putting aside a mistake that can be easily fixed, begs the question on whether `D` (`Discount`) is an article that can be bought and then redeemed later, like a voucher, but, as we can see in a previous table, we already have the `gift_xxxx_xx` stock code for that (unless that is specific to `Dotcomgiftshop`).

```{r}
df %>% 
  group_by(Invoice) %>%
  filter(any(StockCode == "D") &
           !str_starts(Invoice, "C")) %>%
  ungroup()
```

<br>

About the `9` invoices with more than one line,

```{r}
df %>% 
  group_by(Invoice) %>%
  filter(any(StockCode == "D") &
           n() > 1) %>%
  ungroup()
```

for `3` of them it is always `D` the stock code.

```{r}
df %>% 
  group_by(Invoice) %>%
  filter(all(StockCode == "D") &
           n() > 1) %>%
  ungroup()
```

<br>

The `6` remaining can be investigated further, at a first glance we can see that they all have been cancelled.

```{r}
df %>% 
  group_by(Invoice) %>%
  filter(any(StockCode == "D") &
           n() > 1) %>%
  ungroup() %>%
  anti_join(df %>%
              group_by(Invoice) %>%
              filter(all(StockCode == "D") &
                       n() > 1) %>%
              ungroup(), by = "Invoice")
```

<br>

Incidentally the value of discounts in percentage is very high for some of them, it seems more like a refund plus eventual fines.

```{r}
df %>% 
  group_by(Invoice) %>%
  filter(any(StockCode == "D") &
           n() > 1) %>%
  ungroup() %>%
  anti_join(df %>%
              group_by(Invoice) %>%
              filter(all(StockCode == "D") &
                       n() > 1) %>%
              ungroup(), by = "Invoice") %>%
  mutate(Status = if_else(str_detect(StockCode, "D"), "Discount", "Purchases")) %>%
  group_by(Invoice, Status) %>%
  summarise("Value in £" = sum(abs(Quantity * Price)), .groups = "drop_last") %>%
  mutate("Discount Percentage" = formattable::percent(if_else(Status == "Discount",
                                                              `Value in £`[Status == "Discount"] / `Value in £`[Status == "Purchases"],
                                                              NA))) %>%
  arrange(desc(Status), .by_group = TRUE) %>%
  ungroup()
```

<br>

Considering everything, how few valuable information we could extract from the rows that with `D` as a stock code and that there are only `131` of them (out of ``r nrow(df)``), we decide to remove them.

<br>

# - *Description*

Another discrepancy is in the different number of distinct stock codes and descriptions, that should be the same.

```{r}
df %>%
  summarise("Number of Distinct Stock Codes" = n_distinct(StockCode),
            "Number of Distinct Descriptions" = n_distinct(Description))
```

<br>

That is because some stock codes (`2003` out of `4631`) have several descriptions,

```{r}
kable(df %>%
        count(StockCode, Description, name = "Number of Occurrences") %>%
        group_by(StockCode) %>%
        filter(n() > 1) %>%
        group_keys() %>%
        tally(name = "Number of Stockcodes with several Descriptions"), align = "l")
```

of various nature.

```{r}
df %>%
  count(StockCode, Description, name = "Number of Occurrences") %>%
  group_by(StockCode) %>%
  filter(n() > 1) %>%
  ungroup()
```

<br>

For `1564` out of those `2003` an additional description is a missing value (`NA`).

```{r}
kable(df %>%
        count(StockCode, Description, name = "Number of Occurrences") %>%
        group_by(StockCode) %>%
        filter(n() > 1) %>%
        semi_join(df %>%
                    count(StockCode, Description) %>%
                    group_by(StockCode) %>%
                    filter(any(is.na(Description))) %>%
                    ungroup(), by = "StockCode") %>%
        group_keys() %>%
        tally(name = "Number of Stockcodes with NA as Description"), align = "l")
df %>%
  count(StockCode, Description, name = "Number of Occurrences") %>% 
  group_by(StockCode) %>%
  filter(n() > 1) %>%
  ungroup() %>%
  semi_join(df %>%
              count(StockCode, Description) %>%
              group_by(StockCode) %>%
              filter(any(is.na(Description))) %>%
              ungroup(), by = "StockCode")
```

<br>

While the others `439` present typos, updated descriptions or notes.

```{r}
kable(df %>%
        count(StockCode, Description, name = "Number of Occurrences") %>%
        group_by(StockCode) %>%
        filter(n() > 1) %>%
        anti_join(df %>%
                    count(StockCode, Description) %>%
                    group_by(StockCode) %>%
                    filter(any(is.na(Description))) %>%
                    ungroup(), by = "StockCode") %>%
        group_keys() %>%
        tally(name = "Stockcodes with diverse Descriptions"), align = "l")
df %>%
  count(StockCode, Description, name = "Number of Occurrences") %>% 
  group_by(StockCode) %>%
  filter(n() > 1) %>%
  ungroup() %>%
  anti_join(df %>%
              count(StockCode, Description) %>%
              group_by(StockCode) %>%
              filter(any(is.na(Description))) %>%
              ungroup(), by = "StockCode")
```

<br>

The notes are usually written in lower case.

```{r}
df %>%
  count(StockCode, Description, name = "Number of Occurrences") %>% 
  group_by(StockCode) %>%
  filter(n() > 1) %>%
  ungroup() %>%
  anti_join(df %>%
              count(StockCode, Description) %>%
              group_by(StockCode) %>%
              filter(any(is.na(Description))) %>%
              ungroup(), by = "StockCode") %>%
  filter(str_detect(Description, "[:lower:]"))
```

<br>

Furthermore, also amongst the stock code with only one description we experience some issues, like `NAs` values and notes.

```{r}
df %>%
  count(StockCode, Description, name = "Number of Occurrences") %>%
  group_by(StockCode) %>%
  filter(n() == 1 &
           (is.na(Description) |
              str_detect(Description, "[:lower:]")))
```

<br>

We can also encounter the same description pertaining to different stock codes; some they just differ in style

```{r}
df %>%
  count(Description, StockCode, name = "Number of Occurrences") %>%
  group_by(Description) %>%
  filter(n() > 1 &
           str_length(StockCode) != 5 &
           !str_detect(Description, "[:lower:]")) %>%
  ungroup()
```

while others are two different ones.

```{r}
df %>%
  count(Description, StockCode, name = "Number of Occurrences") %>%
  filter(Description != "?") %>%
  group_by(Description) %>%
  filter(n() > 1 &
           str_length(StockCode) == 5 &
           !str_detect(Description, "[:lower:]")) %>%
  ungroup()
```

<br>

So we might say that `Description` is not a column we can rely on too much.

<br>

# - *Quantity*

Moving on to `Quantity`, let's investigate the purchases with a negative value of it,

```{r}
df %>%
  filter(Quantity < 0)
```

that amount to `12326` of them,

```{r}
kable(df %>%
        filter(Quantity < 0) %>%
        tally(name = "Purchases with a negative Quantity"), align = "l")
```


belonging to `6712` invoices, out of which `4591` are cancelled, and for them it makes sense that the quantity is negative, as a way to readjust the inventory levels of the stock code.

```{r}
df %>%
  group_by(Invoice) %>%
  filter(any(Quantity < 0)) %>%
  ungroup() %>%
  distinct(Invoice) %>%
  count(Status = if_else(str_starts(Invoice, "C"), "Cancelled", "Confirmed"), name = "Number of Invoices")
```

But the rest are not cancelled and they seem to have some common concurrences<a name = "Quantity">,</a>

```{r}
df %>%
  filter(Quantity < 0 &
           !str_detect(Invoice, "C"))
```

besides being all single line invoices,

```{r}
kable(df %>%
        filter(Quantity < 0 &
                 !str_detect(Invoice, "C")) %>%
        group_by(Invoice) %>%
        filter(n() > 1) %>%
        ungroup() %>%
        tally(name = "Number of Confirmed Negative Quantity Invoices with more than One Line"), align = "l")
```

like `NAs` or notes in the `Description` column,

```{r}
df %>%
  filter(Quantity < 0 &
           !str_detect(Invoice, "C")) %>%
  count(Description, sort = TRUE, name = "Number of Occurrences")
```

and with all the same value in the `Price`, `CustomerID` and `Country` columns. 

```{r}
df %>%
  filter(Quantity < 0 &
           !str_detect(Invoice, "C")) %>%
  distinct(Price, CustomerID, Country)
```

These invoices run through all the data frame so the issue is not time specific.

```{r}
library(ggplot2)
df %>%
  filter(Quantity < 0 &
           !str_detect(Invoice, "C")) %>%
  ggplot(aes(InvoiceDate, 5)) +
  geom_point() + 
  labs(x = NULL,
       y = NULL,
       title = "Time Placement of Confirmed Invoices with a Negative Quantity") +
  theme(axis.text.y = element_blank(),
        axis.ticks.y = element_blank())
```

We can assume that these are not actual transactions but inventory adjustments, so we will remove them.

We've seen that, when an invoice has a `C`, its quantity is negative but there is one case where that is not true.

```{r}
df %>%
  filter(str_detect(Invoice, "C") &
           Quantity >= 0) 
```

<br>

There are no values equal to `0`,

```{r}
kable(df %>%
        filter(Quantity == 0) %>%
        tally(name = "Number of Rows with 0 Quantity"), align = "l")
```

and no invoices with both positive and negative ones.

```{r}
kable(df %>%
        mutate(class = if_else(Quantity > 0, "Positive Quantity", "Negative Quantity")) %>%
        count(Invoice, class) %>%
        group_by(Invoice) %>%
        filter(n() > 1) %>%
        ungroup() %>%
        tally(name = "Number of Invoices with Both Positive and Negative Quantity Values"), align = "l")
```

<br>

# - *InvoiceDate*

About `InvoiceDate`, let's look for gaps.

```{r}
date_range <- seq(min(as.Date(df$InvoiceDate)), max(as.Date(df$InvoiceDate)), by = 1)
gaps <- tibble("Missing Days" = date_range[!date_range %in% as.Date(df$InvoiceDate)])
ggplot(gaps, aes(`Missing Days`, 5)) +
  geom_point() + 
  labs(x = NULL,
       y = NULL,
       title = "Missing days during our time span") +
  theme(axis.text.y = element_blank(),
        axis.ticks.y = element_blank())
```

<br>

There is a large sequence of missing days around `Christmas 2009` and a smaller one at the beginning of `April 2010` (Easter fell on the `4th of April` that year) but those are not the only ones missing.

```{r}
tibble("Missing Day" = date_range[!date_range %in% as.Date(df$InvoiceDate)], 
       "Day of the Week" = weekdays(date_range[!date_range %in% as.Date(df$InvoiceDate)]))
```

<br>

If we count them we see that `Saturday` is the most frequently missed.

```{r}
tibble("Missing Days" = date_range[!date_range %in% as.Date(df$InvoiceDate)], 
       "Day of the Week" = weekdays(date_range[!date_range %in% as.Date(df$InvoiceDate)])) %>%
  count(`Day of the Week`, sort = TRUE, name = "Number of Occurrences")
```

<br>

I was expecting `Sunday` to be honest given that the clientele is mostly `UK` based. There might be other factors at play here given the nature of the business.

<br>

# - *Price*

Let's look for abnormal prices now and for the negative ones we see that they pertain to the invoices with an `A`.

```{r}
df %>%
  filter(Price < 0)
```

<br>

And for the rows with a price equal to `0` (`3687` of them), 

```{r}
df %>%
  filter(Price == 0)
```

they seem to be a superset of the ones with a negative quantity and a not cancelled invoice (`2121` rows, already seen in the [Quantity](#Quantity) section), that all had the same values in the `Price` (`0`), `CustomerID` (`NA`) and `Country` (`United Kingdom`) columns.

```{r}
df %>%
  filter(Quantity < 0 &
           !str_detect(Invoice, "C"))
```

<br>

Within the `1566` remaining rows of this set, that have a positive `Quantity`,

```{r}
df %>%
  filter(Price == 0 &
           Quantity > 0)
```

we similarly have missing values (`NAs`) in most of the `Description`

```{r}
df %>%
  filter(Price == 0 &
           Quantity > 0) %>%
  count(Description, sort = TRUE, name = "Number of Occurrences")
```

and `CustomerID` columns.

```{r}
df %>%
  filter(Price == 0 &
           Quantity > 0) %>%
  count(CustomerID, sort = TRUE, name = "Number of Occurrences")
```

<br>

These purchases are not concentrated in particular time periods,

```{r}
df %>%
  filter(Price == 0 &
           Quantity > 0) %>%
  ggplot(aes(InvoiceDate, 5)) +
  geom_point() + 
  labs(x = NULL,
       y = NULL,
       title = "Time Placement of Purchases with Price equal to 0 and a positive Quantity") +
  theme(axis.text.y = element_blank(),
        axis.ticks.y = element_blank())
```

<br>

and they mostly pertain to `United Kingdom`.

```{r}
df %>%
  filter(Price == 0 &
           Quantity > 0) %>%
  count(Country, sort = TRUE)
```

<br>

# - *CustomerID*

About `CustomerID`, we have `5` customers, out of ``r length(unique(df$CustomerID))``, that changed country but that is not a concern, more something to keep in mind.

```{r}
df %>%
  count(CustomerID, Country, name = "Number of Occurrences") %>%
  group_by(CustomerID) %>%
  filter(n() > 1 &
           !is.na(CustomerID)) %>%
  ungroup()
```

<br>

# - *Country*

We noticed in the previous document the value `Unspecified` for `15` invoices and `5` (plus `NAs` values) customers in the `Country` column, that is better to change to `NA`.

```{r}
df %>%
  filter(Country == "Unspecified") %>%
  distinct(Country, Invoice, CustomerID) %>%
  arrange(desc(CustomerID))
```

<br>

# - *main takeaways*

Let's recap our findings:

- Invoices starting with a letter different than `C`  
- Stock codes not pertaining to actual transactions  
- A `D` (`Discount`) stock code that doesn't provide valuable information  
- A `Description` column with several values (`NAs`, typos, updated descriptions or notes) for the same stock code, uninformative unique descriptions (`NAs`, notes) for others, the same description assigned to different stock codes  
- `2121` non cancelled invoices with a negative value in the `Quantity` column, all with the same value in the `Price` (`0`), `CustomerID` (`NA`) and `Country` (`United Kingdom`) columns, most likely inventory adjustments. `1` cancelled invoice with a positive value in the `Quantity` column  
- Unexpected gaps in the time frame  
- `3` rows with a negative price (the same as the ones with invoices starting with a letter different than `C`). `3687` rows with price equal to `0`, a set composed of rows with a negative (`2121` rows) and with a positive (`1566`) value in the `Quantity` column 
- `5` customers that changed country  
- `15` invoices and `5` customers with an `Unspecified` value in the `Country` column  

<br>

# - *actions to be performed*

<br>

- Invoices that start with `A`, not actual transactions with a customer, to be removed with

```{r class.source = "fold-show", eval = FALSE}
df %>%
  filter(!str_starts(Invoice, "A"))
```

<br>

- Stock codes not pertaining to actual transactions, to be removed with 

```{r class.source = "fold-show", eval = FALSE}
df %>%
  filter(str_length(StockCode) == 5 |
           str_detect(StockCode, "^\\d{5}[a-zA-Z]{1,2}$") |
           str_detect(StockCode, "PADS|DCGS|SP|gift"))
```

This will take care also of the invoices starting with an `A`, as their stock code (`B`) is not among the one preserved, and, for the same reason, of the invoices with `D` as a stock code.

<br>

- `2121` confirmed invoices with a negative quantity, to be removed with

```{r class.source = "fold-show", eval = FALSE}
df %>%
  filter(!(Quantity < 0 &
           !str_starts(Invoice, "C")))
```

<br>

- `1` cancelled invoice with a positive quantity, to be removed with

```{r class.source = "fold-show", eval = FALSE}
df %>%
  filter(!(str_starts(Invoice, "C") &
             Quantity > 0))
```

But that is not necessary, as the relative stock code is not among the one preserved.

<br>

- `1566` rows with a price equal to `0` and most descriptions and customerIDs empty, to be removed with

```{r class.source = "fold-show", eval = FALSE}
df %>%
  filter(Price != 0)
```

That will take care as well of the `2121` non cancelled invoices with a negative quantity, as they all have a price equal to `0`.

```{r}
kable(df %>%
        filter(Quantity < 0 &
                 !str_detect(Invoice, "C")) %>%
        count("Values of Price" = Price, name = "Number of Occurrences"), align = "l")
```

<br>

So we can remove all non transactions rows with this code, to which we added the manipulation on the `Country` column as well (changing the `Unspecified` values to `NA`).

```{r class.source = "fold-show"}
df_cleaned <- df %>%
  filter(str_length(StockCode) == 5 |
           str_detect(StockCode, "^\\d{5}[a-zA-Z]{1,2}$") |
           str_detect(StockCode, "PADS|DCGS|SP|gift")) %>%
  filter(Price != 0) %>%
  mutate(Country = na_if(Country, "Unspecified"), .keep = "unused", .after = Price)
```

<br>

# - *resulting modifications*

After these manipulations, the data frame has new characteristics: 

- ``r nrow(df_cleaned)`` rows compared to ``r nrow(df)`` (a difference of ``r nrow(df) - nrow(df_cleaned)`` rows)

- new numbers of distinct values for the character columns

```{r}
bind_rows("Cleaned Data Frame" = df_cleaned %>%
            summarise(across(where(is.character), n_distinct)),
          "Original Data Frame" = df %>%
            summarise(across(where(is.character), n_distinct)), .id = "")
```

<br>

- a different percentage of cancelled invoices, that increased to `16.64%` from the previous `15.94%`

```{r}
bind_rows(df_cleaned %>%
            mutate(Status = if_else(str_starts(Invoice, "C"), "Cancelled", "Confirmed")) %>%
            group_by(Status) %>%
            summarize("Distinct Invoices" = n_distinct(Invoice)) %>%
            mutate("Percentage" = formattable::percent(`Distinct Invoices` / sum(`Distinct Invoices`))) %>%
            arrange(desc(`Distinct Invoices`)),
          df %>%
            mutate(Status = if_else(str_starts(Invoice, "C"), "Cancelled", "Confirmed")) %>%
            group_by(Status) %>%
            summarize("Distinct Invoices" = n_distinct(Invoice)) %>%
            mutate("Percentage" = formattable::percent(`Distinct Invoices` / sum(`Distinct Invoices`))) %>%
            arrange(desc(`Distinct Invoices`))) %>%
  mutate("Data Frame" = c("Cleaned", "", "Original", ""), .before = Status)
```

<br>

- and new distributions for the numeric columns, especially `Price`.

```{r}
bind_cols(df_cleaned %>%
            reframe(across(where(is.numeric), ~ summary(.x))) %>%
            rename("Cleaned Quantity" = Quantity, "Cleaned Price" = Price),
          df %>%
            reframe(across(where(is.numeric), ~ summary(.x))) %>%
            rename("Original Quantity" = Quantity, "Original Price" = Price)) %>%
  mutate("Statistic" = c("Min." , "1st Qu.", "Median", "Mean", "3rd Qu.", "Max.")) %>%
  relocate(Statistic, ends_with("y"), everything())
```

<br>

We notice that the gap between distinct stock codes and distinct descriptions widened,

```{r}
bind_rows("Cleaned Data Frame" = df_cleaned %>%
            summarise("Number of Distinct Stock Codes" = n_distinct(StockCode),
                      "Number of Distinct Descriptions" = n_distinct(Description)),
          "Original Data Frame" = df %>%
            summarise("Number of Distinct Stock Codes" = n_distinct(StockCode),
                      "Number of Distinct Descriptions" = n_distinct(Description)), .id = "")
```

despite we removed, from the `Description` column, all missing values

```{r}
kable(df_cleaned %>%
        filter(is.na(Description)) %>%
        tally(name = "Number of NAs in the Description Column"), align = "l")
```

and notes written in lower case.

```{r}
df_cleaned %>%
  count(StockCode, Description, name = "Number of Occurrences") %>%
  filter(str_detect(Description, "[:lower:]"))
```

<br>

The number of invoices per day changed as well,

```{r}
df_cleaned %>%
  group_by("Invoice Day" = as.Date(InvoiceDate)) %>%
  summarise("Cleaned Number of Invoices" = n_distinct(Invoice)) %>%
  left_join(df %>%
              group_by("Invoice Day" = as.Date(InvoiceDate)) %>%
              summarise("Original Number of Invoices" = n_distinct(Invoice)), by = "Invoice Day")
```

with a general decrease in number, 

```{r}
df_cleaned_plot <- df_cleaned %>%
  group_by("Invoice Day" = as.Date(InvoiceDate)) %>%
  mutate("Number of Invoices" = n_distinct(Invoice)) %>%
  ungroup()
colors <- c("Original" = "darkgrey",
            "Cleaned" = "black")
df %>%
  group_by("Invoice Day" = as.Date(InvoiceDate)) %>%
  mutate("Number of Invoices" = n_distinct(Invoice)) %>%
  ungroup() %>%
  ggplot(aes(`Invoice Day`, `Number of Invoices`)) +
  geom_line(aes(color = "Original")) + 
  geom_line(data = df_cleaned_plot, aes(x = `Invoice Day`, y = `Number of Invoices`, color = "Cleaned")) +
  labs(x = "",
       y = "", 
       title = "Number of Invoices per Day, differentiating by Data Frame",
       color = "Legend") +
  scale_color_manual(values = colors) + 
  theme(legend.position = "bottom", 
        legend.title = element_blank())
```

that is more relevant on certain days.

```{r}
df_cleaned %>%
  group_by("Invoice Day" = as.Date(InvoiceDate)) %>%
  summarise("Cleaned Number of Invoices" = n_distinct(Invoice)) %>%
  left_join(df %>%
              group_by("Invoice Day" = as.Date(InvoiceDate)) %>%
              summarise("Original Number of Invoices" = n_distinct(Invoice)), by = "Invoice Day") %>%
  mutate(Percentage = abs((`Cleaned Number of Invoices` - `Original Number of Invoices`) / `Original Number of Invoices`)) %>%
  ggplot(aes(`Invoice Day`, Percentage)) +
  geom_line() +
  scale_y_continuous(labels = scales::label_percent(), limits = c(0, 1)) +
  labs(x = NULL,
       y = NULL,
       title = "Percentages of Removed Invoices per Day")
```

<br>

Furthermore, customer `12745` that was previously located in two countries

```{r}
df %>%
  count(CustomerID, Country, name = "Number of Occurrences") %>%
  group_by(CustomerID) %>%
  filter(n() > 1 &
           !is.na(CustomerID)) %>%
  ungroup()
```

is now exclusively in the `United Kingdom`. 

```{r}
df_cleaned %>%
  filter(CustomerID == "12745") %>%
  count(CustomerID, Country, name = "Number of Occurrences")
```

<br>

And of course the number of customers for each country changed.

```{r}
df_cleaned %>%
  count(Country, wt = n_distinct(CustomerID), sort = TRUE, name = "Cleaned Data Frame") %>%
  full_join(df %>%
              count(Country, wt = n_distinct(CustomerID), name = "Original Data Frame"), by = "Country") %>%
  mutate(across(c(`Cleaned Data Frame`, `Original Data Frame`), ~ coalesce(as.character(.x), "not present")))
```