Growth of E-Commerce in Europe

Author

Vasilis Galitsios

About the project

In this R project, we will use 2 different datasets from Eurostat to evaluate the growth of E-Commerce in Europe.

Import data into R studio

First, we import our first dataset from a .xlsx file (This file contains data from 2010-2019)

library(tidyverse)
library(readxl)
Ecommerce_2019 <- read_excel("C:/Users/vasil/OneDrive/Desktop/Data Analysis/R/Ecommerce/isoc_ec_ibuy_page_linear.xlsx", 
                             col_types = c("text", "text", "text", 
                                           "text", "text", "text", "text", "numeric", 
                                           "numeric", "text"))
head(Ecommerce_2019)

# A tibble: 6 × 10
  DATAFLOW         `LAST UPDATE` freq  indic_is ind_type unit  geo   TIME_PERIOD
  <chr>            <chr>         <chr> <chr>    <chr>    <chr> <chr>       <dbl>
1 ESTAT:ISOC_EC_I… 19/09/23 11:… A     I_BLT12  IND_TOT… PC_I… AL           2018
2 ESTAT:ISOC_EC_I… 19/09/23 11:… A     I_BLT12  IND_TOT… PC_I… AL           2019
3 ESTAT:ISOC_EC_I… 19/09/23 11:… A     I_BLT12  IND_TOT… PC_I… AT           2010
4 ESTAT:ISOC_EC_I… 19/09/23 11:… A     I_BLT12  IND_TOT… PC_I… AT           2011
5 ESTAT:ISOC_EC_I… 19/09/23 11:… A     I_BLT12  IND_TOT… PC_I… AT           2012
6 ESTAT:ISOC_EC_I… 19/09/23 11:… A     I_BLT12  IND_TOT… PC_I… AT           2013
# ℹ 2 more variables: OBS_VALUE <dbl>, OBS_FLAG <chr>

names(Ecommerce_2019)

 [1] "DATAFLOW"    "LAST UPDATE" "freq"        "indic_is"    "ind_type"   
 [6] "unit"        "geo"         "TIME_PERIOD" "OBS_VALUE"   "OBS_FLAG"

Manipulate data

When we look at the dataset we see that the headers are kind of confusing and hard to understand. So first, let’s change the headers so we can communicate more efficiently, our dataset and findings.

Ecommerce_2019_fixed <-Ecommerce_2019 %>%
  select(everything()) %>%
  rename(Time_frequency = freq,
         Last_online_purchase_in = indic_is,
         Age_group = ind_type,
         People_who_used_internet_within = unit,
         Country_Group = geo)

head(Ecommerce_2019_fixed)

# A tibble: 6 × 10
  DATAFLOW         `LAST UPDATE` Time_frequency Last_online_purchase…¹ Age_group
  <chr>            <chr>         <chr>          <chr>                  <chr>    
1 ESTAT:ISOC_EC_I… 19/09/23 11:… A              I_BLT12                IND_TOTAL
2 ESTAT:ISOC_EC_I… 19/09/23 11:… A              I_BLT12                IND_TOTAL
3 ESTAT:ISOC_EC_I… 19/09/23 11:… A              I_BLT12                IND_TOTAL
4 ESTAT:ISOC_EC_I… 19/09/23 11:… A              I_BLT12                IND_TOTAL
5 ESTAT:ISOC_EC_I… 19/09/23 11:… A              I_BLT12                IND_TOTAL
6 ESTAT:ISOC_EC_I… 19/09/23 11:… A              I_BLT12                IND_TOTAL
# ℹ abbreviated name: ¹Last_online_purchase_in
# ℹ 5 more variables: People_who_used_internet_within <chr>,
#   Country_Group <chr>, TIME_PERIOD <dbl>, OBS_VALUE <dbl>, OBS_FLAG <chr>

names(Ecommerce_2019_fixed)

 [1] "DATAFLOW"                        "LAST UPDATE"                    
 [3] "Time_frequency"                  "Last_online_purchase_in"        
 [5] "Age_group"                       "People_who_used_internet_within"
 [7] "Country_Group"                   "TIME_PERIOD"                    
 [9] "OBS_VALUE"                       "OBS_FLAG"

As we look at our dataset again we see that except the headers, there are also a lot of values that are hard to understand, so let’s also fix that.

Ecommerce_2019_fixed$Time_frequency[Ecommerce_2019_fixed$Time_frequency == "A"] <- "Annual"
Ecommerce_2019_fixed$Last_online_purchase_in[Ecommerce_2019_fixed$Last_online_purchase_in == "I_BLT12"] <- "12_months"
Ecommerce_2019_fixed$Age_group[Ecommerce_2019_fixed$Age_group == "IND_TOTAL"] <- "TOTAL"
Ecommerce_2019_fixed$People_who_used_internet_within[Ecommerce_2019_fixed$People_who_used_internet_within == "PC_IND_ILT12"] <- "Last_year"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "AL"] <- "Albania"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "AT"] <- "Austria"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "BA"] <- "Bosnia_and_Herzegovina"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "BE"] <- "Belgium"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "BG"] <- "Bulgaria"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "CH"] <- "Switzerland"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "CY"] <- "Cyprus"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "CZ"] <- "Czechia"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "DE"] <- "Germany"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "DK"] <- "Denmark"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "EA"] <- "Euro_Area(EA11_1999,EA12_2001,EA13_2007,EA15_2008,EA16_2009,EA17_2011,EA18_2014,EA19_2015,EA20_2023)"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "EE"] <- "Estonia"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "EL"] <- "Greece"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "ES"] <- "Spain"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "EU15"] <- "European_Union_15_countries(1995-2004)"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "EU25"] <- "European_Union_25_countries(2004-2006)"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "EU27_2007"] <- "European_Union_27_countries(2007-2013)"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "EU27_2020"] <- "European_Union_27_countries(from_2020)"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "EU28"] <- "European_Union_28_countries(2013-2020)"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "FI"] <- "Finland"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "FR"] <- "France"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "HR"] <- "Croatia"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "HU"] <- "Hungary"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "IE"] <- "Ireland"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "IS"] <- "Iceland"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "IT"] <- "Italy"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "LT"] <- "Lithuania"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "LU"] <- "Luxembourg"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "LV"] <- "Latvia"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "ME"] <- "Montenegro"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "MK"] <- "North_Macedonia"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "MT"] <- "Malta"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "NL"] <- "Netherlands"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "NO"] <- "Norway"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "PL"] <- "Poland"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "PT"] <- "Portugal"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "RO"] <- "Romania"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "RS"] <- "Serbia"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "SE"] <- "Sweden"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "SI"] <- "Slovenia"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "SK"] <- "Slovakia"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "TR"] <- "Türkiye"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "UK"] <- "United_Kingdom"
Ecommerce_2019_fixed$Country_Group[Ecommerce_2019_fixed$Country_Group == "XK"] <- "Kosovo(Under_United_Nations_Security_Council_Resolution_1244/99)"

Ecommerce_2019_fixed %>%
  glimpse() %>%
  select(1:10)

Rows: 383
Columns: 10
$ DATAFLOW                        <chr> "ESTAT:ISOC_EC_IBUY(1.0)", "ESTAT:ISOC…
$ `LAST UPDATE`                   <chr> "19/09/23 11:00:00", "19/09/23 11:00:0…
$ Time_frequency                  <chr> "Annual", "Annual", "Annual", "Annual"…
$ Last_online_purchase_in         <chr> "12_months", "12_months", "12_months",…
$ Age_group                       <chr> "TOTAL", "TOTAL", "TOTAL", "TOTAL", "T…
$ People_who_used_internet_within <chr> "Last_year", "Last_year", "Last_year",…
$ Country_Group                   <chr> "Albania", "Albania", "Austria", "Aust…
$ TIME_PERIOD                     <dbl> 2018, 2019, 2010, 2011, 2012, 2013, 20…
$ OBS_VALUE                       <dbl> 8.19, 10.13, 56.50, 55.72, 59.78, 66.1…
$ OBS_FLAG                        <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…

# A tibble: 383 × 10
   DATAFLOW        `LAST UPDATE` Time_frequency Last_online_purchase…¹ Age_group
   <chr>           <chr>         <chr>          <chr>                  <chr>    
 1 ESTAT:ISOC_EC_… 19/09/23 11:… Annual         12_months              TOTAL    
 2 ESTAT:ISOC_EC_… 19/09/23 11:… Annual         12_months              TOTAL    
 3 ESTAT:ISOC_EC_… 19/09/23 11:… Annual         12_months              TOTAL    
 4 ESTAT:ISOC_EC_… 19/09/23 11:… Annual         12_months              TOTAL    
 5 ESTAT:ISOC_EC_… 19/09/23 11:… Annual         12_months              TOTAL    
 6 ESTAT:ISOC_EC_… 19/09/23 11:… Annual         12_months              TOTAL    
 7 ESTAT:ISOC_EC_… 19/09/23 11:… Annual         12_months              TOTAL    
 8 ESTAT:ISOC_EC_… 19/09/23 11:… Annual         12_months              TOTAL    
 9 ESTAT:ISOC_EC_… 19/09/23 11:… Annual         12_months              TOTAL    
10 ESTAT:ISOC_EC_… 19/09/23 11:… Annual         12_months              TOTAL    
# ℹ 373 more rows
# ℹ abbreviated name: ¹Last_online_purchase_in
# ℹ 5 more variables: People_who_used_internet_within <chr>,
#   Country_Group <chr>, TIME_PERIOD <dbl>, OBS_VALUE <dbl>, OBS_FLAG <chr>

Data visualization

Now that we made our dataset more visually friendly, let’s check the growth of E-Commerce for “European_Union_27_countries(from_2020)”.

We are going to use a line graph with points marks for the visual presentation.

Ecommerce_2019_fixed %>%
  filter(Country_Group == "European_Union_27_countries(from_2020)") %>%
  ggplot(aes(TIME_PERIOD,
             OBS_VALUE)) +
  geom_point(size = 5, colour = "blue", alpha = .3) +
  geom_line(color = "blue", size = 1) +
  scale_x_continuous(breaks = round(seq(min(Ecommerce_2019_fixed$TIME_PERIOD), max(Ecommerce_2019_fixed$TIME_PERIOD), by = 1))) +
  labs(title = "E-Commerce growth in the EU(27 countries from 2020)",
       x = "Year",
       y = "Percentage of internet users who made a purchase online",
       caption = "Source: Eurostat(Online data code: isoc_ec_ibuy)") +
  theme_minimal() +
  theme(plot.title = 
          element_text(size = 18,
                       face = "bold",
                       color = "steelblue",
                       hjust = 0.5),
        axis.text = 
          element_text(size = 10,
                       color = "grey",
                       face = "bold"),
        axis.title = 
          element_text(size = 12,
                       color = "steelblue"))

We can also check any specific country, to see what their data tells us about its growth in E-Commerce popularity.

Let’s take for example Greece.

Ecommerce_2019_fixed %>%
  filter(Country_Group == "Greece") %>%
  ggplot(aes(TIME_PERIOD,
             OBS_VALUE)) +
  geom_point(size = 5, colour = "blue", alpha = .3) +
  geom_line(color = "blue", size = 1) +
  scale_x_continuous(breaks = round(seq(min(Ecommerce_2019_fixed$TIME_PERIOD), max(Ecommerce_2019_fixed$TIME_PERIOD), by = 1))) +
  labs(title = "E-Commerce growth in Greece",
       x = "Year",
       y = "Percentage of internet users who made a purchase online",
       caption = "Source: Eurostat(online data code: isoc_ec_ibuy)") +
  theme_bw() +
  theme(plot.title = 
          element_text(size = 18,
                       face = "bold",
                       color = "steelblue",
                       hjust = 0.5),
        axis.text = 
          element_text(size = 10,
                       color = "grey",
                       face = "bold"),
        axis.title = 
          element_text(size = 12,
                       color = "steelblue"))

2nd dataset

As insightful this dataset is, unfortunately, it only contains data up to 2019.

So we have to find a new dataset for the latest years as well.

library(readxl)
Ecommerce_2022 <- read_excel("C:/Users/vasil/OneDrive/Desktop/Data Analysis/R/Ecommerce/isoc_ec_ib20_page_linear.xlsx", 
                             col_types = c("text", "text", "text", 
                                           "text", "text", "text", "text", "numeric", 
                                           "numeric", "text"))

head(Ecommerce_2022)

# A tibble: 6 × 10
  DATAFLOW         `LAST UPDATE` freq  ind_type indic_is unit  geo   TIME_PERIOD
  <chr>            <chr>         <chr> <chr>    <chr>    <chr> <chr>       <dbl>
1 ESTAT:ISOC_EC_I… 19/09/23 11:… A     IND_TOT… I_BLT12  PC_I… AL           2020
2 ESTAT:ISOC_EC_I… 19/09/23 11:… A     IND_TOT… I_BLT12  PC_I… AL           2021
3 ESTAT:ISOC_EC_I… 19/09/23 11:… A     IND_TOT… I_BLT12  PC_I… AL           2022
4 ESTAT:ISOC_EC_I… 19/09/23 11:… A     IND_TOT… I_BLT12  PC_I… AT           2020
5 ESTAT:ISOC_EC_I… 19/09/23 11:… A     IND_TOT… I_BLT12  PC_I… AT           2021
6 ESTAT:ISOC_EC_I… 19/09/23 11:… A     IND_TOT… I_BLT12  PC_I… AT           2022
# ℹ 2 more variables: OBS_VALUE <dbl>, OBS_FLAG <chr>

This new dataset comes from the same source.

So we are gonna have to repeat the previous steps to make our data more visually understandable.

Fixing headers

Ecommerce_2022_fixed <- Ecommerce_2022 %>%
  select(everything()) %>%
  rename(Time_frequency = freq,
         Age_group = ind_type,
         Last_online_purchase_in = indic_is,
         People_who_used_internet_within = unit,
         Country_Group = geo)

head(Ecommerce_2022_fixed)

# A tibble: 6 × 10
  DATAFLOW         `LAST UPDATE` Time_frequency Age_group Last_online_purchase…¹
  <chr>            <chr>         <chr>          <chr>     <chr>                 
1 ESTAT:ISOC_EC_I… 19/09/23 11:… A              IND_TOTAL I_BLT12               
2 ESTAT:ISOC_EC_I… 19/09/23 11:… A              IND_TOTAL I_BLT12               
3 ESTAT:ISOC_EC_I… 19/09/23 11:… A              IND_TOTAL I_BLT12               
4 ESTAT:ISOC_EC_I… 19/09/23 11:… A              IND_TOTAL I_BLT12               
5 ESTAT:ISOC_EC_I… 19/09/23 11:… A              IND_TOTAL I_BLT12               
6 ESTAT:ISOC_EC_I… 19/09/23 11:… A              IND_TOTAL I_BLT12               
# ℹ abbreviated name: ¹Last_online_purchase_in
# ℹ 5 more variables: People_who_used_internet_within <chr>,
#   Country_Group <chr>, TIME_PERIOD <dbl>, OBS_VALUE <dbl>, OBS_FLAG <chr>

names(Ecommerce_2022_fixed)

 [1] "DATAFLOW"                        "LAST UPDATE"                    
 [3] "Time_frequency"                  "Age_group"                      
 [5] "Last_online_purchase_in"         "People_who_used_internet_within"
 [7] "Country_Group"                   "TIME_PERIOD"                    
 [9] "OBS_VALUE"                       "OBS_FLAG"

Fixing names of specific values

Ecommerce_2022_fixed$Time_frequency[Ecommerce_2022_fixed$Time_frequency == "A"] <- "Annual"
Ecommerce_2022_fixed$Age_group[Ecommerce_2022_fixed$Age_group == "IND_TOTAL"] <- "TOTAL"
Ecommerce_2022_fixed$Last_online_purchase_in[Ecommerce_2022_fixed$Last_online_purchase_in == "I_BLT12"] <- "12_months"
Ecommerce_2022_fixed$People_who_used_internet_within[Ecommerce_2022_fixed$People_who_used_internet_within == "PC_IND_ILT12"] <- "Last_year"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "AL"] <- "Albania"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "AT"] <- "Austria"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "BA"] <- "Bosnia_and_Herzegovina"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "BE"] <- "Belgium"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "BG"] <- "Bulgaria"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "CH"] <- "Switzerland"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "CY"] <- "Cyprus"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "CZ"] <- "Czechia"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "DE"] <- "Germany"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "DK"] <- "Denmark"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "EA"] <- "Euro_Area(EA11_1999,EA12_2001,EA13_2007,EA15_2008,EA16_2009,EA17_2011,EA18_2014,EA19_2015,EA20_2023)"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "EE"] <- "Estonia"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "EL"] <- "Greece"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "ES"] <- "Spain"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "EU27_2020"] <- "European_Union_27_countries(from_2020)"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "FI"] <- "Finland"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "FR"] <- "France"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "HR"] <- "Croatia"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "HU"] <- "Hungary"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "IE"] <- "Ireland"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "IS"] <- "Iceland"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "IT"] <- "Italy"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "LT"] <- "Lithuania"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "LU"] <- "Luxembourg"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "LV"] <- "Latvia"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "ME"] <- "Montenegro"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "MK"] <- "North_Macedonia"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "MT"] <- "Malta"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "NL"] <- "Netherlands"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "NO"] <- "Norway"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "PL"] <- "Poland"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "PT"] <- "Portugal"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "RO"] <- "Romania"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "RS"] <- "Serbia"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "SE"] <- "Sweden"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "SI"] <- "Slovenia"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "SK"] <- "Slovakia"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "TR"] <- "Türkiye"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "UK"] <- "United_Kingdom"
Ecommerce_2022_fixed$Country_Group[Ecommerce_2022_fixed$Country_Group == "XK"] <- "Kosovo(Under_United_Nations_Security_Council_Resolution_1244/99)"

Ecommerce_2022_fixed %>%
  glimpse() %>%
  select(1:10)

Rows: 110
Columns: 10
$ DATAFLOW                        <chr> "ESTAT:ISOC_EC_IB20(1.0)", "ESTAT:ISOC…
$ `LAST UPDATE`                   <chr> "19/09/23 11:00:00", "19/09/23 11:00:0…
$ Time_frequency                  <chr> "Annual", "Annual", "Annual", "Annual"…
$ Age_group                       <chr> "TOTAL", "TOTAL", "TOTAL", "TOTAL", "T…
$ Last_online_purchase_in         <chr> "12_months", "12_months", "12_months",…
$ People_who_used_internet_within <chr> "Last_year", "Last_year", "Last_year",…
$ Country_Group                   <chr> "Albania", "Albania", "Albania", "Aust…
$ TIME_PERIOD                     <dbl> 2020, 2021, 2022, 2020, 2021, 2022, 20…
$ OBS_VALUE                       <dbl> 19.52, 21.44, 34.67, 74.48, 67.98, 69.…
$ OBS_FLAG                        <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…

# A tibble: 110 × 10
   DATAFLOW        `LAST UPDATE` Time_frequency Age_group Last_online_purchase…¹
   <chr>           <chr>         <chr>          <chr>     <chr>                 
 1 ESTAT:ISOC_EC_… 19/09/23 11:… Annual         TOTAL     12_months             
 2 ESTAT:ISOC_EC_… 19/09/23 11:… Annual         TOTAL     12_months             
 3 ESTAT:ISOC_EC_… 19/09/23 11:… Annual         TOTAL     12_months             
 4 ESTAT:ISOC_EC_… 19/09/23 11:… Annual         TOTAL     12_months             
 5 ESTAT:ISOC_EC_… 19/09/23 11:… Annual         TOTAL     12_months             
 6 ESTAT:ISOC_EC_… 19/09/23 11:… Annual         TOTAL     12_months             
 7 ESTAT:ISOC_EC_… 19/09/23 11:… Annual         TOTAL     12_months             
 8 ESTAT:ISOC_EC_… 19/09/23 11:… Annual         TOTAL     12_months             
 9 ESTAT:ISOC_EC_… 19/09/23 11:… Annual         TOTAL     12_months             
10 ESTAT:ISOC_EC_… 19/09/23 11:… Annual         TOTAL     12_months             
# ℹ 100 more rows
# ℹ abbreviated name: ¹Last_online_purchase_in
# ℹ 5 more variables: People_who_used_internet_within <chr>,
#   Country_Group <chr>, TIME_PERIOD <dbl>, OBS_VALUE <dbl>, OBS_FLAG <chr>

Merging the 2 datasets

Now, before we start exploring our new dataset, let’s first conduct a merge with the first one, so we can have a more complete image.

Ecommerce_2010_2022_merged <- rbind(Ecommerce_2019_fixed, Ecommerce_2022_fixed)

Data visualization of the merged datasets

Now, let’s take a look again at the growth of E-Commerce in “European_Union_27_countries(from_2020)”.

Ecommerce_2010_2022_merged %>%
  filter(Country_Group == "European_Union_27_countries(from_2020)") %>%
  ggplot(aes(TIME_PERIOD,
             OBS_VALUE)) +
  geom_point(size = 5, colour = "blue", alpha = .3) +
  geom_line(colour = "blue", size = 1) +
  scale_x_continuous(breaks = round(seq(min(Ecommerce_2010_2022_merged$TIME_PERIOD), max(Ecommerce_2010_2022_merged$TIME_PERIOD), by = 1))) +
  labs(title = "E-Commerce growth in the EU(27 countries from 2020)",
       x = "Year",
       y = "Percentage of internet users who made a purchase online",
       caption = "Source: Eurostat(online data codes: isoc_ec_ibuy and isoc_ec_ib20)") +
  theme_minimal() +
  theme(plot.title = 
          element_text(size = 18,
                       face = "bold",
                       colour = "steelblue",
                       hjust = 0.5),
        axis.text = 
          element_text(size = 10,
                       colour = "grey",
                       face = "bold"),
        axis.title = 
          element_text(size = 12,
                       color = "steelblue"))

And for Greece:

Ecommerce_2010_2022_merged %>%
  filter(Country_Group == "Greece") %>%
  ggplot(aes(TIME_PERIOD,
             OBS_VALUE)) +
  geom_point(size = 5, colour = "blue", alpha = .3) +
  geom_line(colour = "blue", size = 1) +
  scale_x_continuous(breaks = round(seq(min(Ecommerce_2010_2022_merged$TIME_PERIOD), max(Ecommerce_2010_2022_merged$TIME_PERIOD), by = 1))) +
  labs(title = "E-Commerce growth in Greece",
       x = "Year",
       y = "Percentage of internet users who made a purchase online",
       caption = "Source: Eurostat(online data codes: isoc_ec_ibuy and isoc_ec_ib20)") +
  theme_minimal() +
  theme(plot.title = 
          element_text(size = 18,
                       face = "bold",
                       colour = "steelblue",
                       hjust = 0.5),
        axis.text = 
          element_text(size = 10,
                       colour = "grey",
                       face = "bold"),
        axis.title = 
          element_text(size = 12,
                       color = "steelblue"))

Conclusions:

As we see from our data the E-commerce section has been growing at a significant rate over the last 12 years in the EU and our example country, Greece, making it a very interesting and appealing investment.

Sources:

{1} Eurostat(online data code: isoc_ec_ibuy)

{2} Eurostat(online data code: isoc_ec_ib20)