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<h1 class="title toc-ignore">Dataset Creation</h1>
<h4 class="author">Melvin Coleman</h4>
<h4 class="date">12/2/2022</h4>
</div>
<p>Let’s create the dataset we will utilize for the final project. Data
was pulled from the web via scarping and downloaded from websites. The
“data” folder contains the csv files of data downloaded and intended for
use.</p>
<div id="world-cup-records-statistics" class="section level3">
<h3>World Cup Records & Statistics</h3>
<p>This data was loaded data from Wikipedia and contains record and
statistics of the overall team records of the FIFA World Cup.</p>
<pre class="r"><code>wiki_list =
records_stats_html = read_html("https://en.wikipedia.org/wiki/FIFA_World_Cup_records_and_statistics") %>%
html_table(header = TRUE)
wc_stats =
wiki_list[[3]]
wc_stats =
wc_stats %>%
janitor::clean_names() %>%
rename(country = team) %>%
## Delete rank(data ranked in order of WC winners) & team
select(country, everything(), -rank) %>%
## Clean dataset, remove weird characters in names
mutate(
country = str_replace(country, "//[c]", "")
) %>%
apply(., 2, function(country) as.character(gsub("\\[|a\\]","",country))) %>%
apply(., 2, function(country) as.character(gsub("\\[|b\\]","",country))) %>%
apply(., 2, function(country) as.character(gsub("\\[|c\\]","",country))) %>%
apply(., 2, function(country) as.character(gsub("\\[|d\\]","",country))) %>%
apply(., 2, function(country) as.character(gsub("\\[|e\\]","",country))) %>%
apply(., 2, function(country) as.character(gsub("\\[|f\\]","",country))) %>%
as_tibble()
wc_stats</code></pre>
</div>
<div id="fifa-rankings-2022-fifa-rankings" class="section level3">
<h3>Fifa Rankings 2022 <a
href="https://www.2026worldcupnorthamerica.com/fifa-ranking/">Fifa
Rankings</a></h3>
<p>This dataset was created via web scraping.</p>
<pre class="r"><code>wc_rank_html =
read_html("https://www.2026worldcupnorthamerica.com/fifa-ranking/")
rank_text =
wc_rank_html %>%
html_elements(".grippy-host , td:nth-child(1), td:nth-child(1)") %>%
html_text()
rank_text
country_text =
wc_rank_html %>%
html_elements("td:nth-child(2)") %>%
html_text()
country_text
fifa_rankings =
tibble(
rank = rank_text,
country = country_text
) %>%
## Change country names to match other datasets
mutate(
country = str_replace(country, "USA","United States"),
country = str_replace(country, "China PR", "China"),
country = str_replace(country, "IR Iran", "Iran"),
country = str_replace(country, "Korea Republic", "South Korea"),
country = str_replace(country,"Korea DPR","North Korea"),
country = str_replace(country, "Türkiye", "Turkey"),
country = str_replace(country,"Czechia", "Czech Republic"),
country = str_replace(country,"Côte d’Ivoire", "Ivory Coast"),
country = str_replace(country, "Congo DR", "DR Congo")
) %>%
select(country, rank)</code></pre>
</div>
<div id="confederations-dataset" class="section level3">
<h3>Confederations Dataset</h3>
<p>This dataset was downloaded as a csv file and limited to variables of
interest.</p>
<pre class="r"><code>confederations_data =
read_csv(file = "data/fifa_countries_audience.csv", col_names = TRUE) %>%
janitor::clean_names() %>%
## Select variables of interest
select(country, confederation) %>%
mutate(
country = str_replace(country, "Congo DR", "DR Congo")
)</code></pre>
</div>
<div id="top-goal-scorers-per-country" class="section level3">
<h3>Top goal scorers per country</h3>
<p>This dataset was pulled from Wikipedia via web scraping.</p>
<pre class="r"><code>goals_text_html =
read_html("https://en.wikipedia.org/wiki/List_of_top_international_men%27s_football_goal_scorers_by_country") %>%
html_table(header = TRUE)
goals_country_df =
goals_text_html[[1]]
goals_country_df =
goals_country_df %>%
janitor::clean_names() %>%
select(country, player, goals) %>%
## There are countries with more than one goal scorer, let's fix this
aggregate(player ~ country + goals, FUN = paste, collapse = ' & ') %>%
select(country, player, goals) %>%
arrange(country) %>%
## Change Soviet Union to Russia & update goal scorer info
mutate(country = str_replace(country,"Soviet Union\\[b]", "Russia"),
player = str_replace(player, "Oleg Blokhin",
"Alexander Kerzhakov & Artem Dzyuba"))
## Countries to worry about:
## Anguilla, Bulgaria, Curaçao, Denmark,Eritrea, Eswatini, Faroe Islands, France,
## Gibraltar, Iceland, Lebanon, Lesotho, Mongolia, Namibia, Palestine,Romania,
## Scotland, U.S. Virgin Islands, United States
goals_country_df</code></pre>
</div>
<div id="population-data-2021-population" class="section level3">
<h3>Population Data 2021 Population</h3>
<pre class="r"><code>pop_df =
read_csv(file = "data/pop.csv", col_names = TRUE) %>%
janitor::clean_names() %>%
select(country,land_area_km)</code></pre>
<p>Now let’s combine all of our datasets to create our final
dataset.</p>
<p>The datasets we have currently are: <br> - wc_stats (contains world
cup statistics & records) - fifa_rankings (official Fifa rankings
2022) - goals_country_df (top goal scorers per country) - pop_df(land
area of countries) - confederations_data(what confederations each
country is in)</p>
<p>We will combine these datasets by the <code>country</code> variable,
perform some wrangling and output final dataset as .csv file.</p>
<pre class="r"><code>### Put all dataframes into a list & merge by country
merged_df =
list(fifa_rankings, goals_country_df, pop_df,confederations_data)
merged_df2 =
merged_df %>%
reduce(full_join, by= "country") %>%
arrange(country)
final_merge =
merge(wc_stats, merged_df2,by= "country") %>%
## Replace missing data with correct info pulled from the web (sources listed below)
mutate(
## Add missing confederations for countries
confederation = case_when(country %in% c('Wales') ~ 'UEFA', TRUE ~ as.character(confederation)),
confederation = case_when(country %in% c('Bosnia and Herzegovina') ~ 'UEFA',
TRUE ~ as.character(confederation)),
confederation = case_when(country %in% c('England') ~ 'UEFA', TRUE ~ as.character(confederation)),
confederation = case_when(country %in% c('Northern Ireland') ~ 'UEFA',
TRUE ~as.character(confederation)),
confederation = case_when(country %in% c('Republic of Ireland') ~ 'UEFA',
TRUE ~as.character(confederation)),
confederation = case_when(country %in% c('Scotland') ~ 'UEFA',
TRUE ~as.character(confederation)),
confederation = case_when(country %in% c('Trinidad and Tobago') ~ 'CONCACAF',
TRUE ~as.character(confederation)),
confederation = case_when(country %in% c('United Arab Emirates') ~ 'AFC',
TRUE ~as.character(confederation)))</code></pre>
<pre class="r"><code>## Add missing land area by km squared
final_merge =
final_merge %>%
mutate(
land_area_km = case_when(country %in% c('Scotland') ~ 77910,
TRUE ~ as.numeric(land_area_km)),
land_area_km = case_when(country %in% c('Republic of Ireland') ~ 70273,
TRUE ~ as.numeric(land_area_km)),
land_area_km = case_when(country %in% c('Northern Ireland') ~ 14130,
TRUE ~ as.numeric(land_area_km)),
land_area_km = case_when(country %in% c('Bosnia and Herzegovina') ~ 51209,
TRUE ~ as.numeric(land_area_km)),
land_area_km = case_when(country %in% c('Trinidad and Tobago') ~ 5128,
TRUE ~ as.numeric(land_area_km)),
land_area_km = case_when(country %in% c('United Arab Emirates') ~ 83600,
TRUE ~ as.numeric(land_area_km)),
land_area_km = case_when(country %in% c('United Arab Emirates') ~ 83600,
TRUE ~ as.numeric(land_area_km)),
land_area_km = case_when(country %in% c('England') ~ 130279,
TRUE ~ as.numeric(land_area_km)),
land_area_km = case_when(country %in% c('Wales') ~ 20780,
TRUE ~ as.numeric(land_area_km))
)</code></pre>
<p>Output final dataset as <code>.csv</code> file for final project
use.</p>
<pre class="r"><code>write.csv(final_merge, "./data/12_4_dataset.csv")</code></pre>
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