Are humans getting faster? Or is it that more are participating?
100m Mens and Womens races in the Olympics over the last 100 years
Adidas, established in Herzogenaurach, Germany in 1949. Founded by Adolf “Adi” Dassler and brother Rudolf they developed spiked running shoes (spikes) for multiple athletic events. To enhance the quliaty of spiked athletic footwear, he transitioned from a previous model of heavy metal spikes to utilizing canvas and rubber. 1936 they convinced sprinter Jesse Owens to use the hand made spikes at the 1936 Summer Olympics (received 4 gold medals)
Nike, established in Eugene, OR 1964. Originally known as “Blue Ribbon Sports (BRS)” founded at U of O track athlete Phil Knight and his coach Bill Bowerman. The company initially operated in Eugene, OR as a distributor for Japanese shoe maker Onitsuka Tiger, making most sales at track meets out of Knights car. Otis Davis, 1960 Olympic gold medalist, claims Bowerman made the first pair of Nike shoes for him contradicting a claim they were made for Phil Knight.
Reebok, established in Bolton, UK in 1958 - In 1895 Joseph William Foster designed earliest spiked running shoes in the beginning. Opened a small factory called Olympic Works and became famous for “running pumps”. The company began distributing shoes across the Union Jack flag worn by British athletes. They were made amous by 100m champion Harold Abrahams in the 1924 Paris Olympics (Chariots of Fire). 1958 founded “Reebok” (after a type of African antelope)
Rio de Janeiro 2016
rio_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_2016_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-rio_men_100m%>%html_table(fill=TRUE)
rio_men_100m_1<-tables[[11]]
rio_men_100m_2<-tables[[12]]
rio_men_100m_3<-tables[[13]]
rio_men_100m_4<-tables[[14]]
rio_men_100m_5<-tables[[15]]
rio_men_100m_6<-tables[[16]]
rio_men_100m_7<-tables[[17]]
rio_men_100m_8<-tables[[18]]
rio_men_100m_9<-tables[[19]]
rio_men_100m_10<-tables[[20]]
rio_men_100m_11<-tables[[21]]
rio_men_100m_12<-tables[[22]]
rio_men_100m_1 <- rio_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
rio_men_100m_2 <- rio_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
rio_men_100m_3 <- rio_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
rio_men_100m_4 <- rio_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
rio_men_100m_5 <- rio_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
rio_men_100m_6 <- rio_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
rio_men_100m_7 <- rio_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
rio_men_100m_8 <- rio_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "8")
rio_men_100m_9 <- rio_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
rio_men_100m_10 <- rio_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
rio_men_100m_11 <- rio_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
rio_men_100m_12 <- rio_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
rio_men_100m_all <- bind_rows(rio_men_100m_1, rio_men_100m_2, rio_men_100m_3, rio_men_100m_4, rio_men_100m_5, rio_men_100m_6, rio_men_100m_7, rio_men_100m_8, rio_men_100m_9, rio_men_100m_10, rio_men_100m_11, rio_men_100m_12)
rio_men_100m_all <- rio_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=2016,"Host City" ="Rio de Janeiro", "Host Country"="Brazil")
rio_men_100m_all[92, 1] <- "Gold"
rio_men_100m_all[93, 1] <- "Silver"
rio_men_100m_all[94, 1] <- "Bronze"
head(rio_men_100m_all)
## Rank Lane Athlete Nation Reaction Time Notes Heat Year
## 1 1 3 Kemarley Brown Bahrain 0.146 10.13 Q 1 2016
## 2 2 5 Chijindu Ujah Great Britain 0.150 10.13 Q 1 2016
## 3 3 7 Marvin Bracy United States 0.155 10.16 q 1 2016
## 4 4 2 Seye Ogunlewe Nigeria 0.139 10.26 1 2016
## 5 5 1 Femi Ogunode Qatar 0.170 10.28 1 2016
## 6 6 8 Sean Safo-Antwi Ghana 0.145 10.43 1 2016
## Host City Host Country
## 1 Rio de Janeiro Brazil
## 2 Rio de Janeiro Brazil
## 3 Rio de Janeiro Brazil
## 4 Rio de Janeiro Brazil
## 5 Rio de Janeiro Brazil
## 6 Rio de Janeiro Brazil
London 2012
lon12_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_2012_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-lon12_men_100m%>%html_table(fill=TRUE)
lon12_men_100m_1<-tables[[12]]
lon12_men_100m_2<-tables[[13]]
lon12_men_100m_3<-tables[[14]]
lon12_men_100m_4<-tables[[15]]
lon12_men_100m_5<-tables[[16]]
lon12_men_100m_6<-tables[[17]]
lon12_men_100m_7<-tables[[18]]
lon12_men_100m_8<-tables[[19]]
lon12_men_100m_9<-tables[[20]]
lon12_men_100m_10<-tables[[21]]
lon12_men_100m_11<-tables[[22]]
lon12_men_100m_1 <- lon12_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
lon12_men_100m_2 <- lon12_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
lon12_men_100m_3 <- lon12_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
lon12_men_100m_4 <- lon12_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
lon12_men_100m_5 <- lon12_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
lon12_men_100m_6 <- lon12_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
lon12_men_100m_7 <- lon12_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
lon12_men_100m_8 <- lon12_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
lon12_men_100m_9 <- lon12_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
lon12_men_100m_10 <- lon12_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
lon12_men_100m_11 <- lon12_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
lon12_men_100m_all <- bind_rows(lon12_men_100m_1, lon12_men_100m_2, lon12_men_100m_3, lon12_men_100m_4, lon12_men_100m_5, lon12_men_100m_6, lon12_men_100m_7, lon12_men_100m_8, lon12_men_100m_9, lon12_men_100m_10, lon12_men_100m_11)
lon12_men_100m_all <- lon12_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=2012,"Host City" ="London", "Host Country"="United Kingdom")
lon12_men_100m_all[78, 1] <- "Gold"
lon12_men_100m_all[79, 1] <- "Silver"
lon12_men_100m_all[80, 1] <- "Bronze"
head(lon12_men_100m_all)
## Rank Lane Athlete Nation Reaction Time Notes Heat
## 1 1 6 Tyson Gay United States 0.147 10.08 Q 1
## 2 2 5 Richard Thompson Trinidad and Tobago 0.151 10.14 Q 1
## 3 3 7 Gerald Phiri Zambia 0.147 10.16 Q, SB 1
## 4 4 3 Jaysuma Saidy Ndure Norway 0.166 10.28 1
## 5 5 4 Ángel David Rodríguez Spain 0.168 10.34 1
## 6 6 2 Jurgen Themen Suriname 0.169 10.53 1
## Var.8 Year Host City Host Country
## 1 <NA> 2012 London United Kingdom
## 2 <NA> 2012 London United Kingdom
## 3 <NA> 2012 London United Kingdom
## 4 <NA> 2012 London United Kingdom
## 5 <NA> 2012 London United Kingdom
## 6 <NA> 2012 London United Kingdom
Beijing 2008
bei_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_2008_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-bei_men_100m%>%html_table(fill=TRUE)
bei_men_100m_1<-tables[[7]]
bei_men_100m_2<-tables[[8]]
bei_men_100m_3<-tables[[9]]
bei_men_100m_4<-tables[[10]]
bei_men_100m_5<-tables[[11]]
bei_men_100m_6<-tables[[12]]
bei_men_100m_7<-tables[[13]]
bei_men_100m_8<-tables[[14]]
bei_men_100m_9<-tables[[15]]
bei_men_100m_10<-tables[[16]]
bei_men_100m_11<-tables[[17]]
bei_men_100m_12<-tables[[18]]
bei_men_100m_13<-tables[[19]]
bei_men_100m_14<-tables[[20]]
bei_men_100m_15<-tables[[21]]
bei_men_100m_16<-tables[[22]]
bei_men_100m_17<-tables[[23]]
bei_men_100m_18<-tables[[25]]
bei_men_100m_1 <- bei_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
bei_men_100m_2 <- bei_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
bei_men_100m_3 <- bei_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
bei_men_100m_4 <- bei_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
bei_men_100m_5 <- bei_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
bei_men_100m_6 <- bei_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
bei_men_100m_7 <- bei_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
bei_men_100m_8 <- bei_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "8")
bei_men_100m_9 <- bei_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "9")
bei_men_100m_10 <- bei_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "10")
bei_men_100m_11 <- bei_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
bei_men_100m_12 <- bei_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
bei_men_100m_13 <- bei_men_100m_13 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
bei_men_100m_14 <- bei_men_100m_14 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
bei_men_100m_15 <- bei_men_100m_15 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
bei_men_100m_16 <- bei_men_100m_16 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
bei_men_100m_17 <- bei_men_100m_17 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
bei_men_100m_18 <- bei_men_100m_18 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
bei_men_100m_all <- bind_rows(bei_men_100m_1, bei_men_100m_2, bei_men_100m_3, bei_men_100m_4, bei_men_100m_5, bei_men_100m_6, bei_men_100m_7, bei_men_100m_8, bei_men_100m_9, bei_men_100m_10, bei_men_100m_11,bei_men_100m_12,bei_men_100m_13,bei_men_100m_14,bei_men_100m_15,bei_men_100m_16,bei_men_100m_17,bei_men_100m_18)
bei_men_100m_all <- bei_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=2008,"Host City" ="Beijing", "Host Country"="China")
bei_men_100m_all[134,1]<- "Gold"
bei_men_100m_all[135,1]<- "Silver"
bei_men_100m_all[136,1]<- "Bronze"
head(bei_men_100m_all)
## Rank Lane Athlete Nation Reaction Time Notes Var.8 Heat
## 1 1 3 Usain Bolt Jamaica 0.186 10.20 Q <NA> 1
## 2 2 9 Daniel Bailey Antigua and Barbuda 0.198 10.24 Q <NA> 1
## 3 3 6 Vicente de Lima Brazil 0.168 10.26 Q, SB <NA> 1
## 4 4 2 Henry Vizcaíno Cuba 0.157 10.28 q <NA> 1
## 5 5 4 Fabio Cerutti Italy 0.136 10.49 <NA> 1
## 6 6 5 Jurgen Themen Suriname 0.179 10.61 PB <NA> 1
## Year Host City Host Country
## 1 2008 Beijing China
## 2 2008 Beijing China
## 3 2008 Beijing China
## 4 2008 Beijing China
## 5 2008 Beijing China
## 6 2008 Beijing China
Athens 2004
ath_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_2004_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-ath_men_100m%>%html_table(fill=TRUE)
ath_men_100m_1<-tables[[6]]
ath_men_100m_2<-tables[[7]]
ath_men_100m_3<-tables[[8]]
ath_men_100m_4<-tables[[9]]
ath_men_100m_5<-tables[[10]]
ath_men_100m_6<-tables[[11]]
ath_men_100m_7<-tables[[12]]
ath_men_100m_8<-tables[[13]]
ath_men_100m_9<-tables[[14]]
ath_men_100m_10<-tables[[15]]
ath_men_100m_11<-tables[[16]]
ath_men_100m_12<-tables[[17]]
ath_men_100m_13<-tables[[18]]
ath_men_100m_14<-tables[[19]]
ath_men_100m_15<-tables[[20]]
ath_men_100m_16<-tables[[21]]
ath_men_100m_17<-tables[[22]]
ath_men_100m_18<-tables[[23]]
ath_men_100m_1 <- ath_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
ath_men_100m_2 <- ath_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
ath_men_100m_3 <- ath_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
ath_men_100m_4 <- ath_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
ath_men_100m_5 <- ath_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
ath_men_100m_6 <- ath_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
ath_men_100m_7 <- ath_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
ath_men_100m_8 <- ath_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "8")
ath_men_100m_9 <- ath_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "9")
ath_men_100m_10 <- ath_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "10")
ath_men_100m_11 <- ath_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
ath_men_100m_12 <- ath_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
ath_men_100m_13 <- ath_men_100m_13 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
ath_men_100m_14 <- ath_men_100m_14 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
ath_men_100m_15 <- ath_men_100m_15 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
ath_men_100m_16 <- ath_men_100m_16 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
ath_men_100m_17 <- ath_men_100m_17 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
ath_men_100m_18 <- ath_men_100m_18 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
ath_men_100m_all <- bind_rows(ath_men_100m_1, ath_men_100m_2, ath_men_100m_3, ath_men_100m_4, ath_men_100m_5, ath_men_100m_6, ath_men_100m_7, ath_men_100m_8, ath_men_100m_9, ath_men_100m_10, ath_men_100m_11, ath_men_100m_12, ath_men_100m_13, ath_men_100m_14, ath_men_100m_15, ath_men_100m_16, ath_men_100m_17, ath_men_100m_18)
ath_men_100m_all <- ath_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=2004,"Host City" ="Athens", "Host Country"="Greece")
ath_men_100m_all[136,1]<-"Gold"
ath_men_100m_all[137,1]<-"Silver"
ath_men_100m_all[138,1]<-"Bronze"
head(ath_men_100m_all)
## Rank Lane Athlete Nation Reaction Time Notes Heat Year
## 1 1 5 Frankie Fredericks Namibia 0.152 10.12 Q, SB 1 2004
## 2 2 3 Uchenna Emedolu Nigeria 0.222 10.22 Q 1 2004
## 3 3 4 Shingo Suetsugu Japan 0.174 10.27 Q 1 2004
## 4 4 7 Darren Campbell Great Britain 0.159 10.35 1 2004
## 5 5 9 Chen Haijian China 0.181 10.45 1 2004
## 6 6 2 Eric Nkansah Ghana 0.160 10.54 1 2004
## Host City Host Country
## 1 Athens Greece
## 2 Athens Greece
## 3 Athens Greece
## 4 Athens Greece
## 5 Athens Greece
## 6 Athens Greece
Sydney 2000
syd_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_2000_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-syd_men_100m%>%html_table(fill=TRUE)
syd_men_100m_1<-tables[[6]]
syd_men_100m_2<-tables[[7]]
syd_men_100m_3<-tables[[8]]
syd_men_100m_4<-tables[[9]]
syd_men_100m_5<-tables[[10]]
syd_men_100m_6<-tables[[11]]
syd_men_100m_7<-tables[[12]]
syd_men_100m_8<-tables[[13]]
syd_men_100m_9<-tables[[14]]
syd_men_100m_10<-tables[[15]]
syd_men_100m_11<-tables[[16]]
syd_men_100m_12<-tables[[17]]
syd_men_100m_13<-tables[[18]]
syd_men_100m_14<-tables[[19]]
syd_men_100m_15<-tables[[20]]
syd_men_100m_16<-tables[[21]]
syd_men_100m_17<-tables[[22]]
syd_men_100m_18<-tables[[23]]
syd_men_100m_19<-tables[[24]]
syd_men_100m_1 <- syd_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
syd_men_100m_2 <- syd_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
syd_men_100m_3 <- syd_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
syd_men_100m_4 <- syd_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
syd_men_100m_5 <- syd_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
syd_men_100m_6 <- syd_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
syd_men_100m_7 <- syd_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
syd_men_100m_8 <- syd_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "8")
syd_men_100m_9 <- syd_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "9")
syd_men_100m_10 <- syd_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "10")
syd_men_100m_11 <- syd_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "11")
syd_men_100m_12 <- syd_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
syd_men_100m_13 <- syd_men_100m_13 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
syd_men_100m_14 <- syd_men_100m_14 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
syd_men_100m_15 <- syd_men_100m_15 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
syd_men_100m_16 <- syd_men_100m_16 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
syd_men_100m_17 <- syd_men_100m_17 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
syd_men_100m_18 <- syd_men_100m_18 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
syd_men_100m_19 <- syd_men_100m_19 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
syd_men_100m_all <- bind_rows(syd_men_100m_1, syd_men_100m_2, syd_men_100m_3, syd_men_100m_4, syd_men_100m_5, syd_men_100m_6, syd_men_100m_7, syd_men_100m_8, syd_men_100m_9, syd_men_100m_10, syd_men_100m_11, syd_men_100m_12, syd_men_100m_13, syd_men_100m_14, syd_men_100m_15, syd_men_100m_16, syd_men_100m_17, syd_men_100m_18, syd_men_100m_19)
syd_men_100m_all <- syd_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=2000,"Host City" ="Sydney", "Host Country"="Australia")
syd_men_100m_all[152,1]<- "Gold"
syd_men_100m_all[153,1]<- "Silver"
syd_men_100m_all[154,1]<- "Bronze"
head(syd_men_100m_all)
## Rank Lane Athlete Nation Reaction Time Notes Heat Var.7
## 1 1 9 Aziz Zakari Ghana 0.317 10.31 Q 1 <NA>
## 2 2 3 Patrick Johnson Australia 0.152 10.31 Q 1 <NA>
## 3 3 8 Venancio José Spain 0.169 10.36 Q 1 <NA>
## 4 4 5 Martin Lachkovics Austria 0.150 10.41 q 1 <NA>
## 5 5 6 Nicolas Macrozonaris Canada 0.189 10.45 1 <NA>
## 6 6 2 Jamal Al-Saffar Saudi Arabia 0.165 10.54 1 <NA>
## Year Host City Host Country
## 1 2000 Sydney Australia
## 2 2000 Sydney Australia
## 3 2000 Sydney Australia
## 4 2000 Sydney Australia
## 5 2000 Sydney Australia
## 6 2000 Sydney Australia
Atlanta 1996
atl_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_1996_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-atl_men_100m%>%html_table(fill=TRUE)
atl_men_100m_1<-tables[[6]]
atl_men_100m_2<-tables[[7]]
atl_men_100m_3<-tables[[8]]
atl_men_100m_4<-tables[[9]]
atl_men_100m_5<-tables[[10]]
atl_men_100m_6<-tables[[11]]
atl_men_100m_7<-tables[[12]]
atl_men_100m_8<-tables[[13]]
atl_men_100m_9<-tables[[14]]
atl_men_100m_10<-tables[[15]]
atl_men_100m_11<-tables[[16]]
atl_men_100m_12<-tables[[17]]
atl_men_100m_13<-tables[[18]]
atl_men_100m_14<-tables[[19]]
atl_men_100m_15<-tables[[20]]
atl_men_100m_16<-tables[[21]]
atl_men_100m_17<-tables[[22]]
atl_men_100m_18<-tables[[23]]
atl_men_100m_19<-tables[[24]]
atl_men_100m_20<-tables[[25]]
atl_men_100m_1 <- atl_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
atl_men_100m_2 <- atl_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
atl_men_100m_3 <- atl_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
atl_men_100m_4 <- atl_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
atl_men_100m_5 <- atl_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
atl_men_100m_6 <- atl_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
atl_men_100m_7 <- atl_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
atl_men_100m_8 <- atl_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
atl_men_100m_9 <- atl_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
atl_men_100m_10 <- atl_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
atl_men_100m_11 <- atl_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
atl_men_100m_12 <- atl_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "12")
atl_men_100m_13 <- atl_men_100m_13 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
atl_men_100m_14 <- atl_men_100m_14 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
atl_men_100m_15 <- atl_men_100m_15 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
atl_men_100m_16 <- atl_men_100m_16 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
atl_men_100m_17 <- atl_men_100m_17 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
atl_men_100m_18 <- atl_men_100m_18 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
atl_men_100m_19 <- atl_men_100m_19 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
atl_men_100m_20 <- atl_men_100m_20 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
atl_men_100m_all <- bind_rows(atl_men_100m_1, atl_men_100m_2, atl_men_100m_3, atl_men_100m_4, atl_men_100m_5, atl_men_100m_6, atl_men_100m_7, atl_men_100m_8, atl_men_100m_9, atl_men_100m_10, atl_men_100m_11, atl_men_100m_12, atl_men_100m_13, atl_men_100m_14, atl_men_100m_15, atl_men_100m_16, atl_men_100m_17, atl_men_100m_18, atl_men_100m_19, atl_men_100m_20)
atl_men_100m_all <- atl_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=1996,"Host City" ="Atlanta", "Host Country"="United States")
atl_men_100m_all[162,1]<- "Gold"
atl_men_100m_all[163,1]<- "Silver"
atl_men_100m_all[164,1]<- "Bronze"
head(atl_men_100m_all)
## Rank Lane Athlete Nation Reaction Time Notes Heat Year
## 1 1 6 Emmanuel Tuffour Ghana 0.187 10.15 Q 1 1996
## 2 2 5 Bruny Surin Canada 0.168 10.18 Q 1 1996
## 3 3 2 Andrey Fedoriv Russia 0.159 10.39 Q 1 1996
## 4 4 1 Renward Wells Bahamas 0.156 10.48 1 1996
## 5 5 3 Chithaka De Soyza Sri Lanka 0.173 10.55 1 1996
## 6 6 7 Luís Cunha Portugal 0.149 10.65 1 1996
## Host City Host Country
## 1 Atlanta United States
## 2 Atlanta United States
## 3 Atlanta United States
## 4 Atlanta United States
## 5 Atlanta United States
## 6 Atlanta United States
Barcelona 1992
bar_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_1992_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-bar_men_100m%>%html_table(fill=TRUE)
bar_men_100m_1<-tables[[5]]
bar_men_100m_2<-tables[[6]]
bar_men_100m_3<-tables[[7]]
bar_men_100m_4<-tables[[8]]
bar_men_100m_5<-tables[[9]]
bar_men_100m_6<-tables[[10]]
bar_men_100m_7<-tables[[11]]
bar_men_100m_8<-tables[[12]]
bar_men_100m_9<-tables[[13]]
bar_men_100m_10<-tables[[14]]
bar_men_100m_11<-tables[[15]]
bar_men_100m_12<-tables[[16]]
bar_men_100m_13<-tables[[17]]
bar_men_100m_14<-tables[[18]]
bar_men_100m_15<-tables[[19]]
bar_men_100m_16<-tables[[20]]
bar_men_100m_17<-tables[[21]]
bar_men_100m_1 <- bar_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
bar_men_100m_2 <- bar_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
bar_men_100m_3 <- bar_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
bar_men_100m_4 <- bar_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
bar_men_100m_5 <- bar_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
bar_men_100m_6 <- bar_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
bar_men_100m_7 <- bar_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
bar_men_100m_8 <- bar_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "8")
bar_men_100m_9 <- bar_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "9")
bar_men_100m_10 <- bar_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "10")
bar_men_100m_11 <- bar_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
bar_men_100m_12 <- bar_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
bar_men_100m_13 <- bar_men_100m_13 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
bar_men_100m_14 <- bar_men_100m_14 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
bar_men_100m_15 <- bar_men_100m_15 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
bar_men_100m_16 <- bar_men_100m_16 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
bar_men_100m_17 <- bar_men_100m_17 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
bar_men_100m_all <- bind_rows(bar_men_100m_1, bar_men_100m_2, bar_men_100m_3, bar_men_100m_4, bar_men_100m_5, bar_men_100m_6, bar_men_100m_7, bar_men_100m_8, bar_men_100m_9, bar_men_100m_10, bar_men_100m_11, bar_men_100m_12, bar_men_100m_13, bar_men_100m_14, bar_men_100m_15, bar_men_100m_16, bar_men_100m_17)
bar_men_100m_all <- bar_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=1992,"Host City" ="Barcelona", "Host Country"="Spain")
bar_men_100m_all[126,1]<- "Gold"
bar_men_100m_all[127,1]<- "Silver"
bar_men_100m_all[128,1]<- "Bronze"
head(bar_men_100m_all)
## Rank Athlete Nation Time Notes Heat Year Host City
## 1 1 Leroy Burrell United States 10.21 Q 1 1992 Barcelona
## 2 2 Satoru Inoue Japan 10.48 Q 1 1992 Barcelona
## 3 3 Jean-Olivier Zirignon Ivory Coast 10.55 Q 1 1992 Barcelona
## 4 4 Abdulieh Janneh The Gambia 10.71 1 1992 Barcelona
## 5 5 Hassane Illiassou Niger 10.73 1 1992 Barcelona
## 6 6 Khalid Juma Juma Bahrain 10.80 1 1992 Barcelona
## Host Country
## 1 Spain
## 2 Spain
## 3 Spain
## 4 Spain
## 5 Spain
## 6 Spain
Seoul 1988
seo_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_1988_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-seo_men_100m%>%html_table(fill=TRUE)
seo_men_100m_1<-tables[[6]]
seo_men_100m_2<-tables[[7]]
seo_men_100m_3<-tables[[8]]
seo_men_100m_4<-tables[[9]]
seo_men_100m_5<-tables[[10]]
seo_men_100m_6<-tables[[11]]
seo_men_100m_7<-tables[[12]]
seo_men_100m_8<-tables[[13]]
seo_men_100m_9<-tables[[14]]
seo_men_100m_10<-tables[[15]]
seo_men_100m_11<-tables[[16]]
seo_men_100m_12<-tables[[17]]
seo_men_100m_13<-tables[[18]]
seo_men_100m_14<-tables[[19]]
seo_men_100m_15<-tables[[20]]
seo_men_100m_16<-tables[[21]]
seo_men_100m_17<-tables[[22]]
seo_men_100m_18<-tables[[23]]
seo_men_100m_19<-tables[[24]]
seo_men_100m_20<-tables[[25]]
seo_men_100m_21<-tables[[26]]
seo_men_100m_22<-tables[[27]]
seo_men_100m_1 <- seo_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
seo_men_100m_2 <- seo_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
seo_men_100m_3 <- seo_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
seo_men_100m_4 <- seo_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
seo_men_100m_5 <- seo_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
seo_men_100m_6 <- seo_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
seo_men_100m_7 <- seo_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
seo_men_100m_8 <- seo_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "8")
seo_men_100m_9 <- seo_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "9")
seo_men_100m_10 <- seo_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "10")
seo_men_100m_11 <- seo_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "11")
seo_men_100m_12 <- seo_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "12")
seo_men_100m_13 <- seo_men_100m_13 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "13")
seo_men_100m_14 <- seo_men_100m_14 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
seo_men_100m_15 <- seo_men_100m_15 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
seo_men_100m_16 <- seo_men_100m_16 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
seo_men_100m_17 <- seo_men_100m_17 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
seo_men_100m_18 <- seo_men_100m_18 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
seo_men_100m_19 <- seo_men_100m_19 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
seo_men_100m_20 <- seo_men_100m_20 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
seo_men_100m_21 <- seo_men_100m_21 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
seo_men_100m_22 <- seo_men_100m_22 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
seo_men_100m_all <- bind_rows(seo_men_100m_1, seo_men_100m_2, seo_men_100m_3, seo_men_100m_4, seo_men_100m_5, seo_men_100m_6, seo_men_100m_7, seo_men_100m_8, seo_men_100m_9, seo_men_100m_10, seo_men_100m_11, seo_men_100m_12, seo_men_100m_13, seo_men_100m_14, seo_men_100m_15, seo_men_100m_16, seo_men_100m_17, seo_men_100m_18, seo_men_100m_19, seo_men_100m_20, seo_men_100m_21, seo_men_100m_22)
seo_men_100m_all <- seo_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=1988,"Host City" ="Seoul", "Host Country"="South Korea")
seo_men_100m_all[165,1]<- "Gold"
seo_men_100m_all[166,1]<- "Silver"
seo_men_100m_all[167,1]<- "Bronze"
head(seo_men_100m_all)
## Rank Athlete Nation Time Notes Heat Year Host City
## 1 1 Robson da Silva Brazil 10.37 Q 1 1988 Seoul
## 2 2 Ezio Madonia Italy 10.40 Q 1 1988 Seoul
## 3 3 Cheng Hsin-fu Chinese Taipei 10.48 Q 1 1988 Seoul
## 4 4 Thierry Lauret France 10.56 q 1 1988 Seoul
## 5 5 Boevi Lawson Togo 10.59 1 1988 Seoul
## 6 6 Leung Wing Kwong Hong Kong 10.82 1 1988 Seoul
## Host Country
## 1 South Korea
## 2 South Korea
## 3 South Korea
## 4 South Korea
## 5 South Korea
## 6 South Korea
Los Angeles 1984
los_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_1984_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-los_men_100m%>%html_table(fill=TRUE)
los_men_100m_1<-tables[[5]]
los_men_100m_2<-tables[[6]]
los_men_100m_3<-tables[[7]]
los_men_100m_4<-tables[[8]]
los_men_100m_5<-tables[[9]]
los_men_100m_6<-tables[[10]]
los_men_100m_7<-tables[[11]]
los_men_100m_8<-tables[[12]]
los_men_100m_9<-tables[[13]]
los_men_100m_10<-tables[[14]]
los_men_100m_11<-tables[[15]]
los_men_100m_12<-tables[[16]]
los_men_100m_13<-tables[[17]]
los_men_100m_14<-tables[[18]]
los_men_100m_15<-tables[[19]]
los_men_100m_16<-tables[[20]]
los_men_100m_17<-tables[[21]]
los_men_100m_18<-tables[[22]]
los_men_100m_19<-tables[[23]]
los_men_100m_1 <- los_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
los_men_100m_2 <- los_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
los_men_100m_3 <- los_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
los_men_100m_4 <- los_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
los_men_100m_5 <- los_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
los_men_100m_6 <- los_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
los_men_100m_7 <- los_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
los_men_100m_8 <- los_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "8")
los_men_100m_9 <- los_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "9")
los_men_100m_10 <- los_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "10")
los_men_100m_11 <- los_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "11")
los_men_100m_12 <- los_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
los_men_100m_13 <- los_men_100m_13 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
los_men_100m_14 <- los_men_100m_14 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
los_men_100m_15 <- los_men_100m_15 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
los_men_100m_16 <- los_men_100m_16 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
los_men_100m_17 <- los_men_100m_17 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
los_men_100m_18 <- los_men_100m_18 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
los_men_100m_19 <- los_men_100m_19 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
los_men_100m_all <- bind_rows(los_men_100m_1, los_men_100m_2, los_men_100m_3, los_men_100m_4, los_men_100m_5, los_men_100m_6, los_men_100m_7, los_men_100m_8, los_men_100m_9, los_men_100m_10, los_men_100m_11, los_men_100m_12, los_men_100m_13, los_men_100m_14, los_men_100m_15, los_men_100m_16, los_men_100m_17, los_men_100m_18, los_men_100m_19)
los_men_100m_all <- los_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=1984,"Host City" ="Los Angeles", "Host Country"="United States")
los_men_100m_all[138,1]<- "Gold"
los_men_100m_all[139,1]<- "Silver"
los_men_100m_all[140,1]<- "Bronze"
head(los_men_100m_all)
## Rank Athlete Nation Time Notes Heat Year
## 1 1 Carl Lewis United States 10.32 Q 1 1984
## 2 2 Tony Sharpe Canada 10.38 Q 1 1984
## 3 3 Mike McFarlane Great Britain 10.47 Q 1 1984
## 4 4 Hasely Crawford Trinidad and Tobago 10.48 q 1 1984
## 5 5 Peter Van Miltenburg Australia 10.55 q 1 1984
## 6 6 Vicente Daniel Mozambique 10.81 1 1984
## Host City Host Country
## 1 Los Angeles United States
## 2 Los Angeles United States
## 3 Los Angeles United States
## 4 Los Angeles United States
## 5 Los Angeles United States
## 6 Los Angeles United States
Moscow 1980
mos_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_1980_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-mos_men_100m%>%html_table(fill=TRUE)
mos_men_100m_1<-tables[[5]]
mos_men_100m_2<-tables[[6]]
mos_men_100m_3<-tables[[7]]
mos_men_100m_4<-tables[[8]]
mos_men_100m_5<-tables[[9]]
mos_men_100m_6<-tables[[10]]
mos_men_100m_7<-tables[[11]]
mos_men_100m_8<-tables[[12]]
mos_men_100m_9<-tables[[13]]
mos_men_100m_10<-tables[[14]]
mos_men_100m_11<-tables[[15]]
mos_men_100m_12<-tables[[16]]
mos_men_100m_13<-tables[[17]]
mos_men_100m_14<-tables[[18]]
mos_men_100m_15<-tables[[19]]
mos_men_100m_16<-tables[[20]]
mos_men_100m_1 <- mos_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
mos_men_100m_2 <- mos_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
mos_men_100m_3 <- mos_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
mos_men_100m_4 <- mos_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
mos_men_100m_5 <- mos_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
mos_men_100m_6 <- mos_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
mos_men_100m_7 <- mos_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
mos_men_100m_8 <- mos_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "8")
mos_men_100m_9 <- mos_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "9")
mos_men_100m_10 <- mos_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
mos_men_100m_11 <- mos_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
mos_men_100m_12 <- mos_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
mos_men_100m_13 <- mos_men_100m_13 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
mos_men_100m_14 <- mos_men_100m_14 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
mos_men_100m_15 <- mos_men_100m_15 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
mos_men_100m_16 <- mos_men_100m_16 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
mos_men_100m_all <- bind_rows(mos_men_100m_1, mos_men_100m_2, mos_men_100m_3, mos_men_100m_4, mos_men_100m_5, mos_men_100m_6, mos_men_100m_7, mos_men_100m_8, mos_men_100m_9, mos_men_100m_10, mos_men_100m_11, mos_men_100m_12, mos_men_100m_13, mos_men_100m_14, mos_men_100m_15, mos_men_100m_16)
mos_men_100m_all <- mos_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=1980,"Host City" ="Moscow", "Host Country"="Russia")
mos_men_100m_all[114,1]<- "Gold"
mos_men_100m_all[115,1]<- "Silver"
mos_men_100m_all[116,1]<- "Bronze"
head(mos_men_100m_all)
## Rank Athlete Nation Time Notes Heat Year
## 1 1 Silvio Leonard Cuba 10.33 Q 1 1980
## 2 2 Peter Okodogbe Nigeria 10.39 Q 1 1980
## 3 3 Christopher Brathwaite Trinidad and Tobago 10.44 Q 1 1980
## 4 4 Klaus-Dieter Kurrat East Germany 10.53 q 1 1980
## 5 5 Charles Kachenjela Zambia 11.03 1 1980
## 6 6 John Carew Sierra Leone 11.11 1 1980
## Host City Host Country
## 1 Moscow Russia
## 2 Moscow Russia
## 3 Moscow Russia
## 4 Moscow Russia
## 5 Moscow Russia
## 6 Moscow Russia
Montreal 1976
mon_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_1976_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-mon_men_100m%>%html_table(fill=TRUE)
mon_men_100m_1<-tables[[5]]
mon_men_100m_2<-tables[[6]]
mon_men_100m_3<-tables[[7]]
mon_men_100m_4<-tables[[8]]
mon_men_100m_5<-tables[[9]]
mon_men_100m_6<-tables[[10]]
mon_men_100m_7<-tables[[11]]
mon_men_100m_8<-tables[[12]]
mon_men_100m_9<-tables[[13]]
mon_men_100m_10<-tables[[14]]
mon_men_100m_11<-tables[[15]]
mon_men_100m_12<-tables[[16]]
mon_men_100m_13<-tables[[17]]
mon_men_100m_14<-tables[[18]]
mon_men_100m_15<-tables[[19]]
mon_men_100m_16<-tables[[20]]
mon_men_100m_1 <- mon_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
mon_men_100m_2 <- mon_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
mon_men_100m_3 <- mon_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
mon_men_100m_4 <- mon_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
mon_men_100m_5 <- mon_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
mon_men_100m_6 <- mon_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
mon_men_100m_7 <- mon_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
mon_men_100m_8 <- mon_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "8")
mon_men_100m_9 <- mon_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "9")
mon_men_100m_10 <- mon_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
mon_men_100m_11 <- mon_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
mon_men_100m_12 <- mon_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
mon_men_100m_13 <- mon_men_100m_13 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
mon_men_100m_14 <- mon_men_100m_14 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
mon_men_100m_15 <- mon_men_100m_15 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
mon_men_100m_16 <- mon_men_100m_16 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
mon_men_100m_all <- bind_rows(mon_men_100m_1, mon_men_100m_2, mon_men_100m_3, mon_men_100m_4, mon_men_100m_5, mon_men_100m_6, mon_men_100m_7, mon_men_100m_8, mon_men_100m_9, mon_men_100m_10, mon_men_100m_11, mon_men_100m_12, mon_men_100m_13, mon_men_100m_14, mon_men_100m_15, mon_men_100m_16)
mon_men_100m_all <- mon_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=1976,"Host City" ="Montreal", "Host Country"="Canada")
mon_men_100m_all[110,1]<- "Gold"
mon_men_100m_all[111,1]<- "Silver"
mon_men_100m_all[112,1]<- "Bronze"
head(mon_men_100m_all)
## Rank Athlete Nation Time Notes Heat Year Host City
## 1 1 Hasely Crawford Trinidad and Tobago 10.42 Q 1 1976 Montreal
## 2 2 Alexander Thieme East Germany 10.64 Q 1 1976 Montreal
## 3 3 Luciano Caravani Italy 10.66 Q 1 1976 Montreal
## 4 4 Lambert Micha Belgium 10.69 1 1976 Montreal
## 5 5 Gregory Simons Bermuda 10.76 1 1976 Montreal
## 6 6 Bjarni Stefánsson Iceland 11.28 1 1976 Montreal
## Host Country
## 1 Canada
## 2 Canada
## 3 Canada
## 4 Canada
## 5 Canada
## 6 Canada
Munich 1972
mun_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_1972_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-mun_men_100m%>%html_table(fill=TRUE)
mun_men_100m_1<-tables[[5]]
mun_men_100m_2<-tables[[6]]
mun_men_100m_3<-tables[[7]]
mun_men_100m_4<-tables[[8]]
mun_men_100m_5<-tables[[9]]
mun_men_100m_6<-tables[[10]]
mun_men_100m_7<-tables[[11]]
mun_men_100m_8<-tables[[12]]
mun_men_100m_9<-tables[[13]]
mun_men_100m_10<-tables[[14]]
mun_men_100m_11<-tables[[15]]
mun_men_100m_12<-tables[[16]]
mun_men_100m_13<-tables[[17]]
mun_men_100m_14<-tables[[18]]
mun_men_100m_15<-tables[[19]]
mun_men_100m_16<-tables[[20]]
mun_men_100m_17<-tables[[21]]
mun_men_100m_18<-tables[[22]]
mun_men_100m_19<-tables[[23]]
mun_men_100m_20<-tables[[24]]
mun_men_100m_1 <- mun_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
mun_men_100m_2 <- mun_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
mun_men_100m_3 <- mun_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
mun_men_100m_4 <- mun_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
mun_men_100m_5 <- mun_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
mun_men_100m_6 <- mun_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
mun_men_100m_7 <- mun_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
mun_men_100m_8 <- mun_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "8")
mun_men_100m_9 <- mun_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "9")
mun_men_100m_10 <- mun_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "10")
mun_men_100m_11 <- mun_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "11")
mun_men_100m_12 <- mun_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "12")
mun_men_100m_13 <- mun_men_100m_13 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
mun_men_100m_14 <- mun_men_100m_14 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
mun_men_100m_15 <- mun_men_100m_15 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
mun_men_100m_16 <- mun_men_100m_16 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
mun_men_100m_17 <- mun_men_100m_17 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
mun_men_100m_18 <- mun_men_100m_18 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
mun_men_100m_19 <- mun_men_100m_19 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
mun_men_100m_20 <- mun_men_100m_20 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
mun_men_100m_all <- bind_rows(mun_men_100m_1, mun_men_100m_2, mun_men_100m_3, mun_men_100m_4, mun_men_100m_5, mun_men_100m_6, mun_men_100m_7, mun_men_100m_8, mun_men_100m_9, mun_men_100m_10, mun_men_100m_11, mun_men_100m_12, mun_men_100m_13, mun_men_100m_14, mun_men_100m_15, mun_men_100m_16, mun_men_100m_17, mun_men_100m_18, mun_men_100m_19, mun_men_100m_20)
mun_men_100m_all <- mun_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=1972,"Host City" ="Munich", "Host Country"="Germany")
mun_men_100m_all[139,1]<- "Gold"
mun_men_100m_all[140,1]<- "Silver"
mun_men_100m_all[141,1]<- "Bronze"
head(mun_men_100m_all)
## Rank Athlete Nation Time Notes Heat Lane Year
## 1 1 Lennox Miller Jamaica 10.45 Q 1 NA 1972
## 2 2 Amadou Meïté Ivory Coast 10.51 Q 1 NA 1972
## 3 3 Hans-Jürgen Bombach East Germany 10.66 Q 1 NA 1972
## 4 4 Rudy Reid Trinidad and Tobago 10.74 1 NA 1972
## 5 5 Dan Amuke Kenya 10.76 1 NA 1972
## 6 6 Byambajavyn Enkhbaatar Mongolia 10.93 1 NA 1972
## Host City Host Country
## 1 Munich Germany
## 2 Munich Germany
## 3 Munich Germany
## 4 Munich Germany
## 5 Munich Germany
## 6 Munich Germany
Mexico City 1968
mex_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_1968_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-mex_men_100m%>%html_table(fill=TRUE)
mex_men_100m_1<-tables[[5]]
mex_men_100m_2<-tables[[6]]
mex_men_100m_3<-tables[[7]]
mex_men_100m_4<-tables[[8]]
mex_men_100m_5<-tables[[9]]
mex_men_100m_6<-tables[[10]]
mex_men_100m_7<-tables[[11]]
mex_men_100m_8<-tables[[12]]
mex_men_100m_9<-tables[[13]]
mex_men_100m_10<-tables[[14]]
mex_men_100m_11<-tables[[15]]
mex_men_100m_12<-tables[[16]]
mex_men_100m_13<-tables[[17]]
mex_men_100m_14<-tables[[18]]
mex_men_100m_15<-tables[[19]]
mex_men_100m_16<-tables[[20]]
names(mex_men_100m_16)[5] <- "Time"
mex_men_100m_1 <- mex_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
mex_men_100m_2 <- mex_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
mex_men_100m_3 <- mex_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
mex_men_100m_4 <- mex_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
mex_men_100m_5 <- mex_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
mex_men_100m_6 <- mex_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
mex_men_100m_7 <- mex_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
mex_men_100m_8 <- mex_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "8")
mex_men_100m_9 <- mex_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "9")
mex_men_100m_10 <- mex_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
mex_men_100m_11 <- mex_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
mex_men_100m_12 <- mex_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
mex_men_100m_13 <- mex_men_100m_13 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
mex_men_100m_14 <- mex_men_100m_14 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
mex_men_100m_15 <- mex_men_100m_15 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
mex_men_100m_16 <- mex_men_100m_16 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
mex_men_100m_all <- bind_rows(mex_men_100m_1, mex_men_100m_2, mex_men_100m_3, mex_men_100m_4, mex_men_100m_5, mex_men_100m_6, mex_men_100m_7, mex_men_100m_8, mex_men_100m_9, mex_men_100m_10, mex_men_100m_11, mex_men_100m_12, mex_men_100m_13, mex_men_100m_14, mex_men_100m_15, mex_men_100m_16)
mex_men_100m_all <- mex_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=1968,"Host City" ="Mexico City", "Host Country"="Mexico")
mex_men_100m_all[113,1]<- "Gold"
mex_men_100m_all[114,1]<- "Silver"
mex_men_100m_all[115,1]<- "Bronze"
head(mex_men_100m_all)
## Rank Athlete Nation Time Notes Heat Lane Time..a. Year
## 1 1 Charles Greene United States 10.09 Q 1 NA NA 1968
## 2 2 Hideo Iijima Japan 10.24 Q 1 NA NA 1968
## 3 3 Canagasabai Kunalan Singapore 10.47 Q 1 NA NA 1968
## 4 4 Wiesław Maniak Poland 10.49 1 NA NA 1968
## 5 5 Barka Sy Senegal 10.61 1 NA NA 1968
## 6 1 Jim Hines United States 10.26 Q 2 NA NA 1968
## Host City Host Country
## 1 Mexico City Mexico
## 2 Mexico City Mexico
## 3 Mexico City Mexico
## 4 Mexico City Mexico
## 5 Mexico City Mexico
## 6 Mexico City Mexico
Tokyo 1964
tok_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_1964_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-tok_men_100m%>%html_table(fill=TRUE)
tok_men_100m_1<-tables[[6]]
tok_men_100m_2<-tables[[7]]
tok_men_100m_3<-tables[[8]]
tok_men_100m_4<-tables[[9]]
tok_men_100m_5<-tables[[10]]
tok_men_100m_6<-tables[[11]]
tok_men_100m_7<-tables[[12]]
tok_men_100m_8<-tables[[13]]
tok_men_100m_9<-tables[[14]]
tok_men_100m_10<-tables[[15]]
tok_men_100m_11<-tables[[16]]
tok_men_100m_12<-tables[[17]]
tok_men_100m_13<-tables[[18]]
tok_men_100m_14<-tables[[19]]
tok_men_100m_15<-tables[[20]]
tok_men_100m_16<-tables[[21]]
tok_men_100m_17<-tables[[22]]
tok_men_100m_1 <- tok_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
tok_men_100m_2 <- tok_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
tok_men_100m_3 <- tok_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
tok_men_100m_4 <- tok_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
tok_men_100m_5 <- tok_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
tok_men_100m_6 <- tok_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
tok_men_100m_7 <- tok_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
tok_men_100m_8 <- tok_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "8")
tok_men_100m_9 <- tok_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "9")
tok_men_100m_10 <- tok_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "10")
tok_men_100m_11 <- tok_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
tok_men_100m_12 <- tok_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
tok_men_100m_13 <- tok_men_100m_13 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
tok_men_100m_14 <- tok_men_100m_14 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
tok_men_100m_15 <- tok_men_100m_15 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
tok_men_100m_16 <- tok_men_100m_16 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
tok_men_100m_17 <- tok_men_100m_17 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
tok_men_100m_all <- bind_rows(tok_men_100m_1, tok_men_100m_2, tok_men_100m_3, tok_men_100m_4, tok_men_100m_5, tok_men_100m_6, tok_men_100m_7, tok_men_100m_8, tok_men_100m_9, tok_men_100m_10, tok_men_100m_11, tok_men_100m_12, tok_men_100m_13, tok_men_100m_14, tok_men_100m_15, tok_men_100m_16, tok_men_100m_17)
tok_men_100m_all <- tok_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=1964,"Host City" ="Tokyo", "Host Country"="Japan")
tok_men_100m_all[119,1]<- "Gold"
tok_men_100m_all[120,1]<- "Silver"
tok_men_100m_all[121,1]<- "Bronze"
head(tok_men_100m_all)
## Rank Athlete Nation Time Notes Heat Year Host City
## 1 1 Hideo Iijima Japan 10.3 Q 1 1964 Tokyo
## 2 2 Bernard Laidebeur France 10.5 Q 1 1964 Tokyo
## 3 3 Edvin Ozolin Soviet Union 10.5 Q 1 1964 Tokyo
## 4 4 Kenneth Powell India 10.7 1 1964 Tokyo
## 5 5 Zbigniew Syka Poland 10.7 1 1964 Tokyo
## 6 6 Jean-Louis Ravelomanantsoa Madagascar 10.8 1 1964 Tokyo
## Host Country
## 1 Japan
## 2 Japan
## 3 Japan
## 4 Japan
## 5 Japan
## 6 Japan
Rome 1960
rom_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_1960_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-rom_men_100m%>%html_table(fill=TRUE)
rom_men_100m_1<-tables[[5]]
rom_men_100m_2<-tables[[6]]
rom_men_100m_3<-tables[[7]]
rom_men_100m_4<-tables[[8]]
rom_men_100m_5<-tables[[9]]
rom_men_100m_6<-tables[[10]]
rom_men_100m_7<-tables[[11]]
rom_men_100m_8<-tables[[12]]
rom_men_100m_9<-tables[[13]]
rom_men_100m_10<-tables[[14]]
rom_men_100m_11<-tables[[15]]
rom_men_100m_12<-tables[[16]]
rom_men_100m_13<-tables[[17]]
rom_men_100m_14<-tables[[18]]
rom_men_100m_15<-tables[[19]]
rom_men_100m_16<-tables[[20]]
rom_men_100m_1 <- rom_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
rom_men_100m_2 <- rom_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
rom_men_100m_3 <- rom_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
rom_men_100m_4 <- rom_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
rom_men_100m_5 <- rom_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
rom_men_100m_6 <- rom_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
rom_men_100m_7 <- rom_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
rom_men_100m_8 <- rom_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "8")
rom_men_100m_9 <- rom_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "9")
rom_men_100m_10 <- rom_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
rom_men_100m_11 <- rom_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
rom_men_100m_12 <- rom_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
rom_men_100m_13 <- rom_men_100m_13 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
rom_men_100m_14 <- rom_men_100m_14 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
rom_men_100m_15 <- rom_men_100m_15 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
rom_men_100m_16 <- rom_men_100m_16 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
rom_men_100m_all <- bind_rows(rom_men_100m_1, rom_men_100m_2, rom_men_100m_3, rom_men_100m_4, rom_men_100m_5, rom_men_100m_6, rom_men_100m_7, rom_men_100m_8, rom_men_100m_9, rom_men_100m_10, rom_men_100m_11, rom_men_100m_12, rom_men_100m_13, rom_men_100m_14, rom_men_100m_15, rom_men_100m_16)
rom_men_100m_all <- rom_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=1960,"Host City" ="Rome", "Host Country"="Italy")
rom_men_100m_all[99,1]<- "Gold"
rom_men_100m_all[100,1]<- "Silver"
rom_men_100m_all[101,1]<- "Bronze"
head(rom_men_100m_all)
## Rank Athlete Nation Time Notes Heat Year Host City
## 1 1 Enrique Figuerola Cuba 10.4 Q 1 1960 Rome
## 2 2 Carl Fredrik Bunæs Norway 10.7 Q 1 1960 Rome
## 3 3 Yuriy Konovalov Soviet Union 10.7 Q 1 1960 Rome
## 4 4 Suthi Manyakass Thailand 10.8 1 1960 Rome
## 5 5 Mikhail Bachvarov Bulgaria 11.0 1 1960 Rome
## 6 6 Amos Grodzinowsky Israel 11.1 1 1960 Rome
## Host Country
## 1 Italy
## 2 Italy
## 3 Italy
## 4 Italy
## 5 Italy
## 6 Italy
Melbourne 1956
mel_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_1956_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-mel_men_100m%>%html_table(fill=TRUE)
mel_men_100m_1<-tables[[5]]
mel_men_100m_2<-tables[[6]]
mel_men_100m_3<-tables[[7]]
mel_men_100m_4<-tables[[8]]
mel_men_100m_5<-tables[[9]]
mel_men_100m_6<-tables[[10]]
mel_men_100m_7<-tables[[11]]
mel_men_100m_8<-tables[[12]]
mel_men_100m_9<-tables[[13]]
mel_men_100m_10<-tables[[14]]
mel_men_100m_11<-tables[[15]]
mel_men_100m_12<-tables[[16]]
mel_men_100m_13<-tables[[17]]
mel_men_100m_14<-tables[[18]]
mel_men_100m_15<-tables[[19]]
mel_men_100m_16<-tables[[20]]
mel_men_100m_17<-tables[[21]]
mel_men_100m_18<-tables[[22]]
mel_men_100m_19<-tables[[23]]
names(mel_men_100m_19)[5] <- "Time"
mel_men_100m_1 <- mel_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
mel_men_100m_2 <- mel_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
mel_men_100m_3 <- mel_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
mel_men_100m_4 <- mel_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
mel_men_100m_5 <- mel_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
mel_men_100m_6 <- mel_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
mel_men_100m_7 <- mel_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
mel_men_100m_8 <- mel_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "8")
mel_men_100m_9 <- mel_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "9")
mel_men_100m_10 <- mel_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "10")
mel_men_100m_11 <- mel_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "11")
mel_men_100m_12 <- mel_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "12")
mel_men_100m_13 <- mel_men_100m_13 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
mel_men_100m_14 <- mel_men_100m_14 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
mel_men_100m_15 <- mel_men_100m_15 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
mel_men_100m_16 <- mel_men_100m_16 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
mel_men_100m_17 <- mel_men_100m_17 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
mel_men_100m_18 <- mel_men_100m_18 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
mel_men_100m_19 <- mel_men_100m_19 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
mel_men_100m_all <- bind_rows(mel_men_100m_1, mel_men_100m_2, mel_men_100m_3, mel_men_100m_4, mel_men_100m_5, mel_men_100m_6, mel_men_100m_7, mel_men_100m_8, mel_men_100m_9, mel_men_100m_10, mel_men_100m_11, mel_men_100m_12, mel_men_100m_13, mel_men_100m_14, mel_men_100m_15, mel_men_100m_16, mel_men_100m_17, mel_men_100m_18, mel_men_100m_19)
mel_men_100m_all <- mel_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=1956,"Host City" ="Melbourne", "Host Country"="Australia")
mel_men_100m_all[102,1]<- "Gold"
mel_men_100m_all[103,1]<- "Silver"
mel_men_100m_all[104,1]<- "Bronze"
head(mel_men_100m_all)
## Rank Athlete Nation Time Notes Heat Time..hand. Year
## 1 1 Ira Murchison United States 10.67 Q 1 NA 1956
## 2 2 Jan Jarzembowski Poland 10.95 Q 1 NA 1956
## 3 3 Hilmar Þorbjörnsson Iceland 11.12 1 NA 1956
## 4 4 Mario Colarossi Italy 11.14 1 NA 1956
## 5 5 René Ahumada Mexico 11.26 1 NA 1956
## 6 6 Raja bin Ngah Ali Malaya 11.41 1 NA 1956
## Host City Host Country
## 1 Melbourne Australia
## 2 Melbourne Australia
## 3 Melbourne Australia
## 4 Melbourne Australia
## 5 Melbourne Australia
## 6 Melbourne Australia
Helsinki 1952
hel_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_1952_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-hel_men_100m%>%html_table(fill=TRUE)
hel_men_100m_1<-tables[[5]]
hel_men_100m_2<-tables[[6]]
hel_men_100m_3<-tables[[7]]
hel_men_100m_4<-tables[[8]]
hel_men_100m_5<-tables[[9]]
hel_men_100m_6<-tables[[10]]
hel_men_100m_7<-tables[[11]]
hel_men_100m_8<-tables[[12]]
hel_men_100m_9<-tables[[13]]
hel_men_100m_10<-tables[[14]]
hel_men_100m_11<-tables[[15]]
hel_men_100m_12<-tables[[16]]
hel_men_100m_13<-tables[[17]]
hel_men_100m_14<-tables[[18]]
hel_men_100m_15<-tables[[19]]
hel_men_100m_16<-tables[[20]]
hel_men_100m_17<-tables[[21]]
hel_men_100m_18<-tables[[22]]
hel_men_100m_19<-tables[[23]]
hel_men_100m_1 <- hel_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
hel_men_100m_2 <- hel_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
hel_men_100m_3 <- hel_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
hel_men_100m_4 <- hel_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
hel_men_100m_5 <- hel_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
hel_men_100m_6 <- hel_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
hel_men_100m_7 <- hel_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
hel_men_100m_8 <- hel_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "8")
hel_men_100m_9 <- hel_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "9")
hel_men_100m_10 <- hel_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "10")
hel_men_100m_11 <- hel_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "11")
hel_men_100m_12 <- hel_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "12")
hel_men_100m_13 <- hel_men_100m_13 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
hel_men_100m_14 <- hel_men_100m_14 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
hel_men_100m_15 <- hel_men_100m_15 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
hel_men_100m_16 <- hel_men_100m_16 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
hel_men_100m_17 <- hel_men_100m_17 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
hel_men_100m_18 <- hel_men_100m_18 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
hel_men_100m_19 <- hel_men_100m_19 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
hel_men_100m_all <- bind_rows(hel_men_100m_1, hel_men_100m_2, hel_men_100m_3, hel_men_100m_4, hel_men_100m_5, hel_men_100m_6, hel_men_100m_7, hel_men_100m_8, hel_men_100m_9, hel_men_100m_10, hel_men_100m_11, hel_men_100m_12, hel_men_100m_13, hel_men_100m_14, hel_men_100m_15, hel_men_100m_16, hel_men_100m_17, hel_men_100m_18, hel_men_100m_19)
hel_men_100m_all <- hel_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=1952,"Host City" ="Helsinki", "Host Country"="Finland")
hel_men_100m_all[107,1]<- "Gold"
hel_men_100m_all[108,1]<- "Silver"
hel_men_100m_all[109,1]<- "Bronze"
head(hel_men_100m_all)
## Rank Athlete Nation Time Notes Heat Year Host City
## 1 1 John Treloar Australia 10.92 Q 1 1952 Helsinki
## 2 2 Alan Lillington Great Britain 11.06 Q 1 1952 Helsinki
## 3 3 Gabriel Lareya Ghana 11.18 1 1952 Helsinki
## 4 4 Miroslav Horčic Czechoslovakia 11.23 1 1952 Helsinki
## 5 5 Ásmundur Bjarnason Iceland 11.40 1 1952 Helsinki
## 6 6 Youssef Ali Omar Egypt 11.53 1 1952 Helsinki
## Host Country
## 1 Finland
## 2 Finland
## 3 Finland
## 4 Finland
## 5 Finland
## 6 Finland
London 1948
lon48_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_1948_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-lon48_men_100m%>%html_table(fill=TRUE)
lon48_men_100m_1<-tables[[6]]
lon48_men_100m_1[4,4] <- as.factor(11.25)
lon48_men_100m_1[5,4] <- as.factor(11.54)
lon48_men_100m_2<-tables[[7]]
lon48_men_100m_2[4,4] <- as.factor(11.16)
lon48_men_100m_2[5,4] <- as.factor(11.50)
lon48_men_100m_2[6,4] <- as.factor(11.97)
lon48_men_100m_3<-tables[[8]]
lon48_men_100m_4<-tables[[9]]
lon48_men_100m_4[4,4] <- as.factor(11.04)
lon48_men_100m_4[5,4] <- as.factor(11.23)
lon48_men_100m_5<-tables[[10]]
lon48_men_100m_5[4,4] <- as.factor(11.30)
lon48_men_100m_6<-tables[[11]]
lon48_men_100m_6[4,4] <- as.factor(11.08)
lon48_men_100m_6[5,4] <- as.factor(11.32)
lon48_men_100m_6[6,4] <- as.factor(11.69)
lon48_men_100m_7<-tables[[12]]
lon48_men_100m_7[4,4] <- as.factor(11.24)
lon48_men_100m_7[5,4] <- as.factor(11.71)
lon48_men_100m_7[6,4] <- as.factor(11.90)
lon48_men_100m_8<-tables[[13]]
lon48_men_100m_8[4,4] <- as.factor(11.09)
lon48_men_100m_8[5,4] <- as.factor(11.09)
lon48_men_100m_9<-tables[[14]]
lon48_men_100m_9[4,4] <- as.factor(11.22)
lon48_men_100m_9[5,4] <- as.factor(11.35)
lon48_men_100m_10<-tables[[15]]
lon48_men_100m_10[4,4] <- as.factor(11.19)
lon48_men_100m_10[5,4] <- as.factor(11.62)
lon48_men_100m_11<-tables[[16]]
lon48_men_100m_11[4,4] <- as.factor(11.23)
lon48_men_100m_12<-tables[[17]]
lon48_men_100m_12[4,4] <- as.factor(11.36)
lon48_men_100m_12[5,4] <- as.factor(11.45)
lon48_men_100m_12[6,4] <- as.factor(11.78)
lon48_men_100m_13<-tables[[18]]
lon48_men_100m_13[4,4] <- as.factor(10.93)
lon48_men_100m_13[5,4] <- as.factor(10.97)
lon48_men_100m_13[6,4] <- as.factor(11.32)
lon48_men_100m_14<-tables[[19]]
lon48_men_100m_14[4,4] <- as.factor(11.04)
lon48_men_100m_14[5,4] <- as.factor(11.11)
lon48_men_100m_14[6,4] <- as.factor(11.18)
lon48_men_100m_15<-tables[[20]]
lon48_men_100m_15[4,4] <- as.factor(10.82)
lon48_men_100m_15[5,4] <- as.factor(11.08)
lon48_men_100m_15[6,4] <- as.factor(11.10)
lon48_men_100m_16<-tables[[21]]
lon48_men_100m_16[4,4] <- as.factor(11.04)
lon48_men_100m_16[5,4] <- as.factor(11.09)
lon48_men_100m_16[6,4] <- as.factor(11.26)
lon48_men_100m_17<-tables[[22]]
lon48_men_100m_17[4,4] <- as.factor(10.98)
lon48_men_100m_17[5,4] <- as.factor(11.05)
lon48_men_100m_17[6,4] <- as.factor(11.15)
lon48_men_100m_18<-tables[[23]]
lon48_men_100m_18[4,4] <- as.factor(10.74)
lon48_men_100m_18[5,4] <- as.factor(10.82)
lon48_men_100m_18[6,4] <- as.factor(11.01)
lon48_men_100m_19<-tables[[24]]
lon48_men_100m_19[4,4,] <- as.factor(10.61)
lon48_men_100m_19[5,4,] <- as.factor(10.67)
lon48_men_100m_19[6,4,] <- as.factor(10.81)
names(lon48_men_100m_19)[4] <- "Time"
lon48_men_100m_1 <- lon48_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
lon48_men_100m_2 <- lon48_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
lon48_men_100m_3 <- lon48_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
lon48_men_100m_4 <- lon48_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
lon48_men_100m_5 <- lon48_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
lon48_men_100m_6 <- lon48_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
lon48_men_100m_7 <- lon48_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
lon48_men_100m_8 <- lon48_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "8")
lon48_men_100m_9 <- lon48_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "9")
lon48_men_100m_10 <- lon48_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "10")
lon48_men_100m_11 <- lon48_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "11")
lon48_men_100m_12 <- lon48_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "12")
lon48_men_100m_13 <- lon48_men_100m_13 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
lon48_men_100m_14 <- lon48_men_100m_14 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
lon48_men_100m_15 <- lon48_men_100m_15 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
lon48_men_100m_16 <- lon48_men_100m_16 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
lon48_men_100m_17 <- lon48_men_100m_17 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
lon48_men_100m_18 <- lon48_men_100m_18 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
lon48_men_100m_19 <- lon48_men_100m_19 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
lon48_men_100m_all <- bind_rows(lon48_men_100m_1, lon48_men_100m_2, lon48_men_100m_3, lon48_men_100m_4, lon48_men_100m_5, lon48_men_100m_6, lon48_men_100m_7, lon48_men_100m_8, lon48_men_100m_9, lon48_men_100m_10, lon48_men_100m_11, lon48_men_100m_12, lon48_men_100m_13, lon48_men_100m_14, lon48_men_100m_15, lon48_men_100m_16, lon48_men_100m_17, lon48_men_100m_18, lon48_men_100m_19)
lon48_men_100m_all <- lon48_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=1948,"Host City" ="London", "Host Country"="United Kingdom")
lon48_men_100m_all[97,1]<- "Gold"
lon48_men_100m_all[98,1]<- "Silver"
lon48_men_100m_all[99,1]<- "Bronze"
head(lon48_men_100m_all)
## Rank Athlete Nation Time Notes Heat Year Host City
## 1 1 Barney Ewell United States 10.50 Q 1 1948 London
## 2 2 Alastair McCorquodale Great Britain 10.50 Q 1 1948 London
## 3 3 Leslie Laing Jamaica 11.00 1 1948 London
## 4 4 Angel García Cuba 11.25 1 1948 London
## 5 5 Nestor Jacono Malta 11.54 1 1948 London
## 6 1 Mel Patton United States 10.60 Q 2 1948 London
## Host Country
## 1 United Kingdom
## 2 United Kingdom
## 3 United Kingdom
## 4 United Kingdom
## 5 United Kingdom
## 6 United Kingdom
Berlin 1936
ber_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_1936_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-ber_men_100m%>%html_table(fill=TRUE)
ber_men_100m_1<-tables[[5]]
ber_men_100m_2<-tables[[6]]
ber_men_100m_3<-tables[[7]]
ber_men_100m_4<-tables[[8]]
ber_men_100m_5<-tables[[9]]
ber_men_100m_6<-tables[[10]]
ber_men_100m_7<-tables[[11]]
ber_men_100m_8<-tables[[12]]
ber_men_100m_9<-tables[[13]]
ber_men_100m_10<-tables[[14]]
ber_men_100m_11<-tables[[15]]
ber_men_100m_12<-tables[[16]]
ber_men_100m_13<-tables[[17]]
ber_men_100m_14<-tables[[18]]
ber_men_100m_15<-tables[[19]]
ber_men_100m_16<-tables[[20]]
ber_men_100m_17<-tables[[21]]
ber_men_100m_18<-tables[[22]]
ber_men_100m_19<-tables[[23]]
ber_men_100m_1 <- ber_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
ber_men_100m_2 <- ber_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
ber_men_100m_3 <- ber_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
ber_men_100m_4 <- ber_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
ber_men_100m_5 <- ber_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
ber_men_100m_6 <- ber_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
ber_men_100m_7 <- ber_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
ber_men_100m_8 <- ber_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "8")
ber_men_100m_9 <- ber_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "9")
ber_men_100m_10 <- ber_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "10")
ber_men_100m_11 <- ber_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "11")
ber_men_100m_12 <- ber_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "12")
ber_men_100m_13 <- ber_men_100m_13 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
ber_men_100m_14 <- ber_men_100m_14 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
ber_men_100m_15 <- ber_men_100m_15 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
ber_men_100m_16 <- ber_men_100m_16 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
ber_men_100m_17 <- ber_men_100m_17 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
ber_men_100m_18 <- ber_men_100m_18 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
ber_men_100m_19 <- ber_men_100m_19 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
ber_men_100m_all <- bind_rows(ber_men_100m_1, ber_men_100m_2, ber_men_100m_3, ber_men_100m_4, ber_men_100m_5, ber_men_100m_6, ber_men_100m_7, ber_men_100m_8, ber_men_100m_9, ber_men_100m_10, ber_men_100m_11, ber_men_100m_12, ber_men_100m_13, ber_men_100m_14, ber_men_100m_15, ber_men_100m_16, ber_men_100m_17, ber_men_100m_18, ber_men_100m_19)
ber_men_100m_all <- ber_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=1936,"Host City" ="Berlin", "Host Country"="Germany")
ber_men_100m_all[70,1]<- "Gold"
ber_men_100m_all[71,1]<- "Silver"
ber_men_100m_all[72,1]<- "Bronze"
head(ber_men_100m_all)
## Rank Athlete Nation Time Notes Heat Year Host City
## 1 1 Lennart Strandberg Sweden 10.7 Q 1 1936 Berlin
## 2 2 Takayoshi Yoshioka Japan 10.8 Q 1 1936 Berlin
## 3 3 Manfred Kersch Germany 10.8 1 1936 Berlin
## 4 1 Chris Berger Netherlands 10.8 Q 2 1936 Berlin
## 5 2 Pat Dannaher South Africa 11.0 Q 2 1936 Berlin
## 6 3 Bernard Marchand Switzerland 11.2 2 1936 Berlin
## Host Country
## 1 Germany
## 2 Germany
## 3 Germany
## 4 Germany
## 5 Germany
## 6 Germany
Los Angeles 1932
los32_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_1932_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-los32_men_100m%>%html_table(fill=TRUE)
los32_men_100m_1<-tables[[5]]
los32_men_100m_2<-tables[[6]]
los32_men_100m_3<-tables[[7]]
los32_men_100m_4<-tables[[8]]
los32_men_100m_5<-tables[[9]]
los32_men_100m_6<-tables[[10]]
los32_men_100m_7<-tables[[11]]
los32_men_100m_8<-tables[[12]]
los32_men_100m_9<-tables[[13]]
los32_men_100m_10<-tables[[14]]
los32_men_100m_11<-tables[[15]]
los32_men_100m_12<-tables[[16]]
los32_men_100m_13<-tables[[17]]
los32_men_100m_14<-tables[[18]]
los32_men_100m_1 <- los32_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
los32_men_100m_2 <- los32_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
los32_men_100m_3 <- los32_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
los32_men_100m_4 <- los32_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
los32_men_100m_5 <- los32_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
los32_men_100m_6 <- los32_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
los32_men_100m_7 <- los32_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
los32_men_100m_8 <- los32_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
los32_men_100m_9 <- los32_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
los32_men_100m_10 <- los32_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
los32_men_100m_11 <- los32_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
los32_men_100m_12 <- los32_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
los32_men_100m_13 <- los32_men_100m_13 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
los32_men_100m_14 <- los32_men_100m_14 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
los32_men_100m_all <- bind_rows(los32_men_100m_1, los32_men_100m_2, los32_men_100m_3, los32_men_100m_4, los32_men_100m_5, los32_men_100m_6, los32_men_100m_7, los32_men_100m_8, los32_men_100m_9, los32_men_100m_10, los32_men_100m_11, los32_men_100m_12, los32_men_100m_13, los32_men_100m_14)
los32_men_100m_all <- los32_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=1932,"Host City" ="Los Angeles", "Host Country"="United States")
los32_men_100m_all[64,1]<- "Gold"
los32_men_100m_all[65,1]<- "Silver"
los32_men_100m_all[66,1]<- "Bronze"
head(los32_men_100m_all)
## Rank Athlete Nation Time Notes Heat Year Host City
## 1 1 Eddie Tolan United States 10.9 Q 1 1932 Los Angeles
## 2 2 José de Almeida Brazil 11.0 Q 1 1932 Los Angeles
## 3 3 Fernando Ortíz Mexico 11.2 Q 1 1932 Los Angeles
## 4 4 André Théard Haiti 11.4 1 1932 Los Angeles
## 5 5 António Rodrigues Portugal 11.5 1 1932 Los Angeles
## 6 1 George Simpson United States 10.9 <NA> 2 1932 Los Angeles
## Host Country
## 1 United States
## 2 United States
## 3 United States
## 4 United States
## 5 United States
## 6 United States
Amsterdam 1928
ams_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_1928_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-ams_men_100m%>%html_table(fill=TRUE)
ams_men_100m_1<-tables[[5]]
ams_men_100m_2<-tables[[6]]
ams_men_100m_3<-tables[[7]]
ams_men_100m_4<-tables[[8]]
ams_men_100m_5<-tables[[9]]
ams_men_100m_6<-tables[[10]]
ams_men_100m_7<-tables[[11]]
ams_men_100m_8<-tables[[12]]
ams_men_100m_9<-tables[[13]]
ams_men_100m_10<-tables[[14]]
ams_men_100m_11<-tables[[15]]
ams_men_100m_12<-tables[[16]]
ams_men_100m_13<-tables[[17]]
ams_men_100m_14<-tables[[18]]
ams_men_100m_15<-tables[[19]]
ams_men_100m_16<-tables[[20]]
ams_men_100m_17<-tables[[21]]
ams_men_100m_18<-tables[[22]]
ams_men_100m_19<-tables[[23]]
ams_men_100m_20<-tables[[24]]
ams_men_100m_21<-tables[[25]]
ams_men_100m_22<-tables[[26]]
ams_men_100m_23<-tables[[27]]
ams_men_100m_24<-tables[[28]]
ams_men_100m_25<-tables[[29]]
ams_men_100m_1 <- ams_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
ams_men_100m_2 <- ams_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
ams_men_100m_3 <- ams_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
ams_men_100m_4 <- ams_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
ams_men_100m_5 <- ams_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
ams_men_100m_6 <- ams_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
ams_men_100m_7 <- ams_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
ams_men_100m_8 <- ams_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "8")
ams_men_100m_9 <- ams_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "9")
ams_men_100m_10 <- ams_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "10")
ams_men_100m_11 <- ams_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "11")
ams_men_100m_12 <- ams_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "12")
ams_men_100m_13 <- ams_men_100m_13 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "13")
ams_men_100m_14 <- ams_men_100m_14 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "14")
ams_men_100m_15 <- ams_men_100m_15 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "15")
ams_men_100m_16 <- ams_men_100m_16 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "16")
ams_men_100m_17 <- ams_men_100m_17 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
ams_men_100m_18 <- ams_men_100m_18 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
ams_men_100m_19 <- ams_men_100m_19 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
ams_men_100m_20 <- ams_men_100m_20 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
ams_men_100m_21 <- ams_men_100m_21 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
ams_men_100m_22 <- ams_men_100m_22 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
ams_men_100m_23 <- ams_men_100m_23 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
ams_men_100m_24 <- ams_men_100m_24 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
ams_men_100m_25 <- ams_men_100m_25 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
ams_men_100m_all <- bind_rows(ams_men_100m_1, ams_men_100m_2, ams_men_100m_3, ams_men_100m_4, ams_men_100m_5, ams_men_100m_6, ams_men_100m_7, ams_men_100m_8, ams_men_100m_9, ams_men_100m_10, ams_men_100m_11, ams_men_100m_12, ams_men_100m_13, ams_men_100m_14, ams_men_100m_15, ams_men_100m_16, ams_men_100m_17, ams_men_100m_18, ams_men_100m_19, ams_men_100m_20, ams_men_100m_21, ams_men_100m_22, ams_men_100m_23, ams_men_100m_24, ams_men_100m_25)
ams_men_100m_all <- ams_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=1928,"Host City" ="Amsterdam", "Host Country"="Netherlands")
ams_men_100m_all[56,1]<- "Gold"
ams_men_100m_all[57,1]<- "Silver"
ams_men_100m_all[58,1]<- "Bronze"
head(ams_men_100m_all)
## Rank Athlete Nation Time Notes Heat Year Host City
## 1 1 John Fitzpatrick Canada 11.0 Q 1 1928 Amsterdam
## 2 2 Richard Corts Germany 11.0 Q 1 1928 Amsterdam
## 3 3 Willy Dujardin Belgium 11.2 1 1928 Amsterdam
## 4 4 Wilhelm Hennings Netherlands 11.4 1 1928 Amsterdam
## 5 5 Angelos Lambrou Greece 11.4 1 1928 Amsterdam
## 6 1 Sydney Atkinson South Africa 11.2 Q 2 1928 Amsterdam
## Host Country
## 1 Netherlands
## 2 Netherlands
## 3 Netherlands
## 4 Netherlands
## 5 Netherlands
## 6 Netherlands
Paris 1924
par_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_1924_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-par_men_100m%>%html_table(fill=TRUE)
par_men_100m_1<-tables[[5]]
par_men_100m_2<-tables[[6]]
par_men_100m_3<-tables[[7]]
par_men_100m_4<-tables[[8]]
par_men_100m_5<-tables[[9]]
par_men_100m_6<-tables[[10]]
par_men_100m_7<-tables[[11]]
par_men_100m_8<-tables[[12]]
par_men_100m_9<-tables[[13]]
par_men_100m_10<-tables[[14]]
par_men_100m_11<-tables[[15]]
par_men_100m_12<-tables[[16]]
par_men_100m_13<-tables[[17]]
par_men_100m_14<-tables[[18]]
par_men_100m_15<-tables[[19]]
par_men_100m_16<-tables[[20]]
par_men_100m_17<-tables[[21]]
par_men_100m_18<-tables[[22]]
par_men_100m_19<-tables[[23]]
par_men_100m_20<-tables[[24]]
par_men_100m_21<-tables[[25]]
par_men_100m_22<-tables[[26]]
par_men_100m_23<-tables[[27]]
par_men_100m_24<-tables[[28]]
par_men_100m_25<-tables[[29]]
par_men_100m_26<-tables[[30]]
par_men_100m_1 <- par_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
par_men_100m_2 <- par_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
par_men_100m_3 <- par_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
par_men_100m_4 <- par_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
par_men_100m_5 <- par_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
par_men_100m_6 <- par_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
par_men_100m_7 <- par_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
par_men_100m_8 <- par_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "8")
par_men_100m_9 <- par_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "9")
par_men_100m_10 <- par_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "10")
par_men_100m_11 <- par_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "11")
par_men_100m_12 <- par_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "12")
par_men_100m_13 <- par_men_100m_13 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "13")
par_men_100m_14 <- par_men_100m_14 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "14")
par_men_100m_15 <- par_men_100m_15 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "15")
par_men_100m_16 <- par_men_100m_16 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "16")
par_men_100m_17 <- par_men_100m_17 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "17")
par_men_100m_18 <- par_men_100m_18 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
par_men_100m_19 <- par_men_100m_19 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
par_men_100m_20 <- par_men_100m_20 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
par_men_100m_21 <- par_men_100m_21 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
par_men_100m_22 <- par_men_100m_22 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
par_men_100m_23 <- par_men_100m_23 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
par_men_100m_24 <- par_men_100m_24 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
par_men_100m_25 <- par_men_100m_25 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
par_men_100m_26 <- par_men_100m_26 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
par_men_100m_all <- bind_rows(par_men_100m_1, par_men_100m_2, par_men_100m_3, par_men_100m_4, par_men_100m_5, par_men_100m_6, par_men_100m_7, par_men_100m_8, par_men_100m_9, par_men_100m_10, par_men_100m_11, par_men_100m_12, par_men_100m_13, par_men_100m_14, par_men_100m_15, par_men_100m_16, par_men_100m_17, par_men_100m_18, par_men_100m_19, par_men_100m_20, par_men_100m_21, par_men_100m_22, par_men_100m_23, par_men_100m_24, par_men_100m_25, par_men_100m_26)
par_men_100m_all <- par_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=1924,"Host City" ="Paris", "Host Country"="France")
par_men_100m_all[83,1]<- "Gold"
par_men_100m_all[84,1]<- "Silver"
par_men_100m_all[85,1]<- "Bronze"
head(par_men_100m_all)
## Rank Athlete Nation Time Notes Heat Lane Year Host City
## 1 1 Loren Murchison United States 10.8 Q 1 NA 1924 Paris
## 2 2 Arthur Porritt New Zealand 10.9 Q 1 NA 1924 Paris
## 3 1 Cyril Coaffee Canada 11.0 Q 2 NA 1924 Paris
## 4 2 Ernesto Bonacina Italy 11.2 Q 2 NA 1924 Paris
## 5 5 Alois Linka Czechoslovakia 11.6 2 NA 1924 Paris
## 6 1 Charles Paddock United States 11.2 Q 3 NA 1924 Paris
## Host Country
## 1 France
## 2 France
## 3 France
## 4 France
## 5 France
## 6 France
Antwerp 1920
ant_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_1920_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-ant_men_100m%>%html_table(fill=TRUE)
ant_men_100m_1<-tables[[6]]
ant_men_100m_2<-tables[[7]]
ant_men_100m_3<-tables[[8]]
ant_men_100m_4<-tables[[9]]
ant_men_100m_5<-tables[[10]]
ant_men_100m_6<-tables[[11]]
ant_men_100m_7<-tables[[12]]
ant_men_100m_8<-tables[[13]]
ant_men_100m_9<-tables[[14]]
ant_men_100m_10<-tables[[15]]
ant_men_100m_11<-tables[[16]]
ant_men_100m_12<-tables[[17]]
ant_men_100m_13<-tables[[18]]
ant_men_100m_14<-tables[[19]]
ant_men_100m_15<-tables[[20]]
ant_men_100m_16<-tables[[21]]
ant_men_100m_17<-tables[[22]]
ant_men_100m_18<-tables[[23]]
ant_men_100m_19<-tables[[24]]
ant_men_100m_20<-tables[[25]]
ant_men_100m_1 <- ant_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
ant_men_100m_2 <- ant_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
ant_men_100m_3 <- ant_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
ant_men_100m_4 <- ant_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
ant_men_100m_5 <- ant_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
ant_men_100m_6 <- ant_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
ant_men_100m_7 <- ant_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
ant_men_100m_8 <- ant_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "8")
ant_men_100m_9 <- ant_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "9")
ant_men_100m_10 <- ant_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "10")
ant_men_100m_11 <- ant_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "11")
ant_men_100m_12 <- ant_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "12")
ant_men_100m_13 <- ant_men_100m_13 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
ant_men_100m_14 <- ant_men_100m_14 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
ant_men_100m_15 <- ant_men_100m_15 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
ant_men_100m_16 <- ant_men_100m_16 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
ant_men_100m_17 <- ant_men_100m_17 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Quarter Final")
ant_men_100m_18 <- ant_men_100m_18 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
ant_men_100m_19 <- ant_men_100m_19 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
ant_men_100m_20 <- ant_men_100m_20 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
ant_men_100m_all <- bind_rows(ant_men_100m_1, ant_men_100m_2, ant_men_100m_3, ant_men_100m_4, ant_men_100m_5, ant_men_100m_6, ant_men_100m_7, ant_men_100m_8, ant_men_100m_9, ant_men_100m_10, ant_men_100m_11, ant_men_100m_12, ant_men_100m_13, ant_men_100m_14, ant_men_100m_15, ant_men_100m_16, ant_men_100m_17, ant_men_100m_18, ant_men_100m_19, ant_men_100m_20)
ant_men_100m_all <- ant_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=1920,"Host City" ="Antwerp", "Host Country"="Belgium")
ant_men_100m_all[60,1]<- "Gold"
ant_men_100m_all[61,1]<- "Silver"
ant_men_100m_all[62,1]<- "Bronze"
head(ant_men_100m_all)
## Rank Athlete Nation Time Notes Heat Lane Year Host City
## 1 1 William Hill Great Britain 11.0 Q 1 NA 1920 Antwerp
## 2 2 Mario Riccoboni Italy 11.2 Q 1 NA 1920 Antwerp
## 3 3 Marcel Gustin Belgium 11.3 1 NA 1920 Antwerp
## 4 1 René Mourlon France 11.2 Q 2 NA 1920 Antwerp
## 5 2 August Sørensen Denmark 11.3 Q 2 NA 1920 Antwerp
## 6 1 Loren Murchison United States 10.8 Q 3 NA 1920 Antwerp
## Host Country
## 1 Belgium
## 2 Belgium
## 3 Belgium
## 4 Belgium
## 5 Belgium
## 6 Belgium
Stockholm 1912
sto_men_100m<-read_html("https://en.wikipedia.org/wiki/Athletics_at_the_1912_Summer_Olympics_%E2%80%93_Men%27s_100_metres")
tables<-sto_men_100m%>%html_table(fill=TRUE)
sto_men_100m_1<-tables[[5]]
sto_men_100m_2<-tables[[6]]
sto_men_100m_3<-tables[[7]]
sto_men_100m_4<-tables[[8]]
sto_men_100m_5<-tables[[9]]
sto_men_100m_6<-tables[[10]]
sto_men_100m_7<-tables[[11]]
sto_men_100m_8<-tables[[12]]
sto_men_100m_9<-tables[[13]]
sto_men_100m_10<-tables[[14]]
sto_men_100m_11<-tables[[15]]
sto_men_100m_12<-tables[[16]]
sto_men_100m_13<-tables[[17]]
sto_men_100m_14<-tables[[18]]
sto_men_100m_15<-tables[[19]]
sto_men_100m_16<-tables[[20]]
sto_men_100m_17<-tables[[21]]
sto_men_100m_18<-tables[[22]]
sto_men_100m_19<-tables[[23]]
sto_men_100m_20<-tables[[24]]
sto_men_100m_21<-tables[[25]]
sto_men_100m_22<-tables[[26]]
sto_men_100m_23<-tables[[27]]
sto_men_100m_24<-tables[[28]]
sto_men_100m_1 <- sto_men_100m_1 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "1")
sto_men_100m_2 <- sto_men_100m_2 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "2")
sto_men_100m_3 <- sto_men_100m_3 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "3")
sto_men_100m_4 <- sto_men_100m_4 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "4")
sto_men_100m_5 <- sto_men_100m_5 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "5")
sto_men_100m_6 <- sto_men_100m_6 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "6")
sto_men_100m_7 <- sto_men_100m_7 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "7")
sto_men_100m_8 <- sto_men_100m_8 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "8")
sto_men_100m_9 <- sto_men_100m_9 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "9")
sto_men_100m_10 <- sto_men_100m_10 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "10")
sto_men_100m_11 <- sto_men_100m_11 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "11")
sto_men_100m_12 <- sto_men_100m_12 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "12")
sto_men_100m_13 <- sto_men_100m_13 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "13")
sto_men_100m_14 <- sto_men_100m_14 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "14")
sto_men_100m_15 <- sto_men_100m_15 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "15")
sto_men_100m_16 <- sto_men_100m_16 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "16")
sto_men_100m_17 <- sto_men_100m_17 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "17")
sto_men_100m_18 <- sto_men_100m_18 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
sto_men_100m_19 <- sto_men_100m_19 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
sto_men_100m_20 <- sto_men_100m_20 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
sto_men_100m_21 <- sto_men_100m_21 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
sto_men_100m_22 <- sto_men_100m_22 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
sto_men_100m_23 <- sto_men_100m_23 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Semi Final")
sto_men_100m_24 <- sto_men_100m_24 %>% transform(Time = as.numeric(Time), Rank = as.numeric(Rank)) %>% mutate(Heat = "Final")
sto_men_100m_all <- bind_rows(sto_men_100m_1, sto_men_100m_2, sto_men_100m_3, sto_men_100m_4, sto_men_100m_5, sto_men_100m_6, sto_men_100m_7, sto_men_100m_8, sto_men_100m_9, sto_men_100m_10, sto_men_100m_11, sto_men_100m_12, sto_men_100m_13, sto_men_100m_14, sto_men_100m_15, sto_men_100m_16, sto_men_100m_17, sto_men_100m_18, sto_men_100m_19, sto_men_100m_20, sto_men_100m_21, sto_men_100m_22, sto_men_100m_23, sto_men_100m_24)
sto_men_100m_all <- sto_men_100m_all %>%
filter(!is.na(Time))%>%
add_column("Year"=1912,"Host City" ="Stockholm", "Host Country"="Sweden")
sto_men_100m_all[28,1]<- "Gold"
sto_men_100m_all[29,1]<- "Silver"
sto_men_100m_all[30,1]<- "Bronze"
head(sto_men_100m_all)
## Rank Athlete Nation Time Notes Heat Year Host City Host Country
## 1 1 Charles Luther Sweden 12.8 Q 1 1912 Stockholm Sweden
## 2 1 Ivan Möller Sweden 11.5 Q 2 1912 Stockholm Sweden
## 3 1 Ira Courtney United States 11.2 Q 3 1912 Stockholm Sweden
## 4 1 Richard Rice Great Britain 11.4 Q 4 1912 Stockholm Sweden
## 5 1 Victor d'Arcy Great Britain 11.2 Q 5 1912 Stockholm Sweden
## 6 1 Richard Rau Germany 11.5 Q 6 1912 Stockholm Sweden
Combining All Mens 100m Data (All Heats)
mens_100m_all <- bind_rows(
rio_men_100m_all,
bei_men_100m_all,
lon12_men_100m_all,
ath_men_100m_all,
syd_men_100m_all,
atl_men_100m_all,
bar_men_100m_all,
seo_men_100m_all,
los_men_100m_all,
mos_men_100m_all,
mon_men_100m_all,
mun_men_100m_all,
mex_men_100m_all,
tok_men_100m_all,
rom_men_100m_all,
mel_men_100m_all,
hel_men_100m_all,
lon48_men_100m_all,
ber_men_100m_all,
los32_men_100m_all,
ams_men_100m_all,
par_men_100m_all,
ant_men_100m_all,
sto_men_100m_all)
mens_100m_all <- mens_100m_all %>%
relocate("Year", "Host City", "Rank", "Time")
head(mens_100m_all)
## Year Host City Rank Time Lane Athlete Nation Reaction
## 1 2016 Rio de Janeiro 1 10.13 3 Kemarley Brown Bahrain 0.146
## 2 2016 Rio de Janeiro 2 10.13 5 Chijindu Ujah Great Britain 0.150
## 3 2016 Rio de Janeiro 3 10.16 7 Marvin Bracy United States 0.155
## 4 2016 Rio de Janeiro 4 10.26 2 Seye Ogunlewe Nigeria 0.139
## 5 2016 Rio de Janeiro 5 10.28 1 Femi Ogunode Qatar 0.170
## 6 2016 Rio de Janeiro 6 10.43 8 Sean Safo-Antwi Ghana 0.145
## Notes Heat Host Country Var.8 Var.7 Time..a. Time..hand.
## 1 Q 1 Brazil <NA> <NA> NA NA
## 2 Q 1 Brazil <NA> <NA> NA NA
## 3 q 1 Brazil <NA> <NA> NA NA
## 4 1 Brazil <NA> <NA> NA NA
## 5 1 Brazil <NA> <NA> NA NA
## 6 1 Brazil <NA> <NA> NA NA
mens_100m_gold <- mens_100m_all %>%
filter(Rank == "Gold")%>%
group_by(Year, Rank)
head(mens_100m_gold)
## # A tibble: 6 × 15
## # Groups: Year, Rank [6]
## Year `Host City` Rank Time Lane Athlete Nation Reaction Notes Heat
## <dbl> <chr> <chr> <dbl> <int> <chr> <chr> <chr> <chr> <chr>
## 1 2016 Rio de Janeiro Gold 9.81 6 Usain Bo… Jamaica 0.155 SB Final
## 2 2008 Beijing Gold 9.69 4 Usain Bo… Jamaica 0.165 WR Final
## 3 2012 London Gold 9.63 7 Usain Bo… Jamaica 0.165 OR Final
## 4 2004 Athens Gold 9.85 3 Justin G… United … 0.188 PB Final
## 5 2000 Sydney Gold 9.87 5 Maurice … United … 0.197 <NA> Final
## 6 1996 Atlanta Gold 9.84 6 Donovan … Canada <NA> WR Final
## # … with 5 more variables: Host Country <chr>, Var.8 <chr>, Var.7 <chr>,
## # Time..a. <dbl>, Time..hand. <dbl>
str(mens_100m_all)
## 'data.frame': 2687 obs. of 15 variables:
## $ Year : num 2016 2016 2016 2016 2016 ...
## $ Host City : chr "Rio de Janeiro" "Rio de Janeiro" "Rio de Janeiro" "Rio de Janeiro" ...
## $ Rank : chr "1" "2" "3" "4" ...
## $ Time : num 10.1 10.1 10.2 10.3 10.3 ...
## $ Lane : int 3 5 7 2 1 8 9 6 4 8 ...
## $ Athlete : chr "Kemarley Brown" "Chijindu Ujah" "Marvin Bracy" "Seye Ogunlewe" ...
## $ Nation : chr "Bahrain" "Great Britain" "United States" "Nigeria" ...
## $ Reaction : chr "0.146" "0.150" "0.155" "0.139" ...
## $ Notes : chr "Q" "Q" "q" "" ...
## $ Heat : chr "1" "1" "1" "1" ...
## $ Host Country: chr "Brazil" "Brazil" "Brazil" "Brazil" ...
## $ Var.8 : chr NA NA NA NA ...
## $ Var.7 : chr NA NA NA NA ...
## $ Time..a. : num NA NA NA NA NA NA NA NA NA NA ...
## $ Time..hand. : num NA NA NA NA NA NA NA NA NA NA ...
levels(as.factor(mens_100m_all$`Host City`))
## [1] "Amsterdam" "Antwerp" "Athens" "Atlanta"
## [5] "Barcelona" "Beijing" "Berlin" "Helsinki"
## [9] "London" "Los Angeles" "Melbourne" "Mexico City"
## [13] "Montreal" "Moscow" "Munich" "Paris"
## [17] "Rio de Janeiro" "Rome" "Seoul" "Stockholm"
## [21] "Sydney" "Tokyo"
levels(as.factor(mens_100m_all$Nation))
## [1] "Afghanistan" "Albania"
## [3] "Algeria" "American Samoa"
## [5] "Angola" "Antigua and Barbuda"
## [7] "Argentina" "Aruba"
## [9] "Australasia" "Australia"
## [11] "Austria" "Azerbaijan"
## [13] "Bahamas" "Bahrain"
## [15] "Bangladesh" "Barbados"
## [17] "Belgium" "Belize"
## [19] "Benin" "Bermuda"
## [21] "Bohemia" "Bolivia"
## [23] "Botswana" "Brazil"
## [25] "British Virgin Islands" "British West Indies"
## [27] "Brunei" "Bulgaria"
## [29] "Burkina Faso" "Burma"
## [31] "Cambodia" "Cameroon"
## [33] "Canada" "Cayman Islands"
## [35] "Central African Republic" "Ceylon"
## [37] "Chad" "Chile"
## [39] "China" "Chinese Taipei"
## [41] "Colombia" "Comoros"
## [43] "Cook Islands" "Costa Rica"
## [45] "Croatia" "Cuba"
## [47] "Cyprus" "Czech Republic"
## [49] "Czechoslovakia" "Denmark"
## [51] "Dominican Republic" "East Germany"
## [53] "Ecuador" "Egypt"
## [55] "El Salvador" "Equatorial Guinea"
## [57] "Estonia" "Ethiopia"
## [59] "Federated States of Micronesia" "Fiji"
## [61] "Finland" "Formosa"
## [63] "France" "Gabon"
## [65] "Georgia" "Germany"
## [67] "Ghana" "Great Britain"
## [69] "Greece" "Grenada"
## [71] "Guam" "Guatemala"
## [73] "Guinea" "Guinea-Bissau"
## [75] "Guyana" "Haiti"
## [77] "Honduras" "Hong Kong"
## [79] "Hungary" "Iceland"
## [81] "India" "Indonesia"
## [83] "Iran" "Iraq"
## [85] "Ireland" "Israel"
## [87] "Italy" "Ivory Coast"
## [89] "Jamaica" "Japan"
## [91] "Jordan" "Kazakhstan"
## [93] "Kenya" "Khmer Republic"
## [95] "Kiribati" "Kuwait"
## [97] "Kyrgyzstan" "Laos"
## [99] "Latvia" "Lebanon"
## [101] "Lesotho" "Liberia"
## [103] "Libya" "Liechtenstein"
## [105] "Lithuania" "Luxembourg"
## [107] "Macedonia" "Madagascar"
## [109] "Malawi" "Malaya"
## [111] "Malaysia" "Maldives"
## [113] "Mali" "Malta"
## [115] "Marshall Islands" "Mauritania"
## [117] "Mauritius" "Mexico"
## [119] "Monaco" "Mongolia"
## [121] "Morocco" "Mozambique"
## [123] "Namibia" "Nepal"
## [125] "Netherlands" "Netherlands Antilles"
## [127] "New Zealand" "Nicaragua"
## [129] "Niger" "Nigeria"
## [131] "Northern Rhodesia" "Norway"
## [133] "Oman" "Pakistan"
## [135] "Palau" "Palestine"
## [137] "Panama" "Papua New Guinea"
## [139] "Paraguay" "Peru"
## [141] "Philippines" "Poland"
## [143] "Portugal" "Puerto Rico"
## [145] "Qatar" "Republic of China"
## [147] "Republic of the Congo" "Rhodesia"
## [149] "Romania" "Russia"
## [151] "Saint Kitts and Nevis" "Saint Lucia"
## [153] "Saint Vincent and the Grenadines" "San Marino"
## [155] "São Tomé and Príncipe" "Saudi Arabia"
## [157] "Senegal" "Serbia"
## [159] "Seychelles" "Sierra Leone"
## [161] "Singapore" "Slovenia"
## [163] "Solomon Islands" "South Africa"
## [165] "South Korea" "South Yemen"
## [167] "Soviet Union" "Spain"
## [169] "Sri Lanka" "Sudan"
## [171] "Suriname" "Swaziland"
## [173] "Sweden" "Switzerland"
## [175] "Syria" "Taiwan"
## [177] "Tanzania" "Thailand"
## [179] "The Gambia" "Togo"
## [181] "Tonga" "Trinidad and Tobago"
## [183] "Turkey" "Tuvalu"
## [185] "Uganda" "Ukraine"
## [187] "Unified Team" "United Arab Emirates"
## [189] "United States" "United Team of Germany"
## [191] "Upper Volta" "Uruguay"
## [193] "Uzbekistan" "Vanuatu"
## [195] "Venezuela" "Vietnam"
## [197] "Virgin Islands" "West Germany"
## [199] "Yugoslavia" "Zambia"
## [201] "Zimbabwe"
mens_100m_mmm <- mens_100m_all %>%
select(Year, `Host City`, Time) %>%
group_by(Year)%>%
mutate(meanTime = mean(Time), medTime = median(Time), minTime = min(Time), maxTime = max(Time))
head(mens_100m_mmm)
## # A tibble: 6 × 7
## # Groups: Year [1]
## Year `Host City` Time meanTime medTime minTime maxTime
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2016 Rio de Janeiro 10.1 10.2 10.2 9.81 11.9
## 2 2016 Rio de Janeiro 10.1 10.2 10.2 9.81 11.9
## 3 2016 Rio de Janeiro 10.2 10.2 10.2 9.81 11.9
## 4 2016 Rio de Janeiro 10.3 10.2 10.2 9.81 11.9
## 5 2016 Rio de Janeiro 10.3 10.2 10.2 9.81 11.9
## 6 2016 Rio de Janeiro 10.4 10.2 10.2 9.81 11.9
mens_100m_min <- mens_100m_mmm %>%
select(Year, minTime) %>%
group_by(Year)
head(mens_100m_min)
## # A tibble: 6 × 2
## # Groups: Year [1]
## Year minTime
## <dbl> <dbl>
## 1 2016 9.81
## 2 2016 9.81
## 3 2016 9.81
## 4 2016 9.81
## 5 2016 9.81
## 6 2016 9.81
mens_100m_mmmm <- mens_100m_all %>%
select(Year, `Host City`, Time) %>%
group_by(Year)%>%
mutate(meanTime = mean(Time), medTime = median(Time), minTime = min(Time), maxTime = max(Time))
mens_100m_mmmm <- gather(mens_100m_mmmm, 'Time Type', 'mmmTime', 4:7) %>%
mutate(minTime = min(Time))%>%
arrange(desc(Year))
head(mens_100m_mmmm)
## # A tibble: 6 × 6
## # Groups: Year [1]
## Year `Host City` Time `Time Type` mmmTime minTime
## <dbl> <chr> <dbl> <chr> <dbl> <dbl>
## 1 2016 Rio de Janeiro 10.1 meanTime 10.2 9.81
## 2 2016 Rio de Janeiro 10.1 meanTime 10.2 9.81
## 3 2016 Rio de Janeiro 10.2 meanTime 10.2 9.81
## 4 2016 Rio de Janeiro 10.3 meanTime 10.2 9.81
## 5 2016 Rio de Janeiro 10.3 meanTime 10.2 9.81
## 6 2016 Rio de Janeiro 10.4 meanTime 10.2 9.81
Will continue to work on labeling and refining. This is just the beginning of graphing the average and minimum times over the years. (Make a column for min, mean, median to plot)
mens_100m_mmmm <- mens_100m_mmmm %>%
filter(`Time Type` != "maxTime")
levels(as.factor(mens_100m_mmmm$'Time Type'))
## [1] "meanTime" "medTime" "minTime"
mens_100m_gsb <- mens_100m_all %>%
filter(Rank %in% c("Gold", "Silver", "Bronze"))%>%
group_by(Year)
head(mens_100m_gsb)
## # A tibble: 6 × 15
## # Groups: Year [2]
## Year `Host City` Rank Time Lane Athlete Nation Reaction Notes Heat
## <dbl> <chr> <chr> <dbl> <int> <chr> <chr> <chr> <chr> <chr>
## 1 2016 Rio de Janeiro Gold 9.81 6 Usain Bo… Jamaica 0.155 "SB" Final
## 2 2016 Rio de Janeiro Silver 9.89 4 Justin G… United… 0.152 "" Final
## 3 2016 Rio de Janeiro Bronze 9.91 7 Andre De… Canada 0.141 "PB" Final
## 4 2008 Beijing Gold 9.69 4 Usain Bo… Jamaica 0.165 "WR" Final
## 5 2008 Beijing Silver 9.89 5 Richard … Trinid… 0.133 "PB" Final
## 6 2008 Beijing Bronze 9.91 6 Walter D… United… 0.133 "PB" Final
## # … with 5 more variables: Host Country <chr>, Var.8 <chr>, Var.7 <chr>,
## # Time..a. <dbl>, Time..hand. <dbl>
mens_100m_gsbplot <- ggplot(mens_100m_gsb, aes(Year, Time, color = Rank))+
geom_point()+
geom_smooth(se=F, span = 0.37)+
labs(title = "Gold, Silver & Bronze Medals Over 100 Years",
subtitle = "Mens 100m Dash",
y = "Time (in seconds)")+
theme(panel.background = element_blank(),
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.title = element_text(family = "Courier"),
legend.title = element_text(family = "Courier"),
plot.subtitle = element_text(family = "Courier"),
axis.text = element_text(family = "Courier"),
legend.text = element_text(family = "Courier"),
legend.background = element_blank(),
axis.title = element_text(family = "Courier"),
legend.key = element_rect(fill = "white"))+
scale_color_manual(values = c("#FFB48C", "#EBD739", "#B5BCC2"))
mens_100m_gsbplot
ggplot(mens_100m_gsb, aes(Year, Time, color = Rank))+
geom_point()+
geom_smooth(se=F, span = 0.37)+
labs(title = "Gold, Silver & Bronze Medals Over 100 Years",
subtitle = "Mens 100m Dash",
y = "Time (in seconds)")+
theme(panel.background = element_blank(),
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.title = element_text(family = "Courier"),
legend.title = element_text(family = "Courier"),
plot.subtitle = element_text(family = "Courier"),
axis.text = element_text(family = "Courier"),
legend.text = element_text(family = "Courier"),
legend.background = element_blank(),
axis.title = element_text(family = "Courier"),
legend.key = element_rect(fill = "white"))+
scale_color_manual(values = c("#FFB48C", "#EBD739", "#B5BCC2"))+
scale_y_continuous(breaks = c(9, 9.5, 10, 10.5, 11),
labels = c("9", "9.5", "10", "10.5", "11"))
ggplot(mens_100m_gsb, aes(Year, Time, color = Rank))+
geom_point()+
geom_smooth(method = "lm", se = FALSE)+
labs(title = "Gold, Silver & Bronze Medals Over 100 Years",
subtitle = "Mens 100m Dash",
y = "Time (in seconds)")+
theme(panel.background = element_blank(),
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.title = element_text(family = "Courier"),
legend.title = element_text(family = "Courier"),
plot.subtitle = element_text(family = "Courier"),
axis.text = element_text(family = "Courier"),
legend.text = element_text(family = "Courier"),
legend.background = element_blank(),
axis.title = element_text(family = "Courier"),
legend.key = element_rect(fill = "white"))+
scale_color_manual(values = c("#FFB48C", "#EBD739", "#B5BCC2"))
ggplot(mens_100m_gsb, aes(Year, Time, color = Rank))+
geom_point()+
geom_line()+
geom_rect(aes(xmin=1939, xmax=1945, ymin=9.5, ymax=10.9), color = "grey", linetype = "dotted", alpha = 0)+
geom_vline(xintercept = 1967, color = "grey", linetype = 'dashed', size=0.5)+
labs(title = "Gold, Silver & Bronze Medals Over 100 Years",
subtitle = "Mens 100m Dash",
y = "Time (in seconds)")+
theme(panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.title = element_text(family = "Courier"),
legend.title = element_text(family = "Courier"),
plot.subtitle = element_text(family = "Courier"),
axis.text = element_text(family = "Courier"),
legend.text = element_text(family = "Courier"),
axis.title = element_text(family = "Courier"),
legend.key = element_rect(fill = "white"))+
scale_color_manual(values = c("#FFB48C", "#EBD739", "#B5BCC2"))+
geom_text(aes(1928, 9.6), label = "World War II", size = 3.8, family = "Courier", color = "grey")+
geom_text(aes(1977, 9.6), label = "PEDs Banned", size = 3.8, family = "Courier", color = "grey")
mens_100m_gsb_ani <- ggplot(mens_100m_gsb, aes(Year, Time, color = Rank, frame = Year))+
geom_point(size = 5, alpha = 0.6)+
geom_line()+
labs(title = "Gold, Silver & Bronze Medals Over 100 Years",
subtitle = "Mens 100m Dash",
y = "Time (in seconds)")+
theme(panel.background = element_blank(),
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.title = element_text(family = "Courier"),
legend.title = element_text(family = "Courier"),
plot.subtitle = element_text(family = "Courier"),
axis.text = element_text(family = "Courier"),
legend.text = element_text(family = "Courier"),
legend.background = element_blank(),
axis.title = element_text(family = "Courier"),
legend.key = element_rect(fill = "white"))+
scale_color_manual(values = c("#FFB48C", "#EBD739", "#B5BCC2"))
ggplotly(mens_100m_gsb_ani)
mens_100m_mmmplot <- ggplot(mens_100m_mmmm, aes(Year, mmmTime, color = `Time Type`))+
geom_point()+
geom_smooth(se = FALSE)+
theme(panel.background = element_blank(),
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.title = element_text(family = "Courier"),
legend.background = element_blank(),
legend.title = element_text(family = "Courier"),
legend.text = element_text(family = "Courier"),
legend.key = element_blank(),
axis.text = element_text(family = "Courier"),
axis.title = element_text(family = "Courier"))+
scale_y_continuous(limits = c(9.5, 11.25))+
scale_color_manual(values = c("#FC4E07", "#00CCFF", "#E7B800"))+
labs(title = "Minimum, Mean & Median Times Over 100 Years",
subtitle = "Mens 100m Dash",
y = "Time (in seconds)")
mens_100m_mmmplot
ggplot(mens_100m_mmmm, aes(Year, mmmTime, color = `Time Type`))+
geom_point()+
geom_smooth(method = "lm")+
theme(panel.background = element_blank(),
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.title = element_text(family = "Courier"),
legend.background = element_blank(),
legend.key = element_blank(),
legend.title = element_text(family = "Courier"),
legend.text = element_text(family = "Courier"),
axis.text = element_text(family = "Courier"),
axis.title = element_text(family = "Courier"))+
scale_y_continuous(limits = c(9.5, 11.25))+
scale_color_manual(values = c("#FC4E07", "#00AFBB", "#E7B800"))+
labs(title = "Minimum, Mean & Median Times Over 100 Years",
subtitle = "Mens 100m Dash",
y = "Time (in seconds)")
mens_100m_mmm_ani <- ggplot(mens_100m_mmmm, aes(Year, mmmTime, color = `Time Type`, frame = Year))+
geom_point(size = 5)+
theme(panel.background = element_blank(),
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.title = element_text(family = "Courier"),
axis.text = element_text(family = "Courier"),
axis.title = element_text(family = "Courier"),
legend.background = element_blank(),
legend.title = element_text(family = "Courier"),
legend.text = element_text(family = "Courier"))+
scale_color_manual(values = c("#FC4E07", "#00AFBB", "#E7B800"))+
labs(title = "Minimum, Mean & Median Times Over 100 Years",
subtitle = "Mens 100m Dash",
y = "Time (in seconds)")
ggplotly(mens_100m_mmm_ani)
mens_100m_box <- ggplot()+
geom_boxplot(data = atl_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = rio_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = bei_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = lon12_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = ath_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = syd_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = atl_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = bar_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = seo_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = los_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = mos_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = mon_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = mun_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = mex_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = tok_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = rom_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = mel_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = hel_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = lon48_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = ber_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = los32_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = ams_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = par_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = ant_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_boxplot(data = sto_men_100m_all, aes(Time, Year), fill = "oldlace")+
geom_point(data = mens_100m_gold, aes(Time, Year, color = "Rank"), color = "yellow", size=2)+
coord_flip()+
theme(panel.background = element_rect(fill = "#00CCFF"),
plot.background = element_rect(fill = "#00CCFF"),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
plot.title = element_text(family = "Courier-Bold", color = "grey20"),
plot.subtitle = element_text(color = "grey20", family = "Courier"),
axis.text = element_text(color = "grey20", family = "Courier"),
axis.title = element_text(color = "grey20", family = "Courier-Bold"),
axis.ticks = element_blank())+
labs(title = "Are Humans Collectively Gaining Speed?",
subtitle = "100 Years of Mens 100m Race Times at the Olympics")+
scale_x_continuous("Time (in seconds)", limits = c(9.25,12.75), expand = c(0,0))+
scale_y_continuous(breaks = c(1912, 1920, 1928, 1936, 1944, 1952, 1960, 1968, 1976, 1984, 1992, 2000, 2008, 2016),
labels = c("1912", "1920", "1928", "1936", "1944", "1952", "1960", "1968", "1976", "1984", "1992", "2000", "2008", "2016"))+
geom_hline(yintercept = 1967, size = 1, color = "oldlace", linetype = 'dotted')+
geom_rect(aes(xmin=9.25, xmax=12.75, ymin=1939, ymax=1945), fill="oldlace", color = "oldlace", linetype = "dotted", alpha = 0.2, size = 0.8)+
geom_text(aes(9.5, 1933.5), label = "World War II", size = 4.2, family = "Courier-Bold", color = "white")+
geom_text(aes(9.5, 1972), label = "PEDs Banned", size = 4.2, family = "Courier-Bold", color = "white")
mens_100m_box
write.csv(mens_100m_min, "/Users/rochellerafn/RStudio Files/mens_100m_min.csv")
library(readxl)
mens_100m_change1 <- read_xlsx("/Users/rochellerafn/RStudio Files/mens_100m_min.xlsx", sheet = 2, col_names = c("Year", "minTime", "Change"))
mens_100m_change1 <- mens_100m_change1 %>%
filter(!is.na(Change)) %>%
transform(Change = as.numeric(Change))
mens_100m_change1
## Year minTime Change
## 1 Year minTime NA
## 2 1912 10.6 0.00000000
## 3 1920 10.8 1.88679245
## 4 1924 10.6 -1.85185185
## 5 1928 10.6 0.00000000
## 6 1932 10.38 -2.07547170
## 7 1936 10.199999999999999 -1.73410405
## 8 1948 10.3 0.98039216
## 9 1952 10.65 3.39805825
## 10 1956 10.52 -1.22065728
## 11 1960 10.199999999999999 -3.04182510
## 12 1964 9.9 -2.94117647
## 13 1968 9.9 0.00000000
## 14 1972 10.07 1.71717172
## 15 1976 10.06 -0.09930487
## 16 1980 10.11 0.49701789
## 17 1984 9.99 -1.18694362
## 18 1988 9.7899999999999991 -2.00200200
## 19 1992 9.9600000000000009 1.73646578
## 20 1996 9.84 -1.20481928
## 21 2000 9.8699999999999992 0.30487805
## 22 2004 9.85 -0.20263425
## 23 2008 9.69 -1.62436548
## 24 2012 9.6300000000000008 -0.61919505
## 25 2016 9.81 1.86915888
ggplot(mens_100m_change1, aes(Year, Change))+
geom_col()
mens_100m_change2 <- read_xlsx("/Users/rochellerafn/RStudio Files/mens_100m_min.xlsx", sheet = 3, col_names = c("Year", "minTime", "Change", "Change2"))
mens_100m_change2 <- mens_100m_change2 %>%
filter(!is.na(Change)) %>%
transform(Change = as.numeric(Change), Change2 = as.numeric(Change2))
mens_100m_change2
## Year minTime Change Change2
## 1 Year minTime NA NA
## 2 1912 10.6 0.00000000 0.0000000
## 3 1920 10.8 1.88679245 1.8867925
## 4 1924 10.6 -1.85185185 0.0000000
## 5 1928 10.6 0.00000000 0.0000000
## 6 1932 10.38 -2.07547170 -2.0754717
## 7 1936 10.199999999999999 -1.73410405 -3.7735849
## 8 1948 10.3 0.98039216 -2.8301887
## 9 1952 10.65 3.39805825 0.4716981
## 10 1956 10.52 -1.22065728 -0.7547170
## 11 1960 10.199999999999999 -3.04182510 -3.7735849
## 12 1964 9.9 -2.94117647 -6.6037736
## 13 1968 9.9 0.00000000 -6.6037736
## 14 1972 10.07 1.71717172 -5.0000000
## 15 1976 10.06 -0.09930487 -5.0943396
## 16 1980 10.11 0.49701789 -4.6226415
## 17 1984 9.99 -1.18694362 -5.7547170
## 18 1988 9.7899999999999991 -2.00200200 -7.6415094
## 19 1992 9.9600000000000009 1.73646578 -6.0377358
## 20 1996 9.84 -1.20481928 -7.1698113
## 21 2000 9.8699999999999992 0.30487805 -6.8867925
## 22 2004 9.85 -0.20263425 -7.0754717
## 23 2008 9.69 -1.62436548 -8.5849057
## 24 2012 9.6300000000000008 -0.61919505 -9.1509434
## 25 2016 9.81 1.86915888 -7.4528302
ggplot(mens_100m_change2, aes(Year, Change2))+
geom_col()
mens_100m_change3 <- read_xlsx("/Users/rochellerafn/RStudio Files/mens_100m_min.xlsx", sheet = 4, col_names = c("Year", "minTime", "Change", "Change3"))
mens_100m_change3 <- mens_100m_change3 %>%
filter(!is.na(Change)) %>%
transform(Change = as.numeric(Change), Change3 = as.numeric(Change3))
mens_100m_change3
## Year minTime Change Change3
## 1 Year minTime NA NA
## 2 1912 10.6 0.00000000 0.00
## 3 1920 10.8 1.88679245 0.20
## 4 1924 10.6 -1.85185185 -0.20
## 5 1928 10.6 0.00000000 0.00
## 6 1932 10.38 -2.07547170 -0.22
## 7 1936 10.199999999999999 -1.73410405 -0.18
## 8 1948 10.3 0.98039216 0.10
## 9 1952 10.65 3.39805825 0.35
## 10 1956 10.52 -1.22065728 -0.13
## 11 1960 10.199999999999999 -3.04182510 -0.32
## 12 1964 9.9 -2.94117647 -0.30
## 13 1968 9.9 0.00000000 0.00
## 14 1972 10.07 1.71717172 0.17
## 15 1976 10.06 -0.09930487 -0.01
## 16 1980 10.11 0.49701789 0.05
## 17 1984 9.99 -1.18694362 -0.12
## 18 1988 9.7899999999999991 -2.00200200 -0.20
## 19 1992 9.9600000000000009 1.73646578 0.17
## 20 1996 9.84 -1.20481928 -0.12
## 21 2000 9.8699999999999992 0.30487805 0.03
## 22 2004 9.85 -0.20263425 -0.02
## 23 2008 9.69 -1.62436548 -0.16
## 24 2012 9.6300000000000008 -0.61919505 -0.06
## 25 2016 9.81 1.86915888 0.18
ggplot(mens_100m_change3, aes(Year, Change3))+
geom_col()
mens_100m_nations <- mens_100m_all %>%
group_by(Year, Time)%>%
count(Nation)%>%
arrange(Year)
mens_100m_nations
## # A tibble: 2,415 × 4
## # Groups: Year, Time [1,280]
## Year Time Nation n
## <dbl> <dbl> <chr> <int>
## 1 1912 10.6 United States 1
## 2 1912 10.7 United States 3
## 3 1912 10.8 Great Britain 1
## 4 1912 10.8 United States 1
## 5 1912 10.9 Germany 1
## 6 1912 10.9 South Africa 1
## 7 1912 10.9 United States 2
## 8 1912 11 Australasia 1
## 9 1912 11 Canada 1
## 10 1912 11 Great Britain 1
## # … with 2,405 more rows
library(countrycode)
country_code <- countrycode(mens_100m_nations$Nation, origin = 'country.name', destination = 'iso3c')
country_code
## [1] "USA" "USA" "GBR" "USA" "DEU" "ZAF" "USA" NA "CAN" "GBR" "ZAF" "USA"
## [13] "USA" "GBR" "USA" "GBR" "DEU" "SWE" "CZE" "SRB" "SWE" "USA" "ISL" "SWE"
## [25] "GBR" "USA" "BEL" "FRA" "GBR" "NZL" "USA" "AUS" "FRA" "GBR" "NLD" "ZAF"
## [37] "CHE" "USA" "CAN" "FRA" "GBR" "NZL" "ESP" "CHE" "DNK" "FRA" "ITA" "NOR"
## [49] "ESP" "USA" "BEL" "DNK" "GBR" "ITA" "BEL" "CAN" "ITA" "ITA" "NLD" "JPN"
## [61] "ESP" "FRA" "MCO" "GBR" "AUS" "CAN" "USA" "CAN" "NZL" "USA" "AUS" "NZL"
## [73] "USA" "AUS" "CAN" "FRA" "GBR" "HUN" "ITA" "NLD" "USA" "BEL" "FIN" "FRA"
## [85] "ITA" "LUX" "NLD" "NZL" "CAN" "FRA" "HTI" "ITA" "NLD" "ZAF" "CHE" "USA"
## [97] NA "FIN" "GBR" "HUN" "IND" "LUX" "ESP" "SWE" "CAN" "FRA" "HUN" "POL"
## [109] "ESP" "SWE" "CAN" "DNK" "EST" "ITA" "POL" NA "POL" "ESP" "LVA" "CAN"
## [121] "GBR" "ZAF" "USA" "DEU" "USA" "CAN" "DEU" "GBR" "HUN" "ZAF" "USA" "CAN"
## [133] "DEU" "GBR" "ARG" "AUS" "CAN" "CUB" "DEU" "GBR" "HUN" "ZAF" "USA" "AUS"
## [145] "FRA" "NLD" "AUT" "BEL" "ZAF" NA "FIN" "FRA" "GRC" "NLD" "ESP" "CHE"
## [157] "CHE" "LUX" "USA" "ARG" "DEU" "USA" "DEU" "ZAF" "USA" "DEU" "CAN" "NLD"
## [169] "USA" "DEU" "ARG" "USA" "USA" "JPN" "ARG" "BRA" "JPN" "NZL" "ZAF" "USA"
## [181] "JPN" "CAN" "GBR" "JPN" "NZL" "USA" "CAN" "CAN" "BRA" "DEU" "IND" "MEX"
## [193] "NZL" "ZAF" "USA" "ARG" "CAN" NA "DEU" "GBR" "NLD" "ARG" "CAN" NA
## [205] "DEU" "MEX" "DEU" "GBR" "GRC" "BRA" "HTI" "IND" "MEX" "BRA" "CHN" "PRT"
## [217] "USA" "USA" "USA" "DEU" "NLD" "SWE" "USA" "CAN" "GBR" "NLD" "CHE" "USA"
## [229] "CAN" "DEU" "GBR" "HUN" "JPN" "NLD" "ZAF" "SWE" "CHE" "CAN" "DEU" "GBR"
## [241] "GRC" "HUN" "JPN" "NLD" "SWE" "USA" "ARG" "FIN" "HUN" "LUX" "ZAF" "SWE"
## [253] "ARG" "EST" "FRA" "JPN" "NLD" "ZAF" "SWE" "BRA" "FRA" "PHL" "ARG" "CHE"
## [265] "HUN" NA "USA" "USA" "AUS" "GBR" "PAN" "USA" "URY" "AUS" "BRA" "CUB"
## [277] "GBR" "PAN" "USA" "URY" "GBR" "USA" "AUS" "CUB" "GBR" "AUS" "ARG" "AUS"
## [289] "BEL" "FRA" "JAM" "TTO" "GBR" "CUB" "FRA" "BRA" "CAN" "PRT" "BRA" "CAN"
## [301] "AUS" "CHL" "HUN" "ISL" "IND" "JAM" "NLD" "URY" "GBR" "BRA" "PER" "TTO"
## [313] "URY" "CHL" "URY" "ARG" "BRA" "NLD" "BEL" "CUB" "HUN" "NOR" "ZAF" "HUN"
## [325] "AUS" "ARG" "ISL" "PER" "ISL" "ISL" "PAK" "ARG" "CUB" "IND" "BEL" "PHL"
## [337] "PRT" "TUR" "NLD" "TUR" "BEL" "MLT" "GRC" "BMU" "EGY" "MMR" "BMU" "IRQ"
## [349] "MEX" "GBR" "USA" "USA" "JAM" "GBR" "USA" "GBR" "JAM" "USA" "USA" "AUS"
## [361] "USA" "USA" "JAM" "GBR" "AUS" "USA" "RUS" "JAM" "RUS" "CUB" "USA" "AUS"
## [373] "AUS" "CUB" "RUS" "CUB" "NLD" "RUS" "GBR" "IND" "FRA" "FRA" "DEU" "IND"
## [385] "ITA" "FRA" "FRA" "IND" "CHE" "BGR" "GBR" "NLD" "PAK" "DEU" "JPN" "FRA"
## [397] "GBR" "JAM" "CHE" "GBR" "HUN" "ARG" "HUN" "JAM" "ISR" "ARG" "IRL" "ISR"
## [409] "NLD" "NGA" "POL" "RUS" "JPN" "RUS" "PAK" "GHA" "ITA" "PAK" "CUB" "DEU"
## [421] "CHE" NA "CAN" NA "NGA" "CAN" "GBR" "HUN" "NGA" "BGR" "JPN" "FIN"
## [433] "ISL" "VEN" NA "FIN" "MEX" "GRC" "FIN" "EGY" "ISL" "VEN" "USA" "CAN"
## [445] "PRT" "GHA" "PAK" "EGY" "EGY" "CHE" "ISL" "GTM" "ARG" "THA" "THA" "ITA"
## [457] "PRT" "THA" "ITA" "USA" "USA" "USA" "AUS" "USA" "USA" "NZL" "AUS" "TTO"
## [469] "AUS" "USA" "AUS" "NZL" "PAK" "TTO" "USA" "DEU" "POL" "NZL" "POL" "RUS"
## [481] "DEU" "DEU" "RUS" "POL" "TTO" "UGA" "USA" "RUS" "DEU" "CAN" "PAK" "RUS"
## [493] "USA" "CAN" "HUN" "POL" "FRA" "GBR" "ITA" "PAK" "POL" "TTO" "DEU" "HUN"
## [505] "JPN" "ITA" "NGA" "HUN" "NGA" "GBR" "RUS" "AUS" "GBR" "JPN" "NGA" "RUS"
## [517] "DEU" "RUS" "ISL" "ITA" "BRA" "ITA" "VEN" "AUS" "BRA" "VEN" "GBR" "TTO"
## [529] "CAN" "GUY" "JAM" "FRA" "MEX" "PAK" "THA" "BHS" "SGP" "AUS" "TTO" "TTO"
## [541] NA "LBR" "GBR" "IDN" "LBR" "NGA" "CUB" "ETH" NA "NOR" "CAN" "SGP"
## [553] "THA" NA "ETH" "THA" "ETH" "UGA" "USA" "DEU" "CUB" "GBR" "USA" "DEU"
## [565] NA "CAN" "CUB" "FRA" "GBR" "KEN" "POL" "USA" "VEN" "BHS" "CAN" "FRA"
## [577] "GBR" "KEN" "NOR" "POL" "RUS" "USA" "VEN" "BHS" "FRA" "KEN" "ZAF" "UGA"
## [589] "USA" "DEU" "FJI" "FRA" "NOR" "ZAF" "RUS" "USA" "VEN" "BRA" "CAN" "FJI"
## [601] "MEX" "CHE" "THA" "DEU" "VEN" "CAN" "ISL" "IDN" "IRL" NA "MAR" "UGA"
## [613] "AUT" "BEL" "BGR" "GHA" "GRC" "PHL" "DEU" "ISR" "SDN" "VEN" "AUS" "EGY"
## [625] "TWN" "LBR" "PAK" "ESP" "ETH" "IND" "IRQ" "TUR" "KOR" "AFG" "USA" "USA"
## [637] "POL" "BHS" "CAN" "CUB" "AUS" "BHS" "CAN" "CUB" "JAM" "JPN" "POL" "USA"
## [649] "DEU" "AUS" "CUB" "FRA" "CIV" "JAM" "POL" "RUS" "USA" "DEU" "VEN" "AUS"
## [661] "BHS" "CAN" "CHL" "CUB" "FRA" "GBR" "CIV" "JAM" "JPN" "MAR" "POL" "RUS"
## [673] "USA" "DEU" "VEN" "AUS" "CHL" "FRA" "GHA" "GBR" "HUN" "ITA" "JAM" "JPN"
## [685] "KEN" "MYS" "MAR" "NLD" "POL" "TTO" "USA" "DEU" "AUS" "GHA" "GBR" "HUN"
## [697] "IND" "KEN" "MEX" "PHL" "POL" "ZWE" "CHE" "DEU" "VEN" "GBR" "HUN" "MDG"
## [709] "ZMB" "COG" "VEN" "AUS" "BHS" "DOM" "JPN" "PER" "RUS" "THA" "UGA" "COL"
## [721] "PRT" "SEN" "KOR" "CMR" "IRN" "IRQ" "ISR" "PHL" "ESP" "TWN" "MLI" "PAK"
## [733] "VNM" "USA" "JAM" "USA" "USA" "USA" "USA" "CUB" "FRA" "USA" "FRA" "JAM"
## [745] "USA" "CUB" "USA" "JAM" "CUB" "USA" "CAN" "FRA" "FRA" "MDG" "CUB" "CAN"
## [757] "MDG" "USA" "CAN" "CIV" "CUB" "JPN" "CUB" NA "MDG" "USA" "CIV" NA
## [769] NA "CUB" "MDG" "FRA" "JPN" "POL" "DEU" NA "JPN" "CAN" "GBR" "MYS"
## [781] "USA" "FRA" "CHL" "CIV" NA "MYS" "NGA" "SGP" "DEU" "ARG" "CUB" NA
## [793] "FRA" NA "FRA" "GBR" "DEU" "ARG" "FRA" "GBR" "NGA" "SUR" "UGA" "POL"
## [805] "RUS" "NGA" "SGP" "SUR" "DEU" "GHA" "POL" "RUS" "UGA" "DOM" "RUS" "CHL"
## [817] "AUS" "GBR" "DEU" "RUS" "TTO" "POL" "TZA" "PHL" "GHA" "MEX" "SEN" "JAM"
## [829] "KEN" "BHS" "KEN" "ITA" "VEN" "BHS" "ESP" "PRI" "MEX" "CHE" "MAR" "VEN"
## [841] "TWN" "COL" "TWN" "BEL" "SDN" "NIC" "SLV" "RUS" "RUS" "USA" "TTO" "RUS"
## [853] "RUS" "GRC" "USA" "DEU" "SEN" "JAM" "MDG" "SEN" "USA" "JAM" "USA" "JAM"
## [865] NA "RUS" "POL" "RUS" "TTO" "DEU" NA "RUS" NA "FRA" "GBR" "JAM"
## [877] "POL" "DEU" "GBR" "NGA" "FRA" "POL" "SEN" "FIN" NA "DEU" "GRC" "JAM"
## [889] "CHE" "MDG" "POL" "JAM" "MDG" "RUS" "TTO" "USA" NA "JAM" NA "NOR"
## [901] "BHS" "CIV" "TTO" "CAN" "TCD" "FRA" "CIV" "RUS" NA "FIN" "GHA" "CIV"
## [913] "GBR" "FRA" "GBR" "NOR" "TTO" "USA" "CIV" "NGA" "GBR" "CHE" "TWN" NA
## [925] "GBR" "CIV" "KEN" "AUT" "POL" "SUR" "POL" "BRA" NA "FRA" "SUR" "TCD"
## [937] "GHA" NA NA "FRA" "GHA" "BHS" "DEU" "CAN" "FIN" "NGA" "BFA" NA
## [949] "ARG" "PRI" "VEN" "LSO" "TTO" "KEN" "NZL" "BHS" "FIN" "PRI" "TZA" "MYS"
## [961] "TWN" "DEU" "RUS" "CMR" "TCD" "ETH" "POL" NA "BHS" "SGP" "UGA" "MNG"
## [973] "KHM" "COG" "ISL" "LKA" "FIN" "IRN" "PHL" "LBR" "PRI" "PRY" "ZMB" "BOL"
## [985] "KWT" "SAU" "MWI" "HTI" "TTO" "JAM" "RUS" "USA" "TTO" "USA" "USA" "PAN"
## [997] "JAM" "USA" NA "TTO" "BGR" NA "RUS" "USA" NA "JAM" "USA" "BGR"
## [1009] "PAN" "USA" NA "PAN" "USA" "JAM" "RUS" "PAN" "TTO" "USA" "CIV" "BGR"
## [1021] "CIV" "TTO" "USA" "CAN" "CUB" NA "RUS" "CAN" "POL" "TTO" "FRA" "CIV"
## [1033] "POL" "RUS" "BRA" "RUS" "POL" "BRA" "SUR" "DEU" "CAN" "CUB" "POL" "TTO"
## [1045] "POL" "SEN" "RUS" "BRA" "SUR" "CUB" "JAM" "POL" "SWE" "BMU" NA "JAM"
## [1057] "SWE" "BHS" "THA" "ITA" "BMU" "DEU" "CUB" "DEU" "BEL" "POL" "BMU" "RUS"
## [1069] "THA" "TTO" "CUB" "SEN" "SEN" "FRA" "THA" "BMU" "LUX" "PRI" "FRA" "DEU"
## [1081] "ITA" "SEN" "BHS" "GRC" "HUN" "AUS" "BHS" "JPN" "BRB" "MYS" "BLZ" "HTI"
## [1093] "SAU" NA "FJI" "DEU" NA "KWT" "ISL" "IRN" "NIC" "FRA" "GBR" "BGR"
## [1105] "CUB" "CUB" "CUB" "GBR" "GUY" "RUS" "GBR" "ITA" "POL" NA "TTO" "CMR"
## [1117] "FRA" "JAM" "RUS" NA "BGR" "CUB" "CMR" "CUB" "GUY" "NGA" "RUS" "GBR"
## [1129] "POL" "JAM" "RUS" "TTO" NA "GBR" "BGR" "CUB" "NGA" "CUB" "POL" "RUS"
## [1141] "GBR" "POL" "RUS" "TTO" "CUB" "GBR" "POL" "RUS" "CUB" NA "GUY" "RUS"
## [1153] "TTO" "BRA" "FRA" "RUS" "POL" NA "FRA" "BRA" "FRA" "NGA" "DOM" NA
## [1165] "FRA" "COG" NA "POL" "TTO" "JAM" "ITA" "DOM" "ITA" "NGA" "NGA" "COG"
## [1177] "CMR" "GBR" "POL" "GRC" "CUB" "BGR" "NGA" "SYR" "HUN" "HUN" "COG" "BRA"
## [1189] "GRC" "BRA" "NGA" "SEN" "BRA" "TZA" "SEN" "TTO" "TZA" "NLD" "ISL" "BEN"
## [1201] "LBN" "MOZ" "ZMB" "IND" "MLI" "TZA" "SLE" "SLE" "BWA" "GIN" "LSO" "SYC"
## [1213] "KWT" "SLE" "AGO" "ETH" "NPL" "LAO" "PER" "USA" "USA" "USA" "USA" "USA"
## [1225] "CAN" "JAM" "JAM" "USA" "CAN" "GBR" "USA" "JAM" "USA" "JAM" "GBR" "USA"
## [1237] "CAN" "GBR" "CAN" "USA" "CAN" "FRA" "PRI" "GBR" "ITA" "CAN" "ITA" "NGA"
## [1249] "IDN" "USA" "CAN" "GBR" "DEU" "CAN" "NGA" "ESP" "IDN" "JAM" "BRA" "GBR"
## [1261] "SEN" "BEL" "FRA" "GBR" "ITA" "TTO" "FRA" "PAN" "IDN" "DEU" "AUS" "CAN"
## [1273] "ESP" "THA" "BRA" "CHN" "FRA" "BRA" "PRI" "SEN" "DEU" "AUS" "BRA" "ITA"
## [1285] "FRA" "JPN" "TTO" "ITA" "JAM" "BHS" "USA" NA "BHS" "CHN" "FRA" "AUS"
## [1297] "FRA" "GHA" "PRI" "AUS" "THA" "JAM" "PAN" "AUS" "BHS" "JAM" "BHS" "IDN"
## [1309] "COG" "GMB" "BRB" "BRA" NA "DEU" "BRA" "DEU" "GBR" "ITA" "CIV" "KOR"
## [1321] "GUY" "JPN" "BMU" "LBR" "PRT" "GHA" "QAT" "GNQ" "CIV" "MOZ" "LBN" "GTM"
## [1333] "OMN" "PAK" "GMB" "GHA" NA "ZMB" "BLZ" "CMR" "LIE" "ATG" NA "UGA"
## [1345] "SYC" "VGB" "ARE" "SLE" "FJI" "MUS" "MWI" "SWZ" "BGD" "CRI" "SLV" "BWA"
## [1357] "SLB" "CAN" "USA" "GBR" "USA" "USA" "CAN" "USA" "BRA" "CAN" "GBR" "USA"
## [1369] "USA" "USA" "CAN" "USA" "CAN" "JAM" "GBR" "GHA" "JAM" "USA" "BRA" "CAN"
## [1381] "BRA" "JAM" "RUS" "HUN" "USA" "NGA" "GHA" "HUN" "BRA" "IDN" "NGA" "DOM"
## [1393] "ITA" "NGA" "AUT" "FRA" "RUS" "DOM" NA NA "ITA" "RUS" "BRA" "CAN"
## [1405] "FRA" "GHA" "USA" "ITA" "QAT" "FRA" "HUN" "IDN" "AUT" "IDN" "ITA" "FRA"
## [1417] "JAM" "QAT" "SEN" "GHA" "KEN" "ESP" "BRA" "JAM" "ESP" "NGA" "SEN" "JPN"
## [1429] "CHN" "DOM" "ITA" "TWN" "JAM" "JPN" "BEL" "ITA" "KEN" "BEL" "CHN" "FRA"
## [1441] "GBR" "KEN" "HUN" "RUS" "CHN" "MLI" "TWN" "HUN" "DEU" "CHN" "GBR" "HUN"
## [1453] "KOR" "FRA" "NGA" "KOR" "KEN" "DEU" "RUS" "TGO" "CMR" "NGA" "MLI" "ESP"
## [1465] "JPN" "NGA" "JAM" "SEN" "HUN" "LBR" "MEX" "BMU" "TWN" "BHS" "CHL" NA
## [1477] "JPN" "BEN" "CHN" NA "COG" "BRA" "ZWE" "BFA" "CHN" "GBR" "VGB" "BHR"
## [1489] "PRT" "HKG" "BEN" "SAU" "THA" "SLE" "BFA" "OMN" "PAK" "AGO" "BGD" "TON"
## [1501] "UGA" "PNG" "VUT" "LSO" "BRB" "DOM" "DZA" "VNM" "SMR" "ATG" "FJI" "GIN"
## [1513] "LIE" "HTI" "NGA" "LBN" "MDV" "GNQ" "SWZ" NA "MCO" "LBR" "JAM" "GBR"
## [1525] "USA" "GBR" "NAM" "USA" "GBR" "USA" "CAN" "USA" "NGA" "NAM" "NAM" "USA"
## [1537] "CAN" "NGA" "USA" "JAM" "NGA" "USA" "NGA" "BRA" "CAN" "NGA" "USA" "NGA"
## [1549] "BRA" "NAM" NA "CAN" "NGA" "GHA" "NGA" "BRA" "QAT" "JAM" NA "FRA"
## [1561] "GHA" "QAT" "GBR" "FRA" "JAM" "NGA" "CAN" NA "NGA" "SLE" "CMR" "GHA"
## [1573] "FRA" "QAT" NA "GHA" "BRA" "NGA" "GBR" "JPN" "JPN" "CYP" "JPN" "CIV"
## [1585] "JPN" "BRA" "CAN" "CIV" "JPN" "GBR" "ROU" "SEN" "CHE" "CMR" "SLE" "GHA"
## [1597] "KEN" "JAM" "BEL" "FRA" "CAN" "FRA" "GHA" "ESP" "CYP" "GNQ" "CHE" "GHA"
## [1609] "BEL" "TGO" "CAN" "GMB" NA "THA" "NER" "PAN" "HKG" "CAN" "TTO" "BRA"
## [1621] "CYM" "CAF" "BHR" "SMR" "BRB" "PAK" "UGA" "ZWE" "TON" "MLI" "LKA" "LSO"
## [1633] "COG" "HND" "BEN" "BGD" "HTI" "GRD" "GIN" "CRI" "SDN" "FJI" "SWZ" "MRT"
## [1645] "AGO" "PNG" "MDV" "VUT" "BLZ" "COK" "OMN" "LAO" "CAN" "NAM" "TTO" "NAM"
## [1657] "TTO" "NAM" "TTO" "USA" "CAN" "USA" "GBR" "NGA" "GBR" "NGA" "USA" "CAN"
## [1669] "TTO" "NGA" "USA" "USA" "JAM" "CAN" "BRB" "NGA" "USA" "GHA" "BRB" "JAM"
## [1681] "JPN" "USA" "USA" "CAN" "GHA" "JAM" "JPN" "BEL" "GHA" "CYP" "BEL" "CAN"
## [1693] "GHA" "SWE" "USA" "BRA" "GBR" "BRA" "CYP" "GHA" "GBR" "NGA" "GBR" "KNA"
## [1705] "CAN" "JPN" "CHN" "GBR" "UKR" "BEL" "GRC" "CYP" "NAM" "BRB" "DEU" "ITA"
## [1717] "CAN" "MLI" "NZL" "NGA" "RUS" "KNA" "ESP" "NZL" "SWE" "CYP" "BEL" "CHN"
## [1729] "UKR" "FRA" "ITA" "JAM" "MLI" "NGA" "UKR" "BRA" "FRA" "GRC" "NZL" "RUS"
## [1741] "SLE" "FRA" "NGA" "FJI" "LVA" "FRA" "ITA" "SAU" "BRB" "GRC" "ESP" "BRB"
## [1753] "CYP" "UKR" "BHS" "GRC" "JAM" "SWE" "SWE" "CHE" "TGO" "AUS" "KAZ" "ESP"
## [1765] "CIV" "MYS" "LKA" "AUS" "ESP" "ARG" "MUS" "CMR" "JPN" "LBR" "DOM" "BRA"
## [1777] "VCT" "LBR" "PRT" "KEN" "TWN" "TON" "UZB" "GMB" "KOR" "BOL" "SLV" "PNG"
## [1789] "IRL" "KWT" "GNB" "GAB" "KGZ" "BMU" "MLT" "KAZ" "PER" "MRT" "BGD" "BEN"
## [1801] "COM" "STP" "AZE" NA "VNM" "GIN" "NER" "COK" "LBY" "GNQ" "AFG" "CIV"
## [1813] "USA" "TTO" "BRB" "TTO" "USA" "GBR" "USA" "USA" "TTO" "GBR" "AUS" "GAB"
## [1825] "GBR" "TTO" "GBR" "BRB" "GHA" "USA" "GHA" "KNA" "GBR" "KNA" "CAN" "KNA"
## [1837] "USA" "GBR" "GHA" "BRB" "GHA" "JAM" "CIV" "USA" "GHA" "JPN" "AUS" "MUS"
## [1849] "GAB" "GBR" "ITA" "POL" "ZAF" "USA" "BRA" "GBR" "GRC" "NGA" "JAM" "USA"
## [1861] "AUS" "BRA" "GHA" "NGA" "USA" "CYP" "NGA" "CUB" "FRA" "LBR" "GRC" "AUS"
## [1873] "CMR" "CUB" "JAM" "MUS" "NGA" "NGA" "ESP" "LBR" "POL" "FRA" "GBR" "POL"
## [1885] "UKR" "CAN" "JPN" "KNA" "ITA" "CIV" "AUT" "CAN" "JAM" "DEU" "POL" "ISR"
## [1897] "AUS" "AUT" "BRA" "GHA" "ZAF" "CMR" "CAN" "JAM" "JPN" "NGA" "CMR" "JAM"
## [1909] "BHS" "BRA" "CYP" "UKR" "HRV" "ITA" "AUS" "CMR" "ITA" "FIN" "NAM" "ESP"
## [1921] "GTM" "IDN" "SAU" "URY" "ARG" "TTO" "JPN" "BFA" "JPN" "MYS" "IRL" "BGR"
## [1933] "SLV" "HKG" "COM" "IDN" "RUS" NA "GEO" "NGA" "SLE" "KAZ" "MLI" "VNM"
## [1945] "IDN" "ASM" "AZE" "TCD" "PLW" "TON" "GMB" "MLT" "ALB" "SUR" "BRN" "VUT"
## [1957] "SYC" "GUM" "COK" "NER" "CAN" "LAO" "MDG" "JAM" "USA" "PRT" "USA" "USA"
## [1969] "PRT" "USA" "JAM" "JAM" "USA" "PRT" "USA" "JAM" "KNA" "GHA" "KNA" "USA"
## [1981] "KNA" "JAM" "USA" "BRB" "PRT" "USA" "BRB" "GHA" "JAM" "KNA" "BRB" "GBR"
## [1993] "JAM" "NAM" "GBR" "CYM" "FRA" "GBR" "NGA" "SVN" "NAM" "FRA" "GHA" "USA"
## [2005] "GHA" "JPN" "JAM" "CAN" "GHA" "JAM" "AUS" "BRB" "GHA" "NGA" "TTO" "BRA"
## [2017] NA "POL" "AUS" "CYM" "DEU" "JPN" NA "BRA" "NGA" "SVN" "GMB" "FRA"
## [2029] "ITA" "JPN" "BRA" "CAN" "DEU" "GBR" "HUN" "JAM" "ITA" "JAM" "RUS" "BRA"
## [2041] "CAN" "FRA" "GRC" "CIV" "BFA" "JPN" "BRA" "GBR" "NGA" "POL" "GRC" "SAU"
## [2053] "JPN" "HUN" "CIV" "NGA" "GMB" "BMU" "CAN" "TTO" "KAZ" "BFA" "KAZ" "CHN"
## [2065] "BEN" "TTO" "NAM" "CYP" "PRY" "ATG" NA "GHA" "MCO" "COM" "GIN" "FRA"
## [2077] "MLT" "HUN" "HKG" "SLE" "SGP" "GAB" "JOR" "SMR" "FSM" "STP" "ABW" "SLB"
## [2089] "BGD" "AZE" "NIC" "TON" "COK" "ASM" "LAO" "KHM" "KIR" "AFG" "MDV" "JAM"
## [2101] "JAM" "TTO" "JAM" "USA" "JAM" NA "TTO" NA "JAM" "USA" "JAM" "TTO"
## [2113] NA "TTO" "JAM" "TTO" "JAM" "USA" "USA" "KNA" "TTO" "USA" "KNA" "USA"
## [2125] "JAM" "PRT" "USA" "GBR" "PRT" "BHS" "GBR" "BHS" "NOR" "JAM" "FRA" "JAM"
## [2137] "JPN" "KNA" "FRA" "GBR" "JAM" "FRA" "GBR" "NGA" "CAN" "TTO" "USA" "ATG"
## [2149] "JPN" "ATG" "GHA" "SVN" "TTO" "BRB" "JPN" "PRT" "RUS" "USA" "BRA" "FRA"
## [2161] "NGA" "BHS" "CUB" "BRA" "NGA" "BRA" "BRA" "GBR" "ITA" "CAN" "CUB" "ITA"
## [2173] "GHA" "NGA" "RUS" "ESP" "BRB" "COL" NA "ESP" "USA" "CAN" "DEU" "CAN"
## [2185] "COL" "JPN" "NOR" "CHN" "JPN" "CHN" "GBR" "POL" "DEU" "IDN" "NGA" "POL"
## [2197] "SVN" "TTO" "HND" "ITA" "CAF" "CZE" "GMB" "OMN" "UKR" "AZE" "GNB" "BRA"
## [2209] "ECU" "SUR" "COM" "BFA" "HKG" "BGR" "MCO" "SGP" "VUT" "GAB" "TTO" "VCT"
## [2221] "SYC" "COG" "FSM" "BGD" "SLB" "MDV" "TCD" "TON" "MHL" "KIR" "PLW" "COK"
## [2233] "AFG" "TUV" "LAO" "GNQ" "ASM" "JAM" "JAM" "USA" "USA" "USA" "JAM" "JAM"
## [2245] "USA" "USA" "NLD" "JAM" "NLD" "USA" "USA" "TTO" "JAM" "GBR" "TTO" "JAM"
## [2257] "TTO" "GBR" "GBR" "CIV" "JPN" "BHS" "USA" "CAN" "JAM" "JPN" "FRA" "GBR"
## [2269] "ZMB" "ATG" "GBR" "CIV" "TTO" "TTO" "ATG" "CYM" "FRA" "ZMB" "GBR" "GMB"
## [2281] "CHN" "NLD" "GMB" "BHS" "EGY" "NGA" "KNA" "TTO" "POL" "BRA" "BHS" "KNA"
## [2293] "CHN" "NOR" "LTU" "JPN" "KNA" "GUY" "IRN" "PRI" "TTO" "ESP" "BRB" "NGA"
## [2305] "NGA" "EST" "OMN" "BFA" "CAF" "SUR" "COL" "MUS" "BOL" "GRD" "SGP" "GNB"
## [2317] "MDV" "IDN" "COG" "JAM" "JAM" "JAM" "USA" "CAN" "CAN" "JAM" "ZAF" "USA"
## [2329] "FRA" "CIV" "CIV" "ZAF" "GBR" "JAM" "USA" "CIV" "TUR" "CAN" "FRA" "JAM"
## [2341] "JPN" "USA" "FRA" "JAM" "CHN" "USA" "CHN" "JAM" "BHR" "KNA" "ATG" "BHR"
## [2353] "GBR" "JAM" "JPN" "USA" "ZAF" "TUR" "FRA" "GBR" "USA" "CHN" "IRN" "JPN"
## [2365] "GBR" "KNA" "FRA" "ATG" "JPN" "TTO" "CAN" "NLD" "OMN" "IRN" "JPN" "TTO"
## [2377] "CAN" "BRB" "LSO" "NGA" "SAU" "ZMB" "BHS" "QAT" "ZWE" "DEU" "GBR" "TTO"
## [2389] "LBR" "ZAF" "CYM" "DEU" "MAR" "BRA" "CHN" "NLD" "CIV" "NGA" "KOR" "HTI"
## [2401] "KNA" "LCA" "GHA" "IRN" "MDV" "SUR" "BHS" "KNA" "PLW" "BHS" "IDN" "MKD"
## [2413] "SGP" "BRN" "PSE"
mens_100m_ncodes <- mens_100m_nations %>%
add_column('Country Code' = country_code)
mens_100m_ncodes
## # A tibble: 2,415 × 5
## # Groups: Year, Time [1,280]
## Year Time Nation n `Country Code`
## <dbl> <dbl> <chr> <int> <chr>
## 1 1912 10.6 United States 1 USA
## 2 1912 10.7 United States 3 USA
## 3 1912 10.8 Great Britain 1 GBR
## 4 1912 10.8 United States 1 USA
## 5 1912 10.9 Germany 1 DEU
## 6 1912 10.9 South Africa 1 ZAF
## 7 1912 10.9 United States 2 USA
## 8 1912 11 Australasia 1 <NA>
## 9 1912 11 Canada 1 CAN
## 10 1912 11 Great Britain 1 GBR
## # … with 2,405 more rows
mens_100m_ncodes %>%
group_by(Nation)%>%
filter(is.na(`Country Code`))
## # A tibble: 73 × 5
## # Groups: Nation [10]
## Year Time Nation n `Country Code`
## <dbl> <dbl> <chr> <int> <chr>
## 1 1912 11 Australasia 1 <NA>
## 2 1924 11.3 Czechoslovakia 1 <NA>
## 3 1924 11.6 Czechoslovakia 1 <NA>
## 4 1928 11.3 Czechoslovakia 1 <NA>
## 5 1932 11.1 Czechoslovakia 1 <NA>
## 6 1932 11.2 Czechoslovakia 1 <NA>
## 7 1936 11.5 Yugoslavia 1 <NA>
## 8 1952 11.2 Czechoslovakia 1 <NA>
## 9 1952 11.2 Czechoslovakia 1 <NA>
## 10 1952 11.3 Czechoslovakia 1 <NA>
## # … with 63 more rows
# load library
library(rworldmap)
# get map
worldmap <- getMap(resolution = "coarse")
# plot world map
plot(worldmap, col = "white",
fill = T, border = "darkgray",
xlim = c(-180, 180), ylim = c(-90, 90),
bg = "aliceblue",
asp = 1, wrap=c(-180,180))
library(rnaturalearth)
# load data
world <- ne_countries(scale = "medium", returnclass = "sf")
# gene world map
ggplot(data = world) +
geom_sf() +
labs( x = "Longitude", y = "Latitude") +
ggtitle("World map", subtitle = paste0("(", length(unique(world$admin)), " countries)"))
Nope! Too busy lol…
library(gridExtra)
grid.arrange(arrangeGrob(mens_100m_gsbplot, mens_100m_mmmplot, ncol=2, widths=c(2, 2)),
mens_100m_box, nrow=2)
write.csv(mens_100m_gsb, "/Users/rochellerafn/RStudio Files/mens_100m_gsb.csv")
write.csv(mens_100m_mmm, "/Users/rochellerafn/RStudio Files/mens_100m_mmm.csv")
Exponential Regression in R - lm(log(y)~x)
model_mmm <- lm(log(mens_100m_mmmm$mmmTime)~mens_100m_mmmm$Year)
summary(model_mmm)
##
## Call:
## lm(formula = log(mens_100m_mmmm$mmmTime) ~ mens_100m_mmmm$Year)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.06125 -0.03093 0.01134 0.01963 0.04562
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.057e+00 2.298e-02 176.55 <2e-16 ***
## mens_100m_mmmm$Year -8.671e-04 1.163e-05 -74.53 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0282 on 8059 degrees of freedom
## Multiple R-squared: 0.408, Adjusted R-squared: 0.4079
## F-statistic: 5554 on 1 and 8059 DF, p-value: < 2.2e-16
model_gsb <- lm(log(mens_100m_gsb$Time)~mens_100m_gsb$Year)
summary(model_gsb)
##
## Call:
## lm(formula = log(mens_100m_gsb$Time) ~ mens_100m_gsb$Year)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.035003 -0.009850 -0.002573 0.011075 0.038065
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.384e+00 1.249e-01 35.09 <2e-16 ***
## mens_100m_gsb$Year -1.045e-03 6.348e-05 -16.46 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01652 on 70 degrees of freedom
## Multiple R-squared: 0.7947, Adjusted R-squared: 0.7917
## F-statistic: 270.9 on 1 and 70 DF, p-value: < 2.2e-16