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library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(readr)
library(ggplot2)
data <- read.csv("/Users/buyanulziitserennadmid/Downloads/olympic_running.csv")

#clean
dc <- na.omit(data)


#Ergtei 100m eer scatter plot hiih

men_100 <- dc %>%
  filter( Sex == 'men', Length == "100")
ggplot(men_100, aes(x = Year, y = Time)) + 
  geom_point() + 
  theme_classic()+
labs(
  title = "Olympic Men's 100m Sprint",
  x = 'Year',
  y = "Time (seconds)"
)

#ergteigiin ehnii 5hurdiig oloh
fastest_5time <- men_100 %>%
  arrange(Time) %>%
  head(5)
mutate(fastest_5time,Holder = c('Usain Bolt','Usain Bolt','Usain Bolt','Justin Gatlin','Yohan Blake')) %>%
  select (-rownames)

##   Year Length Sex Time        Holder
## 1 2012    100 men 9.63    Usain Bolt
## 2 2008    100 men 9.69    Usain Bolt
## 3 2016    100 men 9.81    Usain Bolt
## 4 1996    100 men 9.84 Justin Gatlin
## 5 2004    100 men 9.85   Yohan Blake

fastest_5time

##   rownames Year Length Sex Time
## 1       30 2012    100 men 9.63
## 2       29 2008    100 men 9.69
## 3       31 2016    100 men 9.81
## 4       26 1996    100 men 9.84
## 5       28 2004    100 men 9.85

#emegteigiin 200m iin histogramm
women_200m <- dc %>%
  filter(Sex == 'women', Length == '200')
library(ggplot2)

ggplot(women_200m, aes(x = Time)) +
  geom_histogram(binwidth = 0.1, fill = "lightblue", color = "black") + 
  labs(
    title = "Histogram of Olympic Women's 200m Sprint Times",
    x = "Time (seconds)",
    y = "Frequency"
  ) +
  theme_minimal() 

#2008 bolon 2012 onii eregtei emegteichuudiin tsagnuudiig negtgesen
result_2008 <- filter(dc, Year == '2008') %>%
  select(Time)
result_2012 <- filter(dc, Year == '2012') %>%
  select(-rownames,-Year)
combined_result <- bind_cols(result_2008, result_2012) %>%
  select(Length, Sex, Time...1, Time...4) %>%
  rename("2008's Time" = Time...1, "2012's Time" = Time...4)

## New names:
## • `Time` -> `Time...1`
## • `Time` -> `Time...4`

combined_result

##    Length   Sex 2008's Time 2012's Time
## 1     100   men        9.69        9.63
## 2     100 women       10.78       10.75
## 3     200   men       19.30       19.32
## 4     200 women       21.74       21.88
## 5     400   men       43.75       43.94
## 6     400 women       49.62       49.55
## 7     800   men      104.65      100.91
## 8     800 women      114.87      117.23
## 9    1500   men      213.11      214.08
## 10   1500 women      240.23      250.74
## 11   5000   men      777.82      821.66
## 12   5000 women      941.40      904.25
## 13  10000   men     1621.17     1650.42
## 14  10000 women     1794.66     1820.75

#speed nertei shine baganad sekunded tuulah zamiig ilerhiilne
speedtei_dc <- dc %>%
  mutate(speed = Length/Time)