title: “Winter Olympics Medals over Time” author: “JunyuMeng-jm4655” date: “2018年2月16日” output: html_document html_document: keep_md: true —
1. Medal Counts over Time
I use the point plot to show the total medal numbers of medals owned by a country and reorder different by its number of total medals. And I use different blue colors to indicates the changes with the time. In this way, reader could not only clearly know the different medal numbers of different countries, but also the changes with time. As the plot showing, most of the countries have made progress with the time while some outliers even won less medals than before. Russia,Germany and USA is the counties who have large total numbers. In addition, I use grid to divide the medal into different sorts. We could compare diffetert countries’ different kinds of medals.For example, althrough Russia is the country has the most total medals, it has most Gold medals within a year rather than silver and bronze.Finland has the most bronze within a year.
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## # Groups: Year [?]
## Year Country sum_medal
## <int> <fct> <int>
## 1 1924 AUT 4
## 2 1924 BEL 5
## 3 1924 CAN 9
## 4 1924 FIN 15
## 5 1924 FRA 12
## 6 1924 GBR 25
## 7 1924 NOR 17
## 8 1924 SUI 9
## 9 1924 SWE 9
## 10 1924 USA 13
## # ... with 345 more rows## # A tibble: 99 x 3
## # Groups: Medal [?]
## Medal Country sum_type
## <fct> <fct> <int>
## 1 Bronze AUS 7
## 2 Bronze AUT 103
## 3 Bronze BEL 7
## 4 Bronze BLR 5
## 5 Bronze BUL 3
## 6 Bronze CAN 107
## 7 Bronze CHN 36
## 8 Bronze CRO 1
## 9 Bronze CZE 35
## 10 Bronze ESP 1
## # ... with 89 more rows
2. Medal Counts adjusted by Population, GDP
I use the Gold medal numbers as the success of a country’s sport career.After calculating the total medals of a country, GDP per Capita and population,I use melt to generate a new variable.Based on this new dataset, I make a geom_line plot and use different color to distinguish the type of variables which could make reader compare among different countries easily. For example, China has very large population but few medals compared to other countries. Slovekia, however, has high score for winter Olymipics,but it is not consistent with its GDP per Capita and small population.
## Country value value
## 1 AUS Medal 5.000000
## 2 AUT Medal 79.000000
## 3 BEL Medal 2.000000
## 4 BLR Medal 6.000000
## 5 BUL Medal 1.000000
## 6 CAN Medal 315.000000
## 7 CHN Medal 16.000000
## 8 CRO Medal 4.000000
## 9 CZE Medal 28.000000
## 10 ESP Medal 1.000000
## 11 EST Medal 4.000000
## 12 FIN Medal 66.000000
## 13 FRA Medal 36.000000
## 14 GBR Medal 34.000000
## 15 GER Medal 218.000000
## 16 ITA Medal 58.000000
## 17 JPN Medal 17.000000
## 18 KAZ Medal 1.000000
## 19 KOR Medal 51.000000
## 20 LIE Medal 2.000000
## 21 NED Medal 42.000000
## 22 NOR Medal 159.000000
## 23 POL Medal 6.000000
## 24 RUS Medal 344.000000
## 25 SLO Medal 2.000000
## 26 SUI Medal 76.000000
## 27 SVK Medal 2.000000
## 28 SWE Medal 127.000000
## 29 UKR Medal 5.000000
## 30 USA Medal 167.000000
## 31 UZB Medal 1.000000
## 32 AUS GDP.per.Capita 281.554815
## 33 AUT GDP.per.Capita 218.874926
## 34 BEL GDP.per.Capita 201.620139
## 35 BLR GDP.per.Capita 28.702282
## 36 BUL GDP.per.Capita 34.967387
## 37 CAN GDP.per.Capita 216.242650
## 38 CHN GDP.per.Capita 40.138419
## 39 CRO GDP.per.Capita 57.679147
## 40 CZE GDP.per.Capita 87.741691
## 41 ESP GDP.per.Capita 129.157912
## 42 EST GDP.per.Capita 85.592521
## 43 FIN GDP.per.Capita 211.555181
## 44 FRA GDP.per.Capita 181.027841
## 45 GBR GDP.per.Capita 219.379848
## 46 GER GDP.per.Capita 206.566570
## 47 ITA GDP.per.Capita 149.789022
## 48 JPN GDP.per.Capita 162.386076
## 49 KAZ GDP.per.Capita 52.549905
## 50 KOR GDP.per.Capita 136.107620
## 51 LIE GDP.per.Capita NA
## 52 NED GDP.per.Capita 221.498840
## 53 NOR GDP.per.Capita 372.001849
## 54 POL GDP.per.Capita 62.772738
## 55 RUS GDP.per.Capita 45.462903
## 56 SLO GDP.per.Capita 103.632699
## 57 SUI GDP.per.Capita 404.725396
## 58 SVK GDP.per.Capita 80.441388
## 59 SWE GDP.per.Capita 252.898368
## 60 UKR GDP.per.Capita 10.574774
## 61 USA GDP.per.Capita 280.578592
## 62 UZB GDP.per.Capita 10.660352
## 63 AUS Population 23.781169
## 64 AUT Population 8.611088
## 65 BEL Population 11.285721
## 66 BLR Population 9.513000
## 67 BUL Population 7.177991
## 68 CAN Population 35.851774
## 69 CHN Population 1371.220000
## 70 CRO Population 4.224404
## 71 CZE Population 10.551219
## 72 ESP Population 46.418269
## 73 EST Population 1.311998
## 74 FIN Population 5.482013
## 75 FRA Population 66.808385
## 76 GBR Population 65.138232
## 77 GER Population 81.413145
## 78 ITA Population 60.802085
## 79 JPN Population 126.958472
## 80 KAZ Population 17.544126
## 81 KOR Population 50.617045
## 82 LIE Population 0.037531
## 83 NED Population 16.936520
## 84 NOR Population 5.195921
## 85 POL Population 37.999494
## 86 RUS Population 144.096812
## 87 SLO Population 2.063768
## 88 SUI Population 8.286976
## 89 SVK Population 5.424050
## 90 SWE Population 9.798871
## 91 UKR Population 45.198200
## 92 USA Population 321.418820
## 93 UZB Population 31.299500
3. Host Country Advantage
I make this plot by clearing the data totally. First, I merge the original data with the new data by “Year”, and include the host country information. Then I generete a new dummy variable(“a”or“b”) through checking whether the host country is consistent with the country which athletes come from. After this, I merged by Year and Country again and use this new dataset to make plot. And to emphasize the difference between country which is a host and country which is not,I use different colors and shapes. I think reader could see it clearly that the host countries usually have more medals than average because the purple dots are distributed at the top of the whole plot.
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4. Country success by sport / discipline / event
I choose skiing as the key sport to analyze. I calculate the total number of skiing sport for a country within one year and we could see the data distribution among different countries. And we could tell which country make a great change intervals while others have stable performance for skiing sport on winter Olympic games. For example, readers can see that Norway make great changes during the history of every time olympic game compared with Russia,but Norway still has a higher level of skiing sport than Russia.
##5. Most successful athletes ###I choose the top 30 athletes who won the most gold medals. I use shape to distinguish different sports and different colors to indicate different countries and I facet the plot according to Gender. The reader can easily tell that female athletes’ number of gold medals is less than male athletes and female athletes’ largest number is greater than female athletes’. Male athletes is more talented for Biathlon than female athletes while female athletes is more talented for Ice Hockey.Canada has excellent female athlets while USA has excellent male athletes. 
##6. Make two plots interactive ### I make a interactive plot for first point = plot because points which indicate changes through time and country are very dense. Reader can only get basic impression.However, when reader want to get specific information, they need zoom in. And a plotly can meet this need In addition, I choose the boxplot to make the second interactive plot for the skiing sport for different countries of different years. Because I would like to let reader could get specific number of medals,like media, mean and son on. For example, reader want to know the biggest number of Russia’s gold medals for a specific winter game. They can use plotly to get the number easily
## # A tibble: 355 x 3
## # Groups: Year [?]
## Year Country sum_medal
## <int> <fct> <int>
## 1 1924 AUT 4
## 2 1924 BEL 5
## 3 1924 CAN 9
## 4 1924 FIN 15
## 5 1924 FRA 12
## 6 1924 GBR 25
## 7 1924 NOR 17
## 8 1924 SUI 9
## 9 1924 SWE 9
## 10 1924 USA 13
## # ... with 345 more rows## # A tibble: 99 x 3
## # Groups: Medal [?]
## Medal Country sum_type
## <fct> <fct> <int>
## 1 Bronze AUS 7
## 2 Bronze AUT 103
## 3 Bronze BEL 7
## 4 Bronze BLR 5
## 5 Bronze BUL 3
## 6 Bronze CAN 107
## 7 Bronze CHN 36
## 8 Bronze CRO 1
## 9 Bronze CZE 35
## 10 Bronze ESP 1
## # ... with 89 more rows## We recommend that you use the dev version of ggplot2 with `ggplotly()`
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## Install it with: `devtools::install_github('tidyverse/ggplot2')`I would like to built a datatable which include the basic information about its economy and sacles as well as the total number of medals which it won during the history of Winter Olympic games. I intend to show some relations between economic development and sports development. This datatable provide searching function and column filters through which the reader can get access to a country’s total medal number, GDP per capita and population.
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