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
Weekly Challenge #278
The first Olympic games were held in Athens in 1896 and only male athletes could participate. Female athletes were admitted in 1900 but they were excluded from certain events. For this challenge, we will look at the data from 1900 to 1936.
The “Participant” dataset includes the sport, the Olympic ID, the athlete’s name, and their country.
The “Details” dataset includes an ID, the Olympic ID, the Games (year and City), and the Medal won or not.
Use both datasets to:
- Find the number of female athletes who participated in each Olympic Games from 1900 to 1936 for each game and create a graphical representation.
- Create a report of the athletes who received a medal for each game including the Olympic ID, the name of the athlete, the country, the game, the sport, and the medals received.
Fun Fact
Figure Skating was listed as one of the sport for the 1908 Summer Olympics.
Data Source:
https://data.world/sports/women-in-the-olympic-games/workspace/project-summary?agentid=sports&datasetid=women-in-the-olympic-games
C:\Users\JBARAL~1\AppData\Local\Temp\Engine_11348_2892e577fd8e4320b5f7ac612fdc914b_\Engine_21836_5d5286fed5564ee798b52b5b3f8e62af_.yxdb
Single
Profile
Details.yxdb
Details.yxdb
Participants.yxdb
Participants.yxdb
C:\Users\JBARAL~1\AppData\Local\Temp\Engine_11348_2892e577fd8e4320b5f7ac612fdc914b_\Engine_21836_b6e229be82d14e6caf9d5cc44b6292bd_.yxdb
Single
Profile
output1.yxdb
Women Participation from 1900 to 1936
output1.yxdb
C:\Users\JBARAL~1\AppData\Local\Temp\Engine_11348_2892e577fd8e4320b5f7ac612fdc914b_\Engine_21836_ac8c419d46a14facaf8f7f8894f2d82e_.yxdb
Single
Report
C:\Users\JBARAL~1\AppData\Local\Temp\Engine_11348_2892e577fd8e4320b5f7ac612fdc914b_\Engine_21836_a9fea6c7dde648bf88739c773b5ec612_.yxdb
Single
Report
output2_medals.yxdb
Medals
output2_medals.yxdb
data
ParseSimple
1
Warn
data_Matched
False
False
"data"
True
True
True
False
False
False
True
False
False
upper
data
Last
1 = IF Contains([1], "Summer") THEN Replace([1], "Summer ", "Summer-") ELSE [1] ...
1
Last
1
id_name_team
Last
id_name_team
bar
markers
h
Count
Games
inside
0
Count
12
false
x+y
Female Athletes in Olympic Games
sans-serif
12
false
700
600
false
closest
0
492.63157894736844
true
Number of Female Athletes
12
0
true
true
true
linear
-1.102396898001299
12.531854243320423
false
Games
category
true
12
auto
0
linear
0
1
0
100
80
200
71
0.43000000000000005
C:\Users\JBARAL~1\AppData\Local\Temp\Engine_11348_2892e577fd8e4320b5f7ac612fdc914b_\Engine_21836_14cfec51689540e7ad6c1a582381db85_.yxdb
Single
Report
Simple
IsNotNull
Medal
True
fixed
2021-12-15 14:25:07
0
2021-12-15 14:25:07
2021-12-15 14:25:07
!IsNull([Medal])
Games = [Year]+" "+[Season]+" "+[City]
Place = IF [Medal]="Gold" THEN "1"
ELSEI...
Basic
100%
Always
Data
==
1
1
Row Rule 1
Basic Table
C:\Users\JBARAL~1\AppData\Local\Temp\Engine_11348_2892e577fd8e4320b5f7ac612fdc914b_\Engine_21836_83661ed2b9fb48ac90f13a5e4d13852d_.yxdb
Single
Report
Games - Ascending
Sport - Ascending
Place - Ascending
Games - Ascending
Sport - Ascending
Place - Ascending
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Horizontal
challenge_278_start_file