Abhiram Jois
Aren’t we all just rows in a larger dataset?
Graph 1:
Concised FIFA dataset
The following is a datset of all the players in the game FIFA-19 and their player attributes. The initial dataset was very large, this is a concised version of the same.
## # A tibble: 18,207 x 17
## Name Age Nationality Overall Potential Club Value Wage Position
## <chr> <dbl> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr>
## 1 L. Messi 31 Argentina 94.7 94.8 FC B~ 110.~ 565K RF
## 2 Cristiano Ronaldo 33 Portugal 94.5 94.5 Juve~ 77M 405K ST
## 3 Neymar Jr 26 Brazil 92.2 93.7 Pari~ 118.~ 290K LW
## 4 De Gea 27 Spain 91.7 93.4 Manc~ 72M 260K GK
## 5 K. De Bruyne 27 Belgium 91.5 92.2 Manc~ 102M 355K RCM
## 6 E. Hazard 27 Belgium 91.2 91.9 Chel~ 93M 340K LF
## 7 L. Modric 32 Croatia 91.4 91.8 Real~ 67M 420K RCM
## 8 L. Suárez 31 Uruguay 91.6 91.7 FC B~ 80M 455K RS
## 9 Sergio Ramos 32 Spain 91.3 91.5 Real~ 51M 380K RCB
## 10 J. Oblak 25 Slovenia 90.7 93.2 Atlé~ 68M 94K GK
## # ... with 18,197 more rows, and 8 more variables: Preferred Foot <chr>,
## # Body Type <chr>, Skill Moves <dbl>, BallControl <dbl>, SprintSpeed <dbl>,
## # GKHandling <dbl>, International Reputation <dbl>, GKReflexes <dbl>What is the value of most internaltionally reputed players and which nationality and club do they belong to?
Graph 2: Making a map with some nice places to eat in Rajajinagar
Interactive dark mode
Graph 3: The successors of the Michigan lake house
The following is a manually curated dataset of the characters in Bojack horseman, season 4, episode 10,11. I created a visual network for the same.
## # A tibble: 16 x 5
## id name sex species profession
## <int> <chr> <chr> <chr> <chr>
## 1 1 Andy Male Dragonfly Homemaker
## 2 2 Beatrice Female Horse Homemaker
## 3 3 Butterscotch Male Horse Writer
## 4 4 Camilia Female Swan Homemaker
## 5 5 Crackerjack Male Horse Soldier
## 6 6 Diane Female Human Writer
## 7 7 Henrietta Female Human Maid
## 8 8 Mrs Sugarman Female Horse Homemaker
## 9 9 Sugarman Male Horse Businessman
## 10 10 Bojack Male Horse Actor
## 11 11 Corban Male Goat Businessman
## 12 12 Sally Male Human Soldier
## 13 13 Todd Male Human Businessman
## 14 14 Mr Peanutbutter Male Dog Actor
## 15 15 Princess Carolyn Female Cat Manager
## 16 16 Hollyhock Female Horse Student## # A tibble: 25 x 2
## from to
## <int> <int>
## 1 2 10
## 2 7 2
## 3 4 2
## 4 9 2
## 5 8 9
## 6 2 11
## 7 2 3
## 8 3 10
## 9 7 3
## 10 8 2
## # ... with 15 more rowsVisual network based on species
## # A tibble: 16 x 5
## id label sex group profession
## <int> <chr> <chr> <chr> <chr>
## 1 1 Andy Male Dragonfly Homemaker
## 2 2 Beatrice Female Horse Homemaker
## 3 3 Butterscotch Male Horse Writer
## 4 4 Camilia Female Swan Homemaker
## 5 5 Crackerjack Male Horse Soldier
## 6 6 Diane Female Human Writer
## 7 7 Henrietta Female Human Maid
## 8 8 Mrs Sugarman Female Horse Homemaker
## 9 9 Sugarman Male Horse Businessman
## 10 10 Bojack Male Horse Actor
## 11 11 Corban Male Goat Businessman
## 12 12 Sally Male Human Soldier
## 13 13 Todd Male Human Businessman
## 14 14 Mr Peanutbutter Male Dog Actor
## 15 15 Princess Carolyn Female Cat Manager
## 16 16 Hollyhock Female Horse StudentReflection
Coding is something I have always enjoyed doing. Similarly this course was enjoyable as well. The course constantly kept me challenged and on my feet at all times. It was nice to spend a lot of waking and sometimes even sleeping hours trying to figure out why something is not working. It was nice to relive that feeling of finally figuring out a faulty code (it’s usually the bracket)). Over all this course was good wrap to the year and was a fun introduction to R. R studio is a very powerful tool which I am sure I will explore a lot more in the years to come. I want to thank Arvind for introducing me to R. He was really patient with all my doubts and kept our enthusiasm high all through. He’s the coolest boomer I know which is kinda sus..