Chapter 3 Data/Operation Abstraction Design

Here we pull the data from source systems first as separate methods and break them down into simpler details which can be readily analyzed. The data operations are effectively abstracted out as R functions and reactive methods of shiny, hence increasing the efficiency of the visual.

The data has been taken from Dr. Torsten Sprenger’s tidytusday post on Apr 9, 2019. We have taken help from tidyverse ecosystem to make meaningful charts with ggplot2, tidyr and dplyr in order to summarize and arrange data

player_dob <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/player_dob.csv")

## Parsed with column specification:
## cols(
##   name = col_character(),
##   grand_slam = col_character(),
##   date_of_birth = col_date(format = ""),
##   date_of_first_title = col_date(format = ""),
##   age = col_double()
## )

grand_slams <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/grand_slams.csv")

## Parsed with column specification:
## cols(
##   year = col_double(),
##   grand_slam = col_character(),
##   name = col_character(),
##   rolling_win_count = col_double(),
##   tournament_date = col_date(format = ""),
##   gender = col_character()
## )

grand_slam_timeline <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/grand_slam_timeline.csv")

## Parsed with column specification:
## cols(
##   player = col_character(),
##   year = col_double(),
##   tournament = col_character(),
##   outcome = col_character(),
##   gender = col_character()
## )