Top 100 Women’s Singles Tennis Scores

This data is about the top 100 Women’s Singles Tennis scores. I will be utilizing it to create a quick visualization.

library("readr")
tennis <-read_csv("top100tennis_clean.csv")

## Parsed with column specification:
## cols(
##   rank = col_double(),
##   country = col_character(),
##   player = col_character(),
##   last_name = col_character(),
##   first_name = col_character(),
##   age = col_double(),
##   points = col_double(),
##   tourn.played = col_double()
## )

I will be using functions from the dply, ggplot2, and plotly libraries. It can be installed with install.packages() if it is not already on the device.

library(dplyr)

## Warning: package 'dplyr' was built under R version 3.6.1

## 
## 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(ggplot2)
library(plotly)

## Warning: package 'plotly' was built under R version 3.6.1

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

Creating the Scatterplot

For this visualization, I used ggplot and ggplotly. The age of the players is represented on the x-axis, and the total points scored by each player is represened by the y-axis.

chart2 <- tennis %>% ggplot(aes(x = age, y = points, size = points, text = paste("Country:", country))) +
  theme_minimal(base_size = 12) +
  geom_point(alpha = 0.8, color = "hotpink") +
  ggtitle("Women's Singles Tennis Players") +
  xlab("Age") + 
  ylab("Total Points") +
  theme_minimal()
chart2 <- ggplotly(chart2)
chart2