DS Labs Assignment

Load the library package

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.0     ✔ readr     2.2.0
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.2     ✔ tibble    3.3.1
✔ lubridate 1.9.5     ✔ tidyr     1.3.2
✔ purrr     1.2.1     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(dslabs)
library(highcharter)

Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo 
Highcharts (www.highcharts.com) is a Highsoft software product which is
not free for commercial and Governmental use

Attaching package: 'highcharter'

The following object is masked from 'package:dslabs':

    stars

Load the dataset

data(research_funding_rates)
head(research_funding_rates)

          discipline applications_total applications_men applications_women
1  Chemical sciences                122               83                 39
2  Physical sciences                174              135                 39
3            Physics                 76               67                  9
4         Humanities                396              230                166
5 Technical sciences                251              189                 62
6  Interdisciplinary                183              105                 78
  awards_total awards_men awards_women success_rates_total success_rates_men
1           32         22           10                26.2              26.5
2           35         26            9                20.1              19.3
3           20         18            2                26.3              26.9
4           65         33           32                16.4              14.3
5           43         30           13                17.1              15.9
6           29         12           17                15.8              11.4
  success_rates_women
1                25.6
2                23.1
3                22.2
4                19.3
5                21.0
6                21.8

dim(research_funding_rates)

[1]  9 10

Clean the dataset by creating three new columns using mutate

research_clean <- research_funding_rates |>
 mutate(
       percent_men = applications_men/applications_total * 100,
       percent_women = applications_women/applications_total * 100,
       percent_women_award = awards_women/awards_total * 100,
       percent_men_award = awards_men/awards_total * 100,
       success_men = success_rates_men/success_rates_total * 100,
       success_women = success_rates_women/success_rates_total * 100) |>
 select(discipline,percent_men,percent_women,percent_women_award,percent_men_award,success_men,success_women)

Create a plot for percentages in applications for men and women

ggplot(research_clean, aes(x = percent_men, y = percent_women, color = discipline)) +
  geom_point(size = 2, alpha = 0.5) +
  geom_line(size = 1) +
  scale_color_brewer(palette = "Set1") +
  labs(title = "Comparison between Men and Women Applications by Discipline", 
       caption = "Research Funding Rates",
       x = "Applications by Men in Various Discplines",
       y = "Applications by Women in Various Disciplines" 
       ) + 
       theme_bw(base_family = "serif")

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?

Create a plot for percentages in awards for men and women

ggplot(research_clean, aes(x = percent_men_award, y = percent_women_award, color = discipline)) +
  geom_point(size = 2, alpha = 0.5) +
  geom_line(size = 1) +
  scale_color_brewer(palette = "Set1") +
  labs(title = "Comparison between Men and Women Awards by Discipline", 
       caption = "Research Funding Rates",
       x = "Awards by Men in Various Discplines",
       y = "Awards by Women in Various Disciplines" 
       ) + 
       theme_bw(base_family ="serif")

`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?

Multivariable graph using highchart to show the success rates

highchart() |>
hc_add_series(data = research_clean,
type = "line",
hcaes(x = success_men,
y = success_women,
group = discipline)) |>
hc_xAxis(title = list(text="Success Men")) |>
hc_yAxis(title = list(text="Success Women")) |>
hc_plotOptions(series = list(marker = list(symbol = "circle"))) |>
hc_legend(align = "right",
verticalAlign = "top")

Description of the visualization

I have picked the research funding rates as a dataset. To create my graph, I started by loading the necessary library packages, then I cleaned my dataset by using mutate to create new variables/columns and by selecting the desired columns I want to focus on. I created four graphs. The first one was to explore the comparison of men and women in relation to applications in the different disciplines, then the comparison of men and women in relation to awards, and finally the comparison of men and women in relation in terms of success using the highcharter package.

The interesting insights I found in the visualizations is that there are more men applicants overall and the disciplines that men tend to apply and be awarded are in sciences whereas women tend to apply in disciplines such as social sciences, humanities, interdisciplinary with a fewer number in technical sciences, physics, etc. What I found most interesting is how high the success rates of women are although their applications and awards were not as high as men.