I added color to this plot so it would be easier to differentiate between the individual points. This plot shows that the relationship between executions and arrests is nonlinear. As the number of arrests increases, the number of executions doesn’t necessarily increase with it.
I added the preferred foot variable to this plot and created a legend with colors. I thought this was helpful because these colors pop against the dark background so it is easy to see which foot players prefer. Most players prefer their right foot and are younger than 40.
8.2 Refinement
library(tidyverse)
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I changed the color of these boxplots to green so they would stand out more against the black of individual observations. These boxplots give visual representation of the variables in a murder dataset.
8.2 Refinement 2
library(ggplot2)library(dplyr)library(gapminder)
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subset <- gapminder %>%filter (year ==2007)mysubset <- subsetplot3 <- ggplot2::ggplot(data = mysubset, aes(x = lifeExp, y = gdpPercap, color = continent)) +geom_density_2d() +geom_point() +labs(title ="Density plot with points colored by continent", subtitle ="Life expectancy generally increases as GDP increases.", caption ="Source- Gapminder Dataset")
I assigned the individual continents each different colors. I also changed the background to be dark. Having the darker background allows the individual points to pop and show up more. This plot shows us that as GDP increases, life expecancy generally increases too. There is a linear relationship between these variables.
p <-ggplot(pip2, aes(x=weight_mean, y=age_mean, color=Nationality))p2 <- p +geom_point(size=5) +annotate(geom ="text", x =75, y=25, label ="France has the lowest average age.", hjust=0) +labs(y="Average Age", x="Average Weight", title="Age and Weight by Nationality", subtitle ="Spain has the lowest average weight.",caption ="FIFA dataset{datasetsICR}")p2
I changed the theme to dark because it helps the colors to pop and stand out against the dark background more. This plot shows the average weight and age of FIFA players by nationality. Spain has the lowest average weight, and France has the lowest average age.
8.3 Refinement 2
p2 +theme(legend.position ="top",plot.title =element_text(size=rel(2),lineheight=.5,family="TT Courier New",face="bold.italic",colour="orange"),axis.text.x =element_text(size=rel(1.1),family="TT Times New Roman",face="bold",color="green"))
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I changed the fonts and the colors of the title text. I also changed the size and location of the text. This helped me emphasize the specific words and information that are most important. This plot shows the average weight and age of FIFA players by nationality. Spain has the lowest average weight, and France has the lowest average age.
8.4 Refinement
p3 <-ggplot(data =subset(FIFA, Age %in% Age),mapping =aes(x = Weight))p1 <- p3 +geom_density(fill ="gray20", color =FALSE,alpha =0.9, mapping =aes(y = ..scaled..)) +geom_vline(data =subset(FIFA, Age %in% Age),aes(xintercept = Weight), color ="white", size =0.5) +geom_text(data =subset(FIFA, Age %in% Age),aes(x = Weight, y = Age, label = Weight), nudge_x =7.5,color ="white", size =3.5, hjust =1) +geom_text(data =subset(FIFA, Age %in% Age),aes(x = Weight, y = Age, label = Age)) +facet_grid(Age ~ ., switch ="y")
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
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p1
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I created subsets by age in order to see the distribution of weight better. This plot shows that weight generally increase until middle age and then starts to decrease.
8.4 Refinement 2
library(gapminder)library(dplyr)subset <- gapminder %>%filter (year ==2007)library(ggplot2)plot1 <-ggplot(subset, aes(x = lifeExp, y = gdpPercap, color = continent)) +geom_density_2d() +geom_point() +labs(title ="Density plot with points colored by continent", subtitle ="Life expectancy generally increases as GDP increases.", caption ="Source- Gapminder Dataset") +facet_wrap(~continent) +labs(title ="Faceted by continent", subtitle ="Life expectancy generally increases as GDP increases.", caption ="Source- Gapminder Dataset")plot1
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I subset each individual continent so I could see the distribution for each one. Through this plot, we see that similar positive correlation between life expectancy and GDP per capita and highlights the variation within and between continents. African countries generally have lower GDP per capita and life expectancy. European countries mostly fall into higher ranges for both metrics.
