Load the datasets
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✔ ggplot2 3.5.1 ✔ tibble 3.2.1
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library (dslabs)library (plotly)
Attaching package: 'plotly'
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setwd ("C:/Users/micha/OneDrive/Documents/DATA 110" )<- read_csv ("murders.csv" )
Rows: 52 Columns: 9
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): state
dbl (8): murder, forcible_rape, robbery, aggravated_assault, burglary, larce...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Source: Wikipedia
# A tibble: 6 × 9
state murder forcible_rape robbery aggravated_assault burglary larceny_theft
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 United… 5.6 31.7 141. 291. 727. 2286.
2 Alabama 8.2 34.3 141. 248. 954. 2650
3 Alaska 4.8 81.1 80.9 465. 622. 2599.
4 Arizona 7.5 33.8 144. 327. 948. 2965.
5 Arkans… 6.7 42.9 91.1 387. 1085. 2711.
6 Califo… 6.9 26 176. 317. 693. 1916.
# ℹ 2 more variables: motor_vehicle_theft <dbl>, population <dbl>
Plot a scatterplot that measures US murders and population size
<- ggplot (murders, aes (x = murder, y = population)) + labs (title = "US Murders by Population Size" ,caption = "Source: Wikipedia" ,x = "Number of Murders" ,y = "Population Size" ) + theme_minimal (base_size = 12 )+ geom_point () # add the points
Remove outliers District of Columbia and United States
<- murders[murders$ state != "District of Columbia" ,]<- murders2[murders2$ state != "United States" ,]
<- ggplot (murders2, aes (x = murder, y = population)) + labs (title = "US Murders by Population Size" ,caption = "Source: Wikipedia" ,x = "Number of Murders per 100,000" ,y = "Population Size" )theme_light (base_size = 12 )
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+ geom_point () # add the points
Add a smoother in black with a confidence interval
<- p2 + geom_smooth (color = "black" )
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Adjust the y-axis scale (population)
<- p3 + geom_point () + ylim (0 ,15000000 )
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Warning: Removed 4 rows containing non-finite outside the scale range
(`stat_smooth()`).
Warning: Removed 4 rows containing missing values or values outside the scale range
(`geom_point()`).
Add different circle sizes for each state based on forcible rape rates
<- p4 + geom_point (alpha = 0.7 , aes (size = forcible_rape), color = "red" ) + theme_bw ()
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Warning: Removed 4 rows containing non-finite outside the scale range
(`stat_smooth()`).
Warning: Removed 4 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 4 rows containing missing values or values outside the scale range
(`geom_point()`).
Create a static graph where each state is labeled
ds_theme_set ()%>% ggplot (aes (x = murder, y = population, label = state)) + geom_abline (col= "black" ) + geom_point (aes (color= forcible_rape), size = 3 ) + geom_text_repel (nudge_x = 0.005 ) + scale_x_log10 ("Murder Rate (log scale)" ) + scale_y_log10 ("Population (log scale)" ) + ggtitle ("US Murders by Population Size (2010)" ) + labs (caption = "Source: Wikipedia" )
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$ legend.justification : chr "center"
$ legend.justification.top : NULL
$ legend.justification.bottom : NULL
$ legend.justification.left : NULL
$ legend.justification.right : NULL
$ legend.justification.inside : NULL
$ legend.location : NULL
$ legend.box : NULL
$ legend.box.just : NULL
$ legend.box.margin : 'margin' num [1:4] 0cm 0cm 0cm 0cm
..- attr(*, "unit")= int 1
$ legend.box.background : list()
..- attr(*, "class")= chr [1:2] "element_blank" "element"
$ legend.box.spacing : 'simpleUnit' num 11points
..- attr(*, "unit")= int 8
[list output truncated]
- attr(*, "class")= chr [1:2] "theme" "gg"
- attr(*, "complete")= logi TRUE
- attr(*, "validate")= logi TRUE
This visualiztion portrays the 2010 US murder rate correlated with the population size of each state. The color for my smoother is black, while the color of the state dots is red. My theme is theme_bw() as well.
