Nsryjdtyk

Here are two columns, both of which are factor variables. The first one is the races of inmates, the second one is whether they recidivated or not. And I'd like to plot the rate of recidivism by race. How should I achieve this?

And I have tried this:

df %>%

  group_by(race, Recidivated) %>%

  summarize(count = n()) %>%

  arrange (-count) %>%

  ggplot(aes(reorder(race, count, FUN = max),

             count, fill = race)) + 

  geom_col() +

  coord_flip() +

  scale_fill_manual(values=palette_9_colors) +

  theme(legend.position = "none") +

  labs(x = "Charge", y = "Count",

       title="Recidivism by Rates",

       subtitle= "Broward County - Source: Propublica",

       caption="UrbanSpatialAnalysis.com") +

  plotTheme()   

The result is a histogram calculating the number of each races. How can I get a plot that visualizes the rate of recidivism by race? Thank you!!!

Here are some of the data!

    > head(df)

   sex age         age_cat             race priors_count two_year_recid

1 Male  69 Greater than 45            Other            0              0

2 Male  34         25 - 45 African-American            0              1

3 Male  24    Less than 25 African-American            4              1

4 Male  44         25 - 45            Other            0              0

5 Male  41         25 - 45        Caucasian           14              1

6 Male  43         25 - 45            Other            3              0

                   r_charge_desc                  c_charge_desc

1                                  Aggravated Assault w/Firearm

2    Felony Battery (Dom Strang) Felony Battery w/Prior Convict

3    Driving Under The Influence          Possession of Cocaine

4                                                       Battery

5 Poss of Firearm by Convic Felo      Possession Burglary Tools

6                                         arrest case no charge

  c_charge_degree r_charge_degree juv_other_count length_of_stay

1               F                               0              1

2               F            (F3)               0             10

3               F            (M1)               1              1

4               M                               0              1

5               F            (F2)               0              6

6               F                               0              1

    Recidivated

1 notRecidivate

2    Recidivate

3    Recidivate

4 notRecidivate

5    Recidivate

6 notRecidivate

race <- sample(c("A", "B", "C", "D"), size = 100, replace = T)

recidivated <- sample(c(TRUE, FALSE), size = 100, replace = T)

df <- data.frame(race, recidivated)

df %>% group_by(race) %>% summarize(recidRate = mean(recidivated)) %>% ggplot(aes(race, recidRate)) + geom_bar(stat = "identity")

You should use TRUE or FALSE for Recidivated if it's a logical variable, and for logicals, mean() is the proportion of TRUE.

Hope this helps :)