Final project-Juveniles crimes,1993–2022
Mimi
2024-06-02
Import Your Data
In the following code hunk, import your data.
data1 <- drop_na(read.csv("C:/shiny/rsconnect/f01.csv"))
data2 <- drop_na(read.csv("C:/shiny/rsconnect/f02.csv"))
data3 <- drop_na(read.csv("C:/shiny/rsconnect/f03.csv"))
data4 <- drop_na(read.csv("C:/shiny/rsconnect/f04.csv"))
data5 <- drop_na(read.csv("C:/shiny/rsconnect/f05.csv"))
data6 <- drop_na(read.csv("C:/shiny/rsconnect/f06.csv"))Part 1
For my first figure, I am going to create a lineplot that plots year on the x axis and rate of nonfatal violent victimization per 1,000 people on the y axis.And the plot has three lines which are grouped by different ages(12 to 17,18 to 24,25 or older).I will create a three column tibble with these data.
data_long <- melt(data1, id.vars = "Year")
data_long <- data_long %>%
mutate(value = as.numeric(as.character(value)))
data_long## Year variable value
## 1 1993 X12.to.17 184.8
## 2 1994 X12.to.17 189.3
## 3 1995 X12.to.17 156.3
## 4 1996 X12.to.17 146.8
## 5 1997 X12.to.17 135.5
## 6 1998 X12.to.17 119.1
## 7 1999 X12.to.17 108.7
## 8 2000 X12.to.17 73.2
## 9 2001 X12.to.17 74.4
## 10 2002 X12.to.17 62.7
## 11 2003 X12.to.17 78.3
## 12 2004 X12.to.17 49.7
## 13 2005 X12.to.17 59.8
## 14 2006 X12.to.17 0.0
## 15 2007 X12.to.17 61.8
## 16 2008 X12.to.17 54.3
## 17 2009 X12.to.17 43.0
## 18 2010 X12.to.17 28.1
## 19 2011 X12.to.17 37.7
## 20 2012 X12.to.17 48.4
## 21 2013 X12.to.17 52.1
## 22 2014 X12.to.17 30.1
## 23 2015 X12.to.17 31.3
## 24 2016 X12.to.17 25.1
## 25 2017 X12.to.17 33.5
## 26 2018 X12.to.17 34.2
## 27 2019 X12.to.17 35.2
## 28 2020 X12.to.17 17.4
## 29 2021 X12.to.17 13.2
## 30 2022 X12.to.17 27.4
## 31 1993 X18.to.24 141.6
## 32 1994 X18.to.24 144.2
## 33 1995 X18.to.24 135.4
## 34 1996 X18.to.24 122.7
## 35 1997 X18.to.24 110.0
## 36 1998 X18.to.24 112.2
## 37 1999 X18.to.24 88.9
## 38 2000 X18.to.24 75.0
## 39 2001 X18.to.24 59.2
## 40 2002 X18.to.24 68.5
## 41 2003 X18.to.24 63.8
## 42 2004 X18.to.24 55.4
## 43 2005 X18.to.24 61.0
## 44 2006 X18.to.24 0.0
## 45 2007 X18.to.24 46.6
## 46 2008 X18.to.24 43.2
## 47 2009 X18.to.24 38.5
## 48 2010 X18.to.24 33.9
## 49 2011 X18.to.24 49.1
## 50 2012 X18.to.24 41.0
## 51 2013 X18.to.24 33.8
## 52 2014 X18.to.24 26.8
## 53 2015 X18.to.24 25.1
## 54 2016 X18.to.24 29.6
## 55 2017 X18.to.24 34.7
## 56 2018 X18.to.24 35.9
## 57 2019 X18.to.24 37.2
## 58 2020 X18.to.24 29.6
## 59 2021 X18.to.24 23.1
## 60 2022 X18.to.24 36.8
## 61 1993 X25.or.older 56.0
## 62 1994 X25.or.older 55.3
## 63 1995 X25.or.older 49.3
## 64 1996 X25.or.older 45.0
## 65 1997 X25.or.older 43.7
## 66 1998 X25.or.older 36.7
## 67 1999 X25.or.older 32.6
## 68 2000 X25.or.older 26.9
## 69 2001 X25.or.older 22.9
## 70 2002 X25.or.older 22.3
## 71 2003 X25.or.older 21.1
## 72 2004 X25.or.older 20.7
## 73 2005 X25.or.older 19.4
## 74 2006 X25.or.older 0.0
## 75 2007 X25.or.older 19.8
## 76 2008 X25.or.older 19.1
## 77 2009 X25.or.older 17.4
## 78 2010 X25.or.older 16.0
## 79 2011 X25.or.older 16.9
## 80 2012 X25.or.older 21.2
## 81 2013 X25.or.older 18.1
## 82 2014 X25.or.older 17.9
## 83 2015 X25.or.older 16.2
## 84 2016 X25.or.older 17.7
## 85 2017 X25.or.older 17.2
## 86 2018 X25.or.older 20.2
## 87 2019 X25.or.older 17.2
## 88 2020 X25.or.older 14.6
## 89 2021 X25.or.older 16.0
## 90 2022 X25.or.older 21.3data_long %>%
ggplot(aes(x = Year, y = value, group = variable, color = variable)) +
geom_line(size = 2) +
theme(legend.position="top") +
theme(panel.grid.major.x = element_line(colour = "gray", linetype = "dotted"))+
scale_y_continuous(breaks = seq(0, 200, by = 20))+
ggtitle("Rate of nonfatal violent victimization(by age of victim) from 1993 to 2022")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Part 2
For my second figure, I am going to create a histogram that includes five sets which means five different years(2018-2022),and each year has three bars which group by age of victim(12 to 17,18 to 24,25 or older).The xlab is year and the ylab is rate of nonfatal violent victimization per 1,000 persons.I will create a three column tibble with these data.
data_long2 <- data2 %>%
pivot_longer(cols = -Year, names_to = "Age_Group", values_to = "Rate")
data_long2$Age_Group <- recode(data_long2$Age_Group,
`X12.to.17` = "12 to 17",
`X18.to.24` = "18 to 24",
`X25.or.older` = "25 or older")
data_long2## # A tibble: 15 × 3
## Year Age_Group Rate
## <int> <chr> <dbl>
## 1 2018 12 to 17 34.2
## 2 2018 18 to 24 35.9
## 3 2018 25 or older 20.2
## 4 2019 12 to 17 35.2
## 5 2019 18 to 24 37.2
## 6 2019 25 or older 17.2
## 7 2020 12 to 17 17.4
## 8 2020 18 to 24 29.6
## 9 2020 25 or older 14.6
## 10 2021 12 to 17 13.2
## 11 2021 18 to 24 23.1
## 12 2021 25 or older 16
## 13 2022 12 to 17 27.4
## 14 2022 18 to 24 36.8
## 15 2022 25 or older 21.3ggplot(data_long2, aes(x = Year, y = Rate, fill = Age_Group)) +
geom_bar(stat = "identity", position = position_dodge()) +
scale_fill_manual(values = c("12 to 17" = "pink2", "18 to 24" = "plum", "25 or older" = "#A6CEE3")) +
labs(y = "Rate per 1,000 persons", fill = "Age Group") +
theme_minimal() +
theme(legend.position = "top")+
ggtitle("Rate of nonfatal violent victimization(by age of victim) from 2018 to 2022")
Part 3
For my third figure, I am going to create a accumulated area chart that plots year on the x axis and number of homicides on the y axis.And different color means different group of age.I will create a three column tibble with these data.
data_long3 <- data3 %>%
pivot_longer(cols = -Year,
names_to = "Age_Group", values_to = "Count")
data_long3$Age_Group <- recode(data_long3$Age_Group,
`X11.or.younger` = "11 or younger",
`X12.to.17` = "12 to 17",
`X18.or.older` = "18 or older")
data_long3## # A tibble: 90 × 3
## Year Age_Group Count
## <int> <chr> <dbl>
## 1 1993 11 or younger 1034
## 2 1993 12 to 17 1852
## 3 1993 18 or older 21641
## 4 1994 11 or younger 964
## 5 1994 12 to 17 1735
## 6 1994 18 or older 20628
## 7 1995 11 or younger 913
## 8 1995 12 to 17 1762
## 9 1995 18 or older 18931
## 10 1996 11 or younger 959
## # ℹ 80 more rowsggplot(data_long3, aes(x = Year, y = Count, fill = Age_Group)) +
geom_area() +
scale_fill_brewer(palette = "Purples", breaks = rev(levels(data_long3$Age_Group)))+
labs(title = "Number of homicides(by age of victim) from 1993 to 2022",
x = "Year", y = "Number",
fill = "Age Group") +
theme_minimal()+
ggtitle("Number of homicides(by age of victim) from 1993 to 2022")
Part 4
For my fourth figure, I am going to create a histogram that includes five sets which means five different years(2018-2022),and each year has three bars which group by age of victim(11 or younger,12 to 17,18 or older).The xlab is year and the ylabis Homicide rate per 100,000 persons.I will create a three column tibble with these data.
data_long4 <- data4 %>%
pivot_longer(cols = -Year, names_to = "Age_Group", values_to = "Rate")
data_long4$Age_Group <- recode(data_long4$Age_Group,
`X11.or.younger` = "11 or younger",
`X12.to.17` = "12 to 17",
`X18.or.older` = "18 or older")
data_long4## # A tibble: 15 × 3
## Year Age_Group Rate
## <int> <chr> <dbl>
## 1 2018 11 or younger 1.31
## 2 2018 12 to 17 2.87
## 3 2018 18 or older 5.93
## 4 2019 11 or younger 1.39
## 5 2019 12 to 17 2.89
## 6 2019 18 or older 5.99
## 7 2020 11 or younger 1.48
## 8 2020 12 to 17 4.41
## 9 2020 18 or older 7.7
## 10 2021 11 or younger 1.55
## 11 2021 12 to 17 4.96
## 12 2021 18 or older 8.17
## 13 2022 11 or younger 1.45
## 14 2022 12 to 17 5.4
## 15 2022 18 or older 7.54ggplot(data_long4, aes(x = Year, y = Rate, fill = Age_Group)) +
geom_bar(stat = "identity", position = position_dodge()) +
scale_fill_manual(values = c("11 or younger" = "pink2", "12 to 17" = "plum", "18 or older" = "#A6CEE3")) +
labs(y = "Rate per 100,000 persons", fill = "Age Group") +
theme_minimal() +
theme(legend.position = "top")+
ggtitle("Homicide rate per 100,000 persons(by age of victim) from 2018 to 2022")
Part 5
For my fifth figure, I am going to create a histogram that includes two sets which means two different years(2021 and 2022),and each year has five bars which group by age of victim(11 or younger,12 to 17,18 to 29,30 or older,Multiple offenders of various ages).The xlab is year and the ylab is Percent of nonfatal violent incidents.I will create a three column tibble with these data.
data_long5 <- data5 %>%
pivot_longer(cols = c("X2021", "X2022"), names_to = "Year", values_to = "Percent")
data_long5## # A tibble: 10 × 3
## Age_Group Year Percent
## <chr> <chr> <dbl>
## 1 11 or younger X2021 1.4
## 2 11 or younger X2022 1.2
## 3 12 to 17 X2021 7.7
## 4 12 to 17 X2022 9.3
## 5 18 to 29 X2021 25
## 6 18 to 29 X2022 23.9
## 7 30 or older X2021 60.3
## 8 30 or older X2022 59.7
## 9 Multiple offenders of various ages X2021 5.7
## 10 Multiple offenders of various ages X2022 5.8ggplot(data_long5, aes(x = Year, y = Percent, fill = Age_Group)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.8), width = 0.7) +
scale_fill_manual(values = c("pink", "pink3", "plum", "plum4", "#A6CEE3")) +
labs(title = "Percent of nonfatal violent incidents(by age of offender) in 2021 and 2022",
x = "Year", y = "Percent",
fill = "Age Group") +
theme_minimal() +
theme(legend.position = "top")
Part 6
For my sixth figure, I am going to create a bar chart that includes twelve sets which means different Offense type,and each type has two bars which means different year(2021 and 2022).The xlab is different offense type and the ylab is Percent of all arrests that were of persons age 17 or younger.I will create a three column tibble with these data.
data_long6 <- data6 %>%
pivot_longer(cols = c("X2021", "X2022"), names_to = "Year", values_to = "Percent")
data_long6## # A tibble: 26 × 3
## Offense.type Year Percent
## <chr> <chr> <dbl>
## 1 Violent crime X2021 8.7
## 2 Violent crime X2022 9.9
## 3 Homicide X2021 8.9
## 4 Homicide X2022 11
## 5 Rape X2021 15.5
## 6 Rape X2022 14.7
## 7 Robbery X2021 17.9
## 8 Robbery X2022 19.9
## 9 Aggravated assault X2021 6.7
## 10 Aggravated assault X2022 7.8
## # ℹ 16 more rowsggplot(data_long6, aes(x = Percent, y = `Offense.type`, fill = Year)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.8), width = 0.7) +
scale_fill_manual(values = c("X2021" = "pink2", "X2022" = "#A6CEE3")) +
labs(title = "Percent of Offense Types for 2021 and 2022",
x = "Percent", y = "Offense Type",
fill = "Year") +
theme_minimal() +
theme(legend.position = "top")
Part 7
For my seventh figure, I am going to create a lineplot that plots year on the x axis and Rate of nonfatal violent victimization per 1,000 persons on the y axis.And the plot has three lines which are grouped by different ages(12 to 17,18 to 24,25 or older).I will create a three column tibble with these data.
data_long2 <- melt(data2, id.vars = "Year")
data_long2 <- data_long2 %>%
mutate(value = as.numeric(as.character(value)))
data_long2## Year variable value
## 1 2018 X12.to.17 34.2
## 2 2019 X12.to.17 35.2
## 3 2020 X12.to.17 17.4
## 4 2021 X12.to.17 13.2
## 5 2022 X12.to.17 27.4
## 6 2018 X18.to.24 35.9
## 7 2019 X18.to.24 37.2
## 8 2020 X18.to.24 29.6
## 9 2021 X18.to.24 23.1
## 10 2022 X18.to.24 36.8
## 11 2018 X25.or.older 20.2
## 12 2019 X25.or.older 17.2
## 13 2020 X25.or.older 14.6
## 14 2021 X25.or.older 16.0
## 15 2022 X25.or.older 21.3data_long2 %>%
ggplot(aes(x = Year, y = value, group = variable, color = variable)) +
geom_line(size = 2) +
theme(legend.position="top") +
theme(panel.grid.major.x = element_line(colour = "gray", linetype = "dotted"))+
scale_y_continuous(breaks = seq(0, 200, by = 20))
ggtitle("Rate of nonfatal violent victimization(by age of victim) from 2018 to 2022")## $title
## [1] "Rate of nonfatal violent victimization(by age of victim) from 2018 to 2022"
##
## attr(,"class")
## [1] "labels"Part 8
For my eighth figure, it is an extension of figure two,I am going to use gganimate create a animation that has five histogram plots which means five different years(2018-2022),and each year has three bars which group by age of victim(12 to 17,18 to 24,25 or older).The xlab is year and the ylab is rate of nonfatal violent victimization per 1,000 persons.Through this app you can choose which year’s plot that you want to look for.
data_long2 <- data2 %>%
pivot_longer(cols = -Year, names_to = "Age_Group", values_to = "Rate")
# Recode Age_Group
data_long2$Age_Group <- recode(data_long2$Age_Group,
`X12.to.17` = "12 to 17",
`X18.to.24` = "18 to 24",
`X25.or.older` = "25 or older")
p <- ggplot(data_long2, aes(x = Year, y = Rate, fill = Age_Group)) +
geom_bar(stat = "identity", position = position_dodge()) +
scale_fill_manual(values = c("12 to 17" = "pink2", "18 to 24" = "plum", "25 or older" = "#A6CEE3")) +
labs(y = "Rate per 1,000 persons", fill = "Age Group") +
theme_minimal() +
theme(legend.position = "top") +
ggtitle("Rate of nonfatal violent victimization (by age of victim) from 2018 to 2022") +
transition_states(Year, transition_length = 1, state_length = 1) +
enter_fade() +
exit_fade()
animate(p)