install.packages("tidyverse")
## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.3'
## (as 'lib' is unspecified)
install.packages("dplyr")
## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.3'
## (as 'lib' is unspecified)
install.packages("ggplot2")
## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.3'
## (as 'lib' is unspecified)
install.packages("reshape2")
## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.3'
## (as 'lib' is unspecified)
install.packages("ggpubr")
## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.3'
## (as 'lib' is unspecified)
install.packages("scales")
## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.3'
## (as 'lib' is unspecified)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── 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(dplyr)
library(ggplot2)
library(reshape2)
##
## Attaching package: 'reshape2'
##
## The following object is masked from 'package:tidyr':
##
## smiths
library(ggpubr)
library(scales)
##
## Attaching package: 'scales'
##
## The following object is masked from 'package:purrr':
##
## discard
##
## The following object is masked from 'package:readr':
##
## col_factor
df <- read.csv("Agency Appropriations by Year.csv")
df <- df %>%
rename("2018" = "FY.2018.Appropriation", "2019" = "FY.2019.Appropriation", "2020" = "FY.2020.Appropriation", "2021" = "FY.2021.Appropriation", "2022" = "FY.2022.Appropriation")
df <- df[(1:15), (1:6)]
df_pie <- melt(df, id="Agency", variable.name = "Year", value.name = "Appropriation")
df <- as.data.frame(t(df))
colnames(df) <- df[1, ]
df <- df[-1, ]
df <- cbind(Year = rownames(df), df)
rownames(df) <- 1:nrow(df)
sapply(df, class)
## Year
## "character"
## Agency Accountability
## "character"
## Agriculture
## "character"
## Commerce and Workforce Development
## "character"
## Digital Transformation and Administration
## "character"
## Energy and Environment
## "character"
## Health and Mental Health
## "character"
## Human Services and Early Childhood Incentives
## "character"
## Public Safety
## "character"
## Science and Technology
## "character"
## Secretary of State and Education
## "character"
## Tourism and Branding
## "character"
## Transportation
## "character"
## Veterans Affairs and Military
## "character"
## Legislature
## "character"
## Judiciary
## "character"
df[, 2:16] <- sapply(df[, 2:16], function(x) as.numeric(gsub(",", "", x)))
sapply(df, class)
## Year
## "character"
## Agency Accountability
## "numeric"
## Agriculture
## "numeric"
## Commerce and Workforce Development
## "numeric"
## Digital Transformation and Administration
## "numeric"
## Energy and Environment
## "numeric"
## Health and Mental Health
## "numeric"
## Human Services and Early Childhood Incentives
## "numeric"
## Public Safety
## "numeric"
## Science and Technology
## "numeric"
## Secretary of State and Education
## "numeric"
## Tourism and Branding
## "numeric"
## Transportation
## "numeric"
## Veterans Affairs and Military
## "numeric"
## Legislature
## "numeric"
## Judiciary
## "numeric"
AA <- ggplot(df, aes(x=Year, y=`Agency Accountability`, group=1)) +
geom_line() +
theme_minimal() +
ylab("Appropriation") +
ggtitle("Agency Accountability") +
theme(plot.title = element_text(size = 10, face = "bold"))
AA
Ag <- ggplot(df, aes(x=Year, y=`Agriculture`, group=1)) +
geom_line() +
theme_minimal() +
ylab("Appropriation") +
ggtitle("Agriculture") +
theme(plot.title = element_text(size = 10, face = "bold"))
Ag
CWD <- ggplot(df, aes(x=Year, y=`Commerce and Workforce Development`, group=1)) +
geom_line() +
theme_minimal() +
ylab("Appropriation") +
ggtitle("Commerce and Workforce Development") +
theme(plot.title = element_text(size = 6, face = "bold"))
CWD
DTA <- ggplot(df, aes(x=Year, y=`Digital Transformation and Administration`, group=1)) +
geom_line() +
theme_minimal() +
ylab("Appropriation") +
ggtitle("Digital Transformation and Administration") +
theme(plot.title = element_text(size = 5, face = "bold"))
DTA
EE <- ggplot(df, aes(x=Year, y=`Energy and Environment`, group=1)) +
geom_line() +
theme_minimal() +
ylab("Appropriation") +
ggtitle("Energy and Environment") +
theme(plot.title = element_text(size = 10, face = "bold"))
EE
HMH <- ggplot(df, aes(x=Year, y=`Health and Mental Health`, group=1)) +
geom_line() +
theme_minimal() +
ylab("Appropriation") +
ggtitle("Health and Mental Health") +
theme(plot.title = element_text(size = 8, face = "bold"))
HMH
HSECI <- ggplot(df, aes(x=Year, y=`Human Services and Early Childhood Incentives`, group=1)) +
geom_line() +
theme_minimal() +
ylab("Appropriation") +
ggtitle("Human Services and Early Childhood Incentives") +
theme(plot.title = element_text(size = 5, face = "bold"))
HSECI
PS <- ggplot(df, aes(x=Year, y=`Public Safety`, group=1)) +
geom_line() +
theme_minimal() +
ylab("Appropriation") +
ggtitle("Public Safety") +
theme(plot.title = element_text(size = 10, face = "bold"))
PS
ST <- ggplot(df, aes(x=Year, y=`Science and Technology`, group=1)) +
geom_line() +
theme_minimal() +
ylab("Appropriation") +
ggtitle("Science and Technology") +
theme(plot.title = element_text(size = 9, face = "bold"))
ST
SSE <- ggplot(df, aes(x=Year, y=`Secretary of State and Education`, group=1)) +
geom_line() +
theme_minimal() +
ylab("Appropriation") +
ggtitle("Secretary of State and Education") +
theme(plot.title = element_text(size = 6, face = "bold"))
SSE
TB <- ggplot(df, aes(x=Year, y=`Tourism and Branding`, group=1)) +
geom_line() +
theme_minimal() +
ylab("Appropriation") +
ggtitle("Tourism and Branding") +
theme(plot.title = element_text(size = 10, face = "bold"))
TB
Tr <- ggplot(df, aes(x=Year, y=`Transportation`, group=1)) +
geom_line() +
theme_minimal() +
ylab("Appropriation") +
ggtitle("Transportation") +
theme(plot.title = element_text(size = 10, face = "bold"))
Tr
VAM <- ggplot(df, aes(x=Year, y=`Veterans Affairs and Military`, group=1)) +
geom_line() +
theme_minimal() +
ylab("Appropriation") +
ggtitle("Veterans Affairs and Military") +
theme(plot.title = element_text(size = 7, face = "bold"))
VAM
Lg <- ggplot(df, aes(x=Year, y=`Legislature`, group=1)) +
geom_line() +
theme_minimal() +
ylab("Appropriation") +
ggtitle("Legislature") +
theme(plot.title = element_text(size = 10, face = "bold"))
Lg
Ju <- ggplot(df, aes(x=Year, y=`Judiciary`, group=1)) +
geom_line() +
theme_minimal() +
ylab("Appropriation") +
ggtitle("Judiciary") +
theme(plot.title = element_text(size = 10, face = "bold"))
Ju
ggarrange(AA, Ag, CWD, DTA, EE, HMH, HSECI, PS, ST, SSE, TB, Tr, VAM, Lg, Ju)
df_pie$Appropriation <- gsub(",", "", df_pie$Appropriation)
df_pie$Appropriation <- gsub(".00", "", df_pie$Appropriation)
df_pie$Appropriation <- as.numeric(df_pie$Appropriation)
ggplot(df_pie, aes(x="", y=Appropriation, fill=Agency)) +
geom_bar(stat="identity", width=1) +
theme_minimal() +
facet_grid(.~ Year) +
xlab("Year") +
ggtitle("Total State Appropriations by Year") +
scale_y_continuous(labels=dollar)
