APPROPRIATIONS_2020f %>%
  mutate(word = reorder(APPROPRIATION, Diff_APPROPRIATIONS_Exp)) %>% 
  ggplot(aes(APPROPRIATION, Diff_APPROPRIATIONS_Exp)) +
  geom_col() +
  xlab(NULL) +
  coord_flip()
## Warning: Removed 9 rows containing missing values (position_stack).

Analysis: Some appropriates that were present in both Fiscal Year of 2019 and 2020, either increased or decreased from Fiscal Year 2019 to 2020.

Note: Police Department GF seems to have an overlap of data, so, the two appropriations associated with Police Department GF both experienced an increase in appropriation in one appropriation, but a decrease in another appropriation from Fiscal Year 2019 to 2020.

APPROPRIATIONS_2020f %>%
  mutate(word = reorder(APPROPRIATION, Percent_Diff_APPROPRIATIONS_Exp)) %>% 
  ggplot(aes(APPROPRIATION, Percent_Diff_APPROPRIATIONS_Exp)) +
  geom_col() +
  xlab(NULL) +
  coord_flip()
## Warning: Removed 9 rows containing missing values (position_stack).

Analysis: The percentage change from based year 2019 to Fiscal Year 2020 is shown in this graphic. Yet again, Police Department saw the largest increase in appropriatsion from Fiscal Year 2019 to 2020; but the department also sa a significant cut to another appropriation that the Dallas Police Department recieves.