Companies House Charts (Day 5 of 5)

My exploratory data analysis of Companies House data in August 2020:

Company status by month

Next, the limitations:

• The delay in processing company status updates is likely to vary across cities

• Status categories aren't clean, especially when a receiver is instructed

And the code snippet:

#group and count each status for each month
temp %>%
filter(CompanyStatus!="Active") %>%
group_by(path_ym,CompanyStatus) %>%
summarise(.groups = "keep",
  path_CompanyStatus_count = n(),
) %>%
ungroup() %>%
mutate(
  CompanyStatus = CompanyStatus %>% as_factor() %>% fct_reorder(path_CompanyStatus_count) %>% fct_rev(),
  CompanyStatus_num = CompanyStatus %>% as.numeric()
) %>%
  filter(CompanyStatus_num<=6) %>%
  select(-CompanyStatus_num) %>%
  mutate(path_ym = ymd(path_ym)) %>%
#graph
  ggplot(aes(x=path_ym,y=path_CompanyStatus_count)) +
  geom_line() +
  facet_wrap(~CompanyStatus,scales = "free_y") +
  #formatting
  labs(
    title = "",x="",y=""
  ) +
  theme_tq() +
  theme(
    legend.position = "none"
  )