Inclusão de pacotes
library(tidyverse)
#> Warning: package 'tidyverse' was built under R version 3.5.3
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#> Warning: package 'stringr' was built under R version 3.5.3
#> Warning: package 'forcats' was built under R version 3.5.3
library(ggthemes)
#> Warning: package 'ggthemes' was built under R version 3.5.3
library(ggforce)
#> Warning: package 'ggforce' was built under R version 3.5.3Lê do arquivo “.rds” preparado em preparo_anos.Rmd
df_all <- read_rds("data/df_all.rds")
nrow(df_all)
#> [1] 705527Renomeia e seleciona colunas a serem utilizadas
df_all_clean <- df_all %>%
# era: rename(), select() deixa df menor
select(ano,
func_cod=`Função`,
func=`Nome Função`,
empenhado=`Valor Empenhado`,
liquidado=`Valor Liquidado`,
pago=`Valor Pago`)Nomes das funções são únicas por código?
df_all_clean %>%
count(func_cod,func) %>%
count(func_cod,name="nn") %>%
count(nn)
#> # A tibble: 1 x 2
#> nn n
#> <int> <int>
#> 1 1 25Função com maior valor empenhado total (top 5)
anos <- unique(df_all_clean$ano%>%as.character)%>%as.integer
df_top_funcs_empenhado <- df_all_clean %>%
group_by(func_cod,func) %>%
summarize(n=n(),
total=sum(empenhado)) %>%
arrange(desc(total)) %>%
head(5) %>%
select(func_cod,func,everything()) %>%
ungroup() %>%
mutate_at(vars(func_cod,func),fct_drop) %>% # so unused levels are dropped
mutate(media_anual=total/length(anos)) %>%
mutate_at(vars(total,media_anual),~(./10^9)%>%as.integer) %>%
rename_at(vars(total,media_anual),~str_c(.,"_b")) # billions
df_top_funcs_empenhado
#> # A tibble: 5 x 5
#> func_cod func n total_b media_anual_b
#> <fct> <fct> <int> <int> <int>
#> 1 09 Previdência Social 11506 7478 1495
#> 2 06 Segurança Pública 77487 3991 798
#> 3 28 Encargos Especiais 15356 3696 739
#> 4 12 Educação 235933 2520 504
#> 5 10 Saúde 78066 1644 328Valores médios anuais
df_top_funcs_empenhado %>%
mutate(func=func%>%fct_inorder()%>%fct_rev) %>%
ggplot(aes(func,media_anual_b)) +
geom_col(aes(fill=func)) +
coord_flip() +
labs(title="Média de Valores Empenhados Anuais por Função",
subtitle=sprintf("Anos %s-%s, Top 5", min(anos),max(anos)),
y="Valor Empenhado Anual Médio (R$ bilhões)") +
theme(legend.position = "none",
axis.title.y=element_blank())
df_all_clean %>%
mutate(func=func%>%fct_reorder(-empenhado,sum)) %>%
filter(as.integer(func)<6) %>%
mutate(func=func%>%fct_drop%>%fct_rev) %>% # for coord_flip
group_by(ano,func) %>%
# milhoes
summarize_at(vars(empenhado,liquidado,pago),
list(~(sum(.)/10^9))) %>%
gather("key","value",-func,-ano) %>%
mutate(key=factor(key,levels=c("pago","liquidado","empenhado"))) %>%
ggplot(aes(func,value,group=key,fill=key)) +
geom_col(position="dodge") +
coord_flip() +
labs(title="Valores Totais por Função",
subtitle="Top 5 por empenho total 2014-8",
y="Valor (R$ bilhões)") +
theme(legend.position = "top",
legend.title = element_blank(),
axis.title.y=element_blank()) +
facet_wrap(~ano)
Empenhado vs Pago das top funções:
df_valores <- df_all_clean %>%
# to avoid warning on factors
{suppressWarnings(semi_join(.,df_top_funcs_empenhado,by="func_cod"))}%>%
group_by(ano,func) %>%
summarize_at(vars(empenhado,liquidado,pago),list(sum=~sum(.)/10^9%>%round(1))) %>%
arrange(desc(empenhado_sum)) %>%
ungroup() %>% # para q proxima linha rode corretamente
mutate(func=func%>%fct_drop%>%fct_inorder) %>%
gather("tipo","valor",ends_with("_sum")) %>%
mutate_at(vars(tipo),~str_sub(.,end=-5)) %>% # remove "_sum" suffix
filter(tipo!="liquidado")
df_valores %>%
ggplot(aes(ano,valor,group=func,color=func)) +
geom_line(size=I(1.2)) +
# scale_y_log10() +
labs(title="Valor Anual por Função",
subtitle=sprintf("Anos %s-%s, Top 5", min(anos),max(anos)),
y = "Valor Total (R$ bilhões)") +
theme(legend.position = "top",
legend.title=element_blank()) +
facet_wrap(~tipo)
Empenhado vs Pago das top funções:
df_valores_ggf <- df_valores%>%
filter(tipo=="empenhado",ano==2017) %>%
group_by(ano,func,tipo)%>%
summarize(valor=last(valor))
df_valores %>%
mutate(func_tipo=func%>%str_c("_",tipo)) %>%
ggplot(aes(ano,valor,
group=func_tipo,
color=func,linetype=tipo)) +
geom_line(size=I(1.2)) +
# to do: ggforce
# geom_mark_ellipse(aes(fill = Species, label = Species)) +
#geom_mark_circle(aes(ano,valor,
# label=func,
# fill=func),
# data=df_valores_ggf,
# expand=.002,
# label.fontsize=8,
# inherit.aes=F)+
#scale_y_log10() +
labs(title="Valor Anual por Função",
subtitle=sprintf("Anos %s-%s, Top 5", min(anos),max(anos)),
y = "Valor Total (R$ bilhões)") +
theme_minimal() +
theme(legend.position = "bottom",
legend.title=element_blank())
ggsave("pics/empenhado vs pago.png",width=11)
#> Saving 11 x 5 in imagehttps://www.data-imaginist.com/2019/the-ggforce-awakens-again/
Distribuição de Valor Empenhado por ano (faceteamento)
df_all_clean %>%
semi_join(df_top_funcs_empenhado,by="func_cod") %>%
mutate(func=func%>%fct_reorder(empenhado,sum)) %>%
ggplot(aes(func,empenhado+1)) +
geom_boxplot(aes(fill=func),outlier.shape=NA) +
scale_y_log10(breaks=10^c(0,3,6,9),labels=c("1","1k","1M","1B")) + #breaks=10^(1:5),labels=10^(1:5)%>%as.integer) +
coord_flip() + # ylim=c(10,.5*10^5)) +
facet_wrap(~ano,ncol=2) +
labs(title="Distribuição do Valor Empenhado",
y = "Valor Empenhado Anual (R$)") +
theme(legend.position = "none",
axis.title.y=element_blank())
#> Warning: Column `func_cod` joining factors with different levels, coercing
#> to character vector