このサイトは、安藤道人・大西連「コロナ禍で生活困窮者への家賃補助と現金貸付が急増：独自入手した厚生労働省データを用いた検証」に掲載しているグラフを、インタラクティブなグラフとして掲載している。

生活保護、住居確保給付金、生活福祉資金（緊急小口資金・総合支援資金）の利用実績の全国集計のグラフであり、各グラフの説明については、元記事を参照してほしい。

また、元データやRMarkdownファイルはGitHubの「household_support_monthly」のフォルダで公開しているほか、本サイトでも一部のコードは確認できる。

1 生活保護

1.1 生活保護受給世帯数

seikatsu <- df_all_long

seikatsu　 <- seikatsu %>% filter(category == "households_total")

seikatsu　 <- seikatsu %>% rename("households_total"= number)

seikatsu$households_total <- seikatsu$households_total/10000

g_seiho_households <- seikatsu %>% ggplot(aes(x = year_month, y = households_total)) +
   　 geom_line(stat = "identity",  colour = "#1177CC") +
      geom_point(stat = "identity", colour = "#1177CC") +
      theme_minimal(base_family = font) +
 　　 scale_x_date(breaks = "2 month", date_labels = "%Y-%m") +
   　 theme(axis.text.x = element_text(angle = 30, hjust = 1)) +
   　 ylab("被保護世帯（単位：1万世帯）")  +
      xlab("年月") +
      scale_y_continuous(limits = c(155, 165),oob = rescale_none)

ggplotly(g_seiho_households)

1.2 生活保護受給世帯数の前月差

seikatsu <-  df_all_long

seikatsu　 <- seikatsu %>% filter(category == "households_total")

seikatsu　 <- seikatsu %>% rename("diff_households_total"= diff)

g_seiho_households_diff <- seikatsu %>% ggplot(aes(x=year_month, y=diff_households_total)) +
   　 geom_line(stat = "identity",  colour = "#1177CC") +
      geom_point(stat = "identity", colour = "#1177CC") +
      theme_minimal(base_family = font) +
 　　 scale_x_date(breaks = "2 month", date_labels = "%Y-%m") +
   　 ylab("被保護世帯（単位：1万世帯）")  +
      xlab("年月") +
      geom_hline(yintercept = 0, color = "black", linetype = "dashed") +
   　 theme(axis.text.x = element_text(angle= 0, hjust = 1))
    
ggplotly(g_seiho_households_diff)

1.3 生活保護受給者数

seikatsu <-  df_all_long

seikatsu　 <- seikatsu %>% filter(category == "persons_total")

seikatsu　 <- seikatsu %>% rename("persons_total"= number)

seikatsu$persons_total <- seikatsu$persons_total/10000

g_seiho_people <- seikatsu %>% ggplot(aes(x=year_month, y=persons_total)) +
   　geom_line(stat = "identity",  colour = "#1177CC") +
      geom_point(stat = "identity", colour = "#1177CC") +
      theme_minimal(base_family = font) +
 　　 scale_x_date(breaks = "2 month", date_labels = "%Y-%m") +
   　 theme(axis.text.x = element_text(angle = 30, hjust = 1))+
   　 ylab("被保護人員数（単位：1万人）")  +
      xlab("年月") +
      scale_y_continuous(limits = c(200,  210),oob = rescale_none)
    
ggplotly(g_seiho_people)

1.4 生活保護受給者数の前月差

seikatsu <-  df_all_long

seikatsu　 <- seikatsu %>% filter(category == "persons_total")

seikatsu　 <- seikatsu %>% rename("diff_persons_total"= diff)

g_seiho_people_diff <- seikatsu %>% ggplot(aes(x=year_month, y=diff_persons_total)) +
   　geom_line(stat = "identity",  colour = "#1177CC") +
      geom_point(stat = "identity", colour = "#1177CC") +
      theme_minimal (base_family = font) +
 　　 scale_x_date(breaks = "2 month", date_labels = "%Y-%m") +
   　 theme(axis.text.x = element_text(angle = 30, hjust = 1))+
   　 ylab("被保護世帯（単位：1万人）")  +
      xlab("年月") +
      geom_hline(yintercept = 0, color = "black", linetype = "dashed") +
      geom_vline(aes(xintercept = as.Date("2020-01-01")))
    
ggplotly(g_seiho_people_diff)

2 住居確保給付金

2.1 住居確保給付金の申請件数

jukyo1901_2006 <- df_long_num %>% select(prefec, date, jukyo_apply)

jukyo1901_2006 <-na.omit(jukyo1901_2006)

jukyo1901_2006　 <- jukyo1901_2006　 %>% filter(prefec == "全国")

jukyo1901_2006 <- jukyo1901_2006 %>% rename("year_month" = date)

g_jukyo_apply <- jukyo1901_2006 %>% ggplot(aes(x = year_month, y = jukyo_apply)) +
 　geom_line(stat = "identity",  colour = "#1177CC") +
   geom_point(stat = "identity", colour = "#1177CC") +
   theme_minimal(base_family = font) +
   scale_x_date(breaks = "2 month", date_labels = "%Y-%m") +
   theme(axis.text.x = element_text(angle = 30, hjust = 1))+
   xlab("年月") +
   ylab("申請件数")+
   labs(colour="prefecture")+
   scale_y_continuous(limits = c(0, 45000),oob = rescale_none) +
   theme(legend.position="none")

ggplotly(g_jukyo_apply)
# グラフ統合用
g_jukyo_apply <- g_jukyo_apply + ggtitle("(a)申請件数") +
     theme(axis.text.x = element_text(angle = 30, hjust = 1))

2.2 住居確保給付金の決定件数

jukyo1901_2006 <- df_long_num %>% select(prefec, date, jukyo_number)

jukyo1901_2006 <-na.omit(jukyo1901_2006)

jukyo1901_2006　 <- jukyo1901_2006　 %>% filter(prefec == "全国")

jukyo1901_2006 <- jukyo1901_2006 %>% rename("year_month" = date, "prefecture" = prefec)

g_jukyo_number <- jukyo1901_2006 %>% ggplot(aes(x = year_month, y = jukyo_number)) +
 　geom_line(stat = "identity",  colour = "#1177CC") +
   geom_point(stat = "identity", colour = "#1177CC") +
   theme_minimal(base_family = font) +
   scale_x_date(breaks = "2 month", date_labels = "%Y-%m") +
   theme(axis.text.x = element_text(angle = 30, hjust=1))+
   xlab("年月") +
   ylab("決定件数")+
   labs(colour="prefecture")+
   scale_y_continuous(limits = c(0, 45000),oob = rescale_none) +
   theme(legend.position="none")

ggplotly(g_jukyo_number)
# グラフ統合用
g_jukyo_number <- g_jukyo_number + ggtitle("(b)決定件数") +
     theme(axis.text.x = element_text(angle = 30, hjust=1))

2.3 住居確保給付金の支給済額

jukyo1901_2006 <- df_long_num %>% select(prefec, date, jukyo_payment_amount)

jukyo1901_2006 <-na.omit(jukyo1901_2006)

jukyo1901_2006　 <- jukyo1901_2006　 %>% filter(prefec == "全国")

jukyo1901_2006$jukyo_payment_amount <- jukyo1901_2006$jukyo_payment_amount/100000

jukyo1901_2006 <- jukyo1901_2006 %>% rename("year_month" = date)

g_jukyo_amounts <- jukyo1901_2006 %>% ggplot(aes(x = year_month, y = jukyo_payment_amount)) +
 　geom_line(stat = "identity",  colour = "#1177CC") +
   geom_point(stat = "identity", colour = "#1177CC") +
   theme_minimal(base_family = font) +
   scale_x_date(breaks = "2 month", date_labels = "%Y-%m") +
   theme(axis.text.x = element_text(angle = 30, hjust=1))+
   xlab("年月") +
   ylab("支給済額（単位：億円）") +
   theme(legend.position="none") 

ggplotly(g_jukyo_amounts)
# 出力用グラフ
g_jukyo_amounts <- g_jukyo_amounts +  labs(title = "図３ 住居確保給付金の支給済額（単位：億円）",
      caption = "注：2019年4月から2020年3月については統計が欠損している。出典：厚生労働省提供資料")

# png出力
ggsave(file = "jukyo_amounts.png", plot = g_jukyo_amounts, width = 5, height = 4)

3 緊急小口資金・総合支援資金

3.1 緊急小口資金の申請件数

koguchi_1901_2008 <- df_long_num %>% select(prefec, date,koguchi_apply)

koguchi_1901_2008 <- na.omit(koguchi_1901_2008)
   
koguchi_1901_2008 <- koguchi_1901_2008  %>% filter(prefec == "全国")

koguchi_1901_2008 <- koguchi_1901_2008　%>% rename("year_month" = date)

g_koguchi_apply <- koguchi_1901_2008 %>% ggplot(aes(x = year_month, y = koguchi_apply)) +
 　geom_line(stat = "identity",  colour = "#1177CC") +
   geom_point(stat = "identity", colour = "#1177CC") +
   theme_minimal (base_family = font) +
   scale_x_date(breaks = "2 month", date_labels = "%Y-%m",
                limit=c(as.Date("2019-01-01"),as.Date("2020-08-01"))) +
   scale_y_continuous(limits = c(0, 200000),oob = rescale_none) +
   theme(axis.text.x = element_text(angle = 30, hjust=1))+
   xlab("年月") +
   ylab("申請件数")+
   theme(legend.position="none") 

ggplotly(g_koguchi_apply)
g_koguchi_apply <- g_koguchi_apply + ggtitle("(a)緊急小口資金の申請件数") +
      theme(axis.text.x = element_text(angle=30, hjust=1))

3.2 緊急小口資金の決定件数

koguchi_1901_2008 <- df_long_num %>% select(prefec, date,koguchi_number)

koguchi_1901_2008 <- na.omit(koguchi_1901_2008)
   
koguchi_1901_2008 <- koguchi_1901_2008  %>% filter(prefec == "全国")

koguchi_1901_2008 <- koguchi_1901_2008　%>% rename("year_month" = date)

g_koguchi_number <- koguchi_1901_2008 %>% ggplot(aes(x = year_month, y =　koguchi_number)) +
 　geom_line(stat = "identity",  colour = "#1177CC") +
   geom_point(stat = "identity", colour = "#1177CC") +
   theme_minimal (base_family = font) +
   scale_x_date(breaks = "2 month", date_labels = "%Y-%m") +
   scale_y_continuous(limits = c(0, 200000),oob = rescale_none) +
   theme(axis.text.x = element_text(angle = 30, hjust=1))+
   xlab("年月") +
   ylab("決定件数")+
   theme(legend.position="none")

ggplotly(g_koguchi_number)
g_koguchi_number <- g_koguchi_number + ggtitle("(b)緊急小口資金の決定件数") +
      theme(axis.text.x = element_text(angle=30, hjust=1))

3.3 総合支援資金の申請件数

sogo_1901_2001 <- df_long_num %>% select(prefec, date,sogo_apply)

sogo_1901_2001 <- na.omit(sogo_1901_2001)
   
sogo_1901_2001 <- sogo_1901_2001 　 %>% filter(prefec == "全国")

sogo_1901_2001  <- sogo_1901_2001 　%>% rename("year_month" = date)

g_sogo_apply <- sogo_1901_2001 %>% ggplot(aes(x = year_month, y =　sogo_apply)) +
 　geom_line(stat = "identity",  colour = "#1177CC") +
   geom_point(stat = "identity", colour = "#1177CC") +
   theme_minimal (base_family = font) +
   scale_x_date(breaks = "2 month", date_labels = "%Y-%m",
                limit=c(as.Date("2019-01-01"),as.Date("2020-08-01"))) +
   scale_y_continuous(limits = c(0, 200000),oob = rescale_none) +
   theme(axis.text.x = element_text(angle = 30, hjust = 1))+
   xlab("年月") +
   ylab("申請件数")+
   theme(legend.position="none")

ggplotly(g_sogo_apply)
# 統合用
g_sogo_apply <- g_sogo_apply + ggtitle("(c)総合支援資金の申請件数")　+
     theme(axis.text.x = element_text(angle=30, hjust=1))

3.4 総合支援資金の決定件数

sogo_1901_2001 <- df_long_num %>% select(prefec, date,sogo_number)

sogo_1901_2001 <- na.omit(sogo_1901_2001)
   
sogo_1901_2001 <- sogo_1901_2001 　 %>% filter(prefec == "全国")

sogo_1901_2001  <- sogo_1901_2001 　%>% rename("year_month" = date)

g_sogo_number  <- sogo_1901_2001 %>% ggplot(aes(x = year_month, y =sogo_number)) +
 　geom_line(stat = "identity",  colour = "#1177CC") +
   geom_point(stat = "identity", colour = "#1177CC") +
   theme_minimal (base_family = font) +
   scale_x_date(breaks = "2 month", date_labels = "%Y-%m") +
   scale_y_continuous(limits = c(0, 200000),oob = rescale_none) +
   theme(axis.text.x = element_text(angle=30, hjust=1))+
   xlab("年月") +
   ylab("決定件数")+
   theme(legend.position="none")

ggplotly(g_sogo_number)
g_sogo_number <- g_sogo_number  + ggtitle("(d)総合支援資金の決定件数")

3.5 緊急小口資金の決定金額

koguchi_1901_2008 <- df_long_num%>% select(prefec, date,koguchi_payment_amount)

koguchi_1901_2008 <- na.omit(koguchi_1901_2008)

koguchi_1901_2008 　 <-koguchi_1901_2008 　 %>% filter(prefec == "全国")

koguchi_1901_2008$koguchi_payment_amount <- koguchi_1901_2008$koguchi_payment_amount/100000

koguchi_1901_2008 <- koguchi_1901_2008　%>% rename("year_month" = date)

g_koguchi_amounts <- koguchi_1901_2008 %>% ggplot(aes(x = year_month, y = koguchi_payment_amount)) +
 　geom_line(stat = "identity",  colour = "#1177CC") +
   geom_point(stat = "identity", colour = "#1177CC") +
   theme_minimal (base_family = font) +
   scale_x_date(breaks = "2 month", date_labels = "%Y-%m") +
   theme(axis.text.x = element_text(angle = 30, hjust=1))+
   xlab("年月") +
   ylab("決定金額（億円）")+
   theme(legend.position="none")

ggplotly(g_koguchi_amounts)
g_koguchi_amounts <- g_koguchi_amounts + ggtitle("(a)緊急小口資金の決定金額") +
      theme(axis.text.x = element_text(angle=30, hjust=1))

3.6 総合支援資金の決定金額

sogo_1901_2001 <- df_long_num %>% select(prefec, date,sogo_payment_amount)

sogo_1901_2001 <- na.omit(sogo_1901_2001)

sogo_1901_2001 　 <-sogo_1901_2001 　 %>% filter(prefec == "全国")

sogo_1901_2001$sogo_payment_amount <- sogo_1901_2001$sogo_payment_amount/100000

sogo_1901_2001  <- sogo_1901_2001 　%>% rename("year_month" = date)

g_sogo_amounts <- sogo_1901_2001 %>% ggplot(aes(x = year_month, y = sogo_payment_amount)) +
 　geom_line(stat = "identity",  colour = "#1177CC") +
   geom_point(stat = "identity", colour = "#1177CC") +
   theme_minimal (base_family = font) +
   scale_x_date(breaks = "2 month", date_labels = "%Y-%m") +
   theme(axis.text.x = element_text(angle=30, hjust=1))+
   xlab("年月") +
   ylab("決定金額（億円）")+
   theme(legend.position="none")

ggplotly(g_sogo_amounts)
g_sogo_amounts <- g_sogo_amounts + ggtitle("(b)総合支援資金の決定金額") +
      theme(axis.text.x = element_text(angle=30, hjust=1))