{"id":1101,"date":"2019-04-08T00:56:55","date_gmt":"2019-04-07T15:56:55","guid":{"rendered":"http:\/\/141.164.34.82\/?p=1101"},"modified":"2019-04-08T01:00:02","modified_gmt":"2019-04-07T16:00:02","slug":"%eb%8c%80%ed%95%9c%eb%af%bc%ea%b5%ad-%ec%9d%b8%ea%b5%ac%ea%b5%ac%ec%a1%b0-%eb%b3%80%ed%99%94%eb%a5%bc-%eb%b3%b4%ec%97%ac%ec%a3%bc%eb%8a%94-%eb%8b%a4%eb%a5%b8-%ea%b7%b8%eb%a6%bc-ggridgesggplot2","status":"publish","type":"post","link":"http:\/\/ds.sumeun.org\/?p=1101","title":{"rendered":"\ub300\ud55c\ubbfc\uad6d \uc778\uad6c\uad6c\uc870 \ubcc0\ud654\ub97c \ubcf4\uc5ec\uc8fc\ub294 \ub2e4\ub978 \uadf8\ub9bc: `ggridges`(`ggplot2` \ud655\uc7a5)"},"content":{"rendered":"

\"\ucd9c\ucc98<\/p>\n

ggplot2<\/code> \ud655\uc7a5 : ggridges<\/code><\/h1>\n

ggridges<\/code> \ud328\ud0a4\uc9c0\ub294 \uacf5\uc2dd\uc0ac\uc774\ud2b8<\/a>\uc5d0\uc11c \uc124\uba85\ud558\uace0 \uc788\ub4ef\uc774 \ubd80\ubd84\uc801\uc73c\ub85c \uacb9\uccd0\uc9c0\ub294 \uc120\uadf8\ub9bc\uc744 \ud1b5\ud574 \uc2dc\uac04 \ub610\ub294 \uacf5\uac04\uc5d0 \ub530\ub77c \ubcc0\ud558\ub294 \ubd84\ud3ec\ub97c \uc2dc\uac01\ud654<\/strong>\ud558\ub294\ub370 \uc720\uc6a9\ud558\ub2e4.<\/p>\n

\uc608\ub97c \ub4e4\uc5b4 \uc704\uc758 \uadf8\ub9bc\uc740 \uc6d4\ubcc4 \uae30\uc628\uc758 \ubd84\ud3ec\ub97c \uc2dc\uac01\ud654\ud558\uace0 \uc788\ub2e4.<\/p>\n

\uc124\uce58<\/h2>\n
install.packages('ggridges')\r\n<\/code><\/pre>\n
\ubd88\ub7ec\ub4e4\uc774\uae30<\/h2>\n
library(ggplot2)\r\nlibrary(ggridges)\r\n<\/code><\/pre>\n
geom_ridgeline<\/code><\/h2>\n
geom_ridgline<\/code>\uc758 \uc8fc\uc694 \uc2dc\uac01\uc801 \ud2b9\uc9c8\uc740 x<\/code>, y<\/code>, height<\/code>, group<\/code>, fill<\/code> \ub4f1\uc774 \uc788\ub2e4.<\/p>\n\n\n\n\n\n\n\n\n\n
\uc2dc\uac01\uc801 \ud2b9\uc9c8<\/th>\n\uc758\ubbf8<\/th>\n<\/tr>\n<\/thead>\n
x<\/code><\/td>\n\\(x\\)-\uc88c\ud45c<\/td>\n<\/tr>\n
y<\/code><\/td>\n\\(y\\)-\uc88c\ud45c : \uc8fc\ub85c \uc2dc\uac04\uc758 \ubcc0\ud654 \ub610\ub294 \uacf5\uac04\uc758 \ubcc0\ud654\uc640 \ub300\uc751\ub41c\ub2e4<\/td>\n<\/tr>\n
height<\/code><\/td>\n\ub2a5\uc120\uc774 \uadf8\ub824\uc9c0\ub294 \ub192\uc774<\/td>\n<\/tr>\n
group<\/code><\/td>\n\ud55c \ub2a5\uc120\uc5d0 \ud574\ub2f9\ud558\ub294 \uc9d1\ub2e8<\/td>\n<\/tr>\n
fill<\/code><\/td>\n\ud55c \ub2a5\uc120\uc758 \uc544\ub7ab \ubd80\ubd84 \ucc44\uc6c0 \uc0c9\uae54<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\ud65c\uc6a9 \uc608: \uc6b0\ub9ac\ub098\ub77c \uc778\uad6c\uad6c\uc870\uc758 \ubcc0\ud654<\/h2>\n
\ub610\ub2e4\ub978 ggplot2<\/code> \ud655\uc7a5\uc778 gganimate<\/code>\uc5d0\uc11c \uc0ac\uc6a9\ud588\ub358 \ub370\uc774\ud130\ub97c ggridges<\/code>\ub97c \uc0ac\uc6a9\ud574\uc11c \uc2dc\uac01\ud654\ubcf4\uc558\ub2e4.<\/p>\n
population.csv<\/code><\/a><\/p>\n
library(dplyr)\r\nlibrary(tidyr)\r\n\r\ndatPop <- data.table::fread('population.csv', encoding='UTF-8')\r\ndatPopAni <- \r\n  datPop %>% \r\n  mutate(year=as.integer(year)) %>% \r\n  gather(key='gender', value='population', male, female)\r\n<\/code><\/pre>\n
\uccab \ubc88\uc9f8 \uadf8\ub9bc<\/h3>\n
ggplot(data=datPopAni, \r\n       aes(x=age2,\r\n           y=year, \r\n           height=population, \r\n           fill=gender, \r\n           group=paste0(year, gender))) + \r\n  geom_ridgeline() \r\n<\/code><\/pre>\n
\"plot<\/p>\n
\ubb54\uac00 \ub108\ubb34 \ubcf5\uc7a1\ud558\ub2e4!<\/p>\n
\ub450 \ubc88\uc9f8 \uadf8\ub9bc<\/h2>\n
\uadf8\ub9bc\uc744 \uc880 \ub354 \ubcf4\uae30 \uc2f6\uac8c \ub9cc\ub4e4\uae30 \uc704\ud574\uc11c \ub370\uc774\ud130\ub97c \uc801\uc808\ud558\uac8c \uac00\uacf5\ud560 \ud544\uc694\uac00 \uc788\ub2e4.<\/p>\n
\uba3c\uc800 \ub108\ubb34 \ub9ce\uc740 \uc5f0\ub3c4\uac00 \uc788\uace0, \\(y\\)-\ucd95 year<\/code>\ub97c \ubcf4\ub2c8 \uc2a4\ucf00\uc77c\uc774 \uc801\uc808\ud558\uc9c0 \uc54a\uc544 \ubcf4\uc778\ub2e4. \uadf8\ub9ac\uace0 \ub2a5\uc120\uc73c\ub85c \uadf8\ub824\uc9c0\ub294 \ub2a5\uc120\ub3c4 \uc9c0\ub098\uce58\uac8c \ubcf5\uc7a1\ud574 \ubcf4\uc778\ub2e4.<\/p>\n
\uba3c\uc800 \ub098\uc774\ub97c 10\uc0b4 \ub2e8\uc704\ub85c \ub04a\uc5b4\uc11c 10\ub300, 20\ub300, 30\ub300 \ub4f1\uc73c\ub85c \ub2e8\uc21c\ud654\ud588\uc73c\uba70,<\/p>\n
datRidge <- \r\n  datPopAni %>% mutate(ageGroup = (age2 %\/% 10)*10) %>% group_by(ageGroup, gender, year) %>% \r\n  summarise(population = sum(population)) \r\n<\/code><\/pre>\n
\uc5f0\ub3c4\ub3c4 10\ub144 \ub2e8\uc704\ub85c \ub04a\uc5b4\uc11c 1960, 1970, 1980, 1990, 2000, 2010, 2020\ub144\ub9cc \uc2dc\uac01\ud654\ud558\uae30\ub85c \ud588\ub2e4.<\/p>\n
datRidge2 <- datRidge %>% filter(year %% 10 ==0)\r\n<\/code><\/pre>\n
\uadf8\ub9ac\uace0 \uc778\uad6c\uc218\ub3c4 year<\/code>\uc640 \ube44\uc2b7\ud55c \uc2a4\ucf00\uc77c\uc774 \ub418\ub3c4\ub85d 200000\uc73c\ub85c \ub098\ub234\ub2e4. \uadf8 \uacb0\uacfc\ub294 \ub2e4\uc74c\uacfc \uac19\ub2e4.<\/p>\n
ggplot(data=datRidge2, \r\n       aes(x=ageGroup,\r\n           y=year, \r\n           height=population\/200000, \r\n           fill=gender, \r\n           group=paste0(year, gender))) + \r\n  geom_ridgeline() \r\n<\/code><\/pre>\n
\"plot<\/p>\n
\uc138 \ubc88\uc9f8 \uadf8\ub9bc<\/h1>\n
\ub9ce\uc774 \ub098\uc544\uc84c\uc9c0\ub9cc \uc55e\ucabd \ub2a5\uc120\ub54c\ubb38\uc5d0 \ub4a4\ucabd \ub2a5\uc120\uc774 \uc644\uc804\ud788 \uac00\ub824\uc9c4\ub2e4\ub294 \ubb38\uc81c\uac00 \uc788\ub2e4. alpha<\/code>\ub97c \ud1b5\ud574 \ub2a5\uc120\uc744 \uc57d\uac04 \ud22c\uba85\ud558\uac8c \uc870\uc815\ud558\uace0, \uc880 \ub354 \uad75\uc740 \uc120\uc744 \uc0ac\uc6a9\ud55c\ub2e4. \uadf8\ub9ac\uace0 year<\/code>\ub97c \uc62c\ub9bc\uc21c\uacfc \ub0b4\ub9bc\uc21c\uc73c\ub85c \ubaa8\ub450 \uc2dc\uac01\ud654\ubcf4\uc558\ub2e4.<\/p>\n
p1 <- ggplot(data=datRidge2, \r\n       aes(x=ageGroup+5,\r\n           y=year, \r\n           height=population\/200000, \r\n           fill=gender, \r\n           group=paste0(year, gender))) + \r\n  geom_ridgeline(alpha=0.4, size=2) +\r\n  scale_x_continuous(breaks=seq(0,100,10), name='age Group') +\r\n  scale_y_reverse(breaks=seq(1960, 2020, 10)) +\r\n  guides(fill='none')\r\nprint(p1)\r\n<\/code><\/pre>\n
\"plot<\/p>\n
p2 <- ggplot(data=datRidge2, \r\n       aes(x=ageGroup+5, \r\n           y=year, \r\n           height=population\/200000, \r\n           fill=gender, \r\n           group=paste0(year, gender))) + \r\n  geom_ridgeline(alpha=0.4, size=2) +\r\n  scale_x_continuous(breaks=seq(0,100,10), name='ageGroup') +\r\n  scale_y_continuous(breaks=seq(1960, 2020, 10), name='') +\r\n  guides(fill='none')\r\nprint(p2)\r\n<\/code><\/pre>\n
\"plot<\/p>\n
\ub450 \uadf8\ub9bc\uc744 \ud55c \uadf8\ub9bc\uc73c\ub85c<\/h3>\n
gridExtra::grid.arrange(p1,p2,ncol=2) \r\n<\/code><\/pre>\n
\"plot<\/p>\n
\ub9cc\uc57d \ud654\uc77c\ub85c \uc800\uc7a5\ud558\uace0 \uc2f6\ub2e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ud55c\ub2e4.<\/p>\n
g <- gridExtra::arrangeGrob(p1, p2, ncol=2)\r\nggsave(file='popCompare.png', g) #https:\/\/stackoverflow.com\/questions\/17059099\/saving-grid-arrange-plot-to-file\r\n<\/code><\/pre>\n
## Saving 7 x 7 in image\r\n<\/pre>\n
\ubcf4\ub108\uc2a4 : ggridges<\/code> + gganimate<\/code><\/h2>\n
\"\"<\/p>\n","protected":false},"excerpt":{"rendered":"
ggplot2 \ud655\uc7a5 : ggridges ggridges \ud328\ud0a4\uc9c0\ub294 \uacf5\uc2dd\uc0ac\uc774\ud2b8\uc5d0\uc11c \uc124\uba85\ud558\uace0 \uc788\ub4ef\uc774 \ubd80\ubd84\uc801\uc73c\ub85c \uacb9\uccd0\uc9c0\ub294 \uc120\uadf8\ub9bc\uc744 \ud1b5\ud574 \uc2dc\uac04 \ub610\ub294 \uacf5\uac04\uc5d0 \ub530\ub77c \ubcc0\ud558\ub294 \ubd84\ud3ec\ub97c \uc2dc\uac01\ud654\ud558\ub294\ub370 \uc720\uc6a9\ud558\ub2e4. \uc608\ub97c \ub4e4\uc5b4 \uc704\uc758 \uadf8\ub9bc\uc740 \uc6d4\ubcc4 \uae30\uc628\uc758 \ubd84\ud3ec\ub97c \uc2dc\uac01\ud654\ud558\uace0 \uc788\ub2e4. \uc124\uce58 install.packages(‘ggridges’) \ubd88\ub7ec\ub4e4\uc774\uae30 library(ggplot2) library(ggridges) geom_ridgeline geom_ridgline\uc758 \uc8fc\uc694 \uc2dc\uac01\uc801 \ud2b9\uc9c8\uc740 x, y, height, group, fill \ub4f1\uc774 \uc788\ub2e4. \uc2dc\uac01\uc801 \ud2b9\uc9c8 \uc758\ubbf8 x \\(x\\)-\uc88c\ud45c y \\(y\\)-\uc88c\ud45c : […]<\/p>\n","protected":false},"author":1,"featured_media":1105,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[28,165],"tags":[188,231,230,229],"jetpack_featured_media_url":"http:\/\/ds.sumeun.org\/wp-content\/uploads\/2019\/04\/popCompare.png","_links":{"self":[{"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=\/wp\/v2\/posts\/1101"}],"collection":[{"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1101"}],"version-history":[{"count":4,"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=\/wp\/v2\/posts\/1101\/revisions"}],"predecessor-version":[{"id":1106,"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=\/wp\/v2\/posts\/1101\/revisions\/1106"}],"wp:featuredmedia":[{"embeddable":true,"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=\/wp\/v2\/media\/1105"}],"wp:attachment":[{"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1101"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1101"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1101"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}