grid.arrange(p1,p2,p3,p4,ncol=2) 2、facet_wrap() 先按分组变量生成多个子图,再按顺序排列 简便格式 ~ 分组变量1 + 分组变量2 facet_wrap( facets, nrow = NULL, ncol = NULL, scales = "fixed", shrink = TRUE, labeller = "label_value", as.table = TRUE, switch = NULL, drop = TRUE, ...
value = sample(1:100, 20), percent = rep(c(10, 30, 30, 20, 10), 4) ) df %>% group_by(status) %>% mutate(xmax = cumsum(percent), xmin = xmax - percent) %>% group_by(gene) %>% mutate(ytmp = value * 100 / sum(value), ymax = cumsum(ytmp), ymin = ymax - ytm...
4、绘图——添加分组并增加分组间隔 #调整柱子显示顺序 data_label = data_label %>% arrange(G, value) #设置分组间的空白间隔 data_label$G<-as.factor(data_label$G) number_empty_bar <- 3 to_add <- data.frame(matrix(NA, number_empty_bar*nlevels(as.factor(data_label$G)), ncol(data_labe...
#调整柱子显示顺序 data_label = data_label %>% arrange(G, value) #设置分组间的空白间隔 data_label$G<-as.factor(data_label$G) number_empty_bar <- 3 to_add <- data.frame(matrix(NA, number_empty_bar*nlevels(as.factor(data_label$G)), ncol(data_label)) ) colnames(to_add) <- coln...
在ggplot中,可以使用`geom_tile()`函数创建热图,并使用`scale_fill_gradient()`函数设置颜色映射。要根据列的值对热图进行排序,可以使用`arrange()`函数对...
df %>% mutate(name=factor(name, levels=rev(lev)),value=replace_na(value,0)) %>% arrange(year,name) %>% group_by(year) %>% mutate(val = cumsum(value)) %>% ungroup() %>% ggplot() + annotate(geom="segment",x=rep(2005.5,7), xend=rep(2021,7), y=seq(10000,70000,10000), ...
arrange(date) %>%# Arrange by date select(-na)# Remove na column d ## # A tibble: 813 x 5 ## date year week day hours ## <date> <chr> <dbl> <fct> <int> ## 1 2018-01-02 2018 2 周二 NA ## 2 2018-01-03 2018 2 周三 ...
x_order <- df %>%group_by(x_factor)%>%summarize(mean_y=mean(y_value))%>%ungroup()%>%arrange(desc(mean_y))%>%select(x_factor); df$x_factor<-factor(df$x_factor,levels=as.character(x_order$x_factor),ordered = TRUE) 参考文档: ...
WHO<-arrange(WHO, desc(D)) #将数据的名字转换为因子,并固定已拍好的country, #同理可以按照聚类的结果进行排列 WHO<-transform(WHO, Country=factor(Country, levels=unique(Country))) require(reshape2) require(ggplot2) require(scales) require(grid) ...
lmres <- gapminder %>%select(country, year, lifeExp) %>%group_by(country) %>%nest(data = c(year, lifeExp)) %>%mutate(lmr = map(data, ~ lm(lifeExp ~ year, data = .x))) %>%mutate(info = map(lmr, extract_lmr)) %>%select(-data, -lmr) %>%unnest(info) %>%arrange(...