library(dplyr) group_by(data, sex) %>% summarize_each(funs(mean), var1, var2, var3...)
在dplyr版本1.0.0之后，上面的summarize_all和summarize_at函数被summarize(across(...))取代，在summarize(across(...))中可以选择要操作的列（此处为val1:val2）。我们还可以在across中提供一个函数列表，并使用粘附规范设置列名（{.col}=原始列名，{.fn}=列表中的函数名）。有关across的更多信息，请参阅...
sum=sum(Sepal.Length)) #计算花萼总长度 sum 1 876.5 #还包含其他类似函数 summarise Summarise each group to fewer rows summarise_all Summarise multiple columns summarise_at Summarise multiple columns summarise_if Summarise multiple columns summarize Summarise each group to...
group_by(season) %>% summarize(rating_ave = mean(imdb_rating), sentiment_ave = mean(sentimentc)) %>% ggplot(data = ., aes(x = sentiment_ave, y = rating_ave, color = season)) + geom_point() # Descriptive fig of pos & neg characters glimpse(tidy.token.schrute) char.sentiment <-...
计算平均值、sd等：library(dplyr) B <- A %>% group_by(Treatment) %>% mutate(upper ...
group_by(cut,color) %>% summarize(avg_price=mean(price)) ggplot(df,aes(x=color,y=avg_price,fill=cut)) + # 设置填充 geom_bar(stat="identity",position="dodge") + # 设置为并列柱状图 (统计变换) geom_text(aes(label = round(avg_price),color = cut), # 新增标签和标签的颜色 ...
library(dplyr)datamean=group_by(iris[,c(1:4)],iris$Species)%>%summarize_each(funs(mean))library(dplyr)datamean=group_by(iris[,c(1:4)],iris$Species)%>%summarize_each(funs(mean))dataquantile=group_by(iris[,c(1:4)],iris$Species)%>%summarize_each(funs(quantile))datazhongshu=group_by...
(自建循环)——rlistjson处理:Rjson+RJSONIO——jsonlite数据抓取:RCurl+XML——httr+xml2循环任务:for/while——apply——plyr::a_ply——并行运算(foreach、parallel)切片索引:subset——dplyr::select+filter聚合运算:aggregate——plyr::ddply+mutate——dplyr::group_by+summarize数据联结:merge——plyr::...
)#gather()函数可以把多列数据合并成一列数据#添加分组信息df$group=rep(c("A","B","C"),each...
Summarize data by event type The final data is grouped by event type and the sum is computed for each damage kind. The two datasets are arranged in descending order of the corresponding numerical value, and only the top 10 event types are taken to present the results. Finally, the data is...