library(dplyr) group_by(data, sex) %>% summarize_each(funs(mean), var1, var2, var3...)
calibration_df <- cv_pred_df %>% mutate(pass = if_else(truth == "pass", 1, 0), pred_rnd = round(prob.pass, 2) ) %>% group_by(pred_rnd) %>% summarize(mean_pred = mean(prob.pass), mean_obs = mean(pass), n = n() ) %>% mutate(group = case_when(n < 100 ~ "<10...
Q8: 根据数据集 iris的第五列拆分数据集后重复上面的Q2到Q7问题 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...
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 ...
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...
例如,我们可以通过使用dplyr库中的filter()函数进行筛选数据,使用group_by()函数进行分组,使用summarize()函数进行汇总,使用arrange()函数进行排序。 三、统计分析 R语言提供了丰富的统计分析库。例如,我们可以使用stats库中的t.test()函数进行双样本t检验,使用ANOVA()函数进行方差分析,使用cor()函数进行相关性分析等...
sample_size = data %>% group_by(name) %>% summarize(num=n()) # Plot data %>% left_join(sample_size) %>% mutate(myaxis = paste0(name, "\n", "n=", num)) %>% ggplot( aes(x=myaxis, y=value, fill=name)) + geom_violin(width=1.4) + ...
(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::...