如何动态地进行集群明智的汇总统计
how to do the cluster wise summary statistics dynamically
假设我将此数据的簇映射到每一行，如下所示：
head(movies.imp)
## num_voted_users num_user_for_reviews num_critic_for_reviews
## 1 886204 3054 723
## 2 471220 1238 302
## 3 275868 994 602
## 4 1144337 2701 813
## 5 8 127 37
## 6 212204 738 462
## movie_facebook_likes cast_total_facebook_likes cluster
## 1 33000 4834 1
## 2 0 48350 1
## 3 85000 11700 1
## 4 164000 106759 1
## 5 0 143 2
## 6 24000 1873 1
为了获得平均集群，我这样做：
a<-as.data.frame(aggregate( .~ cluster, FUN=mean, data=movies.imp))
a
## cluster num_voted_users num_user_for_reviews num_critic_for_reviews
## 1 1 316561.46 831.60526 326.61773
## 2 2 44934.26 180.05922 109.69265
## 3 3 29020.10 80.20408 28.57143
## movie_facebook_likes cast_total_facebook_likes
## 1 33647.263 28282.450
## 2 3119.099 6641.746
## 3 6843.327 2426.755
然后我转换成long:
library(tidyr)
long_df.a <- gather(data=a, value = mean, key=variablenames, num_voted_users,num_user_for_reviews,num_critic_for_reviews,movie_facebook_likes,cast_total_facebook_likes)
long_df.a
long_df.a[,1]<-as.factor(long_df.a[,1]) # converting into a factor
long_df.a[,2]<-as.factor(long_df.a[,2])# converting into a factor
然后我做同样的过程来获得中值、最小值、最大值和标准簇
为以上所有描述性统计计算创建一个数据框
dflong<-cbind(long_df.a,long_df.b[,3],long_df.c[,3],long_df.d[,3],long_df.e[,3])
dflong<-dflong%>% set_names(c("cluster","variablenames","mean","median","min","max","sd")) # renaming the columns
head(dflong)
## cluster variablenames mean median min max
## 1 1 num_voted_users 316561.45706 263332.5 246 1689764
## 2 2 num_voted_users 44934.26451 25256.5 5 469561
## 3 3 num_voted_users 29020.10204 9277.0 15 213483
## 4 1 num_user_for_reviews 831.60526 642.0 1 5060
## 5 2 num_user_for_reviews 180.05922 129.0 1 1690
## 6 3 num_user_for_reviews 80.20408 42.0 1 394
## sd
## 1 231350.9509
## 2 53613.7994
## 3 48491.2638
## 4 659.0410
## 5 181.3630
## 6 105.1669
按集群对数据进行子集化
dflong.1<-dflong %>% filter(cluster==1)
dflong.2<-dflong %>% filter(cluster==2)
dflong.3<-dflong %>% filter(cluster==3)
dflong.combined<-rbind(dflong.1,dflong.2,dflong.3)
head(dflong.combined) # final required output
## cluster variablenames mean median min max
## 1 1 num_voted_users 316561.45706 263332.5 246 1689764
## 2 1 num_user_for_reviews 831.60526 642.0 1 5060
## 3 1 num_critic_for_reviews 326.61773 307.5 2 813
## 4 1 movie_facebook_likes 33647.26316 23000.0 0 349000
## 5 1 cast_total_facebook_likes 28282.45014 21095.0 44 656730
## 6 2 num_voted_users 44934.26451 25256.5 5 469561
## sd
## 1 231350.95086
## 2 659.04103
## 3 142.47953
## 4 37698.06583
## 5 37395.59205
## 6 53613.79942
所以我正在以一种非最佳的方式做事来获取集群汇总统计信息....需要有关如何对动态使用循环进行排序或应用函数以在较少的代码行中获得最终输出的帮助... ..
我以 mtcars 数据集为例。假设 cyl 变量等同于 cluster。您可以在一行代码中获取所有汇总统计信息：
d <- mtcars
s <- d %>% group_by(cyl) %>%
summarise_all(c("mean", "median", "min", "max", "sd"))
# cyl mpg_mean disp_mean hp_mean drat_mean wt_mean qsec_mean vs_mean
# <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 4 26.66364 105.1364 82.63636 4.070909 2.285727 19.13727 0.9090909
# 2 6 19.74286 183.3143 122.28571 3.585714 3.117143 17.97714 0.5714286
# 3 8 15.10000 353.1000 209.21429 3.229286 3.999214 16.77214 0.0000000
# # ... with 43 more variables: ...
我们只剩下重塑数据框以使其成为我们想要的形式：
s <- gather(s, key, value, -cyl)
s <- separate(s, key, c("variable", "stat"))
d.combined <- spread(s, key = stat, value = value)
# # A tibble: 30 × 7
# cyl variable max mean median min sd
# * <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 4 am 1.00 0.7272727 1.00 0.000 0.4670994
# 2 4 carb 2.00 1.5454545 2.00 1.000 0.5222330
# 3 4 disp 146.70 105.1363636 108.00 71.100 26.8715937
# 4 4 drat 4.93 4.0709091 4.08 3.690 0.3654711
# 5 4 gear 5.00 4.0909091 4.00 3.000 0.5393599
# 6 4 hp 113.00 82.6363636 91.00 52.000 20.9345300
# 7 4 mpg 33.90 26.6636364 26.00 21.400 4.5098277
# 8 4 qsec 22.90 19.1372727 18.90 16.700 1.6824452
# 9 4 vs 1.00 0.9090909 1.00 0.000 0.3015113
# 10 4 wt 3.19 2.2857273 2.20 1.513 0.5695637
# # ... with 20 more rows
假设我将此数据的簇映射到每一行，如下所示：
head(movies.imp)
## num_voted_users num_user_for_reviews num_critic_for_reviews
## 1 886204 3054 723
## 2 471220 1238 302
## 3 275868 994 602
## 4 1144337 2701 813
## 5 8 127 37
## 6 212204 738 462
## movie_facebook_likes cast_total_facebook_likes cluster
## 1 33000 4834 1
## 2 0 48350 1
## 3 85000 11700 1
## 4 164000 106759 1
## 5 0 143 2
## 6 24000 1873 1
为了获得平均集群，我这样做：
a<-as.data.frame(aggregate( .~ cluster, FUN=mean, data=movies.imp))
a
## cluster num_voted_users num_user_for_reviews num_critic_for_reviews
## 1 1 316561.46 831.60526 326.61773
## 2 2 44934.26 180.05922 109.69265
## 3 3 29020.10 80.20408 28.57143
## movie_facebook_likes cast_total_facebook_likes
## 1 33647.263 28282.450
## 2 3119.099 6641.746
## 3 6843.327 2426.755
然后我转换成long:
library(tidyr)
long_df.a <- gather(data=a, value = mean, key=variablenames, num_voted_users,num_user_for_reviews,num_critic_for_reviews,movie_facebook_likes,cast_total_facebook_likes)
long_df.a
long_df.a[,1]<-as.factor(long_df.a[,1]) # converting into a factor
long_df.a[,2]<-as.factor(long_df.a[,2])# converting into a factor
然后我做同样的过程来获得中值、最小值、最大值和标准簇
为以上所有描述性统计计算创建一个数据框
dflong<-cbind(long_df.a,long_df.b[,3],long_df.c[,3],long_df.d[,3],long_df.e[,3])
dflong<-dflong%>% set_names(c("cluster","variablenames","mean","median","min","max","sd")) # renaming the columns
head(dflong)
## cluster variablenames mean median min max
## 1 1 num_voted_users 316561.45706 263332.5 246 1689764
## 2 2 num_voted_users 44934.26451 25256.5 5 469561
## 3 3 num_voted_users 29020.10204 9277.0 15 213483
## 4 1 num_user_for_reviews 831.60526 642.0 1 5060
## 5 2 num_user_for_reviews 180.05922 129.0 1 1690
## 6 3 num_user_for_reviews 80.20408 42.0 1 394
## sd
## 1 231350.9509
## 2 53613.7994
## 3 48491.2638
## 4 659.0410
## 5 181.3630
## 6 105.1669
按集群对数据进行子集化
dflong.1<-dflong %>% filter(cluster==1)
dflong.2<-dflong %>% filter(cluster==2)
dflong.3<-dflong %>% filter(cluster==3)
dflong.combined<-rbind(dflong.1,dflong.2,dflong.3)
head(dflong.combined) # final required output
## cluster variablenames mean median min max
## 1 1 num_voted_users 316561.45706 263332.5 246 1689764
## 2 1 num_user_for_reviews 831.60526 642.0 1 5060
## 3 1 num_critic_for_reviews 326.61773 307.5 2 813
## 4 1 movie_facebook_likes 33647.26316 23000.0 0 349000
## 5 1 cast_total_facebook_likes 28282.45014 21095.0 44 656730
## 6 2 num_voted_users 44934.26451 25256.5 5 469561
## sd
## 1 231350.95086
## 2 659.04103
## 3 142.47953
## 4 37698.06583
## 5 37395.59205
## 6 53613.79942
所以我正在以一种非最佳的方式做事来获取集群汇总统计信息....需要有关如何对动态使用循环进行排序或应用函数以在较少的代码行中获得最终输出的帮助... ..
我以 mtcars 数据集为例。假设 cyl 变量等同于 cluster。您可以在一行代码中获取所有汇总统计信息：
d <- mtcars
s <- d %>% group_by(cyl) %>%
summarise_all(c("mean", "median", "min", "max", "sd"))
# cyl mpg_mean disp_mean hp_mean drat_mean wt_mean qsec_mean vs_mean
# <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 4 26.66364 105.1364 82.63636 4.070909 2.285727 19.13727 0.9090909
# 2 6 19.74286 183.3143 122.28571 3.585714 3.117143 17.97714 0.5714286
# 3 8 15.10000 353.1000 209.21429 3.229286 3.999214 16.77214 0.0000000
# # ... with 43 more variables: ...
我们只剩下重塑数据框以使其成为我们想要的形式：
s <- gather(s, key, value, -cyl)
s <- separate(s, key, c("variable", "stat"))
d.combined <- spread(s, key = stat, value = value)
# # A tibble: 30 × 7
# cyl variable max mean median min sd
# * <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 4 am 1.00 0.7272727 1.00 0.000 0.4670994
# 2 4 carb 2.00 1.5454545 2.00 1.000 0.5222330
# 3 4 disp 146.70 105.1363636 108.00 71.100 26.8715937
# 4 4 drat 4.93 4.0709091 4.08 3.690 0.3654711
# 5 4 gear 5.00 4.0909091 4.00 3.000 0.5393599
# 6 4 hp 113.00 82.6363636 91.00 52.000 20.9345300
# 7 4 mpg 33.90 26.6636364 26.00 21.400 4.5098277
# 8 4 qsec 22.90 19.1372727 18.90 16.700 1.6824452
# 9 4 vs 1.00 0.9090909 1.00 0.000 0.3015113
# 10 4 wt 3.19 2.2857273 2.20 1.513 0.5695637
# # ... with 20 more rows