使用dplyr匯總邏輯數據幀
[英]summarise logical dataframe with dplyr
問題描述
我試圖用兩個變量來總結一個數據幀 - 我基本上想要用變量2來分解變量1，以便將結果繪制在100％堆積條形圖中。
我有多個邏輯類型的列，可以分為兩個主要類別，用於創建細分。
我曾嘗試使用gather從dplyr到數據幀轉換為長篇，但輸出是不是我所期望的。
topics_by_variable <- function (dataset, variable_1, variable_2) {
#select variables columns
variable_1_columns <- dataset[, data.table::`%like%`(names(dataset), variable_1)]
variable_2_columns <- dataset[, data.table::`%like%`(names(dataset), variable_2)]
#create new dataframe including only relevant columns
df <- cbind(variable_1_columns, variable_2_columns)
#transform df to long form
new_df <- tidyr::gather(df, variable_2, count, names(variable_2_columns[1]):names(variable_2_columns)[length(names(variable_2_columns))], factor_key=FALSE)
#count topics
topic_count <- function (x) {
t <- sum(x == TRUE)
}
#group by variable 2 and count
new_df <- new_df %>%
dplyr::group_by(variable_2) %>%
dplyr::summarise_at(topic_names, .funs = topic_count)
#transform new_df to longform
final_df <- tidyr::gather(new_df, topic, volume, names(variable_1_columns[1]):names(variable_1_columns)[length(names(variable_1_columns))], factor_key=FALSE)
final_df <- data.frame(final_df)
這是我正在使用的數據集：
structure(list(topic_su = c("TRUE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "TRUE", "TRUE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE"), topic_so = c("FALSE",
"FALSE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE",
"TRUE", "TRUE", "FALSE", "FALSE", "FALSE", "FALSE"), topic_cl = c("FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE"
), topic_in = c("FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "TRUE", "TRUE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE"), topic_qu = c("FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE"), topic_re = c("FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE"), brands_ne = c("TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE"
), brands_st = c("FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE"), brands_co = c("FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE"
), brands_seg = c("FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE"), brands_sen = c("TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE",
"TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE"), brands_ta = c("FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "TRUE"), brands_tc = c("FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", "FALSE",
"FALSE", "FALSE")), class = "data.frame", row.names = c(NA, -39L
))
所需的輸出如下，但是當我使用聚集時，體積數字是總行數，並在所有品牌中重復。
variable_2 topic volume
<chr> <chr> <int>
1 brands_co topic_su 10
2 brands_ne topic_su 17
3 brands_seg topic_su 10
4 brands_sen topic_su 18
5 brands_st topic_su 0
6 brands_ta topic_su 1
7 brands_tc topic_su 0
8 brands_co topic_so 22
9 brands_ne topic_so 17
10 brands_seg topic_so 11
11 brands_sen topic_so 23
12 brands_st topic_so 0
13 brands_ta topic_so 0
14 brands_tc topic_so 0
2 個解決方案
解決方案1
3 已采納 2019-06-24 12:18:46
假設您的數據集是dt您可以執行以下操作：
library(dplyr)
expand.grid(brand = names(dt)[grepl("brands", names(dt))],
topic = names(dt)[grepl("topic", names(dt))],
stringsAsFactors = F) %>%
rowwise() %>%
mutate(volume = sum(dt[brand] == "TRUE" & dt[topic] == "TRUE")) %>%
ungroup()
# # A tibble: 42 x 3
# brand topic volume
# <chr> <chr> <int>
# 1 brands_ne topic_su 17
# 2 brands_st topic_su 0
# 3 brands_co topic_su 10
# 4 brands_seg topic_su 10
# 5 brands_sen topic_su 18
# 6 brands_ta topic_su 1
# 7 brands_tc topic_su 0
# 8 brands_ne topic_so 17
# 9 brands_st topic_so 0
#10 brands_co topic_so 22
# # ... with 32 more rows
該過程執行以下操作：
您將獲得與“品牌”和“主題”匹配的所有列名稱（來自原始數據集），並在它們之間創建所有可能的組合。
對於每個組合，您將獲得原始數據集的相應列，並計算它們都為TRUE的次數。
另一種方法是使用矢量化函數而不是行rowwise ，這可能更快：
# vectorised function
GetVolume = function(x,y) sum(dt[x] == "TRUE" & dt[y] == "TRUE")
GetVolume = Vectorize(GetVolume)
expand.grid(brand = names(dt)[grepl("brands", names(dt))],
topic = names(dt)[grepl("topic", names(dt))],
stringsAsFactors = F) %>%
mutate(volume = GetVolume(brand, topic))
解決方案2
0 2019-06-24 12:27:53
另一個整體解決方案：
library(tidyverse)
## data
head(df)
#> topic_su topic_so topic_cl topic_in topic_qu topic_re brands_ne
#> 1 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
#> 2 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
#> 3 TRUE TRUE FALSE FALSE FALSE FALSE TRUE
#> 4 TRUE TRUE FALSE FALSE FALSE FALSE TRUE
#> 5 TRUE TRUE FALSE FALSE FALSE FALSE TRUE
#> 6 TRUE TRUE FALSE FALSE FALSE FALSE TRUE
#> brands_st brands_co brands_seg brands_sen brands_ta brands_tc
#> 1 FALSE FALSE FALSE TRUE FALSE FALSE
#> 2 FALSE FALSE FALSE TRUE FALSE FALSE
#> 3 FALSE FALSE FALSE TRUE FALSE FALSE
#> 4 FALSE FALSE FALSE TRUE FALSE FALSE
#> 5 FALSE FALSE FALSE TRUE FALSE FALSE
#> 6 FALSE FALSE FALSE TRUE FALSE FALSE
mutate_all(df, as.logical) %>%
gather(key = "topic", value = "topic_value", starts_with("topic")) %>%
gather(key = "variable_2", value = "variable_2_value", -starts_with("topic")) %>%
group_by(topic, variable_2) %>%
summarize(volume = sum(topic_value & variable_2_value))
#> # A tibble: 42 x 3
#> # Groups: topic [6]
#> topic variable_2 volume
#> <chr> <chr> <int>
#> 1 topic_cl brands_co 22
#> 2 topic_cl brands_ne 16
#> 3 topic_cl brands_seg 15
#> 4 topic_cl brands_sen 15
#> 5 topic_cl brands_st 0
#> 6 topic_cl brands_ta 1
#> 7 topic_cl brands_tc 0
#> 8 topic_in brands_co 23
#> 9 topic_in brands_ne 16
#> 10 topic_in brands_seg 15
#> # … with 32 more rows
由reprex包創建於2019-06-24（v0.3.0）
R function 中的 dplyr::summarise() 因“參數不是數字或邏輯”錯誤而失敗
[英]dplyr::summarise() in an R function fails with “argument not numeric or logical” error
用 dplyr 中的匯總創建的平均值減去一個數據幀中的值
[英]subtract the values in one dataframe by the mean created by summarise in dplyr
dplyr summary ：按循環中的多個變量分組並將結果添加到同一數據框中
[英]dplyr summarise : Group by multiple variables in a loop and add results in the same dataframe
如何在 dplyr 和 R 中匯總和子集多級分組 dataframe
[英]How to summarise and subset multi-level grouped dataframe in dplyr and R
使用多個不同的group_by變量（dplyr）來匯總數據幀
[英]Using multiple different group_by variables (dplyr) to summarise a dataframe
dplyr通過將摘要函數應用於另一個數據框來計算新列
[英]dplyr calculate a new column by applying summarise function on another dataframe
dplyr（）分組並獲取計數-錯誤消息評估錯誤：“ summarise_”的適用方法不適用於“邏輯”類的對象
[英]dplyr() grouping and getting counts - error message Evaluation error: no applicable method for 'summarise_' applied to an object of class “logical”
聲明:本站的技術帖子網頁，遵循CC BY-SA 4.0協議，如果您需要轉載，請注明本站網址或者原文地址。任何問題請咨詢:yoyou2525@163.com.
相關問題
dplyr總結邏輯條件
使用匯總（dplyr）的結果來改變原始數據幀
R function 中的 dplyr::summarise() 因“參數不是數字或邏輯”錯誤而失敗
用 dplyr 中的匯總創建的平均值減去一個數據幀中的值
dplyr summary ：按循環中的多個變量分組並將結果添加到同一數據框中
如何在 dplyr 和 R 中匯總和子集多級分組 dataframe
使用多個不同的group_by變量（dplyr）來匯總數據幀
dplyr通過將摘要函數應用於另一個數據框來計算新列
dplyr（）分組並獲取計數-錯誤消息評估錯誤：“ summarise_”的適用方法不適用於“邏輯”類的對象
總結 if 條件中的邏輯語句
相關標簽