r语言 - 如何将调查响应的数据框转换为频率表

Question

我有一个调查结果的R数据帧。每列都是对调查中一个问题的回答。它可以采用值 1 到 10 和 NA。我想把它变成一个频率表。

这是我拥有的数据的一个例子。我假装值从 1 到 3，而不是 1 到 10。

data.frame(
  "Person" = c(1,2,3),
  "Question1" = c(NA, "1", "1"),
  "Question2" = c("1", "2", "3")
)

我想要什么：

data.frame(
  "Question" = c("Question1", "Question2"),
  "Frequency of 1" = c(2, 1),
  "Frequency of 2" = c(0 , 1),
  "Frequency of 3" = c(0, 1)
)

我

尝试使用likert软件包中的likert((，但是我得到的分数结果不正确。这个问题有简单的解决方案吗？

Answer 1

这是一个使用 dplyr 和 purrr 包的解决方案

library(dplyr)
library(purrr)
data.frame(
  "Person" = c(1,2,3),
  "Question1" = c(NA, "1", "1"),
  "Question2" = c("1", "2", "3")
)
df %>% 
  select(-Person) %>% 
  mutate_all(~ factor(.x, levels =  as.character(1:10) ) %>% addNA() ) %>% 
  map(table) %>% 
  transpose() %>% 
  map(as.integer) %>% 
  set_names( ~ paste0("Frequency of ",ifelse(is.na(.), "NA", .))) %>% 
  as_tibble() %>% 
  mutate(Question = setdiff(names(df),"Person")) %>% 
  select(Question,everything(), "Frequency of NA" = `Frequency of ` ) 

Answer 2

data.table解决方案：

require(data.table)
setDT(df)    
# Melt data:
df <- melt(df, id.vars = "Person", value.name = "Question")
# Cast data to required structure:
df <- data.frame(dcast(df, variable ~ Question))
# Rename variables and remove NA count (as per Ops question):
names(df)[1] <- "Question"
names(df)[-1] <- gsub("X", "Frequency of ", names(df)[-1])
df$NA. <- NULL
df
#   Question Frequency of 1 Frequency of 2 Frequency of 3
#1 Question1              2              0              0
#2 Question2              1              1              1

---

或者一行回答：

dcast(melt(setDT(df), id.vars="Person", value.name="Question")[!Question %in% NA][, Question := paste0("Frequency of ", Question)], variable ~ Question)

Answer 3

另一种

tidyverse可能性可能是：

df %>%
 gather(Question, val, -Person, na.rm = TRUE) %>%
 group_by(Question, val) %>%
 summarise(res = length(val)) %>%
 ungroup() %>%
 mutate(val = paste0("Frequency.of.", val)) %>%
 spread(val, res, fill = NA)
  Question  Frequency.of.1 Frequency.of.2 Frequency.of.3
  <chr>              <int>          <int>          <int>
1 Question1              2             NA             NA
2 Question2              1              1              1

在这里，它首先将数据从宽格式转换为长格式。其次，它根据问题计算频率。最后，它创建"Frequency.of."变量并将数据返回到所需的形状。

或者，如果您还想计算每个问题的 NA 值：

df %>%
 gather(Question, val, -Person) %>%
 group_by(Question, val) %>%
 summarise(res = length(val)) %>%
 ungroup() %>%
 mutate(val = paste0("Frequency.of.", val)) %>%
 spread(val, res, fill = NA)
  Question  Frequency.of.1 Frequency.of.2 Frequency.of.3 Frequency.of.NA
  <chr>              <int>          <int>          <int>           <int>
1 Question1              2             NA             NA               1
2 Question2              1              1              1              NA

Answer 4

这不是最优雅的，但可能会有所帮助：df2 是你的数据集。数据：

   df2<-data.frame(
  "Person" = c(1,2,3),
  "Question1" = c(NA, "1", "1"),
  "Question2" = c("1", "2", "3"),stringsAsFactors = F
)

目标：编辑：：您可以按如下方式"自动化"

df2[is.na(df2)]<-0 #To allow numeric manipulation
values<-c("1","2","3")
    Final_df<-sapply(values,function(val) apply(df2[,-1],2,function(x) sum(x==val)))
    Final_df<-as.data.frame(Final_df)
    names(Final_df)<-paste0("Frequency of_",1:ncol(Final_df))

这会产生：

             Frequency of_1          Frequency of_2          Frequency of_3
Question1              2                0                    0
Question2              1                1                    1