按其他因素计算每个因子分组的数量

Question

我知道这个问题的答案很简单，但我已经广泛搜索了论坛，我一直无法找到解决方案。

我有一个名为Data_source的列，这是我想要将变量分组的因素。

我有一系列symptom*变量，我想根据Data_source计数。

出于某种原因，我无法弄清楚如何做到这一点。正常的group_by函数似乎不能正常工作。

这是有问题的数据框架

 df <- wrapr::build_frame(
   "Data_source"  , "Sex"   , "symptoms_decLOC", "symptoms_nausea_vomitting" |
     "1"          , "Female", NA_character_    , NA_character_               |
     "1"          , "Female", NA_character_    , NA_character_               |
     "1"          , "Female", "No"             , NA_character_               |
     "1"          , "Female", "Yes"            , "No"                        |
     "1"          , "Female", "Yes"            , "No"                        |
     "1"          , "Female", "Yes"            , "No"                        |
     "1"          , "Male"  , "Yes"            , "No"                        |
     "1"          , "Female", "Yes"            , "No"                        |
     "2"          , "Female", NA_character_    , NA_character_               |
     "2"          , "Male"  , NA_character_    , NA_character_               |
     "2"          , "Male"  , NA_character_    , NA_character_               |
     "2"          , "Female", "Yes"            , "No"                        |
     "2"          , "Female", "Yes"            , "No"                        |
     "2"          , "Male"  , NA_character_    , NA_character_               |
     "2"          , "Male"  , NA_character_    , NA_character_               |
     "2"          , "Male"  , NA_character_    , NA_character_               |
     "2"          , "Female", NA_character_    , NA_character_               |
     "2"          , "Female", NA_character_    , NA_character_               |
     "2"          , "Male"  , NA_character_    , NA_character_               |
     "2"          , "Female", NA_character_    , NA_character_               )

请注意，性别和症状变量都是包含NA的因素。我尝试了以下方法

df %>% na.omit() %>% group_by(Data_source) %>% count("symptoms_decLOC")

哪个不起作用并且不是最佳的，因为我必须为每一列重复它。理想的是使用类似于lapply(df, count)的东西，但这并没有给我每组的描述。

编辑

在回答下面的问题时，我已经添加了预期的输出。我在excel中对此进行了编辑，为了清晰起见，对group_by进行了颜色编码。

请注意我如何分析每个可能的答案。当我使用dplyr运行时，这里是输出。

> df %>% na.omit() %>% group_by(Data_source) %>% count("symptoms_decLOC")
# A tibble: 2 x 3
# Groups:   Data_source [2]
  Data_source `"symptoms_decLOC"`     n
  <chr>       <chr>               <int>
1 1           symptoms_decLOC         5
2 2           symptoms_decLOC         2

Answer 1

大部分都是这样的：还没弄明白如何包括零计数组...据说添加.drop=FALSE takes care of this，但它不适合我（使用dplyr v.0.8.0.9001）。

library(dplyr)
library(tidyr)
(df
    %>% tidyr::gather(var,val,-Data_source)
    %>% count(Data_source,var,val, .drop=FALSE)
    %>% na.omit()
)

结果：

  Data_source var                       val        n
  <chr>       <chr>                     <chr>  <int>
1 1           Sex                       Female     7
2 1           Sex                       Male       1
3 1           symptoms_decLOC           No         1
4 1           symptoms_decLOC           Yes        5
5 1           symptoms_nausea_vomitting No         5
6 2           Sex                       Female     6
7 2           Sex                       Male       6
8 2           symptoms_decLOC           Yes        2
9 2           symptoms_nausea_vomitting No         2

Answer 2

使用@Ben Bolker的答案来获取每个组的计数，使用spread和gather包含零计数组。

dplyr

library(dplyr)
library(tidyr)

# Count number of occurences by Data_source 
df2 <- 
  df %>% 
  gather(variable, value, -Data_source) %>% 
  count(Data_source, variable, value, name = "counter") %>%
  na.omit() 

# For variable = "Sex", leave as is
# For everything else, in this case symptom* convert into factor to include zero count group
# Then spread with dataframe will NAs filled with 0, re-convert back to long to bind rows
bind_rows(df2 %>%
            filter(variable == "Sex"), 

          df2 %>%
            filter(variable != "Sex") %>%
            mutate(value = factor(value, levels = c("Yes", "No"))) %>%
            spread(key = value, value = counter, fill = 0) %>%
            gather(value, counter, -Data_source, -variable))  %>%

  arrange(Data_source, variable)

data.table

library(data.table)
dt <- data.table(df)

# Melt data by Data source
dt_melt <- melt(dt, id.vars = "Data_source", value.factor = FALSE, variable.factor = FALSE)

# Add counter, if NA then 0 else 1
dt_melt[, counter := 0]
dt_melt[!is.na(value), counter := 1]

# Sum number of occurrences
dt_count <- dt_melt[,list(counter = sum(counter)), by = c("Data_source", "variable", "value")]

# Split into two dt
dt2a <- dt_count[variable == "Sex", ]
dt2b <- dt_count[variable != "Sex" ,]

# only on symptoms variables
# Convert into factor variable
dt2b$value <- factor(dt2b$value, levels = c("Yes", "No"))
dt2b_dcast <- dcast(data = dt2b, formula = Data_source + variable ~ value, value.var = "counter", fill = 0, drop = FALSE)
dt2b_melt <- melt(dt2b_dcast, id.vars = c("Data_source", "variable"), variable.name = "value", value.name = "counter") 

# combine
combined_d <- rbind(dt2a, dt2b_melt)
combined_d[order(Data_source, variable), ]

Answer 3

我不太明白你在问什么，但我想你要计算每个qazxsw poi列中非NA值的数量。

这是一个symptom_*解决方案：

data.table

代码的每个部分正在做什么：

# load library

library(data.table)

# Suppose the table is called "dt". Convert it to a data.table:

setDT(dt)

# convert the wide table to a long one, filter the values that
# aren't NA and count both, by Data_source and by variable
# (variable is the created column with the symptom_* names)

melt(dt, id.vars = 1:2)[!is.na(value), 
                        .N, 
                         by = .(Data_source, variable)]

将melt(dt, id.vars = 1:2)从wide变为long，并将第1列和第2列（Data_source和dt）保持为固定状态。

sex过滤了不是!is.na(value)的值（以前在每个symptom_*标题下）。

NA计算行数。

.N是我们用来计算的分组。 by = .(Data_source, variable)是variable在重塑期间降落的列的名称。