如何使用 R 调用 Dataframe 并在函数中查找 nrow？答案

【问题标题】：How to call a Dataframe and find the nrow within a function using R?如何使用 R 调用 Dataframe 并在函数中查找 nrow？
【发布时间】：2019-03-05 17:46:10
【问题描述】：

我的问题有点棘手。我有一个向量如下

vec <-c("Camera","Battery","Protection")

我有如下数据框 Camera_pos # 一个包含一些列的数据框（我们可以忽略这里的细节）。同样明智的是，我们还有其他数据框，例如 Camera_neg、Battery_pos、Battery_neg、Protection_pos、Protection_neg

所以我有 6 个数据框，其中包含一些观察结果，而这些细节与问题无关。

我正在尝试构建一个从向量和数据帧中提取数据/值的新数据帧。

df <- data.frame(Features = character(),Positive = numeric(), Negative = numeric()) # empty data frame
for(i in 1:length(vec)){
 df$Features[i] = vec[i] # Camera in case of vec[1]
 df$Positive[i] = nrow() # not sure what code to write here, but this code should call the nrow() of Camera_pos ( i =1 is considered here)
df$Negative[i] = nrow() # not sure what code to write here, but this code should call the nrow() of Camera_neg
}

代码应该有点像nrow(vec[i]_pos) 即nrow(Camera_pos) 在 i =1 的情况下。请求您对此提供帮助

P.S : 同样，该函数也应该能够调用其他向量中的元素，因此 df 填充了 3 行和 3 列

输出应该如下所示

Features        Positive         Negative
Camera          3                3
Battery         3                3
Protection      3                3

【问题讨论】：

你好@Arun你能提供一个可重现的例子吗？我推荐你去this问题了解细节？如果我们有数据框可以玩会更容易
嗨，德里克，感谢您的回复。我已经用更多细节编辑了这个问题。如果您需要更多详细信息，请回复。谢谢

标签： r function dataframe data-mining

【解决方案1】：

这是一种方法：

#This would name all the files you have in your working directory
files <- ls()

library(stringr)

df <- data.frame(Features = rep(NA, length(vec)),Positive = rep(NA, length(vec)), Negative = rep(NA, length(vec))) # empty data frame

for(i in 1:length(vec)){
  df$Features[i] = vec[i] # Camera in case of vec[1]
  #Get a temp with only the name of vec[i] of your data.frame
  temp <- files[str_detect(files, vec[i])]
  df$Positive[i] = nrow(get(temp[str_detect(temp, "pos")])) # not sure what code to write here, but this code should call the nrow() of Camera_pos ( i =1 is considered here)
  df$Negative[i] = nrow(get(temp[str_detect(temp, "neg")])) # not sure what code to write here, but this code should call the nrow() of Camera_neg
}

如果你有不明白的地方，我可以更详细地解释

【讨论】：

【解决方案2】：

这是tidyverse 方法

Camera_pos <- data.frame(Text = c("text1","text2","text3"), Score = c(1.45,6.78,6.879))
Camera_neg <- data.frame(Text = c("text1","text2","text3"), Score = c(-0.5,-1.8,-1.4))
Battery_pos <- data.frame(Text = c("text1","text2","text3"), Score = c(0.5,1.8,1.4))
Battery_neg <- data.frame(Text = c("text1","text2","text3"), Score = c(-0.5,-1.8,-1.4))
Protection_pos <- data.frame(Text = c("text1","text2","text3"), Score = c(0.5,1.8,1.4))
Protection_neg <- data.frame(Text = c("text1","text2","text3"), Score = c(-0.5,-1.8,-1.4))

vec <-c("Camera","Battery","Protection")

library(tidyverse)

# get all your environment objetcs
obj_names = ls()

# function the returns the names of your workspace objects that match a pattern
f = function(x) data.frame(x, obj_names = obj_names[grepl(x, obj_names)], stringsAsFactors = F)

map_df(vec, ~f(.x)) %>%                       # apply the function to each pattern
  mutate(d = map(obj_names, ~get(.x))) %>%    # get the datasets
  unnest() %>%                                # unnest data
  mutate(type = ifelse(Score > 0, "Positive", "Negative")) %>%  # get the type of each score
  count(x, type) %>%                          # count combinations
  spread(type, n)                             # reshape

# # A tibble: 3 x 3
#   x          Negative Positive
#   <chr>         <int>    <int>
# 1 Battery           3        3
# 2 Camera            3        3
# 3 Protection        3        3

【讨论】：