了解dplyr管道和汇总功能

如何解决了解dplyr管道和汇总功能

我正在寻找一些帮助，以帮助您理解使用dplyr的管道系统和功能汇总。我觉得我的编码有点冗长，可以简化。所以这里有两个问题，因为我知道我缺少一些概念，但是我不确定在哪里缺乏知识。我在底部包含了完整的代码。预先感谢，因为这个问题要大一些。

1a。根据下面的示例数据并使用dplyr，有没有一种方法可以在不使用中间表的情况下计算每个团队的比赛（日期）？

1b。我已经包含了计算n_games无效的原始方法。为什么？

set.seed(123)
shot_df_ex <- tibble(Team_Name = sample(LETTERS[1:5],250,replace = TRUE),Date = sample(as.Date(c("2019-08-01","2019-09-01","2018-08-01","2018-09-01","2017-08-01","2017-09-01")),size = 250,Type = sample(c("shot","goal"),replace = TRUE,prob = c(0.9,0.1))
)

# count shots per team per game(date)
n_shots_per_game <- shot_df_ex %>% 
  count(Team_Name,Date)

n_shots_per_game

# count games (dates) per team [ISSUES!!!]
# is there a way to do this piping from the shot_df_ex tibble instead of 
#  using an intermediate tibble?

# count number of games using the tibble created above [DOES NOT WORK--WHY?]
n_games <- n_shots_per_game %>% 
  count(Team_Name)

n_games #what is this counting? It should be 6 for each.

# this works,but isn't count() just a quicker way to run
#  group_by() %>% summarise()? 
n_games <- n_shots_per_game %>% 
  group_by(Team_Name) %>% 
  summarise(N_Games=n())

n_games

以下是我创建摘要表的过程。我知道管道是为了减少一些中间变量/表的创建。我在哪里可以结合以下步骤以最少的中间步骤创建最终表。

# load librarys ------------------------------------------------
library(tidyverse)

# build sample shot data ---------------------------------------
set.seed(123)
shot_df_ex <- tibble(Team_Name = sample(LETTERS[1:5],0.1))
)

# calculate data ----------------------------------------------
# since every row is a shot,the following function counts shots for ea. team
n_shots <- shot_df_ex %>% 
  count(Team_Name) %>% 
  rename(N_Shots = n)

n_shots

# do the same for goals for each team
n_goals <- shot_df_ex %>% 
  filter(Type == "goal") %>% 
  count(Team_Name,sort = T) %>% 
  rename(N_Goals = n) %>% 
  arrange(Team_Name)

n_goals

# count shots per team per game(date)
n_shots_per_game <- shot_df_ex %>% 
  count(Team_Name,Date)

n_shots_per_game

# count games (dates) per team [ISSUES!!!]
# is there a way to do this piping from the shot_df_ex tibble instead of 
#  using an intermediate tibble?

# count number of games using the tibble created above [DOES NOT WORK]
n_games <- n_shots_per_game %>% 
  count(Team_Name)

n_games #what is this counting? It should be 6 for each.

# this works,but isn't count() just a quicker way to run
#  group_by() %>% summarise()? 
n_games <- n_shots_per_game %>% 
  group_by(Team_Name) %>% 
  summarise(N_Games=n())

n_games

# combine data ------------------------------------------------
# combine columns and add average shots per game
shot_table_ex <- n_games %>% 
  left_join(n_shots) %>% 
  left_join(n_goals)

# final table with final average calculations
shot_table_ex <- shot_table_ex %>% 
  mutate(Shots_per_Game = round(N_Shots / N_Games,1),Goals_per_Game = round(N_Goals / N_Games,1)) %>% 
  arrange(Team_Name)

shot_table_ex

解决方法

对于1a，您可以直接从tibble（）函数直接传递到count（）。即。

tibble(Team_Name = sample(LETTERS[1:5],250,replace = TRUE),Date = sample(as.Date(c("2019-08-01","2019-09-01","2018-08-01","2018-09-01","2017-08-01","2017-09-01")),size = 250,Type = sample(c("shot","goal"),replace = TRUE,prob = c(0.9,0.1))) %>%
count(Team_Name,Date)

在1b中，count（）使用您的列n（即射门次数）作为权重变量，因此将每个团队的总射门次数而不是行数相加。它会显示一条消息告诉您：

Using `n` as weighting variable i Quiet this message with `wt = n` or count rows with `wt = 1`

使用count(Team_Name,wt=n())将提供您想要的行为。

修改：第2部分

shot_table_ex <- tibble(Team_Name = sample(LETTERS[1:5],0.1))) %>%
     group_by(Team_Name) %>%
     summarise(n_shots = n(),n_goals = sum(Type == "goal"),n_games = n_distinct(Date)) %>%
     mutate(Shots_per_Game = round(n_shots / n_games,1),Goals_per_Game = round(n_goals / n_games,1))

1a。根据下面的示例数据并使用dplyr，有没有一种方法可以在不使用中间表的情况下计算每个团队的比赛（日期）？

这就是我要做的：

shot_df_ex %>% 
  distinct(Team_Name,Date) %>% #Keeps only the cols given and one of each combo
  count(Team_Name)

您还可以使用唯一的：

shot_df_ex %>% 
  group_by(Team_Name) %>%
  summarize(N_Games = length(unique(Date))

1b。我已经包含了计算n_games的原始方法工作。为什么？

您的代码对我有用。您是否保存了中间表？它正在计算每个团队预期的6人。

以下是我创建摘要表的过程。我知道管道是为了减少某些中间产物的产生变量/表。我在哪里可以结合以下步骤来创建决赛桌的中间步骤最少？

shot_df_ex %>% 
  group_by(Team_Name) %>% 
  summarize(
    N_Games = length(unique(Date)),N_Shots = sum(Type == "shot"),N_Goals = sum(Type == "goal")
  ) %>% 
  mutate(Shots_per_Game = round(N_Shots / N_Games,Goals_per_Game = round(N_Goals / N_Games,1))

只要您不需要更改分组，就可以一次使用多个汇总步骤。我们在这里（在sum调用中利用True为1并将False为0的解释。length当然将为我们提供unique产生的向量的长度

此（计数）有效，但是count（）并不是运行group_by（）更快的方法％>％summarise（）吗？

count只是group_by(col) %>% tally()的组合，而tally本质上是summarize(x=n())，所以是的。：）

了解dplyr管道和汇总功能

如何解决了解dplyr管道和汇总功能

解决方法

相关推荐