数据处理，一种下采样 [英] Data manipulation, kind of downsampling

问题描述

我有一个很大的csv文件，下面是数据示例.我将以八个团队为例进行说明.

I have a large csv file, example of the data below. I will use an example of eight teams to illustrate.

home_team    away_team      home_score       away_score         year
belgium      france         2                2                  1990
brazil       uruguay        3                1                  1990
italy        belgium        1                2                  1990
sweden       mexico         3                1                  1990

france       chile          3                1                  1991
brazil       england        2                1                  1991
italy        belgium        1                2                  1991
chile        switzerland    2                2                  1991

我的数据运行了很多年. 我想获得每个团队每年的总得分，请参见下面的示例，

My data runs for many years. I would like to have total number of scores of each team every year, see example below,

team            total_scores          year
belgium         4                     1990
france          2                     1990
brazil          3                     1990
uruguay         1                     1990
italy           1                     1990
sweden          3                     1990
mexico          1                     1990

france          3                     1991
chile           5                     1991
brazil          2                     1991
england         1                     1991
italy           1                     1991
belgium         2                     1991
switzerland     2                     1991

有想法吗?

推荐答案

这是R中的另一种解决方案.

Here is yet another solution in R.

#Packages needed
library(dplyr)
library(magrittr)
library(tidyr)

#Your data
home_team <- c("belgium", "brazil", "italy", "sweden",
               "france", "brazil", "italy", "chile")
away_team <- c("france", "uruguay", "belgium", "mexico",
               "chile", "england", "belgium", "switzerland")
home_score <- c(2,3,1,3,
                3,2,1,2)
away_score <- c(2,1,2,1,
                1,1,2,2)
year <- c(1990, 1990, 1990, 1990,
          1991, 1991, 1991, 1991)

df <- data.frame(home_team, away_team, home_score, away_score, year, stringsAsFactors = FALSE)

df

#   home_team   away_team home_score away_score year
# 1   belgium      france          2          2 1990
# 2    brazil     uruguay          3          1 1990
# 3     italy     belgium          1          2 1990
# 4    sweden      mexico          3          1 1990
# 5    france       chile          3          1 1991
# 6    brazil     england          2          1 1991
# 7     italy     belgium          1          2 1991
# 8     chile switzerland          2          2 1991


#Column names for the new data.frames
my_colnames <- c("team", "score", "year")

#Using select() to create separate home and away datasets
df_home <- df %>% select(matches("home|year")) %>% setNames(my_colnames) %>% mutate(game_where = "home")
df_away <- df %>% select(matches("away|year")) %>% setNames(my_colnames) %>% mutate(game_where = "away")

#rbind()'ing both data.frames
#Grouping the rows together first by the team and then by the year
#Summing up the scores for the aforementioned groupings
#Sorting the newly produced data.frame by year
df_1 <- rbind(df_home, df_away) %>% group_by(team, year) %>% tally(score) %>% arrange(year)

df_1 

 #   team         year     n
 #   <chr>       <dbl> <dbl>
 # 1 belgium      1990     4
 # 2 brazil       1990     3
 # 3 france       1990     2
 # 4 italy        1990     1
 # 5 mexico       1990     1
 # 6 sweden       1990     3
 # 7 uruguay      1990     1
 # 8 belgium      1991     2
 # 9 brazil       1991     2
 #10 chile        1991     3
 #11 england      1991     1
 #12 france       1991     3
 #13 italy        1991     1
 #14 switzerland  1991     2

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