问题
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
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
Thoughts?
回答1:
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
回答2:
Here is a solution using the tidyverse (dplyr and tidyr), in particular the pivot functions from tidyr...
library(tidyverse)
df %>% pivot_longer(cols = -year, #splits non-year columns into home/away and type columns
names_to = c("homeaway", "type"),
names_sep = "_",
values_to = "value",
values_ptypes = list(value = character())) %>%
select(-homeaway) %>% #remove home/away
pivot_wider(names_from = "type", #restore team and score columns (as list columns)
values_from = "value") %>%
unnest(cols = c(team, score)) %>% #unnest the list columns to year, team, score
group_by(year, team) %>%
summarise(total_goals = sum(as.numeric(score)))
# A tibble: 14 x 3
# Groups: year [2]
year team total_goals
<int> <chr> <dbl>
1 1990 belgium 4
2 1990 brazil 3
3 1990 france 2
4 1990 italy 1
5 1990 mexico 1
6 1990 sweden 3
7 1990 uruguay 1
8 1991 belgium 2
9 1991 brazil 2
10 1991 chile 3
11 1991 england 1
12 1991 france 3
13 1991 italy 1
14 1991 switzerland 2
回答3:
You can try:
library(dplyr)
setNames(rbind(df[,c(1,3,5)],
setNames(df[,c(2,4,5)], names(df[,c(1,3,5)]))),
c("Country", "Goals", "Year")) %>%
group_by(Year, Country) %>%
summarize(Total = sum(Goals))
#> # A tibble: 14 x 3
#> # Groups: Year [2]
#> Year Country Total
#> <int> <chr> <int>
#> 1 1990 belgium 4
#> 2 1990 brazil 3
#> 3 1990 france 2
#> 4 1990 italy 1
#> 5 1990 mexico 1
#> 6 1990 sweden 3
#> 7 1990 uruguay 1
#> 8 1991 belgium 2
#> 9 1991 brazil 2
#> 10 1991 chile 3
#> 11 1991 england 1
#> 12 1991 france 3
#> 13 1991 italy 1
#> 14 1991 switzerland 2
Created on 2020-02-21 by the reprex package (v0.3.0)
回答4:
Adding a solution that uses dplyr only.
library(dplyr)
bind_rows(
select(df, team = home_team, score = home_score, year),
select(df, team = away_team, score = away_score, year)
) %>%
group_by(team, year) %>%
summarise(total_scores = sum(score))
来源：https://stackoverflow.com/questions/60339263/data-manipulation-kind-of-downsampling