myData <- read_excel("../00_data/myData_charts.xlsx")
myData
## # A tibble: 10,879 × 8
## team team_name year total home away week weekly_attendance
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 San Francisco 49ers 2000 1057954 541964 515990 1 54626
## 2 San Francisco 49ers 2000 1057954 541964 515990 2 66879
## 3 San Francisco 49ers 2000 1057954 541964 515990 3 65945
## 4 San Francisco 49ers 2000 1057954 541964 515990 4 64127
## 5 San Francisco 49ers 2000 1057954 541964 515990 5 66985
## 6 San Francisco 49ers 2000 1057954 541964 515990 6 68344
## 7 San Francisco 49ers 2000 1057954 541964 515990 7 59870
## 8 San Francisco 49ers 2000 1057954 541964 515990 8 73169
## 9 San Francisco 49ers 2000 1057954 541964 515990 9 68109
## 10 San Francisco 49ers 2000 1057954 541964 515990 10 64900
## # … with 10,869 more rows
Total, Week, Year
Divide it using dplyr::select in a way the two have a common variable, which you could use to join the two.
myData_1half <- myData %>% select(team:year)
myData_2half <- myData %>% select(year:weekly_attendance)
Use tidyr::left_join or other joining functions.
left_join(myData_1half, myData_2half)
## Joining, by = "year"
## # A tibble: 5,883,395 × 8
## team team_name year total home away week weekly_attendance
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 San Francisco 49ers 2000 1057954 541964 515990 1 54626
## 2 San Francisco 49ers 2000 1057954 541964 515990 2 66879
## 3 San Francisco 49ers 2000 1057954 541964 515990 3 65945
## 4 San Francisco 49ers 2000 1057954 541964 515990 4 64127
## 5 San Francisco 49ers 2000 1057954 541964 515990 5 66985
## 6 San Francisco 49ers 2000 1057954 541964 515990 6 68344
## 7 San Francisco 49ers 2000 1057954 541964 515990 7 59870
## 8 San Francisco 49ers 2000 1057954 541964 515990 8 73169
## 9 San Francisco 49ers 2000 1057954 541964 515990 9 68109
## 10 San Francisco 49ers 2000 1057954 541964 515990 10 64900
## # … with 5,883,385 more rows