2.3 Data Frames
## # A tibble: 3 × 10
## Year Age Tm Lg W L W.L ERA G GS
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1942 21 BSN NL 0 0 NA 5.74 4 2
## 2 1946 25 BSN NL 8 5 0.615 2.94 24 16
## 3 1947 26 BSN NL 21 10 0.677 2.33 40 35
## # A tibble: 3 × 10
## Year Age Tm Lg W L W.L ERA G GS
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1942 21 BSN NL 0 0 NA 5.74 4 2
## 2 1946 25 BSN NL 8 5 0.615 2.94 24 16
## 3 1947 26 BSN NL 21 10 0.677 2.33 40 35
2.3.1 Manipulations with Data
spahn <- spahn |>
dplyr::mutate(FIP = (13 * HR + 3 * BB - 2 * SO) / IP)
spahn |>
dplyr::arrange(FIP) |>
dplyr::select(Year, Age, W, L, ERA, FIP) |>
dplyr::slice_head(n = 5)
## # A tibble: 5 × 6
## Year Age W L ERA FIP
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1952 31 14 19 2.98 0.345
## 2 1953 32 23 7 2.1 0.362
## 3 1946 25 8 5 2.94 0.415
## 4 1959 38 21 15 2.96 0.675
## 5 1947 26 21 10 2.33 0.695
What do you notice about Spahn’s FIP?

FIP from Fangraphs
You can combine data with joins.
## [1] "[16 x 28]"
## [1] "[14 x 28]"
## [1] "[30 x 28]"
## [1] "[16 x 34]"
## [1] "[16 x 28]"
## [1] "[16 x 61]"