We are going to be reading in the miltiary active status and FAOSTAT. The FAOSTAT dataset contains information such as regions, group code while the SNL actors
## Rows: 46 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (5): sid, year, first_epid, last_epid, n_episodes
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 614 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): aid
## dbl (5): sid, first_epid, last_epid, n_episodes, season_fraction
## lgl (2): featured, update_anchor
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.The seasons and casts respectively contain data about seasons and casts on SNL over the years. There isn’t much to mutuate or tidy in these two datasets.
valid_tenure <- snl_casts %>%
filter(!is.na(first_epid), !is.na(last_epid)) %>%
summarize(avg_tenure = mean(last_epid - first_epid, na.rm = TRUE))
seasons_tenure <- cross_join(snl_seasons, valid_tenure)
seasons_tenure## # A tibble: 46 × 6
## sid year first_epid last_epid n_episodes avg_tenure
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1975 19751011 19760731 24 NaN
## 2 2 1976 19760918 19770521 22 NaN
## 3 3 1977 19770924 19780520 20 NaN
## 4 4 1978 19781007 19790526 20 NaN
## 5 5 1979 19791013 19800524 20 NaN
## 6 6 1980 19801115 19810411 13 NaN
## 7 7 1981 19811003 19820522 20 NaN
## 8 8 1982 19820925 19830514 20 NaN
## 9 9 1983 19831008 19840512 19 NaN
## 10 10 1984 19841006 19850413 17 NaN
## # ℹ 36 more rows