library(tidyverse)
library(ggplot2)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Challenge 8 Solutions
Joining Data
Challenge Overview
Today’s challenge is to:
- read in multiple data sets, and describe the data set using both words and any supporting information (e.g., tables, etc)
- tidy data (as needed, including sanity checks)
- mutate variables as needed (including sanity checks)
- join two or more data sets and analyze some aspect of the joined data
(be sure to only include the category tags for the data you use!)
Read in data
Read in one (or more) of the following datasets, using the correct R package and command.
- military marriages ⭐⭐
- faostat ⭐⭐
- railroads ⭐⭐⭐
- fed_rate ⭐⭐⭐
- debt ⭐⭐⭐
- us_hh ⭐⭐⭐⭐
- snl ⭐⭐⭐⭐⭐
For this challenge I will be working with the SNL data set.
# Reading the SNL csv files
snl_actors <- read_csv("_data/snl_actors.csv")
snl_casts <- read_csv("_data/snl_casts.csv")
snl_seasons <- read_csv("_data/snl_seasons.csv")# Displaying snl_actors dataset
snl_actors# A tibble: 2,306 × 4
aid url type gender
<chr> <chr> <chr> <chr>
1 Kate McKinnon /Cast/?KaMc cast female
2 Alex Moffat /Cast/?AlMo cast male
3 Ego Nwodim /Cast/?EgNw cast unknown
4 Chris Redd /Cast/?ChRe cast male
5 Kenan Thompson /Cast/?KeTh cast male
6 Carey Mulligan /Guests/?3677 guest andy
7 Marcus Mumford /Guests/?3679 guest male
8 Aidy Bryant /Cast/?AiBr cast female
9 Steve Higgins /Crew/?StHi crew male
10 Mikey Day /Cast/?MiDa cast male
# … with 2,296 more rows# Displaying snl_casts dataset
snl_casts# A tibble: 614 × 8
aid sid featured first_epid last_epid update…¹ n_epi…² seaso…³
<chr> <dbl> <lgl> <dbl> <dbl> <lgl> <dbl> <dbl>
1 A. Whitney Brown 11 TRUE 19860222 NA FALSE 8 0.444
2 A. Whitney Brown 12 TRUE NA NA FALSE 20 1
3 A. Whitney Brown 13 TRUE NA NA FALSE 13 1
4 A. Whitney Brown 14 TRUE NA NA FALSE 20 1
5 A. Whitney Brown 15 TRUE NA NA FALSE 20 1
6 A. Whitney Brown 16 TRUE NA NA FALSE 20 1
7 Alan Zweibel 5 TRUE 19800409 NA FALSE 5 0.25
8 Sasheer Zamata 39 TRUE 20140118 NA FALSE 11 0.524
9 Sasheer Zamata 40 TRUE NA NA FALSE 21 1
10 Sasheer Zamata 41 FALSE NA NA FALSE 21 1
# … with 604 more rows, and abbreviated variable names ¹update_anchor,
# ²n_episodes, ³season_fraction# Displaying snl_seasons dataset
snl_seasons# A tibble: 46 × 5
sid year first_epid last_epid n_episodes
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1975 19751011 19760731 24
2 2 1976 19760918 19770521 22
3 3 1977 19770924 19780520 20
4 4 1978 19781007 19790526 20
5 5 1979 19791013 19800524 20
6 6 1980 19801115 19810411 13
7 7 1981 19811003 19820522 20
8 8 1982 19820925 19830514 20
9 9 1983 19831008 19840512 19
10 10 1984 19841006 19850413 17
# … with 36 more rows# Finding dimension of all 3 snl datasets
dim(snl_actors)[1] 2306 4dim(snl_casts)[1] 614 8dim(snl_seasons)[1] 46 5# Structure of snl_actors dataset
str(snl_actors)spc_tbl_ [2,306 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ aid : chr [1:2306] "Kate McKinnon" "Alex Moffat" "Ego Nwodim" "Chris Redd" ...
$ url : chr [1:2306] "/Cast/?KaMc" "/Cast/?AlMo" "/Cast/?EgNw" "/Cast/?ChRe" ...
$ type : chr [1:2306] "cast" "cast" "cast" "cast" ...
$ gender: chr [1:2306] "female" "male" "unknown" "male" ...
- attr(*, "spec")=
.. cols(
.. aid = col_character(),
.. url = col_character(),
.. type = col_character(),
.. gender = col_character()
.. )
- attr(*, "problems")=<externalptr> # Structure of snl_casts dataset
str(snl_casts)spc_tbl_ [614 × 8] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ aid : chr [1:614] "A. Whitney Brown" "A. Whitney Brown" "A. Whitney Brown" "A. Whitney Brown" ...
$ sid : num [1:614] 11 12 13 14 15 16 5 39 40 41 ...
$ featured : logi [1:614] TRUE TRUE TRUE TRUE TRUE TRUE ...
$ first_epid : num [1:614] 19860222 NA NA NA NA ...
$ last_epid : num [1:614] NA NA NA NA NA NA NA NA NA NA ...
$ update_anchor : logi [1:614] FALSE FALSE FALSE FALSE FALSE FALSE ...
$ n_episodes : num [1:614] 8 20 13 20 20 20 5 11 21 21 ...
$ season_fraction: num [1:614] 0.444 1 1 1 1 ...
- attr(*, "spec")=
.. cols(
.. aid = col_character(),
.. sid = col_double(),
.. featured = col_logical(),
.. first_epid = col_double(),
.. last_epid = col_double(),
.. update_anchor = col_logical(),
.. n_episodes = col_double(),
.. season_fraction = col_double()
.. )
- attr(*, "problems")=<externalptr> # Structure of snl_seasons dataset
str(snl_seasons)spc_tbl_ [46 × 5] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ sid : num [1:46] 1 2 3 4 5 6 7 8 9 10 ...
$ year : num [1:46] 1975 1976 1977 1978 1979 ...
$ first_epid: num [1:46] 19751011 19760918 19770924 19781007 19791013 ...
$ last_epid : num [1:46] 19760731 19770521 19780520 19790526 19800524 ...
$ n_episodes: num [1:46] 24 22 20 20 20 13 20 20 19 17 ...
- attr(*, "spec")=
.. cols(
.. sid = col_double(),
.. year = col_double(),
.. first_epid = col_double(),
.. last_epid = col_double(),
.. n_episodes = col_double()
.. )
- attr(*, "problems")=<externalptr> #Summary of snl_actors
library(summarytools)
print(summarytools::dfSummary(snl_actors,
varnumbers = FALSE,
plain.ascii = FALSE,
style = "grid",
graph.magnif = 0.60,
valid.col = FALSE),
method = 'render',
table.classes = 'table-condensed')Data Frame Summary
snl_actors
Dimensions: 2306 x 4Duplicates: 0
| Variable | Stats / Values | Freqs (% of Valid) | Graph | Missing | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| aid [character] |
|
|
 |
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| url [character] |
|
|
|
57 (2.5%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| type [character] |
|
|
 |
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| gender [character] |
|
|
 |
0 (0.0%) |
Generated by summarytools 1.0.1 (R version 4.2.1)
2022-12-22
#Summary of snl_casts
library(summarytools)
print(summarytools::dfSummary(snl_casts,
varnumbers = FALSE,
plain.ascii = FALSE,
style = "grid",
graph.magnif = 0.60,
valid.col = FALSE),
method = 'render',
table.classes = 'table-condensed')Data Frame Summary
snl_casts
Dimensions: 614 x 8Duplicates: 0
| Variable | Stats / Values | Freqs (% of Valid) | Graph | Missing | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| aid [character] |
|
|
 |
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| sid [numeric] |
|
46 distinct values |  |
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| featured [logical] |
|
|
 |
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| first_epid [numeric] |
|
35 distinct values |  |
564 (91.9%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| last_epid [numeric] |
|
17 distinct values |  |
597 (97.2%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| update_anchor [logical] |
|
|
 |
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| n_episodes [numeric] |
|
22 distinct values |  |
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| season_fraction [numeric] |
|
36 distinct values |  |
0 (0.0%) |
Generated by summarytools 1.0.1 (R version 4.2.1)
2022-12-22
#Summary of snl_seasons
library(summarytools)
print(summarytools::dfSummary(snl_seasons,
varnumbers = FALSE,
plain.ascii = FALSE,
style = "grid",
graph.magnif = 0.60,
valid.col = FALSE),
method = 'render',
table.classes = 'table-condensed')Data Frame Summary
snl_seasons
Dimensions: 46 x 5Duplicates: 0
| Variable | Stats / Values | Freqs (% of Valid) | Graph | Missing | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| sid [numeric] |
|
46 distinct values |  |
0 (0.0%) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| year [numeric] |
|
46 distinct values |  |
0 (0.0%) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| first_epid [numeric] |
|
46 distinct values | |
0 (0.0%) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| last_epid [numeric] |
|
46 distinct values |  |
0 (0.0%) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| n_episodes [numeric] |
|
|
 |
0 (0.0%) |
Generated by summarytools 1.0.1 (R version 4.2.1)
2022-12-22
Briefly describe the data
Saturday Night Live is an American late-night live television sketch comedy and variety show that premiered on NBC in 1975. The snl has 3 datasets “snl_actors.csv”, “snl_casts.csv”, “snl_seasons.csv”. There are no duplicates in all 3 datasets. The “snl_actors.csv” dataset has 2306 observations and 4 variables/attributes and contains information (such as actor name, url, type and gender) about the list of actors who have featured in the SNL show. The “aid” variable has 2306 unique values and acts as the primary key / unique identifier for the dataset. All 4 attributes in this dataset are of datatype character. The “snl_casts.csv” dataset has 614 observations and 8 attributes. This dataset contains information about the cast name “aid”, the seasons in which they have been featured, the number of times they have featured in the show along with each cast’s first and last episode. The “snl_seasons.csv” dataset has 46 observations and 5 attributes. The “sid” variable has 46 unique values and acts as a unique identifier for the dataset. This also indicates that SNL has 46 seasons. All the variables in this dataset are of numerical datatype and contains information about the season number, year it was telecasted, date of the first episode of that season, date of the last episode of that season and the number of episodes in that season. The first premiered season had 24 episodes which is the highest and the season 33 had the lowest number of episodes i.e 12.
Tidy Data and Mutate Variables (as needed)
Is your data already tidy, or is there work to be done? Be sure to anticipate your end result to provide a sanity check, and document your work here.
Are there any variables that require mutation to be usable in your analysis stream? For example, do you need to calculate new values in order to graph them? Can string values be represented numerically? Do you need to turn any variables into factors and reorder for ease of graphics and visualization?
We observe that the “url” attribute in snl_actors dataset has 57 missing values.
#Check for missing/null data in the snl_actors
sum(is.na(snl_actors))[1] 57sum(is.null(snl_actors))[1] 0# Checking which columns have NA values in snl_actors
col <- colnames(snl_actors)
for (c in col){
print(paste0("NA values in ", c, ": ", sum(is.na(snl_actors[,c]))))
}[1] "NA values in aid: 0"
[1] "NA values in url: 57"
[1] "NA values in type: 0"
[1] "NA values in gender: 0"We observe that out of the 57 observations which have missing “url” value, 56 of them have “unknown” value for ‘type’ attribute. This may be the reason for missing “url” value.
# Displaying the 57 actors with missing "url" value.
filter(snl_actors,is.na(snl_actors$url))# A tibble: 57 × 4
aid url type gender
<chr> <chr> <chr> <chr>
1 Don Roy King <NA> unknown male
2 Liam Payne <NA> unknown male
3 Louis Tomlinson <NA> unknown male
4 Zayn Malik <NA> unknown male
5 Caleb Followill <NA> unknown male
6 Jared Followill <NA> unknown male
7 Matthew Followill <NA> unknown male
8 Nathan Followill <NA> unknown male
9 Regine Chassagne <NA> unknown female
10 William Butler <NA> unknown male
# … with 47 more rowsThe “url” contains information in the form of type of actor enclosed in ‘/’ and ‘/?’ followed by a identifier for each actor. Since, we have a unique identifier “aid” and there are no duplicates in the dataset, the attribute “url” seems unnecessary and I am dropping it. We are now left with 2306 observations and 3 attributes for snl_actors.
# Dropping the attribute "url" from the snl_actors
snl_actors <- snl_actors%>%
subset(select = -c(2))
snl_actors# A tibble: 2,306 × 3
aid type gender
<chr> <chr> <chr>
1 Kate McKinnon cast female
2 Alex Moffat cast male
3 Ego Nwodim cast unknown
4 Chris Redd cast male
5 Kenan Thompson cast male
6 Carey Mulligan guest andy
7 Marcus Mumford guest male
8 Aidy Bryant cast female
9 Steve Higgins crew male
10 Mikey Day cast male
# … with 2,296 more rowsThe “type” attribute has 56 “unknown” values. We are retaining these observations for now as we have less data.
table(snl_actors$type)
cast crew guest unknown
154 170 1926 56 # Displaying the 56 actors with type as "unknown".
filter(snl_actors,snl_actors$type=="unknown")# A tibble: 56 × 3
aid type gender
<chr> <chr> <chr>
1 Don Roy King unknown male
2 Liam Payne unknown male
3 Louis Tomlinson unknown male
4 Zayn Malik unknown male
5 Caleb Followill unknown male
6 Jared Followill unknown male
7 Matthew Followill unknown male
8 Nathan Followill unknown male
9 Regine Chassagne unknown female
10 William Butler unknown male
# … with 46 more rowsThe “gender” attribute has 388 “unknown” values. 21 actors have been assigned the gender as “andy” which seems to be a mistake. I changed the value from “andy” to “unknown” for these 21 actors.
table(snl_actors$gender)
andy female male unknown
21 671 1226 388 # Changing the gender from "andy" to "unknown" for the 21 observations
snl_actors <- snl_actors%>%
mutate(gender = replace(gender, gender == "andy", "unknown"))
snl_actors# A tibble: 2,306 × 3
aid type gender
<chr> <chr> <chr>
1 Kate McKinnon cast female
2 Alex Moffat cast male
3 Ego Nwodim cast unknown
4 Chris Redd cast male
5 Kenan Thompson cast male
6 Carey Mulligan guest unknown
7 Marcus Mumford guest male
8 Aidy Bryant cast female
9 Steve Higgins crew male
10 Mikey Day cast male
# … with 2,296 more rows# Sanity check: Check that the "gender" attribute does not have "andy" values. There should be 388+21 = 409 "unknown" values.
table(snl_actors$gender)
female male unknown
671 1226 409 The “snl_actors” dataset is now tidy. Next, we move on to the “snl_casts” dataset.
We observe that the “first_epid” and “last_epid” attributes have 564 and 597 missing values respectively.
#Check for missing/null data in the snl_casts.
sum(is.na(snl_casts))[1] 1161sum(is.null(snl_casts))[1] 0# Checking which columns have NA values in snl_casts
col <- colnames(snl_casts)
for (c in col){
print(paste0("NA values in ", c, ": ", sum(is.na(snl_casts[,c]))))
}[1] "NA values in aid: 0"
[1] "NA values in sid: 0"
[1] "NA values in featured: 0"
[1] "NA values in first_epid: 564"
[1] "NA values in last_epid: 597"
[1] "NA values in update_anchor: 0"
[1] "NA values in n_episodes: 0"
[1] "NA values in season_fraction: 0"Since the attributes “first_epid” and “last_epid” have more than 90% of the values missing and it is difficult to impute the missing values, I decided to drop them from the dataset.
# Dropping the attributes "first_epid" and "last_epid" from the snl_casts
snl_casts <- snl_casts%>%
subset(select = -c(4,5))
snl_casts# A tibble: 614 × 6
aid sid featured update_anchor n_episodes season_fraction
<chr> <dbl> <lgl> <lgl> <dbl> <dbl>
1 A. Whitney Brown 11 TRUE FALSE 8 0.444
2 A. Whitney Brown 12 TRUE FALSE 20 1
3 A. Whitney Brown 13 TRUE FALSE 13 1
4 A. Whitney Brown 14 TRUE FALSE 20 1
5 A. Whitney Brown 15 TRUE FALSE 20 1
6 A. Whitney Brown 16 TRUE FALSE 20 1
7 Alan Zweibel 5 TRUE FALSE 5 0.25
8 Sasheer Zamata 39 TRUE FALSE 11 0.524
9 Sasheer Zamata 40 TRUE FALSE 21 1
10 Sasheer Zamata 41 FALSE FALSE 21 1
# … with 604 more rowsThe “snl_casts” dataset is tidy and left with 614 observations and 6 variables. Finally, we move on to the last dataset “snl_seasons”.
The snl_seasons dataset has no missing/null data.
#Check for missing/null data in the snl_seasons.
sum(is.na(snl_seasons))[1] 0sum(is.null(snl_seasons))[1] 0I converted the “first_epid” and “last_epid” attributes to ymd date format which will be useful while creating visualizations.
# Converting "first_epid" and "last_epid" attributes to ymd date format
library(lubridate)
snl_seasons$first_epid <- ymd(snl_seasons$first_epid)
snl_seasons$last_epid <- ymd(snl_seasons$last_epid)
snl_seasons# A tibble: 46 × 5
sid year first_epid last_epid n_episodes
<dbl> <dbl> <date> <date> <dbl>
1 1 1975 1975-10-11 1976-07-31 24
2 2 1976 1976-09-18 1977-05-21 22
3 3 1977 1977-09-24 1978-05-20 20
4 4 1978 1978-10-07 1979-05-26 20
5 5 1979 1979-10-13 1980-05-24 20
6 6 1980 1980-11-15 1981-04-11 13
7 7 1981 1981-10-03 1982-05-22 20
8 8 1982 1982-09-25 1983-05-14 20
9 9 1983 1983-10-08 1984-05-12 19
10 10 1984 1984-10-06 1985-04-13 17
# … with 36 more rowsSince, the attribute “n_episodes” is present in both snl_casts and snl_seasons datasets, I renamed the attribute “n_episodes” to “seasons_n_episodes” in the snl_seasons dataset.
# Renaming the "n_episodes" column
snl_seasons <- snl_seasons%>%
rename(seasons_n_episodes = n_episodes)
# Displaying the renamed column names
colnames(snl_seasons)[1] "sid" "year" "first_epid"
[4] "last_epid" "seasons_n_episodes"Join Data
Be sure to include a sanity check, and double-check that case count is correct!
I performed left join on snl_casts and snl_actors datasets by using the “aid” attribute as the key. The joined dataset snl_actors_casts has 614 observations and 8 attributes which makes sense as the snl_casts dataset had 614 observations and snl_casts and snl_actors datasets had 6 and 3 attributes respectively. Since, the “aid” attribute is common in both datasets we count it only once.
# performed left join for snl_casts and snl_actors datasets.
snl_actors_casts = merge(x=snl_casts, y=snl_actors, by="aid", all.x=TRUE)
snl_actors_casts aid sid featured update_anchor n_episodes season_fraction
1 A. Whitney Brown 11 TRUE FALSE 8 0.44444444
2 A. Whitney Brown 12 TRUE FALSE 20 1.00000000
3 A. Whitney Brown 13 TRUE FALSE 13 1.00000000
4 A. Whitney Brown 14 TRUE FALSE 20 1.00000000
5 A. Whitney Brown 15 TRUE FALSE 20 1.00000000
6 A. Whitney Brown 16 TRUE FALSE 20 1.00000000
7 Abby Elliott 34 TRUE FALSE 15 0.68181818
8 Abby Elliott 35 TRUE FALSE 22 1.00000000
9 Abby Elliott 36 FALSE FALSE 22 1.00000000
10 Abby Elliott 37 FALSE FALSE 22 1.00000000
11 Adam Sandler 16 TRUE FALSE 9 0.45000000
12 Adam Sandler 17 TRUE FALSE 20 1.00000000
13 Adam Sandler 18 TRUE FALSE 20 1.00000000
14 Adam Sandler 19 FALSE FALSE 20 1.00000000
15 Adam Sandler 20 FALSE FALSE 20 1.00000000
16 Aidy Bryant 38 TRUE FALSE 21 1.00000000
17 Aidy Bryant 39 FALSE FALSE 21 1.00000000
18 Aidy Bryant 40 FALSE FALSE 21 1.00000000
19 Aidy Bryant 41 FALSE FALSE 21 1.00000000
20 Aidy Bryant 42 FALSE FALSE 21 1.00000000
21 Aidy Bryant 43 FALSE FALSE 21 1.00000000
22 Aidy Bryant 44 FALSE FALSE 21 1.00000000
23 Aidy Bryant 45 FALSE FALSE 18 1.00000000
24 Aidy Bryant 46 FALSE FALSE 17 1.00000000
25 Al Franken 3 TRUE FALSE 20 1.00000000
26 Al Franken 4 TRUE FALSE 20 1.00000000
27 Al Franken 5 TRUE FALSE 14 0.70000000
28 Al Franken 11 TRUE FALSE 6 0.33333333
29 Al Franken 14 TRUE FALSE 20 1.00000000
30 Al Franken 15 TRUE FALSE 20 1.00000000
31 Al Franken 16 TRUE FALSE 20 1.00000000
32 Al Franken 17 TRUE FALSE 20 1.00000000
33 Al Franken 18 TRUE FALSE 20 1.00000000
34 Al Franken 19 TRUE FALSE 20 1.00000000
35 Al Franken 20 TRUE FALSE 20 1.00000000
36 Alan Zweibel 5 TRUE FALSE 5 0.25000000
37 Alex Moffat 42 TRUE FALSE 21 1.00000000
38 Alex Moffat 43 TRUE FALSE 21 1.00000000
39 Alex Moffat 44 FALSE FALSE 21 1.00000000
40 Alex Moffat 45 FALSE FALSE 18 1.00000000
41 Alex Moffat 46 FALSE FALSE 17 1.00000000
42 Amy Poehler 27 TRUE FALSE 20 1.00000000
43 Amy Poehler 28 FALSE FALSE 20 1.00000000
44 Amy Poehler 29 FALSE FALSE 20 1.00000000
45 Amy Poehler 30 FALSE TRUE 20 1.00000000
46 Amy Poehler 31 FALSE TRUE 19 1.00000000
47 Amy Poehler 32 FALSE TRUE 20 1.00000000
48 Amy Poehler 33 FALSE TRUE 12 1.00000000
49 Amy Poehler 34 FALSE TRUE 11 0.50000000
50 Ana Gasteyer 22 FALSE FALSE 20 1.00000000
51 Ana Gasteyer 23 FALSE FALSE 20 1.00000000
52 Ana Gasteyer 24 FALSE FALSE 19 1.00000000
53 Ana Gasteyer 25 FALSE FALSE 20 1.00000000
54 Ana Gasteyer 26 FALSE FALSE 20 1.00000000
55 Ana Gasteyer 27 FALSE FALSE 20 1.00000000
56 Andrew Dismukes 46 TRUE FALSE 17 1.00000000
57 Andy Samberg 31 TRUE FALSE 19 1.00000000
58 Andy Samberg 32 FALSE FALSE 20 1.00000000
59 Andy Samberg 33 FALSE FALSE 12 1.00000000
60 Andy Samberg 34 FALSE FALSE 22 1.00000000
61 Andy Samberg 35 FALSE FALSE 22 1.00000000
62 Andy Samberg 36 FALSE FALSE 22 1.00000000
63 Andy Samberg 37 FALSE FALSE 22 1.00000000
64 Ann Risley 6 FALSE FALSE 13 1.00000000
65 Anthony Michael Hall 11 FALSE FALSE 18 1.00000000
66 Beck Bennett 39 TRUE FALSE 21 1.00000000
67 Beck Bennett 40 TRUE FALSE 21 1.00000000
68 Beck Bennett 41 FALSE FALSE 21 1.00000000
69 Beck Bennett 42 FALSE FALSE 21 1.00000000
70 Beck Bennett 43 FALSE FALSE 21 1.00000000
71 Beck Bennett 44 FALSE FALSE 21 1.00000000
72 Beck Bennett 45 FALSE FALSE 18 1.00000000
73 Beck Bennett 46 FALSE FALSE 17 1.00000000
74 Ben Stiller 14 TRUE FALSE 6 0.30000000
75 Beth Cahill 17 TRUE FALSE 15 0.75000000
76 Bill Hader 31 TRUE FALSE 19 1.00000000
77 Bill Hader 32 FALSE FALSE 20 1.00000000
78 Bill Hader 33 FALSE FALSE 12 1.00000000
79 Bill Hader 34 FALSE FALSE 22 1.00000000
80 Bill Hader 35 FALSE FALSE 22 1.00000000
81 Bill Hader 36 FALSE FALSE 22 1.00000000
82 Bill Hader 37 FALSE FALSE 22 1.00000000
83 Bill Hader 38 FALSE FALSE 21 1.00000000
84 Bill Murray 2 FALSE FALSE 12 0.54545455
85 Bill Murray 3 FALSE FALSE 20 1.00000000
86 Bill Murray 4 FALSE TRUE 20 1.00000000
87 Bill Murray 5 FALSE TRUE 20 1.00000000
88 Billy Crystal 10 FALSE FALSE 17 1.00000000
89 Bobby Moynihan 34 TRUE FALSE 22 1.00000000
90 Bobby Moynihan 35 TRUE FALSE 22 1.00000000
91 Bobby Moynihan 36 FALSE FALSE 22 1.00000000
92 Bobby Moynihan 37 FALSE FALSE 22 1.00000000
93 Bobby Moynihan 38 FALSE FALSE 21 1.00000000
94 Bobby Moynihan 39 FALSE FALSE 21 1.00000000
95 Bobby Moynihan 40 FALSE FALSE 21 1.00000000
96 Bobby Moynihan 41 FALSE FALSE 21 1.00000000
97 Bobby Moynihan 42 FALSE FALSE 21 1.00000000
98 Bowen Yang 45 TRUE FALSE 18 1.00000000
99 Bowen Yang 46 TRUE FALSE 17 1.00000000
100 Brad Hall 8 FALSE TRUE 20 1.00000000
101 Brad Hall 9 FALSE TRUE 19 1.00000000
102 Brian Doyle-Murray 5 TRUE FALSE 12 0.60000000
103 Brian Doyle-Murray 7 TRUE TRUE 20 1.00000000
104 Brooks Wheelan 39 TRUE FALSE 21 1.00000000
105 Casey Wilson 33 TRUE FALSE 8 0.66666667
106 Casey Wilson 34 TRUE FALSE 22 1.00000000
107 Cecily Strong 38 TRUE FALSE 21 1.00000000
108 Cecily Strong 39 FALSE TRUE 21 1.00000000
109 Cecily Strong 40 FALSE FALSE 21 1.00000000
110 Cecily Strong 41 FALSE FALSE 21 1.00000000
111 Cecily Strong 42 FALSE FALSE 21 1.00000000
112 Cecily Strong 43 FALSE FALSE 21 1.00000000
113 Cecily Strong 44 FALSE FALSE 21 1.00000000
114 Cecily Strong 45 FALSE FALSE 18 1.00000000
115 Cecily Strong 46 FALSE FALSE 17 1.00000000
116 Charles Rocket 6 FALSE TRUE 13 1.00000000
117 Cheri Oteri 21 FALSE FALSE 20 1.00000000
118 Cheri Oteri 22 FALSE FALSE 20 1.00000000
119 Cheri Oteri 23 FALSE FALSE 20 1.00000000
120 Cheri Oteri 24 FALSE FALSE 19 1.00000000
121 Cheri Oteri 25 FALSE FALSE 20 1.00000000
122 Chevy Chase 1 FALSE TRUE 24 1.00000000
123 Chevy Chase 2 FALSE TRUE 6 0.27272727
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313 cast male
314 cast male
315 cast male
316 cast male
317 cast male
318 cast male
319 cast female
320 cast male
321 cast male
322 cast male
323 cast male
324 cast male
325 cast male
326 cast male
327 cast male
328 cast male
329 cast male
330 cast male
331 cast male
332 cast male
333 cast male
334 cast male
335 cast female
336 cast female
337 cast female
338 cast female
339 cast female
340 cast female
341 cast female
342 cast female
343 cast female
344 cast female
345 cast female
346 cast female
347 cast female
348 cast female
349 cast female
350 cast female
351 cast female
352 cast male
353 cast male
354 cast male
355 cast male
356 cast male
357 cast male
358 cast male
359 cast male
360 cast male
361 cast male
362 cast male
363 cast male
364 cast male
365 cast male
366 cast male
367 cast male
368 cast male
369 cast male
370 cast male
371 cast male
372 cast male
373 cast male
374 cast male
375 cast male
376 cast male
377 cast male
378 cast male
379 cast female
380 cast female
381 cast female
382 cast female
383 cast female
384 cast female
385 cast female
386 cast male
387 cast male
388 cast male
389 cast male
390 cast male
391 cast male
392 cast male
393 cast male
394 cast female
395 cast female
396 cast female
397 cast female
398 cast female
399 cast female
400 cast female
401 cast female
402 cast female
403 cast female
404 cast female
405 cast female
406 cast female
407 cast male
408 cast male
409 cast male
410 cast male
411 cast male
412 cast female
413 cast female
414 cast female
415 cast female
416 cast male
417 cast female
418 cast female
419 cast female
420 cast female
421 cast female
422 cast female
423 cast female
424 cast female
425 cast female
426 cast female
427 cast female
428 cast female
429 cast female
430 cast female
431 cast female
432 cast female
433 cast female
434 cast male
435 cast male
436 cast male
437 cast male
438 cast male
439 cast male
440 cast male
441 cast male
442 cast male
443 cast male
444 cast female
445 cast male
446 cast male
447 cast male
448 cast male
449 cast male
450 cast male
451 cast male
452 cast male
453 cast male
454 cast male
455 cast male
456 cast male
457 cast male
458 cast female
459 cast female
460 cast female
461 cast female
462 cast female
463 cast female
464 cast female
465 cast female
466 cast female
467 cast female
468 cast female
469 cast female
470 cast female
471 cast female
472 cast female
473 cast female
474 cast female
475 cast female
476 cast female
477 cast female
478 cast male
479 cast male
480 cast male
481 cast male
482 cast male
483 cast female
484 cast male
485 cast male
486 cast male
487 cast male
488 cast male
489 cast male
490 cast male
491 cast male
492 cast male
493 cast male
494 cast male
495 cast male
496 cast male
497 cast male
498 cast male
499 cast male
500 cast male
501 cast male
502 cast male
503 cast male
504 cast unknown
505 cast female
506 cast female
507 cast female
508 cast female
509 cast female
510 cast female
511 cast female
512 cast male
513 cast male
514 cast male
515 cast male
516 cast male
517 cast male
518 cast male
519 cast male
520 cast male
521 cast male
522 cast female
523 cast female
524 cast female
525 cast female
526 cast female
527 cast female
528 cast female
529 cast female
530 cast female
531 cast male
532 cast male
533 cast male
534 cast male
535 cast male
536 cast male
537 cast male
538 cast male
539 cast male
540 cast male
541 cast male
542 cast male
543 cast male
544 cast female
545 cast male
546 cast male
547 cast male
548 cast male
549 cast male
550 cast male
551 cast male
552 cast male
553 cast male
554 cast male
555 cast male
556 cast male
557 cast male
558 cast male
559 cast male
560 cast male
561 cast male
562 cast male
563 cast male
564 cast male
565 cast male
566 cast male
567 cast female
568 cast female
569 cast female
570 cast female
571 cast female
572 cast female
573 cast male
574 cast male
575 cast male
576 cast male
577 cast male
578 cast male
579 cast male
580 cast male
581 cast male
582 cast male
583 cast male
584 cast male
585 cast male
586 cast female
587 cast female
588 cast female
589 cast female
590 cast female
591 cast female
592 cast female
593 cast female
594 cast female
595 cast female
596 cast female
597 cast female
598 cast female
599 cast male
600 cast male
601 cast male
602 cast male
603 cast male
604 cast male
605 cast male
606 cast male
607 cast male
608 cast male
609 cast male
610 cast male
611 cast male
612 cast male
613 cast male
614 cast femaleNext, I performed left join on snl_actors_casts and snl_seasons datasets by using the “sid” attribute as the key. The joined dataset snl_actors_casts_seasons has 614 observations and 12 attributes which makes sense as the snl_actors_casts dataset has 614 observations and snl_actors_casts and snl_seasons datasets had 8 and 5 attributes respectively. Since, the “sid” attribute is common in both datasets we count it only once.
# performed left join for snl_actors_casts and snl_seasons datasets.
snl_actors_casts_seasons = merge(x=snl_actors_casts, y=snl_seasons, by="sid", all.x=TRUE)
snl_actors_casts_seasons sid aid featured update_anchor n_episodes season_fraction
1 1 George Coe FALSE FALSE 1 0.04166667
2 1 Laraine Newman FALSE FALSE 24 1.00000000
3 1 Michael O'Donoghue FALSE FALSE 4 0.16666667
4 1 Jane Curtin FALSE FALSE 24 1.00000000
5 1 Chevy Chase FALSE TRUE 24 1.00000000
6 1 Gilda Radner FALSE FALSE 24 1.00000000
7 1 Garrett Morris FALSE FALSE 24 1.00000000
8 1 John Belushi FALSE FALSE 24 1.00000000
9 1 Dan Aykroyd FALSE FALSE 24 1.00000000
10 2 Gilda Radner FALSE FALSE 22 1.00000000
11 2 John Belushi FALSE FALSE 22 1.00000000
12 2 Bill Murray FALSE FALSE 12 0.54545455
13 2 Chevy Chase FALSE TRUE 6 0.27272727
14 2 Dan Aykroyd FALSE FALSE 22 1.00000000
15 2 Laraine Newman FALSE FALSE 22 1.00000000
16 2 Garrett Morris FALSE FALSE 22 1.00000000
17 2 Jane Curtin FALSE TRUE 22 1.00000000
18 3 Al Franken TRUE FALSE 20 1.00000000
19 3 Jane Curtin FALSE TRUE 20 1.00000000
20 3 John Belushi FALSE FALSE 20 1.00000000
21 3 Bill Murray FALSE FALSE 20 1.00000000
22 3 Garrett Morris FALSE FALSE 20 1.00000000
23 3 Laraine Newman FALSE FALSE 20 1.00000000
24 3 Dan Aykroyd FALSE TRUE 20 1.00000000
25 3 Tom Davis TRUE FALSE 20 1.00000000
26 3 Gilda Radner FALSE FALSE 20 1.00000000
27 4 Tom Davis TRUE FALSE 20 1.00000000
28 4 John Belushi FALSE FALSE 20 1.00000000
29 4 Al Franken TRUE FALSE 20 1.00000000
30 4 Laraine Newman FALSE FALSE 20 1.00000000
31 4 Dan Aykroyd FALSE FALSE 20 1.00000000
32 4 Garrett Morris FALSE FALSE 20 1.00000000
33 4 Gilda Radner FALSE FALSE 20 1.00000000
34 4 Jane Curtin FALSE TRUE 20 1.00000000
35 4 Bill Murray FALSE TRUE 20 1.00000000
36 5 Tom Schiller TRUE FALSE 5 0.25000000
37 5 Harry Shearer FALSE FALSE 20 1.00000000
38 5 Jim Downey TRUE FALSE 12 0.60000000
39 5 Don Novello TRUE FALSE 20 1.00000000
40 5 Peter Aykroyd TRUE FALSE 12 0.60000000
41 5 Paul Shaffer TRUE FALSE 16 0.80000000
42 5 Jane Curtin FALSE TRUE 20 1.00000000
43 5 Gilda Radner FALSE FALSE 20 1.00000000
44 5 Al Franken TRUE FALSE 14 0.70000000
45 5 Alan Zweibel TRUE FALSE 5 0.25000000
46 5 Garrett Morris FALSE FALSE 20 1.00000000
47 5 Laraine Newman FALSE FALSE 20 1.00000000
48 5 Tom Davis TRUE FALSE 16 0.80000000
49 5 Brian Doyle-Murray TRUE FALSE 12 0.60000000
50 5 Bill Murray FALSE TRUE 20 1.00000000
51 6 Eddie Murphy FALSE FALSE 10 0.76923077
52 6 Patrick Weathers TRUE FALSE 10 0.76923077
53 6 Denny Dillon FALSE FALSE 13 1.00000000
54 6 Tony Rosato FALSE FALSE 1 0.07692308
55 6 Matthew Laurance TRUE FALSE 10 0.76923077
56 6 Gilbert Gottfried FALSE FALSE 13 1.00000000
57 6 Robin Duke FALSE FALSE 1 0.07692308
58 6 Gail Matthius FALSE TRUE 13 1.00000000
59 6 Laurie Metcalf TRUE FALSE 1 0.07692308
60 6 Yvonne Hudson TRUE FALSE 9 0.69230769
61 6 Emily Prager TRUE FALSE 1 0.07692308
62 6 Charles Rocket FALSE TRUE 13 1.00000000
63 6 Joe Piscopo FALSE FALSE 13 1.00000000
64 6 Ann Risley FALSE FALSE 13 1.00000000
65 6 Tim Kazurinsky FALSE FALSE 1 0.07692308
66 7 Tony Rosato FALSE FALSE 20 1.00000000
67 7 Eddie Murphy FALSE FALSE 20 1.00000000
68 7 Brian Doyle-Murray TRUE TRUE 20 1.00000000
69 7 Joe Piscopo FALSE FALSE 20 1.00000000
70 7 Christine Ebersole FALSE TRUE 20 1.00000000
71 7 Tim Kazurinsky FALSE FALSE 20 1.00000000
72 7 Robin Duke FALSE FALSE 20 1.00000000
73 7 Mary Gross FALSE TRUE 20 1.00000000
74 8 Gary Kroeger FALSE FALSE 20 1.00000000
75 8 Robin Duke FALSE FALSE 20 1.00000000
76 8 Mary Gross FALSE FALSE 20 1.00000000
77 8 Julia Louis-Dreyfus FALSE FALSE 20 1.00000000
78 8 Eddie Murphy FALSE FALSE 20 1.00000000
79 8 Tim Kazurinsky FALSE FALSE 20 1.00000000
80 8 Brad Hall FALSE TRUE 20 1.00000000
81 8 Joe Piscopo FALSE FALSE 20 1.00000000
82 9 Eddie Murphy FALSE FALSE 14 0.73684211
83 9 Mary Gross FALSE FALSE 19 1.00000000
84 9 Joe Piscopo FALSE FALSE 19 1.00000000
85 9 Brad Hall FALSE TRUE 19 1.00000000
86 9 Gary Kroeger FALSE FALSE 19 1.00000000
87 9 Julia Louis-Dreyfus FALSE FALSE 19 1.00000000
88 9 Robin Duke FALSE FALSE 19 1.00000000
89 9 Tim Kazurinsky FALSE FALSE 19 1.00000000
90 9 Jim Belushi FALSE FALSE 19 1.00000000
91 10 Gary Kroeger FALSE FALSE 17 1.00000000
92 10 Pamela Stephenson FALSE FALSE 17 1.00000000
93 10 Jim Belushi FALSE FALSE 17 1.00000000
94 10 Julia Louis-Dreyfus FALSE FALSE 17 1.00000000
95 10 Billy Crystal FALSE FALSE 17 1.00000000
96 10 Rich Hall FALSE FALSE 17 1.00000000
97 10 Christopher Guest FALSE TRUE 17 1.00000000
98 10 Martin Short FALSE FALSE 17 1.00000000
99 10 Harry Shearer FALSE FALSE 10 0.58823529
100 10 Mary Gross FALSE FALSE 17 1.00000000
101 11 A. Whitney Brown TRUE FALSE 8 0.44444444
102 11 Al Franken TRUE FALSE 6 0.33333333
103 11 Randy Quaid FALSE FALSE 18 1.00000000
104 11 Nora Dunn FALSE FALSE 18 1.00000000
105 11 Damon Wayans TRUE FALSE 12 0.66666667
106 11 Dennis Miller FALSE TRUE 18 1.00000000
107 11 Jon Lovitz FALSE FALSE 18 1.00000000
108 11 Anthony Michael Hall FALSE FALSE 18 1.00000000
109 11 Dan Vitale TRUE FALSE 18 1.00000000
110 11 Joan Cusack FALSE FALSE 18 1.00000000
111 11 Danitra Vance FALSE FALSE 18 1.00000000
112 11 Don Novello TRUE FALSE 18 1.00000000
113 11 Terry Sweeney FALSE FALSE 18 1.00000000
114 11 Robert Downey Jr. FALSE FALSE 18 1.00000000
115 12 Nora Dunn FALSE FALSE 20 1.00000000
116 12 Jon Lovitz FALSE FALSE 20 1.00000000
117 12 Victoria Jackson FALSE FALSE 20 1.00000000
118 12 A. Whitney Brown TRUE FALSE 20 1.00000000
119 12 Kevin Nealon TRUE FALSE 20 1.00000000
120 12 Dennis Miller FALSE TRUE 20 1.00000000
121 12 Phil Hartman FALSE FALSE 20 1.00000000
122 12 Dana Carvey FALSE FALSE 20 1.00000000
123 12 Jan Hooks FALSE FALSE 20 1.00000000
124 13 Jon Lovitz FALSE FALSE 13 1.00000000
125 13 A. Whitney Brown TRUE FALSE 13 1.00000000
126 13 Jan Hooks FALSE FALSE 13 1.00000000
127 13 Phil Hartman FALSE FALSE 13 1.00000000
128 13 Kevin Nealon FALSE FALSE 13 1.00000000
129 13 Dennis Miller FALSE TRUE 13 1.00000000
130 13 Dana Carvey FALSE FALSE 13 1.00000000
131 13 Victoria Jackson FALSE FALSE 13 1.00000000
132 13 Nora Dunn FALSE FALSE 13 1.00000000
133 14 Al Franken TRUE FALSE 20 1.00000000
134 14 A. Whitney Brown TRUE FALSE 20 1.00000000
135 14 Jan Hooks FALSE FALSE 20 1.00000000
136 14 Kevin Nealon FALSE FALSE 20 1.00000000
137 14 Dana Carvey FALSE FALSE 20 1.00000000
138 14 Mike Myers TRUE FALSE 11 0.55000000
139 14 Nora Dunn FALSE FALSE 20 1.00000000
140 14 Jon Lovitz FALSE FALSE 20 1.00000000
141 14 Dennis Miller FALSE TRUE 20 1.00000000
142 14 Phil Hartman FALSE FALSE 20 1.00000000
143 14 Victoria Jackson FALSE FALSE 20 1.00000000
144 14 Ben Stiller TRUE FALSE 6 0.30000000
145 15 Jon Lovitz FALSE FALSE 20 1.00000000
146 15 Nora Dunn FALSE FALSE 20 1.00000000
147 15 A. Whitney Brown TRUE FALSE 20 1.00000000
148 15 Dana Carvey FALSE FALSE 20 1.00000000
149 15 Jan Hooks FALSE FALSE 20 1.00000000
150 15 Victoria Jackson FALSE FALSE 20 1.00000000
151 15 Dennis Miller FALSE TRUE 20 1.00000000
152 15 Mike Myers FALSE FALSE 20 1.00000000
153 15 Al Franken TRUE FALSE 20 1.00000000
154 15 Kevin Nealon FALSE FALSE 20 1.00000000
155 15 Phil Hartman FALSE FALSE 20 1.00000000
156 16 Kevin Nealon FALSE FALSE 20 1.00000000
157 16 Adam Sandler TRUE FALSE 9 0.45000000
158 16 Dana Carvey FALSE FALSE 20 1.00000000
159 16 A. Whitney Brown TRUE FALSE 20 1.00000000
160 16 Al Franken TRUE FALSE 20 1.00000000
161 16 Phil Hartman FALSE FALSE 20 1.00000000
162 16 Mike Myers FALSE FALSE 20 1.00000000
163 16 Jan Hooks FALSE FALSE 20 1.00000000
164 16 Dennis Miller FALSE TRUE 20 1.00000000
165 16 David Spade TRUE FALSE 16 0.80000000
166 16 Rob Schneider TRUE FALSE 17 0.85000000
167 16 Victoria Jackson FALSE FALSE 20 1.00000000
168 16 Julia Sweeney TRUE FALSE 16 0.80000000
169 16 Chris Rock TRUE FALSE 20 1.00000000
170 16 Chris Farley TRUE FALSE 20 1.00000000
171 16 Tim Meadows TRUE FALSE 9 0.45000000
172 17 Kevin Nealon FALSE TRUE 20 1.00000000
173 17 Chris Farley FALSE FALSE 20 1.00000000
174 17 David Spade TRUE FALSE 20 1.00000000
175 17 Beth Cahill TRUE FALSE 15 0.75000000
176 17 Julia Sweeney FALSE FALSE 20 1.00000000
177 17 Melanie Hutsell TRUE FALSE 15 0.75000000
178 17 Adam Sandler TRUE FALSE 20 1.00000000
179 17 Mike Myers FALSE FALSE 20 1.00000000
180 17 Phil Hartman FALSE FALSE 20 1.00000000
181 17 Victoria Jackson FALSE FALSE 20 1.00000000
182 17 Dana Carvey FALSE FALSE 20 1.00000000
183 17 Tim Meadows TRUE FALSE 20 1.00000000
184 17 Rob Schneider TRUE FALSE 20 1.00000000
185 17 Robert Smigel TRUE FALSE 20 1.00000000
186 17 Siobhan Fallon TRUE FALSE 20 1.00000000
187 17 Ellen Cleghorne TRUE FALSE 20 1.00000000
188 17 Al Franken TRUE FALSE 20 1.00000000
189 17 Chris Rock FALSE FALSE 20 1.00000000
190 18 Julia Sweeney FALSE FALSE 20 1.00000000
191 18 Kevin Nealon FALSE TRUE 20 1.00000000
192 18 David Spade TRUE FALSE 20 1.00000000
193 18 Mike Myers FALSE FALSE 20 1.00000000
194 18 Phil Hartman FALSE FALSE 20 1.00000000
195 18 Chris Farley FALSE FALSE 20 1.00000000
196 18 Robert Smigel TRUE FALSE 20 1.00000000
197 18 Adam Sandler TRUE FALSE 20 1.00000000
198 18 Tim Meadows TRUE FALSE 20 1.00000000
199 18 Ellen Cleghorne TRUE FALSE 20 1.00000000
200 18 Dana Carvey FALSE FALSE 12 0.60000000
201 18 Chris Rock FALSE FALSE 20 1.00000000
202 18 Melanie Hutsell TRUE FALSE 20 1.00000000
203 18 Rob Schneider FALSE FALSE 20 1.00000000
204 18 Al Franken TRUE FALSE 20 1.00000000
205 19 Julia Sweeney FALSE FALSE 20 1.00000000
206 19 Sarah Silverman TRUE FALSE 18 0.90000000
207 19 Michael McKean FALSE FALSE 6 0.30000000
208 19 Kevin Nealon FALSE TRUE 20 1.00000000
209 19 Jay Mohr TRUE FALSE 18 0.90000000
210 19 David Spade FALSE FALSE 20 1.00000000
211 19 Norm MacDonald TRUE FALSE 19 0.95000000
212 19 Al Franken TRUE FALSE 20 1.00000000
213 19 Adam Sandler FALSE FALSE 20 1.00000000
214 19 Chris Farley FALSE FALSE 20 1.00000000
215 19 Phil Hartman FALSE FALSE 20 1.00000000
216 19 Mike Myers FALSE FALSE 20 1.00000000
217 19 Rob Schneider FALSE FALSE 20 1.00000000
218 19 Ellen Cleghorne FALSE FALSE 20 1.00000000
219 19 Tim Meadows FALSE FALSE 20 1.00000000
220 19 Melanie Hutsell FALSE FALSE 20 1.00000000
221 20 Kevin Nealon FALSE FALSE 20 1.00000000
222 20 Adam Sandler FALSE FALSE 20 1.00000000
223 20 David Spade FALSE FALSE 20 1.00000000
224 20 Chris Elliott FALSE FALSE 20 1.00000000
225 20 Janeane Garofalo FALSE FALSE 14 0.70000000
226 20 Michael McKean FALSE FALSE 20 1.00000000
227 20 Mark McKinney FALSE FALSE 11 0.55000000
228 20 Molly Shannon TRUE FALSE 7 0.35000000
229 20 Jay Mohr TRUE FALSE 20 1.00000000
230 20 Norm MacDonald FALSE TRUE 20 1.00000000
231 20 Chris Farley FALSE FALSE 20 1.00000000
232 20 Mike Myers FALSE FALSE 11 0.55000000
233 20 Ellen Cleghorne FALSE FALSE 20 1.00000000
234 20 Laura Kightlinger TRUE FALSE 20 1.00000000
235 20 Al Franken TRUE FALSE 20 1.00000000
236 20 Tim Meadows FALSE FALSE 20 1.00000000
237 20 Morwenna Banks FALSE FALSE 4 0.20000000
238 21 Chris Kattan TRUE FALSE 6 0.30000000
239 21 Fred Wolf TRUE FALSE 20 1.00000000
240 21 Norm MacDonald FALSE TRUE 20 1.00000000
241 21 Jim Breuer FALSE FALSE 20 1.00000000
242 21 Nancy Walls FALSE FALSE 20 1.00000000
243 21 Tim Meadows FALSE FALSE 20 1.00000000
244 21 Darrell Hammond FALSE FALSE 20 1.00000000
245 21 David Koechner FALSE FALSE 20 1.00000000
246 21 Will Ferrell FALSE FALSE 20 1.00000000
247 21 Molly Shannon FALSE FALSE 20 1.00000000
248 21 Colin Quinn TRUE FALSE 20 1.00000000
249 21 David Spade FALSE FALSE 20 1.00000000
250 21 Cheri Oteri FALSE FALSE 20 1.00000000
251 21 Mark McKinney FALSE FALSE 20 1.00000000
252 22 Darrell Hammond FALSE FALSE 20 1.00000000
253 22 Chris Kattan FALSE FALSE 20 1.00000000
254 22 Molly Shannon FALSE FALSE 20 1.00000000
255 22 Jim Breuer FALSE FALSE 20 1.00000000
256 22 Colin Quinn TRUE FALSE 20 1.00000000
257 22 Tracy Morgan FALSE FALSE 20 1.00000000
258 22 Cheri Oteri FALSE FALSE 20 1.00000000
259 22 Ana Gasteyer FALSE FALSE 20 1.00000000
260 22 Mark McKinney FALSE FALSE 20 1.00000000
261 22 Norm MacDonald FALSE TRUE 20 1.00000000
262 22 Tim Meadows FALSE FALSE 20 1.00000000
263 22 Fred Wolf TRUE FALSE 3 0.15000000
264 22 Will Ferrell FALSE FALSE 20 1.00000000
265 23 Tracy Morgan FALSE FALSE 20 1.00000000
266 23 Norm MacDonald FALSE TRUE 16 0.80000000
267 23 Cheri Oteri FALSE FALSE 20 1.00000000
268 23 Darrell Hammond FALSE FALSE 20 1.00000000
269 23 Tim Meadows FALSE FALSE 20 1.00000000
270 23 Jim Breuer FALSE FALSE 20 1.00000000
271 23 Molly Shannon FALSE FALSE 20 1.00000000
272 23 Chris Kattan FALSE FALSE 20 1.00000000
273 23 Ana Gasteyer FALSE FALSE 20 1.00000000
274 23 Will Ferrell FALSE FALSE 20 1.00000000
275 23 Colin Quinn FALSE TRUE 20 1.00000000
276 24 Darrell Hammond FALSE FALSE 19 1.00000000
277 24 Chris Parnell TRUE FALSE 19 1.00000000
278 24 Cheri Oteri FALSE FALSE 19 1.00000000
279 24 Chris Kattan FALSE FALSE 19 1.00000000
280 24 Colin Quinn FALSE TRUE 19 1.00000000
281 24 Molly Shannon FALSE FALSE 19 1.00000000
282 24 Will Ferrell FALSE FALSE 19 1.00000000
283 24 Tim Meadows FALSE FALSE 19 1.00000000
284 24 Jimmy Fallon TRUE FALSE 19 1.00000000
285 24 Ana Gasteyer FALSE FALSE 19 1.00000000
286 24 Horatio Sanz TRUE FALSE 19 1.00000000
287 24 Tracy Morgan FALSE FALSE 19 1.00000000
288 25 Cheri Oteri FALSE FALSE 20 1.00000000
289 25 Horatio Sanz FALSE FALSE 20 1.00000000
290 25 Ana Gasteyer FALSE FALSE 20 1.00000000
291 25 Colin Quinn FALSE TRUE 20 1.00000000
292 25 Tim Meadows FALSE FALSE 20 1.00000000
293 25 Darrell Hammond FALSE FALSE 20 1.00000000
294 25 Chris Kattan FALSE FALSE 20 1.00000000
295 25 Maya Rudolph TRUE FALSE 3 0.15000000
296 25 Will Ferrell FALSE FALSE 20 1.00000000
297 25 Jimmy Fallon FALSE FALSE 20 1.00000000
298 25 Rachel Dratch TRUE FALSE 18 0.90000000
299 25 Molly Shannon FALSE FALSE 20 1.00000000
300 25 Chris Parnell FALSE FALSE 20 1.00000000
301 25 Tracy Morgan FALSE FALSE 20 1.00000000
302 26 Ana Gasteyer FALSE FALSE 20 1.00000000
303 26 Jerry Minor TRUE FALSE 20 1.00000000
304 26 Maya Rudolph TRUE FALSE 20 1.00000000
305 26 Darrell Hammond FALSE FALSE 20 1.00000000
306 26 Horatio Sanz FALSE FALSE 20 1.00000000
307 26 Tina Fey TRUE TRUE 20 1.00000000
308 26 Rachel Dratch TRUE FALSE 20 1.00000000
309 26 Chris Parnell FALSE FALSE 20 1.00000000
310 26 Will Ferrell FALSE FALSE 20 1.00000000
311 26 Molly Shannon FALSE FALSE 12 0.60000000
312 26 Chris Kattan FALSE FALSE 20 1.00000000
313 26 Jimmy Fallon FALSE TRUE 20 1.00000000
314 26 Tracy Morgan FALSE FALSE 20 1.00000000
315 27 Tina Fey FALSE TRUE 20 1.00000000
316 27 Seth Meyers TRUE FALSE 20 1.00000000
317 27 Tracy Morgan FALSE FALSE 20 1.00000000
318 27 Jeff Richards TRUE FALSE 20 1.00000000
319 27 Amy Poehler TRUE FALSE 20 1.00000000
320 27 Maya Rudolph FALSE FALSE 20 1.00000000
321 27 Jimmy Fallon FALSE TRUE 20 1.00000000
322 27 Chris Kattan FALSE FALSE 20 1.00000000
323 27 Will Ferrell FALSE FALSE 20 1.00000000
324 27 Darrell Hammond FALSE FALSE 20 1.00000000
325 27 Horatio Sanz FALSE FALSE 20 1.00000000
326 27 Dean Edwards TRUE FALSE 20 1.00000000
327 27 Ana Gasteyer FALSE FALSE 20 1.00000000
328 27 Rachel Dratch FALSE FALSE 20 1.00000000
329 27 Chris Parnell FALSE FALSE 8 0.40000000
330 28 Dean Edwards TRUE FALSE 20 1.00000000
331 28 Fred Armisen TRUE FALSE 20 1.00000000
332 28 Chris Kattan FALSE FALSE 20 1.00000000
333 28 Darrell Hammond FALSE FALSE 20 1.00000000
334 28 Amy Poehler FALSE FALSE 20 1.00000000
335 28 Maya Rudolph FALSE FALSE 20 1.00000000
336 28 Chris Parnell FALSE FALSE 20 1.00000000
337 28 Rachel Dratch FALSE FALSE 20 1.00000000
338 28 Seth Meyers TRUE FALSE 20 1.00000000
339 28 Horatio Sanz FALSE FALSE 20 1.00000000
340 28 Tracy Morgan FALSE FALSE 20 1.00000000
341 28 Will Forte TRUE FALSE 20 1.00000000
342 28 Jimmy Fallon FALSE TRUE 20 1.00000000
343 28 Jeff Richards TRUE FALSE 20 1.00000000
344 28 Tina Fey FALSE TRUE 20 1.00000000
345 29 Maya Rudolph FALSE FALSE 20 1.00000000
346 29 Will Forte FALSE FALSE 20 1.00000000
347 29 Jeff Richards FALSE FALSE 10 0.50000000
348 29 Fred Armisen TRUE FALSE 20 1.00000000
349 29 Chris Parnell FALSE FALSE 20 1.00000000
350 29 Seth Meyers FALSE FALSE 20 1.00000000
351 29 Horatio Sanz FALSE FALSE 20 1.00000000
352 29 Jimmy Fallon FALSE TRUE 20 1.00000000
353 29 Rachel Dratch FALSE FALSE 20 1.00000000
354 29 Amy Poehler FALSE FALSE 20 1.00000000
355 29 Finesse Mitchell TRUE FALSE 20 1.00000000
356 29 Kenan Thompson TRUE FALSE 20 1.00000000
357 29 Darrell Hammond FALSE FALSE 20 1.00000000
358 29 Tina Fey FALSE TRUE 20 1.00000000
359 30 Chris Parnell FALSE FALSE 20 1.00000000
360 30 Rachel Dratch FALSE FALSE 20 1.00000000
361 30 Darrell Hammond FALSE FALSE 20 1.00000000
362 30 Rob Riggle TRUE FALSE 20 1.00000000
363 30 Maya Rudolph FALSE FALSE 20 1.00000000
364 30 Jason Sudeikis TRUE FALSE 3 0.15000000
365 30 Horatio Sanz FALSE FALSE 20 1.00000000
366 30 Seth Meyers FALSE FALSE 20 1.00000000
367 30 Tina Fey FALSE TRUE 20 1.00000000
368 30 Will Forte FALSE FALSE 20 1.00000000
369 30 Amy Poehler FALSE TRUE 20 1.00000000
370 30 Fred Armisen FALSE FALSE 20 1.00000000
371 30 Kenan Thompson TRUE FALSE 20 1.00000000
372 30 Finesse Mitchell TRUE FALSE 20 1.00000000
373 31 Chris Parnell FALSE FALSE 19 1.00000000
374 31 Seth Meyers FALSE FALSE 19 1.00000000
375 31 Kristen Wiig TRUE FALSE 15 0.78947368
376 31 Tina Fey FALSE TRUE 19 1.00000000
377 31 Andy Samberg TRUE FALSE 19 1.00000000
378 31 Amy Poehler FALSE TRUE 19 1.00000000
379 31 Darrell Hammond FALSE FALSE 19 1.00000000
380 31 Bill Hader TRUE FALSE 19 1.00000000
381 31 Kenan Thompson FALSE FALSE 19 1.00000000
382 31 Will Forte FALSE FALSE 19 1.00000000
383 31 Finesse Mitchell FALSE FALSE 19 1.00000000
384 31 Maya Rudolph FALSE FALSE 19 1.00000000
385 31 Rachel Dratch FALSE FALSE 19 1.00000000
386 31 Horatio Sanz FALSE TRUE 19 1.00000000
387 31 Fred Armisen FALSE FALSE 19 1.00000000
388 31 Jason Sudeikis TRUE FALSE 19 1.00000000
389 32 Fred Armisen FALSE FALSE 20 1.00000000
390 32 Seth Meyers FALSE TRUE 20 1.00000000
391 32 Bill Hader FALSE FALSE 20 1.00000000
392 32 Will Forte FALSE FALSE 20 1.00000000
393 32 Maya Rudolph FALSE FALSE 20 1.00000000
394 32 Amy Poehler FALSE TRUE 20 1.00000000
395 32 Andy Samberg FALSE FALSE 20 1.00000000
396 32 Kenan Thompson FALSE FALSE 20 1.00000000
397 32 Kristen Wiig FALSE FALSE 20 1.00000000
398 32 Darrell Hammond FALSE FALSE 20 1.00000000
399 32 Jason Sudeikis FALSE FALSE 20 1.00000000
400 33 Seth Meyers FALSE TRUE 12 1.00000000
401 33 Fred Armisen FALSE FALSE 12 1.00000000
402 33 Kenan Thompson FALSE FALSE 12 1.00000000
403 33 Casey Wilson TRUE FALSE 8 0.66666667
404 33 Bill Hader FALSE FALSE 12 1.00000000
405 33 Will Forte FALSE FALSE 12 1.00000000
406 33 Maya Rudolph FALSE FALSE 4 0.33333333
407 33 Andy Samberg FALSE FALSE 12 1.00000000
408 33 Darrell Hammond FALSE FALSE 12 1.00000000
409 33 Amy Poehler FALSE TRUE 12 1.00000000
410 33 Kristen Wiig FALSE FALSE 12 1.00000000
411 33 Jason Sudeikis FALSE FALSE 12 1.00000000
412 34 Michaela Watkins TRUE FALSE 15 0.68181818
413 34 Bobby Moynihan TRUE FALSE 22 1.00000000
414 34 Amy Poehler FALSE TRUE 11 0.50000000
415 34 Bill Hader FALSE FALSE 22 1.00000000
416 34 Seth Meyers FALSE TRUE 22 1.00000000
417 34 Kristen Wiig FALSE FALSE 22 1.00000000
418 34 Darrell Hammond FALSE FALSE 22 1.00000000
419 34 Fred Armisen FALSE FALSE 22 1.00000000
420 34 Casey Wilson TRUE FALSE 22 1.00000000
421 34 Abby Elliott TRUE FALSE 15 0.68181818
422 34 Jason Sudeikis FALSE FALSE 22 1.00000000
423 34 Andy Samberg FALSE FALSE 22 1.00000000
424 34 Kenan Thompson FALSE FALSE 22 1.00000000
425 34 Will Forte FALSE FALSE 22 1.00000000
426 35 Bobby Moynihan TRUE FALSE 22 1.00000000
427 35 Seth Meyers FALSE TRUE 22 1.00000000
428 35 Will Forte FALSE FALSE 22 1.00000000
429 35 Andy Samberg FALSE FALSE 22 1.00000000
430 35 Bill Hader FALSE FALSE 22 1.00000000
431 35 Fred Armisen FALSE FALSE 22 1.00000000
432 35 Abby Elliott TRUE FALSE 22 1.00000000
433 35 Kenan Thompson FALSE FALSE 22 1.00000000
434 35 Kristen Wiig FALSE FALSE 22 1.00000000
435 35 Jenny Slate TRUE FALSE 22 1.00000000
436 35 Jason Sudeikis FALSE FALSE 22 1.00000000
437 35 Nasim Pedrad TRUE FALSE 22 1.00000000
438 36 Abby Elliott FALSE FALSE 22 1.00000000
439 36 Paul Brittain TRUE FALSE 22 1.00000000
440 36 Jason Sudeikis FALSE FALSE 22 1.00000000
441 36 Bobby Moynihan FALSE FALSE 22 1.00000000
442 36 Jay Pharoah TRUE FALSE 22 1.00000000
443 36 Andy Samberg FALSE FALSE 22 1.00000000
444 36 Seth Meyers FALSE TRUE 22 1.00000000
445 36 Bill Hader FALSE FALSE 22 1.00000000
446 36 Kenan Thompson FALSE FALSE 22 1.00000000
447 36 Fred Armisen FALSE FALSE 22 1.00000000
448 36 Taran Killam TRUE FALSE 22 1.00000000
449 36 Kristen Wiig FALSE FALSE 22 1.00000000
450 36 Nasim Pedrad TRUE FALSE 22 1.00000000
451 36 Vanessa Bayer TRUE FALSE 22 1.00000000
452 37 Seth Meyers FALSE TRUE 22 1.00000000
453 37 Bobby Moynihan FALSE FALSE 22 1.00000000
454 37 Abby Elliott FALSE FALSE 22 1.00000000
455 37 Jason Sudeikis FALSE FALSE 22 1.00000000
456 37 Jay Pharoah TRUE FALSE 22 1.00000000
457 37 Bill Hader FALSE FALSE 22 1.00000000
458 37 Nasim Pedrad FALSE FALSE 22 1.00000000
459 37 Kenan Thompson FALSE FALSE 22 1.00000000
460 37 Taran Killam TRUE FALSE 22 1.00000000
461 37 Andy Samberg FALSE FALSE 22 1.00000000
462 37 Paul Brittain TRUE FALSE 12 0.54545455
463 37 Fred Armisen FALSE FALSE 22 1.00000000
464 37 Kate McKinnon TRUE FALSE 5 0.22727273
465 37 Kristen Wiig FALSE FALSE 22 1.00000000
466 37 Vanessa Bayer TRUE FALSE 22 1.00000000
467 38 Bill Hader FALSE FALSE 21 1.00000000
468 38 Seth Meyers FALSE TRUE 21 1.00000000
469 38 Aidy Bryant TRUE FALSE 21 1.00000000
470 38 Kate McKinnon TRUE FALSE 21 1.00000000
471 38 Tim Robinson TRUE FALSE 21 1.00000000
472 38 Jason Sudeikis FALSE FALSE 21 1.00000000
473 38 Kenan Thompson FALSE FALSE 21 1.00000000
474 38 Bobby Moynihan FALSE FALSE 21 1.00000000
475 38 Taran Killam FALSE FALSE 21 1.00000000
476 38 Jay Pharoah FALSE FALSE 21 1.00000000
477 38 Vanessa Bayer FALSE FALSE 21 1.00000000
478 38 Nasim Pedrad FALSE FALSE 21 1.00000000
479 38 Cecily Strong TRUE FALSE 21 1.00000000
480 38 Fred Armisen FALSE FALSE 21 1.00000000
481 39 Noel Wells TRUE FALSE 21 1.00000000
482 39 Nasim Pedrad FALSE FALSE 21 1.00000000
483 39 Seth Meyers FALSE TRUE 13 0.61904762
484 39 Bobby Moynihan FALSE FALSE 21 1.00000000
485 39 Sasheer Zamata TRUE FALSE 11 0.52380952
486 39 Jay Pharoah FALSE FALSE 21 1.00000000
487 39 Mike O'Brien TRUE FALSE 21 1.00000000
488 39 Beck Bennett TRUE FALSE 21 1.00000000
489 39 Vanessa Bayer FALSE FALSE 21 1.00000000
490 39 Kenan Thompson FALSE FALSE 21 1.00000000
491 39 Aidy Bryant FALSE FALSE 21 1.00000000
492 39 Cecily Strong FALSE TRUE 21 1.00000000
493 39 Colin Jost TRUE TRUE 8 0.38095238
494 39 Brooks Wheelan TRUE FALSE 21 1.00000000
495 39 Kyle Mooney TRUE FALSE 21 1.00000000
496 39 Taran Killam FALSE FALSE 21 1.00000000
497 39 John Milhiser TRUE FALSE 21 1.00000000
498 39 Kate McKinnon FALSE FALSE 21 1.00000000
499 40 Michael Che TRUE TRUE 21 1.00000000
500 40 Jay Pharoah FALSE FALSE 21 1.00000000
501 40 Aidy Bryant FALSE FALSE 21 1.00000000
502 40 Kenan Thompson FALSE FALSE 21 1.00000000
503 40 Pete Davidson TRUE FALSE 21 1.00000000
504 40 Vanessa Bayer FALSE FALSE 21 1.00000000
505 40 Beck Bennett TRUE FALSE 21 1.00000000
506 40 Sasheer Zamata TRUE FALSE 21 1.00000000
507 40 Cecily Strong FALSE FALSE 21 1.00000000
508 40 Bobby Moynihan FALSE FALSE 21 1.00000000
509 40 Kyle Mooney TRUE FALSE 21 1.00000000
510 40 Taran Killam FALSE FALSE 21 1.00000000
511 40 Kate McKinnon FALSE FALSE 21 1.00000000
512 40 Colin Jost TRUE TRUE 21 1.00000000
513 40 Leslie Jones TRUE FALSE 18 0.85714286
514 41 Taran Killam FALSE FALSE 21 1.00000000
515 41 Vanessa Bayer FALSE FALSE 21 1.00000000
516 41 Beck Bennett FALSE FALSE 21 1.00000000
517 41 Cecily Strong FALSE FALSE 21 1.00000000
518 41 Aidy Bryant FALSE FALSE 21 1.00000000
519 41 Kate McKinnon FALSE FALSE 21 1.00000000
520 41 Sasheer Zamata FALSE FALSE 21 1.00000000
521 41 Kenan Thompson FALSE FALSE 21 1.00000000
522 41 Bobby Moynihan FALSE FALSE 21 1.00000000
523 41 Pete Davidson TRUE FALSE 21 1.00000000
524 41 Jay Pharoah FALSE FALSE 21 1.00000000
525 41 Michael Che TRUE TRUE 21 1.00000000
526 41 Colin Jost FALSE TRUE 21 1.00000000
527 41 Kyle Mooney FALSE FALSE 21 1.00000000
528 41 Leslie Jones TRUE FALSE 21 1.00000000
529 41 Jon Rudnitsky TRUE FALSE 21 1.00000000
530 42 Vanessa Bayer FALSE FALSE 21 1.00000000
531 42 Leslie Jones FALSE FALSE 21 1.00000000
532 42 Beck Bennett FALSE FALSE 21 1.00000000
533 42 Pete Davidson FALSE FALSE 21 1.00000000
534 42 Sasheer Zamata FALSE FALSE 21 1.00000000
535 42 Bobby Moynihan FALSE FALSE 21 1.00000000
536 42 Colin Jost FALSE TRUE 21 1.00000000
537 42 Kenan Thompson FALSE FALSE 21 1.00000000
538 42 Aidy Bryant FALSE FALSE 21 1.00000000
539 42 Kate McKinnon FALSE FALSE 21 1.00000000
540 42 Cecily Strong FALSE FALSE 21 1.00000000
541 42 Mikey Day TRUE FALSE 21 1.00000000
542 42 Melissa Villasenor TRUE FALSE 21 1.00000000
543 42 Michael Che FALSE TRUE 21 1.00000000
544 42 Kyle Mooney FALSE FALSE 21 1.00000000
545 42 Alex Moffat TRUE FALSE 21 1.00000000
546 43 Alex Moffat TRUE FALSE 21 1.00000000
547 43 Beck Bennett FALSE FALSE 21 1.00000000
548 43 Leslie Jones FALSE FALSE 21 1.00000000
549 43 Pete Davidson FALSE FALSE 21 1.00000000
550 43 Colin Jost FALSE TRUE 21 1.00000000
551 43 Melissa Villasenor TRUE FALSE 21 1.00000000
552 43 Luke Null TRUE FALSE 21 1.00000000
553 43 Kyle Mooney FALSE FALSE 21 1.00000000
554 43 Cecily Strong FALSE FALSE 21 1.00000000
555 43 Chris Redd TRUE FALSE 21 1.00000000
556 43 Mikey Day TRUE FALSE 21 1.00000000
557 43 Heidi Gardner TRUE FALSE 21 1.00000000
558 43 Kate McKinnon FALSE FALSE 21 1.00000000
559 43 Michael Che FALSE TRUE 21 1.00000000
560 43 Kenan Thompson FALSE FALSE 21 1.00000000
561 43 Aidy Bryant FALSE FALSE 21 1.00000000
562 44 Beck Bennett FALSE FALSE 21 1.00000000
563 44 Ego Nwodim TRUE FALSE 21 1.00000000
564 44 Aidy Bryant FALSE FALSE 21 1.00000000
565 44 Chris Redd TRUE FALSE 21 1.00000000
566 44 Colin Jost FALSE TRUE 21 1.00000000
567 44 Mikey Day FALSE FALSE 21 1.00000000
568 44 Pete Davidson FALSE FALSE 21 1.00000000
569 44 Leslie Jones FALSE FALSE 21 1.00000000
570 44 Kenan Thompson FALSE FALSE 21 1.00000000
571 44 Melissa Villasenor FALSE FALSE 21 1.00000000
572 44 Kate McKinnon FALSE FALSE 21 1.00000000
573 44 Alex Moffat FALSE FALSE 21 1.00000000
574 44 Heidi Gardner TRUE FALSE 21 1.00000000
575 44 Kyle Mooney FALSE FALSE 21 1.00000000
576 44 Michael Che FALSE TRUE 21 1.00000000
577 44 Cecily Strong FALSE FALSE 21 1.00000000
578 45 Mikey Day FALSE FALSE 18 1.00000000
579 45 Bowen Yang TRUE FALSE 18 1.00000000
580 45 Alex Moffat FALSE FALSE 18 1.00000000
581 45 Melissa Villasenor FALSE FALSE 18 1.00000000
582 45 Pete Davidson FALSE FALSE 18 1.00000000
583 45 Colin Jost FALSE TRUE 18 1.00000000
584 45 Kate McKinnon FALSE FALSE 18 1.00000000
585 45 Kyle Mooney FALSE FALSE 18 1.00000000
586 45 Chris Redd FALSE FALSE 18 1.00000000
587 45 Kenan Thompson FALSE FALSE 18 1.00000000
588 45 Beck Bennett FALSE FALSE 18 1.00000000
589 45 Michael Che FALSE TRUE 18 1.00000000
590 45 Heidi Gardner FALSE FALSE 18 1.00000000
591 45 Cecily Strong FALSE FALSE 18 1.00000000
592 45 Chloe Fineman TRUE FALSE 18 1.00000000
593 45 Aidy Bryant FALSE FALSE 18 1.00000000
594 45 Ego Nwodim TRUE FALSE 18 1.00000000
595 46 Kate McKinnon FALSE FALSE 17 1.00000000
596 46 Chloe Fineman TRUE FALSE 17 1.00000000
597 46 Kyle Mooney FALSE FALSE 17 1.00000000
598 46 Colin Jost FALSE TRUE 17 1.00000000
599 46 Melissa Villasenor FALSE FALSE 17 1.00000000
600 46 Andrew Dismukes TRUE FALSE 17 1.00000000
601 46 Kenan Thompson FALSE FALSE 17 1.00000000
602 46 Ego Nwodim FALSE FALSE 17 1.00000000
603 46 Chris Redd FALSE FALSE 17 1.00000000
604 46 Michael Che FALSE TRUE 17 1.00000000
605 46 Lauren Holt TRUE FALSE 17 1.00000000
606 46 Pete Davidson FALSE FALSE 17 1.00000000
607 46 Heidi Gardner FALSE FALSE 17 1.00000000
608 46 Mikey Day FALSE FALSE 17 1.00000000
609 46 Punkie Johnson TRUE FALSE 17 1.00000000
610 46 Bowen Yang TRUE FALSE 17 1.00000000
611 46 Cecily Strong FALSE FALSE 17 1.00000000
612 46 Aidy Bryant FALSE FALSE 17 1.00000000
613 46 Alex Moffat FALSE FALSE 17 1.00000000
614 46 Beck Bennett FALSE FALSE 17 1.00000000
type gender year first_epid last_epid seasons_n_episodes
1 cast male 1975 1975-10-11 1976-07-31 24
2 cast female 1975 1975-10-11 1976-07-31 24
3 cast male 1975 1975-10-11 1976-07-31 24
4 cast female 1975 1975-10-11 1976-07-31 24
5 cast male 1975 1975-10-11 1976-07-31 24
6 cast female 1975 1975-10-11 1976-07-31 24
7 cast male 1975 1975-10-11 1976-07-31 24
8 cast male 1975 1975-10-11 1976-07-31 24
9 cast male 1975 1975-10-11 1976-07-31 24
10 cast female 1976 1976-09-18 1977-05-21 22
11 cast male 1976 1976-09-18 1977-05-21 22
12 cast male 1976 1976-09-18 1977-05-21 22
13 cast male 1976 1976-09-18 1977-05-21 22
14 cast male 1976 1976-09-18 1977-05-21 22
15 cast female 1976 1976-09-18 1977-05-21 22
16 cast male 1976 1976-09-18 1977-05-21 22
17 cast female 1976 1976-09-18 1977-05-21 22
18 cast male 1977 1977-09-24 1978-05-20 20
19 cast female 1977 1977-09-24 1978-05-20 20
20 cast male 1977 1977-09-24 1978-05-20 20
21 cast male 1977 1977-09-24 1978-05-20 20
22 cast male 1977 1977-09-24 1978-05-20 20
23 cast female 1977 1977-09-24 1978-05-20 20
24 cast male 1977 1977-09-24 1978-05-20 20
25 cast male 1977 1977-09-24 1978-05-20 20
26 cast female 1977 1977-09-24 1978-05-20 20
27 cast male 1978 1978-10-07 1979-05-26 20
28 cast male 1978 1978-10-07 1979-05-26 20
29 cast male 1978 1978-10-07 1979-05-26 20
30 cast female 1978 1978-10-07 1979-05-26 20
31 cast male 1978 1978-10-07 1979-05-26 20
32 cast male 1978 1978-10-07 1979-05-26 20
33 cast female 1978 1978-10-07 1979-05-26 20
34 cast female 1978 1978-10-07 1979-05-26 20
35 cast male 1978 1978-10-07 1979-05-26 20
36 cast male 1979 1979-10-13 1980-05-24 20
37 cast male 1979 1979-10-13 1980-05-24 20
38 cast male 1979 1979-10-13 1980-05-24 20
39 cast male 1979 1979-10-13 1980-05-24 20
40 cast male 1979 1979-10-13 1980-05-24 20
41 cast male 1979 1979-10-13 1980-05-24 20
42 cast female 1979 1979-10-13 1980-05-24 20
43 cast female 1979 1979-10-13 1980-05-24 20
44 cast male 1979 1979-10-13 1980-05-24 20
45 cast male 1979 1979-10-13 1980-05-24 20
46 cast male 1979 1979-10-13 1980-05-24 20
47 cast female 1979 1979-10-13 1980-05-24 20
48 cast male 1979 1979-10-13 1980-05-24 20
49 cast male 1979 1979-10-13 1980-05-24 20
50 cast male 1979 1979-10-13 1980-05-24 20
51 cast male 1980 1980-11-15 1981-04-11 13
52 cast male 1980 1980-11-15 1981-04-11 13
53 cast male 1980 1980-11-15 1981-04-11 13
54 cast male 1980 1980-11-15 1981-04-11 13
55 cast male 1980 1980-11-15 1981-04-11 13
56 unknown male 1980 1980-11-15 1981-04-11 13
57 cast female 1980 1980-11-15 1981-04-11 13
58 cast female 1980 1980-11-15 1981-04-11 13
59 cast female 1980 1980-11-15 1981-04-11 13
60 cast female 1980 1980-11-15 1981-04-11 13
61 cast female 1980 1980-11-15 1981-04-11 13
62 cast male 1980 1980-11-15 1981-04-11 13
63 cast male 1980 1980-11-15 1981-04-11 13
64 cast female 1980 1980-11-15 1981-04-11 13
65 cast male 1980 1980-11-15 1981-04-11 13
66 cast male 1981 1981-10-03 1982-05-22 20
67 cast male 1981 1981-10-03 1982-05-22 20
68 cast male 1981 1981-10-03 1982-05-22 20
69 cast male 1981 1981-10-03 1982-05-22 20
70 cast female 1981 1981-10-03 1982-05-22 20
71 cast male 1981 1981-10-03 1982-05-22 20
72 cast female 1981 1981-10-03 1982-05-22 20
73 cast female 1981 1981-10-03 1982-05-22 20
74 cast male 1982 1982-09-25 1983-05-14 20
75 cast female 1982 1982-09-25 1983-05-14 20
76 cast female 1982 1982-09-25 1983-05-14 20
77 cast female 1982 1982-09-25 1983-05-14 20
78 cast male 1982 1982-09-25 1983-05-14 20
79 cast male 1982 1982-09-25 1983-05-14 20
80 cast male 1982 1982-09-25 1983-05-14 20
81 cast male 1982 1982-09-25 1983-05-14 20
82 cast male 1983 1983-10-08 1984-05-12 19
83 cast female 1983 1983-10-08 1984-05-12 19
84 cast male 1983 1983-10-08 1984-05-12 19
85 cast male 1983 1983-10-08 1984-05-12 19
86 cast male 1983 1983-10-08 1984-05-12 19
87 cast female 1983 1983-10-08 1984-05-12 19
88 cast female 1983 1983-10-08 1984-05-12 19
89 cast male 1983 1983-10-08 1984-05-12 19
90 cast male 1983 1983-10-08 1984-05-12 19
91 cast male 1984 1984-10-06 1985-04-13 17
92 cast female 1984 1984-10-06 1985-04-13 17
93 cast male 1984 1984-10-06 1985-04-13 17
94 cast female 1984 1984-10-06 1985-04-13 17
95 cast male 1984 1984-10-06 1985-04-13 17
96 cast male 1984 1984-10-06 1985-04-13 17
97 cast male 1984 1984-10-06 1985-04-13 17
98 cast male 1984 1984-10-06 1985-04-13 17
99 cast male 1984 1984-10-06 1985-04-13 17
100 cast female 1984 1984-10-06 1985-04-13 17
101 cast male 1985 1985-11-09 1986-05-24 18
102 cast male 1985 1985-11-09 1986-05-24 18
103 cast male 1985 1985-11-09 1986-05-24 18
104 cast female 1985 1985-11-09 1986-05-24 18
105 cast male 1985 1985-11-09 1986-05-24 18
106 cast male 1985 1985-11-09 1986-05-24 18
107 cast male 1985 1985-11-09 1986-05-24 18
108 cast male 1985 1985-11-09 1986-05-24 18
109 cast male 1985 1985-11-09 1986-05-24 18
110 cast female 1985 1985-11-09 1986-05-24 18
111 cast female 1985 1985-11-09 1986-05-24 18
112 cast male 1985 1985-11-09 1986-05-24 18
113 cast male 1985 1985-11-09 1986-05-24 18
114 cast male 1985 1985-11-09 1986-05-24 18
115 cast female 1986 1986-10-11 1987-05-23 20
116 cast male 1986 1986-10-11 1987-05-23 20
117 cast female 1986 1986-10-11 1987-05-23 20
118 cast male 1986 1986-10-11 1987-05-23 20
119 cast male 1986 1986-10-11 1987-05-23 20
120 cast male 1986 1986-10-11 1987-05-23 20
121 cast male 1986 1986-10-11 1987-05-23 20
122 cast male 1986 1986-10-11 1987-05-23 20
123 cast female 1986 1986-10-11 1987-05-23 20
124 cast male 1987 1987-10-17 1988-02-27 13
125 cast male 1987 1987-10-17 1988-02-27 13
126 cast female 1987 1987-10-17 1988-02-27 13
127 cast male 1987 1987-10-17 1988-02-27 13
128 cast male 1987 1987-10-17 1988-02-27 13
129 cast male 1987 1987-10-17 1988-02-27 13
130 cast male 1987 1987-10-17 1988-02-27 13
131 cast female 1987 1987-10-17 1988-02-27 13
132 cast female 1987 1987-10-17 1988-02-27 13
133 cast male 1988 1988-10-08 1989-05-20 20
134 cast male 1988 1988-10-08 1989-05-20 20
135 cast female 1988 1988-10-08 1989-05-20 20
136 cast male 1988 1988-10-08 1989-05-20 20
137 cast male 1988 1988-10-08 1989-05-20 20
138 cast male 1988 1988-10-08 1989-05-20 20
139 cast female 1988 1988-10-08 1989-05-20 20
140 cast male 1988 1988-10-08 1989-05-20 20
141 cast male 1988 1988-10-08 1989-05-20 20
142 cast male 1988 1988-10-08 1989-05-20 20
143 cast female 1988 1988-10-08 1989-05-20 20
144 cast male 1988 1988-10-08 1989-05-20 20
145 cast male 1989 1989-09-30 1990-05-19 20
146 cast female 1989 1989-09-30 1990-05-19 20
147 cast male 1989 1989-09-30 1990-05-19 20
148 cast male 1989 1989-09-30 1990-05-19 20
149 cast female 1989 1989-09-30 1990-05-19 20
150 cast female 1989 1989-09-30 1990-05-19 20
151 cast male 1989 1989-09-30 1990-05-19 20
152 cast male 1989 1989-09-30 1990-05-19 20
153 cast male 1989 1989-09-30 1990-05-19 20
154 cast male 1989 1989-09-30 1990-05-19 20
155 cast male 1989 1989-09-30 1990-05-19 20
156 cast male 1990 1990-09-29 1991-05-18 20
157 cast male 1990 1990-09-29 1991-05-18 20
158 cast male 1990 1990-09-29 1991-05-18 20
159 cast male 1990 1990-09-29 1991-05-18 20
160 cast male 1990 1990-09-29 1991-05-18 20
161 cast male 1990 1990-09-29 1991-05-18 20
162 cast male 1990 1990-09-29 1991-05-18 20
163 cast female 1990 1990-09-29 1991-05-18 20
164 cast male 1990 1990-09-29 1991-05-18 20
165 unknown male 1990 1990-09-29 1991-05-18 20
166 cast male 1990 1990-09-29 1991-05-18 20
167 cast female 1990 1990-09-29 1991-05-18 20
168 cast female 1990 1990-09-29 1991-05-18 20
169 cast male 1990 1990-09-29 1991-05-18 20
170 cast male 1990 1990-09-29 1991-05-18 20
171 cast male 1990 1990-09-29 1991-05-18 20
172 cast male 1991 1991-09-28 1992-05-16 20
173 cast male 1991 1991-09-28 1992-05-16 20
174 unknown male 1991 1991-09-28 1992-05-16 20
175 cast female 1991 1991-09-28 1992-05-16 20
176 cast female 1991 1991-09-28 1992-05-16 20
177 cast female 1991 1991-09-28 1992-05-16 20
178 cast male 1991 1991-09-28 1992-05-16 20
179 cast male 1991 1991-09-28 1992-05-16 20
180 cast male 1991 1991-09-28 1992-05-16 20
181 cast female 1991 1991-09-28 1992-05-16 20
182 cast male 1991 1991-09-28 1992-05-16 20
183 cast male 1991 1991-09-28 1992-05-16 20
184 cast male 1991 1991-09-28 1992-05-16 20
185 cast male 1991 1991-09-28 1992-05-16 20
186 cast female 1991 1991-09-28 1992-05-16 20
187 cast female 1991 1991-09-28 1992-05-16 20
188 cast male 1991 1991-09-28 1992-05-16 20
189 cast male 1991 1991-09-28 1992-05-16 20
190 cast female 1992 1992-09-26 1993-05-15 20
191 cast male 1992 1992-09-26 1993-05-15 20
192 unknown male 1992 1992-09-26 1993-05-15 20
193 cast male 1992 1992-09-26 1993-05-15 20
194 cast male 1992 1992-09-26 1993-05-15 20
195 cast male 1992 1992-09-26 1993-05-15 20
196 cast male 1992 1992-09-26 1993-05-15 20
197 cast male 1992 1992-09-26 1993-05-15 20
198 cast male 1992 1992-09-26 1993-05-15 20
199 cast female 1992 1992-09-26 1993-05-15 20
200 cast male 1992 1992-09-26 1993-05-15 20
201 cast male 1992 1992-09-26 1993-05-15 20
202 cast female 1992 1992-09-26 1993-05-15 20
203 cast male 1992 1992-09-26 1993-05-15 20
204 cast male 1992 1992-09-26 1993-05-15 20
205 cast female 1993 1993-09-25 1994-05-14 20
206 cast female 1993 1993-09-25 1994-05-14 20
207 cast male 1993 1993-09-25 1994-05-14 20
208 cast male 1993 1993-09-25 1994-05-14 20
209 cast male 1993 1993-09-25 1994-05-14 20
210 unknown male 1993 1993-09-25 1994-05-14 20
211 cast male 1993 1993-09-25 1994-05-14 20
212 cast male 1993 1993-09-25 1994-05-14 20
213 cast male 1993 1993-09-25 1994-05-14 20
214 cast male 1993 1993-09-25 1994-05-14 20
215 cast male 1993 1993-09-25 1994-05-14 20
216 cast male 1993 1993-09-25 1994-05-14 20
217 cast male 1993 1993-09-25 1994-05-14 20
218 cast female 1993 1993-09-25 1994-05-14 20
219 cast male 1993 1993-09-25 1994-05-14 20
220 cast female 1993 1993-09-25 1994-05-14 20
221 cast male 1994 1994-09-24 1995-05-13 20
222 cast male 1994 1994-09-24 1995-05-13 20
223 unknown male 1994 1994-09-24 1995-05-13 20
224 cast male 1994 1994-09-24 1995-05-13 20
225 cast female 1994 1994-09-24 1995-05-13 20
226 cast male 1994 1994-09-24 1995-05-13 20
227 cast male 1994 1994-09-24 1995-05-13 20
228 cast female 1994 1994-09-24 1995-05-13 20
229 cast male 1994 1994-09-24 1995-05-13 20
230 cast male 1994 1994-09-24 1995-05-13 20
231 cast male 1994 1994-09-24 1995-05-13 20
232 cast male 1994 1994-09-24 1995-05-13 20
233 cast female 1994 1994-09-24 1995-05-13 20
234 cast female 1994 1994-09-24 1995-05-13 20
235 cast male 1994 1994-09-24 1995-05-13 20
236 cast male 1994 1994-09-24 1995-05-13 20
237 cast female 1994 1994-09-24 1995-05-13 20
238 cast male 1995 1995-09-30 1996-05-18 20
239 cast male 1995 1995-09-30 1996-05-18 20
240 cast male 1995 1995-09-30 1996-05-18 20
241 cast male 1995 1995-09-30 1996-05-18 20
242 cast female 1995 1995-09-30 1996-05-18 20
243 cast male 1995 1995-09-30 1996-05-18 20
244 cast male 1995 1995-09-30 1996-05-18 20
245 cast male 1995 1995-09-30 1996-05-18 20
246 cast male 1995 1995-09-30 1996-05-18 20
247 cast female 1995 1995-09-30 1996-05-18 20
248 cast male 1995 1995-09-30 1996-05-18 20
249 unknown male 1995 1995-09-30 1996-05-18 20
250 cast female 1995 1995-09-30 1996-05-18 20
251 cast male 1995 1995-09-30 1996-05-18 20
252 cast male 1996 1996-09-28 1997-05-17 20
253 cast male 1996 1996-09-28 1997-05-17 20
254 cast female 1996 1996-09-28 1997-05-17 20
255 cast male 1996 1996-09-28 1997-05-17 20
256 cast male 1996 1996-09-28 1997-05-17 20
257 cast male 1996 1996-09-28 1997-05-17 20
258 cast female 1996 1996-09-28 1997-05-17 20
259 cast female 1996 1996-09-28 1997-05-17 20
260 cast male 1996 1996-09-28 1997-05-17 20
261 cast male 1996 1996-09-28 1997-05-17 20
262 cast male 1996 1996-09-28 1997-05-17 20
263 cast male 1996 1996-09-28 1997-05-17 20
264 cast male 1996 1996-09-28 1997-05-17 20
265 cast male 1997 1997-09-27 1998-05-09 20
266 cast male 1997 1997-09-27 1998-05-09 20
267 cast female 1997 1997-09-27 1998-05-09 20
268 cast male 1997 1997-09-27 1998-05-09 20
269 cast male 1997 1997-09-27 1998-05-09 20
270 cast male 1997 1997-09-27 1998-05-09 20
271 cast female 1997 1997-09-27 1998-05-09 20
272 cast male 1997 1997-09-27 1998-05-09 20
273 cast female 1997 1997-09-27 1998-05-09 20
274 cast male 1997 1997-09-27 1998-05-09 20
275 cast male 1997 1997-09-27 1998-05-09 20
276 cast male 1998 1998-09-26 1999-05-15 19
277 cast male 1998 1998-09-26 1999-05-15 19
278 cast female 1998 1998-09-26 1999-05-15 19
279 cast male 1998 1998-09-26 1999-05-15 19
280 cast male 1998 1998-09-26 1999-05-15 19
281 cast female 1998 1998-09-26 1999-05-15 19
282 cast male 1998 1998-09-26 1999-05-15 19
283 cast male 1998 1998-09-26 1999-05-15 19
284 cast male 1998 1998-09-26 1999-05-15 19
285 cast female 1998 1998-09-26 1999-05-15 19
286 cast male 1998 1998-09-26 1999-05-15 19
287 cast male 1998 1998-09-26 1999-05-15 19
288 cast female 1999 1999-10-02 2000-05-20 20
289 cast male 1999 1999-10-02 2000-05-20 20
290 cast female 1999 1999-10-02 2000-05-20 20
291 cast male 1999 1999-10-02 2000-05-20 20
292 cast male 1999 1999-10-02 2000-05-20 20
293 cast male 1999 1999-10-02 2000-05-20 20
294 cast male 1999 1999-10-02 2000-05-20 20
295 cast female 1999 1999-10-02 2000-05-20 20
296 cast male 1999 1999-10-02 2000-05-20 20
297 cast male 1999 1999-10-02 2000-05-20 20
298 cast female 1999 1999-10-02 2000-05-20 20
299 cast female 1999 1999-10-02 2000-05-20 20
300 cast male 1999 1999-10-02 2000-05-20 20
301 cast male 1999 1999-10-02 2000-05-20 20
302 cast female 2000 2000-10-07 2001-05-19 20
303 cast male 2000 2000-10-07 2001-05-19 20
304 cast female 2000 2000-10-07 2001-05-19 20
305 cast male 2000 2000-10-07 2001-05-19 20
306 cast male 2000 2000-10-07 2001-05-19 20
307 cast female 2000 2000-10-07 2001-05-19 20
308 cast female 2000 2000-10-07 2001-05-19 20
309 cast male 2000 2000-10-07 2001-05-19 20
310 cast male 2000 2000-10-07 2001-05-19 20
311 cast female 2000 2000-10-07 2001-05-19 20
312 cast male 2000 2000-10-07 2001-05-19 20
313 cast male 2000 2000-10-07 2001-05-19 20
314 cast male 2000 2000-10-07 2001-05-19 20
315 cast female 2001 2001-09-29 2002-05-18 20
316 cast male 2001 2001-09-29 2002-05-18 20
317 cast male 2001 2001-09-29 2002-05-18 20
318 cast male 2001 2001-09-29 2002-05-18 20
319 cast female 2001 2001-09-29 2002-05-18 20
320 cast female 2001 2001-09-29 2002-05-18 20
321 cast male 2001 2001-09-29 2002-05-18 20
322 cast male 2001 2001-09-29 2002-05-18 20
323 cast male 2001 2001-09-29 2002-05-18 20
324 cast male 2001 2001-09-29 2002-05-18 20
325 cast male 2001 2001-09-29 2002-05-18 20
326 cast male 2001 2001-09-29 2002-05-18 20
327 cast female 2001 2001-09-29 2002-05-18 20
328 cast female 2001 2001-09-29 2002-05-18 20
329 cast male 2001 2001-09-29 2002-05-18 20
330 cast male 2002 2002-10-05 2003-05-17 20
331 cast male 2002 2002-10-05 2003-05-17 20
332 cast male 2002 2002-10-05 2003-05-17 20
333 cast male 2002 2002-10-05 2003-05-17 20
334 cast female 2002 2002-10-05 2003-05-17 20
335 cast female 2002 2002-10-05 2003-05-17 20
336 cast male 2002 2002-10-05 2003-05-17 20
337 cast female 2002 2002-10-05 2003-05-17 20
338 cast male 2002 2002-10-05 2003-05-17 20
339 cast male 2002 2002-10-05 2003-05-17 20
340 cast male 2002 2002-10-05 2003-05-17 20
341 cast male 2002 2002-10-05 2003-05-17 20
342 cast male 2002 2002-10-05 2003-05-17 20
343 cast male 2002 2002-10-05 2003-05-17 20
344 cast female 2002 2002-10-05 2003-05-17 20
345 cast female 2003 2003-10-04 2004-05-15 20
346 cast male 2003 2003-10-04 2004-05-15 20
347 cast male 2003 2003-10-04 2004-05-15 20
348 cast male 2003 2003-10-04 2004-05-15 20
349 cast male 2003 2003-10-04 2004-05-15 20
350 cast male 2003 2003-10-04 2004-05-15 20
351 cast male 2003 2003-10-04 2004-05-15 20
352 cast male 2003 2003-10-04 2004-05-15 20
353 cast female 2003 2003-10-04 2004-05-15 20
354 cast female 2003 2003-10-04 2004-05-15 20
355 cast male 2003 2003-10-04 2004-05-15 20
356 cast male 2003 2003-10-04 2004-05-15 20
357 cast male 2003 2003-10-04 2004-05-15 20
358 cast female 2003 2003-10-04 2004-05-15 20
359 cast male 2004 2004-10-02 2005-05-21 20
360 cast female 2004 2004-10-02 2005-05-21 20
361 cast male 2004 2004-10-02 2005-05-21 20
362 cast male 2004 2004-10-02 2005-05-21 20
363 cast female 2004 2004-10-02 2005-05-21 20
364 cast male 2004 2004-10-02 2005-05-21 20
365 cast male 2004 2004-10-02 2005-05-21 20
366 cast male 2004 2004-10-02 2005-05-21 20
367 cast female 2004 2004-10-02 2005-05-21 20
368 cast male 2004 2004-10-02 2005-05-21 20
369 cast female 2004 2004-10-02 2005-05-21 20
370 cast male 2004 2004-10-02 2005-05-21 20
371 cast male 2004 2004-10-02 2005-05-21 20
372 cast male 2004 2004-10-02 2005-05-21 20
373 cast male 2005 2005-10-01 2006-05-20 19
374 cast male 2005 2005-10-01 2006-05-20 19
375 cast female 2005 2005-10-01 2006-05-20 19
376 cast female 2005 2005-10-01 2006-05-20 19
377 cast male 2005 2005-10-01 2006-05-20 19
378 cast female 2005 2005-10-01 2006-05-20 19
379 cast male 2005 2005-10-01 2006-05-20 19
380 cast male 2005 2005-10-01 2006-05-20 19
381 cast male 2005 2005-10-01 2006-05-20 19
382 cast male 2005 2005-10-01 2006-05-20 19
383 cast male 2005 2005-10-01 2006-05-20 19
384 cast female 2005 2005-10-01 2006-05-20 19
385 cast female 2005 2005-10-01 2006-05-20 19
386 cast male 2005 2005-10-01 2006-05-20 19
387 cast male 2005 2005-10-01 2006-05-20 19
388 cast male 2005 2005-10-01 2006-05-20 19
389 cast male 2006 2006-09-30 2007-05-19 20
390 cast male 2006 2006-09-30 2007-05-19 20
391 cast male 2006 2006-09-30 2007-05-19 20
392 cast male 2006 2006-09-30 2007-05-19 20
393 cast female 2006 2006-09-30 2007-05-19 20
394 cast female 2006 2006-09-30 2007-05-19 20
395 cast male 2006 2006-09-30 2007-05-19 20
396 cast male 2006 2006-09-30 2007-05-19 20
397 cast female 2006 2006-09-30 2007-05-19 20
398 cast male 2006 2006-09-30 2007-05-19 20
399 cast male 2006 2006-09-30 2007-05-19 20
400 cast male 2007 2007-09-29 2008-05-17 12
401 cast male 2007 2007-09-29 2008-05-17 12
402 cast male 2007 2007-09-29 2008-05-17 12
403 cast female 2007 2007-09-29 2008-05-17 12
404 cast male 2007 2007-09-29 2008-05-17 12
405 cast male 2007 2007-09-29 2008-05-17 12
406 cast female 2007 2007-09-29 2008-05-17 12
407 cast male 2007 2007-09-29 2008-05-17 12
408 cast male 2007 2007-09-29 2008-05-17 12
409 cast female 2007 2007-09-29 2008-05-17 12
410 cast female 2007 2007-09-29 2008-05-17 12
411 cast male 2007 2007-09-29 2008-05-17 12
412 cast female 2008 2008-09-13 2009-05-16 22
413 cast male 2008 2008-09-13 2009-05-16 22
414 cast female 2008 2008-09-13 2009-05-16 22
415 cast male 2008 2008-09-13 2009-05-16 22
416 cast male 2008 2008-09-13 2009-05-16 22
417 cast female 2008 2008-09-13 2009-05-16 22
418 cast male 2008 2008-09-13 2009-05-16 22
419 cast male 2008 2008-09-13 2009-05-16 22
420 cast female 2008 2008-09-13 2009-05-16 22
421 cast female 2008 2008-09-13 2009-05-16 22
422 cast male 2008 2008-09-13 2009-05-16 22
423 cast male 2008 2008-09-13 2009-05-16 22
424 cast male 2008 2008-09-13 2009-05-16 22
425 cast male 2008 2008-09-13 2009-05-16 22
426 cast male 2009 2009-09-26 2010-05-15 22
427 cast male 2009 2009-09-26 2010-05-15 22
428 cast male 2009 2009-09-26 2010-05-15 22
429 cast male 2009 2009-09-26 2010-05-15 22
430 cast male 2009 2009-09-26 2010-05-15 22
431 cast male 2009 2009-09-26 2010-05-15 22
432 cast female 2009 2009-09-26 2010-05-15 22
433 cast male 2009 2009-09-26 2010-05-15 22
434 cast female 2009 2009-09-26 2010-05-15 22
435 cast female 2009 2009-09-26 2010-05-15 22
436 cast male 2009 2009-09-26 2010-05-15 22
437 cast female 2009 2009-09-26 2010-05-15 22
438 cast female 2010 2010-09-25 2011-05-21 22
439 cast male 2010 2010-09-25 2011-05-21 22
440 cast male 2010 2010-09-25 2011-05-21 22
441 cast male 2010 2010-09-25 2011-05-21 22
442 cast male 2010 2010-09-25 2011-05-21 22
443 cast male 2010 2010-09-25 2011-05-21 22
444 cast male 2010 2010-09-25 2011-05-21 22
445 cast male 2010 2010-09-25 2011-05-21 22
446 cast male 2010 2010-09-25 2011-05-21 22
447 cast male 2010 2010-09-25 2011-05-21 22
448 cast male 2010 2010-09-25 2011-05-21 22
449 cast female 2010 2010-09-25 2011-05-21 22
450 cast female 2010 2010-09-25 2011-05-21 22
451 cast female 2010 2010-09-25 2011-05-21 22
452 cast male 2011 2011-09-24 2012-05-19 22
453 cast male 2011 2011-09-24 2012-05-19 22
454 cast female 2011 2011-09-24 2012-05-19 22
455 cast male 2011 2011-09-24 2012-05-19 22
456 cast male 2011 2011-09-24 2012-05-19 22
457 cast male 2011 2011-09-24 2012-05-19 22
458 cast female 2011 2011-09-24 2012-05-19 22
459 cast male 2011 2011-09-24 2012-05-19 22
460 cast male 2011 2011-09-24 2012-05-19 22
461 cast male 2011 2011-09-24 2012-05-19 22
462 cast male 2011 2011-09-24 2012-05-19 22
463 cast male 2011 2011-09-24 2012-05-19 22
464 cast female 2011 2011-09-24 2012-05-19 22
465 cast female 2011 2011-09-24 2012-05-19 22
466 cast female 2011 2011-09-24 2012-05-19 22
467 cast male 2012 2012-09-15 2013-05-18 21
468 cast male 2012 2012-09-15 2013-05-18 21
469 cast female 2012 2012-09-15 2013-05-18 21
470 cast female 2012 2012-09-15 2013-05-18 21
471 cast male 2012 2012-09-15 2013-05-18 21
472 cast male 2012 2012-09-15 2013-05-18 21
473 cast male 2012 2012-09-15 2013-05-18 21
474 cast male 2012 2012-09-15 2013-05-18 21
475 cast male 2012 2012-09-15 2013-05-18 21
476 cast male 2012 2012-09-15 2013-05-18 21
477 cast female 2012 2012-09-15 2013-05-18 21
478 cast female 2012 2012-09-15 2013-05-18 21
479 cast female 2012 2012-09-15 2013-05-18 21
480 cast male 2012 2012-09-15 2013-05-18 21
481 cast female 2013 2013-09-28 2014-05-17 21
482 cast female 2013 2013-09-28 2014-05-17 21
483 cast male 2013 2013-09-28 2014-05-17 21
484 cast male 2013 2013-09-28 2014-05-17 21
485 cast female 2013 2013-09-28 2014-05-17 21
486 cast male 2013 2013-09-28 2014-05-17 21
487 cast male 2013 2013-09-28 2014-05-17 21
488 cast male 2013 2013-09-28 2014-05-17 21
489 cast female 2013 2013-09-28 2014-05-17 21
490 cast male 2013 2013-09-28 2014-05-17 21
491 cast female 2013 2013-09-28 2014-05-17 21
492 cast female 2013 2013-09-28 2014-05-17 21
493 cast male 2013 2013-09-28 2014-05-17 21
494 cast male 2013 2013-09-28 2014-05-17 21
495 cast male 2013 2013-09-28 2014-05-17 21
496 cast male 2013 2013-09-28 2014-05-17 21
497 cast male 2013 2013-09-28 2014-05-17 21
498 cast female 2013 2013-09-28 2014-05-17 21
499 cast male 2014 2014-09-27 2015-05-16 21
500 cast male 2014 2014-09-27 2015-05-16 21
501 cast female 2014 2014-09-27 2015-05-16 21
502 cast male 2014 2014-09-27 2015-05-16 21
503 cast male 2014 2014-09-27 2015-05-16 21
504 cast female 2014 2014-09-27 2015-05-16 21
505 cast male 2014 2014-09-27 2015-05-16 21
506 cast female 2014 2014-09-27 2015-05-16 21
507 cast female 2014 2014-09-27 2015-05-16 21
508 cast male 2014 2014-09-27 2015-05-16 21
509 cast male 2014 2014-09-27 2015-05-16 21
510 cast male 2014 2014-09-27 2015-05-16 21
511 cast female 2014 2014-09-27 2015-05-16 21
512 cast male 2014 2014-09-27 2015-05-16 21
513 cast female 2014 2014-09-27 2015-05-16 21
514 cast male 2015 2015-10-03 2016-05-21 21
515 cast female 2015 2015-10-03 2016-05-21 21
516 cast male 2015 2015-10-03 2016-05-21 21
517 cast female 2015 2015-10-03 2016-05-21 21
518 cast female 2015 2015-10-03 2016-05-21 21
519 cast female 2015 2015-10-03 2016-05-21 21
520 cast female 2015 2015-10-03 2016-05-21 21
521 cast male 2015 2015-10-03 2016-05-21 21
522 cast male 2015 2015-10-03 2016-05-21 21
523 cast male 2015 2015-10-03 2016-05-21 21
524 cast male 2015 2015-10-03 2016-05-21 21
525 cast male 2015 2015-10-03 2016-05-21 21
526 cast male 2015 2015-10-03 2016-05-21 21
527 cast male 2015 2015-10-03 2016-05-21 21
528 cast female 2015 2015-10-03 2016-05-21 21
529 cast male 2015 2015-10-03 2016-05-21 21
530 cast female 2016 2016-10-01 2017-05-20 21
531 cast female 2016 2016-10-01 2017-05-20 21
532 cast male 2016 2016-10-01 2017-05-20 21
533 cast male 2016 2016-10-01 2017-05-20 21
534 cast female 2016 2016-10-01 2017-05-20 21
535 cast male 2016 2016-10-01 2017-05-20 21
536 cast male 2016 2016-10-01 2017-05-20 21
537 cast male 2016 2016-10-01 2017-05-20 21
538 cast female 2016 2016-10-01 2017-05-20 21
539 cast female 2016 2016-10-01 2017-05-20 21
540 cast female 2016 2016-10-01 2017-05-20 21
541 cast male 2016 2016-10-01 2017-05-20 21
542 cast female 2016 2016-10-01 2017-05-20 21
543 cast male 2016 2016-10-01 2017-05-20 21
544 cast male 2016 2016-10-01 2017-05-20 21
545 cast male 2016 2016-10-01 2017-05-20 21
546 cast male 2017 2017-09-30 2018-05-19 21
547 cast male 2017 2017-09-30 2018-05-19 21
548 cast female 2017 2017-09-30 2018-05-19 21
549 cast male 2017 2017-09-30 2018-05-19 21
550 cast male 2017 2017-09-30 2018-05-19 21
551 cast female 2017 2017-09-30 2018-05-19 21
552 cast male 2017 2017-09-30 2018-05-19 21
553 cast male 2017 2017-09-30 2018-05-19 21
554 cast female 2017 2017-09-30 2018-05-19 21
555 cast male 2017 2017-09-30 2018-05-19 21
556 cast male 2017 2017-09-30 2018-05-19 21
557 cast female 2017 2017-09-30 2018-05-19 21
558 cast female 2017 2017-09-30 2018-05-19 21
559 cast male 2017 2017-09-30 2018-05-19 21
560 cast male 2017 2017-09-30 2018-05-19 21
561 cast female 2017 2017-09-30 2018-05-19 21
562 cast male 2018 2018-09-29 2019-05-18 21
563 cast unknown 2018 2018-09-29 2019-05-18 21
564 cast female 2018 2018-09-29 2019-05-18 21
565 cast male 2018 2018-09-29 2019-05-18 21
566 cast male 2018 2018-09-29 2019-05-18 21
567 cast male 2018 2018-09-29 2019-05-18 21
568 cast male 2018 2018-09-29 2019-05-18 21
569 cast female 2018 2018-09-29 2019-05-18 21
570 cast male 2018 2018-09-29 2019-05-18 21
571 cast female 2018 2018-09-29 2019-05-18 21
572 cast female 2018 2018-09-29 2019-05-18 21
573 cast male 2018 2018-09-29 2019-05-18 21
574 cast female 2018 2018-09-29 2019-05-18 21
575 cast male 2018 2018-09-29 2019-05-18 21
576 cast male 2018 2018-09-29 2019-05-18 21
577 cast female 2018 2018-09-29 2019-05-18 21
578 cast male 2019 2019-09-28 2020-05-09 18
579 cast male 2019 2019-09-28 2020-05-09 18
580 cast male 2019 2019-09-28 2020-05-09 18
581 cast female 2019 2019-09-28 2020-05-09 18
582 cast male 2019 2019-09-28 2020-05-09 18
583 cast male 2019 2019-09-28 2020-05-09 18
584 cast female 2019 2019-09-28 2020-05-09 18
585 cast male 2019 2019-09-28 2020-05-09 18
586 cast male 2019 2019-09-28 2020-05-09 18
587 cast male 2019 2019-09-28 2020-05-09 18
588 cast male 2019 2019-09-28 2020-05-09 18
589 cast male 2019 2019-09-28 2020-05-09 18
590 cast female 2019 2019-09-28 2020-05-09 18
591 cast female 2019 2019-09-28 2020-05-09 18
592 cast female 2019 2019-09-28 2020-05-09 18
593 cast female 2019 2019-09-28 2020-05-09 18
594 cast unknown 2019 2019-09-28 2020-05-09 18
595 cast female 2020 2020-10-03 2021-04-10 17
596 cast female 2020 2020-10-03 2021-04-10 17
597 cast male 2020 2020-10-03 2021-04-10 17
598 cast male 2020 2020-10-03 2021-04-10 17
599 cast female 2020 2020-10-03 2021-04-10 17
600 cast male 2020 2020-10-03 2021-04-10 17
601 cast male 2020 2020-10-03 2021-04-10 17
602 cast unknown 2020 2020-10-03 2021-04-10 17
603 cast male 2020 2020-10-03 2021-04-10 17
604 cast male 2020 2020-10-03 2021-04-10 17
605 cast female 2020 2020-10-03 2021-04-10 17
606 cast male 2020 2020-10-03 2021-04-10 17
607 cast female 2020 2020-10-03 2021-04-10 17
608 cast male 2020 2020-10-03 2021-04-10 17
609 cast unknown 2020 2020-10-03 2021-04-10 17
610 cast male 2020 2020-10-03 2021-04-10 17
611 cast female 2020 2020-10-03 2021-04-10 17
612 cast female 2020 2020-10-03 2021-04-10 17
613 cast male 2020 2020-10-03 2021-04-10 17
614 cast male 2020 2020-10-03 2021-04-10 17We have successfully merged all three datasets and the snl_actors_casts_seasons final dataset contains information about the actors, casts and seasons in a single dataset. We can now use this dataset for creating visualizations and analyzing the data.
For the first visualization, I created a bar graph representing the distribution of casts for each season of the SNL show. We can observe that the number of casts involved in each season of the SNL show has been more than 10 consistently from the 13th season. It would be interesting to visualize whether the number of episodes in a season has any impact on the number of casts featured in a season.
# Bar graph representing the distribution of casts over the seasons.
library(ggplot2)
ggplot(snl_actors_casts_seasons, aes(x = sid)) +
geom_bar(fill="#F8766D", width = 0.8) +
labs(title = "Distribution of casts over the seasons",
y = "Count", x = "Season Number") +
theme(axis.text.x=element_text(angle=90, hjust=1))
For the next visualization, I created a bar graph representing the gender distribution of casts over the years. This would help us understand more about the representation of male and female casts in the show which cannot be interpreted from the previous visualization. Since one season is premiered each year, representing the gender distribution over the years or over the seasons would result in the same visualization. It is quite evident from the visualization below that more than 50% of the casts involved in the SNL show from the beginning of the show are male. It would be nice to see more female casts in the SNL show in future.
# Bar graph representing the gender distribution of casts over the years.
ggplot(snl_actors_casts_seasons, aes(x = year, fill = gender)) +
geom_bar(width = 0.8) +
labs(title = "Gender distribution of casts over the years",
y = "Count", x = "Year") +
theme(axis.text.x=element_text(angle=90, hjust=1))