Daily Show

The below data depicts depicts how the catorgy of guests on The Daily Show changed over time. We can see for example how the frequency of actors decline.

actorsgrouped <- guests %>% 
    group_by(YEAR,guest_category) %>%
    summarize(count = n())%>% 
    filter(guest_category == 'Acting')

## `summarise()` regrouping output by 'YEAR' (override with `.groups` argument)

actorsgrouped

## # A tibble: 17 x 3
## # Groups:   YEAR [17]
##     YEAR guest_category count
##    <int> <chr>          <int>
##  1  1999 Acting           108
##  2  2000 Acting           100
##  3  2001 Acting            92
##  4  2002 Acting            84
##  5  2003 Acting            74
##  6  2004 Acting            51
##  7  2005 Acting            44
##  8  2006 Acting            44
##  9  2007 Acting            25
## 10  2008 Acting            26
## 11  2009 Acting            22
## 12  2010 Acting            45
## 13  2011 Acting            42
## 14  2012 Acting            33
## 15  2013 Acting            60
## 16  2014 Acting            47
## 17  2015 Acting            33

While the frequency of politician guests increased and remianed steady around 2004.

journalistgrouped <- guests %>% 
    group_by(YEAR,guest_category) %>%
    summarize(count = n())%>% 
    filter(guest_category == 'Politician')

## `summarise()` regrouping output by 'YEAR' (override with `.groups` argument)

journalistgrouped

## # A tibble: 17 x 3
## # Groups:   YEAR [17]
##     YEAR guest_category count
##    <int> <chr>          <int>
##  1  1999 Politician         2
##  2  2000 Politician        13
##  3  2001 Politician         3
##  4  2002 Politician         8
##  5  2003 Politician        14
##  6  2004 Politician        32
##  7  2005 Politician        22
##  8  2006 Politician        25
##  9  2007 Politician        21
## 10  2008 Politician        27
## 11  2009 Politician        26
## 12  2010 Politician        25
## 13  2011 Politician        23
## 14  2012 Politician        29
## 15  2013 Politician        11
## 16  2014 Politician        13
## 17  2015 Politician        14

The most prononced in the progression of guests coming from the media.

mediagrouped <- guests %>% 
    group_by(YEAR,guest_category) %>%
    summarize(count = n())%>% 
    filter(guest_category == 'Media')

## `summarise()` regrouping output by 'YEAR' (override with `.groups` argument)

mediagrouped

## # A tibble: 17 x 3
## # Groups:   YEAR [17]
##     YEAR guest_category count
##    <int> <chr>          <int>
##  1  1999 Media             11
##  2  2000 Media             21
##  3  2001 Media             30
##  4  2002 Media             39
##  5  2003 Media             41
##  6  2004 Media             45
##  7  2005 Media             54
##  8  2006 Media             47
##  9  2007 Media             47
## 10  2008 Media             77
## 11  2009 Media             59
## 12  2010 Media             50
## 13  2011 Media             50
## 14  2012 Media             52
## 15  2013 Media             51
## 16  2014 Media             53
## 17  2015 Media             24