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The Normal Curve

EDP 613

Week 5

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Prepping a New R Script

  1. Open up a blank R script using the menu path File > New File > R Script.
2 / 38

Prepping a New R Script

  1. Open up a blank R script using the menu path File > New File > R Script.

  2. Save this script as whatever.R (replacing the term whatever) in your R folder. Remember to note where the file is!

2 / 38

Prepping a New R Script

  1. Open up a blank R script using the menu path File > New File > R Script.

  2. Save this script as whatever.R (replacing the term whatever) in your R folder. Remember to note where the file is!

  3. After you have saved this file as whatever.R, go to the menu and this week try running the following alternative to Session > Set Working Directory > To Source File Location at the top of your script

setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
2 / 38

Getting ready for this session

Get the files

  • Box Office.csv

  • teampolview.csv

3 / 38

and save it in the same location as this script.

  • Install the packages viridis and patchwork.

  • Load up tidyverse and viridis

This week try using pacman to do it

pacman::p_load(tidyverse,
               patchwork,
               viridis)
4 / 38

Last week's R activity

5 / 38

Load up data

boxoffice <- read_csv("Box Office.csv")
6 / 38

Before we go on

Thes solutions are just one of many ways to get to the actual answer. Your work may and will likely vary.

7 / 38

1. What is the average number of positive reviews for the top five movies?

count: false

boxoffice
# A tibble: 718 × 10
    Rank AllPos AllNeg TopPos TopNeg Movie         Relea…¹ Relea…² Reven…³  year
   <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <chr>         <chr>   <chr>     <dbl> <dbl>
 1     1    238     48     38      3 Avatar (Fox)  12/18/… Dec      7.61e8  2009
 2     2    145     21     36      8 Titanic (Par… 12/19/… Dec      6.59e8  1997
 3     3    268     22     38      7 Marvel's The… 5/4/12  May      6.23e8  2012
 4     4    268     18     42      4 The Dark Kni… 7/18/08 Jul      5.35e8  2008
 5     5    107     81     19     31 Star Wars: E… 5/19/99 May      4.75e8  1999
 6     6     63      4     15      2 Star Wars (F… 5/25/77 May      4.61e8  1977
 7     7    256     39     34     11 The Dark Kni… 7/20/12 Jul      4.48e8  2012
 8     8    185     23     36      5 Shrek 2 (Dre… 5/19/04 May      4.41e8  2004
 9     9     92      2     17      1 E.T.: The Ex… 6/11/82 Jun      4.35e8  1982
10    10    119    101     16     25 Pirates of t… 7/7/06  Jul      4.23e8  2006
# … with 708 more rows, and abbreviated variable names ¹​ReleaseDate,
#   ²​ReleaseMonth, ³​Revenues
8 / 38
boxoffice %>%
  arrange(Rank)
# A tibble: 718 × 10
    Rank AllPos AllNeg TopPos TopNeg Movie         Relea…¹ Relea…² Reven…³  year
   <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <chr>         <chr>   <chr>     <dbl> <dbl>
 1     1    238     48     38      3 Avatar (Fox)  12/18/… Dec      7.61e8  2009
 2     2    145     21     36      8 Titanic (Par… 12/19/… Dec      6.59e8  1997
 3     3    268     22     38      7 Marvel's The… 5/4/12  May      6.23e8  2012
 4     4    268     18     42      4 The Dark Kni… 7/18/08 Jul      5.35e8  2008
 5     5    107     81     19     31 Star Wars: E… 5/19/99 May      4.75e8  1999
 6     6     63      4     15      2 Star Wars (F… 5/25/77 May      4.61e8  1977
 7     7    256     39     34     11 The Dark Kni… 7/20/12 Jul      4.48e8  2012
 8     8    185     23     36      5 Shrek 2 (Dre… 5/19/04 May      4.41e8  2004
 9     9     92      2     17      1 E.T.: The Ex… 6/11/82 Jun      4.35e8  1982
10    10    119    101     16     25 Pirates of t… 7/7/06  Jul      4.23e8  2006
# … with 708 more rows, and abbreviated variable names ¹​ReleaseDate,
#   ²​ReleaseMonth, ³​Revenues
8 / 38
boxoffice %>%
  arrange(Rank) %>%
  head(5)
# A tibble: 5 × 10
   Rank AllPos AllNeg TopPos TopNeg Movie          Relea…¹ Relea…² Reven…³  year
  <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <chr>          <chr>   <chr>     <dbl> <dbl>
1     1    238     48     38      3 Avatar (Fox)   12/18/… Dec      7.61e8  2009
2     2    145     21     36      8 Titanic (Para… 12/19/… Dec      6.59e8  1997
3     3    268     22     38      7 Marvel's The … 5/4/12  May      6.23e8  2012
4     4    268     18     42      4 The Dark Knig… 7/18/08 Jul      5.35e8  2008
5     5    107     81     19     31 Star Wars: Ep… 5/19/99 May      4.75e8  1999
# … with abbreviated variable names ¹​ReleaseDate, ²​ReleaseMonth, ³​Revenues
8 / 38
boxoffice %>%
  arrange(Rank) %>%
  head(5) %>%
  summarize(mean_pos =
              mean(AllPos,
                   na.rm = TRUE))
# A tibble: 1 × 1
  mean_pos
     <dbl>
1     205.
8 / 38
boxoffice %>%
  arrange(Rank) %>%
  head(5) %>%
  summarize(mean_pos =
              mean(AllPos,
                   na.rm = TRUE)) %>%
  pull()
[1] 205.2
8 / 38

2. What are the average number of negative reviews for the bottom five movies?

count: false

boxoffice
# A tibble: 718 × 10
    Rank AllPos AllNeg TopPos TopNeg Movie         Relea…¹ Relea…² Reven…³  year
   <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <chr>         <chr>   <chr>     <dbl> <dbl>
 1     1    238     48     38      3 Avatar (Fox)  12/18/… Dec      7.61e8  2009
 2     2    145     21     36      8 Titanic (Par… 12/19/… Dec      6.59e8  1997
 3     3    268     22     38      7 Marvel's The… 5/4/12  May      6.23e8  2012
 4     4    268     18     42      4 The Dark Kni… 7/18/08 Jul      5.35e8  2008
 5     5    107     81     19     31 Star Wars: E… 5/19/99 May      4.75e8  1999
 6     6     63      4     15      2 Star Wars (F… 5/25/77 May      4.61e8  1977
 7     7    256     39     34     11 The Dark Kni… 7/20/12 Jul      4.48e8  2012
 8     8    185     23     36      5 Shrek 2 (Dre… 5/19/04 May      4.41e8  2004
 9     9     92      2     17      1 E.T.: The Ex… 6/11/82 Jun      4.35e8  1982
10    10    119    101     16     25 Pirates of t… 7/7/06  Jul      4.23e8  2006
# … with 708 more rows, and abbreviated variable names ¹​ReleaseDate,
#   ²​ReleaseMonth, ³​Revenues
9 / 38
boxoffice %>%
  arrange(Rank)
# A tibble: 718 × 10
    Rank AllPos AllNeg TopPos TopNeg Movie         Relea…¹ Relea…² Reven…³  year
   <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <chr>         <chr>   <chr>     <dbl> <dbl>
 1     1    238     48     38      3 Avatar (Fox)  12/18/… Dec      7.61e8  2009
 2     2    145     21     36      8 Titanic (Par… 12/19/… Dec      6.59e8  1997
 3     3    268     22     38      7 Marvel's The… 5/4/12  May      6.23e8  2012
 4     4    268     18     42      4 The Dark Kni… 7/18/08 Jul      5.35e8  2008
 5     5    107     81     19     31 Star Wars: E… 5/19/99 May      4.75e8  1999
 6     6     63      4     15      2 Star Wars (F… 5/25/77 May      4.61e8  1977
 7     7    256     39     34     11 The Dark Kni… 7/20/12 Jul      4.48e8  2012
 8     8    185     23     36      5 Shrek 2 (Dre… 5/19/04 May      4.41e8  2004
 9     9     92      2     17      1 E.T.: The Ex… 6/11/82 Jun      4.35e8  1982
10    10    119    101     16     25 Pirates of t… 7/7/06  Jul      4.23e8  2006
# … with 708 more rows, and abbreviated variable names ¹​ReleaseDate,
#   ²​ReleaseMonth, ³​Revenues
9 / 38
boxoffice %>%
  arrange(Rank) %>%
  tail(5)
# A tibble: 5 × 10
   Rank AllPos AllNeg TopPos TopNeg Movie          Relea…¹ Relea…² Reven…³  year
  <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <chr>          <chr>   <chr>     <dbl> <dbl>
1   714    151     45     26     10 Cloverfield (… 1/18/08 Jan      8.00e7  2008
2   715     19     15      3      1 Footloose (19… 2/17/84 Feb      8.00e7  1984
3   716     39     96      6     24 Dear John (So… 2/5/10  Feb      8.00e7  2010
4   717      5      8      0      1 A Star Is Bor… 12/17/… Dec      8.00e7  1976
5   718     46      2      6      0 Fantasia (Dis… 11/13/… Nov      8.00e7  1940
# … with abbreviated variable names ¹​ReleaseDate, ²​ReleaseMonth, ³​Revenues
9 / 38
boxoffice %>%
  arrange(Rank) %>%
  tail(5) %>%
  summarize(mean_neg =
              mean(AllNeg,
                   na.rm = TRUE))
# A tibble: 1 × 1
  mean_neg
     <dbl>
1     33.2
9 / 38
boxoffice %>%
  arrange(Rank) %>%
  tail(5) %>%
  summarize(mean_neg =
              mean(AllNeg,
                   na.rm = TRUE)) %>%
  pull()
[1] 33.2
9 / 38

3. How were movies released over the years? Provide counts and a visualization.

count: false

boxoffice
# A tibble: 718 × 10
    Rank AllPos AllNeg TopPos TopNeg Movie         Relea…¹ Relea…² Reven…³  year
   <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <chr>         <chr>   <chr>     <dbl> <dbl>
 1     1    238     48     38      3 Avatar (Fox)  12/18/… Dec      7.61e8  2009
 2     2    145     21     36      8 Titanic (Par… 12/19/… Dec      6.59e8  1997
 3     3    268     22     38      7 Marvel's The… 5/4/12  May      6.23e8  2012
 4     4    268     18     42      4 The Dark Kni… 7/18/08 Jul      5.35e8  2008
 5     5    107     81     19     31 Star Wars: E… 5/19/99 May      4.75e8  1999
 6     6     63      4     15      2 Star Wars (F… 5/25/77 May      4.61e8  1977
 7     7    256     39     34     11 The Dark Kni… 7/20/12 Jul      4.48e8  2012
 8     8    185     23     36      5 Shrek 2 (Dre… 5/19/04 May      4.41e8  2004
 9     9     92      2     17      1 E.T.: The Ex… 6/11/82 Jun      4.35e8  1982
10    10    119    101     16     25 Pirates of t… 7/7/06  Jul      4.23e8  2006
# … with 708 more rows, and abbreviated variable names ¹​ReleaseDate,
#   ²​ReleaseMonth, ³​Revenues
10 / 38
boxoffice %>%
  group_by(year)
# A tibble: 718 × 10
# Groups:   year [55]
    Rank AllPos AllNeg TopPos TopNeg Movie         Relea…¹ Relea…² Reven…³  year
   <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <chr>         <chr>   <chr>     <dbl> <dbl>
 1     1    238     48     38      3 Avatar (Fox)  12/18/… Dec      7.61e8  2009
 2     2    145     21     36      8 Titanic (Par… 12/19/… Dec      6.59e8  1997
 3     3    268     22     38      7 Marvel's The… 5/4/12  May      6.23e8  2012
 4     4    268     18     42      4 The Dark Kni… 7/18/08 Jul      5.35e8  2008
 5     5    107     81     19     31 Star Wars: E… 5/19/99 May      4.75e8  1999
 6     6     63      4     15      2 Star Wars (F… 5/25/77 May      4.61e8  1977
 7     7    256     39     34     11 The Dark Kni… 7/20/12 Jul      4.48e8  2012
 8     8    185     23     36      5 Shrek 2 (Dre… 5/19/04 May      4.41e8  2004
 9     9     92      2     17      1 E.T.: The Ex… 6/11/82 Jun      4.35e8  1982
10    10    119    101     16     25 Pirates of t… 7/7/06  Jul      4.23e8  2006
# … with 708 more rows, and abbreviated variable names ¹​ReleaseDate,
#   ²​ReleaseMonth, ³​Revenues
10 / 38
boxoffice %>%
  group_by(year) %>%
  count(name = "number of movies")
# A tibble: 55 × 2
# Groups:   year [55]
    year `number of movies`
   <dbl>              <int>
 1  1937                  1
 2  1939                  1
 3  1940                  2
 4  1942                  1
 5  1950                  1
 6  1953                  1
 7  1955                  1
 8  1956                  1
 9  1961                  1
10  1964                  1
# … with 45 more rows
10 / 38
boxoffice %>%
  group_by(year) %>%
  count(name = "number of movies") %>%
  ungroup()
# A tibble: 55 × 2
    year `number of movies`
   <dbl>              <int>
 1  1937                  1
 2  1939                  1
 3  1940                  2
 4  1942                  1
 5  1950                  1
 6  1953                  1
 7  1955                  1
 8  1956                  1
 9  1961                  1
10  1964                  1
# … with 45 more rows
10 / 38

Save as a variable

boxoffice_annualnum <- 
  boxoffice %>%
  group_by(year) %>%
  count(name = "number of movies") %>%
  ungroup()
11 / 38

4. Which measure of central tendency is the best to describe the average number of movies over the years?

Since the data is skewed, the median is the best indicator of the true average

  • Median

count: false

boxoffice_annualnum
# A tibble: 55 × 2
    year `number of movies`
   <dbl>              <int>
 1  1937                  1
 2  1939                  1
 3  1940                  2
 4  1942                  1
 5  1950                  1
 6  1953                  1
 7  1955                  1
 8  1956                  1
 9  1961                  1
10  1964                  1
# … with 45 more rows
12 / 38
boxoffice_annualnum %>%
  summarize(median =
              median(`number of movies`,
                     na.rm = TRUE))
# A tibble: 1 × 1
  median
   <int>
1      6
12 / 38
boxoffice_annualnum %>%
  summarize(median =
              median(`number of movies`,
                     na.rm = TRUE)) %>%
  pull()
[1] 6
12 / 38
  • Mean

count: false

boxoffice_annualnum
# A tibble: 55 × 2
    year `number of movies`
   <dbl>              <int>
 1  1937                  1
 2  1939                  1
 3  1940                  2
 4  1942                  1
 5  1950                  1
 6  1953                  1
 7  1955                  1
 8  1956                  1
 9  1961                  1
10  1964                  1
# … with 45 more rows
13 / 38
boxoffice_annualnum %>%
  summarize(mean =
              mean(`number of movies`,
                     na.rm = TRUE))
# A tibble: 1 × 1
   mean
  <dbl>
1  13.1
13 / 38
boxoffice_annualnum %>%
  summarize(mean =
              mean(`number of movies`,
                     na.rm = TRUE)) %>%
  pull()
[1] 13.05455
13 / 38
  • Plot

count: false

ggplot(boxoffice_annualnum,
       aes(year,
           `number of movies`,
           fill = `number of movies`))

14 / 38
ggplot(boxoffice_annualnum,
       aes(year,
           `number of movies`,
           fill = `number of movies`)) +
  geom_bar(stat = "identity")

14 / 38
ggplot(boxoffice_annualnum,
       aes(year,
           `number of movies`,
           fill = `number of movies`)) +
  geom_bar(stat = "identity") +
  theme_minimal()

14 / 38
ggplot(boxoffice_annualnum,
       aes(year,
           `number of movies`,
           fill = `number of movies`)) +
  geom_bar(stat = "identity") +
  theme_minimal() +
  scale_fill_viridis_c(direction = -1)

14 / 38

15 / 38

5. Which year has the most number of ranked movies?

count: false

boxoffice
# A tibble: 718 × 10
    Rank AllPos AllNeg TopPos TopNeg Movie         Relea…¹ Relea…² Reven…³  year
   <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <chr>         <chr>   <chr>     <dbl> <dbl>
 1     1    238     48     38      3 Avatar (Fox)  12/18/… Dec      7.61e8  2009
 2     2    145     21     36      8 Titanic (Par… 12/19/… Dec      6.59e8  1997
 3     3    268     22     38      7 Marvel's The… 5/4/12  May      6.23e8  2012
 4     4    268     18     42      4 The Dark Kni… 7/18/08 Jul      5.35e8  2008
 5     5    107     81     19     31 Star Wars: E… 5/19/99 May      4.75e8  1999
 6     6     63      4     15      2 Star Wars (F… 5/25/77 May      4.61e8  1977
 7     7    256     39     34     11 The Dark Kni… 7/20/12 Jul      4.48e8  2012
 8     8    185     23     36      5 Shrek 2 (Dre… 5/19/04 May      4.41e8  2004
 9     9     92      2     17      1 E.T.: The Ex… 6/11/82 Jun      4.35e8  1982
10    10    119    101     16     25 Pirates of t… 7/7/06  Jul      4.23e8  2006
# … with 708 more rows, and abbreviated variable names ¹​ReleaseDate,
#   ²​ReleaseMonth, ³​Revenues
16 / 38
boxoffice %>%
  group_by(year)
# A tibble: 718 × 10
# Groups:   year [55]
    Rank AllPos AllNeg TopPos TopNeg Movie         Relea…¹ Relea…² Reven…³  year
   <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <chr>         <chr>   <chr>     <dbl> <dbl>
 1     1    238     48     38      3 Avatar (Fox)  12/18/… Dec      7.61e8  2009
 2     2    145     21     36      8 Titanic (Par… 12/19/… Dec      6.59e8  1997
 3     3    268     22     38      7 Marvel's The… 5/4/12  May      6.23e8  2012
 4     4    268     18     42      4 The Dark Kni… 7/18/08 Jul      5.35e8  2008
 5     5    107     81     19     31 Star Wars: E… 5/19/99 May      4.75e8  1999
 6     6     63      4     15      2 Star Wars (F… 5/25/77 May      4.61e8  1977
 7     7    256     39     34     11 The Dark Kni… 7/20/12 Jul      4.48e8  2012
 8     8    185     23     36      5 Shrek 2 (Dre… 5/19/04 May      4.41e8  2004
 9     9     92      2     17      1 E.T.: The Ex… 6/11/82 Jun      4.35e8  1982
10    10    119    101     16     25 Pirates of t… 7/7/06  Jul      4.23e8  2006
# … with 708 more rows, and abbreviated variable names ¹​ReleaseDate,
#   ²​ReleaseMonth, ³​Revenues
16 / 38
boxoffice %>%
  group_by(year) %>%
  tally()
# A tibble: 55 × 2
    year     n
   <dbl> <int>
 1  1937     1
 2  1939     1
 3  1940     2
 4  1942     1
 5  1950     1
 6  1953     1
 7  1955     1
 8  1956     1
 9  1961     1
10  1964     1
# … with 45 more rows
16 / 38
boxoffice %>%
  group_by(year) %>%
  tally() %>%
  rename(`number of movies` = n)
# A tibble: 55 × 2
    year `number of movies`
   <dbl>              <int>
 1  1937                  1
 2  1939                  1
 3  1940                  2
 4  1942                  1
 5  1950                  1
 6  1953                  1
 7  1955                  1
 8  1956                  1
 9  1961                  1
10  1964                  1
# … with 45 more rows
16 / 38
boxoffice %>%
  group_by(year) %>%
  tally() %>%
  rename(`number of movies` = n) %>%
  ungroup()
# A tibble: 55 × 2
    year `number of movies`
   <dbl>              <int>
 1  1937                  1
 2  1939                  1
 3  1940                  2
 4  1942                  1
 5  1950                  1
 6  1953                  1
 7  1955                  1
 8  1956                  1
 9  1961                  1
10  1964                  1
# … with 45 more rows
16 / 38
boxoffice %>%
  group_by(year) %>%
  tally() %>%
  rename(`number of movies` = n) %>%
  ungroup() %>%
  filter(`number of movies` ==
           max(`number of movies`))
# A tibble: 1 × 2
   year `number of movies`
  <dbl>              <int>
1  2010                 43
16 / 38

or

count: false

boxoffice
# A tibble: 718 × 10
    Rank AllPos AllNeg TopPos TopNeg Movie         Relea…¹ Relea…² Reven…³  year
   <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <chr>         <chr>   <chr>     <dbl> <dbl>
 1     1    238     48     38      3 Avatar (Fox)  12/18/… Dec      7.61e8  2009
 2     2    145     21     36      8 Titanic (Par… 12/19/… Dec      6.59e8  1997
 3     3    268     22     38      7 Marvel's The… 5/4/12  May      6.23e8  2012
 4     4    268     18     42      4 The Dark Kni… 7/18/08 Jul      5.35e8  2008
 5     5    107     81     19     31 Star Wars: E… 5/19/99 May      4.75e8  1999
 6     6     63      4     15      2 Star Wars (F… 5/25/77 May      4.61e8  1977
 7     7    256     39     34     11 The Dark Kni… 7/20/12 Jul      4.48e8  2012
 8     8    185     23     36      5 Shrek 2 (Dre… 5/19/04 May      4.41e8  2004
 9     9     92      2     17      1 E.T.: The Ex… 6/11/82 Jun      4.35e8  1982
10    10    119    101     16     25 Pirates of t… 7/7/06  Jul      4.23e8  2006
# … with 708 more rows, and abbreviated variable names ¹​ReleaseDate,
#   ²​ReleaseMonth, ³​Revenues
17 / 38
boxoffice %>%
  group_by(year)
# A tibble: 718 × 10
# Groups:   year [55]
    Rank AllPos AllNeg TopPos TopNeg Movie         Relea…¹ Relea…² Reven…³  year
   <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <chr>         <chr>   <chr>     <dbl> <dbl>
 1     1    238     48     38      3 Avatar (Fox)  12/18/… Dec      7.61e8  2009
 2     2    145     21     36      8 Titanic (Par… 12/19/… Dec      6.59e8  1997
 3     3    268     22     38      7 Marvel's The… 5/4/12  May      6.23e8  2012
 4     4    268     18     42      4 The Dark Kni… 7/18/08 Jul      5.35e8  2008
 5     5    107     81     19     31 Star Wars: E… 5/19/99 May      4.75e8  1999
 6     6     63      4     15      2 Star Wars (F… 5/25/77 May      4.61e8  1977
 7     7    256     39     34     11 The Dark Kni… 7/20/12 Jul      4.48e8  2012
 8     8    185     23     36      5 Shrek 2 (Dre… 5/19/04 May      4.41e8  2004
 9     9     92      2     17      1 E.T.: The Ex… 6/11/82 Jun      4.35e8  1982
10    10    119    101     16     25 Pirates of t… 7/7/06  Jul      4.23e8  2006
# … with 708 more rows, and abbreviated variable names ¹​ReleaseDate,
#   ²​ReleaseMonth, ³​Revenues
17 / 38
boxoffice %>%
  group_by(year) %>%
  summarise(`number of movies` = n())
# A tibble: 55 × 2
    year `number of movies`
   <dbl>              <int>
 1  1937                  1
 2  1939                  1
 3  1940                  2
 4  1942                  1
 5  1950                  1
 6  1953                  1
 7  1955                  1
 8  1956                  1
 9  1961                  1
10  1964                  1
# … with 45 more rows
17 / 38
boxoffice %>%
  group_by(year) %>%
  summarise(`number of movies` = n()) %>%
  ungroup()
# A tibble: 55 × 2
    year `number of movies`
   <dbl>              <int>
 1  1937                  1
 2  1939                  1
 3  1940                  2
 4  1942                  1
 5  1950                  1
 6  1953                  1
 7  1955                  1
 8  1956                  1
 9  1961                  1
10  1964                  1
# … with 45 more rows
17 / 38
boxoffice %>%
  group_by(year) %>%
  summarise(`number of movies` = n()) %>%
  ungroup() %>%
  filter(`number of movies` ==
           max(`number of movies`))
# A tibble: 1 × 2
   year `number of movies`
  <dbl>              <int>
1  2010                 43
17 / 38

or

count: false

boxoffice
# A tibble: 718 × 10
    Rank AllPos AllNeg TopPos TopNeg Movie         Relea…¹ Relea…² Reven…³  year
   <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <chr>         <chr>   <chr>     <dbl> <dbl>
 1     1    238     48     38      3 Avatar (Fox)  12/18/… Dec      7.61e8  2009
 2     2    145     21     36      8 Titanic (Par… 12/19/… Dec      6.59e8  1997
 3     3    268     22     38      7 Marvel's The… 5/4/12  May      6.23e8  2012
 4     4    268     18     42      4 The Dark Kni… 7/18/08 Jul      5.35e8  2008
 5     5    107     81     19     31 Star Wars: E… 5/19/99 May      4.75e8  1999
 6     6     63      4     15      2 Star Wars (F… 5/25/77 May      4.61e8  1977
 7     7    256     39     34     11 The Dark Kni… 7/20/12 Jul      4.48e8  2012
 8     8    185     23     36      5 Shrek 2 (Dre… 5/19/04 May      4.41e8  2004
 9     9     92      2     17      1 E.T.: The Ex… 6/11/82 Jun      4.35e8  1982
10    10    119    101     16     25 Pirates of t… 7/7/06  Jul      4.23e8  2006
# … with 708 more rows, and abbreviated variable names ¹​ReleaseDate,
#   ²​ReleaseMonth, ³​Revenues
18 / 38
boxoffice %>%
  group_by(year)
# A tibble: 718 × 10
# Groups:   year [55]
    Rank AllPos AllNeg TopPos TopNeg Movie         Relea…¹ Relea…² Reven…³  year
   <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <chr>         <chr>   <chr>     <dbl> <dbl>
 1     1    238     48     38      3 Avatar (Fox)  12/18/… Dec      7.61e8  2009
 2     2    145     21     36      8 Titanic (Par… 12/19/… Dec      6.59e8  1997
 3     3    268     22     38      7 Marvel's The… 5/4/12  May      6.23e8  2012
 4     4    268     18     42      4 The Dark Kni… 7/18/08 Jul      5.35e8  2008
 5     5    107     81     19     31 Star Wars: E… 5/19/99 May      4.75e8  1999
 6     6     63      4     15      2 Star Wars (F… 5/25/77 May      4.61e8  1977
 7     7    256     39     34     11 The Dark Kni… 7/20/12 Jul      4.48e8  2012
 8     8    185     23     36      5 Shrek 2 (Dre… 5/19/04 May      4.41e8  2004
 9     9     92      2     17      1 E.T.: The Ex… 6/11/82 Jun      4.35e8  1982
10    10    119    101     16     25 Pirates of t… 7/7/06  Jul      4.23e8  2006
# … with 708 more rows, and abbreviated variable names ¹​ReleaseDate,
#   ²​ReleaseMonth, ³​Revenues
18 / 38
boxoffice %>%
  group_by(year) %>%
  mutate(`number of movies` = n())
# A tibble: 718 × 11
# Groups:   year [55]
    Rank AllPos AllNeg TopPos TopNeg Movie Relea…¹ Relea…² Reven…³  year numbe…⁴
   <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <chr> <chr>   <chr>     <dbl> <dbl>   <int>
 1     1    238     48     38      3 Avat… 12/18/… Dec      7.61e8  2009      38
 2     2    145     21     36      8 Tita… 12/19/… Dec      6.59e8  1997      18
 3     3    268     22     38      7 Marv… 5/4/12  May      6.23e8  2012      31
 4     4    268     18     42      4 The … 7/18/08 Jul      5.35e8  2008      39
 5     5    107     81     19     31 Star… 5/19/99 May      4.75e8  1999      27
 6     6     63      4     15      2 Star… 5/25/77 May      4.61e8  1977       5
 7     7    256     39     34     11 The … 7/20/12 Jul      4.48e8  2012      31
 8     8    185     23     36      5 Shre… 5/19/04 May      4.41e8  2004      33
 9     9     92      2     17      1 E.T.… 6/11/82 Jun      4.35e8  1982       5
10    10    119    101     16     25 Pira… 7/7/06  Jul      4.23e8  2006      29
# … with 708 more rows, and abbreviated variable names ¹​ReleaseDate,
#   ²​ReleaseMonth, ³​Revenues, ⁴​`number of movies`
18 / 38
boxoffice %>%
  group_by(year) %>%
  mutate(`number of movies` = n()) %>%
  ungroup()
# A tibble: 718 × 11
    Rank AllPos AllNeg TopPos TopNeg Movie Relea…¹ Relea…² Reven…³  year numbe…⁴
   <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <chr> <chr>   <chr>     <dbl> <dbl>   <int>
 1     1    238     48     38      3 Avat… 12/18/… Dec      7.61e8  2009      38
 2     2    145     21     36      8 Tita… 12/19/… Dec      6.59e8  1997      18
 3     3    268     22     38      7 Marv… 5/4/12  May      6.23e8  2012      31
 4     4    268     18     42      4 The … 7/18/08 Jul      5.35e8  2008      39
 5     5    107     81     19     31 Star… 5/19/99 May      4.75e8  1999      27
 6     6     63      4     15      2 Star… 5/25/77 May      4.61e8  1977       5
 7     7    256     39     34     11 The … 7/20/12 Jul      4.48e8  2012      31
 8     8    185     23     36      5 Shre… 5/19/04 May      4.41e8  2004      33
 9     9     92      2     17      1 E.T.… 6/11/82 Jun      4.35e8  1982       5
10    10    119    101     16     25 Pira… 7/7/06  Jul      4.23e8  2006      29
# … with 708 more rows, and abbreviated variable names ¹​ReleaseDate,
#   ²​ReleaseMonth, ³​Revenues, ⁴​`number of movies`
18 / 38
boxoffice %>%
  group_by(year) %>%
  mutate(`number of movies` = n()) %>%
  ungroup() %>%
  distinct(year, .keep_all=TRUE)
# A tibble: 55 × 11
    Rank AllPos AllNeg TopPos TopNeg Movie Relea…¹ Relea…² Reven…³  year numbe…⁴
   <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <chr> <chr>   <chr>     <dbl> <dbl>   <int>
 1     1    238     48     38      3 Avat… 12/18/… Dec      7.61e8  2009      38
 2     2    145     21     36      8 Tita… 12/19/… Dec      6.59e8  1997      18
 3     3    268     22     38      7 Marv… 5/4/12  May      6.23e8  2012      31
 4     4    268     18     42      4 The … 7/18/08 Jul      5.35e8  2008      39
 5     5    107     81     19     31 Star… 5/19/99 May      4.75e8  1999      27
 6     6     63      4     15      2 Star… 5/25/77 May      4.61e8  1977       5
 7     8    185     23     36      5 Shre… 5/19/04 May      4.41e8  2004      33
 8     9     92      2     17      1 E.T.… 6/11/82 Jun      4.35e8  1982       5
 9    10    119    101     16     25 Pira… 7/7/06  Jul      4.23e8  2006      29
10    11     84     10     14      1 The … 6/15/94 Jun      4.23e8  1994      14
# … with 45 more rows, and abbreviated variable names ¹​ReleaseDate,
#   ²​ReleaseMonth, ³​Revenues, ⁴​`number of movies`
18 / 38
boxoffice %>%
  group_by(year) %>%
  mutate(`number of movies` = n()) %>%
  ungroup() %>%
  distinct(year, .keep_all=TRUE) %>%
  filter(`number of movies` ==
           max(`number of movies`))
# A tibble: 1 × 11
   Rank AllPos AllNeg TopPos TopNeg Movie  Relea…¹ Relea…² Reven…³  year numbe…⁴
  <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <chr>  <chr>   <chr>     <dbl> <dbl>   <int>
1    12    252      3     41      0 Toy S… 6/18/10 Jun      4.15e8  2010      43
# … with abbreviated variable names ¹​ReleaseDate, ²​ReleaseMonth, ³​Revenues,
#   ⁴​`number of movies`
18 / 38
boxoffice %>%
  group_by(year) %>%
  mutate(`number of movies` = n()) %>%
  ungroup() %>%
  distinct(year, .keep_all=TRUE) %>%
  filter(`number of movies` ==
           max(`number of movies`)) %>%
  select(year, `number of movies`)
# A tibble: 1 × 2
   year `number of movies`
  <dbl>              <int>
1  2010                 43
18 / 38

Imaginary bonus: What are the top ranked movie by year?

count: false

boxoffice
# A tibble: 718 × 10
    Rank AllPos AllNeg TopPos TopNeg Movie         Relea…¹ Relea…² Reven…³  year
   <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <chr>         <chr>   <chr>     <dbl> <dbl>
 1     1    238     48     38      3 Avatar (Fox)  12/18/… Dec      7.61e8  2009
 2     2    145     21     36      8 Titanic (Par… 12/19/… Dec      6.59e8  1997
 3     3    268     22     38      7 Marvel's The… 5/4/12  May      6.23e8  2012
 4     4    268     18     42      4 The Dark Kni… 7/18/08 Jul      5.35e8  2008
 5     5    107     81     19     31 Star Wars: E… 5/19/99 May      4.75e8  1999
 6     6     63      4     15      2 Star Wars (F… 5/25/77 May      4.61e8  1977
 7     7    256     39     34     11 The Dark Kni… 7/20/12 Jul      4.48e8  2012
 8     8    185     23     36      5 Shrek 2 (Dre… 5/19/04 May      4.41e8  2004
 9     9     92      2     17      1 E.T.: The Ex… 6/11/82 Jun      4.35e8  1982
10    10    119    101     16     25 Pirates of t… 7/7/06  Jul      4.23e8  2006
# … with 708 more rows, and abbreviated variable names ¹​ReleaseDate,
#   ²​ReleaseMonth, ³​Revenues
19 / 38
boxoffice %>%
  group_by(year)
# A tibble: 718 × 10
# Groups:   year [55]
    Rank AllPos AllNeg TopPos TopNeg Movie         Relea…¹ Relea…² Reven…³  year
   <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <chr>         <chr>   <chr>     <dbl> <dbl>
 1     1    238     48     38      3 Avatar (Fox)  12/18/… Dec      7.61e8  2009
 2     2    145     21     36      8 Titanic (Par… 12/19/… Dec      6.59e8  1997
 3     3    268     22     38      7 Marvel's The… 5/4/12  May      6.23e8  2012
 4     4    268     18     42      4 The Dark Kni… 7/18/08 Jul      5.35e8  2008
 5     5    107     81     19     31 Star Wars: E… 5/19/99 May      4.75e8  1999
 6     6     63      4     15      2 Star Wars (F… 5/25/77 May      4.61e8  1977
 7     7    256     39     34     11 The Dark Kni… 7/20/12 Jul      4.48e8  2012
 8     8    185     23     36      5 Shrek 2 (Dre… 5/19/04 May      4.41e8  2004
 9     9     92      2     17      1 E.T.: The Ex… 6/11/82 Jun      4.35e8  1982
10    10    119    101     16     25 Pirates of t… 7/7/06  Jul      4.23e8  2006
# … with 708 more rows, and abbreviated variable names ¹​ReleaseDate,
#   ²​ReleaseMonth, ³​Revenues
19 / 38
boxoffice %>%
  group_by(year) %>%
  filter(Rank == max(Rank))
# A tibble: 55 × 10
# Groups:   year [55]
    Rank AllPos AllNeg TopPos TopNeg Movie         Relea…¹ Relea…² Reven…³  year
   <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <chr>         <chr>   <chr>     <dbl> <dbl>
 1   128     64      3     15      2 Gone with th… 12/15/… Dec      1.99e8  1939
 2   145     39      1      6      0 Snow White a… 12/21/… Dec      1.85e8  1937
 3   249     36      1      5      0 101 Dalmatia… 1/25/61 Jan      1.45e8  1961
 4   408     36      1      4      0 American Gra… 8/1/73  Aug      1.15e8  1973
 5   424     49      2      5      2 One Flew Ove… 11/20/… Nov      1.12e8  1975
 6   427     28      5      3      2 Doctor Zhiva… 12/22/… Dec      1.12e8  1965
 7   430      7     15      0      1 Porky's (Fox) 3/19/82 Mar      1.11e8  1982
 8   473     42      6      5      2 The Graduate… 12/21/… Dec      1.05e8  1967
 9   488     41      4      4      0 Bambi (Disne… 8/13/42 Aug      1.03e8  1942
10   496     40      5      3      3 Butch Cassid… 9/23/69 Sep      1.02e8  1969
# … with 45 more rows, and abbreviated variable names ¹​ReleaseDate,
#   ²​ReleaseMonth, ³​Revenues
19 / 38
boxoffice %>%
  group_by(year) %>%
  filter(Rank == max(Rank)) %>%
  select(Rank, Movie, year)
# A tibble: 55 × 3
# Groups:   year [55]
    Rank Movie                                             year
   <dbl> <chr>                                            <dbl>
 1   128 Gone with the Wind (MGM)                          1939
 2   145 Snow White and the Seven Dwarfs (Disney / RKO)    1937
 3   249 101 Dalmatians (1961) (Disney)                    1961
 4   408 American Graffiti (Universal)                     1973
 5   424 One Flew Over the Cuckoo's Nest (United Artists)  1975
 6   427 Doctor Zhivago (MGM)                              1965
 7   430 Porky's (Fox)                                     1982
 8   473 The Graduate (AVCO Embassy)                       1967
 9   488 Bambi (Disney / RKO)                              1942
10   496 Butch Cassidy and the Sundance Kid (Fox)          1969
# … with 45 more rows
19 / 38
boxoffice %>%
  group_by(year) %>%
  filter(Rank == max(Rank)) %>%
  select(Rank, Movie, year) %>%
  arrange(-year)
# A tibble: 55 × 3
# Groups:   year [55]
    Rank Movie                                          year
   <dbl> <chr>                                         <dbl>
 1   658 Wrath of the Titans (Warner Bros.)             2012
 2   705 Zookeeper (Sony / Columbia)                    2011
 3   716 Dear John (Sony / Screen Gems)                 2010
 4   656 Up in the Air (Paramount)                      2009
 5   714 Cloverfield (Paramount)                        2008
 6   711 Disturbia (Paramount / DreamWorks)             2007
 7   712 Nacho Libre (Paramount)                        2006
 8   708 The Dukes of Hazzard (Warner Bros.)            2005
 9   706 Alien Vs. Predator (Fox)                       2004
10   704 The Texas Chainsaw Massacre (2003) (New Line)  2003
# … with 45 more rows
19 / 38
boxoffice %>%
  group_by(year) %>%
  filter(Rank == max(Rank)) %>%
  select(Rank, Movie, year) %>%
  arrange(-year) %>%
  ungroup()
# A tibble: 55 × 3
    Rank Movie                                          year
   <dbl> <chr>                                         <dbl>
 1   658 Wrath of the Titans (Warner Bros.)             2012
 2   705 Zookeeper (Sony / Columbia)                    2011
 3   716 Dear John (Sony / Screen Gems)                 2010
 4   656 Up in the Air (Paramount)                      2009
 5   714 Cloverfield (Paramount)                        2008
 6   711 Disturbia (Paramount / DreamWorks)             2007
 7   712 Nacho Libre (Paramount)                        2006
 8   708 The Dukes of Hazzard (Warner Bros.)            2005
 9   706 Alien Vs. Predator (Fox)                       2004
10   704 The Texas Chainsaw Massacre (2003) (New Line)  2003
# … with 45 more rows
19 / 38
top_movie_year <- 
  boxoffice %>%
  group_by(year) %>%
  filter(Rank == max(Rank)) %>%
  select(Rank, Movie, year) %>%
  arrange(-year) %>%
  ungroup()
20 / 38

Numerical

count: false

ggplot(top_movie_year,
       aes(year,
           Rank,
           fill = Movie))

21 / 38
ggplot(top_movie_year,
       aes(year,
           Rank,
           fill = Movie)) +
  geom_bar(stat = "identity",
           show.legend = FALSE)

21 / 38
ggplot(top_movie_year,
       aes(year,
           Rank,
           fill = Movie)) +
  geom_bar(stat = "identity",
           show.legend = FALSE) +
  theme_minimal()

21 / 38
ggplot(top_movie_year,
       aes(year,
           Rank,
           fill = Movie)) +
  geom_bar(stat = "identity",
           show.legend = FALSE) +
  theme_minimal() +
  scale_fill_viridis_d(direction = -1)

21 / 38
ggplot(top_movie_year,
       aes(year,
           Rank,
           fill = Movie)) +
  geom_bar(stat = "identity",
           show.legend = FALSE) +
  theme_minimal() +
  scale_fill_viridis_d(direction = -1) +
  labs(title = "Top Movies by Year and Rank",
       subtitle = "According to Rotten Tomatoes")

21 / 38

Categorical

count: false

ggplot(top_movie_year,
       aes(year,
           Movie,
           fill = Movie))

22 / 38
ggplot(top_movie_year,
       aes(year,
           Movie,
           fill = Movie)) +
  geom_bar(stat = "identity",
           show.legend = FALSE)

22 / 38
ggplot(top_movie_year,
       aes(year,
           Movie,
           fill = Movie)) +
  geom_bar(stat = "identity",
           show.legend = FALSE) +
  theme_minimal()

22 / 38
ggplot(top_movie_year,
       aes(year,
           Movie,
           fill = Movie)) +
  geom_bar(stat = "identity",
           show.legend = FALSE) +
  theme_minimal() +
  scale_fill_viridis_d(direction = -1)

22 / 38
ggplot(top_movie_year,
       aes(year,
           Movie,
           fill = Movie)) +
  geom_bar(stat = "identity",
           show.legend = FALSE) +
  theme_minimal() +
  scale_fill_viridis_d(direction = -1) +
  labs(title = "Top Movies by Year",
       subtitle = "According to Rotten Tomatoes")

22 / 38

That looks off. Let's try

23 / 38

That looks off. Let's try

  • reordering the bars which we can do a command called reorder()
23 / 38

That looks off. Let's try

  • reordering the bars which we can do a command called reorder()

  • setting limits on the horizontal axis which we can do with a command called xlim()

23 / 38

That looks off. Let's try

  • reordering the bars which we can do a command called reorder()

  • setting limits on the horizontal axis which we can do with a command called xlim()

ggplot(top_movie_year,
       aes(year,
           reorder(Movie, -year),
           fill = Movie))

23 / 38
ggplot(top_movie_year,
       aes(year,
           reorder(Movie, -year),
           fill = Movie)) +
  geom_bar(stat = "identity",
           show.legend = FALSE)

23 / 38
ggplot(top_movie_year,
       aes(year,
           reorder(Movie, -year),
           fill = Movie)) +
  geom_bar(stat = "identity",
           show.legend = FALSE) +
  theme_minimal()

23 / 38
ggplot(top_movie_year,
       aes(year,
           reorder(Movie, -year),
           fill = Movie)) +
  geom_bar(stat = "identity",
           show.legend = FALSE) +
  theme_minimal() +
  scale_fill_viridis_d(direction = -1)

23 / 38
ggplot(top_movie_year,
       aes(year,
           reorder(Movie, -year),
           fill = Movie)) +
  geom_bar(stat = "identity",
           show.legend = FALSE) +
  theme_minimal() +
  scale_fill_viridis_d(direction = -1) +
  labs(title = "Top Movies by Year",
       subtitle = "According to Rotten Tomatoes")

23 / 38
ggplot(top_movie_year,
       aes(year,
           reorder(Movie, -year),
           fill = Movie)) +
  geom_bar(stat = "identity",
           show.legend = FALSE) +
  theme_minimal() +
  scale_fill_viridis_d(direction = -1) +
  labs(title = "Top Movies by Year",
       subtitle = "According to Rotten Tomatoes") +
  coord_cartesian(xlim = c(1900, 2015))

23 / 38

Ok now on to the normal curve!

24 / 38

Load up data

nfl_pol <- read_csv("teampolview.csv")
25 / 38

Data Wrangling

count: false

nfl_pol
# A tibble: 33 × 25
   Team  Total…¹ Total…² Asian…³ Black…⁴ Hispa…⁵ White…⁶ Other…⁷ Total…⁸ Asian…⁹
   <chr>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
 1 Ariz…     148      39       2       7       7      20       3      71       4
 2 Atla…     188      59       3      27       5      23       1      75       3
 3 Balt…     150      56       5      14       3      30       4      65       3
 4 Buff…      92      22       2       3       1      15       1      46       7
 5 Caro…     164      51       4      16       3      26       2      64       3
 6 Chic…     285      94       5      16       8      63       2     129       9
 7 Cinc…     106      37       0       6       1      29       1      32       2
 8 Clev…     105      34       2       3       3      24       2      42       3
 9 Dall…     438     128       5      30      17      66      10     170       9
10 Denv…     313     100       4      15       7      68       6     122       3
# … with 23 more rows, 15 more variables: `Black Independent` <dbl>,
#   `Hispanic Independent` <dbl>, `White Independent` <dbl>,
#   `Other Independent` <dbl>, `Total Republican` <dbl>,
#   `Asian Republican` <dbl>, `Black Republican` <dbl>,
#   `Hispanic Republican` <dbl>, Republican <dbl>, `Other Republican` <dbl>,
#   `GOP%` <chr>, `Dem%` <chr>, `Ind%` <chr>, `White%` <chr>,
#   `Nonwhite%` <chr>, and abbreviated variable names ¹​`Total Respondents`, …
26 / 38
nfl_pol %>%
  select(Team,
         `Total Respondents`, `Total Democrats`,
         Republican, `Other Republican`)
# A tibble: 33 × 5
   Team               `Total Respondents` `Total Democrats` Republican Other R…¹
   <chr>                            <dbl>             <dbl>      <dbl>     <dbl>
 1 Arizona Cardinals                  148                39         30         2
 2 Atlanta Falcons                    188                59         41         3
 3 Baltimore Ravens                   150                56         26         1
 4 Buffalo Bills                       92                22         16         0
 5 Carolina Panthers                  164                51         44         1
 6 Chicago Bears                      285                94         54         1
 7 Cincinnati Bengals                 106                37         30         2
 8 Cleveland Browns                   105                34         26         2
 9 Dallas Cowboys                     438               128        123         6
10 Denver Broncos                     313               100         84         3
# … with 23 more rows, and abbreviated variable name ¹​`Other Republican`
26 / 38
nfl_pol %>%
  select(Team,
         `Total Respondents`, `Total Democrats`,
         Republican, `Other Republican`) %>%
  rowwise(Team)
# A tibble: 33 × 5
# Rowwise:  Team
   Team               `Total Respondents` `Total Democrats` Republican Other R…¹
   <chr>                            <dbl>             <dbl>      <dbl>     <dbl>
 1 Arizona Cardinals                  148                39         30         2
 2 Atlanta Falcons                    188                59         41         3
 3 Baltimore Ravens                   150                56         26         1
 4 Buffalo Bills                       92                22         16         0
 5 Carolina Panthers                  164                51         44         1
 6 Chicago Bears                      285                94         54         1
 7 Cincinnati Bengals                 106                37         30         2
 8 Cleveland Browns                   105                34         26         2
 9 Dallas Cowboys                     438               128        123         6
10 Denver Broncos                     313               100         84         3
# … with 23 more rows, and abbreviated variable name ¹​`Other Republican`
26 / 38
nfl_pol %>%
  select(Team,
         `Total Respondents`, `Total Democrats`,
         Republican, `Other Republican`) %>%
  rowwise(Team) %>%
  mutate(`Total Republicans` = sum(c(Republican,`Other Republican`)))
# A tibble: 33 × 6
# Rowwise:  Team
   Team               `Total Respondents` Total Democr…¹ Repub…² Other…³ Total…⁴
   <chr>                            <dbl>          <dbl>   <dbl>   <dbl>   <dbl>
 1 Arizona Cardinals                  148             39      30       2      32
 2 Atlanta Falcons                    188             59      41       3      44
 3 Baltimore Ravens                   150             56      26       1      27
 4 Buffalo Bills                       92             22      16       0      16
 5 Carolina Panthers                  164             51      44       1      45
 6 Chicago Bears                      285             94      54       1      55
 7 Cincinnati Bengals                 106             37      30       2      32
 8 Cleveland Browns                   105             34      26       2      28
 9 Dallas Cowboys                     438            128     123       6     129
10 Denver Broncos                     313            100      84       3      87
# … with 23 more rows, and abbreviated variable names ¹​`Total Democrats`,
#   ²​Republican, ³​`Other Republican`, ⁴​`Total Republicans`
26 / 38
nfl_pol %>%
  select(Team,
         `Total Respondents`, `Total Democrats`,
         Republican, `Other Republican`) %>%
  rowwise(Team) %>%
  mutate(`Total Republicans` = sum(c(Republican,`Other Republican`))) %>%
  select(-c(Republican,`Other Republican`))
# A tibble: 33 × 4
# Rowwise:  Team
   Team               `Total Respondents` `Total Democrats` `Total Republicans`
   <chr>                            <dbl>             <dbl>               <dbl>
 1 Arizona Cardinals                  148                39                  32
 2 Atlanta Falcons                    188                59                  44
 3 Baltimore Ravens                   150                56                  27
 4 Buffalo Bills                       92                22                  16
 5 Carolina Panthers                  164                51                  45
 6 Chicago Bears                      285                94                  55
 7 Cincinnati Bengals                 106                37                  32
 8 Cleveland Browns                   105                34                  28
 9 Dallas Cowboys                     438               128                 129
10 Denver Broncos                     313               100                  87
# … with 23 more rows
26 / 38
nfl_pol %>%
  select(Team,
         `Total Respondents`, `Total Democrats`,
         Republican, `Other Republican`) %>%
  rowwise(Team) %>%
  mutate(`Total Republicans` = sum(c(Republican,`Other Republican`))) %>%
  select(-c(Republican,`Other Republican`)) %>%
  mutate(percent_dem = round(`Total Democrats`/`Total Respondents`,2))
# A tibble: 33 × 5
# Rowwise:  Team
   Team               `Total Respondents` `Total Democrats` Total Repu…¹ perce…²
   <chr>                            <dbl>             <dbl>        <dbl>   <dbl>
 1 Arizona Cardinals                  148                39           32    0.26
 2 Atlanta Falcons                    188                59           44    0.31
 3 Baltimore Ravens                   150                56           27    0.37
 4 Buffalo Bills                       92                22           16    0.24
 5 Carolina Panthers                  164                51           45    0.31
 6 Chicago Bears                      285                94           55    0.33
 7 Cincinnati Bengals                 106                37           32    0.35
 8 Cleveland Browns                   105                34           28    0.32
 9 Dallas Cowboys                     438               128          129    0.29
10 Denver Broncos                     313               100           87    0.32
# … with 23 more rows, and abbreviated variable names ¹​`Total Republicans`,
#   ²​percent_dem
26 / 38
nfl_pol %>%
  select(Team,
         `Total Respondents`, `Total Democrats`,
         Republican, `Other Republican`) %>%
  rowwise(Team) %>%
  mutate(`Total Republicans` = sum(c(Republican,`Other Republican`))) %>%
  select(-c(Republican,`Other Republican`)) %>%
  mutate(percent_dem = round(`Total Democrats`/`Total Respondents`,2)) %>%
  mutate(percent_rep = round(`Total Republicans`/`Total Respondents`,2))
# A tibble: 33 × 6
# Rowwise:  Team
   Team               `Total Respondents` Total Democr…¹ Total…² perce…³ perce…⁴
   <chr>                            <dbl>          <dbl>   <dbl>   <dbl>   <dbl>
 1 Arizona Cardinals                  148             39      32    0.26    0.22
 2 Atlanta Falcons                    188             59      44    0.31    0.23
 3 Baltimore Ravens                   150             56      27    0.37    0.18
 4 Buffalo Bills                       92             22      16    0.24    0.17
 5 Carolina Panthers                  164             51      45    0.31    0.27
 6 Chicago Bears                      285             94      55    0.33    0.19
 7 Cincinnati Bengals                 106             37      32    0.35    0.3 
 8 Cleveland Browns                   105             34      28    0.32    0.27
 9 Dallas Cowboys                     438            128     129    0.29    0.29
10 Denver Broncos                     313            100      87    0.32    0.28
# … with 23 more rows, and abbreviated variable names ¹​`Total Democrats`,
#   ²​`Total Republicans`, ³​percent_dem, ⁴​percent_rep
26 / 38

Give it a variable

nfl_percentages <- 
  nfl_pol %>%
  select(Team, 
         `Total Respondents`, 
         `Total Democrats`, 
          Republican,
         `Other Republican`) %>%
  rowwise(Team) %>%
  mutate(`Total Republicans` = sum(c(Republican,`Other Republican`))) %>%
  select(-c(Republican,
            `Other Republican`)) %>% 
  mutate(percent_dem = round(`Total Democrats`/`Total Respondents`,2)) %>%
  mutate(percent_rep = round(`Total Republicans`/`Total Respondents`,2))
27 / 38

Plot for Democrats by Team

count: false

ggplot(nfl_percentages,
       aes(reorder(Team, percent_dem),
           percent_dem,
           fill = percent_dem))

28 / 38
ggplot(nfl_percentages,
       aes(reorder(Team, percent_dem),
           percent_dem,
           fill = percent_dem)) +
  geom_bar(stat="identity")

28 / 38
ggplot(nfl_percentages,
       aes(reorder(Team, percent_dem),
           percent_dem,
           fill = percent_dem)) +
  geom_bar(stat="identity") +
  coord_flip()

28 / 38
ggplot(nfl_percentages,
       aes(reorder(Team, percent_dem),
           percent_dem,
           fill = percent_dem)) +
  geom_bar(stat="identity") +
  coord_flip() +
  theme_minimal()

28 / 38

Plot for Republicans by Team

count: false

ggplot(nfl_percentages,
       aes(reorder(Team, percent_rep),
           percent_rep,
           fill = percent_rep))

29 / 38
ggplot(nfl_percentages,
       aes(reorder(Team, percent_rep),
           percent_rep,
           fill = percent_rep)) +
  geom_bar(stat="identity")

29 / 38
ggplot(nfl_percentages,
       aes(reorder(Team, percent_rep),
           percent_rep,
           fill = percent_rep)) +
  geom_bar(stat="identity") +
  coord_flip()

29 / 38
ggplot(nfl_percentages,
       aes(reorder(Team, percent_rep),
           percent_rep,
           fill = percent_rep)) +
  geom_bar(stat="identity") +
  coord_flip() +
  theme_minimal()

29 / 38

Let's compare them!

But first we need to assign variables

p1 <- 
  ggplot(nfl_percentages, 
         aes(reorder(Team, percent_dem),
             percent_dem,
             fill = percent_dem)) +
  geom_bar(stat="identity") +
  coord_flip() +
  theme_minimal()
p2 <- 
  ggplot(nfl_percentages, 
         aes(reorder(Team, percent_rep), 
             percent_rep,
             fill = percent_rep)) +
  geom_bar(stat="identity") +
  coord_flip() +
  theme_minimal()
30 / 38

Patch it together using Patchwork

p1 + p2

31 / 38

A better way

That's not really a comparison...at least not teamwise! Let's try something different

32 / 38

More Data Wrangling: Going from wide to long using pivot_longer

Pivot Longer

33 / 38

Let's pivot!

count: false

nfl_percentages
# A tibble: 33 × 6
# Rowwise:  Team
   Team               `Total Respondents` Total Democr…¹ Total…² perce…³ perce…⁴
   <chr>                            <dbl>          <dbl>   <dbl>   <dbl>   <dbl>
 1 Arizona Cardinals                  148             39      32    0.26    0.22
 2 Atlanta Falcons                    188             59      44    0.31    0.23
 3 Baltimore Ravens                   150             56      27    0.37    0.18
 4 Buffalo Bills                       92             22      16    0.24    0.17
 5 Carolina Panthers                  164             51      45    0.31    0.27
 6 Chicago Bears                      285             94      55    0.33    0.19
 7 Cincinnati Bengals                 106             37      32    0.35    0.3 
 8 Cleveland Browns                   105             34      28    0.32    0.27
 9 Dallas Cowboys                     438            128     129    0.29    0.29
10 Denver Broncos                     313            100      87    0.32    0.28
# … with 23 more rows, and abbreviated variable names ¹​`Total Democrats`,
#   ²​`Total Republicans`, ³​percent_dem, ⁴​percent_rep
34 / 38
nfl_percentages %>%
  pivot_longer(c(percent_dem, percent_rep),
               names_to = "type",
               values_to = "political_percentages")
# A tibble: 66 × 6
   Team              `Total Respondents` `Total Democrats` Total…¹ type  polit…²
   <chr>                           <dbl>             <dbl>   <dbl> <chr>   <dbl>
 1 Arizona Cardinals                 148                39      32 perc…    0.26
 2 Arizona Cardinals                 148                39      32 perc…    0.22
 3 Atlanta Falcons                   188                59      44 perc…    0.31
 4 Atlanta Falcons                   188                59      44 perc…    0.23
 5 Baltimore Ravens                  150                56      27 perc…    0.37
 6 Baltimore Ravens                  150                56      27 perc…    0.18
 7 Buffalo Bills                      92                22      16 perc…    0.24
 8 Buffalo Bills                      92                22      16 perc…    0.17
 9 Carolina Panthers                 164                51      45 perc…    0.31
10 Carolina Panthers                 164                51      45 perc…    0.27
# … with 56 more rows, and abbreviated variable names ¹​`Total Republicans`,
#   ²​political_percentages
34 / 38

Give it a variable

nlf_percentages_long <- 
  nfl_percentages %>%
  pivot_longer(c(percent_dem, percent_rep),
               names_to = "type",
               values_to = "political_percentages")
35 / 38

More Data Wrangling: Pivoting

count: false

ggplot(nlf_percentages_long,
       aes(Team,
           political_percentages,
           fill =  political_percentages))

36 / 38
ggplot(nlf_percentages_long,
       aes(Team,
           political_percentages,
           fill =  political_percentages)) +
  geom_bar(stat="identity")

36 / 38
ggplot(nlf_percentages_long,
       aes(Team,
           political_percentages,
           fill =  political_percentages)) +
  geom_bar(stat="identity") +
  coord_flip()

36 / 38
ggplot(nlf_percentages_long,
       aes(Team,
           political_percentages,
           fill =  political_percentages)) +
  geom_bar(stat="identity") +
  coord_flip() +
  theme_minimal()

36 / 38
ggplot(nlf_percentages_long,
       aes(Team,
           political_percentages,
           fill =  political_percentages)) +
  geom_bar(stat="identity") +
  coord_flip() +
  theme_minimal() +
  facet_wrap(.~type)

36 / 38

Your turn

Try these on your own

  1. Compare how the different ethnicities within each political party differ.

  2. Compare how each specific ethnicity between each political party differ.

  3. Which ethnicity in each political party is the most conservative? the most liberal?

37 / 38

That's it for today!

38 / 38

Prepping a New R Script

  1. Open up a blank R script using the menu path File > New File > R Script.
2 / 38
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