Module 7 Data Wrangling

7.1 Load libraries

Load tidyverse using library()

Our data for this module is an excel spreadsheet, so we need to install a new package to handle this type of data.

install.packages("readxl")

7.2 Read your data in

After readxl package installation is done:

  1. load readxl using library()

  2. check your working environment with getwd() and dir()

  3. load your data

nfl_salary <- read_excel("data/nfl_salary.xlsx")
  1. inspect your data with summary(), glimpse() and View()
glimpse(nfl_salary)
## Rows: 800
## Columns: 11
## $ year                <dbl> 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2…
## $ Cornerback          <dbl> 11265916, 11000000, 10000000, 10000000, 10000000,…
## $ `Defensive Lineman` <dbl> 17818000, 16200000, 12476000, 11904706, 11762782,…
## $ Linebacker          <dbl> 16420000, 15623000, 11825000, 10083333, 10020000,…
## $ `Offensive Lineman` <dbl> 15960000, 12800000, 11767500, 10358200, 10000000,…
## $ Quarterback         <dbl> 17228125, 16000000, 14400000, 14100000, 13510000,…
## $ `Running Back`      <dbl> 12955000, 10873833, 9479000, 7700000, 7500000, 70…
## $ Safety              <dbl> 8871428, 8787500, 8282500, 8000000, 7804333, 7652…
## $ `Special Teamer`    <dbl> 4300000, 3725000, 3556176, 3500000, 3250000, 3225…
## $ `Tight End`         <dbl> 8734375, 8591000, 8290000, 7723333, 6974666, 6133…
## $ `Wide Receiver`     <dbl> 16250000, 14175000, 11424000, 11415000, 10800000,…
  1. How many observations are there?

  2. What variables are there in the data?

7.3 Summarise data

QUESTIONS:

  1. Have salaries for different NFL positions increased between 2011 and 2018?

  2. What positions pay more and less?

Let’s summarise the mean salary for Quarterback by year.

nfl_salary %>%
  group_by(year) %>%
  summarise(quarterback_mean_salary = mean(Quarterback, na.rm = TRUE))
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 8 x 2
##    year quarterback_mean_salary
##   <dbl>                   <dbl>
## 1  2011                3376113.
## 2  2012                3496408.
## 3  2013                3450185.
## 4  2014                4234160.
## 5  2015                4225789.
## 6  2016                5499939.
## 7  2017                5329727.
## 8  2018                6593769.

What would we do to add the mean salary for Cornerback?

nfl_salary %>%
  group_by(year) %>%
  summarise(quarterback_mean_salary = mean(Quarterback, na.rm = TRUE),
            cornerback_mean_salary = mean(Cornerback, na.rm = TRUE))
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 8 x 3
##    year quarterback_mean_salary cornerback_mean_salary
##   <dbl>                   <dbl>                  <dbl>
## 1  2011                3376113.               3037766.
## 2  2012                3496408.               3132916.
## 3  2013                3450185.               2901798.
## 4  2014                4234160.               3038278.
## 5  2015                4225789.               3758543.
## 6  2016                5499939.               4201470.
## 7  2017                5329727.               4125692.
## 8  2018                6593769.               4659704.

Let’s stop and think about how our data is organized. Is our data tidy?

We have columns that mix two type of variables:

  1. categorical variable for position

  2. numeric variable for salary

7.4 Tidy data

In order to make our data easier to work with, we need to make sure each column in our data represents just one variable. To do that for our nfl_salary dataframe, we need to pivot it.

nfl_salary_tidy <- nfl_salary %>%
  pivot_longer(cols = -year,
               names_to = "position",
               values_to = "salary")

Always inspect your new data frame.

glimpse(nfl_salary_tidy)
## Rows: 8,000
## Columns: 3
## $ year     <dbl> 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, …
## $ position <chr> "Cornerback", "Defensive Lineman", "Linebacker", "Offensive …
## $ salary   <dbl> 11265916, 17818000, 16420000, 15960000, 17228125, 12955000, …

How many positions are there in the data? We can now do a count() with our categorical variable for position

nfl_salary_tidy %>%
  count(position)
## # A tibble: 10 x 2
##    position              n
##    <chr>             <int>
##  1 Cornerback          800
##  2 Defensive Lineman   800
##  3 Linebacker          800
##  4 Offensive Lineman   800
##  5 Quarterback         800
##  6 Running Back        800
##  7 Safety              800
##  8 Special Teamer      800
##  9 Tight End           800
## 10 Wide Receiver       800

We can add year to our group_by to check how many observations per position across year

nfl_salary_tidy %>%
  count(position, year)
## # A tibble: 80 x 3
##    position           year     n
##    <chr>             <dbl> <int>
##  1 Cornerback         2011   100
##  2 Cornerback         2012   100
##  3 Cornerback         2013   100
##  4 Cornerback         2014   100
##  5 Cornerback         2015   100
##  6 Cornerback         2016   100
##  7 Cornerback         2017   100
##  8 Cornerback         2018   100
##  9 Defensive Lineman  2011   100
## 10 Defensive Lineman  2012   100
## # … with 70 more rows

Let’s check for NAs (i.e., missing data), we can do that by using is.na() and filter().

nfl_salary_tidy %>%
  filter(is.na(salary)) %>%
  count(position, year)
## # A tibble: 9 x 3
##   position        year     n
##   <chr>          <dbl> <int>
## 1 Quarterback     2011     3
## 2 Quarterback     2012    12
## 3 Quarterback     2013     7
## 4 Quarterback     2014    11
## 5 Quarterback     2015     3
## 6 Quarterback     2016     5
## 7 Quarterback     2017     3
## 8 Quarterback     2018    11
## 9 Special Teamer  2011     1

We can remove these rows from our data frame.

nfl_salary_tidy_clean <- nfl_salary_tidy %>%
  filter(!is.na(salary))

Inspect your new data frame.

glimpse(nfl_salary_tidy_clean)
## Rows: 7,944
## Columns: 3
## $ year     <dbl> 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, …
## $ position <chr> "Cornerback", "Defensive Lineman", "Linebacker", "Offensive …
## $ salary   <dbl> 11265916, 17818000, 16420000, 15960000, 17228125, 12955000, …

Now we can do our salary summarise() in a cleaner way. We are going to do a mean() of our numeric variable salary by year AND position.

nfl_salary_tidy_clean %>%
  group_by(year, position) %>%
  summarise(mean_salary = mean(salary))
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## # A tibble: 80 x 3
## # Groups:   year [8]
##     year position          mean_salary
##    <dbl> <chr>                   <dbl>
##  1  2011 Cornerback           3037766.
##  2  2011 Defensive Lineman    4306995.
##  3  2011 Linebacker           4016045.
##  4  2011 Offensive Lineman    4662748.
##  5  2011 Quarterback          3376113.
##  6  2011 Running Back         1976341.
##  7  2011 Safety               2241891.
##  8  2011 Special Teamer       1244069.
##  9  2011 Tight End            1608100.
## 10  2011 Wide Receiver        2996590.
## # … with 70 more rows

We can do the group_by both ways (first year and then position or vice-versa).

nfl_salary_tidy_clean %>%
  group_by(position, year) %>%
  summarise(mean_salary = mean(salary)) %>%
  arrange(mean_salary)
## `summarise()` regrouping output by 'position' (override with `.groups` argument)
## # A tibble: 80 x 3
## # Groups:   position [10]
##    position        year mean_salary
##    <chr>          <dbl>       <dbl>
##  1 Special Teamer  2013    1235892.
##  2 Special Teamer  2011    1244069.
##  3 Special Teamer  2014    1264493.
##  4 Special Teamer  2012    1313043.
##  5 Special Teamer  2015    1348637.
##  6 Special Teamer  2016    1394443.
##  7 Special Teamer  2017    1459552.
##  8 Special Teamer  2018    1571447.
##  9 Tight End       2011    1608100.
## 10 Tight End       2012    1664520.
## # … with 70 more rows

Add a - (minus) sign to the argument in arrange() to arrange your results by decreasing order of mean_salary.

nfl_salary_tidy_clean %>%
  group_by(position, year) %>%
  summarise(mean_salary = mean(salary)) %>%
  arrange(-mean_salary)
## `summarise()` regrouping output by 'position' (override with `.groups` argument)
## # A tibble: 80 x 3
## # Groups:   position [10]
##    position           year mean_salary
##    <chr>             <dbl>       <dbl>
##  1 Offensive Lineman  2018    7522647.
##  2 Defensive Lineman  2018    7202360.
##  3 Quarterback        2018    6593769.
##  4 Offensive Lineman  2017    6370947.
##  5 Defensive Lineman  2017    6202601.
##  6 Wide Receiver      2018    5627721.
##  7 Quarterback        2016    5499939.
##  8 Offensive Lineman  2016    5410392.
##  9 Quarterback        2017    5329727.
## 10 Linebacker         2018    5293675.
## # … with 70 more rows

We can also add arrange() to our code block.

nfl_salary_tidy_clean %>%
  group_by(position, year) %>%
  summarise(mean_salary = mean(salary)) %>%
  arrange()
## `summarise()` regrouping output by 'position' (override with `.groups` argument)
## # A tibble: 80 x 3
## # Groups:   position [10]
##    position           year mean_salary
##    <chr>             <dbl>       <dbl>
##  1 Cornerback         2011    3037766.
##  2 Cornerback         2012    3132916.
##  3 Cornerback         2013    2901798.
##  4 Cornerback         2014    3038278.
##  5 Cornerback         2015    3758543.
##  6 Cornerback         2016    4201470.
##  7 Cornerback         2017    4125692.
##  8 Cornerback         2018    4659704.
##  9 Defensive Lineman  2011    4306995.
## 10 Defensive Lineman  2012    4693730.
## # … with 70 more rows

7.4.1 Viz Demo

We can also visualize our data using ggplot().

First we save our summary results in a new dataframe called nfl_salary_summary.

nfl_salary_summary <- nfl_salary_tidy_clean %>%
  group_by(position, year) %>%
  summarise(mean_salary = mean(salary)) %>%
  arrange()
## `summarise()` regrouping output by 'position' (override with `.groups` argument)

Then we plot it.

nfl_salary_summary %>%
  ggplot(aes(x = year, y = mean_salary,
             color = position,
             group = position)) +
  geom_point() +
  geom_line()

7.5 Transform Data

Now that our data is tidy, we can transform our data by adding new variables/columns to it.

It seems some salaries for certain positions show a higher increase across the years than the salaries for other positions. In other words, the proportion of what position makes in relation to total money spent in salaries for each each.

We can check this is true by creating a sum() of salaries for each year and a count of players using n():

nfl_salary_tidy_clean %>%
  group_by(year, position) %>%
  summarise(player_count = n(),
            total_per_position = sum(salary)) 
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## # A tibble: 80 x 4
## # Groups:   year [8]
##     year position          player_count total_per_position
##    <dbl> <chr>                    <int>              <dbl>
##  1  2011 Cornerback                 100          303776605
##  2  2011 Defensive Lineman          100          430699528
##  3  2011 Linebacker                 100          401604548
##  4  2011 Offensive Lineman          100          466274753
##  5  2011 Quarterback                 97          327482939
##  6  2011 Running Back               100          197634074
##  7  2011 Safety                     100          224189136
##  8  2011 Special Teamer              99          123162874
##  9  2011 Tight End                  100          160810030
## 10  2011 Wide Receiver              100          299659044
## # … with 70 more rows

We can then add mutate() to our code block to calculate sum() of all salaries per year.

nfl_salary_tidy_clean %>%
  group_by(year, position) %>%
  summarise(player_count = n(),
            total_per_position = sum(salary)) %>%
  mutate(total_per_year = sum(total_per_position)) 
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## # A tibble: 80 x 5
## # Groups:   year [8]
##     year position          player_count total_per_position total_per_year
##    <dbl> <chr>                    <int>              <dbl>          <dbl>
##  1  2011 Cornerback                 100          303776605     2935293531
##  2  2011 Defensive Lineman          100          430699528     2935293531
##  3  2011 Linebacker                 100          401604548     2935293531
##  4  2011 Offensive Lineman          100          466274753     2935293531
##  5  2011 Quarterback                 97          327482939     2935293531
##  6  2011 Running Back               100          197634074     2935293531
##  7  2011 Safety                     100          224189136     2935293531
##  8  2011 Special Teamer              99          123162874     2935293531
##  9  2011 Tight End                  100          160810030     2935293531
## 10  2011 Wide Receiver              100          299659044     2935293531
## # … with 70 more rows

Now we can calculate the percentage cost of each position by the total salaries for each year, we can do that all in the same mutate().

nfl_salary_tidy_clean %>%
  group_by(year, position) %>%
  summarise(player_count = n(),
            total_per_position = sum(salary)) %>%
  mutate(total_per_year = sum(total_per_position),
         percentage_cost = total_per_position/total_per_year) 
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## # A tibble: 80 x 6
## # Groups:   year [8]
##     year position   player_count total_per_posit… total_per_year percentage_cost
##    <dbl> <chr>             <int>            <dbl>          <dbl>           <dbl>
##  1  2011 Cornerback          100        303776605     2935293531          0.103 
##  2  2011 Defensive…          100        430699528     2935293531          0.147 
##  3  2011 Linebacker          100        401604548     2935293531          0.137 
##  4  2011 Offensive…          100        466274753     2935293531          0.159 
##  5  2011 Quarterba…           97        327482939     2935293531          0.112 
##  6  2011 Running B…          100        197634074     2935293531          0.0673
##  7  2011 Safety              100        224189136     2935293531          0.0764
##  8  2011 Special T…           99        123162874     2935293531          0.0420
##  9  2011 Tight End           100        160810030     2935293531          0.0548
## 10  2011 Wide Rece…          100        299659044     2935293531          0.102 
## # … with 70 more rows

Add arrange() to see higher percentages at the top.

nfl_salary_tidy_clean %>%
  group_by(year, position) %>%
  summarise(player_count = n(),
            total_per_position = sum(salary)) %>%
  mutate(total_per_year = sum(total_per_position),
         percentage_cost = total_per_position/total_per_year) %>%
  arrange(-percentage_cost)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## # A tibble: 80 x 6
## # Groups:   year [8]
##     year position   player_count total_per_posit… total_per_year percentage_cost
##    <dbl> <chr>             <int>            <dbl>          <dbl>           <dbl>
##  1  2018 Offensive…          100        752264724     4557047519           0.165
##  2  2014 Defensive…          100        503535499     3154183189           0.160
##  3  2011 Offensive…          100        466274753     2935293531           0.159
##  4  2017 Offensive…          100        637094749     4027571325           0.158
##  5  2018 Defensive…          100        720236012     4557047519           0.158
##  6  2013 Defensive…          100        454787761     2920039442           0.156
##  7  2014 Offensive…          100        489885308     3154183189           0.155
##  8  2013 Offensive…          100        453489965     2920039442           0.155
##  9  2012 Defensive…          100        469373045     3032589536           0.155
## 10  2017 Defensive…          100        620260110     4027571325           0.154
## # … with 70 more rows

7.5.1 Viz Demo

We can also visualize our data using ggplot().

First we save our summary results in a new dataframe called nfl_salary_summary.

nfl_salary_summary <- nfl_salary_tidy_clean %>%
  group_by(year, position) %>%
  summarise(player_count = n(),
            total_per_position = sum(salary)) %>%
  mutate(total_per_year = sum(total_per_position),
         percentage_cost = total_per_position/total_per_year) %>%
  arrange(-percentage_cost)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)

Then we plot it.

nfl_salary_summary %>%
  ggplot(aes(x = year, y = percentage_cost,
             color = position,
             group = position)) +
  geom_point() +
  geom_line()

7.6 DATA CHALLENGE 02

Accept data challenge 02 assignment