Import data

# csv file
hockey <- read_csv("../00_data/myData_Hockey.csv")
hockey
## # A tibble: 251 × 10
##     ...1 raw_rank player      years total_goals url_number raw_link link  active
##    <dbl>    <dbl> <chr>       <chr>       <dbl>      <dbl> <chr>    <chr> <chr> 
##  1     1        1 Wayne Gret… 1979…         894          1 /player… http… Retir…
##  2     2        2 Gordie Howe 1946…         801          2 /player… http… Retir…
##  3     3        3 Jaromir Ja… 1990…         766          3 /player… http… Retir…
##  4     4        4 Brett Hull  1986…         741          4 /player… http… Retir…
##  5     5        5 Marcel Dio… 1971…         731          5 /player… http… Retir…
##  6     6        6 Phil Espos… 1963…         717          6 /player… http… Retir…
##  7     7        7 Mike Gartn… 1979…         708          7 /player… http… Retir…
##  8     8        8 Alex Ovech… 2005…         700          8 /player… http… Active
##  9     9        9 Mark Messi… 1979…         694          9 /player… http… Retir…
## 10    10       10 Steve Yzer… 1983…         692         10 /player… http… Retir…
## # ℹ 241 more rows
## # ℹ 1 more variable: yr_start <dbl>
# excel file
data <- read_excel("../00_data/Salaries.xlsx")
data
## # A tibble: 397 × 6
##    rank      discipline yrs.since.phd yrs.service sex    salary
##    <chr>     <chr>              <dbl>       <dbl> <chr>   <dbl>
##  1 Prof      B                     19          18 Male   139750
##  2 Prof      B                     20          16 Male   173200
##  3 AsstProf  B                      4           3 Male    79750
##  4 Prof      B                     45          39 Male   115000
##  5 Prof      B                     40          41 Male   141500
##  6 AssocProf B                      6           6 Male    97000
##  7 Prof      B                     30          23 Male   175000
##  8 Prof      B                     45          45 Male   147765
##  9 Prof      B                     21          20 Male   119250
## 10 Prof      B                     18          18 Female 129000
## # ℹ 387 more rows

State one question

Plot data

ggplot(hockey, aes(x = player, y = total_goals)) +
  geom_bar(stat = "identity")

ggplot(hockey, aes(x = player, y = total_goals)) +
  geom_boxplot() +
  labs(title = "Total Goals Per Player")

ggplot(hockey, aes(x = player <26, y = total_goals)) +
  geom_boxplot() +
  labs(title = "Total Goals Per Player Top 25")

Interpret

In order to more easily visualize a section of data, I intuitively put“<26” in ggplot function to only plot the top 25 players and their respective goal amounts. I was wondering if there is a different and more repeatable way to do this with a subsection of data.