Contents

Tidy Tuesday - Olympics

Intro

This code was written live on twitch.tv @ https://twitch.tv/theeatgamelove. Follow us on our socials to follow along during our next session!

TidyTuesday

Join the R4DS Online Learning Community in the weekly #TidyTuesday event!

Every week we post a raw dataset, a chart or article related to that dataset, and ask you to explore the data. While the dataset will be “tamed”, it will not always be tidy! As such you might need to apply various R for Data Science techniques to wrangle the data into a true tidy format.

The goal of TidyTuesday is to apply your R skills, get feedback, explore other’s work, and connect with the greater #RStats community!

As such we encourage everyone of all skills to participate!

Packages

1
2
3
4
5
library(tidyverse)
library(tidytuesdayR)
library(kowr)
library(ggbump)
library(cowplot)

Load the weekly Data

Dowload the weekly data and make available in the tt object.

1
tt <- tt_load("2021-07-27")

Readme

Take a look at the readme for the weekly data to get insight on the dataset. This includes a data dictionary, source, and a link to an article on the data.

1
tt

Glimpse Data

Take an initial look at the format of the data available.

1
2
tt %>% 
  map(glimpse)
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
## Rows: 271,116
## Columns: 15
## $ id     <dbl> 1, 2, 3, 4, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, ...
## $ name   <chr> "A Dijiang", "A Lamusi", "Gunnar Nielsen Aaby", "Edgar Linde...
## $ sex    <chr> "M", "M", "M", "M", "F", "F", "F", "F", "F", "F", "M", "M", ...
## $ age    <dbl> 24, 23, 24, 34, 21, 21, 25, 25, 27, 27, 31, 31, 31, 31, 33, ...
## $ height <dbl> 180, 170, NA, NA, 185, 185, 185, 185, 185, 185, 188, 188, 18...
## $ weight <dbl> 80, 60, NA, NA, 82, 82, 82, 82, 82, 82, 75, 75, 75, 75, 75, ...
## $ team   <chr> "China", "China", "Denmark", "Denmark/Sweden", "Netherlands"...
## $ noc    <chr> "CHN", "CHN", "DEN", "DEN", "NED", "NED", "NED", "NED", "NED...
## $ games  <chr> "1992 Summer", "2012 Summer", "1920 Summer", "1900 Summer", ...
## $ year   <dbl> 1992, 2012, 1920, 1900, 1988, 1988, 1992, 1992, 1994, 1994, ...
## $ season <chr> "Summer", "Summer", "Summer", "Summer", "Winter", "Winter", ...
## $ city   <chr> "Barcelona", "London", "Antwerpen", "Paris", "Calgary", "Cal...
## $ sport  <chr> "Basketball", "Judo", "Football", "Tug-Of-War", "Speed Skati...
## $ event  <chr> "Basketball Men's Basketball", "Judo Men's Extra-Lightweight...
## $ medal  <chr> NA, NA, NA, "Gold", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## Rows: 230
## Columns: 3
## $ NOC    <chr> "AFG", "AHO", "ALB", "ALG", "AND", "ANG", "ANT", "ANZ", "ARG...
## $ region <chr> "Afghanistan", "Curacao", "Albania", "Algeria", "Andorra", "...
## $ notes  <chr> NA, "Netherlands Antilles", NA, NA, NA, NA, "Antigua and Bar...
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
## $olympics
## # A tibble: 271,116 x 15
##       id name  sex     age height weight team  noc   games  year season city 
##    <dbl> <chr> <chr> <dbl>  <dbl>  <dbl> <chr> <chr> <chr> <dbl> <chr>  <chr>
##  1     1 A Di~ M        24    180     80 China CHN   1992~  1992 Summer Barc~
##  2     2 A La~ M        23    170     60 China CHN   2012~  2012 Summer Lond~
##  3     3 Gunn~ M        24     NA     NA Denm~ DEN   1920~  1920 Summer Antw~
##  4     4 Edga~ M        34     NA     NA Denm~ DEN   1900~  1900 Summer Paris
##  5     5 Chri~ F        21    185     82 Neth~ NED   1988~  1988 Winter Calg~
##  6     5 Chri~ F        21    185     82 Neth~ NED   1988~  1988 Winter Calg~
##  7     5 Chri~ F        25    185     82 Neth~ NED   1992~  1992 Winter Albe~
##  8     5 Chri~ F        25    185     82 Neth~ NED   1992~  1992 Winter Albe~
##  9     5 Chri~ F        27    185     82 Neth~ NED   1994~  1994 Winter Lill~
## 10     5 Chri~ F        27    185     82 Neth~ NED   1994~  1994 Winter Lill~
## # ... with 271,106 more rows, and 3 more variables: sport <chr>, event <chr>,
## #   medal <chr>
## 
## $regions
## # A tibble: 230 x 3
##    NOC   region      notes               
##    <chr> <chr>       <chr>               
##  1 AFG   Afghanistan <NA>                
##  2 AHO   Curacao     Netherlands Antilles
##  3 ALB   Albania     <NA>                
##  4 ALG   Algeria     <NA>                
##  5 AND   Andorra     <NA>                
##  6 ANG   Angola      <NA>                
##  7 ANT   Antigua     Antigua and Barbuda 
##  8 ANZ   Australia   Australasia         
##  9 ARG   Argentina   <NA>                
## 10 ARM   Armenia     <NA>                
## # ... with 220 more rows

Data Cleaning

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
d <-
  tt$olympics %>% 
  mutate(
    age_bucket = case_when(
      age < 13 ~ "Kid",
      age < 20 ~ "Teenager",
      age < 65 ~ "Adult",
      TRUE ~ "Senior"
    ),
    decade = year %/% 10 * 10
  )

Custom Functions

1
2
3
4
5
6
7
counts <- partial(count, sort = TRUE)

count_p <- function(.data, x) {
  .data %>% 
    counts({{x}}) %>% 
    add_proportion(percent_format = TRUE)
}

Exploration

Lets check out the data!

1
2
3
d %>% 
counts(medal) %>% 
add_proportion(percent_format = TRUE)
1
## i Selecting by 'n'
1
2
3
4
5
6
7
## # A tibble: 4 x 4
##   medal       n      p p_format
##   <chr>   <int>  <dbl> <chr>   
## 1 <NA>   231333 0.853  85.33%  
## 2 Gold    13372 0.0493 4.93%   
## 3 Bronze  13295 0.0490 4.90%   
## 4 Silver  13116 0.0484 4.84%
1
2
d %>% 
  count_p(sex)
1
## i Selecting by 'n'
1
2
3
4
5
## # A tibble: 2 x 4
##   sex        n     p p_format
##   <chr>  <int> <dbl> <chr>   
## 1 M     196594 0.725 72.51%  
## 2 F      74522 0.275 27.49%
1
2
d %>% 
  count_p(sport)
1
## i Selecting by 'n'
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
## # A tibble: 66 x 4
##    sport                    n      p p_format
##    <chr>                <int>  <dbl> <chr>   
##  1 Athletics            38624 0.142  14.25%  
##  2 Gymnastics           26707 0.0985 9.85%   
##  3 Swimming             23195 0.0856 8.56%   
##  4 Shooting             11448 0.0422 4.22%   
##  5 Cycling              10859 0.0401 4.01%   
##  6 Fencing              10735 0.0396 3.96%   
##  7 Rowing               10595 0.0391 3.91%   
##  8 Cross Country Skiing  9133 0.0337 3.37%   
##  9 Alpine Skiing         8829 0.0326 3.26%   
## 10 Wrestling             7154 0.0264 2.64%   
## # ... with 56 more rows
1
2
d %>% 
  count_p(age)
1
## i Selecting by 'n'
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
## # A tibble: 75 x 4
##      age     n      p p_format
##    <dbl> <int>  <dbl> <chr>   
##  1    23 21875 0.0807 8.07%   
##  2    24 21720 0.0801 8.01%   
##  3    22 20814 0.0768 7.68%   
##  4    25 19707 0.0727 7.27%   
##  5    21 19164 0.0707 7.07%   
##  6    26 17675 0.0652 6.52%   
##  7    27 16025 0.0591 5.91%   
##  8    20 15258 0.0563 5.63%   
##  9    28 14043 0.0518 5.18%   
## 10    19 11643 0.0429 4.29%   
## # ... with 65 more rows
1
2
d %>% 
  count_p(age_bucket)
1
## i Selecting by 'n'
1
2
3
4
5
6
7
## # A tibble: 4 x 4
##   age_bucket      n        p p_format
##   <chr>       <int>    <dbl> <chr>   
## 1 Adult      228985 0.845    84.46%  
## 2 Teenager    32250 0.119    11.90%  
## 3 Senior       9828 0.0363   3.63%   
## 4 Kid            53 0.000195 0.02%
1
2
d %>% 
  count_p(decade)
1
## i Selecting by 'n'
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
## # A tibble: 13 x 4
##    decade     n       p p_format
##     <dbl> <int>   <dbl> <chr>   
##  1   2000 49357 0.182   18.21%  
##  2   1990 36958 0.136   13.63%  
##  3   2010 35901 0.132   13.24%  
##  4   1980 35201 0.130   12.98%  
##  5   1960 29194 0.108   10.77%  
##  6   1970 22461 0.0828  8.28%   
##  7   1950 15792 0.0582  5.82%   
##  8   1920 15559 0.0574  5.74%   
##  9   1930 10722 0.0395  3.95%   
## 10   1900  8071 0.0298  2.98%   
## 11   1940  7480 0.0276  2.76%   
## 12   1910  4040 0.0149  1.49%   
## 13   1890   380 0.00140 0.14%

Ages

Who are the young’ns and who are the old’ns

1
2
3
d %>% 
  arrange(age) %>% 
  head()
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
## # A tibble: 6 x 17
##      id name  sex     age height weight team  noc   games  year season city 
##   <dbl> <chr> <chr> <dbl>  <dbl>  <dbl> <chr> <chr> <chr> <dbl> <chr>  <chr>
## 1 71691 Dimi~ M        10     NA     NA Ethn~ GRE   1896~  1896 Summer Athi~
## 2 22411 Magd~ F        11    152     NA Grea~ GBR   1932~  1932 Winter Lake~
## 3 37333 Carl~ M        11     NA     NA Spain ESP   1992~  1992 Summer Barc~
## 4 40129 Luig~ F        11     NA     NA Italy ITA   1928~  1928 Summer Amst~
## 5 47618 Sonj~ F        11    155     45 Norw~ NOR   1924~  1924 Winter Cham~
## 6 51268 Beat~ F        11    151     38 Roma~ ROU   1968~  1968 Winter Gren~
## # ... with 5 more variables: sport <chr>, event <chr>, medal <chr>,
## #   age_bucket <chr>, decade <dbl>

Young kids

1
2
3
4
5
d %>% 
  filter(age_bucket == "Kid") %>% 
  ggplot() +
  aes(x = year, fill = season) +
  geom_histogram(color = "black")
1
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
d %>% 
  filter(age_bucket == "Kid") %>% 
  counts(sport) %>% 
  mutate(sport = fct_reorder(sport, n, .desc = TRUE)) %>% 
  ggplot() +
  aes(
    x = sport,
    y = n,
    fill = sport
  ) +
  geom_col(
    show.legend = FALSE,
    color = "black"
  ) +
  theme_minimal() +
  scale_fill_zodiac()

1
2
3
4
d %>% 
  filter(age_bucket == "Kid") %>% 
  counts(team) %>% 
  add_proportion(percent_format = TRUE)
1
## i Selecting by 'n'
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
## # A tibble: 27 x 4
##    team              n      p p_format
##    <chr>         <int>  <dbl> <chr>   
##  1 Puerto Rico       8 0.151  15.09%  
##  2 Italy             5 0.0943 9.43%   
##  3 Colombia          4 0.0755 7.55%   
##  4 Great Britain     4 0.0755 7.55%   
##  5 France            3 0.0566 5.66%   
##  6 Mexico            3 0.0566 5.66%   
##  7 Spain             3 0.0566 5.66%   
##  8 Cote d'Ivoire     2 0.0377 3.77%   
##  9 Hungary           2 0.0377 3.77%   
## 10 South Africa      2 0.0377 3.77%   
## # ... with 17 more rows

Seniors

1
2
3
4
5
d %>% 
  filter(age_bucket == "Senior") %>% 
  ggplot() +
  aes(x = year, fill = season) +
  geom_histogram(color = "black")
1
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

As time goes on, less and less seniors are competing in the Olympics

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
d %>% 
  filter(age_bucket == "Senior") %>% 
  mutate(sport = fct_lump(sport, n = 12)) %>% 
  counts(sport) %>% 
  filter(sport != "Other") %>% 
  mutate(sport = fct_reorder(sport, n)) %>% 
  ggplot() +
  aes(
    x = sport,
    y = n,
    fill = sport
  ) +
  geom_col(
    show.legend = FALSE,
    color = "black"
  ) +
  theme_minimal() +
  scale_fill_zodiac() +
  coord_flip()

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
senior_decades <-
  d %>% 
  filter(age_bucket == "Senior") %>% 
  mutate(sport = fct_lump(sport, n = 5)) %>% 
  counts(decade, sport) %>% 
  filter(sport != "Other") %>% 
  arrange(sport, decade, n) %>% 
  group_by(decade) %>% 
  mutate(rank = rank(n, ties.method = "random")) %>% 
  ungroup()

senior_decades %>% 
  ggplot() +
  aes(
    x = decade,
    y = rank,
    color = sport,
    group = sport
  ) +
  geom_point(size = 5) +
  geom_bump(size = 1.1, smooth = 8) +
  theme_minimal_grid(font_size = 14, line_size = 0) +
  scale_color_zodiac() +
  scale_x_continuous(limits = c(1889.6, 2010.4)) +
  labs(
    y = "Rank",
    x = NULL
  ) +
  scale_y_reverse() 

1
2
3
4
d %>% 
  filter(age_bucket == "Senior") %>% 
  counts(team) %>% 
  add_proportion(percent_format = TRUE)
1
## i Selecting by 'n'
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
## # A tibble: 418 x 4
##    team               n      p p_format
##    <chr>          <int>  <dbl> <chr>   
##  1 France          1133 0.115  11.53%  
##  2 Greece           798 0.0812 8.12%   
##  3 Great Britain    747 0.0760 7.60%   
##  4 Belgium          587 0.0597 5.97%   
##  5 Italy            347 0.0353 3.53%   
##  6 Switzerland      332 0.0338 3.38%   
##  7 United States    284 0.0289 2.89%   
##  8 Czechoslovakia   231 0.0235 2.35%   
##  9 Egypt            225 0.0229 2.29%   
## 10 Canada           223 0.0227 2.27%   
## # ... with 408 more rows

What year did the first women compete?

1
2
3
4
5
year_sex <-
  d %>% 
  count(year, season, sex) %>% 
  group_by(year) %>% 
  add_proportion(percent_format = TRUE)
1
## i Selecting by 'n'
1
year_sex
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
## # A tibble: 101 x 6
## # Groups:   year [35]
##     year season sex       n       p p_format
##    <dbl> <chr>  <chr> <int>   <dbl> <chr>   
##  1  1896 Summer M       380 1       100.00% 
##  2  1900 Summer F        33 0.0170  1.70%   
##  3  1900 Summer M      1903 0.983   98.30%  
##  4  1904 Summer F        16 0.0123  1.23%   
##  5  1904 Summer M      1285 0.988   98.77%  
##  6  1906 Summer F        11 0.00635 0.63%   
##  7  1906 Summer M      1722 0.994   99.37%  
##  8  1908 Summer F        47 0.0152  1.52%   
##  9  1908 Summer M      3054 0.985   98.48%  
## 10  1912 Summer F        87 0.0215  2.15%   
## # ... with 91 more rows
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
year_sex %>% 
  ggplot() +
  aes(
    x = year,
    y = n,
    color = sex,
    group = sex
  ) +
  geom_line() +
  facet_wrap(~ season)

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
year_sex %>% 
  ggplot() +
  aes(
    x = year,
    y = p,
    color = sex,
    group = sex
  ) +
  geom_line(size = 1.1) +
  facet_wrap(~ season) +
  geom_hline(yintercept = 0.5, color = "red") +
  theme_minimal() +
  scale_color_viridis_d() +
  scale_y_continuous(label = scales::percent)

1
2
d %>% 
  counts(year, season, name, medal)
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
## # A tibble: 198,484 x 5
##     year season name                             medal     n
##    <dbl> <chr>  <chr>                            <chr> <int>
##  1  1932 Summer Robert Tait McKenzie             <NA>     43
##  2  1928 Summer Alfrd (Arnold-) Hajs (Guttmann-) <NA>     28
##  3  1932 Summer Miltiades Manno                  <NA>     26
##  4  1948 Summer Alfred James Munnings            <NA>     25
##  5  1932 Summer Wilhelm (William) Hunt Diederich <NA>     19
##  6  1932 Summer Acee Blue Eagle                  <NA>     18
##  7  1928 Summer Stanisaw Noakowski               <NA>     17
##  8  1936 Summer Jean Lucien Nicolas Jacoby       <NA>     17
##  9  1928 Summer Georges-mile Fauvelle            <NA>     16
## 10  1928 Summer ngel Zrraga Argelles             <NA>     16
## # ... with 198,474 more rows
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
d_name_medal <-
  d %>% 
  count(name, sex, noc, medal) %>% 
  group_by(name) %>% 
  filter(any(!is.na(medal))) %>% 
  mutate(medal = ifelse(is.na(medal), "No Medal", medal)) %>% 
  pivot_wider(
    names_from = medal,
    values_from = n,
    values_fill = 0
  ) %>% 
  janitor::clean_names() %>% 
  mutate(
    total_medals = gold + silver + bronze,
    total_events = total_medals + no_medal,
    p_medal = total_medals / total_events
  ) %>% 
  arrange(desc(total_medals))

d_name_medal
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
## # A tibble: 29,145 x 10
## # Groups:   name [28,202]
##    name  sex   noc    gold bronze silver no_medal total_medals total_events
##    <chr> <chr> <chr> <int>  <int>  <int>    <int>        <int>        <int>
##  1 "Mic~ M     USA      23      2      3        2           28           30
##  2 "Lar~ F     URS       9      4      5        1           18           19
##  3 "Nik~ M     URS       7      3      5        9           15           24
##  4 "Bor~ M     URS       7      2      4       11           13           24
##  5 "Edo~ M     ITA       6      2      5        1           13           14
##  6 "Ole~ M     NOR       8      1      4       14           13           27
##  7 "Tak~ M     JPN       5      4      4       20           13           33
##  8 "Ale~ M     RUS       4      6      2        9           12           21
##  9 "Dar~ F     USA       4      4      4        1           12           13
## 10 "Jen~ F     USA       8      1      3        5           12           17
## # ... with 29,135 more rows, and 1 more variable: p_medal <dbl>

Top 10 Men and women

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
plot_me <-
  d_name_medal %>% 
  group_by(sex) %>% 
  arrange(desc(total_medals)) %>% 
  mutate(rank = row_number()) %>% 
  filter(rank %in% 1:10) %>% 
  arrange(sex, rank)

medal_colors <- c(
  Gold = "#d5a500",
  Silver = "#b7b7b7",
  Bronze = "#a17419"
)

plot_me %>% 
  filter(sex == "F") %>% 
  mutate(
    name_clean = paste0(str_sub(name, 1, 10), "...(", noc, ")"),
    name_clean = fct_reorder(name_clean, total_medals),
  ) %>% 
  pivot_longer(
    gold:silver,
    names_to = "medal",
    values_to = "count"
  ) %>% 
  mutate(
    medal = str_to_title(medal),
    medal = factor(medal, levels = c("Gold", "Silver", "Bronze"))
  ) %>% 
  ggplot() +
  aes(
    x = name_clean,
    y = count,
    fill = medal,
    label = noc
  ) +
  geom_col(color = "black") +
  coord_flip() +
  theme_minimal() +
  labs(
    x = NULL,
    y = "Total Medals",
    fill = "Medal",
    title = "Top 10 Decorated Females in the Olympics"
  ) +
  scale_fill_manual(values = medal_colors)

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
plot_me %>% 
  filter(sex == "M") %>% 
  mutate(
    name_clean = paste0(str_sub(name, 1, 10), "...(", noc, ")"),
    name_clean = fct_reorder(name_clean, total_medals),
  ) %>% 
  pivot_longer(
    gold:silver,
    names_to = "medal",
    values_to = "count"
  ) %>% 
  mutate(
    medal = str_to_title(medal),
    medal = factor(medal, levels = c("Gold", "Silver", "Bronze"))
  ) %>% 
  ggplot() +
  aes(
    x = name_clean,
    y = count,
    fill = medal,
    label = noc
  ) +
  geom_col(color = "black") +
  coord_flip() +
  theme_minimal() +
  labs(
    x = NULL,
    y = "Total Medals",
    fill = "Medal",
    title = "Top 10 Decorated Males in the Olympics"
  ) +
  scale_fill_manual(values = medal_colors)

Save Image

Save your image for sharing. Be sure to use the #TidyTuesday hashtag in your post on twitter!

1
2
3
4
# This will save your most recent plot
ggsave(
filename = "My TidyTuesday Plot.png",
device = "png")
1
## Saving 7 x 5 in image

Reproducibility Receipt

Time Info
1
2
3

[1] "2021-08-25 13:20:29 CDT"


Repo Info
1
2
3
4
5

Local:    master C:/Users/lexim/Documents/Projects/eatGameLove
Remote:   master @ origin (https://github.com/alexismeskowski/eatGameLove.git)
Head:     [5884f85] 2021-08-25: Added an image to the Olympics Tidy Tuesday post <3


Session Info
  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100

- Session info ---------------------------------------------------------------
 setting  value                       
 version  R version 4.0.3 (2020-10-10)
 os       Windows 10 x64              
 system   x86_64, mingw32             
 ui       RTerm                       
 language (EN)                        
 collate  English_United States.1252  
 ctype    English_United States.1252  
 tz       America/Chicago             
 date     2021-08-25                  

- Packages -------------------------------------------------------------------
 package      * version    date       lib source                        
 assertthat     0.2.1      2019-03-21 [1] CRAN (R 4.0.3)                
 backports      1.1.10     2020-09-15 [1] CRAN (R 4.0.3)                
 blob           1.2.1      2020-01-20 [1] CRAN (R 4.0.3)                
 blogdown       1.3        2021-04-14 [1] CRAN (R 4.0.5)                
 bookdown       0.21       2020-10-13 [1] CRAN (R 4.0.3)                
 broom          0.7.2      2020-10-20 [1] CRAN (R 4.0.3)                
 bslib          0.2.5.1    2021-05-18 [1] CRAN (R 4.0.5)                
 cellranger     1.1.0      2016-07-27 [1] CRAN (R 4.0.3)                
 cli            3.0.0      2021-06-30 [1] CRAN (R 4.0.5)                
 clipr          0.7.1      2020-10-08 [1] CRAN (R 4.0.3)                
 colorspace     1.4-1      2019-03-18 [1] CRAN (R 4.0.3)                
 cowplot      * 1.1.1      2020-12-30 [1] CRAN (R 4.0.5)                
 crayon         1.3.4      2017-09-16 [1] CRAN (R 4.0.3)                
 curl           4.3        2019-12-02 [1] CRAN (R 4.0.3)                
 DBI            1.1.0      2019-12-15 [1] CRAN (R 4.0.3)                
 dbplyr         1.4.4      2020-05-27 [1] CRAN (R 4.0.3)                
 desc           1.2.0      2018-05-01 [1] CRAN (R 4.0.3)                
 details      * 0.2.1      2020-01-12 [1] CRAN (R 4.0.5)                
 digest         0.6.27     2020-10-24 [1] CRAN (R 4.0.3)                
 dplyr        * 1.0.2      2020-08-18 [1] CRAN (R 4.0.3)                
 ellipsis       0.3.1      2020-05-15 [1] CRAN (R 4.0.3)                
 evaluate       0.14       2019-05-28 [1] CRAN (R 4.0.3)                
 fansi          0.4.1      2020-01-08 [1] CRAN (R 4.0.3)                
 farver         2.0.3      2020-01-16 [1] CRAN (R 4.0.3)                
 forcats      * 0.5.0      2020-03-01 [1] CRAN (R 4.0.3)                
 fs             1.5.0      2020-07-31 [1] CRAN (R 4.0.3)                
 generics       0.1.0      2020-10-31 [1] CRAN (R 4.0.3)                
 ggbump       * 0.1.0      2020-04-24 [1] CRAN (R 4.0.5)                
 ggplot2      * 3.3.2      2020-06-19 [1] CRAN (R 4.0.3)                
 git2r          0.27.1     2020-05-03 [1] CRAN (R 4.0.3)                
 glue           1.4.2      2020-08-27 [1] CRAN (R 4.0.3)                
 gtable         0.3.0      2019-03-25 [1] CRAN (R 4.0.3)                
 haven          2.3.1      2020-06-01 [1] CRAN (R 4.0.3)                
 hms            0.5.3      2020-01-08 [1] CRAN (R 4.0.3)                
 htmltools      0.5.1.1    2021-01-22 [1] CRAN (R 4.0.5)                
 httr           1.4.2      2020-07-20 [1] CRAN (R 4.0.3)                
 janitor        2.1.0      2021-01-05 [1] CRAN (R 4.0.5)                
 jquerylib      0.1.4      2021-04-26 [1] CRAN (R 4.0.5)                
 jsonlite       1.7.1      2020-09-07 [1] CRAN (R 4.0.3)                
 knitr          1.30       2020-09-22 [1] CRAN (R 4.0.3)                
 kowr         * 0.0.0.9000 2021-07-29 [1] Github (koderkow/kowr@193c157)
 labeling       0.4.2      2020-10-20 [1] CRAN (R 4.0.3)                
 lifecycle      0.2.0      2020-03-06 [1] CRAN (R 4.0.3)                
 lubridate      1.7.9      2020-06-08 [1] CRAN (R 4.0.3)                
 magrittr       1.5        2014-11-22 [1] CRAN (R 4.0.3)                
 modelr         0.1.8      2020-05-19 [1] CRAN (R 4.0.3)                
 munsell        0.5.0      2018-06-12 [1] CRAN (R 4.0.3)                
 pillar         1.4.6      2020-07-10 [1] CRAN (R 4.0.3)                
 pkgconfig      2.0.3      2019-09-22 [1] CRAN (R 4.0.3)                
 png            0.1-7      2013-12-03 [1] CRAN (R 4.0.3)                
 purrr        * 0.3.4      2020-04-17 [1] CRAN (R 4.0.3)                
 R6             2.5.0      2020-10-28 [1] CRAN (R 4.0.3)                
 Rcpp           1.0.5      2020-07-06 [1] CRAN (R 4.0.3)                
 readr        * 1.4.0      2020-10-05 [1] CRAN (R 4.0.3)                
 readxl         1.3.1      2019-03-13 [1] CRAN (R 4.0.3)                
 reprex         0.3.0      2019-05-16 [1] CRAN (R 4.0.3)                
 rlang          0.4.11     2021-04-30 [1] CRAN (R 4.0.5)                
 rmarkdown      2.9        2021-06-15 [1] CRAN (R 4.0.5)                
 rprojroot      1.3-2      2018-01-03 [1] CRAN (R 4.0.3)                
 rstudioapi     0.11       2020-02-07 [1] CRAN (R 4.0.3)                
 rvest          0.3.6      2020-07-25 [1] CRAN (R 4.0.3)                
 sass           0.4.0      2021-05-12 [1] CRAN (R 4.0.5)                
 scales         1.1.1      2020-05-11 [1] CRAN (R 4.0.3)                
 selectr        0.4-2      2019-11-20 [1] CRAN (R 4.0.3)                
 sessioninfo    1.1.1      2018-11-05 [1] CRAN (R 4.0.4)                
 snakecase      0.11.0     2019-05-25 [1] CRAN (R 4.0.5)                
 stringi        1.5.3      2020-09-09 [1] CRAN (R 4.0.3)                
 stringr      * 1.4.0      2019-02-10 [1] CRAN (R 4.0.3)                
 tibble       * 3.0.4      2020-10-12 [1] CRAN (R 4.0.3)                
 tidyr        * 1.1.2      2020-08-27 [1] CRAN (R 4.0.3)                
 tidyselect     1.1.0      2020-05-11 [1] CRAN (R 4.0.3)                
 tidytuesdayR * 1.0.1      2020-07-10 [1] CRAN (R 4.0.5)                
 tidyverse    * 1.3.0      2019-11-21 [1] CRAN (R 4.0.3)                
 usethis        1.6.3      2020-09-17 [1] CRAN (R 4.0.3)                
 utf8           1.1.4      2018-05-24 [1] CRAN (R 4.0.3)                
 vctrs          0.3.4      2020-08-29 [1] CRAN (R 4.0.3)                
 viridisLite    0.3.0      2018-02-01 [1] CRAN (R 4.0.3)                
 withr          2.3.0      2020-09-22 [1] CRAN (R 4.0.3)                
 xfun           0.24       2021-06-15 [1] CRAN (R 4.0.5)                
 xml2           1.3.2      2020-04-23 [1] CRAN (R 4.0.3)                
 yaml           2.2.1      2020-02-01 [1] CRAN (R 4.0.3)                

[1] C:/R/library
[2] C:/Program Files/R/R-4.0.3/library