An Introduction to tidyverse

Kazuharu Yanagimoto

January 13, 2023

Pipe Operator `|>`

|> inserts the previous object into the first argument of the next function

mtcars |>
  subset(cyl==4) |>
  head()

                mpg cyl  disp hp drat    wt  qsec vs am gear carb
Datsun 710     22.8   4 108.0 93 3.85 2.320 18.61  1  1    4    1
Merc 240D      24.4   4 146.7 62 3.69 3.190 20.00  1  0    4    2
Merc 230       22.8   4 140.8 95 3.92 3.150 22.90  1  0    4    2
Fiat 128       32.4   4  78.7 66 4.08 2.200 19.47  1  1    4    1
Honda Civic    30.4   4  75.7 52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla 33.9   4  71.1 65 4.22 1.835 19.90  1  1    4    1

head(subset(mtcars, cyl==4))

                mpg cyl  disp hp drat    wt  qsec vs am gear carb
Datsun 710     22.8   4 108.0 93 3.85 2.320 18.61  1  1    4    1
Merc 240D      24.4   4 146.7 62 3.69 3.190 20.00  1  0    4    2
Merc 230       22.8   4 140.8 95 3.92 3.150 22.90  1  0    4    2
Fiat 128       32.4   4  78.7 66 4.08 2.200 19.47  1  1    4    1
Honda Civic    30.4   4  75.7 52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla 33.9   4  71.1 65 4.22 1.835 19.90  1  1    4    1

Pipe Operator `|>`

To insert it to the second or later argument, you can use _

mtcars |>
  lm(mpg ~ disp, data = _)


Call:
lm(formula = mpg ~ disp, data = mtcars)

Coefficients:
(Intercept)         disp  
   29.59985     -0.04122

%>% in tidyverse used to be used
|> is officially supported and recommended after R version 4.1
|> works without loading any package, but %>% needs magnitr

`tibble`

In tidyverse packages, data frames are handled by tibble format.

tibble(
  x = 1:5, 
  y = 1, 
  z = x ^ 2 + y
)

# A tibble: 5 × 3
      x     y     z
  <int> <dbl> <dbl>
1     1     1     2
2     2     1     5
3     3     1    10
4     4     1    17
5     5     1    26

tibble::tribble(
  ~x, ~y, ~z,
  "a", 2, 3.6,
  "b", 1, 8.5
)

# A tibble: 2 × 3
  x         y     z
  <chr> <dbl> <dbl>
1 a         2   3.6
2 b         1   8.5

For detail, see Tibbles vs. data.frame in Wickham and Grolemund (2017)

dplyr Basics

`glimpse`

library(palmerpenguins) # Example Dataset
penguins |>
  head()

# A tibble: 6 × 8
  species island    bill_length_mm bill_depth_mm flipper_l…¹ body_…² sex    year
  <fct>   <fct>              <dbl>         <dbl>       <int>   <int> <fct> <int>
1 Adelie  Torgersen           39.1          18.7         181    3750 male   2007
2 Adelie  Torgersen           39.5          17.4         186    3800 fema…  2007
3 Adelie  Torgersen           40.3          18           195    3250 fema…  2007
4 Adelie  Torgersen           NA            NA            NA      NA <NA>   2007
5 Adelie  Torgersen           36.7          19.3         193    3450 fema…  2007
6 Adelie  Torgersen           39.3          20.6         190    3650 male   2007
# … with abbreviated variable names ¹flipper_length_mm, ²body_mass_g

penguins |>
  glimpse()

Rows: 344
Columns: 8
$ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
$ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
$ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
$ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
$ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
$ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
$ sex               <fct> male, female, female, NA, female, male, female, male…
$ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…

`select`

penguins |>
  select(species, body_mass_g:year, starts_with("bill")) |>
  head()

# A tibble: 6 × 6
  species body_mass_g sex     year bill_length_mm bill_depth_mm
  <fct>         <int> <fct>  <int>          <dbl>         <dbl>
1 Adelie         3750 male    2007           39.1          18.7
2 Adelie         3800 female  2007           39.5          17.4
3 Adelie         3250 female  2007           40.3          18  
4 Adelie           NA <NA>    2007           NA            NA  
5 Adelie         3450 female  2007           36.7          19.3
6 Adelie         3650 male    2007           39.3          20.6

penguins |>
  select(-year) |>
  head()

# A tibble: 6 × 7
  species island    bill_length_mm bill_depth_mm flipper_length_mm body_…¹ sex  
  <fct>   <fct>              <dbl>         <dbl>             <int>   <int> <fct>
1 Adelie  Torgersen           39.1          18.7               181    3750 male 
2 Adelie  Torgersen           39.5          17.4               186    3800 fema…
3 Adelie  Torgersen           40.3          18                 195    3250 fema…
4 Adelie  Torgersen           NA            NA                  NA      NA <NA> 
5 Adelie  Torgersen           36.7          19.3               193    3450 fema…
6 Adelie  Torgersen           39.3          20.6               190    3650 male 
# … with abbreviated variable name ¹body_mass_g

`filter`

penguins |>
  filter(year >= 2008, between(bill_length_mm, 39, 41)) |>
  head()

# A tibble: 6 × 8
  species island    bill_length_mm bill_depth_mm flipper_l…¹ body_…² sex    year
  <fct>   <fct>              <dbl>         <dbl>       <int>   <int> <fct> <int>
1 Adelie  Biscoe              39.6          17.7         186    3500 fema…  2008
2 Adelie  Biscoe              40.1          18.9         188    4300 male   2008
3 Adelie  Biscoe              39            17.5         186    3550 fema…  2008
4 Adelie  Biscoe              40.6          18.8         193    3800 male   2008
5 Adelie  Torgersen           39.7          18.4         190    3900 male   2008
6 Adelie  Torgersen           39.6          17.2         196    3550 fema…  2008
# … with abbreviated variable names ¹flipper_length_mm, ²body_mass_g

penguins |>
  filter(!is.na(sex)) |>
  head()

# A tibble: 6 × 8
  species island    bill_length_mm bill_depth_mm flipper_l…¹ body_…² sex    year
  <fct>   <fct>              <dbl>         <dbl>       <int>   <int> <fct> <int>
1 Adelie  Torgersen           39.1          18.7         181    3750 male   2007
2 Adelie  Torgersen           39.5          17.4         186    3800 fema…  2007
3 Adelie  Torgersen           40.3          18           195    3250 fema…  2007
4 Adelie  Torgersen           36.7          19.3         193    3450 fema…  2007
5 Adelie  Torgersen           39.3          20.6         190    3650 male   2007
6 Adelie  Torgersen           38.9          17.8         181    3625 fema…  2007
# … with abbreviated variable names ¹flipper_length_mm, ²body_mass_g

`rename`

penguins |>
  rename(gender = sex) |> # I know it is penguin
  head()

# A tibble: 6 × 8
  species island    bill_length_mm bill_depth_mm flipper_…¹ body_…² gender  year
  <fct>   <fct>              <dbl>         <dbl>      <int>   <int> <fct>  <int>
1 Adelie  Torgersen           39.1          18.7        181    3750 male    2007
2 Adelie  Torgersen           39.5          17.4        186    3800 female  2007
3 Adelie  Torgersen           40.3          18          195    3250 female  2007
4 Adelie  Torgersen           NA            NA           NA      NA <NA>    2007
5 Adelie  Torgersen           36.7          19.3        193    3450 female  2007
6 Adelie  Torgersen           39.3          20.6        190    3650 male    2007
# … with abbreviated variable names ¹flipper_length_mm, ²body_mass_g

You can rename when you select

penguins |>
  select(time = year, gender = sex, starts_with("bill")) |>
  head()

# A tibble: 6 × 4
   time gender bill_length_mm bill_depth_mm
  <int> <fct>           <dbl>         <dbl>
1  2007 male             39.1          18.7
2  2007 female           39.5          17.4
3  2007 female           40.3          18  
4  2007 <NA>             NA            NA  
5  2007 female           36.7          19.3
6  2007 male             39.3          20.6

`mutate`

penguins |>
  mutate(
    flipper_length_cm = flipper_length_mm / 10,
    sex = recode(sex, male = "Male", female = "Female"),
    species_by_sex = case_when(
      species == "Adelie" & sex == "Male" ~ "Am",
      species == "Adelie" & sex == "Female" ~ "Af",
      species == "Chinstrap" & sex == "Memale" ~ "Cm",
      species == "Chinstrap" & sex == "Female" ~ "Cf",
      species == "Gentoo" & sex == "Male" ~ "Gm",
      species == "Gentoo" & sex == "Female" ~ "Gf",
    )) |>
  head()

# A tibble: 6 × 10
  species island    bill_l…¹ bill_…² flipp…³ body_…⁴ sex    year flipp…⁵ speci…⁶
  <fct>   <fct>        <dbl>   <dbl>   <int>   <int> <fct> <int>   <dbl> <chr>  
1 Adelie  Torgersen     39.1    18.7     181    3750 Male   2007    18.1 Am     
2 Adelie  Torgersen     39.5    17.4     186    3800 Fema…  2007    18.6 Af     
3 Adelie  Torgersen     40.3    18       195    3250 Fema…  2007    19.5 Af     
4 Adelie  Torgersen     NA      NA        NA      NA <NA>   2007    NA   <NA>   
5 Adelie  Torgersen     36.7    19.3     193    3450 Fema…  2007    19.3 Af     
6 Adelie  Torgersen     39.3    20.6     190    3650 Male   2007    19   Am     
# … with abbreviated variable names ¹bill_length_mm, ²bill_depth_mm,
#   ³flipper_length_mm, ⁴body_mass_g, ⁵flipper_length_cm, ⁶species_by_sex

`summarize` & `group_by`

penguins |>
  filter(!is.na(sex)) |>
  group_by(species, sex) |>
  summarize(
    mean_bill_length_mm = mean(bill_length_mm, na.rm = TRUE),
    sd_bill_length_mm = sd(bill_length_mm, na.rm = TRUE)
  )

# A tibble: 6 × 4
# Groups:   species [3]
  species   sex    mean_bill_length_mm sd_bill_length_mm
  <fct>     <fct>                <dbl>             <dbl>
1 Adelie    female                37.3              2.03
2 Adelie    male                  40.4              2.28
3 Chinstrap female                46.6              3.11
4 Chinstrap male                  51.1              1.56
5 Gentoo    female                45.6              2.05
6 Gentoo    male                  49.5              2.72

`across` & `mutate`

penguins |>
  mutate(across(bill_length_mm:bill_depth_mm, ~.x / 10)) |>
  rename_at(vars(bill_length_mm:bill_depth_mm), ~str_replace(.x, "mm", "cm")) |>
  head()

# A tibble: 6 × 8
  species island    bill_length_cm bill_depth_cm flipper_l…¹ body_…² sex    year
  <fct>   <fct>              <dbl>         <dbl>       <int>   <int> <fct> <int>
1 Adelie  Torgersen           3.91          1.87         181    3750 male   2007
2 Adelie  Torgersen           3.95          1.74         186    3800 fema…  2007
3 Adelie  Torgersen           4.03          1.8          195    3250 fema…  2007
4 Adelie  Torgersen          NA            NA             NA      NA <NA>   2007
5 Adelie  Torgersen           3.67          1.93         193    3450 fema…  2007
6 Adelie  Torgersen           3.93          2.06         190    3650 male   2007
# … with abbreviated variable names ¹flipper_length_mm, ²body_mass_g

You can replace existing columns at once by across()
~ creates an anonymous (temporal) function. .x is the argument

`across` & `summarize`

penguins |>
  filter(!is.na(sex)) |>
  group_by(species, sex) |>
  summarize(
    across(bill_length_mm:body_mass_g,
          list(mean = ~mean(.x, na.rm = TRUE),
               sd = ~sd(.x, na.rm = TRUE)))
  )

# A tibble: 6 × 10
# Groups:   species [3]
  species  sex   bill_…¹ bill_…² bill_…³ bill_…⁴ flipp…⁵ flipp…⁶ body_…⁷ body_…⁸
  <fct>    <fct>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
1 Adelie   fema…    37.3    2.03    17.6   0.943    188.    5.60   3369.    269.
2 Adelie   male     40.4    2.28    19.1   1.02     192.    6.60   4043.    347.
3 Chinstr… fema…    46.6    3.11    17.6   0.781    192.    5.75   3527.    285.
4 Chinstr… male     51.1    1.56    19.3   0.761    200.    5.98   3939.    362.
5 Gentoo   fema…    45.6    2.05    14.2   0.540    213.    3.90   4680.    282.
6 Gentoo   male     49.5    2.72    15.7   0.741    222.    5.67   5485.    313.
# … with abbreviated variable names ¹bill_length_mm_mean, ²bill_length_mm_sd,
#   ³bill_depth_mm_mean, ⁴bill_depth_mm_sd, ⁵flipper_length_mm_mean,
#   ⁶flipper_length_mm_sd, ⁷body_mass_g_mean, ⁸body_mass_g_sd

You can summarize multiple columns by multiple functions at once
The names in the list becomes the suffix of the new columns

`janitor::tabyl()`

penguins |>
  janitor::tabyl(species)

   species   n   percent
    Adelie 152 0.4418605
 Chinstrap  68 0.1976744
    Gentoo 124 0.3604651

penguins |>
  janitor::tabyl(species, sex)

   species female male NA_
    Adelie     73   73   6
 Chinstrap     34   34   0
    Gentoo     58   61   5

penguins |>
  janitor::tabyl(species) |>
  janitor::adorn_percentages()

   species         n     percent
    Adelie 0.9971014 0.002898551
 Chinstrap 0.9971014 0.002898551
    Gentoo 0.9971014 0.002898551

penguins |>
  janitor::tabyl(species, sex) |>
  janitor::adorn_percentages(denominator = "row")

   species    female      male        NA_
    Adelie 0.4802632 0.4802632 0.03947368
 Chinstrap 0.5000000 0.5000000 0.00000000
    Gentoo 0.4677419 0.4919355 0.04032258

janitor::tabyl() gives frequency tables in tibble format

`*_join()`

inner_join(df1, df2)
left_join(df1, df2)
right_join(df1, df2)
full_join(df1, df2)
semi_join(df1, df2)
anti_join(df1, df2)

In most of cases, left_join is enough. For details of other functions, read Rational data section in Wickham and Grolemund (2017).

`left_join()`

library(nycflights13) # Dataset
flights |> glimpse()

Rows: 336,776
Columns: 19
$ year           <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2…
$ month          <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ day            <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ dep_time       <int> 517, 533, 542, 544, 554, 554, 555, 557, 557, 558, 558, …
$ sched_dep_time <int> 515, 529, 540, 545, 600, 558, 600, 600, 600, 600, 600, …
$ dep_delay      <dbl> 2, 4, 2, -1, -6, -4, -5, -3, -3, -2, -2, -2, -2, -2, -1…
$ arr_time       <int> 830, 850, 923, 1004, 812, 740, 913, 709, 838, 753, 849,…
$ sched_arr_time <int> 819, 830, 850, 1022, 837, 728, 854, 723, 846, 745, 851,…
$ arr_delay      <dbl> 11, 20, 33, -18, -25, 12, 19, -14, -8, 8, -2, -3, 7, -1…
$ carrier        <chr> "UA", "UA", "AA", "B6", "DL", "UA", "B6", "EV", "B6", "…
$ flight         <int> 1545, 1714, 1141, 725, 461, 1696, 507, 5708, 79, 301, 4…
$ tailnum        <chr> "N14228", "N24211", "N619AA", "N804JB", "N668DN", "N394…
$ origin         <chr> "EWR", "LGA", "JFK", "JFK", "LGA", "EWR", "EWR", "LGA",…
$ dest           <chr> "IAH", "IAH", "MIA", "BQN", "ATL", "ORD", "FLL", "IAD",…
$ air_time       <dbl> 227, 227, 160, 183, 116, 150, 158, 53, 140, 138, 149, 1…
$ distance       <dbl> 1400, 1416, 1089, 1576, 762, 719, 1065, 229, 944, 733, …
$ hour           <dbl> 5, 5, 5, 5, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6, 6…
$ minute         <dbl> 15, 29, 40, 45, 0, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 59, 0…
$ time_hour      <dttm> 2013-01-01 05:00:00, 2013-01-01 05:00:00, 2013-01-01 0…

planes |> glimpse()

Rows: 3,322
Columns: 9
$ tailnum      <chr> "N10156", "N102UW", "N103US", "N104UW", "N10575", "N105UW…
$ year         <int> 2004, 1998, 1999, 1999, 2002, 1999, 1999, 1999, 1999, 199…
$ type         <chr> "Fixed wing multi engine", "Fixed wing multi engine", "Fi…
$ manufacturer <chr> "EMBRAER", "AIRBUS INDUSTRIE", "AIRBUS INDUSTRIE", "AIRBU…
$ model        <chr> "EMB-145XR", "A320-214", "A320-214", "A320-214", "EMB-145…
$ engines      <int> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ seats        <int> 55, 182, 182, 182, 55, 182, 182, 182, 182, 182, 55, 55, 5…
$ speed        <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ engine       <chr> "Turbo-fan", "Turbo-fan", "Turbo-fan", "Turbo-fan", "Turb…

`left_join()`

flights |>
  left_join(planes, by = "tailnum", suffix = c("", "_built")) |>
  select(year, month, day, dep_time, arr_time, carrier, flight, tailnum, year_built, type, model)

# A tibble: 336,776 × 11
    year month   day dep_time arr_t…¹ carrier flight tailnum year_…² type  model
   <int> <int> <int>    <int>   <int> <chr>    <int> <chr>     <int> <chr> <chr>
 1  2013     1     1      517     830 UA        1545 N14228     1999 Fixe… 737-…
 2  2013     1     1      533     850 UA        1714 N24211     1998 Fixe… 737-…
 3  2013     1     1      542     923 AA        1141 N619AA     1990 Fixe… 757-…
 4  2013     1     1      544    1004 B6         725 N804JB     2012 Fixe… A320…
 5  2013     1     1      554     812 DL         461 N668DN     1991 Fixe… 757-…
 6  2013     1     1      554     740 UA        1696 N39463     2012 Fixe… 737-…
 7  2013     1     1      555     913 B6         507 N516JB     2000 Fixe… A320…
 8  2013     1     1      557     709 EV        5708 N829AS     1998 Fixe… CL-6…
 9  2013     1     1      557     838 B6          79 N593JB     2004 Fixe… A320…
10  2013     1     1      558     753 AA         301 N3ALAA       NA <NA>  <NA> 
# … with 336,766 more rows, and abbreviated variable names ¹arr_time,
#   ²year_built

If the key name is different, you can write by = c("LEFT_KEY" = "RIGHT_KEY)

tidyr Basics

Tidy Data Approach

Tidy Data (Wickham (2014), Wickham and Grolemund (2017))

Each variable must have its own column.
Each observation must have its own row.
Each value must have its own cell.

Hence, tidy data is in long format rather than in wide format (unlike Stata…)

`pivot_longer` & `pivot_wider`

This data is not tidy

tb1

# A tibble: 3 × 3
  country     `1999` `2000`
  <chr>        <dbl>  <dbl>
1 Afghanistan    745   2666
2 Brazil       37737  80488
3 China       212258 213766

tb2

# A tibble: 6 × 4
  country      year type           count
  <chr>       <dbl> <chr>          <dbl>
1 Afghanistan  1999 cases            745
2 Afghanistan  1999 population  19987071
3 Afghanistan  2000 cases           2666
4 Afghanistan  2000 population  20595360
5 Brazil       1999 cases          37737
6 Brazil       1999 population 172006362

Make it tidy

tb1 |>
  pivot_longer(c(`1999`, `2000`),
    names_to = "year", values_to = "cases")

# A tibble: 6 × 3
  country     year   cases
  <chr>       <chr>  <dbl>
1 Afghanistan 1999     745
2 Afghanistan 2000    2666
3 Brazil      1999   37737
4 Brazil      2000   80488
5 China       1999  212258
6 China       2000  213766

tb2 |>
  pivot_wider(names_from = type, values_from = count)

# A tibble: 3 × 4
  country      year cases population
  <chr>       <dbl> <dbl>      <dbl>
1 Afghanistan  1999   745   19987071
2 Afghanistan  2000  2666   20595360
3 Brazil       1999 37737  172006362

`separate` & `unite`

This data is not tidy

tb1

# A tibble: 6 × 3
  country      year rate             
  <chr>       <dbl> <chr>            
1 Afghanistan  1999 745/19987071     
2 Afghanistan  2000 2666/20595360    
3 Brazil       1999 37737/172006362  
4 Brazil       2000 80488/174504898  
5 China        1999 212258/1272915272
6 China        2000 213766/1280428583

tb2

# A tibble: 6 × 5
  country     century year_d2  cases population
  <chr>         <dbl>   <dbl>  <dbl>      <dbl>
1 Afghanistan      19      99    745   19987071
2 Afghanistan      20       0   2666   20595360
3 Brazil           19      99  37737  172006362
4 Brazil           20       0  80488  174504898
5 China            19      99 212258 1272915272
6 China            20       0 213766 1280428583

Make it tidy

tb1 |> 
  separate(rate, into = c("cases", "population")) |>
  mutate(across(c("cases", "population"), as.numeric))

# A tibble: 6 × 4
  country      year  cases population
  <chr>       <dbl>  <dbl>      <dbl>
1 Afghanistan  1999    745   19987071
2 Afghanistan  2000   2666   20595360
3 Brazil       1999  37737  172006362
4 Brazil       2000  80488  174504898
5 China        1999 212258 1272915272
6 China        2000 213766 1280428583

tb2 |>
  unite(year, century, year_d2, sep = "") |>
  mutate(year = as.integer(year))

# A tibble: 6 × 4
  country      year  cases population
  <chr>       <int>  <dbl>      <dbl>
1 Afghanistan  1999    745   19987071
2 Afghanistan   200   2666   20595360
3 Brazil       1999  37737  172006362
4 Brazil        200  80488  174504898
5 China        1999 212258 1272915272
6 China         200 213766 1280428583

ggplot2 Basics

Grammer of Graphics

Grammar of Graphics (Wilkinson 1999)

Graphics are made of distinct layers of grammatical elements
Plots are built with appropriate aesthetic mappings to make these plots meaningful

ggplot2

designed to represent the grammar of graphics
connects layers by + instead of |>

Aethentics & Geometries

penguins |>
  ggplot(aes(x = flipper_length_mm,
             y = bill_length_mm)) +
  geom_point()

geom_*() functions specify the type of plots (density, bar, scatter,…)

Statistics

penguins |>
  ggplot(aes(x = flipper_length_mm,
             y = bill_length_mm)) +
  geom_point() +
  facet_wrap(~species) +
  stat_smooth(formula = 'y ~ x',
              method = "lm", se = FALSE)

Coordinates

penguins |>
  filter(species == "Adelie") |>
  ggplot(aes(x = flipper_length_mm,
             y = bill_length_mm)) +
  geom_point() +
  stat_smooth(formula = 'y ~ x',
              method = "lm", se = FALSE) +
  coord_cartesian(xlim = c(170, 230),
                  ylim = c(30, 60))

coord_cartesan() is a zoom-in and zoom-out function
It is more recommended than scale_*_continuous(), which removes points if they are out of xlim (ylim)

Themes

penguins |>
  ggplot(aes(x = flipper_length_mm,
             y = bill_length_mm)) +
  geom_point() +
  facet_wrap(~species) +
  stat_smooth(formula = 'y ~ x',
              method = "lm", se = FALSE) +
  theme(axis.text.x = element_text(color = "red"))

You can configure all the elements in a plot. For more detail, read documents

Size of Points

penguins |>
  ggplot(aes(x = flipper_length_mm,
             y = bill_length_mm)) +
  geom_point(size = 4)

For geom_line(), use linewidth = 4

Color

penguins |>
  ggplot(aes(x = flipper_length_mm,
             y = bill_length_mm,
             color = species)) +
  geom_point(size = 4)

For geom_bar(), use fill = species

Color & Shape

penguins |>
  ggplot(aes(x = flipper_length_mm,
             y = bill_length_mm,
             color = species,
             shape = species)) +
  geom_point(size = 4)

Built-in Themes

penguins |>
  ggplot(aes(x = flipper_length_mm,
             y = bill_length_mm,
             color = species,
             shape = species)) +
  geom_point(size = 4) +
  theme_minimal()

For more built-in themes, you can find in ggtheme

Handson

Open the Quarto Notebook code/10_notebooks/handson_tidyverse.qmd
Answer the question in the notebook
You can find my answer as a qmd file or webpage
The webpage version folds codes. It allows you to read only the code where you get stacked

References

Wickham, Hadley. 2014. “Tidy Data.” Journal of Statistical Software 59 (10): 1–23. https://doi.org/10.18637/jss.v059.i10.

Wickham, Hadley, and Garrett Grolemund. 2017. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. 1st edition. Sebastopol, CA: O’Reilly Media. https://r4ds.had.co.nz/.

Wilkinson, Leland. 1999. The Grammar of Graphics. Springer New York.

Pipe Operator |>

Pipe Operator |>

tibble

dplyr Basics

glimpse

select

filter

rename

mutate

summarize & group_by

across & mutate

across & summarize

janitor::tabyl()

*_join()

left_join()

left_join()

tidyr Basics

Tidy Data Approach

pivot_longer & pivot_wider

separate & unite

ggplot2 Basics

Grammer of Graphics

Aethentics & Geometries

Facets

Statistics

Coordinates

Themes

Size of Points

Color

Color & Shape

Built-in Themes

Handson

References

Pipe Operator `|>`

Pipe Operator `|>`

`tibble`

`glimpse`

`select`

`filter`

`rename`

`mutate`

`summarize` & `group_by`

`across` & `mutate`

`across` & `summarize`

`janitor::tabyl()`

`*_join()`

`left_join()`

`left_join()`

`pivot_longer` & `pivot_wider`

`separate` & `unite`