reprex 실습

[shanmdphd]

https://youtu.be/U0x_w3-4y-A 영상을 참고하여
아무 코드나 작성해서 reprex::reprex() 로 클립보드에 복사하여 댓글에 붙여넣기 해 주세요.

기한: 2023-04-14 23:59

library(NonCompart)
tblNCA(Theoph)
#>    Subject       b0  CMAX CMAXD TMAX TLAG CLST     CLSTP  TLST    LAMZHL
#> 1        1 2.368785 10.50    NA 1.12    0 3.28 3.2801465 24.37 14.304378
#> 2        2 2.411237  8.33    NA 1.92    0 0.90 0.8886398 24.30  6.659342
#> 3        3 2.529712  8.20    NA 1.02    0 1.05 1.0550967 24.17  6.766087
#> 4        4 2.592755  8.60    NA 1.07    0 1.15 1.1564216 24.65  6.981247
#> 5        5 2.551092 11.40    NA 1.00    0 1.57 1.5556951 24.35  8.002264
#> 6        6 2.033404  6.44    NA 1.15    0 0.92 0.9412712 23.85  7.894998
#> 7        7 2.288550  7.09    NA 3.48    0 1.15 1.1607192 24.22  7.846668
#> 8        8 2.170403  7.56    NA 2.02    0 1.25 1.2285268 24.12  8.510038
#> 9        9 2.124648  9.03    NA 0.63    0 1.12 1.1164831 24.43  8.405999
#> 10      10 2.657705 10.21    NA 3.55    0 2.42 2.4136923 23.70  9.246916
#> 11      11 2.147594  8.00    NA 0.98    0 0.86 0.8598066 24.08  7.261237
#> 12      12 2.824493  9.75    NA 3.52    0 1.17 1.1755390 24.15  6.286508
#>          LAMZ LAMZLL LAMZUL LAMZNPT     CORRXY        R2     R2ADJ    AUCLST
#> 1  0.04845700   9.05  24.37       3 -0.9999999 0.9999997 0.9999995 148.92305
#> 2  0.10408644   7.03  24.30       4 -0.9985967 0.9971954 0.9957931  91.52680
#> 3  0.10244431   9.00  24.17       3 -0.9996624 0.9993250 0.9986499  99.28650
#> 4  0.09928702   9.02  24.65       3 -0.9994619 0.9989241 0.9978483 106.79630
#> 5  0.08661888   7.02  24.35       4 -0.9993234 0.9986472 0.9979708 121.29440
#> 6  0.08779574   2.03  23.85       7 -0.9991203 0.9982413 0.9978896  73.77555
#> 7  0.08833650   6.98  24.22       4 -0.9993349 0.9986702 0.9980053  90.75340
#> 8  0.08145054   3.53  24.12       6 -0.9954961 0.9910124 0.9887655  88.55995
#> 9  0.08245863   8.80  24.43       3 -0.9997218 0.9994437 0.9988873  86.32615
#> 10 0.07495982   9.38  23.70       3 -0.9997543 0.9995087 0.9990174 138.36810
#> 11 0.09545856   9.03  24.08       3 -0.9999991 0.9999983 0.9999965  80.09360
#> 12 0.11025949   9.03  24.15       3 -0.9996984 0.9993968 0.9987936 119.97750
#>       AUCALL    AUCIFO AUCIFOD    AUCIFP AUCIFPD    AUCPEO    AUCPEP   AUMCLST
#> 1  148.92305 216.61193      NA 216.61496      NA 31.248917 31.249876 1459.0711
#> 2   91.52680 100.17346      NA 100.06432      NA  8.631687  8.532030  706.5866
#> 3   99.28650 109.53597      NA 109.58572      NA  9.357173  9.398325  803.1859
#> 4  106.79630 118.37888      NA 118.44356      NA  9.784331  9.833594  901.0842
#> 5  121.29440 139.41978      NA 139.25463      NA 13.000579 12.897403 1017.1143
#> 6   73.77555  84.25442      NA  84.49670      NA 12.437174 12.688246  609.1524
#> 7   90.75340 103.77180      NA 103.89315      NA 12.545221 12.647366  782.4199
#> 8   88.55995 103.90669      NA 103.64305      NA 14.769730 14.552931  739.5346
#> 9   86.32615  99.90872      NA  99.86607      NA 13.594978 13.558076  705.2296
#> 10 138.36810 170.65206      NA 170.56791      NA 18.918002 18.878001 1278.1800
#> 11  80.09360  89.10274      NA  89.10072      NA 10.110962 10.108918  617.2422
#> 12 119.97750 130.58883      NA 130.63907      NA  8.125757  8.161087  977.8807
#>      AUMCIFO   AUMCIFP  AUMCPEO  AUMCPEP VZFO VZFP CLFO CLFP MRTEVLST  MRTEVIFO
#> 1  4505.5348 4505.6709 67.61603 67.61701    0    0    0    0 9.797483 20.800031
#> 2   999.7723  996.0716 29.32525 29.06267    0    0    0    0 7.719996  9.980411
#> 3  1150.9648 1152.6529 30.21629 30.31850    0    0    0    0 8.089578 10.507642
#> 4  1303.2524 1305.4981 30.85881 30.97775    0    0    0    0 8.437410 11.009163
#> 5  1667.7216 1661.7937 39.01174 38.79419    0    0    0    0 8.385501 11.961873
#> 6   978.4285  986.9665 37.74176 38.28034    0    0    0    0 8.256833 11.612785
#> 7  1245.0984 1249.4111 37.16000 37.37691    0    0    0    0 8.621383 11.998427
#> 8  1298.1158 1288.5201 43.03015 42.60589    0    0    0    0 8.350666 12.493092
#> 9  1201.7715 1200.2124 41.31750 41.24126    0    0    0    0 8.169363 12.028695
#> 10 2473.9934 2470.8765 48.33535 48.27018    0    0    0    0 9.237534 14.497296
#> 11  928.5600  928.4900 33.52694 33.52193    0    0    0    0 7.706511 10.421227
#> 12 1330.3840 1332.0528 26.49636 26.58844    0    0    0    0 8.150534 10.187579
#>     MRTEVIFP
#> 1  20.800368
#> 2   9.954313
#> 3  10.518276
#> 4  11.022112
#> 5  11.933490
#> 6  11.680533
#> 7  12.025924
#> 8  12.432287
#> 9  12.018220
#> 10 14.486174
#> 11 10.420679
#> 12 10.196436

^{Created on 2023-04-14 with reprex v2.0.2}

[minjijuliakang]

students |>
rename(
student_id = Student ID,
full_name = Full Name
)
#> Error in rename(students, student_id = Student ID, full_name = Full Name): could not find function "rename"

[shanmdphd]

잘 했습니다.

[shanmdphd]

https://www.youtube.com/watch?v=w9DfC2BHGPA Julia, 프로필 사진 설정하시고 이거 보시고 프로필 Repo도 생성해 보세요~

[junghyun0]

[Jihong757]

library(tidyverse)

students <- read_csv("https://pos.it/r4ds-students-csv")
#> Rows: 6 Columns: 5
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (4): Full Name, favourite.food, mealPlan, AGE
#> dbl (1): Student ID
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.


students_rename <- students |> 
  rename(
     `Student ID` = student_id,
     `Full Name` = full_name 
  )
#> Error in `rename()`:
#> ! Can't rename columns that don't exist.
#> ✖ Column `student_id` doesn't exist.

#> Backtrace:
#>      ▆
#>   1. ├─dplyr::rename(students, `Student ID` = student_id, `Full Name` = full_name)
#>   2. ├─dplyr:::rename.data.frame(students, `Student ID` = student_id, `Full Name` = full_name)
#>   3. │ └─tidyselect::eval_rename(expr(c(...)), .data)
#>   4. │   └─tidyselect:::rename_impl(...)
#>   5. │     └─tidyselect:::eval_select_impl(...)
#>   6. │       ├─tidyselect:::with_subscript_errors(...)
#>   7. │       │ └─rlang::try_fetch(...)
#>   8. │       │   └─base::withCallingHandlers(...)
#>   9. │       └─tidyselect:::vars_select_eval(...)
#>  10. │         └─tidyselect:::walk_data_tree(expr, data_mask, context_mask)
#>  11. │           └─tidyselect:::eval_c(expr, data_mask, context_mask)
#>  12. │             └─tidyselect:::reduce_sels(node, data_mask, context_mask, init = init)
#>  13. │               └─tidyselect:::walk_data_tree(new, data_mask, context_mask)
#>  14. │                 └─tidyselect:::as_indices_sel_impl(...)
#>  15. │                   └─tidyselect:::as_indices_impl(...)
#>  16. │                     └─tidyselect:::chr_as_locations(x, vars, call = call, arg = arg)
#>  17. │                       └─vctrs::vec_as_location(...)
#>  18. └─vctrs (local) `<fn>`()
#>  19.   └─vctrs:::stop_subscript_oob(...)
#>  20.     └─vctrs:::stop_subscript(...)
#>  21.       └─rlang::abort(...)

^{Created on 2023-04-17 with reprex v2.0.2}

[shanmdphd]

library(ggplot2)
ggplot(Indometh, aes(x=time, y=conc)) + 
  stat_summary(
    fun=mean, 
    geom = "line", 
    linewidth = 0.3
  ) +
  stat_summary(
    fun = mean, 
    geom = "point", 
    size = 2
  ) +
  stat_summary(
    fun.data = "mean_cl_boot", 
    geom = "errorbar", 
    col = "red", 
    width = 0.3
  )

^{Created on 2023-04-18 with reprex v2.0.2}

[shanmdphd]

library(ggplot2)
# devtools::install_github("dpastoor/PKPDdatasets")
library(PKPDdatasets) 

PKPDdatasets::sd_oral_richpk |> 
  ggplot(aes(Time, Conc)) +
  stat_summary(
    fun=mean, 
    geom = "line", 
    linewidth = 0.3
  ) +
  stat_summary(
    fun = mean, 
    geom = "point", 
    size = 2
  ) +
  stat_summary(
    fun.data = "mean_cl_boot", 
    geom = "errorbar", 
    width = 0.3
  ) +
  facet_grid(Race ~ Gender)

^{Created on 2023-04-18 with reprex v2.0.2}

[shanmdphd]

library(tidyverse)
age <- c(1, 2, 3, 5, 7, 11, 13, 18)
df <- tibble(age)

df |> 
  mutate(stage = if_else(age <= 1, 
                         'infant', 
                         if_else(age <=2, 
                                 'toddler',
                                 if_else(age <= 12, 
                                         'childhood', 
                                         'adolescent'))))
#> # A tibble: 8 × 2
#>     age stage     
#>   <dbl> <chr>     
#> 1     1 infant    
#> 2     2 toddler   
#> 3     3 childhood 
#> 4     5 childhood 
#> 5     7 childhood 
#> 6    11 childhood 
#> 7    13 adolescent
#> 8    18 adolescent

df |> 
  mutate(stage = case_when(
    age <= 1 ~ 'infant',
    age <= 2 ~ 'toddler',
    age <= 12 ~ 'childhood',
    age <= 18 ~ 'adolescent'
  ))
#> # A tibble: 8 × 2
#>     age stage     
#>   <dbl> <chr>     
#> 1     1 infant    
#> 2     2 toddler   
#> 3     3 childhood 
#> 4     5 childhood 
#> 5     7 childhood 
#> 6    11 childhood 
#> 7    13 adolescent
#> 8    18 adolescent

^{Created on 2023-04-25 with reprex v2.0.2}

[shanmdphd]

library(tidyverse)
age <- c(1, 2, 3, 5, 7, 11, 13, 18, 43, 50)
df <- tibble(age)

df |> 
  mutate(stage = case_when(
    age <= 1 ~ 'infant',
    age <= 2 ~ 'toddler',
    age <= 12 ~ 'childhood',
    age <= 18 ~ 'adolescent'
  ))
#> # A tibble: 10 × 2
#>      age stage     
#>    <dbl> <chr>     
#>  1     1 infant    
#>  2     2 toddler   
#>  3     3 childhood 
#>  4     5 childhood 
#>  5     7 childhood 
#>  6    11 childhood 
#>  7    13 adolescent
#>  8    18 adolescent
#>  9    43 <NA>      
#> 10    50 <NA>

df |> 
  mutate(stage = case_when(
    age <= 1 ~ 'infant',
    age <= 2 ~ 'toddler',
    age <= 12 ~ 'childhood',
    age <= 18 ~ 'adolescent',
    TRUE ~ '꼰대'
  ))
#> # A tibble: 10 × 2
#>      age stage     
#>    <dbl> <chr>     
#>  1     1 infant    
#>  2     2 toddler   
#>  3     3 childhood 
#>  4     5 childhood 
#>  5     7 childhood 
#>  6    11 childhood 
#>  7    13 adolescent
#>  8    18 adolescent
#>  9    43 꼰대      
#> 10    50 꼰대

^{Created on 2023-04-25 with reprex v2.0.2}

[shanmdphd]

library(tidyverse)
df <- tibble(name=c("Pil", "Cha", "Jun", "Kelly", "David", "Julia", NA, NA),
             food=c("Mexican", NA, "ddukboki", "bread", NA, "Japanese", "Korean", NA))
df |> 
  mutate(name_or_food = coalesce(name, food, "Whatever")) |> 
  mutate(name_na = coalesce(name, "Noname"))
#> # A tibble: 8 × 4
#>   name  food     name_or_food name_na
#>   <chr> <chr>    <chr>        <chr>  
#> 1 Pil   Mexican  Pil          Pil    
#> 2 Cha   <NA>     Cha          Cha    
#> 3 Jun   ddukboki Jun          Jun    
#> 4 Kelly bread    Kelly        Kelly  
#> 5 David <NA>     David        David  
#> 6 Julia Japanese Julia        Julia  
#> 7 <NA>  Korean   Korean       Noname 
#> 8 <NA>  <NA>     Whatever     Noname

^{Created on 2023-05-19 with reprex v2.0.2}