A glimpse of the Tidyverse

dplyr
tidyr
data wrangling
Published

11 January 2024

Introduction

Goal for Today

Introduce you to R and Rstudio.

What Are R and Rstudio?

What Is R?

R is an open source programming language with origins in C and FORTRAN. Advantages:

  • Flexibility
  • It’s free (and open source)!
  • Ease of handling advanced computational models
  • Ease of handling multiple data sets in one session
  • Higher demand in industries.

But more importantly, it’s free.

Some disadvantages:

  • “Bleeding” edge? (Even then…)
  • Higher learning curve
  • A “programming language” and not a “program.”

Rstudio will help with the learning curve component.

Getting Started in R and Rstudio

Let’s get started in Rstudio first. Select “Tools” in the menu.

  • Scroll to “Global Options” (should be at the bottom)
  • On the pop-up, select “pane layout.”
  • Rearrange so that “Source” is top left, “Console” is top right”, and the files/plots/packages/etc. is the bottom right.
  • Save

Getting Started in R and Rstudio

Hit Ctrl-Shift-N (Cmd-Shift-N if you’re on a Mac) to open up a new script.

  • Minimize the “Environment/History/Connections/Git” pane in the bottom left.
  • Adjust the console output to your liking.

This should maximize your Rstudio experience, esp. as you’ll eventually start writing documents in Rstudio.

  • That should maximize your Rstudio experience, esp. as you begin to write documents in Rstudio as well.

A Few Commands to Get Started

getwd() will spit out your current working directory.

By default, assuming your username is “Bongani”:

  • Windows: "D:/" (notice the forward slashes!)

Creating Objects

R is an “object-oriented” programming language.

  • i.e. inputs create outputs that may be assigned to objects in the workspace.

For example:


a <- 3
b <- 4 
this_is_a_long_object_name_and_you_should_not_do_this <- 5
d <- pi # notice there are a few built-in functions/objects

Sometimes it’s useful to see all the mess you’ve created in your workspace

ls()
#> [1] "a"                                                    
#> [2] "b"                                                    
#> [3] "d"                                                    
#> [4] "insert_html_math"                                     
#> [5] "insert_inc_bullet"                                    
#> [6] "insert_pause"                                         
#> [7] "insert_slide_break"                                   
#> [8] "make_latex_decorator"                                 
#> [9] "this_is_a_long_object_name_and_you_should_not_do_this"

Install Packages

R depends on user-created libraries to do much of its functionality. We’re going to start with a few for the sake of this exercise.

# This will take a while, mostly for tidyverse
install.packages(c("tidyverse","devtools"))

# Once it's installed:
library(tidyverse)
library(devtools)

Load Data

You can load data from your hard drive, or even the internet. Some commands:

Just make sure to apply it to an object.

# Note: hypothetical data
Apply <- haven::read_dta("D:\MY THESIS\BONGANIfinalologit.dta")

Cunemp <- read_tsv("D:\MY THESIS\BONGANIfinalla.data.64.County") 

Load Data

Some R packages, like pharmacoSmoking package, has built-in data. For example:

pwt_sample= pharmacoSmoking |> 
  as.tibble()
names(pwt_sample)
#>  [1] "id"             "ttr"            "relapse"        "grp"           
#>  [5] "age"            "gender"         "race"           "employment"    
#>  [9] "yearsSmoking"   "levelSmoking"   "ageGroup2"      "ageGroup4"     
#> [13] "priorAttempts"  "longestNoSmoke"

Tidyverse

Tidyverse

The tidyverse is a suite of functions/packages that totally rethink base R. Some functions we’ll discuss:

I cannot fully discuss everything from the tidyverse. That’s why there’s Google/Stackexchange. :P

%>%

The pipe (%>%) allows you to chain together a series of tidyverse functions.

  • This is especially useful when you’re recoding data and you want to make sure you got everything right before saving the data.

You can chain together a host of tidyverse commands within it.

glimpse() and summary()

glimpse() and summary() will get you some basic descriptions of your data. For example:

pwt_sample %>% glimpse() # notice the pipe
#> Rows: 125
#> Columns: 14
#> $ id             <int> 21, 113, 39, 80, 87, 29, 16, 35, 54, 70, 84, 85, 25, 47…
#> $ ttr            <int> 182, 14, 5, 16, 0, 182, 14, 77, 2, 0, 12, 182, 21, 3, 1…
#> $ relapse        <int> 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1…
#> $ grp            <fct> patchOnly, patchOnly, combination, combination, combina…
#> $ age            <int> 36, 41, 25, 54, 45, 43, 66, 78, 40, 38, 64, 51, 37, 65,…
#> $ gender         <fct> Male, Male, Female, Male, Male, Male, Male, Female, Fem…
#> $ race           <fct> white, white, white, white, white, hispanic, black, bla…
#> $ employment     <fct> ft, other, other, ft, other, ft, pt, other, ft, ft, oth…
#> $ yearsSmoking   <int> 26, 27, 12, 39, 30, 30, 54, 56, 25, 23, 30, 35, 23, 50,…
#> $ levelSmoking   <fct> heavy, heavy, heavy, heavy, heavy, heavy, heavy, light,…
#> $ ageGroup2      <fct> 21-49, 21-49, 21-49, 50+, 21-49, 21-49, 50+, 50+, 21-49…
#> $ ageGroup4      <fct> 35-49, 35-49, 21-34, 50-64, 35-49, 35-49, 65+, 65+, 35-…
#> $ priorAttempts  <int> 0, 3, 3, 0, 0, 2, 0, 10, 4, 10, 12, 1, 5, 6, 5, 2, 1, 1…
#> $ longestNoSmoke <int> 0, 90, 21, 0, 0, 1825, 0, 15, 7, 90, 365, 7, 1095, 180,…

glimpse() and summary()

summary() is technically not a tidyverse function, but it works within the pipe.

pwt_sample= pharmacoSmoking
pwt_sample %>% summary()
#>        id              ttr            relapse               grp    
#>  Min.   :  1.00   Min.   :  0.00   Min.   :0.000   combination:61  
#>  1st Qu.: 33.00   1st Qu.:  8.00   1st Qu.:0.000   patchOnly  :64  
#>  Median : 67.00   Median : 49.00   Median :1.000                   
#>  Mean   : 66.15   Mean   : 77.44   Mean   :0.712                   
#>  3rd Qu.: 99.00   3rd Qu.:182.00   3rd Qu.:1.000                   
#>  Max.   :130.00   Max.   :182.00   Max.   :1.000                   
#>       age           gender         race    employment  yearsSmoking  
#>  Min.   :22.00   Female:81   black   :38   ft   :72   Min.   : 9.00  
#>  1st Qu.:41.00   Male  :44   hispanic: 8   other:39   1st Qu.:22.00  
#>  Median :49.00               other   : 2   pt   :14   Median :30.00  
#>  Mean   :48.84               white   :77              Mean   :30.88  
#>  3rd Qu.:56.00                                        3rd Qu.:39.00  
#>  Max.   :86.00                                        Max.   :56.00  
#>  levelSmoking ageGroup2  ageGroup4  priorAttempts     longestNoSmoke  
#>  heavy:89     21-49:66   21-34:16   Min.   :   0.00   Min.   :   0.0  
#>  light:36     50+  :59   35-49:50   1st Qu.:   1.00   1st Qu.:   7.0  
#>                          50-64:48   Median :   2.00   Median :  90.0  
#>                          65+  :11   Mean   :  12.68   Mean   : 539.7  
#>                                     3rd Qu.:   5.00   3rd Qu.: 365.0  
#>                                     Max.   :1000.00   Max.   :6205.0

select()

select() will grab (or omit) columns from the data.

# grab everything
pwt_sample %>% select(everything()) |> head()
#>    id ttr relapse         grp age gender     race employment yearsSmoking
#> 1  21 182       0   patchOnly  36   Male    white         ft           26
#> 2 113  14       1   patchOnly  41   Male    white      other           27
#> 3  39   5       1 combination  25 Female    white      other           12
#> 4  80  16       1 combination  54   Male    white         ft           39
#> 5  87   0       1 combination  45   Male    white      other           30
#> 6  29 182       0 combination  43   Male hispanic         ft           30
#>   levelSmoking ageGroup2 ageGroup4 priorAttempts longestNoSmoke
#> 1        heavy     21-49     35-49             0              0
#> 2        heavy     21-49     35-49             3             90
#> 3        heavy     21-49     21-34             3             21
#> 4        heavy       50+     50-64             0              0
#> 5        heavy     21-49     35-49             0              0
#> 6        heavy     21-49     35-49             2           1825

select()

# grab everything, but drop the id variable.
pwt_sample %>% select(-id) 
#>     ttr relapse         grp age gender     race employment yearsSmoking
#> 1   182       0   patchOnly  36   Male    white         ft           26
#> 2    14       1   patchOnly  41   Male    white      other           27
#> 3     5       1 combination  25 Female    white      other           12
#> 4    16       1 combination  54   Male    white         ft           39
#> 5     0       1 combination  45   Male    white      other           30
#> 6   182       0 combination  43   Male hispanic         ft           30
#> 7    14       1   patchOnly  66   Male    black         pt           54
#> 8    77       1   patchOnly  78 Female    black      other           56
#> 9     2       1   patchOnly  40 Female    black         ft           25
#> 10    0       1   patchOnly  38   Male    black         ft           23
#> 11   12       1   patchOnly  64 Female    black      other           30
#> 12  182       0 combination  51   Male    black         ft           35
#> 13   21       1   patchOnly  37 Female    white         pt           23
#> 14    3       1   patchOnly  65   Male    white      other           50
#> 15  170       1   patchOnly  42 Female    white         ft           30
#> 16   25       1   patchOnly  40   Male    white      other           22
#> 17    4       1   patchOnly  65 Female    white      other           50
#> 18  182       0 combination  52 Female    white      other           19
#> 19  140       1 combination  43   Male    white         ft           27
#> 20   63       1 combination  34 Female    white         ft           18
#> 21   15       1 combination  46 Female    white      other           26
#> 22  140       1 combination  60   Male    white         ft           42
#> 23  110       1 combination  49 Female    white      other           35
#> 24  182       0 combination  58 Female    white         ft           38
#> 25    0       1   patchOnly  48   Male hispanic      other           33
#> 26  182       0 combination  54 Female hispanic      other           38
#> 27   15       1   patchOnly  49 Female    black         pt           35
#> 28  182       0   patchOnly  55   Male    black         ft           39
#> 29    4       1   patchOnly  33   Male    black         ft           12
#> 30   56       1 combination  49 Female    black         ft           37
#> 31    2       1 combination  46   Male    black         ft           25
#> 32   80       1   patchOnly  34 Female    other         ft           18
#> 33  182       0 combination  46 Female    white         pt           25
#> 34   56       1   patchOnly  52   Male    black      other           25
#> 35    0       1   patchOnly  52 Female    black         ft           30
#> 36   14       1   patchOnly  48 Female    white         ft           34
#> 37   14       1   patchOnly  48 Female    white         ft           39
#> 38   28       1   patchOnly  56 Female    white         ft           36
#> 39  182       0   patchOnly  58   Male    white      other           41
#> 40    6       1   patchOnly  60   Male    white         ft           50
#> 41  182       0   patchOnly  55   Male    white         ft           40
#> 42   14       1   patchOnly  43 Female    white         ft           28
#> 43   15       1   patchOnly  55 Female    white         ft           42
#> 44  182       0 combination  70 Female    white      other           52
#> 45   75       1 combination  62 Female    white         pt           46
#> 46   30       1 combination  86   Male    white      other           40
#> 47    4       1 combination  52   Male    white         ft           33
#> 48   56       1 combination  27 Female    white         ft           13
#> 49  182       0 combination  52   Male    white         ft           35
#> 50  182       0 combination  62   Male    white         ft           35
#> 51    5       1 combination  57 Female    white         ft           47
#> 52    8       1 combination  40   Male    white         ft           27
#> 53  140       1 combination  49   Male    white      other           14
#> 54   20       1 combination  68 Female    white      other           53
#> 55   63       1 combination  47 Female    white         pt           15
#> 56   30       1 combination  46 Female    white         ft           34
#> 57    8       1 combination  55   Male    white         pt           40
#> 58   50       1 combination  29   Male    white         ft           12
#> 59   14       1 combination  64 Female    white         pt           45
#> 60    0       1 combination  52   Male    white         ft           38
#> 61   84       1   patchOnly  38 Female hispanic         ft           10
#> 62    0       1   patchOnly  35   Male hispanic      other           20
#> 63  105       1   patchOnly  50 Female hispanic      other           33
#> 64  182       0 combination  63 Female hispanic      other           45
#> 65  182       0   patchOnly  58   Male    black         ft           40
#> 66  182       0   patchOnly  56   Male    black         ft           16
#> 67    7       1   patchOnly  44 Female    black      other           31
#> 68  182       0   patchOnly  34 Female    black         ft           20
#> 69    0       1   patchOnly  49 Female    black         ft           36
#> 70    8       1   patchOnly  43 Female    black      other           24
#> 71    1       1   patchOnly  39 Female    black         ft           27
#> 72  182       0 combination  41 Female    black         ft           20
#> 73   12       1 combination  46 Female    black      other           30
#> 74  182       0 combination  46 Female    black         ft           21
#> 75   49       1   patchOnly  53 Female    white         pt           18
#> 76  182       0   patchOnly  58   Male    white         ft           46
#> 77  182       0   patchOnly  40 Female    white         ft           25
#> 78    2       1   patchOnly  62 Female    white      other           49
#> 79  182       0   patchOnly  53   Male    white         ft           38
#> 80   56       1   patchOnly  44 Female    white         ft           35
#> 81  182       0   patchOnly  64 Female    white      other           47
#> 82    0       1   patchOnly  50 Female    white         ft           30
#> 83   28       1   patchOnly  47   Male    white         ft           22
#> 84  155       1   patchOnly  49 Female    white         pt           35
#> 85    2       1   patchOnly  51   Male    white      other           36
#> 86    0       1   patchOnly  41 Female    white      other           26
#> 87    0       1   patchOnly  22 Female    white         pt           10
#> 88    1       1   patchOnly  22 Female    white         pt            9
#> 89  140       1   patchOnly  34 Female    white         ft           18
#> 90    1       1   patchOnly  48 Female    white         ft           30
#> 91   28       1   patchOnly  30 Female    white         ft           12
#> 92    1       1   patchOnly  31   Male    white         ft           19
#> 93  182       0   patchOnly  44 Female    white         ft           30
#> 94   77       1   patchOnly  56 Female    white         ft           44
#> 95   56       1   patchOnly  29 Female    white         ft           10
#> 96  182       0 combination  69   Male    white      other           50
#> 97  182       0 combination  41   Male    white         ft           29
#> 98  182       0 combination  52 Female    white      other           33
#> 99  182       0 combination  52 Female    white         ft           30
#> 100 182       0 combination  53   Male    white         ft           30
#> 101  21       1 combination  31 Female    white         ft           15
#> 102  60       1 combination  70 Female    white      other           54
#> 103   0       1 combination  43   Male    white      other           29
#> 104 182       0 combination  58 Female    white         ft           36
#> 105  65       1 combination  48 Female    white         ft           34
#> 106 182       0 combination  72   Male    white      other           55
#> 107 182       0 combination  61 Female    white      other           45
#> 108 182       0 combination  53 Female    white         ft           41
#> 109 182       0 combination  63 Female    other      other           52
#> 110   2       1 combination  40 Female hispanic         pt           23
#> 111  40       1   patchOnly  39 Female    black         ft           20
#> 112 100       1   patchOnly  60 Female    black         ft           20
#> 113   1       1   patchOnly  54 Female    black      other           34
#> 114  45       1   patchOnly  68 Female    black         ft           25
#> 115  14       1   patchOnly  54 Female    black         ft           22
#> 116  30       1   patchOnly  51   Male    black      other           30
#> 117  42       1 combination  39 Female    black         ft           23
#> 118   2       1 combination  47 Female    black         ft           33
#> 119 182       0 combination  33 Female    black         ft           10
#> 120  60       1 combination  27 Female    black         ft           11
#> 121  10       1 combination  45 Female    black         ft           32
#> 122   0       1 combination  36 Female    black         ft           20
#> 123 170       1 combination  39   Male    black         ft           20
#> 124  15       1 combination  56 Female    black      other           39
#> 125 182       0 combination  50 Female    black         pt           30
#>     levelSmoking ageGroup2 ageGroup4 priorAttempts longestNoSmoke
#> 1          heavy     21-49     35-49             0              0
#> 2          heavy     21-49     35-49             3             90
#> 3          heavy     21-49     21-34             3             21
#> 4          heavy       50+     50-64             0              0
#> 5          heavy     21-49     35-49             0              0
#> 6          heavy     21-49     35-49             2           1825
#> 7          heavy       50+       65+             0              0
#> 8          light       50+       65+            10             15
#> 9          heavy     21-49     35-49             4              7
#> 10         light     21-49     35-49            10             90
#> 11         heavy       50+     50-64            12            365
#> 12         heavy       50+     50-64             1              7
#> 13         light     21-49     35-49             5           1095
#> 14         heavy       50+       65+             6            180
#> 15         heavy     21-49     35-49             5            240
#> 16         heavy     21-49     35-49             2              2
#> 17         light       50+       65+             1              2
#> 18         light       50+     50-64             1              7
#> 19         heavy     21-49     35-49             5            120
#> 20         heavy     21-49     21-34             8             90
#> 21         heavy     21-49     35-49             2             14
#> 22         light       50+     50-64             6           2920
#> 23         heavy     21-49     35-49            10             60
#> 24         heavy       50+     50-64             1              0
#> 25         heavy     21-49     35-49             4            120
#> 26         light       50+     50-64            30           3650
#> 27         light     21-49     35-49             4            540
#> 28         heavy       50+     50-64             1             28
#> 29         light     21-49     21-34             1            730
#> 30         heavy     21-49     35-49             3           2920
#> 31         light     21-49     35-49             2           1095
#> 32         heavy     21-49     21-34             1            120
#> 33         heavy     21-49     35-49             3            365
#> 34         light       50+     50-64            10              7
#> 35         heavy       50+     50-64             2             42
#> 36         heavy     21-49     35-49             6           2555
#> 37         heavy     21-49     35-49             1              2
#> 38         heavy       50+     50-64             5            180
#> 39         light       50+     50-64           100            180
#> 40         heavy       50+     50-64             6             30
#> 41         heavy       50+     50-64             1            180
#> 42         heavy     21-49     35-49             4            365
#> 43         heavy       50+     50-64             2             60
#> 44         heavy       50+       65+             3              1
#> 45         heavy       50+     50-64             8           1095
#> 46         light       50+       65+             4           2190
#> 47         heavy       50+     50-64             2              2
#> 48         light     21-49     21-34             1              7
#> 49         heavy       50+     50-64             3            730
#> 50         heavy       50+     50-64             1           2555
#> 51         heavy       50+     50-64             0              0
#> 52         heavy     21-49     35-49             2             90
#> 53         heavy     21-49     35-49             0              0
#> 54         heavy       50+       65+             0              0
#> 55         heavy     21-49     35-49             1             90
#> 56         light     21-49     35-49            20             90
#> 57         heavy       50+     50-64             6            913
#> 58         heavy     21-49     21-34             0              0
#> 59         heavy       50+     50-64             4             60
#> 60         heavy       50+     50-64             4              7
#> 61         heavy     21-49     35-49             2              3
#> 62         light     21-49     35-49             2              7
#> 63         heavy       50+     50-64             2             30
#> 64         heavy       50+     50-64             1             28
#> 65         heavy       50+     50-64             0              0
#> 66         light       50+     50-64             3            700
#> 67         heavy     21-49     35-49             2              2
#> 68         heavy     21-49     21-34             5             90
#> 69         heavy     21-49     35-49             1           6205
#> 70         light     21-49     35-49             2              3
#> 71         heavy     21-49     35-49             1              1
#> 72         light     21-49     35-49             6            270
#> 73         light     21-49     35-49             2             55
#> 74         light     21-49     35-49             1           1095
#> 75         heavy       50+     50-64             6           3650
#> 76         heavy       50+     50-64            10             14
#> 77         heavy     21-49     35-49             1           2920
#> 78         heavy       50+     50-64             1              8
#> 79         heavy       50+     50-64             5           1095
#> 80         heavy     21-49     35-49             8            180
#> 81         heavy       50+     50-64             4            365
#> 82         heavy       50+     50-64             1             90
#> 83         heavy     21-49     35-49             3              4
#> 84         heavy     21-49     35-49             1           1095
#> 85         heavy       50+     50-64             1              5
#> 86         heavy     21-49     35-49             1              7
#> 87         heavy     21-49     21-34             3              2
#> 88         heavy     21-49     21-34             2              3
#> 89         light     21-49     21-34             2           2190
#> 90         light     21-49     35-49            10           1095
#> 91         light     21-49     21-34             0              0
#> 92         heavy     21-49     21-34            10            120
#> 93         heavy     21-49     35-49             1              3
#> 94         heavy       50+     50-64             4           1095
#> 95         light     21-49     21-34             8            240
#> 96         heavy       50+       65+             6           5475
#> 97         heavy     21-49     35-49            20            180
#> 98         heavy       50+     50-64             5            270
#> 99         heavy       50+     50-64             3           1095
#> 100        heavy       50+     50-64             3           3285
#> 101        heavy     21-49     21-34             4             90
#> 102        heavy       50+       65+             1             90
#> 103        heavy     21-49     35-49            12              6
#> 104        light       50+     50-64             2             90
#> 105        heavy     21-49     35-49          1000            548
#> 106        light       50+       65+            30             30
#> 107        heavy       50+     50-64             1             60
#> 108        heavy       50+     50-64             3             60
#> 109        heavy       50+     50-64             2            180
#> 110        heavy     21-49     35-49             2              3
#> 111        light     21-49     35-49             3            210
#> 112        light       50+     50-64             5           1825
#> 113        light       50+     50-64             2            365
#> 114        light       50+       65+             2              7
#> 115        heavy       50+     50-64             5             10
#> 116        heavy       50+     50-64             2             30
#> 117        light     21-49     35-49             2           1825
#> 118        light     21-49     35-49             4            365
#> 119        light     21-49     21-34             2              1
#> 120        light     21-49     21-34             2             14
#> 121        heavy     21-49     35-49             1             75
#> 122        heavy     21-49     35-49             1            270
#> 123        light     21-49     35-49             3            180
#> 124        heavy       50+     50-64             3              7
#> 125        heavy       50+     50-64             0              0

select()

# grab just these three columns.
pwt_sample %>% select(ttr, grp, gender)
#>     ttr         grp gender
#> 1   182   patchOnly   Male
#> 2    14   patchOnly   Male
#> 3     5 combination Female
#> 4    16 combination   Male
#> 5     0 combination   Male
#> 6   182 combination   Male
#> 7    14   patchOnly   Male
#> 8    77   patchOnly Female
#> 9     2   patchOnly Female
#> 10    0   patchOnly   Male
#> 11   12   patchOnly Female
#> 12  182 combination   Male
#> 13   21   patchOnly Female
#> 14    3   patchOnly   Male
#> 15  170   patchOnly Female
#> 16   25   patchOnly   Male
#> 17    4   patchOnly Female
#> 18  182 combination Female
#> 19  140 combination   Male
#> 20   63 combination Female
#> 21   15 combination Female
#> 22  140 combination   Male
#> 23  110 combination Female
#> 24  182 combination Female
#> 25    0   patchOnly   Male
#> 26  182 combination Female
#> 27   15   patchOnly Female
#> 28  182   patchOnly   Male
#> 29    4   patchOnly   Male
#> 30   56 combination Female
#> 31    2 combination   Male
#> 32   80   patchOnly Female
#> 33  182 combination Female
#> 34   56   patchOnly   Male
#> 35    0   patchOnly Female
#> 36   14   patchOnly Female
#> 37   14   patchOnly Female
#> 38   28   patchOnly Female
#> 39  182   patchOnly   Male
#> 40    6   patchOnly   Male
#> 41  182   patchOnly   Male
#> 42   14   patchOnly Female
#> 43   15   patchOnly Female
#> 44  182 combination Female
#> 45   75 combination Female
#> 46   30 combination   Male
#> 47    4 combination   Male
#> 48   56 combination Female
#> 49  182 combination   Male
#> 50  182 combination   Male
#> 51    5 combination Female
#> 52    8 combination   Male
#> 53  140 combination   Male
#> 54   20 combination Female
#> 55   63 combination Female
#> 56   30 combination Female
#> 57    8 combination   Male
#> 58   50 combination   Male
#> 59   14 combination Female
#> 60    0 combination   Male
#> 61   84   patchOnly Female
#> 62    0   patchOnly   Male
#> 63  105   patchOnly Female
#> 64  182 combination Female
#> 65  182   patchOnly   Male
#> 66  182   patchOnly   Male
#> 67    7   patchOnly Female
#> 68  182   patchOnly Female
#> 69    0   patchOnly Female
#> 70    8   patchOnly Female
#> 71    1   patchOnly Female
#> 72  182 combination Female
#> 73   12 combination Female
#> 74  182 combination Female
#> 75   49   patchOnly Female
#> 76  182   patchOnly   Male
#> 77  182   patchOnly Female
#> 78    2   patchOnly Female
#> 79  182   patchOnly   Male
#> 80   56   patchOnly Female
#> 81  182   patchOnly Female
#> 82    0   patchOnly Female
#> 83   28   patchOnly   Male
#> 84  155   patchOnly Female
#> 85    2   patchOnly   Male
#> 86    0   patchOnly Female
#> 87    0   patchOnly Female
#> 88    1   patchOnly Female
#> 89  140   patchOnly Female
#> 90    1   patchOnly Female
#> 91   28   patchOnly Female
#> 92    1   patchOnly   Male
#> 93  182   patchOnly Female
#> 94   77   patchOnly Female
#> 95   56   patchOnly Female
#> 96  182 combination   Male
#> 97  182 combination   Male
#> 98  182 combination Female
#> 99  182 combination Female
#> 100 182 combination   Male
#> 101  21 combination Female
#> 102  60 combination Female
#> 103   0 combination   Male
#> 104 182 combination Female
#> 105  65 combination Female
#> 106 182 combination   Male
#> 107 182 combination Female
#> 108 182 combination Female
#> 109 182 combination Female
#> 110   2 combination Female
#> 111  40   patchOnly Female
#> 112 100   patchOnly Female
#> 113   1   patchOnly Female
#> 114  45   patchOnly Female
#> 115  14   patchOnly Female
#> 116  30   patchOnly   Male
#> 117  42 combination Female
#> 118   2 combination Female
#> 119 182 combination Female
#> 120  60 combination Female
#> 121  10 combination Female
#> 122   0 combination Female
#> 123 170 combination   Male
#> 124  15 combination Female
#> 125 182 combination Female

group_by()

group_by() might be the most powerful function in tidyverse.

  • tl;dr: it allows you to perform functions within specific subsets (groups) of the data.
# Notice we can chain some pipes together
pwt_sample %>%
  # group by gender
  group_by(gender) %>%
  # Get me the first observation, by group.
  slice(1)
#> # A tibble: 2 × 14
#> # Groups:   gender [2]
#>      id   ttr relapse grp           age gender race  employment yearsSmoking
#>   <int> <int>   <int> <fct>       <int> <fct>  <fct> <fct>             <int>
#> 1    39     5       1 combination    25 Female white other                12
#> 2    21   182       0 patchOnly      36 Male   white ft                   26
#> # ℹ 5 more variables: levelSmoking <fct>, ageGroup2 <fct>, ageGroup4 <fct>,
#> #   priorAttempts <int>, longestNoSmoke <int>

group_by()

Notice what would happen in the absence of group_by()

pwt_sample %>%
  # Get me the first observation for each gender
  slice(1) #Forgot to group_by()
#>   id ttr relapse       grp age gender  race employment yearsSmoking
#> 1 21 182       0 patchOnly  36   Male white         ft           26
#>   levelSmoking ageGroup2 ageGroup4 priorAttempts longestNoSmoke
#> 1        heavy     21-49     35-49             0              0

Caveat: if you’re applying a group-specific function (that you need once), it’s generally advisable to “ungroup” (i.e. ungroup()) the data when you’re done.

summarize()

summarize() creates condensed summaries of the data, for whatever it is you want.

pwt_sample %>%
    # How many observations are in the data?
    dplyr::summarize(n = n())
#>     n
#> 1 125

summarize()

# Note: works *wonderfully* with group_by()
pwt_sample %>%
    group_by(gender) %>%
    # Give me the max time to relapse observed in the data.
    dplyr::summarize(maxttr = max(ttr, na.rm=T))
#> # A tibble: 2 × 2
#>   gender maxttr
#>   <fct>   <int>
#> 1 Female    182
#> 2 Male      182

mutate()

mutate() creates new columns while retaining original dimensions of the data (unlike summarize()).

pwt_sample %>%
    # Convert rgdpna from real GDP in millions to real GDP in billions
    mutate(ttrmonths = ttr/30)
#>      id ttr relapse         grp age gender     race employment yearsSmoking
#> 1    21 182       0   patchOnly  36   Male    white         ft           26
#> 2   113  14       1   patchOnly  41   Male    white      other           27
#> 3    39   5       1 combination  25 Female    white      other           12
#> 4    80  16       1 combination  54   Male    white         ft           39
#> 5    87   0       1 combination  45   Male    white      other           30
#> 6    29 182       0 combination  43   Male hispanic         ft           30
#> 7    16  14       1   patchOnly  66   Male    black         pt           54
#> 8    35  77       1   patchOnly  78 Female    black      other           56
#> 9    54   2       1   patchOnly  40 Female    black         ft           25
#> 10   70   0       1   patchOnly  38   Male    black         ft           23
#> 11   84  12       1   patchOnly  64 Female    black      other           30
#> 12   85 182       0 combination  51   Male    black         ft           35
#> 13   25  21       1   patchOnly  37 Female    white         pt           23
#> 14   47   3       1   patchOnly  65   Male    white      other           50
#> 15   59 170       1   patchOnly  42 Female    white         ft           30
#> 16   63  25       1   patchOnly  40   Male    white      other           22
#> 17  102   4       1   patchOnly  65 Female    white      other           50
#> 18    3 182       0 combination  52 Female    white      other           19
#> 19   15 140       1 combination  43   Male    white         ft           27
#> 20   32  63       1 combination  34 Female    white         ft           18
#> 21   79  15       1 combination  46 Female    white      other           26
#> 22   90 140       1 combination  60   Male    white         ft           42
#> 23  110 110       1 combination  49 Female    white      other           35
#> 24  127 182       0 combination  58 Female    white         ft           38
#> 25  119   0       1   patchOnly  48   Male hispanic      other           33
#> 26   33 182       0 combination  54 Female hispanic      other           38
#> 27   62  15       1   patchOnly  49 Female    black         pt           35
#> 28   67 182       0   patchOnly  55   Male    black         ft           39
#> 29  112   4       1   patchOnly  33   Male    black         ft           12
#> 30   60  56       1 combination  49 Female    black         ft           37
#> 31   93   2       1 combination  46   Male    black         ft           25
#> 32  122  80       1   patchOnly  34 Female    other         ft           18
#> 33  130 182       0 combination  46 Female    white         pt           25
#> 34   19  56       1   patchOnly  52   Male    black      other           25
#> 35   65   0       1   patchOnly  52 Female    black         ft           30
#> 36    4  14       1   patchOnly  48 Female    white         ft           34
#> 37   20  14       1   patchOnly  48 Female    white         ft           39
#> 38   22  28       1   patchOnly  56 Female    white         ft           36
#> 39   26 182       0   patchOnly  58   Male    white      other           41
#> 40   43   6       1   patchOnly  60   Male    white         ft           50
#> 41  107 182       0   patchOnly  55   Male    white         ft           40
#> 42  111  14       1   patchOnly  43 Female    white         ft           28
#> 43  117  15       1   patchOnly  55 Female    white         ft           42
#> 44    8 182       0 combination  70 Female    white      other           52
#> 45   12  75       1 combination  62 Female    white         pt           46
#> 46   13  30       1 combination  86   Male    white      other           40
#> 47   23   4       1 combination  52   Male    white         ft           33
#> 48   30  56       1 combination  27 Female    white         ft           13
#> 49   34 182       0 combination  52   Male    white         ft           35
#> 50   36 182       0 combination  62   Male    white         ft           35
#> 51   38   5       1 combination  57 Female    white         ft           47
#> 52   44   8       1 combination  40   Male    white         ft           27
#> 53   61 140       1 combination  49   Male    white      other           14
#> 54   68  20       1 combination  68 Female    white      other           53
#> 55   69  63       1 combination  47 Female    white         pt           15
#> 56   82  30       1 combination  46 Female    white         ft           34
#> 57   97   8       1 combination  55   Male    white         pt           40
#> 58  106  50       1 combination  29   Male    white         ft           12
#> 59  114  14       1 combination  64 Female    white         pt           45
#> 60  120   0       1 combination  52   Male    white         ft           38
#> 61   40  84       1   patchOnly  38 Female hispanic         ft           10
#> 62   49   0       1   patchOnly  35   Male hispanic      other           20
#> 63  125 105       1   patchOnly  50 Female hispanic      other           33
#> 64  123 182       0 combination  63 Female hispanic      other           45
#> 65    7 182       0   patchOnly  58   Male    black         ft           40
#> 66    9 182       0   patchOnly  56   Male    black         ft           16
#> 67   37   7       1   patchOnly  44 Female    black      other           31
#> 68   52 182       0   patchOnly  34 Female    black         ft           20
#> 69   86   0       1   patchOnly  49 Female    black         ft           36
#> 70   94   8       1   patchOnly  43 Female    black      other           24
#> 71  104   1       1   patchOnly  39 Female    black         ft           27
#> 72   42 182       0 combination  41 Female    black         ft           20
#> 73   75  12       1 combination  46 Female    black      other           30
#> 74  100 182       0 combination  46 Female    black         ft           21
#> 75    1  49       1   patchOnly  53 Female    white         pt           18
#> 76    6 182       0   patchOnly  58   Male    white         ft           46
#> 77   11 182       0   patchOnly  40 Female    white         ft           25
#> 78   24   2       1   patchOnly  62 Female    white      other           49
#> 79   27 182       0   patchOnly  53   Male    white         ft           38
#> 80   31  56       1   patchOnly  44 Female    white         ft           35
#> 81   56 182       0   patchOnly  64 Female    white      other           47
#> 82   72   0       1   patchOnly  50 Female    white         ft           30
#> 83   78  28       1   patchOnly  47   Male    white         ft           22
#> 84   81 155       1   patchOnly  49 Female    white         pt           35
#> 85   83   2       1   patchOnly  51   Male    white      other           36
#> 86   88   0       1   patchOnly  41 Female    white      other           26
#> 87   91   0       1   patchOnly  22 Female    white         pt           10
#> 88   95   1       1   patchOnly  22 Female    white         pt            9
#> 89   99 140       1   patchOnly  34 Female    white         ft           18
#> 90  101   1       1   patchOnly  48 Female    white         ft           30
#> 91  115  28       1   patchOnly  30 Female    white         ft           12
#> 92  116   1       1   patchOnly  31   Male    white         ft           19
#> 93  124 182       0   patchOnly  44 Female    white         ft           30
#> 94  126  77       1   patchOnly  56 Female    white         ft           44
#> 95  129  56       1   patchOnly  29 Female    white         ft           10
#> 96    2 182       0 combination  69   Male    white      other           50
#> 97    5 182       0 combination  41   Male    white         ft           29
#> 98   14 182       0 combination  52 Female    white      other           33
#> 99   18 182       0 combination  52 Female    white         ft           30
#> 100  51 182       0 combination  53   Male    white         ft           30
#> 101  57  21       1 combination  31 Female    white         ft           15
#> 102  64  60       1 combination  70 Female    white      other           54
#> 103  76   0       1 combination  43   Male    white      other           29
#> 104  92 182       0 combination  58 Female    white         ft           36
#> 105  98  65       1 combination  48 Female    white         ft           34
#> 106 103 182       0 combination  72   Male    white      other           55
#> 107 105 182       0 combination  61 Female    white      other           45
#> 108 121 182       0 combination  53 Female    white         ft           41
#> 109  96 182       0 combination  63 Female    other      other           52
#> 110  46   2       1 combination  40 Female hispanic         pt           23
#> 111  28  40       1   patchOnly  39 Female    black         ft           20
#> 112  53 100       1   patchOnly  60 Female    black         ft           20
#> 113  55   1       1   patchOnly  54 Female    black      other           34
#> 114  73  45       1   patchOnly  68 Female    black         ft           25
#> 115  77  14       1   patchOnly  54 Female    black         ft           22
#> 116 109  30       1   patchOnly  51   Male    black      other           30
#> 117  17  42       1 combination  39 Female    black         ft           23
#> 118  45   2       1 combination  47 Female    black         ft           33
#> 119  48 182       0 combination  33 Female    black         ft           10
#> 120  50  60       1 combination  27 Female    black         ft           11
#> 121  74  10       1 combination  45 Female    black         ft           32
#> 122  89   0       1 combination  36 Female    black         ft           20
#> 123 108 170       1 combination  39   Male    black         ft           20
#> 124 118  15       1 combination  56 Female    black      other           39
#> 125 128 182       0 combination  50 Female    black         pt           30
#>     levelSmoking ageGroup2 ageGroup4 priorAttempts longestNoSmoke  ttrmonths
#> 1          heavy     21-49     35-49             0              0 6.06666667
#> 2          heavy     21-49     35-49             3             90 0.46666667
#> 3          heavy     21-49     21-34             3             21 0.16666667
#> 4          heavy       50+     50-64             0              0 0.53333333
#> 5          heavy     21-49     35-49             0              0 0.00000000
#> 6          heavy     21-49     35-49             2           1825 6.06666667
#> 7          heavy       50+       65+             0              0 0.46666667
#> 8          light       50+       65+            10             15 2.56666667
#> 9          heavy     21-49     35-49             4              7 0.06666667
#> 10         light     21-49     35-49            10             90 0.00000000
#> 11         heavy       50+     50-64            12            365 0.40000000
#> 12         heavy       50+     50-64             1              7 6.06666667
#> 13         light     21-49     35-49             5           1095 0.70000000
#> 14         heavy       50+       65+             6            180 0.10000000
#> 15         heavy     21-49     35-49             5            240 5.66666667
#> 16         heavy     21-49     35-49             2              2 0.83333333
#> 17         light       50+       65+             1              2 0.13333333
#> 18         light       50+     50-64             1              7 6.06666667
#> 19         heavy     21-49     35-49             5            120 4.66666667
#> 20         heavy     21-49     21-34             8             90 2.10000000
#> 21         heavy     21-49     35-49             2             14 0.50000000
#> 22         light       50+     50-64             6           2920 4.66666667
#> 23         heavy     21-49     35-49            10             60 3.66666667
#> 24         heavy       50+     50-64             1              0 6.06666667
#> 25         heavy     21-49     35-49             4            120 0.00000000
#> 26         light       50+     50-64            30           3650 6.06666667
#> 27         light     21-49     35-49             4            540 0.50000000
#> 28         heavy       50+     50-64             1             28 6.06666667
#> 29         light     21-49     21-34             1            730 0.13333333
#> 30         heavy     21-49     35-49             3           2920 1.86666667
#> 31         light     21-49     35-49             2           1095 0.06666667
#> 32         heavy     21-49     21-34             1            120 2.66666667
#> 33         heavy     21-49     35-49             3            365 6.06666667
#> 34         light       50+     50-64            10              7 1.86666667
#> 35         heavy       50+     50-64             2             42 0.00000000
#> 36         heavy     21-49     35-49             6           2555 0.46666667
#> 37         heavy     21-49     35-49             1              2 0.46666667
#> 38         heavy       50+     50-64             5            180 0.93333333
#> 39         light       50+     50-64           100            180 6.06666667
#> 40         heavy       50+     50-64             6             30 0.20000000
#> 41         heavy       50+     50-64             1            180 6.06666667
#> 42         heavy     21-49     35-49             4            365 0.46666667
#> 43         heavy       50+     50-64             2             60 0.50000000
#> 44         heavy       50+       65+             3              1 6.06666667
#> 45         heavy       50+     50-64             8           1095 2.50000000
#> 46         light       50+       65+             4           2190 1.00000000
#> 47         heavy       50+     50-64             2              2 0.13333333
#> 48         light     21-49     21-34             1              7 1.86666667
#> 49         heavy       50+     50-64             3            730 6.06666667
#> 50         heavy       50+     50-64             1           2555 6.06666667
#> 51         heavy       50+     50-64             0              0 0.16666667
#> 52         heavy     21-49     35-49             2             90 0.26666667
#> 53         heavy     21-49     35-49             0              0 4.66666667
#> 54         heavy       50+       65+             0              0 0.66666667
#> 55         heavy     21-49     35-49             1             90 2.10000000
#> 56         light     21-49     35-49            20             90 1.00000000
#> 57         heavy       50+     50-64             6            913 0.26666667
#> 58         heavy     21-49     21-34             0              0 1.66666667
#> 59         heavy       50+     50-64             4             60 0.46666667
#> 60         heavy       50+     50-64             4              7 0.00000000
#> 61         heavy     21-49     35-49             2              3 2.80000000
#> 62         light     21-49     35-49             2              7 0.00000000
#> 63         heavy       50+     50-64             2             30 3.50000000
#> 64         heavy       50+     50-64             1             28 6.06666667
#> 65         heavy       50+     50-64             0              0 6.06666667
#> 66         light       50+     50-64             3            700 6.06666667
#> 67         heavy     21-49     35-49             2              2 0.23333333
#> 68         heavy     21-49     21-34             5             90 6.06666667
#> 69         heavy     21-49     35-49             1           6205 0.00000000
#> 70         light     21-49     35-49             2              3 0.26666667
#> 71         heavy     21-49     35-49             1              1 0.03333333
#> 72         light     21-49     35-49             6            270 6.06666667
#> 73         light     21-49     35-49             2             55 0.40000000
#> 74         light     21-49     35-49             1           1095 6.06666667
#> 75         heavy       50+     50-64             6           3650 1.63333333
#> 76         heavy       50+     50-64            10             14 6.06666667
#> 77         heavy     21-49     35-49             1           2920 6.06666667
#> 78         heavy       50+     50-64             1              8 0.06666667
#> 79         heavy       50+     50-64             5           1095 6.06666667
#> 80         heavy     21-49     35-49             8            180 1.86666667
#> 81         heavy       50+     50-64             4            365 6.06666667
#> 82         heavy       50+     50-64             1             90 0.00000000
#> 83         heavy     21-49     35-49             3              4 0.93333333
#> 84         heavy     21-49     35-49             1           1095 5.16666667
#> 85         heavy       50+     50-64             1              5 0.06666667
#> 86         heavy     21-49     35-49             1              7 0.00000000
#> 87         heavy     21-49     21-34             3              2 0.00000000
#> 88         heavy     21-49     21-34             2              3 0.03333333
#> 89         light     21-49     21-34             2           2190 4.66666667
#> 90         light     21-49     35-49            10           1095 0.03333333
#> 91         light     21-49     21-34             0              0 0.93333333
#> 92         heavy     21-49     21-34            10            120 0.03333333
#> 93         heavy     21-49     35-49             1              3 6.06666667
#> 94         heavy       50+     50-64             4           1095 2.56666667
#> 95         light     21-49     21-34             8            240 1.86666667
#> 96         heavy       50+       65+             6           5475 6.06666667
#> 97         heavy     21-49     35-49            20            180 6.06666667
#> 98         heavy       50+     50-64             5            270 6.06666667
#> 99         heavy       50+     50-64             3           1095 6.06666667
#> 100        heavy       50+     50-64             3           3285 6.06666667
#> 101        heavy     21-49     21-34             4             90 0.70000000
#> 102        heavy       50+       65+             1             90 2.00000000
#> 103        heavy     21-49     35-49            12              6 0.00000000
#> 104        light       50+     50-64             2             90 6.06666667
#> 105        heavy     21-49     35-49          1000            548 2.16666667
#> 106        light       50+       65+            30             30 6.06666667
#> 107        heavy       50+     50-64             1             60 6.06666667
#> 108        heavy       50+     50-64             3             60 6.06666667
#> 109        heavy       50+     50-64             2            180 6.06666667
#> 110        heavy     21-49     35-49             2              3 0.06666667
#> 111        light     21-49     35-49             3            210 1.33333333
#> 112        light       50+     50-64             5           1825 3.33333333
#> 113        light       50+     50-64             2            365 0.03333333
#> 114        light       50+       65+             2              7 1.50000000
#> 115        heavy       50+     50-64             5             10 0.46666667
#> 116        heavy       50+     50-64             2             30 1.00000000
#> 117        light     21-49     35-49             2           1825 1.40000000
#> 118        light     21-49     35-49             4            365 0.06666667
#> 119        light     21-49     21-34             2              1 6.06666667
#> 120        light     21-49     21-34             2             14 2.00000000
#> 121        heavy     21-49     35-49             1             75 0.33333333
#> 122        heavy     21-49     35-49             1            270 0.00000000
#> 123        light     21-49     35-49             3            180 5.66666667
#> 124        heavy       50+     50-64             3              7 0.50000000
#> 125        heavy       50+     50-64             0              0 6.06666667

mutate()

Note: this also works well with group_by()

pwt_sample %>%
    group_by(gender) %>%
    # divide ttr over the gender's max, for some reason.
  mutate(ttrprop = ttr/max(ttr, na.rm=T)) |> 
  select(ttrprop) |> 
  head()
#> # A tibble: 6 × 2
#> # Groups:   gender [2]
#>   gender ttrprop
#>   <fct>    <dbl>
#> 1 Male    1     
#> 2 Male    0.0769
#> 3 Female  0.0275
#> 4 Male    0.0879
#> 5 Male    0     
#> 6 Male    1

filter()

filter() is a great diagnostic tool for subsetting your data to look at specific observations.

  • Notice the use of double-equal signs (==) for the filter() functions.
pwt_sample %>%
  filter(race== "black") |> 
  head()
#>   id ttr relapse         grp age gender  race employment yearsSmoking
#> 1 16  14       1   patchOnly  66   Male black         pt           54
#> 2 35  77       1   patchOnly  78 Female black      other           56
#> 3 54   2       1   patchOnly  40 Female black         ft           25
#> 4 70   0       1   patchOnly  38   Male black         ft           23
#> 5 84  12       1   patchOnly  64 Female black      other           30
#> 6 85 182       0 combination  51   Male black         ft           35
#>   levelSmoking ageGroup2 ageGroup4 priorAttempts longestNoSmoke
#> 1        heavy       50+       65+             0              0
#> 2        light       50+       65+            10             15
#> 3        heavy     21-49     35-49             4              7
#> 4        light     21-49     35-49            10             90
#> 5        heavy       50+     50-64            12            365
#> 6        heavy       50+     50-64             1              7

Don’t Forget to Assign

When you’re done, don’t forget to assign what you’ve done to an object.

pwt_sample %>%
    group_by(gender) %>%
    # divide ttr over the gender's max, for some reason.
  mutate(ttrprop = ttr/max(ttr, na.rm=T)) |> 
  select(ttrprop) |> 
  head() -> NewObjectName

tidyverse’s greatest feature is the ability to see what you’re coding in real time before commiting/overwrting data.