A glimpse of the Tidyverse
dplyr
tidyr
data wrangling
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.
getwd()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/objectsSometimes 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:
haven::read_dta()for Stata .dta fileshaven::read_spss()for SPSS filesread_csv()for CSV filesreadxl::read_excel()for MS Excel spreadsheetsread_tsv()for tab-separated values.
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:
%>%(the pipe)glimpse()andsummary()select()group_by()summarize()mutate()filter()
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. :6205.0select()
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 1825select()
# 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 0select()
# 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 Femalegroup_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 0Caveat: 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 125summarize()
# 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 182mutate()
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 6.06666667mutate()
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 1filter()
filter() is a great diagnostic tool for subsetting your data to look at specific observations.
- Notice the use of double-equal signs (
==) for thefilter()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 7Don’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() -> NewObjectNametidyverse’s greatest feature is the ability to see what you’re coding in real time before commiting/overwrting data.