Practice the pipe
library(tidyverse)
library(rcfss)
Using gun_deaths from the rcfss library, answer the following question:
For each education category, how many white males where killed in 2012?
Write your code using all four methods:
- Intermediate steps
- Overwrite the original
- Function composition
Piping
data("gun_deaths") gun_deaths## # A tibble: 100,798 x 10 ## id year month intent police sex age race place education ## <dbl> <dbl> <chr> <chr> <dbl> <chr> <dbl> <chr> <chr> <fct> ## 1 1 2012 Jan Suicide 0 M 34 Asian/Pacif… Home BA+ ## 2 2 2012 Jan Suicide 0 F 21 White Street Some col… ## 3 3 2012 Jan Suicide 0 M 60 White Other s… BA+ ## 4 4 2012 Feb Suicide 0 M 64 White Home BA+ ## 5 5 2012 Feb Suicide 0 M 31 White Other s… HS/GED ## 6 6 2012 Feb Suicide 0 M 17 Native Amer… Home Less tha… ## 7 7 2012 Feb Undeter… 0 M 48 White Home HS/GED ## 8 8 2012 Mar Suicide 0 M 41 Native Amer… Home HS/GED ## 9 9 2012 Feb Acciden… 0 M 50 White Other s… Some col… ## 10 10 2012 Feb Suicide 0 M NA Black Home <NA> ## # … with 100,788 more rows
Intermediate steps
Click for the solution
gun_deaths1 <- filter(gun_deaths, sex == "M", race == "White", year == 2012)
gun_deaths2 <- group_by(gun_deaths1, education)
(gun_deaths3 <- summarize(gun_deaths2, n = n()))
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 5 x 2
## education n
## <fct> <int>
## 1 Less than HS 2858
## 2 HS/GED 7912
## 3 Some college 4258
## 4 BA+ 3029
## 5 <NA> 285
Overwrite the original
Hint: make sure to save a copy of gun_deaths as gun_deaths2 for this code chunk.
Click for the solution
gun_deaths2 <- gun_deaths # copy for demonstration purposes
gun_deaths2 <- filter(gun_deaths2, sex == "M", race == "White", year == 2012)
gun_deaths2 <- group_by(gun_deaths2, education)
(gun_deaths2 <- summarize(gun_deaths2, n = n()))
## # A tibble: 5 x 2
## education n
## <fct> <int>
## 1 Less than HS 2858
## 2 HS/GED 7912
## 3 Some college 4258
## 4 BA+ 3029
## 5 <NA> 285
Function composition
Click for the solution
summarize(
group_by(
filter(gun_deaths, sex == "M", race == "White", year == 2012),
education
),
n = n()
)
## # A tibble: 5 x 2
## education n
## <fct> <int>
## 1 Less than HS 2858
## 2 HS/GED 7912
## 3 Some college 4258
## 4 BA+ 3029
## 5 <NA> 285
Piped operation
Click for the solution
gun_deaths %>%
filter(sex == "M", race == "White", year == 2012) %>%
group_by(education) %>%
summarize(n = n())
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 5 x 2
## education n
## <fct> <int>
## 1 Less than HS 2858
## 2 HS/GED 7912
## 3 Some college 4258
## 4 BA+ 3029
## 5 <NA> 285
# alternative using count()
gun_deaths %>%
filter(sex == "M", race == "White", year == 2012) %>%
count(education)
## # A tibble: 5 x 2
## education n
## <fct> <int>
## 1 Less than HS 2858
## 2 HS/GED 7912
## 3 Some college 4258
## 4 BA+ 3029
## 5 <NA> 285
Note that all methods produce the same answer. But which did you find easiest to implement?
Session Info
devtools::session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.0.4 (2021-02-15)
## os macOS Big Sur 10.16
## system x86_64, darwin17.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/Chicago
## date 2021-05-25
##
## ─ Packages ───────────────────────────────────────────────────────────────────
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