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:
data("gun_deaths")
gun_deaths## # A tibble: 100,798 x 10
## id year month intent police sex age
## <int> <int> <dbl> <chr> <int> <chr> <int>
## 1 1 2012 1 Suicide 0 M 34
## 2 2 2012 1 Suicide 0 F 21
## 3 3 2012 1 Suicide 0 M 60
## 4 4 2012 2 Suicide 0 M 64
## 5 5 2012 2 Suicide 0 M 31
## 6 6 2012 2 Suicide 0 M 17
## 7 7 2012 2 Undetermined 0 M 48
## 8 8 2012 3 Suicide 0 M 41
## 9 9 2012 2 Accidental 0 M 50
## 10 10 2012 2 Suicide 0 M NA
## # ... with 100,788 more rows, and 3 more variables: race <chr>,
## # place <chr>, education <fctr>
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()))## # A tibble: 5 x 2
## education n
## <fctr> <int>
## 1 Less than HS 2858
## 2 HS/GED 7912
## 3 Some college 4258
## 4 BA+ 3029
## 5 NA 285Hint: make sure to save a copy of gun_deaths as gun_deaths2 for this code chunk.
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
## <fctr> <int>
## 1 Less than HS 2858
## 2 HS/GED 7912
## 3 Some college 4258
## 4 BA+ 3029
## 5 NA 285
summarize(
group_by(
filter(gun_deaths, sex == "M", race == "White", year == 2012),
education),
n = n()
)## # A tibble: 5 x 2
## education n
## <fctr> <int>
## 1 Less than HS 2858
## 2 HS/GED 7912
## 3 Some college 4258
## 4 BA+ 3029
## 5 NA 285
gun_deaths %>%
filter(sex == "M", race == "White", year == 2012) %>%
group_by(education) %>%
summarize(n = n())## # A tibble: 5 x 2
## education n
## <fctr> <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
## <fctr> <int>
## 1 Less than HS 2858
## 2 HS/GED 7912
## 3 Some college 4258
## 4 BA+ 3029
## 5 NA 285Note that all methods produce the same answer. But which did you find easiest to implement?
devtools::session_info()## Session info -------------------------------------------------------------## setting value
## version R version 3.4.3 (2017-11-30)
## system x86_64, darwin15.6.0
## ui X11
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## tz America/Chicago
## date 2018-04-04## Packages -----------------------------------------------------------------## package * version date source
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## yaml 2.1.18 2018-03-08 CRAN (R 3.4.4)This work is licensed under the CC BY-NC 4.0 Creative Commons License.