The babynames data

Data Manipulation with dplyr

James Chapman

Curriculum Manager, DataCamp

The babynames data

babynames

# A tibble: 332,595 x 3
    year name    number
   <dbl> <chr>    <int>
 1  1880 Aaron      102
 2  1880 Ab           5
 3  1880 Abbie       71
 4  1880 Abbott       5
 5  1880 Abby         6
 6  1880 Abe         50
 7  1880 Abel         9
 8  1880 Abigail     12
 9  1880 Abner       27
10  1880 Abraham     81
# … with 332,585 more rows

Frequency of a name

babynames %>%
  filter(name == "Amy")

# A tibble: 28 x 3
    year name  number
   <dbl> <chr>  <int>
 1  1880 Amy      167
 2  1885 Amy      240
 3  1890 Amy      275
 4  1895 Amy      303
 5  1900 Amy      335
 6  1905 Amy      269
 7  1910 Amy      287
 8  1915 Amy      624
 9  1920 Amy      624
10  1925 Amy      560
# … with 18 more rows

Amy plot

babynames_filtered <- babynames %>%
  filter(name == "Amy")

library(ggplot2)

ggplot(babynames_filtered, aes(x = year, y = number))

Amy plot

babynames_filtered <- babynames %>%
  filter(name == "Amy")

library(ggplot2)

ggplot(babynames_filtered, aes(x = year, y = number)) +
  geom_line()

amy-name-plot

Filter for multiple names

%in%: used to filter for multiple values

babynames_multiple <- babynames %>%
  filter(name %in% c("Amy", "Christopher"))

When was each name most common?

babynames %>%
  group_by(name) %>%
  slice_max(number, n = 1)

# A tibble: 54,881 x 3
# Groups:   name [48,040]
    year name     number
   <dbl> <chr>     <int>
 1  1880 Arch         61
 2  1880 Bird         17
 3  1880 Ednah         6
 4  1880 Erasmus       5
 5  1880 Garfield    122
 6  1880 Harve        17
 7  1880 Lidie         7
 8  1880 Loula        13
 9  1880 Lovisa        5
10  1880 Lulie         8
# … with 54,871 more rows

Let's practice!

Data Manipulation with dplyr