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✖ dplyr::filter() masks stats::filter()
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library(gt)library(wordcloud2)library(babynames)
filter(babynames, year ==1998)
# A tibble: 27,899 × 5
year sex name n prop
<dbl> <chr> <chr> <int> <dbl>
1 1998 F Emily 26181 0.0135
2 1998 F Hannah 21373 0.0110
3 1998 F Samantha 20193 0.0104
4 1998 F Sarah 19879 0.0103
5 1998 F Ashley 19874 0.0103
6 1998 F Alexis 19778 0.0102
7 1998 F Taylor 18572 0.00958
8 1998 F Jessica 18235 0.00941
9 1998 F Madison 17786 0.00918
10 1998 F Elizabeth 15186 0.00784
# ℹ 27,889 more rows
babynames |>filter(year ==1998) |>slice_max(prop, n =5)
# A tibble: 5 × 5
year sex name n prop
<dbl> <chr> <chr> <int> <dbl>
1 1998 M Michael 36614 0.0181
2 1998 M Jacob 36014 0.0178
3 1998 M Matthew 31142 0.0154
4 1998 M Joshua 28088 0.0139
5 1998 F Emily 26181 0.0135
babynames |>filter(year ==1998, sex =="F") |>mutate(percent =round(prop *100, 1)) |>slice_max(prop, n =10) |>select(year, sex, name, percent) |>gt() |>tab_header(title ="Most common girl names in 1998")
babynames |>filter(year ==1998) |># use only one yearfilter(sex =="F") |># use only one sexslice_max(prop, n=100) |># use the top 100 namesselect(name, n) |># select the two relevant variables: the name and how often it occurswordcloud2(size = .5, shape ="square", color ="red") +WCtheme(1) # generate the word cloud
Create a word cloud with a year and sex of your choice, size = .5, red colored words, a square shape, and theme 1. Graphing changes in popularity over time
babynames |># start with the datafilter(name =="Naomi", sex =="F") |># choose the name and sexggplot(aes(x = year, y = prop)) +# put year on the x-axis and prop (proportion) on ygeom_line() # make it a line graph
babynames |># start with the datafilter(name =="Naomi", sex =="F") |># choose the name and sexmutate(percent =round(prop *100, 1)) |># create a new variable called percentggplot(aes(x = year, y = percent)) +# put year on the x-axis and prop (proportion) on ygeom_line(color ="blue") # make it a line graph and give the line a color
I was born in 1998. My name was most popular in the years 1917-1925, with a drop off in 1950’s, and re-spiked in 2010. In both these spikes, the name only hit a 0.2 percent in popularity.
babynames |># Start with the datasetfilter(name =="Naomi", sex =="F") |># only look at the name you wantslice_max(prop, n =1) # get the year with the top number for that name
# A tibble: 1 × 5
year sex name n prop
<dbl> <chr> <chr> <int> <dbl>
1 2017 F Naomi 3823 0.00204
Denton was born in 1997 so the uptrend fits.
babynames |># Start with the datasetfilter(name =="Denton", sex =="M") |># only look at the name and sex you wantslice_max(prop, n =10) # get the top 10 names
# A tibble: 10 × 5
year sex name n prop
<dbl> <chr> <chr> <int> <dbl>
1 1897 M Denton 9 0.0000738
2 1881 M Denton 7 0.0000646
3 1908 M Denton 10 0.0000601
4 1888 M Denton 6 0.0000462
5 1942 M Denton 65 0.0000462
6 1911 M Denton 11 0.0000456
7 1914 M Denton 31 0.0000454
8 1913 M Denton 24 0.0000448
9 1935 M Denton 47 0.0000440
10 1994 M Denton 89 0.0000437
Copy and paste the chunk above and try a few different names and sexes. Could you have guessed when people you know were born? Denton yes, Adelle no, John yes, Sondra yes,
babynames |>filter(name =="Sondra") |>ggplot(aes(x = year, y = n)) +geom_line()
The name Alice peaked in popularity 55years after the release of the book Alice in Wonderland, had another spike around the 1950’s when the animated Disney version came out, and yet another spike in popularity in 2010 when the live action version was released.
The name Jenny peaked around the 1970’s and 80’s and was slowly becomming less popular even with the release of award winning movie “Forest Gump”.
The name Naomi was most popular around the end of this study and the chart had started trending up in the 70’s making it more and more likely around 1998 that I would be given the name as well.
babynames |>filter(name =="Naomi"| name =="Jenny"| name =="Alice") |>filter(sex =="F") |>ggplot(aes(x = year, y = n, color = name)) +geom_line()
It looks like the name Diana peaked in the 1950’s just before the time princess Diana was born and has slowly gone down in popularity over time. The name Jennifer was also most popular around the time she was born, however, it wasn’t as popular when and after she became famous for starring on the hit show “Friends”. Hannah, a pseudonym for Miley Cyrus’s show “Hannah Montana” had also peaked in popularity just before the beginning of her show and her becommnig famous.
babynames |>filter(name =="Jennifer"| name =="Hannah"| name =="Diana") |>filter(sex =="F") |>filter(year >1940) |>ggplot(aes(x = year, y = n, color = name)) +geom_line()
