My Name Is Sara

Introduction

I am writing this for Arvind’s class. Its kind of interesting.

My First Piece of R Code

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(ggformula)

Loading required package: scales

Attaching package: 'scales'

The following object is masked from 'package:purrr':

    discard

The following object is masked from 'package:readr':

    col_factor

Loading required package: ggridges

New to ggformula?  Try the tutorials: 
    learnr::run_tutorial("introduction", package = "ggformula")
    learnr::run_tutorial("refining", package = "ggformula")

library(babynames)

Using the babynames library to get a list of data

babynames

# A tibble: 1,924,665 × 5
    year sex   name          n   prop
   <dbl> <chr> <chr>     <int>  <dbl>
 1  1880 F     Mary       7065 0.0724
 2  1880 F     Anna       2604 0.0267
 3  1880 F     Emma       2003 0.0205
 4  1880 F     Elizabeth  1939 0.0199
 5  1880 F     Minnie     1746 0.0179
 6  1880 F     Margaret   1578 0.0162
 7  1880 F     Ida        1472 0.0151
 8  1880 F     Alice      1414 0.0145
 9  1880 F     Bertha     1320 0.0135
10  1880 F     Sarah      1288 0.0132
# ℹ 1,924,655 more rows

Filtering the data

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

# A tibble: 222 × 5
    year sex   name      n    prop
   <dbl> <chr> <chr> <int>   <dbl>
 1  1880 F     Sara    165 0.00169
 2  1881 F     Sara    147 0.00149
 3  1882 F     Sara    180 0.00156
 4  1883 F     Sara    183 0.00152
 5  1884 F     Sara    197 0.00143
 6  1885 F     Sara    215 0.00151
 7  1886 F     Sara    247 0.00161
 8  1887 F     Sara    214 0.00138
 9  1888 F     Sara    293 0.00155
10  1889 F     Sara    286 0.00151
# ℹ 212 more rows

Creating a graph

babynames %>% filter(name == "Sara") %>% 
  gf_line(n~year)

Sara was a more popular name than I thought it was.

Filtering data and plotting names with different spellings

babynames %>% filter(name == "Sara"| name == "Sarah") %>% gf_line(n~year)

Filtering the data (part 2)

babynames %>% filter(name == "Maya"| name == "Maaya")

# A tibble: 98 × 5
    year sex   name      n       prop
   <dbl> <chr> <chr> <int>      <dbl>
 1  1940 F     Maya      5 0.00000423
 2  1942 F     Maya      5 0.0000036 
 3  1943 F     Maya      5 0.00000348
 4  1949 F     Maya     10 0.0000057 
 5  1950 F     Maya      7 0.00000398
 6  1951 F     Maya      8 0.00000433
 7  1952 F     Maya      7 0.00000368
 8  1953 F     Maya     15 0.00000778
 9  1954 F     Maya      9 0.00000452
10  1955 F     Maya      9 0.00000449
# ℹ 88 more rows

Graph of the received data

babynames %>% filter(name == "Maya"| name == "Maaya") %>% gf_line (n~year)