load packages needed

library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.2     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── 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

read the birthweight data

birthweight <- read.csv("data/birthweight_data.csv")

calculate the mean birthweight separately for twins and singletons

birthweight_summary <- birthweight %>% 
  group_by(plurality) %>% 
  summarise(
    mean_weight = mean(birthweight),
    sd_weight = sd(birthweight)
  ) %>% 
  ungroup ()

view the output table

birthweight_summary
## # A tibble: 2 × 3
##   plurality mean_weight sd_weight
##   <chr>           <dbl>     <dbl>
## 1 singleton       3248.      570.
## 2 twin            2311.      593.

5. identify the earliest (i.e. the minimum value) gestational age for each ethicity group

earliest_gestational <- birthweight %>% 
  group_by(child_ethn) %>% 
  summarise(
    min_gestational = min(as.numeric(gestation_age_w), na.rm = TRUE)
  ) %>% 
  ungroup ()
## Warning: There were 2 warnings in `summarise()`.
## The first warning was:
## ℹ In argument: `min_gestational = min(as.numeric(gestation_age_w), na.rm =
##   TRUE)`.
## ℹ In group 3: `child_ethn = "Caucasian"`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.

view output table

earliest_gestational
## # A tibble: 10 × 2
##    child_ethn                        min_gestational
##    <chr>                                       <dbl>
##  1 Aboriginal/Torres Strait Islander              33
##  2 African/African-American                       26
##  3 Caucasian                                      26
##  4 East Asian                                     33
##  5 Hispanic/Latino                                37
##  6 Middle-Eastern                                 28
##  7 Missing                                        36
##  8 Polynesian/Melanesian                          28
##  9 South Asian                                    28
## 10 South-East Asian                               29

7. download a picture of a baby from the internet and insert it into your document below

Baby
Baby

write summary to file

write_csv(birthweight_summary, "mean_birthweight_summary.csv")

Knit document and publish to RPubs