Exercises 9 - Numeric Summaries Solutions

We’ll continue to work on the same .Rmd file from the previous exercise.

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9.1 Summarize Counts

1. We’ll consider the names of interest we had from before. Filter the data to only include those names then group the data by name and sex. Now use summarize() to create a new variable called total that is the sum of the counts (remove NA’s with na.rm = TRUE).

names <- c("Justin", "George", "Alexander", "Jacob", "Anderson")
BabyNamesFull %>%
    filter(name %in% names) %>%
    group_by(name, sex) %>%                          
    summarise(total = sum(count, na.rm = TRUE)) 

## `summarise()` has grouped output by 'name'. You can override using the `.groups` argument.

## # A tibble: 10 x 3
## # Groups:   name [5]
##    name      sex     total
##    <chr>     <chr>   <dbl>
##  1 Alexander F        4466
##  2 Alexander M      684145
##  3 Anderson  F         950
##  4 Anderson  M       28016
##  5 George    F        9942
##  6 George    M     1470178
##  7 Jacob     F        2259
##  8 Jacob     M      941483
##  9 Justin    F        3797
## 10 Justin    M      778247

1. Filter the BabyNamesFull data object to only include rows where the count is more than 50000. Save this as an R object. Then, create a contingency table to count the number of times each name appears.

temp <- BabyNamesFull %>%
  filter(count >50000)
table(temp$name)

## 
##      Ashley Christopher       David     Deborah       Debra       James 
##           1          12          25           3           1          49 
##       Jason    Jennifer     Jessica        John       Linda        Lisa 
##           6          14           3          49          10           5 
##        Mark        Mary     Matthew     Michael    Patricia     Richard 
##           6          46           1          46           4          12 
##      Robert     William 
##          51          26

1. Let’s investigate the total number of names in each year. Group the data by year and find the sum of the count variable. Once you’ve done that, run summary() on the total counts.

temp <- BabyNamesFull %>% 
  group_by(year) %>%
  summarize(total = sum(count))
summary(temp$total)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  192700 1728436 3092162 2535652 3677142 4199919

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