Read in the data from source.

BIRTHS <- readr::read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/births/US_births_1994-2003_CDC_NCHS.csv")
## Parsed with column specification:
## cols(
##   year = col_double(),
##   month = col_double(),
##   date_of_month = col_double(),
##   day_of_week = col_double(),
##   births = col_double()
## )

Replace day_of_week with name of day instead of number.

BIRTHS <- dplyr::mutate(BIRTHS, day_of_week = replace(day_of_week, day_of_week == "1", "Monday"))
BIRTHS <- dplyr::mutate(BIRTHS, day_of_week = replace(day_of_week, day_of_week == "2", "Tuesday"))
BIRTHS <- dplyr::mutate(BIRTHS, day_of_week = replace(day_of_week, day_of_week == "3", "Wednesday"))
BIRTHS <- dplyr::mutate(BIRTHS, day_of_week = replace(day_of_week, day_of_week == "4", "Thursday"))
BIRTHS <- dplyr::mutate(BIRTHS, day_of_week = replace(day_of_week, day_of_week == "5", "Friday"))
BIRTHS <- dplyr::mutate(BIRTHS, day_of_week = replace(day_of_week, day_of_week == "6", "Saturday"))
BIRTHS <- dplyr::mutate(BIRTHS, day_of_week = replace(day_of_week, day_of_week == "7", "Sunday"))
BIRTHS
## # A tibble: 3,652 x 5
##     year month date_of_month day_of_week births
##    <dbl> <dbl>         <dbl> <chr>        <dbl>
##  1  1994     1             1 Saturday      8096
##  2  1994     1             2 Sunday        7772
##  3  1994     1             3 Monday       10142
##  4  1994     1             4 Tuesday      11248
##  5  1994     1             5 Wednesday    11053
##  6  1994     1             6 Thursday     11406
##  7  1994     1             7 Friday       11251
##  8  1994     1             8 Saturday      8653
##  9  1994     1             9 Sunday        7910
## 10  1994     1            10 Monday       10498
## # ... with 3,642 more rows

Add a column in the data to hold the name of the month, based on the number of the month.

BIRTHS <- dplyr::mutate(BIRTHS, monthName = replace(month, month == "1", "Janruary"))
BIRTHS <- dplyr::mutate(BIRTHS, monthName = replace(month, month == "2", "February"))
BIRTHS <- dplyr::mutate(BIRTHS, monthName = replace(month, month == "3", "March"))
BIRTHS <- dplyr::mutate(BIRTHS, monthName = replace(month, month == "4", "April"))
BIRTHS <- dplyr::mutate(BIRTHS, monthName = replace(month, month == "5", "May"))
BIRTHS <- dplyr::mutate(BIRTHS, monthName = replace(month, month == "6", "June"))
BIRTHS <- dplyr::mutate(BIRTHS, monthName = replace(month, month == "7", "July"))
BIRTHS <- dplyr::mutate(BIRTHS, monthName = replace(month, month == "8", "August"))
BIRTHS <- dplyr::mutate(BIRTHS, monthName = replace(month, month == "9", "September"))
BIRTHS <- dplyr::mutate(BIRTHS, monthName = replace(month, month == "10", "October"))
BIRTHS <- dplyr::mutate(BIRTHS, monthName = replace(month, month == "11", "November"))
BIRTHS <- dplyr::mutate(BIRTHS, monthName = replace(month, month == "12", "December"))
BIRTHS
## # A tibble: 3,652 x 6
##     year month date_of_month day_of_week births monthName
##    <dbl> <dbl>         <dbl> <chr>        <dbl> <chr>    
##  1  1994     1             1 Saturday      8096 1        
##  2  1994     1             2 Sunday        7772 1        
##  3  1994     1             3 Monday       10142 1        
##  4  1994     1             4 Tuesday      11248 1        
##  5  1994     1             5 Wednesday    11053 1        
##  6  1994     1             6 Thursday     11406 1        
##  7  1994     1             7 Friday       11251 1        
##  8  1994     1             8 Saturday      8653 1        
##  9  1994     1             9 Sunday        7910 1        
## 10  1994     1            10 Monday       10498 1        
## # ... with 3,642 more rows

Birth data arranged by the number of births, and stored in ARG_BIRTHS.

ARG_BIRTHS <- dplyr::arrange(BIRTHS, desc(births))
ARG_BIRTHS
## # A tibble: 3,652 x 6
##     year month date_of_month day_of_week births monthName
##    <dbl> <dbl>         <dbl> <chr>        <dbl> <chr>    
##  1  1999     9             9 Thursday     14540 9        
##  2  2003    12            30 Tuesday      14438 December 
##  3  2003     9            16 Tuesday      14145 9        
##  4  2003     9             3 Wednesday    14119 9        
##  5  2003     9            23 Tuesday      14036 9        
##  6  2002     9            12 Thursday     13982 9        
##  7  2001    12            28 Friday       13918 December 
##  8  2003     9            10 Wednesday    13908 9        
##  9  2002     9            24 Tuesday      13884 9        
## 10  2002     9            17 Tuesday      13883 9        
## # ... with 3,642 more rows

Group birth data by each month in the over all dataset, and get the total number of births in those months. Then arrange the data by the month. Then plot the data.

MONTHLY_BIRTHS <- BIRTHS %>%
                  group_by(month) %>%
                    summarise(monthlyBirths = sum(births)) %>%
                    arrange(month)

plot(MONTHLY_BIRTHS$monthlyBirths, type = "o", col = "red", xlab = "Month", ylab = "Number of births",
     main = "NUMBER OF BIRTHS PER MONTH")

Group birth data based on each year, and sum the total births for that year.

YEARLY_BIRTHS <- BIRTHS %>%
  group_by(year) %>%
  summarise(yearlyBirths = sum(year)) %>%
  arrange(year)
  
YEARLY_BIRTHS
## # A tibble: 10 x 2
##     year yearlyBirths
##    <dbl>        <dbl>
##  1  1994       727810
##  2  1995       728175
##  3  1996       730536
##  4  1997       728905
##  5  1998       729270
##  6  1999       729635
##  7  2000       732000
##  8  2001       730365
##  9  2002       730730
## 10  2003       731095