library(dplyr)
## Warning: package 'dplyr' was built under R version 4.3.3
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(dtplyr)
## Warning: package 'dtplyr' was built under R version 4.3.3
library(magrittr)
## Warning: package 'magrittr' was built under R version 4.3.3

Data set is retrieved from GITHUB and saved to data frame US_Births.

Created data frame for the year 1999

births_1999 <- US_Births %>% 
  filter(year == 1999)

Created a data frame called months_table with an index and the months of the year. This table will be joined to the the original data frame births_1999.

months_txt <- c("january", "february", "march", "april", "may", "june", "july", "august", "september", "october", "november", "december")

months_id <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)

months_table <- data.frame(months_id, months_txt)

Created a data frame called days_table with an index and the days of the week. This table will be joined to the the original data frame births_1999.

days_id <- c(1, 2, 3, 4, 5, 6, 7)

days_txt <- c("monday", "tuesday", "wednesday", "thursday", "friday", "saturday", "sunday")

days_table <- data.frame(days_id, days_txt)

Joining data frames days_table and months_table to the original data frame births_1999.

births_1999 <- births_1999 %>% inner_join(months_table, by = c("month" = "months_id"))

births_1999 <- births_1999 %>% inner_join(days_table, by = c("day_of_week" = "days_id"))

conclusion: The data frame (births_1999) used here is is a subset of the original data set. It contains information for the year 1999. The month and the day_of_week columns contains numbers that represented a month of the year, or a day of the week. Two character columns were added to clearly show the month and days. This makes for better reading.