Exercises 6 - dplyr Solutions

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

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6.1 Fix the BabyNameSupp Data

Edit your code chunk that reads the data in from a csv file and add the argument col_types = cols(sex = col_character()) to the read_csv() code.

BabyNamesSupp <- 
  read_csv("https://github.com/jbpost2/Basics-of-R-for-Data-Science-and-Statistics/raw/master/datasets/BabyNamesSupp.csv",
           col_types = cols(sex = col_character()))  # fixes `sex`

6.2 Combining Data Sets with dplyr::bind_rows()

1. Write a quick note below your last R code chunk that mentions that we are going to combine the two data sets into one.

2. Use the bind_rows() function from dplyr to combine BabyNames & BabyNamesSupp via bind_rows(BabyNames, BabyNamesSupp) into an object called BabyNamesFull.

BabyNamesFull <- bind_rows(BabyNames, BabyNamesSupp)

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6.3 Filter and Arrange Rows

• Filter the BabyNamesFull data to include only the names we are interested in (Hint: use %in%). Just have this print out to the console.

• Continue the previous code to then reorder the rows descending on the count variable.

# vector of names
names <- c("Justin", "Alexander", "Jacob", "Anderson", "George")

BabyNamesFull %>%
  filter(name %in% names) %>% 
  head() #This is just added so we don't have too much print out

##        name sex count year
## 1    George   F    26 1880
## 2    George   M  5126 1880
## 3     Jacob   M   404 1880
## 4 Alexander   M   211 1880
## 5  Anderson   M    43 1880
## 6    Justin   M     8 1880

BabyNamesFull %>%
  filter(name %in% names) %>% 
  arrange(desc(count)) %>%
  head() #Added for less printing

##     name sex count year
## 1  Jacob   M 35997 1998
## 2  Jacob   M 35344 1999
## 3 Justin   M 35034 1988
## 4  Jacob   M 34462 2000
## 5  Jacob   M 34140 1997
## 6 Justin   M 32842 1989

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