Data_IO_Lab Key

In this lab you can use the interactive console to explore or Knit the document. Remember anything you type here can be “sent” to the console with Cmd-Enter (OS-X) or Ctrl-Enter (Windows/Linux) in an R code chunk.

Part 1

1. Read in the Youth Tobacco study from URL http://jhudatascience.org/intro_to_R_class/data/Youth_Tobacco_Survey_YTS_Data.csv and assign it to a variable named youth.

library(readr)
youth <- read_csv("http://jhudatascience.org/intro_to_R_class/data/Youth_Tobacco_Survey_YTS_Data.csv")

## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   .default = col_character(),
##   YEAR = col_double(),
##   Data_Value = col_double(),
##   Data_Value_Std_Err = col_double(),
##   Low_Confidence_Limit = col_double(),
##   High_Confidence_Limit = col_double(),
##   Sample_Size = col_double(),
##   DisplayOrder = col_double()
## )
## ℹ Use `spec()` for the full column specifications.

2. Use head() to display the first few rows of the data frame.

head(youth)

## # A tibble: 6 x 31
##    YEAR LocationAbbr LocationDesc TopicType  TopicDesc  MeasureDesc   DataSource
##   <dbl> <chr>        <chr>        <chr>      <chr>      <chr>         <chr>     
## 1  2015 AZ           Arizona      Tobacco U… Cessation… Percent of C… YTS       
## 2  2015 AZ           Arizona      Tobacco U… Cessation… Percent of C… YTS       
## 3  2015 AZ           Arizona      Tobacco U… Cessation… Percent of C… YTS       
## 4  2015 AZ           Arizona      Tobacco U… Cessation… Quit Attempt… YTS       
## 5  2015 AZ           Arizona      Tobacco U… Cessation… Quit Attempt… YTS       
## 6  2015 AZ           Arizona      Tobacco U… Cessation… Quit Attempt… YTS       
## # … with 24 more variables: Response <chr>, Data_Value_Unit <chr>,
## #   Data_Value_Type <chr>, Data_Value <dbl>, Data_Value_Footnote_Symbol <chr>,
## #   Data_Value_Footnote <chr>, Data_Value_Std_Err <dbl>,
## #   Low_Confidence_Limit <dbl>, High_Confidence_Limit <dbl>, Sample_Size <dbl>,
## #   Gender <chr>, Race <chr>, Age <chr>, Education <chr>, GeoLocation <chr>,
## #   TopicTypeId <chr>, TopicId <chr>, MeasureId <chr>, StratificationID1 <chr>,
## #   StratificationID2 <chr>, StratificationID3 <chr>, StratificationID4 <chr>,
## #   SubMeasureID <chr>, DisplayOrder <dbl>

3. Use spec() to display the list of column names and their type specification.

spec(youth)

## cols(
##   YEAR = col_double(),
##   LocationAbbr = col_character(),
##   LocationDesc = col_character(),
##   TopicType = col_character(),
##   TopicDesc = col_character(),
##   MeasureDesc = col_character(),
##   DataSource = col_character(),
##   Response = col_character(),
##   Data_Value_Unit = col_character(),
##   Data_Value_Type = col_character(),
##   Data_Value = col_double(),
##   Data_Value_Footnote_Symbol = col_character(),
##   Data_Value_Footnote = col_character(),
##   Data_Value_Std_Err = col_double(),
##   Low_Confidence_Limit = col_double(),
##   High_Confidence_Limit = col_double(),
##   Sample_Size = col_double(),
##   Gender = col_character(),
##   Race = col_character(),
##   Age = col_character(),
##   Education = col_character(),
##   GeoLocation = col_character(),
##   TopicTypeId = col_character(),
##   TopicId = col_character(),
##   MeasureId = col_character(),
##   StratificationID1 = col_character(),
##   StratificationID2 = col_character(),
##   StratificationID3 = col_character(),
##   StratificationID4 = col_character(),
##   SubMeasureID = col_character(),
##   DisplayOrder = col_double()
## )

4. Load the readxl package with the library() command.

If it is not installed, install it via: RStudio --> Tools --> Install Packages. You can also try install.packages("readxl").

library(readxl)

5. Download the dataset of monuments from: http://jhudatascience.org/intro_to_R_class/data/Monuments.xlsx file to Monuments.xlsx.

download.file("http://jhudatascience.org/intro_to_R_class/data/Monuments.xlsx",
              destfile = "Monuments.xlsx",
              overwrite = TRUE, 
              mode = "wb")

6. Use the read_excel() function in the readxl package to read the Monuments.xlsx file and call the output mon.

mon <- read_excel("Monuments.xlsx")

Part 2

7. Learn your working directory by running getwd()

getwd()

## [1] "/Users/avahoffman/Dropbox/JHSPH/intro_to_r/Data_IO/lab"

8. Write out the mon object as a CSV file calling it “monuments.csv”, using write_csv(). Where is the file now?

write_csv(mon, "monuments.csv")

Bonus

9. Write one of the variables from the Basic R lab to your working directory in rds format. Call the file my_variable.rds.

y <- c(10, 20, 30, 40, 50, 60)
write_rds(y, "my_variable.rds")