## Loading, Saving, Converting Data -- the finished follow-along script ## This is the completed version of the script built on the guide page. ## It runs from top to bottom, repeatedly, without errors: the deliberate ## error and prompt lines from the page are commented out below (each with ## a note), and the saves use overwrite = TRUE. Run the whole file with the ## Source icon, to the right of Run in the script toolbar. ## ---- 1. Load the package and a dataset --------------------------------- ## Load the jstats package (needed once per R session) library(jstats) ## Load the package dataset called community ## It's loaded as a data frame into the Environment jload("community") ## ---- 2. Objects, the assignment operator, and the Global Environment ---- ## "Objects" other than data frames can go into the Environment ## The "Assignment Operator" (less-than followed by a dash) is a key ## base R fundamental ## Take the thing on the right and assign it to what's on the left ## Another base R fundamental is how to refer to one variable within ## a data frame ## Take the Region variable from the community data frame and assign it ## to a new object ## The new object we'll call "MyObject" MyObject <- community$Region # base R ## The name on the left is entirely yours to choose, and the thing on the ## right can be anything at all -- here, just the number 5 x <- 5 ## You are not limited to one dataset at a time ## Load a second package dataset, clinic, alongside community jload("clinic") ## ---- 3. Cleaning up ---------------------------------------------------- ## Remove MyObject from the Environment ## Note that this is a base R function (j is not the first letter) rm(MyObject) # base R ## Remove x as well rm(x) ## Remove the community data frame rm(community) ## Remove the clinic data frame rm(clinic) ## ---- 4. How much output you see ---------------------------------------- ## The output level is set to "standard" by default ## After you learn more about jstats (and R) you might not want all of ## that output joutput("minimal") ## With minimal output, you get less information jload("community") ## Set the output level back to standard joutput("standard") ## ---- 5. Functions with no arguments, and getting help ------------------ ## A few functions work correctly without input between the parentheses, ## but most require input ## On the guide page, the next line demonstrates one of jstats's error ## messages -- it is disabled here so this file runs without errors # jload() ## But this one will show you the current output settings for jstats joutput() ## Here's a base R function that works without arguments/input ## List the objects/data frames in the Environment ls() # base R ## Here's another one from base R ## A working directory is the "path" to where your project is stored ## on your computer ## If you're not working in a project, you have to set this yourself (finicky) getwd() # base R ## The technical name for a function's inputs are "Arguments" ## View help files describing necessary and optional Arguments by running... ?jload ## The same ? help works for base R functions and functions from any ## installed package ?ls ## And this one opens the package overview: every jstats function, ## grouped by purpose ?jstats ## ---- 6. The overwrite prompt ------------------------------------------- ## On the guide page, the next line demonstrates the y/n overwrite prompt in ## the Console -- it is disabled here so an unattended run doesn't pause # jload("community") ## Set an optional overwrite argument/input to overwrite without the prompt jload("community", overwrite = TRUE) ## ---- 7. Saving to a Data folder ---------------------------------------- ## Use the assignment operator to copy community to a new data frame ## called MyData MyData <- community ## Save it with no folder specified -- where does the file go? ## The console message shows the full path: it landed in your project's ## working directory, the default location ## It's the same path the base R getwd() command showed you earlier jsave(MyData, "WhereDidIGo.rds", overwrite = TRUE) ## Delete that test file ## file.remove() is base R -- the file-on-disk equivalent of the rm() ## you used for data frames. It replies with a cryptic [1] TRUE file.remove("WhereDidIGo.rds") # base R ## Now name a Data folder of your own ## jstats creates the folder right away (if it isn't already there) joptions(data.dir = "Data") ## Save the dataset to the Data folder and call it MyData_r.rds ## Specify R format with the .rds extension jsave(MyData, "MyData_r.rds", overwrite = TRUE) ## ---- 8. Gone from the Environment -------------------------------------- ## Remove both data frames from the Environment ## What happens to the file you just saved? The next block finds out rm(MyData) # base R rm(community) ## ---- 9. Getting it back, and saving across formats --------------------- ## The file survived: jload() brings the saved dataset straight back ## (overwrite = TRUE in case an earlier run left MyData_r in the Environment) jload("MyData_r.rds", overwrite = TRUE) ## The original community dataset carries SPSS-style missing values ## So it saves to SPSS format (.sav) without a problem jsave(MyData_r, "MyData_spss.sav", overwrite = TRUE) ## But the SPSS-style missing-value codes need to be converted ## if you want to save to a format other than R or SPSS ## On the guide page, the next line demonstrates that error -- ## it is disabled here so this file runs without errors # jsave(MyData_r, "MyData_stata.dta") ## Convert to Stata format (.dta) MyData_stata <- jconvert(MyData_r, to = "stata") jsave(MyData_stata, "MyData_stata.dta", overwrite = TRUE) # No error ## ---- 10. Reloading, and how missing codes travel ----------------------- ## You removed data frames one at a time earlier; this clears them all at once ## It is the code equivalent of the broom button in the Environment pane rm(list = ls()) # base R ## Load all data sets back jload("MyData_r.rds") jload("MyData_spss.sav") jload("MyData_stata.dta") ## Examine how the missing codes are kept in the different formats jfreq(MyData_r, Education) # will print whatever was originally stored jfreq(MyData_spss, Education) # SPSS-style declared missing codes jfreq(MyData_stata, Education) # Stata-style declared missing codes MyData_r_stata <- jconvert(MyData_r, to = "stata") jfreq(MyData_r_stata, Education) # Now the codes display in Stata format ## ---- 11. Turning up the digits ----------------------------------------- ## By default, jstats shows 3 decimal places jdesc(MyData_r, Age) # note how many decimals the Age mean shows here ## Turn the display up to the maximum of 7 decimal places joutput(digits = 7) jdesc(MyData_r, Age) # the same mean, now with more decimals shown ## ---- 12. The proof that nothing was lost ------------------------------- ## Ask for the mean of Age from each of the three reloaded data frames jdesc(MyData_r, Age) jdesc(MyData_spss, Age) jdesc(MyData_stata, Age) ## All three report the identical mean, to the last decimal: ## same data, three file formats, nothing lost along the way ## And one base R caution, shown live (the guide page explains): ## Income carries declared missing-value codes (-99, -98) jdesc(MyData_spss, Income) # the correct mean -- jdesc leaves the codes out ## Now ask base R's mean() for the same column. It knows nothing about the ## declarations, so the -99s and -98s go straight into the average mean(MyData_spss$Income) # base R -- a poisoned mean ## Set the display back to the default before you go joutput(digits = 3) ## Put the data folder setting back to its default (the working directory) too ## NULL clears the setting ## A finished script leaves your session exactly as it found it joptions(data.dir = NULL)