## ----eval=FALSE---------------------------------------------------------------
# name <- civis::users_list_me()$name
# paste(name, "is really awesome!")

## ----eval=FALSE---------------------------------------------------------------
# library(civis)
# 
# # First we'll load a dataframe of the famous iris dataset
# data(iris)
# 
# # We'll set a default database and define the table where want to
# # store our data
# options(civis.default_db = "my_database")
# iris_tablename <- "my_schema.my_table"
# 
# # Next we'll push the data to the database table
# write_civis(iris, iris_tablename)
# 
# # Great, now let's read it back
# df <- read_civis(iris_tablename)
# 
# # Hmmm, I'm more partial to setosa myself. Let's write a custom sql query.
# # We'll need to wrap our query string in `sql` to let read_civis know we
# # are passing in a sql command rather than a tablename.
# query_str <- paste("SELECT * FROM", iris_tablename, "WHERE Species = 'setosa'")
# iris_setosa <- read_civis(sql(query_str))
# 
# # Now let's store this data along with a note as a serialized R object
# # on a remote file system. We could store any object remotely this way.
# data <- list(data = iris_setosa, special_note = "The best iris species")
# file_id <- write_civis_file(data)
# 
# # Finally, let's read back our data from the remote file system.
# data2 <- read_civis(file_id)
# data2[["special_note"]]
# 
# ## [1] "The best iris species"

## ----eval=FALSE---------------------------------------------------------------
# library(civis)
# 
# # It really is a great dataset
# data(iris)
# 
# # Gradient boosting or random forest, who will win?
# gb_model <- civis_ml_gradient_boosting_classifier(iris, "Species")
# rf_model <- civis_ml_random_forest_classifier(iris, "Species")
# macroavgs <- list(gb_model = gb_model$metrics$metrics$roc_auc_macroavg,
#                   rf_model = rf_model$metrics$metrics$roc_auc_macroavg)
# macroavgs
# 
# ## $gb_model
# ## [1] 0.9945333
# ##
# ## $rf_model
# ## [1] 0.9954667