juliasilge · juliasilge · Apr 17, 2023
diff --git a/chapters/chapter1.md b/chapters/chapter1.md
@@ -57,7 +57,7 @@ The first time you run a code exercise, it may take a little while for your Dock
 
 **Wherever you see `___` in a code exercise, replace it with the correct code as instructed. Run the code (via the button) to see if it will run, and submit it (via the other button) to check if it's correct.**
 
-The `tidyverse` metapackage is loaded for you, so you can use readr and ggplot2. 
+The tidyverse metapackage is loaded for you, so you can use ggplot2 and other functions for exploratory data analysis. The arrow package is also loaded, so you can read our data stored as a [Parquet](https://parquet.apache.org/) file. 
 
 - Take a look at the `cars2018` object using `glimpse()`.
 - Use the appropriate column from the data set in the call to `aes()` so you can plot a histogram of fuel efficiency (miles per gallon, `mpg`).

diff --git a/data/c1_car_vars.parquet b/data/c1_car_vars.parquet
diff --git a/data/c1_car_vars.rds b/data/c1_car_vars.rds
diff --git a/data/c1_fit_lm.rds b/data/c1_fit_lm.rds
diff --git a/data/c1_fit_rf.rds b/data/c1_fit_rf.rds
diff --git a/data/c1_lm_res.rds b/data/c1_lm_res.rds
diff --git a/data/c1_rf_res.rds b/data/c1_rf_res.rds
diff --git a/data/c1_test.parquet b/data/c1_test.parquet
diff --git a/data/c1_test.rds b/data/c1_test.rds
diff --git a/data/c1_train.parquet b/data/c1_train.parquet
diff --git a/data/c1_train.rds b/data/c1_train.rds
diff --git a/data/c1_train_10_percent.parquet b/data/c1_train_10_percent.parquet
diff --git a/data/c1_train_10_percent.rds b/data/c1_train_10_percent.rds
diff --git a/exercises/exc_01_03.R b/exercises/exc_01_03.R
@@ -1,5 +1,6 @@
 library(tidyverse)
-cars2018 <- read_csv("data/cars2018.csv")
+library(arrow)
+cars2018 <- read_parquet("data/cars2018.parquet")
 
 # Print the cars2018 object
 glimpse(___)

diff --git a/exercises/exc_01_04.R b/exercises/exc_01_04.R
@@ -1,5 +1,6 @@
 library(tidyverse)
-cars2018 <- read_csv("data/cars2018.csv")
+library(arrow)
+cars2018 <- read_parquet("data/cars2018.parquet")
 
 # Deselect the 2 columns to create cars_vars
 car_vars <- cars2018 %>%

diff --git a/exercises/exc_01_06.R b/exercises/exc_01_06.R
@@ -1,4 +1,5 @@
-car_vars <- readRDS("data/c1_car_vars.rds")
+library(arrow)
+car_vars <- read_parquet("data/c1_car_vars.parquet")
 
 # Load tidymodels
 ___

diff --git a/exercises/exc_01_07_1.R b/exercises/exc_01_07_1.R
@@ -1,5 +1,6 @@
-car_train <- readRDS("data/c1_train.rds")
-car_test <- readRDS("data/c1_test.rds")
+library(arrow)
+car_train <- read_parquet("data/c1_train.parquet")
+car_test <- read_parquet("data/c1_test.parquet")
 
 # Load tidymodels
 ___

diff --git a/exercises/exc_01_07_2.R b/exercises/exc_01_07_2.R
@@ -1,7 +1,7 @@
 library(tidymodels)
-
-car_train <- readRDS("data/c1_train.rds")
-car_test <- readRDS("data/c1_test.rds")
+library(arrow)
+car_train <- read_parquet("data/c1_train.parquet")
+car_test <- read_parquet("data/c1_test.parquet")
 
 # Build a random forest model specification
 rf_mod <- ___ %>%

diff --git a/exercises/exc_01_08.R b/exercises/exc_01_08.R
@@ -1,6 +1,8 @@
-car_train <- readRDS("data/c1_train.rds")
-fit_lm <- readRDS("data/c1_fit_lm.rds")
-fit_rf <- readRDS("data/c1_fit_rf.rds")
+library(tidyverse)
+library(arrow)
+car_train <- read_parquet("data/c1_train.parquet")
+fit_lm <- read_rds("data/c1_fit_lm.rds")
+fit_rf <- read_rds("data/c1_fit_rf.rds")
 
 # Load tidymodels
 library(___)

diff --git a/exercises/exc_01_09.R b/exercises/exc_01_09.R
@@ -1,8 +1,8 @@
 library(tidymodels)
-
-car_test <- readRDS("data/c1_test.rds")
-fit_lm <- readRDS("data/c1_fit_lm.rds")
-fit_rf <- readRDS("data/c1_fit_rf.rds")
+library(tidyverse)
+car_test <- read_parquet("data/c1_test.parquet")
+fit_lm <- read_rds("data/c1_fit_lm.rds")
+fit_rf <- read_rds("data/c1_fit_rf.rds")
 
 # Create the new columns
 results <- ___ %>%

diff --git a/exercises/exc_01_11.R b/exercises/exc_01_11.R
@@ -1,6 +1,6 @@
 library(tidymodels)
-
-car_train <- readRDS("data/c1_train_10_percent.rds")
+library(arrow)
+car_train <- read_parquet("data/c1_train_10_percent.parquet")
 
 lm_mod <- linear_reg() %>%
     set_engine("lm")

diff --git a/exercises/exc_01_12_1.R b/exercises/exc_01_12_1.R
@@ -1,7 +1,8 @@
+library(tidyverse)
 library(tidymodels)
 
-lm_res <- readRDS("data/c1_lm_res.rds")
-rf_res <- readRDS("data/c1_rf_res.rds")
+lm_res <- read_rds("data/c1_lm_res.rds")
+rf_res <- read_rds("data/c1_rf_res.rds")
 
 results <-  bind_rows(___ %>%
                           collect_predictions() %>%

diff --git a/exercises/exc_01_12_2.R b/exercises/exc_01_12_2.R
@@ -1,7 +1,8 @@
+library(tidyverse)
 library(tidymodels)
 
-lm_res <- readRDS("data/c1_lm_res.rds")
-rf_res <- readRDS("data/c1_rf_res.rds")
+lm_res <- read_rds("data/c1_lm_res.rds")
+rf_res <- read_rds("data/c1_rf_res.rds")
 
 results <-  bind_rows(lm_res %>%
                           collect_predictions() %>%

diff --git a/exercises/solution_01_03.R b/exercises/solution_01_03.R
@@ -1,5 +1,6 @@
 library(tidyverse)
-cars2018 <- read_csv("data/cars2018.csv")
+library(arrow)
+cars2018 <- read_parquet("data/cars2018.parquet")
 
 # Print the cars2018 object
 glimpse(cars2018)

diff --git a/exercises/solution_01_04.R b/exercises/solution_01_04.R
@@ -1,5 +1,6 @@
 library(tidyverse)
-cars2018 <- read_csv("data/cars2018.csv")
+library(arrow)
+cars2018 <- read_parquet("data/cars2018.parquet")
 
 # Deselect the 2 columns to create cars_vars
 car_vars <- cars2018 %>%

diff --git a/exercises/solution_01_06.R b/exercises/solution_01_06.R
@@ -1,4 +1,5 @@
-car_vars <- readRDS("data/c1_car_vars.rds")
+library(arrow)
+car_vars <- read_parquet("data/c1_car_vars.parquet")
 
 # Load tidymodels
 library(tidymodels)

diff --git a/exercises/solution_01_07_1.R b/exercises/solution_01_07_1.R
@@ -1,5 +1,6 @@
-car_train <- readRDS("data/c1_train.rds")
-car_test <- readRDS("data/c1_test.rds")
+library(arrow)
+car_train <- read_parquet("data/c1_train.parquet")
+car_test <- read_parquet("data/c1_test.parquet")
 
 # Load tidymodels
 library(tidymodels)

diff --git a/exercises/solution_01_07_2.R b/exercises/solution_01_07_2.R
@@ -1,7 +1,7 @@
 library(tidymodels)
-
-car_train <- readRDS("data/c1_train.rds")
-car_test <- readRDS("data/c1_test.rds")
+library(arrow)
+car_train <- read_parquet("data/c1_train.parquet")
+car_test <- read_parquet("data/c1_test.parquet")
 
 # Build a random forest model specification
 rf_mod <- rand_forest() %>%

diff --git a/exercises/solution_01_08.R b/exercises/solution_01_08.R
@@ -1,6 +1,8 @@
-car_train <- readRDS("data/c1_train.rds")
-fit_lm <- readRDS("data/c1_fit_lm.rds")
-fit_rf <- readRDS("data/c1_fit_rf.rds")
+library(tidyverse)
+library(arrow)
+car_train <- read_parquet("data/c1_train.parquet")
+fit_lm <- read_rds("data/c1_fit_lm.rds")
+fit_rf <- read_rds("data/c1_fit_rf.rds")
 
 # Load tidymodels
 library(tidymodels)

diff --git a/exercises/solution_01_09.R b/exercises/solution_01_09.R
@@ -1,8 +1,8 @@
 library(tidymodels)
-
-car_test <- readRDS("data/c1_test.rds")
-fit_lm <- readRDS("data/c1_fit_lm.rds")
-fit_rf <- readRDS("data/c1_fit_rf.rds")
+library(tidyverse)
+car_test <- read_parquet("data/c1_test.parquet")
+fit_lm <- read_rds("data/c1_fit_lm.rds")
+fit_rf <- read_rds("data/c1_fit_rf.rds")
 
 # Create the new columns
 results <- car_test %>%

diff --git a/exercises/solution_01_11.R b/exercises/solution_01_11.R
@@ -1,6 +1,6 @@
 library(tidymodels)
-
-car_train <- readRDS("data/c1_train_10_percent.rds")
+library(arrow)
+car_train <- read_parquet("data/c1_train_10_percent.parquet")
 
 lm_mod <- linear_reg() %>%
     set_engine("lm")

diff --git a/exercises/solution_01_12_1.R b/exercises/solution_01_12_1.R
@@ -1,7 +1,8 @@
+library(tidyverse)
 library(tidymodels)
 
-lm_res <- readRDS("data/c1_lm_res.rds")
-rf_res <- readRDS("data/c1_rf_res.rds")
+lm_res <- read_rds("data/c1_lm_res.rds")
+rf_res <- read_rds("data/c1_rf_res.rds")
 
 results <-  bind_rows(lm_res %>%
                           collect_predictions() %>%

diff --git a/exercises/solution_01_12_2.R b/exercises/solution_01_12_2.R
@@ -1,7 +1,8 @@
+library(tidyverse)
 library(tidymodels)
 
-lm_res <- readRDS("data/c1_lm_res.rds")
-rf_res <- readRDS("data/c1_rf_res.rds")
+lm_res <- read_rds("data/c1_lm_res.rds")
+rf_res <- read_rds("data/c1_rf_res.rds")
 
 results <-  bind_rows(lm_res %>%
                           collect_predictions() %>%