diff --git a/RBeginnersExercise2.Rmd b/RBeginnersExercise2.Rmd
index 9f4e61ad730956f51ea8f9bd12a668d87f731140..e6030a5c42c9b8a2f00848ad1578cc77b86c80cd 100644
--- a/RBeginnersExercise2.Rmd
+++ b/RBeginnersExercise2.Rmd
@@ -101,28 +101,30 @@ Try to find the datatype of all columns in the  `students` table by using the `s
 
 ```{r}
 # Write your code below
-sapply(students, class)
+
 ```
 
-## 2.2.5 Summary of dataset
 
-Use the `summary()` function to get a summary of all columns in the `menu` dataset.
+## 2.2.5 Find unique values in a column
+
+The `unique()` function in R can be used to find all unique values in a specific column in a table, e.g., `unique(tableName$columnName)`. Use the `unique()` function to find all values in the `Price` column in the `menu` dataset, without duplication.
 
 ```{r}
 # Write your code below
 
 ```
 
-## 2.2.6 Find unique values in a column
-
-The `unique()` function in R can be used to find all unique values in a specific column in a table, e.g., `unique(tableName$columnName)`. Use the `unique()` function to find all values in the `Price` column in the `menu` dataset, without duplication.
+By using the `sapply()` function, find the unique values in each column in the `menu` table. 
 
 ```{r}
 # Write your code below
 
 ```
 
-By using the `sapply()` function, find the unique values in each column in the `menu` table. 
+
+## 2.2.6 Summary of dataset
+
+Use the `summary()` function to get a summary of all columns in the `menu` dataset.
 
 ```{r}
 # Write your code below
@@ -224,7 +226,7 @@ menu[3, 2] = 4.50
 menu # Check to make sure the missing value is being replaced correctly. 
 ```
 
-For a small dataset, the method above is plausible. For a larger dataset, the better way is to replace missing value(s) in a batch using the function `replace_na` in the `dbplyr` package.
+For a small dataset, the method above is plausible. For a larger dataset, the better way is to replace missing value(s) in a batch using the function `replace_na` in the `tidyr` package.
 
 ```{r}
 library(tidyr) # The package which includes the replace_na function. 
diff --git a/RBeginnersExercise2_Sol.Rmd b/RBeginnersExercise2_Sol.Rmd
index 3c84d070db06ecc4e13900f3110e77b0c44ab510..8d36ba9557db7cbbd223388033060a60cd8dcc00 100644
--- a/RBeginnersExercise2_Sol.Rmd
+++ b/RBeginnersExercise2_Sol.Rmd
@@ -105,15 +105,6 @@ sapply(students, class)
 ```
 
 
-## 2.2.5 Summary of dataset
-
-Use the `summary()` function to get a summary of all columns in the `menu` dataset.
-
-```{r}
-# Write your code below
-summary(menu)
-```
-
 ## 2.2.6 Find unique values in a column
 
 The `unique()` function in R can be used to find all unique values in a specific column in a table, e.g., `unique(tableName$columnName)`. Use the `unique()` function to find all values in the `Price` column in the `menu` dataset, without duplication.
@@ -131,6 +122,16 @@ sapply(menu, unique)
 ```
 
 
+## 2.2.5 Summary of dataset
+
+Use the `summary()` function to get a summary of all columns in the `menu` dataset.
+
+```{r}
+# Write your code below
+summary(menu)
+```
+
+
 ## 2.2.7 Incomplete cases in dataset
 
 In R, incomplete cases are rows in dataset that have `Na` value(s). These incomplete cases can be viewed using the function `complete.cases`.
@@ -226,7 +227,7 @@ menu[3, 2] = 4.50
 menu # Check to make sure the missing value is being replaced correctly. 
 ```
 
-For a small dataset, the method above is plausible. For a larger dataset, the better way is to replace missing value(s) in a batch using the function `replace_na` in the `dbplyr` package.
+For a small dataset, the method above is plausible. For a larger dataset, the better way is to replace missing value(s) in a batch using the function `replace_na` in the `tidyr` package.
 
 ```{r}
 library(tidyr) # The package which includes the replace_na function.