**Course Title:** Mastering R Programming: Data Analysis, Visualization, and Beyond **Section Title:** Data Manipulation with dplyr and tidyr **Topic:** Key `dplyr` verbs: `filter()`, `select()`, `mutate()`, `summarize()`, and `group_by()`. In this topic, we will delve into the key verbs of the `dplyr` package, which is a fundamental tool for data manipulation in R. The `dplyr` package is part of the `tidyverse` ecosystem, a collection of R packages designed for data science. If you haven't installed `dplyr` yet, you can do so by running `install.packages("dplyr")` in your R console. ### Key `dplyr` verbs #### 1. `filter()` The `filter()` verb is used to subset rows based on conditions. Its syntax is `filter(data, condition)`, where `data` is the data frame and `condition` is a logical expression. ``` library(dplyr) library(mtcars) # Filter rows where mpg is greater than 30 filtered_data <- mtcars %>% filter(mpg > 30) glimpse(filtered_data) ``` In the above example, we filtered the `mtcars` data frame to get only the rows where `mpg` is greater than 30. #### 2. `select()` The `select()` verb is used to select specific columns from a data frame. Its syntax is `select(data, ..., -col1, -col2)`, where `data` is the data frame, `...` represents the columns you want to keep, and `-col1, -col2` represents the columns you want to drop. ``` library(dplyr) library(mtcars) # Select columns mpg, cyl, and gear selected_data <- mtcars %>% select(mpg, cyl, gear) glimpse(selected_data) ``` In the above example, we selected only the columns `mpg`, `cyl`, and `gear` from the `mtcars` data frame. #### 3. `mutate()` The `mutate()` verb is used to add new columns or modify existing columns in a data frame. Its syntax is `mutate(data, ..., col1 = expression, col2 = expression)`, where `data` is the data frame, `...` represents the new or modified columns, and `expression` represents the expression that defines the new or modified columns. ```r library(dplyr) library(mtcars) # Create a new column mpg_per_cyl by dividing mpg by cyl mutated_data <- mtcars %>% mutate(mpg_per_cyl = mpg / cyl) glimpse(mutated_data) ``` In the above example, we added a new column `mpg_per_cyl` to the `mtcars` data frame by dividing `mpg` by `cyl`. #### 4. `summarize()` The `summarize()` verb is used to compute aggregation functions on a data frame. Its syntax is `summarize(data, ..., col1 = expression, col2 = expression)`, where `data` is the data frame, `...` represents the aggregated columns, and `expression` represents the expression that defines the aggregated columns. ``` library(dplyr) library(mtcars) # Compute the mean and standard deviation of mpg summarized_data <- mtcars %>% summarize(mean_mpg = mean(mpg), sd_mpg = sd(mpg)) glimpse(summarized_data) ``` In the above example, we computed the mean and standard deviation of `mpg` from the `mtcars` data frame. #### 5. `group_by()` The `group_by()` verb is used to group a data frame by one or more columns. Its syntax is `group_by(data, ..., col1, col2)`, where `data` is the data frame and `...` represents the grouping columns. ``` library(dplyr) library(mtcars) # Group by cyl and gear grouped_data <- mtcars %>% group_by(cyl, gear) glimpse(grouped_data) ``` In the above example, we grouped the `mtcars` data frame by `cyl` and `gear`. This allows us to perform aggregation functions on each group. By combining these verbs, you can perform complex data manipulation tasks in an efficient and elegant way. For more information on `dplyr`, please refer to the [dplyr documentation](https://dplyr.tidyverse.org/). ### Exercises 1. Filter the `mtcars` data frame to get only the rows where `mpg` is less than 20. 2. Select only the columns `mpg`, `cyl`, and `disp` from the `mtcars` data frame. 3. Create a new column `mpg_per_disp` by dividing `mpg` by `disp`. 4. Compute the mean and standard deviation of `mpg` for each group of `cyl`. 5. Group the `mtcars` data frame by `gear` and compute the sum of `mpg` for each group. ### Conclusion In this topic, we covered the key `dplyr` verbs for data manipulation in R. We demonstrated how to use `filter()`, `select()`, `mutate()`, `summarize()`, and `group_by()` to perform common data manipulation tasks. These verbs can be combined to perform complex data manipulation tasks in an efficient and elegant way. If you have any questions or need help with the exercises, please leave a comment below. In the next topic, we will cover data reshaping with `tidyr`: Pivoting and unpivoting data using `gather()` and `spread()`.