Additional Exercises

Exercise 1

Subset the chicago_air dataset to only include records where the temperature is below 30 degrees and the pressure is above 1000 hPa. Use the filter() function from the dplyr package.

Click for Solution

Load the dplyr package using library() and use logical expressions to get records where temp is less than 30 and pressure is greater than 1000.

library(dplyr)

cold_high_pressure <- filter(chicago_air, temp < 30, pressure > 1000)

cold_high_pressure

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Exercise 2

Create a new column in the chicago_air data frame called ozone_category that categorizes ozone levels into "Low", "Moderate", and "High". Use the mutate() function from the dplyr package.

Click for Solution

Use mutate() to create a new column ozone_category based on conditional statements for ozone values.

library(dplyr)

chicago_air <- mutate(chicago_air, 
                      ozone_category = case_when(
                        ozone < 0.030 ~ "Low",
                        ozone >= 0.030 & ozone < 0.060 ~ "Moderate",
                        ozone >= 0.060 ~ "High"
                      ))

head(chicago_air)

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Exercise 3

Sort the chicago_air data frame by temperature in ascending order and then by pressure in descending order. Use the arrange() function from the dplyr package.

Click for Solution

Use arrange() with temp and desc(pressure) to sort the data frame.

library(dplyr)

sorted_air <- arrange(chicago_air, temp, desc(pressure))

head(sorted_air)

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Exercise 4

Use the apply() function to create a vector of the standard deviation values from the numeric columns in the chicago_air data frame.

Click for Solution

Subset the chicago_air data frame to the numeric columns and use the apply() function with sd.

chicago_numeric <- chicago_air[, c("ozone", "temp", "pressure")]

sd_values <- apply(chicago_numeric, MARGIN = 2, FUN = sd, na.rm = TRUE)

sd_values

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Exercise 5

Write a function called temp_range that takes a data frame and returns the difference between the maximum and minimum temperature values.

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Define the function temp_range and use max() and min() to calculate the range of temperatures.

temp_range <- function(data) {
  max(data$temp, na.rm = TRUE) - min(data$temp, na.rm = TRUE)
}

temp_range(chicago_air)

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Exercise 6

Create a function named subset_by_weekday that takes a data frame and a weekday (as an integer) as arguments and returns a subset of the data frame for that weekday.

Click for Solution

Define the function subset_by_weekday to filter the data frame based on the weekday column.

subset_by_weekday <- function(data, weekday) {
  filter(data, weekday == weekday)
}

subset_by_weekday(chicago_air, 3)

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Exercise 7

Use the dplyr package to create a new data frame that groups the chicago_air data by month and calculates the average temperature for each month.

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Use group_by() and summarize() to group by month and calculate the average temp.

library(dplyr)

monthly_avg_temp <- chicago_air %>%
  group_by(month) %>%
  summarize(avg_temp = mean(temp, na.rm = TRUE))

monthly_avg_temp

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Exercise 8

Write a function called convert_date that takes a data frame and converts the date column from a character to a Date object.

Click for Solution

Define the function convert_date and use as.Date() to convert the date column.

convert_date <- function(data) {
  data$date <- as.Date(data$date)
  return(data)
}

chicago_air <- convert_date(chicago_air)
str(chicago_air)

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Exercise 9

Use a for() loop to create a vector of average ozone levels for each month in the chicago_air data frame.

Click for Solution

Loop through each month, filter the data, and calculate the mean ozone level.

average_ozone <- c()

for (month in 1:12) {
  monthly_data <- filter(chicago_air, month == month)
  average_ozone[month] <- mean(monthly_data$ozone, na.rm = TRUE)
}

average_ozone

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Exercise 10

Combine the warm and cool data frames from the lesson using the rbind() function instead of bind_rows().

Click for Solution

Use rbind() to combine warm and cool data frames.

recombined_rbind <- rbind(warm, cool)

nrow(recombined_rbind) == nrow(chicago_air)

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