These exercises accompany the Data Manipulation Part 2 tutorial: http://rpubs.com/NateByers/DataManip2. These exercises use data frames from the region5air library. Run the following code to clean out your global environment and load the data you need:
rm(list = ls())
library(dplyr)
library(region5air)
data(airdata)
data(chicago_air)mutate() function to add a column named “violation” to the chicago_air dataset that indicates when the ozone value was above 0.070 ppm. Hint: you will make the new column using a single equal sign, violation =. The value you want is a logical comparison: violation = ozone values greater than 0.07.group_by() function to group the airdata data frame by site, parameter, and duration. Then use filter() to make a data frame called max_params with maximum values for each monitor/parameter/duration combination. Hint: in filter() you will use value ==. You want to keep numbers in the “value” column that equal the maximum for each group (use max(), and don’t forget na.rm = TRUE).airdata data frame by site, parameter, and duration. Use summarize() to create a data frame called mean_params with mean values for each monitor/parameter/duration combination. Hint: you will use a single = symbol, mean_value =. You want to use the mean() function on numbers in the “value”" column for each group (don’t forget na.rm = TRUE).%>%, to string together this series of dplyr operations: subset airdata down to ozone (44201); group by site and datetime; use summarize to find the mean value for each site/datetime combination; and filter down to the 4th highest value for each year. Note: this data set only has 1 hour values. This exercise would make more sense with 8 hour data, for comparison to the NAAQS.chicago_air <- mutate(chicago_air, violation = ozone > 0.070)
head(chicago_air)## date ozone temp solar month weekday violation
## 1 2013-01-01 0.032 17 0.65 1 3 FALSE
## 2 2013-01-02 0.020 15 0.61 1 4 FALSE
## 3 2013-01-03 0.021 28 0.17 1 5 FALSE
## 4 2013-01-04 0.028 18 0.62 1 6 FALSE
## 5 2013-01-05 0.025 26 0.48 1 7 FALSE
## 6 2013-01-06 0.026 36 0.47 1 1 FALSEairdata <- group_by(airdata, site, parameter, duration)
max_params <- filter(airdata, value == max(value, na.rm = TRUE))mean_params <- summarize(airdata, mean_value = mean(value, na.rm = TRUE))Without pipes, this is how we would do it.
# subset down to ozone
ozone <- filter(airdata, parameter == "44201")
# group ozone by site and datetime
ozone <- group_by(ozone, site, datetime)
# replace the value column with the mean value for each monitor at a datetime
# --taking the mean when there are multiple pocs
ozone <- summarize(ozone, value = mean(value, na.rm = TRUE))
# create a year column so you can group by year
ozone <- mutate(ozone, year = substr(datetime, start = 1, stop = 2))
# group now on site and year
ozone <- group_by(ozone, site, year)
# filter down to the fourth highest value when the value column
# is ranked in descending order per site and year
ozone_4rth_high <- filter(ozone, row_number(desc(value)) == 4)Here are the same operations with pipes.
ozone_4rth_high <- airdata %>%
filter(parameter == "44201") %>%
group_by(site, datetime) %>%
summarize(value = mean(value, na.rm = TRUE)) %>%
mutate(year = substr(datetime, 1, 4)) %>%
group_by(site, year) %>%
filter(row_number(desc(value)) == 4)