Solutions Day 2

I NEED TO GO OVER THIS ONE MORE TIME VS THE LESSON TO MAKE SURE THEY MATCH.

Goals

To learn about: arrange, filter, slice, group_by, summarize

To find several values from our data:

The coldest and warmest days
The rainiest and snowiest days
Years with most snow days
Years with most 100+ days
Years with most rain
Earliest day to reach 100+ each year

With this lesson we’ll just use Texas data. (You theoretically could use a different state, but would need to adjust your code to import the right data, use valid cities, etc.)

Setup

library(tidyverse)

Import

Import your cleaned data using read_rds() and save it into an object:

tx_clean <- read_rds("data-processed/tx_clean.rds")

Arrange

Find the coldest day, warmest day, most snow, most rain.

Coldest day

tx_clean |> 
  arrange(tmin) |> 
  select(city, date, tmin)

Hotest day

tx_clean |> 
  arrange(desc(tmax)) |> 
  select(city, date, tmax)

OYO: Most rain

Find the days with the most rain.

tx_clean |> 
  arrange(desc(rain)) |> 
  select(city, date, rain)

OYO: Most snow

Find the days with the most snow.

tx_clean |> 
  arrange(desc(snow)) |> 
  select(city, date, snow)

Filter

Find days that are 100+.

tx_clean |> 
  filter(tmax >= 100) |> 
  select(city, date, tmax)

Filter for days in Dallas that are 100+

tx_clean |> 
  filter(tmax >= 100, city == "Dallas") |> 
  select(city, date, tmax)

Find days where it snowed, or there is snow still on the ground.

tx_clean |> 
  filter(snow > 0 | snwd > 0) |> 
  select(city, date, snow, snwd)

OYO: Snow days in Dallas

Find days where it snowed or there is snow on the ground, but only in Dallas.

tx_clean |> 
  filter(snow > 0 | snwd > 0, city == "Dallas") |> 
  select(city, date, snow, snwd)

Slice

Use slice_min to find the coldest day in our data.

tx_clean |> 
  slice_min(tmin) |> 
  select(city, date, tmin)

Group and slice

Add group_by to find the coldest day in each city.

tx_clean |> 
  group_by(city) |> 
  slice_min(tmin) |> 
  select(city, date, tmin)

OYO: Hottest day in each city

Use group_by and slice_max to find the hottest days in each city. Note there might be some ties.

tx_clean |> 
  group_by(city) |> 
  slice_max(tmax) |> 
  select(city, date, tmax)

Multiple groups

Hottest day each year in each city

tx_clean |> 
  group_by(yr, city) |> 
  slice_max(tmax) |> 
  select(city, tmax) |> 
  distinct()

Adding missing grouping variables: `yr`

Summarize

Summarize to find our first date, last date and number of rows.

tx_clean |> 
  summarize(
    e_date = min(date),
    l_date = max(date),
    cnt = n()
  )

Group and summarize

Group the data by city and find the first date, last date and number of rows.

tx_clean |> 
  group_by(city) |> 
  summarise(
    e_date = min(date),
    l_date = max(date),
    cnt = n()
  )

Add city and yr as a group:

tx_clean |> 
  group_by(city, yr) |> 
  summarise(
    e_date = min(date),
    l_date = max(date),
    cnt = n()
  )

`summarise()` has grouped output by 'city'. You can override using the
`.groups` argument.

Group and summarize: Count

Find the number of days in Austin that were 100+.

tx_clean |> 
  filter(city == "Austin", tmax >= 100) |> 
  group_by(yr) |> 
  summarize(hot_days = n()) |> 
  arrange(desc(hot_days))

Find the years with the most 100+ degree days in each city.

tx_clean |> 
  filter(tmax >= 100) |> 
  group_by(city, yr) |> 
  summarize(hot_days = n()) |> 
  arrange(desc(hot_days))

`summarise()` has grouped output by 'city'. You can override using the
`.groups` argument.

OYO: Most snow days by city each year

Count only the days that where it snowed.

tx_clean |> 
  filter(snow > 0) |> 
  group_by(city, yr) |> 
  summarise(snow_days = n()) |> 
  arrange(desc(snow_days))

`summarise()` has grouped output by 'city'. You can override using the
`.groups` argument.

Group and Summarize: Math

Years with most rain in each city.

tx_yr_rain <- tx_clean |> 
  filter(yr > 1939, yr < 2023) |>
  group_by(city, yr) |> 
  summarise(tot_rain = sum(rain, na.rm = TRUE)) |> 
  arrange(city, desc(tot_rain))

`summarise()` has grouped output by 'city'. You can override using the
`.groups` argument.

tx_yr_rain

The most rain in each city, sliced:

tx_yr_rain |> 
  group_by(city) |> 
  slice_max(tot_rain, n = 3)

The least rain in each city, sliced:

tx_yr_rain |> 
  group_by(city) |> 
  slice_min(tot_rain, n = 3)

OYO: Years with most snow

Find the years with the most total snow in each city

tx_yr_snow <- tx_clean |> 
  group_by(city, yr) |> 
  summarize(tot_snow = sum(snow)) |> 
  arrange(city, desc(tot_snow))

`summarise()` has grouped output by 'city'. You can override using the
`.groups` argument.

tx_yr_snow

Most snow, sliced:

tx_yr_snow |> 
  group_by(city) |> 
  slice_max(tot_snow, n = 3)

Working through logic

Getting average monthly rain for each city.

First get the total rain for each month/year:

tx_mn_yr_rain <- tx_clean |> 
  filter(yr >= 1940, yr <= 2022) |>
  group_by(city, mn, yr) |>
  summarize(mn_yr_rain = sum(rain, na.rm = TRUE))

`summarise()` has grouped output by 'city', 'mn'. You can override using the
`.groups` argument.

tx_mn_yr_rain

Then calculate the average for the months in each city:

city_avg_rain <- tx_mn_yr_rain |> 
  group_by(city, mn) |>
  summarise(avg_mn_rain = mean(mn_yr_rain))

`summarise()` has grouped output by 'city'. You can override using the
`.groups` argument.

city_avg_rain

And as a tease, we plot it:

city_avg_rain |> 
  ggplot(aes(x = mn, y = avg_mn_rain, group = city)) +
  geom_line(aes(color = city)) +
  ylim(0,6) +
  labs(
    title = "Average monthly rainfall, 1940-2022",
    x = "", y = "Average monthly rain",
    color = "City"
  )

Challenge: Earliest 100+ day each city

For each city, find the earliest day of a year in which it reached 100 degrees.

tx_clean |> 
  filter(tmax >= 100) |> 
  group_by(city) |> 
  slice_min(yd) |> 
  select(city, date, tmax)