- Use the
merge()function to join two datasets. - Deal with missings and impute data.
- Identify relevant observations using
quantile(). - Practice your GitHub skills.
For this lab we will be dealing with the meteorological dataset met.
In this case, we will use data.table to answer some questions
regarding the met dataset, while at the same time practice your
Git+GitHub skills for this project.
This markdown document should be rendered using github_document
document.
-
Go to wherever you are planning to store the data on your computer, and create a folder for this project
-
In that folder, save this template as “README.Rmd”. This will be the markdown file where all the magic will happen.
-
Go to your GitHub account and create a new repository of the same name that your local folder has, e.g., “JSC370-labs”.
-
Initialize the Git project, add the “README.Rmd” file, and make your first commit.
-
Add the repo you just created on GitHub.com to the list of remotes, and push your commit to origin while setting the upstream.
Most of the steps can be done using command line:
# Step 1
cd ~/Documents
mkdir JSC370-labs
cd JSC370-labs
# Step 2
wget https://raw.githubusercontent.com/JSC370/jsc370-2023/main/labs/lab05/lab05-wrangling-gam.Rmd
mv lab05-wrangling-gam.Rmd README.Rmd
# if wget is not available,
curl https://raw.githubusercontent.com/JSC370/jsc370-2023/main/labs/lab05/lab05-wrangling-gam.Rmd --output README.Rmd
# Step 3
# Happens on github
# Step 4
git init
git add README.Rmd
git commit -m "First commit"
# Step 5
git remote add origin git@github.com:[username]/JSC370-labs
git push -u origin masterYou can also complete the steps in R (replace with your paths/username when needed)
# Step 1
setwd("~/Documents")
dir.create("JSC370-labs")
setwd("JSC370-labs")
# Step 2
download.file(
"https://raw.githubusercontent.com/JSC370/jsc370-2023/main/labs/lab05/lab05-wrangling-gam.Rmd",
destfile = "README.Rmd"
)
# Step 3: Happens on Github
# Step 4
system("git init && git add README.Rmd")
system('git commit -m "First commit"')
# Step 5
system("git remote add origin git@github.com:[username]/JSC370-labs")
system("git push -u origin master")Once you are done setting up the project, you can now start working with the MET data.
- Load the
data.table(and thedtplyranddplyrpackages).
# Install packages if they are not already installed
packages <- c("data.table", "dtplyr", "dplyr")
new_packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new_packages)) install.packages(new_packages)
# Load the required libraries
library(data.table)
library(dtplyr)
library(dplyr)##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:data.table':
##
## between, first, last
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
- Load the met data from https://raw.githubusercontent.com/JSC370/JSC370-2024/main/data/met_all_2023.gz, and also the station data. For the latter, you can use the code we used during lecture to pre-process the stations data:
# Download the data
stations <- fread("ftp://ftp.ncdc.noaa.gov/pub/data/noaa/isd-history.csv")
stations[, USAF := as.integer(USAF)]## Warning in eval(jsub, SDenv, parent.frame()): NAs introduced by coercion
# Dealing with NAs and 999999
stations[, USAF := fifelse(USAF == 999999, NA_integer_, USAF)]
stations[, CTRY := fifelse(CTRY == "", NA_character_, CTRY)]
stations[, STATE := fifelse(STATE == "", NA_character_, STATE)]
# Selecting the three relevant columns, and keeping unique records
stations <- unique(stations[, list(USAF, CTRY, STATE)])
# Dropping NAs
stations <- stations[!is.na(USAF)]
# Removing duplicates
stations[, n := 1:.N, by = .(USAF)]
stations <- stations[n == 1,][, n := NULL]
# Read in the met data
met <- data.table::fread("met_all.gz")- Merge the data as we did during the lecture. Use the
merge()code and you can also try the tidy way withleft_join()
met <- merge(
x = met,
y = stations,
all.x = TRUE, all.y = FALSE,
by.x = "USAFID", by.y = "USAF"
)Across all weather stations, what stations have the median values of
temperature, wind speed, and atmospheric pressure? Using the
quantile() function, identify these three stations. Do they coincide?
# medians across all stations and times
medians <- met[, .(
temp_50 = quantile(temp, probs = .5, na.rm = TRUE),
wind.sp_50 = quantile(wind.sp, probs = .5, na.rm = TRUE),
atm.press_50 = quantile(atm.press, probs = .5, na.rm = TRUE)
)]
medians## temp_50 wind.sp_50 atm.press_50
## <num> <num> <num>
## 1: 23.5 2.1 1014.1
# medians by station (keep state)
station_med <- met[, .(
temp = quantile(temp, probs = .5, na.rm = TRUE),
wind.sp = quantile(wind.sp, probs = .5, na.rm = TRUE),
atm.press = quantile(atm.press, probs = .5, na.rm = TRUE)
), by = .(USAFID, STATE)]# Find the stations that are the closest to the overall medians
# Median temperature stations
station_med[, temp_dist := abs(temp - medians$temp_50)]
median_temp_station <- station_med[temp_dist == 0]
median_temp_station## USAFID STATE temp wind.sp atm.press temp_dist
## <int> <char> <num> <num> <num> <num>
## 1: 720501 VA 23.5 1.5 NA 0
## 2: 722031 AL 23.5 0.0 NA 0
## 3: 722148 NC 23.5 0.0 NA 0
## 4: 723055 NC 23.5 0.0 NA 0
## 5: 723067 NC 23.5 1.5 NA 0
## 6: 723177 NC 23.5 0.0 NA 0
## 7: 725564 NE 23.5 2.6 NA 0
# Median wind speed stations
station_med[, wind.sp_dist := abs(wind.sp - medians$wind.sp_50)]
median_wind.sp_station <- station_med[wind.sp_dist == 0]
median_wind.sp_station## USAFID STATE temp wind.sp atm.press temp_dist wind.sp_dist
## <int> <char> <num> <num> <num> <num> <num>
## 1: 720110 TX 31.0 2.1 NA 7.5 0
## 2: 720258 MN 17.0 2.1 NA 6.5 0
## 3: 720266 IN 21.0 2.1 NA 2.5 0
## 4: 720272 WA 18.0 2.1 NA 5.5 0
## 5: 720273 TX 28.6 2.1 NA 5.1 0
## ---
## 339: 726583 MN 21.0 2.1 NA 2.5 0
## 340: 726589 MN 20.0 2.1 NA 3.5 0
## 341: 726603 MN 20.7 2.1 NA 2.8 0
## 342: 726626 WI 16.6 2.1 NA 6.9 0
## 343: 726813 ID 22.8 2.1 1011.75 0.7 0
# Median atmospheric pressure stations
station_med[, atm.press_dist := abs(atm.press - medians$atm.press_50)]
median_atm.press_station <- station_med[atm.press_dist == 0]
median_atm.press_station## USAFID STATE temp wind.sp atm.press temp_dist wind.sp_dist atm.press_dist
## <int> <char> <num> <num> <num> <num> <num> <num>
## 1: 722420 TX 30.0 4.6 1014.1 6.5 2.5 0
## 2: 723830 CA 23.3 5.1 1014.1 0.2 3.0 0
## 3: 724885 NV 24.7 2.6 1014.1 1.2 0.5 0
## 4: 724940 CA 18.9 5.1 1014.1 4.6 3.0 0
## 5: 725376 MI 22.8 3.1 1014.1 0.7 1.0 0
## 6: 725975 OR 16.1 2.1 1014.1 7.4 0.0 0
## 7: 726183 ME 18.9 0.0 1014.1 4.6 2.1 0
## 8: 726375 MI 21.1 3.1 1014.1 2.4 1.0 0
## 9: 726579 MN 20.0 3.1 1014.1 3.5 1.0 0
## 10: 726584 MN 20.0 3.1 1014.1 3.5 1.0 0
## 11: 726590 SD 20.0 3.1 1014.1 3.5 1.0 0
Knit the document, commit your changes, and save it on GitHub. Don’t
forget to add README.md to the tree, the first time you render it.
Just like the previous question, you are asked to identify what is the most representative, the median, station per state. This time, instead of looking at one variable at a time, look at the euclidean distance. If multiple stations show in the median, select the one located at the lowest latitude.
Knit the doc and save it on GitHub.
For each state, identify what is the station that is closest to the
mid-point of the state. Combining these with the stations you identified
in the previous question, use leaflet() to visualize all ~100 points
in the same figure, applying different colors for those identified in
this question.
Knit the doc and save it on GitHub.
Using the quantile() function, generate a summary table that shows the
number of states included, average temperature, wind-speed, and
atmospheric pressure by the variable “average temperature level,” which
you’ll need to create.
Start by computing the states’ average temperature. Use that measurement to classify them according to the following criteria:
- low: temp < 20
- Mid: temp >= 20 and temp < 25
- High: temp >= 25
Once you are done with that, you can compute the following:
- Number of entries (records),
- Number of NA entries,
- Number of stations,
- Number of states included, and
- Mean temperature, wind-speed, and atmospheric pressure.
All by the levels described before.
Knit the document, commit your changes, and push them to GitHub.
Let’s practice running regression models with smooth functions on X. We
need the mgcv package and gam() function to do this.
-
using your data with the median values per station, examine the association between median temperature (y) and median wind speed (x). Create a scatterplot of the two variables using ggplot2. Add both a linear regression line and a smooth line.
-
fit both a linear model and a spline model (use
gam()with a cubic regression spline on wind speed). Summarize and plot the results from the models and interpret which model is the best fit and why.