Introduction

  1. Introduction to the U.S. Daily Climate Normals Dataset

The U.S. Daily Climate Normals (1981-2010) dataset provides a comprehensive overview of average daily meteorological conditions across the United States and its territories. This dataset is a valuable resource for understanding typical climate patterns, including temperature, precipitation, and other weather variables.

library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.4.1
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## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
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library(corrplot)
## Warning: package 'corrplot' was built under R version 4.4.1
## corrplot 0.94 loaded
climate_data <- read_csv("NORMAL_DLY_sample_csv.csv")
## Rows: 365 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): STATION, STATION_NAME
## dbl (7): ELEVATION, LATITUDE, LONGITUDE, DATE, DLY-TMIN-NORMAL, DLY-TMAX-NOR...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
  1. Check missing values
missing_values <- sum(is.na(climate_data))

if (missing_values == 0) {
  message("There are 0 empty values in the dataset.")
} else {
  message(paste("There are", missing_values, "empty values in the dataset."))
}
## There are 0 empty values in the dataset.
  1. Show Dataset
head(climate_data)
## # A tibble: 6 × 9
##   STATION     STATION_NAME ELEVATION LATITUDE LONGITUDE   DATE `DLY-TMIN-NORMAL`
##   <chr>       <chr>            <dbl>    <dbl>     <dbl>  <dbl>             <dbl>
## 1 GHCND:USC0… PETERSBURG …      466.     48.0     -98.0 2.01e7               -33
## 2 GHCND:USC0… PETERSBURG …      466.     48.0     -98.0 2.01e7               -35
## 3 GHCND:USC0… PETERSBURG …      466.     48.0     -98.0 2.01e7               -36
## 4 GHCND:USC0… PETERSBURG …      466.     48.0     -98.0 2.01e7               -38
## 5 GHCND:USC0… PETERSBURG …      466.     48.0     -98.0 2.01e7               -39
## 6 GHCND:USC0… PETERSBURG …      466.     48.0     -98.0 2.01e7               -41
## # ℹ 2 more variables: `DLY-TMAX-NORMAL` <dbl>, `MTD-PRCP-NORMAL` <dbl>
summary(climate_data)
##    STATION          STATION_NAME         ELEVATION        LATITUDE    
##  Length:365         Length:365         Min.   :466.3   Min.   :48.04  
##  Class :character   Class :character   1st Qu.:466.3   1st Qu.:48.04  
##  Mode  :character   Mode  :character   Median :466.3   Median :48.04  
##                                        Mean   :466.3   Mean   :48.04  
##                                        3rd Qu.:466.3   3rd Qu.:48.04  
##                                        Max.   :466.3   Max.   :48.04  
##    LONGITUDE           DATE          DLY-TMIN-NORMAL DLY-TMAX-NORMAL
##  Min.   :-98.01   Min.   :20100101   Min.   :-48     Min.   :138.0  
##  1st Qu.:-98.01   1st Qu.:20100402   1st Qu.: 73     1st Qu.:247.0  
##  Median :-98.01   Median :20100702   Median :307     Median :531.0  
##  Mean   :-98.01   Mean   :20100668   Mean   :282     Mean   :493.2  
##  3rd Qu.:-98.01   3rd Qu.:20101001   3rd Qu.:489     3rd Qu.:725.0  
##  Max.   :-98.01   Max.   :20101231   Max.   :567     Max.   :805.0  
##  MTD-PRCP-NORMAL 
##  Min.   :  1.00  
##  1st Qu.: 27.00  
##  Median : 56.00  
##  Mean   : 87.59  
##  3rd Qu.:131.00  
##  Max.   :376.00

Exploratory Data Analysis

  1. Descriptive Statistics
summary(climate_data[, c("DLY-TMIN-NORMAL", "DLY-TMAX-NORMAL", "MTD-PRCP-NORMAL")])
##  DLY-TMIN-NORMAL DLY-TMAX-NORMAL MTD-PRCP-NORMAL 
##  Min.   :-48     Min.   :138.0   Min.   :  1.00  
##  1st Qu.: 73     1st Qu.:247.0   1st Qu.: 27.00  
##  Median :307     Median :531.0   Median : 56.00  
##  Mean   :282     Mean   :493.2   Mean   : 87.59  
##  3rd Qu.:489     3rd Qu.:725.0   3rd Qu.:131.00  
##  Max.   :567     Max.   :805.0   Max.   :376.00
hist(climate_data$`DLY-TMIN-NORMAL`, main = "Histogram of Daily Minimum Temperature")

hist(climate_data$`DLY-TMAX-NORMAL`, main = "Histogram of Daily Maximum Temperature")

hist(climate_data$`MTD-PRCP-NORMAL`, main = "Histogram of Monthly Total Precipitation")

Inferential Statistic

  1. Correlation
correlation_matrix <- cor(climate_data[, c("DLY-TMIN-NORMAL", "DLY-TMAX-NORMAL", "MTD-PRCP-NORMAL")])
corrplot(correlation_matrix, method = "circle", type = "upper")

  1. Regression
model <- lm(`DLY-TMIN-NORMAL` ~ `DLY-TMAX-NORMAL`, data = climate_data)
summary(model)
## 
## Call:
## lm(formula = `DLY-TMIN-NORMAL` ~ `DLY-TMAX-NORMAL`, data = climate_data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -22.4337 -13.9647  -0.9196  14.2645  27.1539 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       -1.605e+02  1.788e+00  -89.74   <2e-16 ***
## `DLY-TMAX-NORMAL`  8.973e-01  3.267e-03  274.66   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.83 on 363 degrees of freedom
## Multiple R-squared:  0.9952, Adjusted R-squared:  0.9952 
## F-statistic: 7.544e+04 on 1 and 363 DF,  p-value: < 2.2e-16
ggplot(climate_data, aes(x = `DLY-TMAX-NORMAL`, y = `DLY-TMIN-NORMAL`)) +
  geom_point() +
  geom_abline(intercept = coef(model)[1], slope = coef(model)[2], color = "red") +
  labs(x = "Daily Maximum Temperature", y = "Daily Minimum Temperature") +
  ggtitle("Scatter Plot: Daily Minimum Temperature vs. Daily Maximum Temperature")