Project Part 1

Global Warming.

1. I downloaded Global Warming data from Our Wolrd In Data. I selected this data because I wanted to see the sea-surface water temperature overtime.

2. This is the link used fo the data.

3. The following code chunks loads the package I will use to read in and prepare the data for analysis.

library(tidyverse)

4. Read the data in

climate_change <- read_csv(here::here("_posts", "2022-05-09-project-part-1","climate-change.csv"))

5. Use Glimpse to see the names and types of columns.

glimpse(climate_change)

Rows: 10,085
Columns: 20
$ Entity                                  <chr> "Antarctica", "Antar…
$ Date                                    <date> 1992-01-01, 1992-01…
$ `Combined measurements`                 <dbl> NA, NA, NA, NA, NA, …
$ `Seasonal variation`                    <dbl> 418.3103, 425.3770, …
$ `Monthly averaged...5`                  <dbl> NA, NA, NA, NA, NA, …
$ `Annual averaged...6`                   <dbl> NA, NA, NA, NA, NA, …
$ monthly_sea_surface_temperature_anomaly <dbl> NA, NA, NA, NA, NA, …
$ `Sea surface temp (lower-bound)`        <dbl> NA, NA, NA, NA, NA, …
$ `Sea surface temp (upper-bound)`        <dbl> NA, NA, NA, NA, NA, …
$ `Monthly pH measurement`                <dbl> NA, NA, NA, NA, NA, …
$ `Annual average`                        <dbl> NA, NA, NA, NA, NA, …
$ `Temperature anomaly`                   <dbl> NA, NA, NA, NA, NA, …
$ `Church & White`                        <dbl> NA, NA, NA, NA, NA, …
$ `University of Hawaii`                  <dbl> NA, NA, NA, NA, NA, …
$ Average                                 <dbl> NA, NA, NA, NA, NA, …
$ arctic_sea_ice_osisaf                   <dbl> NA, NA, NA, NA, NA, …
$ `Monthly averaged...17`                 <dbl> NA, NA, NA, NA, NA, …
$ `Annual averaged...18`                  <dbl> NA, NA, NA, NA, NA, …
$ `Monthly averaged...19`                 <dbl> NA, NA, NA, NA, NA, …
$ `Annual averaged...20`                  <dbl> NA, NA, NA, NA, NA, …

#view(climate_change)

6. Use output from glimpse (and view) to prepare the data for analysis

• Create the object World that is list of the regions I want to extract from the dataset

• Change the name of 1st columns to Region and the 4th columns to Temperature anomaly

• Use filter to extract the rows that I want to keep: Year >= 1880-01-15 and Region in World.

• Select the columns to keep: eegion, Date, temperature_anomaly

• Assign the output to regional_anomaly

regional_anomaly <- climate_change %>%
  rename(region = 1, temperature_anomaly = `Temperature anomaly`) %>%
  filter(Date >= "1880-01-15", region == "World") %>%
  select(region, Date, temperature_anomaly)

Check that the total for 1880 equals the total in the graph

regional_anomaly %>% 
  filter(Date=="1880-06-15")

# A tibble: 1 × 3
  region Date       temperature_anomaly
  <chr>  <date>                   <dbl>
1 World  1880-06-15                -0.2

Add a picture

picture_climate

write_csv(regional_anomaly, file = "regional_anomaly.csv")