DATA607 p2 #3
Angel Gallardo
2024-03-03
Abstract: Data includes the yearlyaverage change of temperature in celsius for each country. We will change the results to Farenheit and find which country has the bigger temperature change on average. Do they follow a normal distribution? How does it compare with US?
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(dplyr)
climate <- read.csv("https://raw.githubusercontent.com/Angelogallardo05/Week6/main/Indicator_3_1_Climate_Indicators_Annual_Mean_Global_Surface_Temperature_577579683071085080.csv")
head(climate)## ObjectId Country ISO2 ISO3
## 1 1 Afghanistan, Islamic Rep. of AF AFG
## 2 2 Albania AL ALB
## 3 3 Algeria DZ DZA
## 4 4 American Samoa AS ASM
## 5 5 Andorra, Principality of AD AND
## 6 6 Angola AO AGO
## Indicator
## 1 Temperature change with respect to a baseline climatology, corresponding to the period 1951-1980
## 2 Temperature change with respect to a baseline climatology, corresponding to the period 1951-1980
## 3 Temperature change with respect to a baseline climatology, corresponding to the period 1951-1980
## 4 Temperature change with respect to a baseline climatology, corresponding to the period 1951-1980
## 5 Temperature change with respect to a baseline climatology, corresponding to the period 1951-1980
## 6 Temperature change with respect to a baseline climatology, corresponding to the period 1951-1980
## Unit
## 1 Degree Celsius
## 2 Degree Celsius
## 3 Degree Celsius
## 4 Degree Celsius
## 5 Degree Celsius
## 6 Degree Celsius
## Source
## 1 Food and Agriculture Organization of the United Nations (FAO). 2022. FAOSTAT Climate Change, Climate Indicators, Temperature change. License: CC BY-NC-SA 3.0 IGO. Extracted from: https://www.fao.org/faostat/en/#data/ET. Accessed on 2023-03-28.
## 2 Food and Agriculture Organization of the United Nations (FAO). 2022. FAOSTAT Climate Change, Climate Indicators, Temperature change. License: CC BY-NC-SA 3.0 IGO. Extracted from: https://www.fao.org/faostat/en/#data/ET. Accessed on 2023-03-28.
## 3 Food and Agriculture Organization of the United Nations (FAO). 2022. FAOSTAT Climate Change, Climate Indicators, Temperature change. License: CC BY-NC-SA 3.0 IGO. Extracted from: https://www.fao.org/faostat/en/#data/ET. Accessed on 2023-03-28.
## 4 Food and Agriculture Organization of the United Nations (FAO). 2022. FAOSTAT Climate Change, Climate Indicators, Temperature change. License: CC BY-NC-SA 3.0 IGO. Extracted from: https://www.fao.org/faostat/en/#data/ET. Accessed on 2023-03-28.
## 5 Food and Agriculture Organization of the United Nations (FAO). 2022. FAOSTAT Climate Change, Climate Indicators, Temperature change. License: CC BY-NC-SA 3.0 IGO. Extracted from: https://www.fao.org/faostat/en/#data/ET. Accessed on 2023-03-28.
## 6 Food and Agriculture Organization of the United Nations (FAO). 2022. FAOSTAT Climate Change, Climate Indicators, Temperature change. License: CC BY-NC-SA 3.0 IGO. Extracted from: https://www.fao.org/faostat/en/#data/ET. Accessed on 2023-03-28.
## CTS.Code CTS.Name
## 1 ECCS Surface Temperature Change
## 2 ECCS Surface Temperature Change
## 3 ECCS Surface Temperature Change
## 4 ECCS Surface Temperature Change
## 5 ECCS Surface Temperature Change
## 6 ECCS Surface Temperature Change
## CTS.Full.Descriptor
## 1 Environment, Climate Change, Climate Indicators, Surface Temperature Change
## 2 Environment, Climate Change, Climate Indicators, Surface Temperature Change
## 3 Environment, Climate Change, Climate Indicators, Surface Temperature Change
## 4 Environment, Climate Change, Climate Indicators, Surface Temperature Change
## 5 Environment, Climate Change, Climate Indicators, Surface Temperature Change
## 6 Environment, Climate Change, Climate Indicators, Surface Temperature Change
## X1961 X1962 X1963 X1964 X1965 X1966 X1967 X1968 X1969 X1970 X1971
## 1 -0.113 -0.164 0.847 -0.764 -0.244 0.226 -0.371 -0.423 -0.539 0.813 0.619
## 2 0.627 0.326 0.075 -0.166 -0.388 0.559 -0.074 0.081 -0.013 -0.106 -0.195
## 3 0.164 0.114 0.077 0.250 -0.100 0.433 -0.026 -0.067 0.291 0.116 -0.385
## 4 0.079 -0.042 0.169 -0.140 -0.562 0.181 -0.368 -0.187 0.132 -0.047 -0.477
## 5 0.736 0.112 -0.752 0.308 -0.490 0.415 0.637 0.018 -0.137 0.121 -0.326
## 6 0.041 -0.152 -0.190 -0.229 -0.196 0.175 -0.081 -0.193 0.188 0.248 -0.097
## X1972 X1973 X1974 X1975 X1976 X1977 X1978 X1979 X1980 X1981 X1982
## 1 -1.124 0.232 -0.489 -0.445 -0.286 0.513 0.129 0.361 0.600 0.483 -0.346
## 2 -0.069 -0.288 -0.139 -0.211 -0.683 0.545 -0.814 0.203 -0.414 -0.351 0.173
## 3 -0.348 -0.015 -0.503 -0.539 -0.782 0.504 0.012 0.654 0.232 0.215 0.399
## 4 -0.067 0.330 -0.308 -0.118 -0.177 0.156 0.092 0.341 0.350 0.179 0.280
## 5 -0.499 0.025 -0.371 0.246 -0.045 -0.093 -0.163 0.058 -0.188 0.178 1.044
## 6 -0.035 0.475 -0.158 -0.029 -0.313 0.272 0.037 0.291 0.279 -0.071 0.164
## X1983 X1984 X1985 X1986 X1987 X1988 X1989 X1990 X1991 X1992 X1993
## 1 0.164 0.145 0.283 -0.141 0.391 0.919 -0.205 0.730 -0.168 -0.294 0.220
## 2 -0.128 -0.270 -0.103 0.569 -0.106 0.370 -0.066 0.795 -0.269 0.106 0.076
## 3 0.560 -0.004 0.508 0.296 0.975 1.304 0.386 1.266 0.031 -0.312 0.552
## 4 0.313 0.277 0.256 0.394 0.354 0.509 0.143 0.497 0.641 0.344 -0.069
## 5 0.859 -0.157 0.059 0.387 0.397 0.883 1.162 1.736 0.231 0.386 0.174
## 6 0.487 0.631 0.694 0.176 0.689 0.572 -0.055 0.687 0.341 0.466 0.256
## X1994 X1995 X1996 X1997 X1998 X1999 X2000 X2001 X2002 X2003 X2004 X2005
## 1 0.430 0.359 -0.116 0.471 0.675 1.198 0.993 1.311 1.365 0.587 1.373 0.401
## 2 1.330 -0.172 -0.038 0.075 0.795 0.670 1.065 1.532 0.492 0.970 0.444 0.189
## 3 0.732 0.595 0.846 1.059 1.109 1.476 0.820 1.856 1.258 1.585 0.988 1.264
## 4 0.189 0.755 0.784 NA NA 0.242 0.626 0.904 1.152 0.716 0.191 0.801
## 5 1.508 1.279 0.570 1.788 1.018 1.055 1.050 1.480 0.835 1.949 0.936 0.851
## 6 0.212 0.753 0.370 0.107 1.064 0.417 0.169 0.295 0.735 0.889 0.414 1.021
## X2006 X2007 X2008 X2009 X2010 X2011 X2012 X2013 X2014 X2015 X2016 X2017 X2018
## 1 1.720 0.675 0.704 0.895 1.613 1.397 0.223 1.281 0.456 1.093 1.555 1.540 1.544
## 2 0.345 1.316 0.978 0.910 1.191 1.055 1.487 1.333 1.198 1.569 1.464 1.121 2.028
## 3 1.395 1.220 1.185 0.945 2.265 1.398 1.147 1.192 1.690 1.121 1.757 1.512 1.210
## 4 0.403 1.032 0.670 NA 1.311 0.854 0.924 1.257 1.170 1.009 1.539 1.435 1.189
## 5 1.485 1.024 0.946 1.413 0.471 1.677 1.265 0.831 1.946 1.690 1.990 1.925 1.919
## 6 0.561 0.885 0.501 0.708 1.194 0.880 0.552 1.044 0.828 1.331 1.609 0.870 1.395
## X2019 X2020 X2021 X2022
## 1 0.910 0.498 1.327 2.012
## 2 1.675 1.498 1.536 1.518
## 3 1.115 1.926 2.330 1.688
## 4 1.539 1.430 1.268 1.256
## 5 1.964 2.562 1.533 3.243
## 6 1.752 1.162 1.553 1.212remove duplicate and unecessary columns
climate <- climate[, -c(4, 5, 7, 8, 9)]remove the X in the names of the Year’s columns
names(climate)[6:ncol(climate)] <- gsub("^X", "", names(climate)[6:ncol(climate)])Remove rows with NA
climate_clean <- na.omit(climate)#Add “World” to the ISO2 column , resulting in no blanks for that column
climate_clean$ISO2[155] <- "World"climate_clean <- climate_clean %>%
mutate(across(6:last_col(), ~ . * 9/5))change the column description from degree celsius to Degree Farenheit
climate_clean <- climate_clean %>%
mutate(Unit = if_else(Unit == "Degree Celsius", "Degree Fahrenheit", Unit))climate_clean <- climate_clean %>%
mutate(average_temp_change = rowMeans(select(., 6:last_col()), na.rm = TRUE))#Sort by highest to lowest average temperature change by country
sorted_data <- climate_clean %>%
arrange(desc(average_temp_change))analyze mongolia, normal distribution of temperatur change, has a normal distribution
mongolia_data <- climate_clean %>%
filter(Country == "Mongolia")
# Extract temperature change values
temperature_change <- unlist(mongolia_data[, 6:ncol(mongolia_data)], use.names = FALSE)
# Calculate mean and standard deviation
mean_temp_change <- mean(temperature_change, na.rm = TRUE)
sd_temp_change <- sd(temperature_change, na.rm = TRUE)
# Generate simulated normal distribution
simulated_data_mongolia <- rnorm(n = 1000, mean = mean_temp_change, sd = sd_temp_change)
# Create histogram
ggplot() +
geom_histogram(aes(x = simulated_data_mongolia), binwidth = 0.05, fill = "skyblue", color = "black") +
labs(title = "Simulated Normal Distribution of Temperature Change for Mongolia",
x = "Temperature Change",
y = "Frequency")
quartiles <- quantile(simulated_data_mongolia, probs = c(0.25, 0.5, 0.75))
# Print quartiles
print(quartiles)## 25% 50% 75%
## 0.5076652 1.5849147 2.5580434repeat for US, also a normal distribution for temperature change
US temperature changes quite lower than Mongolia. It has a more normal distribution with a shorter range.
us_data <- climate_clean %>%
filter(Country == "United States")
# Extract temperature change values
temperature_change <- unlist(us_data[, 6:ncol(us_data)], use.names = FALSE)
# Calculate mean and standard deviation
usmean_temp_change <- mean(temperature_change, na.rm = TRUE)
ussd_temp_change <- sd(temperature_change, na.rm = TRUE)
# Generate simulated normal distribution
simulated_data_us <- rnorm(n = 1000, mean = usmean_temp_change, sd = ussd_temp_change)
# Create histogram
ggplot() +
geom_histogram(aes(x = simulated_data_us), binwidth = 0.05, fill = "skyblue", color = "black") +
labs(title = "Simulated Normal Distribution of Temperature Change for Mongolia",
x = "Temperature Change",
y = "Frequency")
quartiles <- quantile(simulated_data_us, probs = c(0.25, 0.5, 0.75))
# Print quartiles
print(quartiles)## 25% 50% 75%
## 0.1937738 0.8820742 1.6075091