Is Climate change real?
CLIMATE CHANGE
Climate change refers to long-term shifts in temperatures and weather patterns. These shifts may be natural, but since the 1800s, human activities have been the main driver of climate change, primarily due to the burning of fossil fuels (like coal, oil and gas), which produces heat-trapping gases. (United Nations)
Climate change has always been a topic for generations. In this age and technology, there are news and videos, more than ever, that explain and show the impact of the so call Climate Change to the world we are living. But is Climate change real?
To say climate change is real, let us determine if, through out the years, a change in global temperatures happened. If so, Climate change is real as the forerunner of such event, the long-term shifts in temperatures, will tell us that it is happening and have caused changes in weather pattern as well.
This report limits the study of temperature changes in Antarctica - a very delicate area that if such temperature changes occur, plays a vital role in the global precedent of such weather events.
Download Raw
#Download from github repo
temp <- read.csv("https://raw.githubusercontent.com/inciladc/rfiles/main/datasets/worldtemp.csv")Inspect Raw
## 'data.frame': 9656 obs. of 66 variables:
## $ Area.Code : int 2 2 2 2 2 2 2 2 2 2 ...
## $ Area : chr "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
## $ Months.Code : int 7001 7001 7002 7002 7003 7003 7004 7004 7005 7005 ...
## $ Months : chr "January" "January" "February" "February" ...
## $ Element.Code: int 7271 6078 7271 6078 7271 6078 7271 6078 7271 6078 ...
## $ Element : chr "Temperature change" "Standard Deviation" "Temperature change" "Standard Deviation" ...
## $ Unit : chr "°C" "°C" "°C" "°C" ...
## $ Y1961 : num 0.777 1.95 -1.743 2.597 0.516 ...
## $ Y1962 : num 0.062 1.95 2.465 2.597 1.336 ...
## $ Y1963 : num 2.744 1.95 3.919 2.597 0.403 ...
## $ Y1964 : num -5.232 1.95 -0.202 2.597 1.659 ...
## $ Y1965 : num 1.868 1.95 -0.096 2.597 -0.909 ...
## $ Y1966 : num 3.629 1.95 3.397 2.597 -0.069 ...
## $ Y1967 : num -1.432 1.95 0.296 2.597 -0.759 ...
## $ Y1968 : num 0.389 1.95 -2.055 2.597 0.496 ...
## $ Y1969 : num -2.3 1.95 -3.17 2.6 2.48 ...
## $ Y1970 : num 0.804 1.95 1.809 2.597 -0.915 ...
## $ Y1971 : num -1.487 1.95 0.816 2.597 1.658 ...
## $ Y1972 : num -1.3 1.95 -7.72 2.6 -1.78 ...
## $ Y1973 : num -2.951 1.95 1.838 2.597 -0.473 ...
## $ Y1974 : num -1.18 1.95 -3.71 2.6 1 ...
## $ Y1975 : num -0.49 1.95 -1.239 2.597 -0.585 ...
## $ Y1976 : num 2.41 1.95 -1.62 2.6 -2.82 ...
## $ Y1977 : num -3.014 1.95 -0.156 2.597 3.377 ...
## $ Y1978 : num -0.663 1.95 -0.369 2.597 -1.536 ...
## $ Y1979 : num 1.14 1.95 1.07 2.6 -1.42 ...
## $ Y1980 : num -0.393 1.95 -1.222 2.597 -0.628 ...
## $ Y1981 : num 1.72 1.95 1.09 2.6 1.17 ...
## $ Y1982 : num 0.678 1.95 -2.101 2.597 -1.781 ...
## $ Y1983 : num 0.524 1.95 0.46 2.597 -2.406 ...
## $ Y1984 : num -0.058 1.95 -4.321 2.597 1.761 ...
## $ Y1985 : num 0.435 1.95 2.467 2.597 -0.16 ...
## $ Y1986 : num 0.332 1.95 -0.286 2.597 -3.087 ...
## $ Y1987 : num 2.65 1.95 1.41 2.6 1.33 ...
## $ Y1988 : num 1.15 1.95 0.17 2.597 -0.056 ...
## $ Y1989 : num -1.108 1.95 -2.89 2.597 0.065 ...
## $ Y1990 : num 0.634 1.95 -0.31 2.597 -1 ...
## $ Y1991 : num 0.018 1.95 -1.373 2.597 -0.901 ...
## $ Y1992 : num 0.582 1.95 -0.12 2.597 -2.22 ...
## $ Y1993 : num -0.821 1.95 1.414 2.597 -1.449 ...
## $ Y1994 : num 1.09 1.95 -1.41 2.6 1.31 ...
## $ Y1995 : num 1.297 1.95 -0.149 2.597 -1.451 ...
## $ Y1996 : num -0.718 1.95 0.87 2.597 -0.336 ...
## $ Y1997 : num 1.426 1.95 0.043 2.597 -0.005 ...
## $ Y1998 : num 0.95 1.95 -0.54 2.597 -0.576 ...
## $ Y1999 : num 0.859 1.95 3.222 2.597 -0.217 ...
## $ Y2000 : num 1.565 1.95 -0.901 2.597 -0.267 ...
## $ Y2001 : num -0.603 1.95 0.707 2.597 1.229 ...
## $ Y2002 : num 1.606 1.95 0.985 2.597 1.949 ...
## $ Y2003 : num 2.479 1.95 1.816 2.597 -0.158 ...
## $ Y2004 : num 2.71 1.95 2.87 2.6 2.75 ...
## $ Y2005 : num 0.109 1.95 -1.506 2.597 1.663 ...
## $ Y2006 : num -1.61 1.95 4.72 2.6 1.85 ...
## $ Y2007 : num 0.431 1.95 1.645 2.597 -0.201 ...
## $ Y2008 : num -5.55 1.95 -2.33 2.6 4.17 ...
## $ Y2009 : num 1.52 1.95 2.49 2.6 2.36 ...
## $ Y2010 : num 3.6 1.95 1.21 2.6 3.39 ...
## $ Y2011 : num 1.179 1.95 0.321 2.597 0.748 ...
## $ Y2012 : num -0.583 1.95 -3.201 2.597 -0.527 ...
## $ Y2013 : num 1.23 1.95 1.49 2.6 2.25 ...
## $ Y2014 : num 1.755 1.95 -3.187 2.597 -0.076 ...
## $ Y2015 : num 1.943 1.95 2.699 2.597 -0.497 ...
## $ Y2016 : num 3.42 1.95 2.25 2.6 2.3 ...
## $ Y2017 : num 1.201 1.95 -0.323 2.597 0.834 ...
## $ Y2018 : num 2 1.95 2.71 2.6 4.42 ...
## $ Y2019 : num 2.951 1.95 0.086 2.597 0.234 ...Change in Temperature
#store temperature data in object delta_temp
delta_temp <- temp %>% filter(Element=="Temperature change")Area of focus Antarctica
#Choose Antarctica only for this stury
a <- delta_temp %>% filter(Area=="Antarctica")VISUALIZE YEARLY DELTA TREND
Year by Year 
Month by month
b <- a
b <- b %>% pivot_longer(cols = c(Y1961:Y2019), names_to = "years")
b %>% filter(Months!="NA") %>% ggplot(aes(years,y=value,col=Months, group=Months, label=Months)) + geom_point()+theme(axis.text.x = element_text(angle = 90))+ geom_hline(yintercept = 0.0)
Monthly Temperature Evaluation from 1961 - 2019
ANNUAL AVERAGE CHANGE IN TEMPERATURE
#Compute mean of Years from 1961-2019
mean_peryear<- colMeans(a[,c(8:66)])Plotting the Annual Mean Temperatures 
PREDICTION IN 10 YEARS
Data cleaning and preparation
# store f to g
g <- f#remove Y
j <- g %>% mutate_at("Years", str_replace,"Y","")
#format date to years
j$Years <- format(j$Years,format="%Y")Predict using Prophet
#setting the date and store at object ds
ds <- j$Years
ds <- format(ds, format="%m/%d/%Y")
ds <- ymd(ds, truncated = 2L)
#setting the mean temperatures and store in object y
y <- j$mean_peryear
#store both as a data frame
df_pred <- data.frame(ds,y)#prepare prediction and store at m
m <- prophet(df_pred)#set data frame and predict for the next 10 years
future <- make_future_dataframe(m,periods = 10, freq = "year")
forecast <- predict(m,future)Prediction from year 2000-2019 data in 10 yrs
dyplot.prophet(m,forecast)## Warning: `select_()` was deprecated in dplyr 0.7.0.
## Please use `select()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.summary(forecast)## ds trend additive_terms
## Min. :1961-01-01 00:00:00 Min. :0.2999 Min. :-0.5520
## 1st Qu.:1978-01-01 00:00:00 1st Qu.:0.5128 1st Qu.:-0.5252
## Median :1995-01-01 00:00:00 Median :0.7256 Median :-0.4918
## Mean :1995-01-01 03:07:49 Mean :0.7256 Mean :-0.5044
## 3rd Qu.:2012-01-01 00:00:00 3rd Qu.:0.9384 3rd Qu.:-0.4518
## Max. :2029-01-01 00:00:00 Max. :1.1512 Max. :-0.4518
## additive_terms_lower additive_terms_upper yearly yearly_lower
## Min. :-0.5520 Min. :-0.5520 Min. :-0.5520 Min. :-0.5520
## 1st Qu.:-0.5252 1st Qu.:-0.5252 1st Qu.:-0.5252 1st Qu.:-0.5252
## Median :-0.4918 Median :-0.4918 Median :-0.4918 Median :-0.4918
## Mean :-0.5044 Mean :-0.5044 Mean :-0.5044 Mean :-0.5044
## 3rd Qu.:-0.4518 3rd Qu.:-0.4518 3rd Qu.:-0.4518 3rd Qu.:-0.4518
## Max. :-0.4518 Max. :-0.4518 Max. :-0.4518 Max. :-0.4518
## yearly_upper multiplicative_terms multiplicative_terms_lower
## Min. :-0.5520 Min. :0 Min. :0
## 1st Qu.:-0.5252 1st Qu.:0 1st Qu.:0
## Median :-0.4918 Median :0 Median :0
## Mean :-0.5044 Mean :0 Mean :0
## 3rd Qu.:-0.4518 3rd Qu.:0 3rd Qu.:0
## Max. :-0.4518 Max. :0 Max. :0
## multiplicative_terms_upper yhat_lower yhat_upper
## Min. :0 Min. :-0.88608 Min. :0.4482
## 1st Qu.:0 1st Qu.:-0.65899 1st Qu.:0.6740
## Median :0 Median :-0.44804 Median :0.8884
## Mean :0 Mean :-0.44418 Mean :0.8903
## 3rd Qu.:0 3rd Qu.:-0.23905 3rd Qu.:1.1009
## Max. :0 Max. : 0.04115 Max. :1.3547
## trend_lower trend_upper yhat
## Min. :0.2999 Min. :0.2999 Min. :-0.2145046
## 1st Qu.:0.5128 1st Qu.:0.5128 1st Qu.: 0.0000123
## Median :0.7256 Median :0.7256 Median : 0.2212236
## Mean :0.7256 Mean :0.7256 Mean : 0.2211403
## 3rd Qu.:0.9384 3rd Qu.:0.9384 3rd Qu.: 0.4364788
## Max. :1.1512 Max. :1.1512 Max. : 0.6994661Prediction from year 2000-2019 data in 10 yrs
## Disabling weekly seasonality. Run prophet with weekly.seasonality=TRUE to override this.## Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.## n.changepoints greater than number of observations. Using 15dyplot.prophet(m2000,forecast2k) summary(forecast2k)## ds trend additive_terms
## Min. :2000-01-01 00:00:00 Min. :0.05428 Min. :-0.3286
## 1st Qu.:2007-04-02 06:00:00 1st Qu.:0.32514 1st Qu.:-0.2567
## Median :2014-07-02 12:00:00 Median :0.59600 Median : 0.1033
## Mean :2014-07-02 12:00:00 Mean :0.59600 Mean : 0.1038
## 3rd Qu.:2021-10-01 18:00:00 3rd Qu.:0.86686 3rd Qu.: 0.4648
## Max. :2029-01-01 00:00:00 Max. :1.13772 Max. : 0.5371
## additive_terms_lower additive_terms_upper yearly yearly_lower
## Min. :-0.3286 Min. :-0.3286 Min. :-0.3286 Min. :-0.3286
## 1st Qu.:-0.2567 1st Qu.:-0.2567 1st Qu.:-0.2567 1st Qu.:-0.2567
## Median : 0.1033 Median : 0.1033 Median : 0.1033 Median : 0.1033
## Mean : 0.1038 Mean : 0.1038 Mean : 0.1038 Mean : 0.1038
## 3rd Qu.: 0.4648 3rd Qu.: 0.4648 3rd Qu.: 0.4648 3rd Qu.: 0.4648
## Max. : 0.5371 Max. : 0.5371 Max. : 0.5371 Max. : 0.5371
## yearly_upper multiplicative_terms multiplicative_terms_lower
## Min. :-0.3286 Min. :0 Min. :0
## 1st Qu.:-0.2567 1st Qu.:0 1st Qu.:0
## Median : 0.1033 Median :0 Median :0
## Mean : 0.1038 Mean :0 Mean :0
## 3rd Qu.: 0.4648 3rd Qu.:0 3rd Qu.:0
## Max. : 0.5371 Max. :0 Max. :0
## multiplicative_terms_upper yhat_lower yhat_upper
## Min. :0 Min. :-0.90018 Min. :0.3279
## 1st Qu.:0 1st Qu.:-0.19194 1st Qu.:1.0100
## Median :0 Median : 0.09411 Median :1.2991
## Mean :0 Mean : 0.09157 Mean :1.3075
## 3rd Qu.:0 3rd Qu.: 0.40554 3rd Qu.:1.6139
## Max. :0 Max. : 1.04837 Max. :2.2874
## trend_lower trend_upper yhat
## Min. :0.05428 Min. :0.05428 Min. :-0.2743
## 1st Qu.:0.32514 1st Qu.:0.32514 1st Qu.: 0.3886
## Median :0.59600 Median :0.59600 Median : 0.6993
## Mean :0.59600 Mean :0.59600 Mean : 0.6998
## 3rd Qu.:0.86686 3rd Qu.:0.86686 3rd Qu.: 1.0101
## Max. :1.13772 Max. :1.13772 Max. : 1.6749