Datadive12

Select a column of your data that encodes time (e.g., “date”, “timestamp”, “year”, etc.). Convert this into a Date in R.

I am going to use year in my time based data. I think It would be interesting to see the time series of energy usage in the united states compared with renewable energy usage. I will need to select only the columns where the country code is equivalent to the USA’s.

library(tidyverse)

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library(ggthemes)
library(ggrepel)

#time series
library(xts)

## Warning: package 'xts' was built under R version 4.3.2

## Loading required package: zoo

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library(tsibble)

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setwd("C:/Users/kaitl/OneDrive/Documents/590_Working")

#update data types of dataframe
energy <- read_delim("./590_FinalData1.csv", delim = ",", col_types = "icciiciiiiiiii")

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

energy1 <- energy
energy1[energy1 == '..'] <- NA

energy1$year <- as.Date(energy1$year , format = "d%/m%/%y")
#print(energy1$year)

• Choose a column of data to analyze over time. This should be a “response-like” variable that is of particular interest.

  Renewable Energy Usage

• Create a tsibble object of just the date and response variable. Then, plot your data over time. Consider different windows of time. What stands out?

  # # create a tsibble of renewable energy consumption for the United States
  # energy_ <- energy1 |>
  #   filter(country_name == "United States") |>
  #   select(year, ren_energy_cons) |>
  #   distinct()
  # 
  # energy_ts <- as_tsibble(energy_, index=year) |>
  #   index_by(date = date(year)) 
  # 
  # energy_ts

# #plot data over time
# # an "xts" object separate from the original
# energy_xts <- xts(x = energy_ts$ren_energy_cons, 
#                   order.by = energy_ts$date)
# 
# energy_xts <- setNames(energy_xts, "ren. eng. use")
# 
# #loess method
# energy_ts |>
#   filter_index("1975" ~ "2000") |>
#   drop_na() |>
#   ggplot(mapping = aes(x = year, y = ren_energy_cons)) +
#   geom_point(size=1, shape='O') +
#   geom_smooth(span=0.2, color = 'blue', se=FALSE) + #adjust span to see changes in plot 0.1, 0.2, 0.3..
#   labs(title = "U.S. Renewable Energy Consumption During 1975") +
#   theme_hc()

I do notice an oscillation in the data, but as its doing this it is also headed in a positive direction.

• Use linear regression to detect any upwards or downwards trends

{# {r} # # model <- lm(ren_energy_cons ~ year , data = energy1, na.action = na.omit) # # rsquared <- summary(model)$r.squared # # energy1 |> # ggplot(mapping = aes(x = ren_energy_cons, # y = year)) + # geom_point() + # geom_smooth(method = 'lm', color = 'gray', linetype = 'dashed', # se = FALSE) + # geom_smooth(se = FALSE) + # labs(title = "Renweable Energy Consumption vs. Year", # subtitle = paste("Linear Fit R-Squared =", round(rsquared, 3))) + # theme_classic()

• Calculate ACF

# energy_ts |>
#   mutate(years = lag(year, 7)) |>
#   drop_na()
# acf(energy_ts, ci = 0.95, na.action = na.exclude)

This looks like it is telling us that there is a period of 10 in the data