4 ggplot2

4.1 Preparation

raw_df <- readRDS("rp_201801_202005_df.rds")

4.2 Scatterplot

First let’s preprocess our data to get production in May 2020 and hours between 10:00 AM and 5:00 PM.

plot_df1 <- 
  raw_df %>% 
    filter(dt >= "2020-05-01" & dt < "2020-06-01" & lubridate::hour(dt) >= 10 & lubridate::hour(dt) <= 17) %>%   transmute(hour_of_day = lubridate::hour(dt),wind_lic,sun_ul)

print(plot_df1,n=3)

# A tibble: 248 × 3
  hour_of_day wind_lic sun_ul
        <int>    <dbl>  <dbl>
1          17    2055.  1442.
2          16    2036.  2523.
3          15    1875.  3402.
# ℹ 245 more rows

Let’s plot licensed wind production against unlicensed solar production for May 2020 for hours between 10-17. We can say that

Usually during morning time solar production is high and wind production is kind of low.
Expectedly solar production is decreasing in the afternoon

ggplot(plot_df1, aes(x = wind_lic, y = sun_ul, color = as.character(hour_of_day))) + geom_point()

ggplot2 is quite flexible. We can move aes from ggplot object to geom_point object.

ggplot(plot_df1) + geom_point(aes(x = wind_lic, y = sun_ul, color = as.character(hour_of_day)))

4.3 Line Chart

Here I want to compare wind and solar production in a time series plot.

plot_df2 <- raw_df %>% filter(dt >= "2020-05-01" & dt < "2020-06-01") %>% select(dt,wind_lic,sun_ul) 

print(plot_df2,n=3)

# A tibble: 744 × 3
  dt                  wind_lic sun_ul
  <dttm>                 <dbl>  <dbl>
1 2020-05-31 23:00:00    1434. 0.0582
2 2020-05-31 22:00:00    1577. 0.032 
3 2020-05-31 21:00:00    1858. 0.0335
# ℹ 741 more rows

ggplot(plot_df2) + 
  geom_line(aes(x=dt,y=wind_lic,color="wind")) + 
  geom_line(aes(x=dt,y=sun_ul,color="solar"))

What if I don’t want to repeat geom_line functions? I can use pivot_longer function to get all in a single geom_line.

ggplot(plot_df2 %>% pivot_longer(.,cols=-dt),aes(x=dt,y=value,color=name)) + geom_line()

4.4 Bar Chart

Now I’d like to get the May 2020 production and I’d like to differentiate Licensed and Unlicensed.

plot_df3 <- raw_df %>% filter(dt >= "2020-05-01" & dt < "2020-06-01") %>% summarise(across(-dt,sum)) %>% pivot_longer(.,everything()) %>% mutate(type = ifelse( grepl("_lic+$",name),"Licensed","Unlicensed"))

print(plot_df3,n=3)

# A tibble: 16 × 3
  name              value type    
  <chr>             <dbl> <chr>   
1 wind_lic       1346260. Licensed
2 geothermal_lic  654089. Licensed
3 biogas_lic       56839. Licensed
# ℹ 13 more rows

Let’s plot total productions using a bar chart. To improve readability, we reorder by production and differentiate Licensed/Unlicensed using color.

ggplot(plot_df3,aes(x=reorder(name,-value),y=value,fill=type)) + geom_bar(stat = "identity") + theme(axis.text.x = element_text(angle=60,vjust=1,hjust=1))

4.5 Pie Chart

ggplot(plot_df3 %>% filter(type=="Licensed"),aes(x="",y=value,fill=name)) + geom_bar(stat="identity") + coord_polar("y")

4.6 Theming and Customization

Let’s get our charts better looks!

sc_plot <- ggplot(plot_df1) + geom_point(aes(x = wind_lic, y = sun_ul, color=as.character(hour_of_day)))

sc_plot

sc_plot2 <- sc_plot + theme_minimal()
sc_plot2

sc_plot3 <-
sc_plot2 + labs(x="Licensed Wind Production (MWh)", y="Unlicensed Solar Production (MWh)", color="Hour of Day", title = "Licensed Wind vs Unlicensed Solar", subtitle = "Renewable production in May 2020, between 10:00-17:00 each day") 

sc_plot3

sc_plot3 + theme(legend.position = "top",axis.text.x = element_text(angle=45,hjust=1,vjust=1)) + scale_y_continuous(labels=function(x) format(x, big.mark = ".", decimal.mark = ",", scientific = FALSE)) + scale_x_continuous(labels=function(x) format(x, big.mark = ".", decimal.mark = ",", scientific = FALSE))