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How to scatterplot based different column conditions in r?

Time:01-18

My dataframe looks like this:

dput(B8A)
structure(list(ID = c("AUR", "AUR", "AUR", "AUR", "AUR", "AUR", 
"LAM", "LAM", "LAM", "LAM", "LAM", "LAM", "P0", "P01", "P02", 
"P1", "P18", "P2", "P22", "P23", "P24", "P3", "P4"), nD_SOS = c(129L, 
349L, 256L, 319L, 306L, 128L, 309L, 244L, 134L, 356L, 131L, 302L, 
276L, 296L, 294L, 310L, 337L, 291L, 308L, 295L, 308L, 303L, 310L
), nNDVI_SOS = c(119L, 374L, 272L, 329L, 328L, 152L, 317L, 246L, 
129L, 378L, 152L, 315L, 358L, 314L, 310L, 305L, 363L, 305L, 281L, 
274L, 285L, 302L, 311L), crop = c("sunfl", "wwht", "wrap", "wwht", 
"fbn", "sunfl", "wwht", "wmust_vetch", "irr_corn", "wwht", "irr_corn", 
"wwht", "wrap", "wrap", "wrap", "wrap", "wwht", "wrap", "wrap", 
"wrap", "wrap", "wrap", "wrap"), year = c(2016, 2016, 2017, 2018, 
2019, 2020, 2015, 2016, 2017, 2017, 2019, 2019, 2016, 2016, 2016, 
2017, 2018, 2017, 2015, 2015, 2015, 2017, 2018)), row.names = c(NA, 
-23L), class = "data.frame")

I want to do a scatterplot between nD_SOS and nNDVI_SOS based on different column conditions. The condition is to get the scatterplot only for the year 2017 and the crop wrap.

I've been using this code for plotting:

p3 <- ggplot(nB8A_SOS_KRR25_crop, aes(x=nNDVI_SOS, y=nD_SOS))  
  geom_point()  
  stat_regline_equation(label.x=30, label.y=310)  
  stat_cor(aes(label=..rr.label..), label.x=30, label.y=290) 
  geom_smooth(method=lm , color="red", fill="#69b3a2", se=TRUE)  
  ggtitle("B8A KRR 25")  
  theme_ipsum()

p3

The idea is to add a piece of code to show the data for the specific year and crop.

Any help will be much appreciatted.

CodePudding user response:

df %>%  
  filter(year == 2017, 
         crop == "wrap") %>% 
  ggplot()   
  aes(x = nNDVI_SOS, y = nD_SOS)   
  geom_point()   
  stat_regline_equation(label.x=30, label.y=310)  
  stat_cor(aes(label=..rr.label..), label.x=30, label.y=290) 
  geom_smooth(method=lm , color="red", fill="#69b3a2", se=TRUE)  
  ggtitle("B8A KRR 25")  
  theme_ipsum()

enter image description here

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