Plotting precip2 Data (by time and station)

#Precipitation over Days Scatter Plot For Each Station
date <- unique(data2[, 6])                       #date
date <- format(sort(date))                       #sorting dates and converting from numeric to char string
date2 <- as.Date(date, format = "%Y%m%d")        #converting dates into proper date format
station_ids_names <- unique(data2[, 1:2])        #station names
good <- 1:36
good <- good[-c(3, 9, 19, 20, 21, 35)]           #takes out stations that don't have precipitations
for (i in good){                                 #loop goes through stations that have precipitations
  indicator <- !is.na(precip2[,i])               #indicator variable to take out NAs of plots
  station_title <- paste(station_ids_names[i ,2], 
              "Precipitation")           #changing title for each station for plots
  plot(date2[indicator], precip2[indicator, i], col = "blue", main = station_title, 
       xlab = "Time (months)", ylab = "Total Precipitation (in)", type = "h")
}

#Cal Poly SLO Plot
indicator <- !is.na(precip2[,35])              
station_title <- "Cal Poly SLO Precipitation"      
plot(date2[indicator], precip2[indicator, 35], col = "blue", main = station_title, 
     xlab = "Time (months)", ylab = "Total Precipitation (in)", type = "h")

Plotting precip2 Data (by time)

good <- 1:36
good <- good[-c(3, 9, 19, 20, 21)]
plot(date2, precip2[, 1], type = "h", pch = 1, 
     main = "Precipitation over Time", xlab = "Time (months)", 
     ylab = "Total Precipitation (in)", col = "blue", ylim = c(0, 27))
for (x in good){
  points(date2, precip2[, x], type = "h", pch = 1, col = "blue")
}

Plotting temps_min2 and temps_max2 Data (by time and station)

#Temperatures over Time Scatter Plot
station_ids_names <- unique(data2[, 1:2])    #station ids and names
date <- unique(data2[, 6])                   #date
date <- format(sort(date))                   #sorting dates and converting from numeric to character string
date2 <- as.Date(date, format = "%Y%m%d")  
good2 <- 1:36
good2 <- good2[-c(2, 6, 7, 8, 13, 16, 22,
                  23, 24, 26, 27, 30, 31, 
                  32, 33, 34, 35, 36)]       #removing all stations without min and max temperatures
for (x in good2) {                           #loops through all stations with min and max temperatures
  indicator <- !is.na(temps_min2[,x]) &      #indicator to remove NAs from plot
               !is.na(temps_max2[,x])
  temp_title <- paste(station_ids_names[x, 2], "Max/Min Temperatures") #changing title
  temp_d <- date2[indicator]
  y1 <- temps_min2[indicator, x]
  y2 <- temps_max2[indicator, x]
  yr <- range(c(y1, y2))
  plot(temp_d, y1, type = "p", pch = 1, main = temp_title, xlab = "Time (months)", 
       ylab = "Temperature (Fahrenheit)", col = "blue", ylim = yr)
  points(temp_d, y2, type = "p", pch = 1, col = "red")
  abline(lm(y1~temp_d), col = "blue", lwd = 2)
  abline(lm(y2~temp_d), col = "red", lwd = 2)
}

#Cal Poly SLO Plot
indicator <- !is.na(temps_min2[,35]) & !is.na(temps_max2[,35])
temp_title <- "Cal Poly SLO Max/Min Temperatures"
temp_d <- date2[indicator]
y1 <- temps_min2[indicator, 35]
y2 <- temps_max2[indicator, 35]
yr <- range(c(y1, y2))
trange <- as.Date(c("19280101", "20170101"), format = "%Y%m%d")
plot(temp_d, y1, type = "p", pch = 1, main = temp_title, xlab = "Time (months)", 
ylab = "Temperature (Fahrenheit)", col = "blue", ylim = c(34,90), xlim = trange)
points(temp_d, y2, type = "p", pch = 1, col = "red")
abline(lm(y1~temp_d), col = "blue", lwd = 2)
abline(lm(y2~temp_d), col = "red", lwd = 2)

Plotting temps_min2 and temps_max2 Data (by time)

good2 <- 1:36
good2 <- good2[-c(2, 6, 7, 8, 13, 16, 22,
                  23, 24, 26, 27, 30, 31, 
                  32, 33, 34, 36)]  
plot(date2, temps_min2[, 1], type = "p", pch = 1, main = "Mean Temperature over Time", 
     xlab = "Time (months)", ylab = "Mean Temperature (Fahrenheit)", 
     col = "blue", ylim = c(15, 110))
avg_slope1_num <- 0                            #initializing slope numerator for min temps
avg_slope2_num <- 0                            #initializing slope numerator for max temps
avg_intercept1_num <- 0                        #initializing intercapt numerator for min temps
avg_intercept2_num <- 0                        #initializing intercapt numerator for max temps
c = 0                                          #initializing index variable
for (z in good2){
  points(date2, temps_min2[, z], type = "p", 
         pch = 1, col = "blue")                #adding min temps points in blue
  points(date2, temps_max2[, z], type = "p", 
         pch = 1, col = "red")                 #adding max temps points in red
  require(stats)
  reg1 <- lm(temps_min2[, z] ~ date2)          #regression for min temps
  reg2 <- lm(temps_max2[, z] ~ date2)          #regression for max temps
  coeff1 <- coefficients(reg1)                 #coefficients for min temps
  coeff2 <- coefficients(reg2)                 #coefficients for max temps
  slope1 <- round(coeff1[2],1)                 #slope for min temps
  slope2 <- round(coeff2[2],1)                 #slope for max temps
  intercept1 <- round(coeff1[1],1)             #intercept for min temps
  intercept2 <- round(coeff2[1],1)             #intercept for max temps
  avg_slope1_num <- avg_slope1_num + slope1    #adding min temps slope to avg slope numerator
  avg_slope2_num <- avg_slope2_num + slope2    #adding max temps slope to avg slope numerator
  avg_intercept1_num <- avg_intercept1_num + 
                            intercept1         #adding min temps intercept to avg intercept num
  avg_intercept2_num <- avg_intercept2_num + 
                            intercept2         #adding max temps intercept to avg intercept num
  c = c + 1                                    #indexing
}
avg_slope1 = avg_slope1_num / c                #avg min temps slope num divded by indexing number
avg_slope2 = avg_slope2_num / c                #avg max temps slope num divded by indexing number
avg_intercept1 = avg_intercept1_num / c        #avg min temps interc num divded by indexing number
avg_intercept2 = avg_intercept2_num / c        #avg max temps interc num divded by indexing number
abline(avg_intercept1, avg_slope1, lwd = 2)    #regression line for min temps
abline(avg_intercept2, avg_slope2, lwd = 2)    #regression line for max temps