In altruisticDataAnalysis/red-dragon:

red-dragon

San Francisco Yearly Average of Daily Max Temp

For Daily Weather Data at Station USW00023272

From 1921 to 2017

The program below wrangles data using R code. The data is from the weather station nearest my home in Duboce Triangle, SF, CA. The National Oceanic and Atmospheric Administration (NOAA) National Centers For Environmental Information is the data source. The daily weather data summaries are for all time, relative to this SF weather station existence.

    library(tidyverse)
    sfDaily <- read_csv("data/sf1921to2017GhcnDaily.csv")
    sfDaily <- sfDaily[,colSums(is.na(sfDaily))<nrow(sfDaily)]
    sfDaily$YEAR <- substring(sfDaily$DATE,1,4)
    sfYearMax <- sfDaily %>%
        group_by(sfDaily$YEAR) %>%
        summarise((sfDaily = mean(TMAX)))
    plot(sfYearMax)

daylight <- read_delim("data/sfDurationOfDaylight2017.txt", delim = "\t")
daylight <- as.tibble(daylight)
str(daylight)
daylightJan <- select(daylight, Day, 'Jan.')
daylightFeb <- select(daylight, Day, 'Feb.')
daylightMar <- select(daylight, Day, 'Mar.')
daylightApr <- select(daylight, Day, 'Apr.')
daylightMay <- select(daylight, Day, 'May')
daylightJun <- select(daylight, Day, 'June')
daylightJul <- select(daylight, Day, 'July')
daylightAug <- select(daylight, Day, 'Aug.')
daylightSep <- select(daylight, Day, 'Sep.')
daylightOct <- select(daylight, Day, 'Oct.')
daylightNov <- select(daylight, Day, 'Nov.')
daylightDec <- select(daylight, Day, 'Dec.')

daylightJan$Month <- 'Jan'
daylightFeb$Month <- 'Feb'
daylightMar$Month <- 'Mar'
daylightApr$Month <- 'Apr'
daylightMay$Month <- 'May'
daylightJun$Month <- 'Jun'
daylightJul$Month <- 'Jul'
daylightAug$Month <- 'Aug'
daylightSep$Month <- 'Sep'
daylightOct$Month <- 'Oct'
daylightNov$Month <- 'Nov'
daylightDec$Month <- 'Dec'

daylightJan <- as.data.frame(daylightJan)
daylightFeb <- as.data.frame(daylightFeb)
daylightMar <- as.data.frame(daylightMar)
daylightApr <- as.data.frame(daylightApr)
daylightMay <- as.data.frame(daylightMay)
daylightJun <- as.data.frame(daylightJun)
daylightJul <- as.data.frame(daylightJul)
daylightAug <- as.data.frame(daylightAug)
daylightSep <- as.data.frame(daylightSep)
daylightOct <- as.data.frame(daylightOct)
daylightNov <- as.data.frame(daylightNov)
daylightDec <- as.data.frame(daylightDec)

#colnames(df)[names(df)=="varName"] <- "newVarName"
colnames(daylightJan)[names(daylightJan)=="Jan."] <- "Daylight"
colnames(daylightFeb)[names(daylightFeb)=="Feb."] <- "Daylight"
colnames(daylightMar)[names(daylightMar)=="Mar."] <- "Daylight"
colnames(daylightApr)[names(daylightApr)=="Apr."] <- "Daylight"
colnames(daylightMay)[names(daylightMay)=="May"] <- "Daylight"
colnames(daylightJun)[names(daylightJun)=="June"] <- "Daylight"
colnames(daylightJul)[names(daylightJul)=="July"] <- "Daylight"
colnames(daylightAug)[names(daylightAug)=="Aug."] <- "Daylight"
colnames(daylightSep)[names(daylightSep)=="Sep."] <- "Daylight"
colnames(daylightOct)[names(daylightOct)=="Oct."] <- "Daylight"
colnames(daylightNov)[names(daylightNov)=="Nov."] <- "Daylight"
colnames(daylightDec)[names(daylightDec)=="Dec."] <- "Daylight"

daylightYear <- rbind(daylightJan, daylightFeb)
daylightYear <- rbind(daylightYear, daylightMar)
daylightYear <- rbind(daylightYear, daylightApr)
daylightYear <- rbind(daylightYear, daylightMay)
daylightYear <- rbind(daylightYear, daylightJun)
daylightYear <- rbind(daylightYear, daylightJul)
daylightYear <- rbind(daylightYear, daylightAug)
daylightYear <- rbind(daylightYear, daylightSep)
daylightYear <- rbind(daylightYear, daylightOct)
daylightYear <- rbind(daylightYear, daylightNov)
daylightYear <- rbind(daylightYear, daylightDec)

daylightYear <- na.omit(daylightYear)

daylightYear$dayOfYear <- daylightYear %>%
  mutate(1:365)
# colnames(df)[names(df)=="varName"] <- "newVarName"
colnames(daylightYear)[names(daylightYear)=="dayOfYear.1:365"] <- "dayOfYear"

#daylightYear <- select(daylightYear,3,1,2)

as.character(daylightYear$Day)
daylightYear$`Day of Year` <- paste(daylightYear$Month, daylightYear$Day)
#daylightYear <- select(daylightYear, 4,3)
plot(daylightYear$Daylight)
#require(ggplot2)
#theme_set(theme_bw()) # Change the theme to my preference
#ggplot(aes(x = daylightYear$dayOfYear, y = daylightYear$Daylight), data = daylightYear) + geom_point()

MIT License

Copyright (c) 2017 Jeffrey Long

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

altruisticDataAnalysis/red-dragon documentation built on May 10, 2019, 1:22 p.m.