inst/pm25_consulate_2015.R

In masalmon/usaqmindia: Data from the U.S. Embassy and Consulate Air Quality Monitors in India

###############################################################
# 2015 csv file for january to november
###############################################################
file <- read_csv("inst/extdata/jan-nov2015.csv", skip = 5,
                 col_names = c("Date", "Time",
                               "PM2.5_Delhi", "PM2.5_Chennai",
                               "PM2.5_Kolkata", "PM2.5_Mumbai",
                               "PM2.5_Hyderabad"),
                 col_types = "ccnnnnn",
                 na = c("", "NA", "NoData", "-999",
                        "---", "InVld", "PwrFail"))

which24 <- which(grepl("24:00 AM", file$Date))

file <- file %>%
  mutate(Time = ifelse(grepl("24:00 AM", Date),
                       "12:00 AM", Time)) %>%
  mutate(Date = dmy(gsub(" 24:00 AM", "", Date)))

file$Date[which24] <- file$Date[which24] + days(1)

file <- file %>%
  filter(!is.na(Date)) %>%
  mutate(datetime = paste(as.character(Date), Time)) %>%
  mutate(datetime = parse_date_time(datetime,
                                    "%Y-%m-%d I:M p")) %>%
  mutate(datetime = force_tz(datetime,
                                    "Asia/Kolkata")) %>%
  select(datetime, everything()) %>%
  select(- Date, - Time)

data_us <- bind_rows(data_us, file)
###############################################################
# 2015 file for december is a pdf, yikee
###############################################################

# f <- "inst/extdata/jan-dec_2015.pdf"
# out1 <- extract_tables(f)
# save(out1, file = "inst/extdata/us_data.RData")
load("inst/extdata/us_data.RData")


# apply said functions
all_pm <- bind_rows(lapply(out1, transform_tableau))
names(all_pm) <- "name"
# now separate
all_pm <- all_pm %>% separate(col = name,
                              sep = ",",
                              into = c("PM2.5_Chennai",
                                       "PM2.5_Kolkata",
                                       "PM2.5_Hyderabad",
                                       "PM2.5_Mumbai",
                                       "PM2.5_Delhi"))

all_pm <- all_pm %>% mutate(PM2.5_Chennai = as.numeric(PM2.5_Chennai),
                            PM2.5_Kolkata = as.numeric(PM2.5_Kolkata),
                            PM2.5_Hyderabad = as.numeric(PM2.5_Hyderabad),
                            PM2.5_Mumbai = as.numeric(PM2.5_Mumbai),
                            PM2.5_Delhi = as.numeric(PM2.5_Delhi))

# now find the corresponding times
# not that easy since part of the pdf is unreadable

times_us <- bind_rows(lapply(out1, horaire_tableau))
names(times_us) <- "datetime"
times_us <- times_us %>%
  mutate(datetime = as.POSIXct(datetime, origin = "1970-01-01",
                               tz = "Asia/Kolkata"))

all_pm <-  cbind(all_pm, times_us)
names(all_pm)[6] <- "datetime"
first_interesting <- max(which(month(all_pm$datetime) == 11))+2
all_pm <- all_pm[first_interesting:nrow(all_pm),]
all_pm$datetime <- seq(from = ymd_hms("2015-12-01 01:00:00", tz = "Asia/Kolkata"),
                       to = ymd_hms("2016-01-01 00:00:00", tz = "Asia/Kolkata"),
                       by = "1 hour")
data_us <- bind_rows(data_us, all_pm)
rm(out1)
rm(all_pm)