Choosing stations in Peru pt. 1, Meteorological Stations
Introduction
Last summer I spent a few months in San Martín, Peru, toward the western border of the Amazon. My trip was dedicated to identifying areas of the region that are affected by seasonal flooding. I was directed to the district of San Rafael, where I secured the support of the local government. The authorities directed me to two smaller communities within their district: Libertad and Panamá. I will write more on the interviews and my direct findings in a future post. For now, I want to get into the nitty-gritty of identifying stations to inform a local climate study in the San Rafael District. This will be the first in a series of posts related to my dissertation research. In today’s post, I will strive to select stations and perform some cursory data exploration. Let’s start by loading the necessary libraries.
library(dplyr)
library(plotly)
library(purrr)
library(senamhiR)
library(tibble)
library(timevis)
library(zoo)
Station search
When I was on the ground in San Rafael, I checked the GPS coordinates on my phone. I was standing at -7.023833°, -76.465222°. I’ll use these coordinates as the target for nearby stations, using my
senamhiR R package. By the looks of it, there are just a handful of stations within 20 km of San Rafael.
nearby <- station_search(target = c(-7.023833, -76.465222), dist = 1:20)
nearby
#> # A tibble: 6 x 15
#> # Rowwise:
#> Station StationID Type Configuration `Data Start` `Data End` `Period (Yr)`
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 SAN CR… 221803 CON H 1968 1980 13
#> 2 BELLAV… 000382 CON M 1963 2020 58
#> 3 NUEVO … 153312 CON M 1963 2020 58
#> 4 LA UNI… 000384 CON M 1970 2020 51
#> 5 PICOTA 153313 CON M 1963 2020 58
#> 6 PICOTA 230715 CON H 2003 2020 18
#> # … with 8 more variables: `Station Status` <chr>, Latitude <dbl>,
#> # Longitude <dbl>, Altitude <int>, Region <chr>, Province <chr>,
#> # District <chr>, Dist <dbl>
I love maps, so let’s look at these stations on a map. The red dot is the “target” from my GPS reading.
# This block won't run because it addAwesomeMarkers doesn't seem to work in hugodown
map_stations(nearby)
By the looks of it, I have four meteorological stations and two river stations in close proximity to my site. Unfortunately, only the meteorological stations have long-term (≥ 30 years) climate data.
nearby %>% filter(`Period (Yr)` >= 30)
#> # A tibble: 4 x 15
#> # Rowwise:
#> Station StationID Type Configuration `Data Start` `Data End` `Period (Yr)`
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 BELLAV… 000382 CON M 1963 2020 58
#> 2 NUEVO … 153312 CON M 1963 2020 58
#> 3 LA UNI… 000384 CON M 1970 2020 51
#> 4 PICOTA 153313 CON M 1963 2020 58
#> # … with 8 more variables: `Station Status` <chr>, Latitude <dbl>,
#> # Longitude <dbl>, Altitude <int>, Region <chr>, Province <chr>,
#> # District <chr>, Dist <dbl>
Four stations it is. Let’s test these stations! The first step, of course, is to acquire the data. I’ll download these to a list, without collapsing the stations into a single table, for now.
Data audit
The completeness of these data sets is important to check. I will apply the
quick_audit() function to each station. I will filter these tables so that any year that is 100% missing for a given variable will be omitted, hence the year column will be discontinuous.
dat_audit <- lapply(dat, quick_audit, by = "year")
Station 1: BELLAVISTA (000382)
dat_audit[[1]] %>% filter_at(2:ncol(.), any_vars(. < 100)) %>% print(n = 50)
#> # A tibble: 45 x 13
#> Year `Tmax (C) pct N… `Tmin (C) pct N… `TBS07 (C) pct … `TBS13 (C) pct …
#> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 1963 91.5 91.5 91.5 100
#> 2 1964 0 0 0 8.47
#> 3 1965 0.274 0.274 0.274 3.56
#> 4 1966 0 0 0 9.04
#> 5 1967 0 0 0 0
#> 6 1968 0 0 0 0
#> 7 1969 0 0 0 0.274
#> 8 1970 0 0 0 0.274
#> 9 1971 0.274 0.274 0.274 0.548
#> 10 1972 0.820 0.820 0.820 0.820
#> 11 1973 16.2 16.2 16.2 16.2
#> 12 1974 0.274 0 0 0
#> 13 1975 0 0 0 0.274
#> 14 1976 0 0 0 0
#> 15 1977 0 0 0 0
#> 16 1978 1.10 1.10 1.10 1.10
#> 17 1979 0.822 0.822 0.822 1.10
#> 18 1980 3.28 3.28 3.28 3.28
#> 19 1981 41.9 42.7 41.9 41.9
#> 20 1995 16.2 16.2 16.2 16.2
#> 21 1996 0 0 0 0
#> 22 1997 0 0 0 0
#> 23 1998 0 0 0 0
#> 24 1999 0 0 0 0
#> 25 2000 0 0 0 0
#> 26 2001 0 0 0 0
#> 27 2002 0 0 0 0
#> 28 2003 0 0 0 0
#> 29 2004 0 0 0 0
#> 30 2005 0 0 0 0
#> 31 2006 0 18.6 0 0
#> 32 2007 0 0 0 0
#> 33 2008 0 0 0 0
#> 34 2009 0 0 0 0
#> 35 2010 0 0 0 0
#> 36 2011 0 0 0 0
#> 37 2012 0 0 0 0
#> 38 2013 0 0 0 0
#> 39 2014 0 0 0 0
#> 40 2015 0 0 0 0
#> 41 2016 92.1 92.1 92.1 92.1
#> 42 2017 0 3.01 0 0
#> 43 2018 0 0.274 0 0
#> 44 2019 0 0 0 0
#> 45 2020 83.6 83.6 83.6 83.6
#> # … with 8 more variables: `TBS19 (C) pct NA` <dbl>, `TBH07 (C) pct NA` <dbl>,
#> # `TBH13 (C) pct NA` <dbl>, `TBH19 (C) pct NA` <dbl>, `Prec07 (mm) pct
#> # NA` <dbl>, `Prec19 (mm) pct NA` <dbl>, `Direccion del Viento pct NA` <dbl>,
#> # `Velocidad del Viento (m/s) pct NA` <dbl>
We can see that Bellavista seems to have a full array of data for all variables. This is very good! There is a large stretch of missing data from the early 1980s to the mid ’90s. I have heard (colloquially) that a lot of stations were abandoned during the period of internal conflict in Peru. Sendero Luminoso was active in the Bellavista region, so that might explain this large gap. While I don’t have 30 years of continuous data from Bellavista, I am pretty content with the 25 years preceding the present.
Station 2: LA UNION (000384)
dat_audit[[2]] %>% filter_at(2:ncol(.), any_vars(. < 100)) %>% print(n = 50)
#> # A tibble: 41 x 13
#> Year `Tmax (C) pct N… `Tmin (C) pct N… `TBS07 (C) pct … `TBS13 (C) pct …
#> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 1970 58.1 58.1 58.1 58.1
#> 2 1971 1.10 0.274 0.274 0.822
#> 3 1972 9.02 8.74 8.74 9.02
#> 4 1973 42.7 42.7 42.7 42.7
#> 5 1974 46.6 21.9 21.9 21.9
#> 6 1975 0.274 0.822 0.274 1.10
#> 7 1976 0.546 0.546 0.546 0.546
#> 8 1977 27.1 27.1 27.1 27.1
#> 9 1978 0.274 0.274 0.274 0.274
#> 10 1979 0 0 0 0
#> 11 1980 0 0 0 0
#> 12 1981 83.8 83.8 83.8 83.8
#> 13 1982 58.6 58.6 58.6 58.6
#> 14 1993 18.4 18.6 18.6 18.4
#> 15 1994 0 0 0 0
#> 16 1995 0 2.74 0 0
#> 17 1996 0 0 0 0
#> 18 1997 0 0 0 0
#> 19 1998 0 0 0 0
#> 20 1999 0 0 0 0
#> 21 2000 0.273 0 0 0
#> 22 2001 0 0 0 0
#> 23 2002 0 0 0 0
#> 24 2003 0 0 0 0
#> 25 2004 0 0 0 0
#> 26 2005 0 0 0 0
#> 27 2006 0 0 0 0
#> 28 2007 0 0 0 0
#> 29 2008 0 0 0 0
#> 30 2009 0 0 0 0
#> 31 2010 0 0 0 0
#> 32 2011 8.49 8.49 8.49 8.49
#> 33 2012 0 0 0 0
#> 34 2013 0 0 0 0
#> 35 2014 0 0 0 0
#> 36 2015 0 0 0 0
#> 37 2016 83.6 83.6 83.6 83.6
#> 38 2017 12.3 12.3 12.3 12.3
#> 39 2018 0 0 0 0
#> 40 2019 8.49 8.49 8.49 8.49
#> 41 2020 83.6 83.6 83.6 83.6
#> # … with 8 more variables: `TBS19 (C) pct NA` <dbl>, `TBH07 (C) pct NA` <dbl>,
#> # `TBH13 (C) pct NA` <dbl>, `TBH19 (C) pct NA` <dbl>, `Prec07 (mm) pct
#> # NA` <dbl>, `Prec19 (mm) pct NA` <dbl>, `Direccion del Viento pct NA` <dbl>,
#> # `Velocidad del Viento (m/s) pct NA` <dbl>
Union shows a similar gap in data continuity as Bellavista. However, it is still useful, as it has fairly complete data since 1993 or 1994
Station 3: PICOTA (153313)
I have worked with the Picota data before, so I know that there is no temperature data at this station. Given my focus on flooding, I am more interested in the precipitation record. Let’s see what we have available to us.
dat_audit[[3]] %>% select_at(.var = vars(Year, starts_with("Prec"))) %>% filter_at(2:ncol(.), any_vars(. < 100)) %>% print(n = 99)
#> # A tibble: 58 x 3
#> Year `Prec07 (mm) pct NA` `Prec19 (mm) pct NA`
#> <int> <dbl> <dbl>
#> 1 1963 66.6 66.6
#> 2 1964 0 0
#> 3 1965 33.4 33.4
#> 4 1966 0 0
#> 5 1967 0 0
#> 6 1968 0 0
#> 7 1969 0 0
#> 8 1970 3.29 2.74
#> 9 1971 0.274 0.548
#> 10 1972 0 0
#> 11 1973 0 0
#> 12 1974 0 0
#> 13 1975 0 0
#> 14 1976 0 0
#> 15 1977 0 0
#> 16 1978 0 0
#> 17 1979 0 0
#> 18 1980 0 0
#> 19 1981 0 0
#> 20 1982 8.49 8.49
#> 21 1983 0 0
#> 22 1984 0 0
#> 23 1985 0 0
#> 24 1986 0 0
#> 25 1987 0 0
#> 26 1988 0 0
#> 27 1989 0 0
#> 28 1990 0 0
#> 29 1991 0 0
#> 30 1992 0 0
#> 31 1993 0 0
#> 32 1994 0 0
#> 33 1995 0 0
#> 34 1996 0 0
#> 35 1997 0 0
#> 36 1998 0 0
#> 37 1999 0 0
#> 38 2000 0 0
#> 39 2001 0 0
#> 40 2002 0.274 0
#> 41 2003 0 0
#> 42 2004 0 0
#> 43 2005 0 0
#> 44 2006 0 0
#> 45 2007 0 0
#> 46 2008 0 0
#> 47 2009 0 0
#> 48 2010 0 0
#> 49 2011 0 0
#> 50 2012 0 0
#> 51 2013 0.274 0
#> 52 2014 0 0
#> 53 2015 0 0
#> 54 2016 42.1 41.8
#> 55 2017 0.274 0
#> 56 2018 0 0
#> 57 2019 0 0
#> 58 2020 75.1 75.1
Indeed, just precipitation, but nearly a complete record since the 1960s! Picota is downstream of my target site, but I think that the precipitation info here may still prove to be useful.
Station 4: NUEVO LIMA (153312)
Finally, we have Nuevo Lima, which appears to be another rain gauge station, though there is some very sparse temperature data since around 2012 (not shown here).
dat_audit[[4]] %>% select_at(.var = vars(Year, starts_with("Prec"))) %>% filter_at(2:ncol(.), any_vars(. < 100)) %>% print(n = 99)
#> # A tibble: 58 x 3
#> Year `Prec07 (mm) pct NA` `Prec19 (mm) pct NA`
#> <int> <dbl> <dbl>
#> 1 1963 87.4 87.4
#> 2 1964 25.1 25.1
#> 3 1965 0.274 0
#> 4 1966 1.64 4.11
#> 5 1967 18.9 22.5
#> 6 1968 22.1 23.5
#> 7 1969 16.4 17.3
#> 8 1970 27.9 23.8
#> 9 1971 21.1 15.6
#> 10 1972 23.2 19.4
#> 11 1973 9.32 14.2
#> 12 1974 11.5 11.2
#> 13 1975 12.6 12.3
#> 14 1976 8.47 5.74
#> 15 1977 5.75 4.66
#> 16 1978 4.93 6.58
#> 17 1979 2.47 6.03
#> 18 1980 2.73 5.46
#> 19 1981 3.29 8.77
#> 20 1982 3.56 5.48
#> 21 1983 4.11 4.38
#> 22 1984 6.01 6.56
#> 23 1985 6.58 3.84
#> 24 1986 11.2 3.84
#> 25 1987 8.77 2.47
#> 26 1988 17.5 0
#> 27 1989 8.77 0
#> 28 1990 7.12 3.29
#> 29 1991 7.12 4.66
#> 30 1992 6.83 5.19
#> 31 1993 7.12 9.86
#> 32 1994 4.11 6.85
#> 33 1995 1.92 0.548
#> 34 1996 0 0
#> 35 1997 0.274 0
#> 36 1998 0 0
#> 37 1999 0 0
#> 38 2000 0 0
#> 39 2001 0 0
#> 40 2002 0.274 0.274
#> 41 2003 0 0
#> 42 2004 0 0
#> 43 2005 0 0
#> 44 2006 0.548 0
#> 45 2007 0.274 0.822
#> 46 2008 1.91 3.83
#> 47 2009 1.92 3.84
#> 48 2010 2.47 3.01
#> 49 2011 0.548 1.92
#> 50 2012 8.74 10.7
#> 51 2013 1.10 1.37
#> 52 2014 4.11 5.75
#> 53 2015 0.274 0
#> 54 2016 83.6 83.6
#> 55 2017 0.548 0
#> 56 2018 0 0
#> 57 2019 0 0
#> 58 2020 83.6 83.6
Another station with a little bit of patchiness, but overall a fairly complete and long-term precipitation record up to 2015.
Visualizing these data
Monthly average precipitation
OK, let’s take a look at these data. First, I’ll plot the monthly average precipitation. I will include a record of the completeness of the data using the aforementioned
quick_audit(), which I wrote for the senamhiR package. As written,
quick_audit() only works on one station at a time, so I’ll use the
map() functions in the purrr package to get the desired data. I am going to limit my visualization to the past 25-years (1993 to 2017).
# This has to be nested because of the attribute stored in each table, which is lost when passed to quick_audit. In a future re-write of quick_audit, I will handle this better.
dat_audit <- dat %>%
map_df(~add_column(
quick_audit(
filter(., Fecha >= "1993-01-01" & Fecha <= "2017-12-31"),
variables = c("Prec07", "Prec19"), by = "month"),
StationID = attr(., "StationID"), .before = 1)
)
# Binding rows causes yearmon objects to lose attributes; I'll rename it while I'm at it
dat_audit <- dat_audit %>% mutate(Yearmon = as.yearmon(`Year-month`)) %>%
select(StationID, Yearmon, `Prec07 (mm) pct NA`, `Prec19 (mm) pct NA`)
dat_audit
#> # A tibble: 1,200 x 4
#> StationID Yearmon `Prec07 (mm) pct NA` `Prec19 (mm) pct NA`
#> <chr> <yearmon> <dbl> <dbl>
#> 1 000382 Jan 1993 100 100
#> 2 000382 Feb 1993 100 100
#> 3 000382 Mar 1993 100 100
#> 4 000382 Apr 1993 100 100
#> 5 000382 May 1993 100 100
#> 6 000382 Jun 1993 100 100
#> 7 000382 Jul 1993 100 100
#> 8 000382 Aug 1993 100 100
#> 9 000382 Sep 1993 100 100
#> 10 000382 Oct 1993 100 100
#> # … with 1,190 more rows
Now I will get the aggregate monthly total precipitation, and join the information about the percent of missing values. As a first-time visualization, I’ll only filter fully missing months (100% NA values). I just want to get a quick visual, but for a more robust visualization, I would certainly consider a lower threshold.
First, I will do some data cleaning of the daily data to make my life easier.
dat <- dat %>%
collapse_pcd %>% # Uses the collapse_pcd function from senamhiR
filter(Fecha >= "1993-01-01" & Fecha <= "2017-12-31") %>% # filter the data
rename(p07 = `Prec07 (mm)`, p19 = `Prec19 (mm)`) %>% # rename some vars for easier typing
mutate(Prec = case_when(is.na(p07) & is.na(p19) ~ as.numeric(NA), # avoid false zeroes
TRUE ~ rowSums(.[11:12], na.rm = TRUE)), # keep one p even if other is NA
Day = factor(case_when(is.na(Prec) ~ as.character(NA),
Prec < 1 ~ "dry",
TRUE ~ "wet"))) # add wet and dry day variables
Now I will create the aggregate monthly series and plot this using plotly.
dat_monthly <- dat %>% group_by(StationID, Yearmon = as.yearmon(Fecha)) %>%
summarize(Tot_Prec = sum(Prec, na.rm = TRUE),
dry_days = sum(Day == "dry", na.rm = TRUE),
wet_days = sum(Day == "wet", na.rm = TRUE))
dat_monthly <- left_join(dat_monthly, dat_audit) %>%
filter(`Prec07 (mm) pct NA` != 100 & `Prec19 (mm) pct NA` != 100) %>%
group_by(StationID, Month = format(Yearmon, format = "%m")) %>%
summarize(Tot_Prec = mean(Tot_Prec, na.rm = TRUE),
dry_days = mean(dry_days, na.rm = TRUE),
wet_days = mean(wet_days, na.rm = TRUE))
plot_ly(data = dat_monthly, x = ~Month, y = ~Tot_Prec, color = ~StationID) %>%
add_bars() %>%
layout(title = "Average monthly total precipitation (mm)",
yaxis = list(title = "Total monthly precipitation (mm)"))
#> Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
#> Please use `arrange()` instead.
#> See vignette('programming') for more help
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_warnings()` to see where this warning was generated.
By the looks of it, all four stations exhibit fairly similar precipitation patterns. The region shows a double-peak precipitation pattern, with highest rainfall occurring in March, and a second, smaller peak in November. These peaks are consistent with the displacement of the intertropical convergence zone, which shifts north and south based on the solar equinoxes. San Rafael experiences the lowest rainfall in June, July and August, when the ITCZ is to the north. This is a super neat pattern that will require a deeper analysis in another post or paper.
Wet- and dry-day frequencies and spells
How about wet days and dry days? First, let’s take a look at the average number of wet and dry days in a month. Note that the code for these numbers was in the previous code boxes.
subplot(
plot_ly(data = dat_monthly, x = ~Month, color = ~StationID, y = ~wet_days) %>%
add_bars(),
plot_ly(data = dat_monthly, x = ~Month, color = ~StationID, y = ~dry_days) %>%
add_bars() %>%
layout(title = "Average monthly (top) wet (≥ 1 mm p) and (bottom) dry days (< 1 mm p)",
yaxis = list(title = "Number of monthly wet/dry days"),
showlegend = FALSE),
nrows = 2
)
The top panel in the above shows the average number of wet days (days with ≥ 1 mm precipitation) for each month at each station. By the looks of it, none of the stations exceed an average maximum of 11.5 dry days in March. Throughout the year, it seems to rain more than 1 mm on somewhere between 5 and 8 days each month, on average. The bottom panel shows the number of dry-days which, as you might imagine, is the exact opposite of the number of wet days, as I have defined dry days here as the number of days with less than 1 mm of precipitation).
Wet and dry spells may be more interesting than the total number of wet or dry days as a possible indicator of flood events. Let’s take a look at extended (3 days or more) periods of wet days, similar to what I did in a previous post about cold weather events in Toronto.
fx <- function(x, y = NULL, day = NULL, comm = NULL, ...) {
event <- rle(x == day)
event$values[event$lengths < 3] <- FALSE
if (!inherits(comm, "function")) {
# This will return the lengths of the events
lens <- NULL
for (i in 1:length(event[[1]])) {
if (!is.na(event$values[i]) & event$values[i]) {
lens <- c(lens, rep(event$lengths[i], event$lengths[i]))
} else {
lens <- c(lens, rep(NA, event$lengths[i]))
}
}
lens
} else {
# This will return summary variables of the events
vals <- NULL
for (i in 1:length(event[[1]])) {
if (!is.na(event$values[i]) & event$values[i]) {
pull <- c(rep(FALSE, sum(event$lengths[1:(i - 1)])),
rep(TRUE, event$lengths[i]))
pull <- c(pull, rep(FALSE, length(y) - length(pull)))
vals <- c(vals, rep(comm(y[pull], ...), event$lengths[i]))
} else {
vals <- c(vals, rep(NA, event$lengths[i]))
}
}
vals
}
}
dat_days <- dat %>% group_by(StationID) %>%
mutate(dry_start = as.Date(fx(Day, Fecha, "dry", min)),
dry_end = as.Date(fx(Day, Fecha, "dry", max)),
dryspell = fx(Day, day = "dry"),
wet_start = as.Date(fx(Day, Fecha, "wet", min)),
wet_end = as.Date(fx(Day, Fecha, "wet", max)),
wetspell = fx(Day, day = "wet"))
The figure below shows a timeline of wet spells detected at each station from 1993 to 2017. The number of events is pretty dense, so some zooming and panning is required, but the timeline allows us to get a quick idea of where longer-duration events have occurred.
timedat <- dat_days %>%
filter(!is.na(wetspell)) %>%
group_by(StationID, wet_start, wet_end, wetspell) %>%
summarize(Tot_Prec = sum(Prec),
most_prec = max(Prec, na.rm = TRUE),
least_prec = min(Prec, na.rm = TRUE),
avg_dly_prec = mean(Prec, na.rm = TRUE)) %>%
ungroup() %>%
mutate(content = paste(wetspell, "days"),
start = wet_start,
end = wet_end,
group = StationID,
title = paste("Total:", Tot_Prec, "mm; Max:", most_prec,
"mm/day; Min:", least_prec, "mm/day; Avg:",
round(avg_dly_prec, 1), "mm/day")) %>%
select(content, start, end, group, title)
#> `summarise()` regrouping output by 'StationID', 'wet_start', 'wet_end' (override with `.groups` argument)
groups <- tibble(id = unique(timedat$group), content = id)
timevis::timevis(timedat, groups, options = list(stack = FALSE), width = 720, height = 240)
Preliminary summary
In this post, I have examined the data availability at four meteorological stations near my study site in San Rafael, in San Martín, Peru. I have identified a two-peak precipitation regime, and have classified some clustering of precipitation. This information will be useful to examine alongside some river flow data to begin to unravel the relationship between precipitation and river discharge and level. As my analysis progresses, I may need to consider more rain data from areas upstream of my study site, as well as river data from tributaries to the Huallaga River. In my next post, I will look at some river stations that might inform my current study.
This post was compiled on 2020-10-09 18:50:07. Since that time, there may have been changes to the packages that were used in this post. If you can no longer use this code, please notify the author in the comments below.
Packages Used in this post
sessioninfo::package_info(dependencies = "Depends")
#> package * version date lib source
#> assertthat 0.2.1 2019-03-21 [1] RSPM (R 4.0.0)
#> cli 2.0.2 2020-02-28 [1] RSPM (R 4.0.0)
#> colorspace 1.4-1 2019-03-18 [1] RSPM (R 4.0.0)
#> crayon 1.3.4 2017-09-16 [1] RSPM (R 4.0.0)
#> crosstalk 1.1.0.1 2020-03-13 [1] RSPM (R 4.0.0)
#> curl 4.3 2019-12-02 [1] RSPM (R 4.0.0)
#> data.table 1.13.0 2020-07-24 [1] RSPM (R 4.0.2)
#> digest 0.6.25 2020-02-23 [1] RSPM (R 4.0.0)
#> downlit 0.2.0 2020-09-25 [1] RSPM (R 4.0.2)
#> dplyr * 1.0.2 2020-08-18 [1] RSPM (R 4.0.2)
#> ellipsis 0.3.1 2020-05-15 [1] RSPM (R 4.0.0)
#> evaluate 0.14 2019-05-28 [1] RSPM (R 4.0.0)
#> fansi 0.4.1 2020-01-08 [1] RSPM (R 4.0.0)
#> farver 2.0.3 2020-01-16 [1] RSPM (R 4.0.0)
#> fastmap 1.0.1 2019-10-08 [1] RSPM (R 4.0.0)
#> fs 1.5.0 2020-07-31 [1] RSPM (R 4.0.2)
#> generics 0.0.2 2018-11-29 [1] RSPM (R 4.0.0)
#> geosphere 1.5-10 2019-05-26 [1] RSPM (R 4.0.0)
#> ggplot2 * 3.3.2 2020-06-19 [1] RSPM (R 4.0.1)
#> glue 1.4.2 2020-08-27 [1] RSPM (R 4.0.2)
#> gtable 0.3.0 2019-03-25 [1] RSPM (R 4.0.0)
#> htmltools 0.5.0 2020-06-16 [1] RSPM (R 4.0.1)
#> htmlwidgets 1.5.1 2019-10-08 [1] RSPM (R 4.0.0)
#> httpuv 1.5.4 2020-06-06 [1] RSPM (R 4.0.2)
#> httr 1.4.2 2020-07-20 [1] RSPM (R 4.0.2)
#> hugodown 0.0.0.9000 2020-10-08 [1] Github (r-lib/hugodown@18911fc)
#> jsonlite 1.7.1 2020-09-07 [1] RSPM (R 4.0.2)
#> knitr 1.30 2020-09-22 [1] RSPM (R 4.0.2)
#> later 1.1.0.1 2020-06-05 [1] RSPM (R 4.0.2)
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#>
#> [1] /home/conor/Library
#> [2] /usr/local/lib/R/site-library
#> [3] /usr/local/lib/R/library
Conor I. Anderson, PhD
Alumnus, Climate Lab
Conor is a recent PhD graduate from the Department of Physical and Environmental Sciences at the University of Toronto Scarborough (UTSC).