Replicating NYT Weather App

  • December 2016
  • R

So much time since my last post so I want to post something, no matter what it is, but I hope this will be somehow helpfull

In this post I will show some new features for the next version of highcharter package. The main feature added is hc_add_series now is a generic function! This mean you can add the data argument can be numeric, data frame, time series (ts, xts, ohlc) amonth others so the syntaxis will be a little cleaner.

What we’ll do here? We’ll make an interactive version of the well-well-know-and-a-little-repeated Tufte weather chart.

There are good ggplot versions if you can start and

But our focus will be replicate the New York Time App: How Much Warmer Was Your City in 2015? where you can choose among over 3K cities!. So let’s start. So we need a interactive charting library and shiny.


If you search/explore in the devTools in the previous link you can know where is the path of the used data. So to be clear:

All the data used in this post is from – Me.

We’ll load the tidyverse, download the data, and create an auxiliar variable dt to store the date time in numeric format.


url_base <- ""
file <- "new-york_ny.csv" # "san-francisco_ca.csv"
url_file <- file.path(url_base, file)

data <- read_csv(url_file)
data <- mutate(data, dt = datetime_to_timestamp(date))

date month temp_max temp_min temp_rec_max temp_rec_min temp_avg_max temp_avg_min temp_rec_high temp_rec_low precip_value precip_actual precip_normal precip_rec snow_rec annual_average_temperature departure_from_normal total_precipitation precipitation_departure_from_normal dt
2015-01-01 1 39 27 62 -4 39 28 NULL NULL 0.00 5.23 3.65 NULL NULL NA NA NA NA 1.42e+12
2015-01-02 1 42 35 68 2 39 28 NULL NULL 0.00 NA NA NULL NULL NA NA NA NA 1.42e+12
2015-01-03 1 42 33 64 -4 39 28 NULL NULL 0.71 NA NA NULL NA NA NA NA NA 1.42e+12
2015-01-04 1 56 41 66 -3 39 27 NULL NULL 1.01 NA NA NULL NULL NA NA NA NA 1.42e+12
2015-01-05 1 49 21 64 -4 38 27 NULL NULL 1.01 NA NA NULL NULL NA NA NA NA 1.42e+12
2015-01-06 1 22 19 72 -2 38 27 NULL NULL 1.06 NA NA NULL NULL NA NA NA NA 1.42e+12


Due the data is ready we’ll start to create the chart (a highchart object):

hc <- highchart() %>%
  hc_xAxis(type = "datetime", showLastLabel = FALSE,
           dateTimeLabelFormats = list(month = "%B")) %>% 
  hc_tooltip(shared = TRUE, useHTML = TRUE,
             headerFormat = as.character(tags$small("{point.x: %b %d}", tags$br()))) %>% 
  hc_plotOptions(series = list(borderWidth = 0, pointWidth = 4)) %>% 



No data to display. All acording to the plan XD.

Temperature Data

We’ll select the temperature columns from the data and do some wrangling, gather, spread, separate and recodes to get a nice tidy data frame.

dtempgather <- data %>% 
  select(dt, starts_with("temp")) %>% 
  select(-temp_rec_high, -temp_rec_low) %>% 
  rename(temp_actual_max = temp_max,
         temp_actual_min = temp_min) %>% 
  gather(key, value, -dt) %>% 
  mutate(key = str_replace(key, "temp_", "")) 

dtempspread <- dtempgather %>% 
  separate(key, c("serie", "type"), sep = "_") %>% 
  spread(type, value)

temps <- dtempspread %>% 
  mutate(serie = factor(serie, levels = c("rec", "avg", "actual")),
         serie = fct_recode(serie, Record = "rec", Normal = "avg", Observed = "actual"))

dt serie max min
1.42e+12 Observed 39 27
1.42e+12 Normal 39 28
1.42e+12 Record 62 -4
1.42e+12 Observed 42 35
1.42e+12 Normal 39 28
1.42e+12 Record 68 2

Now whe can add this data to the highchart object using hc_add_series:

hc <- hc %>% 
  hc_add_series(temps, type = "columnrange",
                hcaes(dt, low = min, high = max, group = serie),
                color = c("#ECEBE3", "#C8B8B9", "#A90048")) 



A really similar chart of what we want!

The original chart show records of temprerature. So we need to filter the days with temperature records using the columns temp_rec_high and temp_rec_low, then some gathers and tweaks. Then set some options to show the points, like use fill color and some longer radius.

records <- data %>%
  select(dt, temp_rec_high, temp_rec_low) %>% 
  filter(temp_rec_high != "NULL" | temp_rec_low != "NULL") %>% 
  dmap_if(is.character, str_extract, "\\d+") %>% 
  dmap_if(is.character, as.numeric) %>% 
  gather(type, value, -dt) %>% 
  filter(! %>% 
  mutate(type = str_replace(type, "temp_rec_", ""),
         type = paste("This year record", type))

pointsyles <- list(
  symbol = "circle",
  lineWidth= 1,
  radius= 4,
  fillColor= "#FFFFFF",
  lineColor= NULL

dt type value
1.44e+12 This year record high 95
1.44e+12 This year record high 97
1.45e+12 This year record high 74
1.45e+12 This year record high 67
1.45e+12 This year record high 67
1.45e+12 This year record high 68
hc <- hc %>% 
  hc_add_series(records, "point", hcaes(x = dt, y = value, group = type),
                marker = pointsyles)



We’re good.

Precipitation Data

A nice feture of the NYTs app is and the chart is show the precipitaion by month. This data is in other axis. So we need to create a list with 2 axis using the create_yaxis helper and the adding this axis to the chart.

axis <- create_yaxis(
  naxis = 2,
  heights = c(3,1),
  sep = 0.05,
  turnopposite = FALSE,
  showLastLabel = FALSE,
  startOnTick = FALSE)

Manually add titles (I know this can be more elegant) and options.

axis[[1]]$title <- list(text = "Temperature")
axis[[1]]$labels <- list(format = "{value}?F")

axis[[2]]$title <- list(text = "Precipitation")
axis[[2]]$min <- 0

hc <- hc_yAxis_multiples(hc, axis)



The 2 axis are ready, now we need add the data. We will add 12 series -one for each month- but we want to asociate 1 legend for all these 12 series, so we need to use id and linkedTo parameters and obviously. That’s why the id will be a 'p' for the firt element and then NA to the other 11. And then linked this 11 to the first series (id = 'p').

precip <- select(data, dt, precip_value, month)

hc <- hc %>%
  hc_add_series(precip, type = "area", hcaes(dt, precip_value, group = month),
                name = "Precipitation", color = "#008ED0", lineWidth = 1,
                yAxis = 1, fillColor = "#EBEAE2", 
                id = c("p", rep(NA, 11)), linkedTo = c(NA, rep("p", 11)))

The same way we’ll add the normal precipitations by month.

precipnormal <- data %>% 
  select(dt, precip_normal, month) %>% 
  group_by(month) %>% 
  filter(row_number() %in% c(1, n())) %>%
  ungroup() %>% 

hc <- hc %>% 
  hc_add_series(precipnormal, "line", hcaes(x = dt, y = precip_normal, group = month),
                name = "Normal Precipitation", color = "#008ED0", yAxis = 1,
                id = c("np", rep(NA, 11)), linkedTo = c(NA, rep("np", 11)),
                lineWidth = 1)


Curious how the chart looks? Me too! Nah, I saw the chart before this post.



With R you can create a press style chart with some wrangling and charting. Now with a little of love we can make the code resuable to make a shiny app.


Someone put the grid lines for the 2 axis as the original NYT app please to these charts! I will grateful if someone code that details.

See you :B!

giphy gif source

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