Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
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 https://rpubs.com/tyshynsk/133318 and https://rpubs.com/bradleyboehmke/weather_graphic.
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.
Data
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 http://www.nytimes.com – Me.
We’ll load the tidyverse, download the data, and create an auxiliar
variable dt to store the date time in numeric format.
library(printr) library(tidyverse) library(highcharter) library(lubridate) library(stringr) library(forcats) url_base <- "http://graphics8.nytimes.com/newsgraphics/2016/01/01/weather/assets" 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)) head(data)
| 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 |
Setup
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)) %>%
hc_add_theme(hc_theme_smpl())
hc
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"))
head(temps)
| 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"))
hc
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(!is.na(value)) %>%
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
)
head(records)
| 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)
hc
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)
hc
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() %>%
fill(precip_normal)
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)
Volia
Curious how the chart looks? Me too! Nah, I saw the chart before this post.
hc
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.
Homework
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!
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.