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#30DayChartChallenge
@30DayChartChall
A #DataViz challenge by
@CedScherer
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@dr_xeo
Support:
@ShijiaWendy
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@shinysci
📢 2nd edition in April 2022
Worldwide30DayChartChallenge.orgJoined February 2021
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#30DayChartChallenge’s Tweets

#30DayChartChallenge Retweeted
Nicola Rennie
@nrennie35
Next week I'm talking about the #30DayChartChallenge - all about plots, #rstats, #ggplot2, and "how to make a flowchart in {ggplot2}" 📊📊📊 Come and join us! meetup.com/rladies-nairob
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R-Ladies Nairobi 🇰🇪
@RLadiesNairobi
·
✨ The #30DayChartChallenge took place last month, and for our next meetup, we have @nrennie35 share her experience participating in the challenge! All are welcome!! ⭐ Date: April 26th, 2022, 8 PM - 10 PM (GMT+3) To RSVP: meetup.com/rladies-nairob #RStats #rladies
Talk poster: #30DayChartChallenge: Nicola's Experience Our speaker: @nrennie35 Abstract of the talk: During April 2022, I participated in the #30DayChartChallenge - a data visualisation challenge where participants create a new chart each day based on the daily prompt. In this talk, I'll discuss why and how I created 30 charts, the aspects I found most challenging, and some interesting things I discovered in R along the way!
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#30DayChartChallenge Retweeted
USGS Data Science
@USGS_DataSci
We loved sharing our science and #DataViz with you throughout April for the #30DayChartChallenge! Many thanks to all of our contributors
@USGS
!👏 Read our blog post about the challenge: waterdata.usgs.gov/blog/chart-cha You can find all our contributions in this 🧵
waterdata.usgs.gov
The 30 Day Chart Challenge with the USGS VizLab - Water Data For The Nation Blog
A round up of @USGS_Datasci tweets for the
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#30DayChartChallenge Retweeted
USGS Data Science
@USGS_DataSci
#Day1, Comparisons: Part-to-Whole #30DayChartChallenge
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USGS Data Science
@USGS_DataSci
·
Day 1 of the #30DayChartChallenge 📅 Part-to-Whole! Parts of the whole lake phytoplankton community using @EPAWater NLA 2017 data. Part-to-whole comparison from phytoplankton to fatty acids in 3 parts by Michael Meyer #DataViz #rstats: github.com/USGS-VIZLAB/ch 1/3
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A panel of replicate maps that each show the spatial distribution of lake algal communities across the US. Blue-green algae and green algae are in highest density in the SE US. Cryptophytes, diatoms, and golden algae show a similar but weaker spatial pattern. Dinoflagellates, Euglenoids and yellow-green algae are the least common across lake communities.
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#30DayChartChallenge Retweeted
USGS Data Science
@USGS_DataSci
#Day1, Comparisons: Part-to-Whole #30DayChartChallenge
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USGS Data Science
@USGS_DataSci
·
Which states have the most water? This animation shows the % of the total area of each state that is covered by water, morphing between a standard map & an area-weighted cartogram for day 1 of the #30DayChartChallenge📅Part-to-Whole #DataViz #rstats #r4ds:github.com/USGS-VIZLAB/ch
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A choropleth map of CONUS comparing the percent area of water in each state transforms into a contiguous cartogram map where each state is distorted to reflect the percent area water. A bar chart indicates that RI, DC, LA, FL and NC have the highest percent area water, all above 15%. NM, AZ, CO, KS, and NE have the lowest percent area water, all below 2% land area. Most states are between 5 – 10% water. Data in the chart are available in table form at https://www.usgs.gov/special-topics/water-science-school/science/how-wet-your-state-water-area-each-state
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#30DayChartChallenge Retweeted
USGS Data Science
@USGS_DataSci
#Day2, Comparisons: Pictogram #30DayChartChallenge
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USGS Data Science
@USGS_DataSci
·
Introducing Salty, a new friend of @USGS_Water’s Drippy! 💧 Salty is here to teach you about #saline lakes, which make up nearly half of global lake volume! Learn more about saline water: usgs.gov/special-topics Day 2 of the #30DayChartChallenge 📅 Pictogram
Nearly half of global lake volume is saline. Many saline lakes are endorheic, or closed, meaning they have no outflowing water. With no outlets, water evaporation can cause salt and other solutes to concentrate in the water. The visual depicts 100 raindrops (10x10) with 48 of these raindrops filled in. A raindrop with a face holding a salt shaker, is shown to be saying “that’s approximately 85,400 km of salty water!” At the bottom of the visual it says, “water volumes estimated in: Igor Shiklomanov’s chapter ‘World fresh water resources’ in Peter H. Gleik (editor), 1993, Water in Crisis: A Guide to the World’s Fresh Water resources”
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#30DayChartChallenge Retweeted
USGS Data Science
@USGS_DataSci
#Day3, Comparisons: Historical #30DayChartChallenge
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USGS Data Science
@USGS_DataSci
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Are U.S. lake temperatures warming or cooling and are they reaching mid-year warmth earlier or later? Check out this wheat field inspired #DataViz for some hints. Day 3 historical comparison #30DayChartChallenge by Jordan Read 1/5 #rstats code: github.com/USGS-VIZLAB/ch
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A map of CONUS using angled wheat field vectors to compare lake growing degree days between the past (1981-1990) and present (2011-2020). The vectors are mapped to the number of growing degree days earlier or later on the x-axis, and the difference in temperature on the y-axis, resulting in vector angles spanning 360 degrees. The map shows that the SE has strong directional changes towards earlier and warmer GDD. The NE and upper midwest trend towards warmer and later GDD. Western states of UT, WY, ID, and MT all have trends towards later, with colder temperatures in some areas. Along the west coast, this pattern switches to earlier and warmer. The data are from doi.org/10.5066/P9CEMS0M
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#30DayChartChallenge Retweeted
USGS Data Science
@USGS_DataSci
#Day4, Comparisons: Flora #30DayChartChallenge
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USGS Data Science
@USGS_DataSci
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Spring has sprung in many parts of the contiguous U.S.!🌱🌸 Comparing the timing of spring leaf out in 2022 (so far) to the 30-year average (1991-2020) with @USANPN data. For day 4 of #30DayChartChallenge 📅 Comparisons - flora #DataViz #rstats code: github.com/USGS-VIZLAB/ch
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Spring has sprung: the timing of spring leaf out in the contiguous U.S. as of April 4th, 2022. The visual depicts the portions of the U.S. where spring leaf out has occurred, and the total area of land experiencing the first day of spring for each day of the current year. Spring has started in most states, with the exception of the northernmost states and parts of western states, like MT, ID, WY, CO, UT. Spring crawls from the southernmost parts up the country. Compared to the 30 year average, spring timing follows a similar pattern, with some places experiencing spring leaf out earlier, and some places later. In particular, most of CA, the west coast, and the lower midwest had an earlier spring, as much as 4 weeks earlier than normal. High elevation locations and the SE had later spring than normal.
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#30DayChartChallenge Retweeted
USGS Data Science
@USGS_DataSci
#Day5, Comparisons: Slope #30DayChartChallenge
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USGS Data Science
@USGS_DataSci
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The percent area loss of ice across the 10 largest glaciers in @GlacierNPS since 1966. 🏞 Day 5 of the #30DayChartChallenge📅 Slope by Matthew Morriss @USGS_UT #DataViz made with #rstats github.com/USGS-VIZLAB/ch Using @USGS data: sciencebase.gov/catalog/item/5
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Decline of the 10 largest glaciers in Glacier National Park, using the total percent area loss since 1966 at four timepoints. A slope chart shows all glaciers starting at 100% in 1966 and declining in area over time. Total percent loss ranges from –11% to –54% across glaciers by 2015, with the greatest decline in the Agassiz Glacier. The second greatest loss was the Grinnell Glacier, (45%) which is shown in an inset the shrinking perimeter of the glacier.
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#30DayChartChallenge Retweeted
USGS Data Science
@USGS_DataSci
#Day6, Comparisons: OWID Data #30DayChartChallenge
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USGS Data Science
@USGS_DataSci
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There’s a split in basic drinking water access between urban and rural environments in countries around the world. #DataViz inspired by @pabloalvrez #Day6 #30DayChartChallenge OWID @OurWorldInData by Matthew Morriss #rstats code: github.com/USGS-VIZLAB/ch
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A chart compares basic access to safe drinking water in urban and rural areas around the world. Access, measured as the percent of the population meeting basic drinking water requirements, is shown for 155 countries. Urban areas tend to have higher access to drinking water compared to rural areas. Globally, approximately 95% of urban populations have basic drinking water access compared to around 0% of rural populations. In some countries, as low as 15% of rural populations have access to drinking water, while all urban areas have at least 50% access. The data come from OWID water, sanitation, and hygiene data explorer, WHO/UNICEF Joint Monitoring Program for Water Supply and Sanitation.
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#30DayChartChallenge Retweeted
USGS Data Science
@USGS_DataSci
#Day7, Distributions: Physical #30DayChartChallenge
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USGS Data Science
@USGS_DataSci
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What makes mountain lakes bluer? ⛰ Looking at 36 years of lake color data for over 42k lakes for the #30DayChartChallenge📅 Day 7 physical distribution by Simon Topp #rstats code: github.com/USGS-VIZLAB/ch Data using @USGS_Landsat: doi.org/10.5281/zenodo
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How do Mountains Influence Lake Color? Distribution of over 42k lakes and their color in relation to the topography of the US. A collection of graphics show the relationships between lake color, elevation, time of year, and the total number of lakes. This broadly shows that during the summer months high elevation lakes are blue, but iced over during the winter. Low and mid-elevation lakes are greener than the blue high elevation lakes, and spring algae blooms are noticeable in March and April. In space, lakes are distributed across the US, with the median lake color over 36 years tending to be greener in the center of the country, and bluer lakes in the NE and Sierras.
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#30DayChartChallenge Retweeted
USGS Data Science
@USGS_DataSci
#Day8, Distributions: Mountains #30DayChartChallenge
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USGS Data Science
@USGS_DataSci
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What are the major sources of sediment in North Carolina streams? Going from the coast to the mountains, land use change & stream channels contribute to higher levels of sediment Day 8 #30DayChartChallenge 📅 ggridges mountains by Krissy Hopkins #rstats: github.com/USGS-VIZLAB/ch
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Where is sediment coming from in North Carolina? Comparing sediment sources for HUC8 basins, data from doi.org/10.5066/P97MV16H. A ridgeline plot shows major sediment courses in three ecoregions of NC: coastal plain, piedmont, and mountains. In the Coastal plain, stream channels and agriculture are the top sediment sources. In piedmont and mountains agriculture makes the lowest contributions, followed by development, land use change, and stream channels. The amount of sediment from stream channels is the highest and shows a wider range of sediment per year normalized by area, Mg/km2.
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#30DayChartChallenge Retweeted
USGS Data Science
@USGS_DataSci
#Day9, Distributions: Statistics #30DayChartChallenge
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USGS Data Science
@USGS_DataSci
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With thousands of active #streamgages nationwide, you’re never too far from a @USGS_Water monitoring location. How far away is your closest gage?⌚🗺dashboard.waterdata.usgs.gov/app/nwd/?regio Day 9 #30DayChartChallenge 📅 Distribution-statistics by Sam Oliver #rstats code: github.com/USGS-VIZLAB/ch
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A map of USGS streamgages and population density shows the proximity of gages to people. In addition, a line chart is overlaid showing the total population within 3 distances from any gage in the US. These distances represent a normal walking distance, biking distance, and driving distance. 6,731,533 people are within walking (< 2km), 106,186,269 within biking (< 6 km) and 299,932,698 within driving (<20km)
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#30DayChartChallenge Retweeted
USGS Data Science
@USGS_DataSci
#Day11, Distributions: Circular #30DayChartChallenge
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USGS Data Science
@USGS_DataSci
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When are rivers wet or dry? To answer this we look at average daily streamflow across 1,865 @USGS_Water gage sites, over 70 years. Day 11 #30DayChartChallenge 📅 Circular by Caelan Simeone from @USGS_OR #rstats code: github.com/USGS-VIZLAB/ch #DataRetrieval
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When are U.S. rivers wet or dry? This circular area chart displays the total flow rate in million cubic feet per second throughout a year. Total flow is derived from 1,865 USGS NWIS gages with near complete data for 1951-2020. The amount of flow in rivers in the continental United States varies throughout the year and across the country, but overall where we have gages we see that late winter/early spring months (March - May) tend to have the highest flows and late summer/early fall months tend to have the lowest flows. Extreme events can occur within these long-term ranges, but can also stray outside of them: for example extreme low flows in November 1952 or extreme high flows in June 1972.
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#30DayChartChallenge Retweeted
USGS Data Science
@USGS_DataSci
#Day11, Distributions: Circular #30DayChartChallenge
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USGS Data Science
@USGS_DataSci
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Shoreline development factor is the ratio of the lake shoreline to the circumference of a circle of the same area, aka how circular a lake is 🔵. Here are 100 U.S. lakes, from least to most circular for day 11 #30DayChartChallenge 📅circular #python code: github.com/USGS-VIZLAB/ch
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A progression of 100 lakes, from an Oxbow lake in Tennessee that is long and sinuous, to the near perfectly circular Big Oak Lake in Missouri.
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#30DayChartChallenge Retweeted
USGS Data Science
@USGS_DataSci
#Day14, Relationships: 3-Dimensional #30DayChartChallenge
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USGS Data Science
@USGS_DataSci
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Do lakes change in size? Mapping the number of lakes growing vs shrinking compared to the 20yr mean (1995-2015) across the contiguous US for #Day14 of #30DayChartChallenge 📅 3-Dimensional by Michael Meyer #DataViz #rstats code: github.com/USGS-VIZLAB/ch
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#30DayChartChallenge Retweeted
USGS Data Science
@USGS_DataSci
#Day19, Timeseries: Global Change #30DayChartChallenge
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USGS Data Science
@USGS_DataSci
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Is streamflow drought becoming more common? Here we plot the duration of streamflow drought events at 122 @USGS_Water streamgages in the Upper Colorado River Basin over 40+ years. Day 19 #30DayChartChallenge 📅 Global Change by Scott Hamshaw #rstats code: github.com/USGS-VIZLAB/ch
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A time series of streamflow drought events occurring across 122 stream gages in the Upper Colorado River basin from 1980 to present. Periods of streamflow drought are indicated for each of 122 gages in a beeswarm-like chart, with time on the x-axis. During time periods where many gages experienced drought, the swarm bulges due to many co-occurring events. Widespread drought events appear in 1982, 2002-2003, 2012-2013, and 2018. It appears that larger drought events are more common in the recent past, and that drought duration is generally increasing over time. The largest period of simultaneous drought across gages occurred in 2002. Across gages, the Recapture Creek sites near Blanding, UT has had some of the longest drought events, with 266 and 354 days of drought starting in 2002 and 2018, respectively.
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#30DayChartChallenge Retweeted
USGS Data Science
@USGS_DataSci
#Day20, Timeseries: New Tool #30DayChartChallenge
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USGS Data Science
@USGS_DataSci
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Our @USGS_Water Next Generation Monitoring Location pages display important #water data like gage height and discharge. Find a monitoring location near you by using the National Water Dashboard: dashboard.waterdata.usgs.gov/app/nwd/?regio For day 20 #30DayChartChallenge New Tool 🔧🔨
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Screenshot of a USGS NextGen Monitoring Location page. It shows the Long Green Creek at Glen Arm, MD.
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#30DayChartChallenge Retweeted
USGS Data Science
@USGS_DataSci
#Day20, Timeseries: New Tool #30DayChartChallenge
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USGS Data Science
@USGS_DataSci
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Many rivers in Alaska are still unmonitored or are no longer being monitored. The Surface Water Ocean Topography satellite (launching Nov 22) will estimate river flow across the globe! 🛰 #Day20 of #30DayChartChallenge📅New Tool by Merritt Harlan #rstats: github.com/USGS-VIZLAB/ch
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A figure showing the a) geographic and b) temporal coverage of discharge observations from USGS gages and the expected contributions of the Surface Water Ocean Topography (SWOT) satellite. Many rivers in Alaska, particularly close to the Arctic are not currently being monitored, and monitoring stations have declined in availability over time. The SWOT satellite mission will dramatically increase the number of large river reaches being monitored, from an approximate 35 river reaches to 2,500 river reaches.
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#30DayChartChallenge Retweeted
USGS Data Science
@USGS_DataSci
#Day21, Timeseries: Down/Upwards #30DayChartChallenge
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USGS Data Science
@USGS_DataSci
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Where the fresh water of the Delaware River meets estuarine salt water is called the salt front. It moves up and down in response to weather conditions in the basin. #30DayChartChallenge #Day21📅Down/upwards by Theodore Thompson & Salme Cook #python code: github.com/USGS-VIZLAB/ch
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The rise and fall of the Delaware River salt front. Over time, the location of the salt front moves in response to regional weather patterns, sea level rise, and human impacts. The precise location of the salt front in the Delaware River Basin is based on the location in the river where the average chloride concentration is 250 mg/L. Two polar charts show the location of the salt front along the Delaware River in 2016 and 2019. When there is lots of precipitation, as was the case in early 2019, increased flow in the Delaware River pushes the salt front downstream. When conditions are very dry, as was the case in fall 2016, the salt front gradually moves further and further upstream, passing Wilmington, Delaware, and nearing Philadelphia, Pennsylvania. During both years, the salt front is highest upstream, encroaching Philadelphia, during the months of September, October, and November, while located further downstream below Wilmington from Jan – June.
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#30DayChartChallenge Retweeted
USGS Data Science
@USGS_DataSci
#Day23, Timeseries: Tiles #30DayChartChallenge
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USGS Data Science
@USGS_DataSci
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Visualizing temporal patterns of salinity at upstream and downstream sites on the lower Connecticut River Estuary over 10 years. Day 23 #30DayChartChallenge 📅 Tiles by John Mullaney #DataViz #NWIS #ggplot2 #rstats: github.com/USGS-VIZLAB/ch
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Timeseries plots of maximum daily salinity on the lower Connecticut River 2011-2021 for an upstream site on the river and a downstream site in the estuary. The timeseries chart uses tiles to show annual patterns in salinity compared across 10 years. At the upstream site, Essex CT (01194750), freshwater dominates the beginning of the year up until May, with some moments of low salinity levels (oligohaline). Starting in June, the upstream site increases in salinity, with moderate salinity levels (mesohaline) through November. At the downstream site, salinity levels are high (polyhaline) for the majority of the year, with short periods of freshwater during April. This reflects the timing of snowmelt and increased streamflow. Across years, the upstream site appears to be getting saltier earlier. There is no obvious trend across years at the downstream site. A map indicates that the two sites are relatively close (~6 km apart) in the Long Island Sound and surrounded by tidal marshes that re
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#30DayChartChallenge Retweeted
USGS Data Science
@USGS_DataSci
#Day23, Timeseries: Tiles #30DayChartChallenge
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USGS Data Science
@USGS_DataSci
·
120 years of land cover in the Delaware River Basin (1900 to 2020) using @USGS_EROS FORE-SCE model data. #Day23 #30DayChartChallenge 📅 Tiles by Margaux Sleckman #rstats: github.com/USGS-VIZLAB/ch Data: sciencebase.gov/catalog/item/6
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Land cover is animated through time in the Delaware River Basin. The visual shows a map and chart reflecting the proportion of land area in 8 major land cover classes: water, agriculture, barren, forest, grassland, wetland and developed areas (low and high intensity). Time is broken into 10 year intervals. Through time, forest cover replaces agriculture up until 1970. Simultaneously developed areas replace agriculture and grasslands through 2020. Forest is the dominant land cover class.
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#30DayChartChallenge Retweeted
USGS Data Science
@USGS_DataSci
#Day27, Uncertainties: Future #30DayChartChallenge
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USGS Data Science
@USGS_DataSci
·
Our team forecasts Delaware River Basin stream temperatures up to 7 days ahead. Each day, our #DeepLearning model ingests observed temperatures with #DataAssimilation. How does DA work? A 🧵: #30DayChartChallenge #Day27📅Future by Jeremy Diaz #python code: github.com/USGS-VIZLAB/ch
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