Joe Russell, Adnan Fiaz
Jeremy Singer-Vine sends out a newsletter every week where he highlights a number of interesting open datasets (you can explore all the datasets here). At Mango we are all for open data so we thought we would also share some of the open datasets we think are fun to explore.
Open Food Facts
Open Food Facts is a collaborative, free and open database of food products. It is a prime example of how effective crowdsourcing your data is. People from around the world have collected details about more than 300.000 food products and uploaded the information through mobile apps. The data is available as a MongoDB dump, CSV export and an experimental API. We have downloaded the CSV export and will try to visualise the ingredients across all products.
# http://world.openfoodfacts.org/data/en.openfoodfacts.org.products.csv foodFacts <- read_tsv("data/en.openfoodfacts.org.products.csv") dim(foodFacts) ##  295958 162 library(stringr) ingredients <- foodFacts %>% # ideally, the unnest_tokens function is what we want but it was too slow #tidytext::unnest_tokens(ingredient, ingredients_text, # token = stringr::str_split, pattern=",|\\(|\\)|\\[|\\]") %>% # so instead we chose a different approach involving transmute and unlist # transmute will give us a list-column transmute(ingredients = str_split(ingredients_text, pattern=",|\\(|\\)|\\[|\\]")) %>% # unlist will convert the list-column into a character vector unlist() %>% # enframe will convert the vector into a data frame which is easier to groupby enframe() %>% # now we clean up some of the text mutate(value = str_replace(value, "org|organic", ""), value = str_replace(value, "-> en:\\w+", ""), value = str_replace_all(value, "'", ""), value = str_trim(value)) %>% # and finally filter some of the weirder entries filter(value!="", value!=".", !str_detect(value, "completed|\\d")) %>% # to then group by and count the ingredients count(value) %>% arrange(desc(n)) head(ingredients, 10) ## # A tibble: 10 x 2 ## value n ##
## 1 salt 122183 ## 2 sugar 88463 ## 3 water 80037 ## 4 sel 38852 ## 5 sucre 29971 ## 6 eau 28502 ## 7 citric acid 28475 ## 8 riboflavin 21527 ## 9 milk 21265 ## 10 niacin 21201
There are no surprises at the top but further down there are a few ingredients that are odd. Let’s create a word cloud to show the relative frequencies.
library(wordcloud) top100 <- head(ingredients, 100) wordcloud::wordcloud(top100$value, top100$n)
Ingredients are only one aspect of this very interesting dataset. We could go on and look at the co-occurrence of certain ingredients (using network analysis) and then continue analysing their quantities. We could also include the data on nutritional value and calculate correlations. The data could also use some more cleaning considering there are some ingredients in different languages (e.g. water and eau).
Following in the edible theme, VAM collate commodity prices from the globe’s poorer nations and use them in helping to identify food insecurity hotspots. The data we will be using can be downloaded here, from which we’ll attempt to visualise how prices have changed over the past 20 years.
# https://data.humdata.org/dataset/wfp-food-prices #Providing column names and types to make workings easier later on colnames <- c("Country_ID", "Country", "Region_ID", "Region", "Market_ID", "Market", "Commodity_ID", "Commodity", "Currency_ID", "Currency", "Sector_ID", "Sector", "Units_ID", "Units", "Month", "Year", "Price", "Commodity_Source") coltypes <- cols(Year = "n", Price = "n", Month = "n") foodPrices <- read_csv("data/FoodPrices/WFPVAM_FoodPrices_13-03-2017.csv", col_names = colnames, col_types = coltypes) %>% filter(row_number() != 1) #Large number of commodities - won't be able to plot them all! length(unique(foodPrices$Commodity)) ##  304 #Overall price trend - trend of all commodity prices over time overallPriceTrend <- foodPrices %>% group_by(Commodity, Year) %>% #As different commodities will clearly have different prices, we'll make #them comparable by scaling based on their max price within our timeframe summarise(globalAveragePrice = mean(Price)/max(Price)) #Food prices trend over time, grouped by commodity - same as above, #this time selecting a smaller sample for plotting commodoties <- c("Wheat", "Milk", "Coffee", "Bananas", "Sugar") priceTrend <- foodPrices %>% select(Price, Commodity, Year) %>% #selecting our reduced commodities filter(Commodity %in% commodoties) %>% group_by(Commodity, Year) %>% summarise(globalAveragePrice = mean(Price)/max(Price)) priceTrend ## # A tibble: 77 x 3 ## # Groups: Commodity [?] ## Commodity Year globalAveragePrice ##
## 1 Bananas 2008 1.0000000 ## 2 Bananas 2009 0.4487272 ## 3 Bananas 2010 0.1727820 ## 4 Bananas 2011 0.2909874 ## 5 Bananas 2012 0.4470188 ## 6 Bananas 2013 0.1271515 ## 7 Bananas 2014 0.1263784 ## 8 Bananas 2015 0.1129815 ## 9 Bananas 2016 0.2050480 ## 10 Bananas 2017 0.2630983 ## # ... with 67 more rows
We can see what our data looks like for each commodity.
Now let’s create a graphic.
library(forcats) #We'll create a ggplot graphic, using geom_smooth #Specify some colours semi-related to their genuine appearence colours <- c("Bananas" = "#fea814", "Coffee" = "#383838", "Sugar" = "#4fcfff", "Milk" = "#cccccc", "Wheat" = "#005692") #Specify commodity levels for use in our legend fctLevels <- c("Coffee", "Bananas", "Milk", "Sugar", "Wheat") ggplot(priceTrend, aes(x = Year, y = globalAveragePrice)) + geom_smooth(aes(colour = fct_relevel(Commodity, fctLevels)), se = FALSE, size = 1.8, linetype = 5) + geom_smooth(data = overallPriceTrend, colour = "red", se = FALSE, size = 3.5) + geom_line(aes(size = "All Commodities", linetype = NA), colour = "red") + scale_colour_manual(values = colours) + scale_x_continuous(breaks = seq(1992, 2017, 2)) + scale_y_continuous(limits = c(0, 1), labels = c("Min", "Max"), breaks = c(0, 1)) + labs(title = "Average Global Commodity Prices over Time", subtitle = "Commodity Price Relative to Max in Period", caption = "Data from https://data.humdata.org/dataset/wfp-food-prices", x = "", y = "", colour = "Commodity", size = "") + theme_classic()
The trend for our selected commodities seems to show a gradual decrease in prices, as all but coffee and milk prices are now lower than at the beginning of our timeframe. This is somewhat reflected in our overall price trend, as we can see there has been a slight downward trend, although this has been somewhat negated in the past three years.
In our analysis we took only a mere peek into the dataset. For example, we could look at seasonality trends, subset by country or region or even by market. Indeed it could be taken a step further, as VAM have, and be used as a tool for predicting when and where food security will occur in the future.
North Korea Missile Tests
The next dataset is from the James Martin Center for Nonproliferation Studies (CNS) North Korea Missile Test Database. We agree it’s not a fun topic but we also wanted to show the breadth of open data that is out there. You can get the data as an Excel file but it is also hosted on data.world. And fortunately for the R community data.world have a R package to access their API.
library(data.world) # We've set the configuration in a previous session path <- "ian/the-cns-north-korea-missile-test-database" missiles <- query(qry_sql("SELECT f1, facility_latitude, facility_longitude, distance_travelled_km, facility_name FROM missile_tests WHERE success='Success'"), path) # additional filtering outside of query missiles <- missiles %>% filter(distance_travelled_km!="Unknown", distance_travelled_km!="N/A") %>% drop_na() %>% mutate(distance_travelled_km = as.integer(distance_travelled_km), facility_name = substr(facility_name, 0, 20)) head(missiles) ## # A tibble: 6 x 5 ## f1 facility_latitude facility_longitude distance_travelled_km ##
## 1 101 39.65960 124.7057 1000 ## 2 102 39.65960 124.7057 1000 ## 3 12 40.85000 129.6667 500 ## 4 40 38.99083 127.6236 200 ## 5 41 38.99083 127.6236 200 ## 6 42 38.99083 127.6236 200 ## # ... with 1 more variables: facility_name
The data contains information on the launch location but not on the precise location of where the missile landed. However we can use the distance travelled to approximate this by calculating a radius.
# slightly adapted from https://stackoverflow.com/questions/34183049/plot-circle-with-a-certain-radius-around-point-on-a-map-in-ggplot2#34187454 # drop duplicate locations and distances dups <- duplicated(missiles %>% select(facility_name, distance_travelled_km)) missiles <- missiles %>% filter(!dups) # define the circle we want for each missile circles <- data_frame(f1 = rep(missiles$f1, each = 100), angle = rep(seq(0, 2*pi, length.out = 100), nrow(missiles))) meanLatitude <- mean(missiles$facility_latitude) missile_circles <- missiles %>% # length per longitude changes with latitude, so need correction mutate(radiusLon = distance_travelled_km/111/cos(meanLatitude/57.3), radiusLat = distance_travelled_km/111) %>% left_join(circles) %>% mutate(longitude = facility_longitude + radiusLon * cos(angle), latitude = facility_latitude + radiusLat * sin(angle))
So now we have our circles we can plot them on a map.
library(ggmap) nk = get_map(location = c(lon = mean(missile_circles$facility_longitude), lat = mean(missile_circles$facility_latitude)), zoom = 5, maptype = "terrain") ggmap(nk, extent = "panel") + geom_point(aes(x = facility_longitude, y = facility_latitude, colour=facility_name), data = missiles) + ########### add circles geom_polygon(data = missile_circles, aes(longitude, latitude, group = f1, colour=facility_name), alpha = 0)
These are obviously not missile ranges and none of the missiles will have gone over China. The CNS have also created visualisations with this data and from that we can see that the time dimension is important. That is something we could add to our visualisation or we could perform more analyses on the success/fail dimension.
The Bureau of Transportation Statistics are a leading source of U.S. transportation systems data, helping to shape transportation policy and research projects across the US. We pulled through their data, selecting the Month and DayOfWeek fields, as well as Departure and Arrival delays. The site only allows us to download data one month at a time, so we need to begin by reading in 12 files to a list and binding them together.
# https://www.transtats.bts.gov/DL_SelectFields.asp # Month files were downloaded to separate csvs, so we'll read them in and combine them files <- list.files(path = "data/FlightData/", pattern = "*.csv", full.names = TRUE) flightsRaw <- map_df(files, read_csv)
Now lets recode our Months and Weekdays to be character names, rather than just integers. Then we can prepare our data for plotting. In this plot, we’ll be aiming to plot the difference between departure and arrival delays, grouped by Weekday and Month.
#Begin by renaming integer values to days of the week and months flightData <- flightsRaw %>% mutate(DAY_OF_WEEK = as.factor(DAY_OF_WEEK), DAY_OF_WEEK = fct_recode(DAY_OF_WEEK, Monday = "1", Tuesday = "2", Wenesday = "3", Thursday = "4", Friday = "5", Saturday = "6", Sunday = "7"), MONTH = as.factor(MONTH), MONTH = fct_recode(MONTH, January = "1", February = "2", March = "3", April = "4", May = "5", June = "6", July = "7", August = "8", September = "9", October = "10", November = "11", December = "12")) #Summarise the data to show the mean delay/arrival time split by Month and Weekday monthDayDelays <- flightData %>% select(DEP_DELAY, ARR_DELAY, MONTH, DAY_OF_WEEK) %>% group_by(MONTH, DAY_OF_WEEK) %>% #Calculate means split by Month and Weekday summarise(Departures = mean(DEP_DELAY, na.rm = TRUE), Arrivals = mean(ARR_DELAY, na.rm = TRUE)) %>% filter(!is.na(MONTH)) %>% ungroup() %>% #Gather our data so Depature and Arrival delay times are stored within one column gather(key = delayType, value = Delay, Arrivals, Departures) %>% #Reorder Departure/Arrival factors so Depatures appear first in our graphic mutate(delayType = fct_rev(delayType)) monthDayDelays ## # A tibble: 168 x 4 ## MONTH DAY_OF_WEEK delayType Delay ##
## 1 January Monday Arrivals 3.08526171 ## 2 January Tuesday Arrivals 0.49934650 ## 3 January Wenesday Arrivals -0.64372809 ## 4 January Thursday Arrivals -0.61050874 ## 5 January Friday Arrivals 3.37778214 ## 6 January Saturday Arrivals 0.05099645 ## 7 January Sunday Arrivals 4.17985079 ## 8 February Monday Arrivals 1.30443327 ## 9 February Tuesday Arrivals 5.09620906 ## 10 February Wenesday Arrivals 3.00267779 ## # ... with 158 more rows
We now have our Delay times in one column, with delayType either arrival or departure. We also have our Month and Weekday columns for grouping our data in the plot.
Now let’s create a graphic. Note, the colours used for our graphic were obtained from Color Brewer 2.0.
#Specify colours vector for use in scale_fill_manual. colours <- c("#ffffb2", "#fed976", "#feb24c", "#fd8d3c", "#fc4e2a", "#e31a1c", "#b10026") #Graphic ggplot(monthDayDelays, aes(x = MONTH, y = Delay)) + #Adding reference lines to our (soon to become) circular chart geom_hline(yintercept = seq(0, 20, by = 5), alpha = 0.1) + #Bars for every day of the week grouped by month geom_bar(stat = "identity", aes(fill = DAY_OF_WEEK), position = "dodge", colour = "black") + #This bit is hacky. To get a single label to appear on our reference lines at zero degrees, we select a #single datapoint from January (Month at zero degrees) and use it as our data argument. #monthDayDelays[1, ] is a datapoint for mean Arrival time in January geom_text(data = monthDayDelays[1, ], y = 5, label = "5", size = 3) + geom_text(data = monthDayDelays[1, ], y = 10, label = "10", size = 3) + geom_text(data = monthDayDelays[1, ], y = 15, label = "15", size = 3) + geom_text(data = monthDayDelays[1, ], y = 20, label = "20", size = 3) + #monthDayDelays[85, ] is a datapoint for mean Departure time in January geom_text(data = monthDayDelays[85, ], y = 10, label = "10", size = 3) + geom_text(data = monthDayDelays[85, ], y = 15, label = "15", size = 3) + geom_text(data = monthDayDelays[85, ], y = 20, label = "20", size = 3) + #Specify colours for chart scale_fill_manual(values = rev(colours)) + #Remove yAxis scale from side of plot scale_y_continuous(breaks = NULL) + #Make plot circular, starting at due north coord_polar(theta = "x", start = -pi/12) + #Separate our departure and arrival plots facet_wrap(~delayType, labeller = label_parsed) + #Add labels labs(title = "Average Minute Delay of American Flights - 2016", x = "", y = "", fill = "", caption = "Data from https://www.transtats.bts.gov/DL_SelectFields.asp") + theme_minimal(base_size = 8) + #Make our facet titles pretty theme(strip.text.x = element_text(size = 15, colour = "#f03b20"))
Interestingly, we can see that even if your flight is delayed, you can expect that delay to have reduced upon arrival. In fact, on average 5 minutes and 25 seconds will be recovered during air time.
Perhaps not so interestingly, we can see that the months with the longest delays fall in the summer and winter holidays. Conversely, if you’re not bound by school holidays, November looks to be an excellent time to travel, as you can expect to arrive a whole 2 minutes early!
Again with this analysis, there are many more pathways we could explore. For example, FiveThirtyEight have produced a similar delay chart, only this time grouped by airport. This, when combined with our brief analysis, makes your dream, undelayed, vacation a possible reality!!
This blogpost turned out longer than we expected but that’s what happens when you have fun. If you have a cool dataset that’s fun to analyse let us know on twitter. You can find the code for this blogpost on GitHub.