| A | B | C | D | E | F | G | H | I | J | K | L | M | N | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Screencast | Date | Timestamp | Timestamp (with hours) | 1 | 2 | 3 | Timestamp (sec) | Link | Description | Functions | Packages | Concepts | |
2 | College Majors and Income | 2018-10-15 | 1:45 | 0:1:45 | 0 | 1 | 45 | 105 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=105s | Using read_csv function to import data directly from Github to R (without cloning the repository) | read_csv | |||
3 | College Majors and Income | 2018-10-15 | 7:20 | 0:7:20 | 0 | 7 | 20 | 440 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=440s | Creating a histogram (geom_histogram), then a boxplot (geom_boxplot), to explore the distribution of salaries | geom_histogram | geom_boxplot | |||
4 | College Majors and Income | 2018-10-15 | 8:55 | 0:8:55 | 0 | 8 | 55 | 535 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=535s | Using fct_reorder function to sort boxplot of college majors by salary | fct_reorder | |||
5 | College Majors and Income | 2018-10-15 | 9:35 | 0:9:35 | 0 | 9 | 35 | 575 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=575s | Using dollar_format function from scales package to convert scientific notation to dollar format (e.g., "4e+04" becomes "$40,000") | dollar_format | scales | ||
6 | College Majors and Income | 2018-10-15 | 14:10 | 0:14:10 | 0 | 14 | 10 | 850 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=850s | Creating a dotplot (geom_point) of 20 top-earning majors (includes adjusting axis, using the colour aesthetic, and adding error bars) | geom_point | |||
7 | College Majors and Income | 2018-10-15 | 17:45 | 0:17:45 | 0 | 17 | 45 | 1065 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=1065s | Using str_to_title function to convert string from ALL CAPS to Title Case | str_to_title | |||
8 | College Majors and Income | 2018-10-15 | 20:45 | 0:20:45 | 0 | 20 | 45 | 1245 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=1245s | Creating a Bland-Altman graph to explore relationship between sample size and median salary | ||||
9 | College Majors and Income | 2018-10-15 | 21:45 | 0:21:45 | 0 | 21 | 45 | 1305 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=1305s | Using geom_text_repel function from ggrepel package to get text labels on scatter plot points | geom_text_repel | ggrepel | ||
10 | College Majors and Income | 2018-10-15 | 28:30 | 0:28:30 | 0 | 28 | 30 | 1710 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=1710s | Using count function's wt argument to specify what should be counted (default is number of rows) | count | |||
11 | College Majors and Income | 2018-10-15 | 30:00 | 0:30:00 | 0 | 30 | 00 | 1800 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=1800s | Spicing up a dull bar graph by adding a redundant colour aesthetic (trick from Julia Silge) | ||||
12 | College Majors and Income | 2018-10-15 | 36:20 | 0:36:20 | 0 | 36 | 20 | 2180 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=2180s | Starting to explore relationship between gender and salary | ||||
13 | College Majors and Income | 2018-10-15 | 37:10 | 0:37:10 | 0 | 37 | 10 | 2230 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=2230s | Creating a stacked bar graph (geom_col) of gender breakdown within majors | geom_col | |||
14 | College Majors and Income | 2018-10-15 | 40:15 | 0:40:15 | 0 | 40 | 15 | 2415 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=2415s | Using summarise_at to aggregate men and women from majors into categories of majors | summarise_at | |||
15 | College Majors and Income | 2018-10-15 | 45:30 | 0:45:30 | 0 | 45 | 30 | 2730 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=2730s | Graphing scatterplot (geom_point) of share of women and median salary | geom_point | |||
16 | College Majors and Income | 2018-10-15 | 47:10 | 0:47:10 | 0 | 47 | 10 | 2830 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=2830s | Using geom_smooth function to add a line of best fit to scatterplot above | geom_smooth | |||
17 | College Majors and Income | 2018-10-15 | 48:40 | 0:48:40 | 0 | 48 | 40 | 2920 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=2920s | Explanation of why not to aggregate first when performing a statistical test (including explanation of Simpson's Paradox) | ||||
18 | College Majors and Income | 2018-10-15 | 49:55 | 0:49:55 | 0 | 49 | 55 | 2995 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=2995s | Fixing geom_smooth so that we get one overall line while still being able to map to the colour aesthetic | geom_smooth | |||
19 | College Majors and Income | 2018-10-15 | 51:10 | 0:51:10 | 0 | 51 | 10 | 3070 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=3070s | Predicting median salary from share of women with weighted linear regression (to take sample sizes into account) | lm | |||
20 | College Majors and Income | 2018-10-15 | 56:05 | 0:56:05 | 0 | 56 | 05 | 3365 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=3365s | Using nest function and tidy function from the broom package to apply a linear model to many categories at once | nest | tidy | broom | ||
21 | College Majors and Income | 2018-10-15 | 58:05 | 0:58:05 | 0 | 58 | 05 | 3485 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=3485s | Using p.adjust function to adjust p-values to correct for multiple testing (using FDR, False Discovery Rate) | p.adjust | |||
22 | College Majors and Income | 2018-10-15 | 1:04:50 | 1:04:50 | 1 | 04 | 50 | 3890 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=3890s | Showing how to add an appendix to an RMarkdown file with code that doesn't run when compiled | ||||
23 | College Majors and Income | 2018-10-15 | 1:09:00 | 1:09:00 | 1 | 09 | 00 | 4140 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=4140s | Using fct_lump function to aggregate major categories into the top four and an "Other" category | fct_lump | |||
24 | College Majors and Income | 2018-10-15 | 1:10:05 | 1:10:05 | 1 | 10 | 05 | 4205 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=4205s | Adding sample size to the size aesthetic within the aes function | ||||
25 | College Majors and Income | 2018-10-15 | 1:10:50 | 1:10:50 | 1 | 10 | 50 | 4250 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=4250s | Using ggplotly function from plotly package to create an interactive scatterplot (tooltips appear when moused over) | ggplotly | plotly | ||
26 | College Majors and Income | 2018-10-15 | 1:15:55 | 1:15:55 | 1 | 15 | 55 | 4555 | https://www.youtube.com/watch?v=nx5yhXAQLxw&t=4555s | Exploring IQR (Inter-Quartile Range) of salaries by major | ||||
27 | Horror Movie Profits | 2018-10-23 | 2:50 | 0:2:50 | 0 | 2 | 50 | 170 | https://www.youtube.com/watch?v=3-DRwg9yeNA&t=170s | Using parse_date function from lubridate package to convert date formatted as character to date class (should have used mdy function though) | parse_date | lubridate | ||
28 | Horror Movie Profits | 2018-10-23 | 7:45 | 0:7:45 | 0 | 7 | 45 | 465 | https://www.youtube.com/watch?v=3-DRwg9yeNA&t=465s | Using fct_lump function to aggregate distributors into top 6 (by number of movies) and and "Other" category | fct_lump | |||
29 | Horror Movie Profits | 2018-10-23 | 8:50 | 0:8:50 | 0 | 8 | 50 | 530 | https://www.youtube.com/watch?v=3-DRwg9yeNA&t=530s | Investigating strange numbers in the data and discovering duplication | ||||
30 | Horror Movie Profits | 2018-10-23 | 12:40 | 0:12:40 | 0 | 12 | 40 | 760 | https://www.youtube.com/watch?v=3-DRwg9yeNA&t=760s | Using problems function to look at parsing errors when importing data | problems | |||
31 | Horror Movie Profits | 2018-10-23 | 14:35 | 0:14:35 | 0 | 14 | 35 | 875 | https://www.youtube.com/watch?v=3-DRwg9yeNA&t=875s | Using arrange and distinct function and its .keep_all argument to de-duplicate observations | arrange | distinct | |||
32 | Horror Movie Profits | 2018-10-23 | 16:10 | 0:16:10 | 0 | 16 | 10 | 970 | https://www.youtube.com/watch?v=3-DRwg9yeNA&t=970s | Using geom_boxplot function to create a boxplot of budget by distributor | goem_boxplot | |||
33 | Horror Movie Profits | 2018-10-23 | 19:20 | 0:19:20 | 0 | 19 | 20 | 1160 | https://www.youtube.com/watch?v=3-DRwg9yeNA&t=1160s | Using floor function to bin release years into decades (e.g., "1970" and "1973" both become "1970") | floor | |||
34 | Horror Movie Profits | 2018-10-23 | 21:30 | 0:21:30 | 0 | 21 | 30 | 1290 | https://www.youtube.com/watch?v=3-DRwg9yeNA&t=1290s | Using summarise_at function to apply the same function to multiple variables at the same time | summarise_at | |||
35 | Horror Movie Profits | 2018-10-23 | 24:10 | 0:24:10 | 0 | 24 | 10 | 1450 | https://www.youtube.com/watch?v=3-DRwg9yeNA&t=1450s | Using geom_line to visualize multiple metrics at the same time | geom_line | |||
36 | Horror Movie Profits | 2018-10-23 | 26:00 | 0:26:00 | 0 | 26 | 00 | 1560 | https://www.youtube.com/watch?v=3-DRwg9yeNA&t=1560s | Using facet_wrap function to graph small multiples of genre-budget boxplots by distributor | facet_wrap | |||
37 | Horror Movie Profits | 2018-10-23 | 28:35 | 0:28:35 | 0 | 28 | 35 | 1715 | https://www.youtube.com/watch?v=3-DRwg9yeNA&t=1715s | Starting analysis of profit ratio of movies | ||||
38 | Horror Movie Profits | 2018-10-23 | 32:50 | 0:32:50 | 0 | 32 | 50 | 1970 | https://www.youtube.com/watch?v=3-DRwg9yeNA&t=1970s | Using paste0 function in a custom function to show labels of multiple (e.g., "4X" or "6X" to mean "4 times" or "6 times") | paste0 | |||
39 | Horror Movie Profits | 2018-10-23 | 41:20 | 0:41:20 | 0 | 41 | 20 | 2480 | https://www.youtube.com/watch?v=3-DRwg9yeNA&t=2480s | Starting analysis of the most common genres over time | ||||
40 | Horror Movie Profits | 2018-10-23 | 45:55 | 0:45:55 | 0 | 45 | 55 | 2755 | https://www.youtube.com/watch?v=3-DRwg9yeNA&t=2755s | Starting analysis of the most profitable individual horror movies | ||||
41 | Horror Movie Profits | 2018-10-23 | 51:45 | 0:51:45 | 0 | 51 | 45 | 3105 | https://www.youtube.com/watch?v=3-DRwg9yeNA&t=3105s | Using paste0 function to add release date of movie to labels in a bar graph | paste0 | |||
42 | Horror Movie Profits | 2018-10-23 | 53:25 | 0:53:25 | 0 | 53 | 25 | 3205 | https://www.youtube.com/watch?v=3-DRwg9yeNA&t=3205s | Using geom_text function, along with its check_overlap argument, to add labels to some points on a scatterplot | geom_text | |||
43 | Horror Movie Profits | 2018-10-23 | 58:10 | 0:58:10 | 0 | 58 | 10 | 3490 | https://www.youtube.com/watch?v=3-DRwg9yeNA&t=3490s | Using ggplotly function from plotly package to create an interactive scatterplot | ggplotly | plotly | ||
44 | Horror Movie Profits | 2018-10-23 | 1:00:55 | 1:00:55 | 1 | 00 | 55 | 3655 | https://www.youtube.com/watch?v=3-DRwg9yeNA&t=3655s | Reviewing unexplored areas of investigation | ||||
45 | R Downloads | 2018-10-30 | 5:20 | 0:5:20 | 0 | 5 | 20 | 320 | https://www.youtube.com/watch?v=nms9F-XubJU&t=320s | Using geom_line function to visualize changes over time | geom_line | |||
46 | R Downloads | 2018-10-30 | 7:35 | 0:7:35 | 0 | 7 | 35 | 455 | https://www.youtube.com/watch?v=nms9F-XubJU&t=455s | Starting to decompose time series data into day-of-week trend and overall trend (lots of lubridate package functions) | lubridate | |||
47 | R Downloads | 2018-10-30 | 9:50 | 0:9:50 | 0 | 9 | 50 | 590 | https://www.youtube.com/watch?v=nms9F-XubJU&t=590s | Using floor_date function from lubridate package to round dates down to the week level | ||||
48 | R Downloads | 2018-10-30 | 10:05 | 0:10:05 | 0 | 10 | 05 | 605 | https://www.youtube.com/watch?v=nms9F-XubJU&t=605s | Using min function to drop incomplete/partial week at the start of the dataset | ||||
49 | R Downloads | 2018-10-30 | 12:20 | 0:12:20 | 0 | 12 | 20 | 740 | https://www.youtube.com/watch?v=nms9F-XubJU&t=740s | Using countrycode function from countrycode package to replace two-letter country codes with full names (e.g., "CA" becomes "Canada") | countrycode | countrycode | ||
50 | R Downloads | 2018-10-30 | 17:20 | 0:17:20 | 0 | 17 | 20 | 1040 | https://www.youtube.com/watch?v=nms9F-XubJU&t=1040s | Using fct_lump function to get top N categories within a categorical variable and classify the rest as "Other" | fct_lump | |||
51 | R Downloads | 2018-10-30 | 20:30 | 0:20:30 | 0 | 20 | 30 | 1230 | https://www.youtube.com/watch?v=nms9F-XubJU&t=1230s | Using hour function from lubridate package to pull out integer hour value from a datetime variable | hour | lubridate | ||
52 | R Downloads | 2018-10-30 | 22:20 | 0:22:20 | 0 | 22 | 20 | 1340 | https://www.youtube.com/watch?v=nms9F-XubJU&t=1340s | Using facet_wrap function to graph small multiples of downloads by country, then changing scales argument to allow different scales on y-axis | facet_wrap | |||
53 | R Downloads | 2018-10-30 | 31:00 | 0:31:00 | 0 | 31 | 00 | 1860 | https://www.youtube.com/watch?v=nms9F-XubJU&t=1860s | Starting analysis of downloads by IP address | ||||
54 | R Downloads | 2018-10-30 | 35:20 | 0:35:20 | 0 | 35 | 20 | 2120 | https://www.youtube.com/watch?v=nms9F-XubJU&t=2120s | Using as.POSIXlt to combine separate date and time variables to get a single datetime variable | as.POSIXlt | |||
55 | R Downloads | 2018-10-30 | 36:35 | 0:36:35 | 0 | 36 | 35 | 2195 | https://www.youtube.com/watch?v=nms9F-XubJU&t=2195s | Using lag function to calculate time between downloads (time between events) per IP address (comparable to SQL window function) | lag | |||
56 | R Downloads | 2018-10-30 | 38:05 | 0:38:05 | 0 | 38 | 05 | 2285 | https://www.youtube.com/watch?v=nms9F-XubJU&t=2285s | Using as.numeric function to convert variable from a time interval object to a numeric variable (number in seconds) | as.numeric | |||
57 | R Downloads | 2018-10-30 | 38:40 | 0:38:40 | 0 | 38 | 40 | 2320 | https://www.youtube.com/watch?v=nms9F-XubJU&t=2320s | Explanation of a bimodal log-normal distribution | ||||
58 | R Downloads | 2018-10-30 | 39:05 | 0:39:05 | 0 | 39 | 05 | 2345 | https://www.youtube.com/watch?v=nms9F-XubJU&t=2345s | Handy trick for setting easy-to-interpret intervals for time data on scale_x_log10 function's breaks argument | scale_x_log10 | |||
59 | R Downloads | 2018-10-30 | 47:40 | 0:47:40 | 0 | 47 | 40 | 2860 | https://www.youtube.com/watch?v=nms9F-XubJU&t=2860s | Starting to explore package downloads | ||||
60 | R Downloads | 2018-10-30 | 52:15 | 0:52:15 | 0 | 52 | 15 | 3135 | https://www.youtube.com/watch?v=nms9F-XubJU&t=3135s | Adding 1 to the numerator and denominator when calculating a ratio to get around dividing by zero | ||||
61 | R Downloads | 2018-10-30 | 57:55 | 0:57:55 | 0 | 57 | 55 | 3475 | https://www.youtube.com/watch?v=nms9F-XubJU&t=3475s | Showing how to look at package download data over time using cran_downloads function from the cranlogs package | cran_downloads | cranlogs | ||
62 | US Wind Turbines | 2018-11-06 | 3:50 | 0:3:50 | 0 | 3 | 50 | 230 | https://www.youtube.com/watch?v=O1oDIQV6VKU&t=230s | Using count function to explore categorical variables | count | |||
63 | US Wind Turbines | 2018-11-06 | 5:00 | 0:5:00 | 0 | 5 | 00 | 300 | https://www.youtube.com/watch?v=O1oDIQV6VKU&t=300s | Creating a quick-and-dirty map using geom_point function and latitude and longitude data | geom_point | Visualisation | ||
64 | US Wind Turbines | 2018-11-06 | 6:10 | 0:6:10 | 0 | 6 | 10 | 370 | https://www.youtube.com/watch?v=O1oDIQV6VKU&t=370s | Explaining need for mapproj package when plotting maps in ggplot2 | coord_map | mapproj | ||
65 | US Wind Turbines | 2018-11-06 | 7:35 | 0:7:35 | 0 | 7 | 35 | 455 | https://www.youtube.com/watch?v=O1oDIQV6VKU&t=455s | Using borders function to add US state borders to map | borders | Visualisation | ||
66 | US Wind Turbines | 2018-11-06 | 10:45 | 0:10:45 | 0 | 10 | 45 | 645 | https://www.youtube.com/watch?v=O1oDIQV6VKU&t=645s | Using fct_lump to get the top 6 project categories and put the rest in a lumped "Other" category | fct_lump | |||
67 | US Wind Turbines | 2018-11-06 | 11:30 | 0:11:30 | 0 | 11 | 30 | 690 | https://www.youtube.com/watch?v=O1oDIQV6VKU&t=690s | Changing data so that certain categories' points appear in front of other categories' points on the map | Visualisation | |||
68 | US Wind Turbines | 2018-11-06 | 14:15 | 0:14:15 | 0 | 14 | 15 | 855 | https://www.youtube.com/watch?v=O1oDIQV6VKU&t=855s | Taking the centroid (average longitude and latitude) of points across a geographic area as a way to aggregate categories to one point | ||||
69 | US Wind Turbines | 2018-11-06 | 19:40 | 0:19:40 | 0 | 19 | 40 | 1180 | https://www.youtube.com/watch?v=O1oDIQV6VKU&t=1180s | Using ifelse function to clean missing data that is coded as "-9999" | ifelse | |||
70 | US Wind Turbines | 2018-11-06 | 26:00 | 0:26:00 | 0 | 26 | 00 | 1560 | https://www.youtube.com/watch?v=O1oDIQV6VKU&t=1560s | Asking, "How has turbine capacity changed over time?" | ||||
71 | US Wind Turbines | 2018-11-06 | 33:15 | 0:33:15 | 0 | 33 | 15 | 1995 | https://www.youtube.com/watch?v=O1oDIQV6VKU&t=1995s | Exploring different models of wind turbines | ||||
72 | US Wind Turbines | 2018-11-06 | 38:00 | 0:38:00 | 0 | 38 | 00 | 2280 | https://www.youtube.com/watch?v=O1oDIQV6VKU&t=2280s | Using mutate_if function to find NA values (coded as -9999) in multiple columns and replace them with an actual NA | mutate_if | Data cleaning and manipulation | ||
73 | US Wind Turbines | 2018-11-06 | 45:40 | 0:45:40 | 0 | 45 | 40 | 2740 | https://www.youtube.com/watch?v=O1oDIQV6VKU&t=2740s | Reviewing documentation for gganimate package | gganimate | |||
74 | US Wind Turbines | 2018-11-06 | 47:00 | 0:47:00 | 0 | 47 | 00 | 2820 | https://www.youtube.com/watch?v=O1oDIQV6VKU&t=2820s | Attempting to set up gganimate map | gganimate | Visualisation | ||
75 | US Wind Turbines | 2018-11-06 | 48:55 | 0:48:55 | 0 | 48 | 55 | 2935 | https://www.youtube.com/watch?v=O1oDIQV6VKU&t=2935s | Understanding gganimate package using a "Hello World" / toy example, then trying to debug turbine animation | gganimate | Visualisation | ||
76 | US Wind Turbines | 2018-11-06 | 56:45 | 0:56:45 | 0 | 56 | 45 | 3405 | https://www.youtube.com/watch?v=O1oDIQV6VKU&t=3405s | Using is.infinite function to get rid of troublesome Inf values | is.infinite | Data cleaning and manipulation | ||
77 | US Wind Turbines | 2018-11-06 | 57:55 | 0:57:55 | 0 | 57 | 55 | 3475 | https://www.youtube.com/watch?v=O1oDIQV6VKU&t=3475s | Quick hack for getting cumulative data from a table using crossing function (though it does end up with some duplication) | crossing | Data cleaning and manipulation | ||
78 | US Wind Turbines | 2018-11-06 | 1:01:45 | 1:01:45 | 1 | 01 | 45 | 3705 | https://www.youtube.com/watch?v=O1oDIQV6VKU&t=3705s | Diagnosis of gganimate issue (points between integer years are being interpolated) | gganimate | Visualisation | ||
79 | US Wind Turbines | 2018-11-06 | 1:04:35 | 1:04:35 | 1 | 04 | 35 | 3875 | https://www.youtube.com/watch?v=O1oDIQV6VKU&t=3875s | Pseudo-successful gganimate map (cumulative points show up, but some points are missing) | gganimate | |||
80 | US Wind Turbines | 2018-11-06 | 1:05:40 | 1:05:40 | 1 | 05 | 40 | 3940 | https://www.youtube.com/watch?v=O1oDIQV6VKU&t=3940s | Summary of screencast | ||||
81 | Malaria Incidence | 2018-11-12 | 2:45 | 0:2:45 | 0 | 2 | 45 | 165 | https://www.youtube.com/watch?v=5_6O2oDy5Jk&t=165s | Importing data using the malariaAtlas package | malariaAtlas | |||
82 | Malaria Incidence | 2018-11-12 | 14:10 | 0:14:10 | 0 | 14 | 10 | 850 | https://www.youtube.com/watch?v=5_6O2oDy5Jk&t=850s | Using geom_line function to visualize malaria prevalence over time | geom_line | |||
83 | Malaria Incidence | 2018-11-12 | 15:10 | 0:15:10 | 0 | 15 | 10 | 910 | https://www.youtube.com/watch?v=5_6O2oDy5Jk&t=910s | Quick map visualization using longitude and latitude coordinates and the geom_point function | geom_point | |||
84 | Malaria Incidence | 2018-11-12 | 18:40 | 0:18:40 | 0 | 18 | 40 | 1120 | https://www.youtube.com/watch?v=5_6O2oDy5Jk&t=1120s | Using borders function to add Kenyan country borders to map | borders | |||
85 | Malaria Incidence | 2018-11-12 | 19:50 | 0:19:50 | 0 | 19 | 50 | 1190 | https://www.youtube.com/watch?v=5_6O2oDy5Jk&t=1190s | Using scale_colour_gradient2 function to change the colour scale of points on the map | scale_colour_gradient2 | |||
86 | Malaria Incidence | 2018-11-12 | 20:40 | 0:20:40 | 0 | 20 | 40 | 1240 | https://www.youtube.com/watch?v=5_6O2oDy5Jk&t=1240s | Using arrange function to ensure that certain points on a map appear in front of/behind other points | arrange | |||
87 | Malaria Incidence | 2018-11-12 | 21:50 | 0:21:50 | 0 | 21 | 50 | 1310 | https://www.youtube.com/watch?v=5_6O2oDy5Jk&t=1310s | Aggregating data into decades using the truncated division operator %/% | %/% | |||
88 | Malaria Incidence | 2018-11-12 | 24:45 | 0:24:45 | 0 | 24 | 45 | 1485 | https://www.youtube.com/watch?v=5_6O2oDy5Jk&t=1485s | Starting to look at aggregated malaria data (instead of country-specific data) | ||||
89 | Malaria Incidence | 2018-11-12 | 26:50 | 0:26:50 | 0 | 26 | 50 | 1610 | https://www.youtube.com/watch?v=5_6O2oDy5Jk&t=1610s | Using sample and unique functions to randomly select a few countries, which are then graphed | sample | unique | |||
90 | Malaria Incidence | 2018-11-12 | 28:30 | 0:28:30 | 0 | 28 | 30 | 1710 | https://www.youtube.com/watch?v=5_6O2oDy5Jk&t=1710s | Using last function to select the most recent observation from a set of arranged data | last | |||
91 | Malaria Incidence | 2018-11-12 | 32:55 | 0:32:55 | 0 | 32 | 55 | 1975 | https://www.youtube.com/watch?v=5_6O2oDy5Jk&t=1975s | Creating a Bland-Altman plot to explore relationship between current incidence and change in incidence in past 15 years | ||||
92 | Malaria Incidence | 2018-11-12 | 35:45 | 0:35:45 | 0 | 35 | 45 | 2145 | https://www.youtube.com/watch?v=5_6O2oDy5Jk&t=2145s | Using anti_join function to find which countries are not in the malaria dataset | anti_join | |||
93 | Malaria Incidence | 2018-11-12 | 36:40 | 0:36:40 | 0 | 36 | 40 | 2200 | https://www.youtube.com/watch?v=5_6O2oDy5Jk&t=2200s | Using the iso3166 dataset set in the maps package to match three-letter country code (i.e., the ISO 3166 code) with country names | maps | |||
94 | Malaria Incidence | 2018-11-12 | 38:30 | 0:38:30 | 0 | 38 | 30 | 2310 | https://www.youtube.com/watch?v=5_6O2oDy5Jk&t=2310s | Creating a world map using geom_polygon function (and eventually theme_void and coord_map functions) | geom_polygon | theme_void | coord_map | |||
95 | Malaria Incidence | 2018-11-12 | 39:00 | 0:39:00 | 0 | 39 | 00 | 2340 | https://www.youtube.com/watch?v=5_6O2oDy5Jk&t=2340s | Getting rid of Antarctica from world map | ||||
96 | Malaria Incidence | 2018-11-12 | 42:35 | 0:42:35 | 0 | 42 | 35 | 2555 | https://www.youtube.com/watch?v=5_6O2oDy5Jk&t=2555s | Using facet_wrap function to create small multiples of world map for different time periods | facet_wrap | |||
97 | Malaria Incidence | 2018-11-12 | 47:30 | 0:47:30 | 0 | 47 | 30 | 2850 | https://www.youtube.com/watch?v=5_6O2oDy5Jk&t=2850s | Starting to create an animated map of malaria deaths (actual code writing starts at 57:45) | ||||
98 | Malaria Incidence | 2018-11-12 | 51:25 | 0:51:25 | 0 | 51 | 25 | 3085 | https://www.youtube.com/watch?v=5_6O2oDy5Jk&t=3085s | Starting with a single year after working through some bugs | ||||
99 | Malaria Incidence | 2018-11-12 | 52:10 | 0:52:10 | 0 | 52 | 10 | 3130 | https://www.youtube.com/watch?v=5_6O2oDy5Jk&t=3130s | Using regex_inner_join function from the fuzzyjoin package to join map datasets because one of them has values in regular expressions | regex_inner_join | fuzzyjoin | ||
100 | Malaria Incidence | 2018-11-12 | 55:15 | 0:55:15 | 0 | 55 | 15 | 3315 | https://www.youtube.com/watch?v=5_6O2oDy5Jk&t=3315s | As alternative to fuzzyjoin package in above step, using str_remove function to get rid of unwanted regex | str_remove |