2022 UC Carpentries Fall Workshop (R )

**Workshop Details** Dates: September 6th - 13th, 2022 Time: 9am - 12pm **Workshop Agenda:** https://ucsdlib.github.io/2022-09-06-carpentries-uc/ **Lesson:** https://swcarpentry.github.io/r-novice-gapminder/ ## Day 1 - 3: Introduction to R **Software Installation:** * R Studio downloads: https://www.r-project.org/ - download the free version * Online/Cloud R Studio interface: https://rstudio.cloud/ *This is an online interface that can be used when unable to download R Studio* * Git software: https://git-scm.com/downloads **Lesson Data (download)** * [Gapminder data](https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/gh-pages/_episodes_rmd/data/gapminder_data.csv) * right click in browser window and Save As: gapminder_data.csv * [Feline-data](https://kthoma2484.github.io/2022-06-13-UCSD/data/feline-data.csv) * [Gapminder Wide data](https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/gh-pages/_episodes_rmd/data/gapminder_wide.csv) * * right click in browser window and Save As: gapminder_wide.csv ## NOTES: A copy of the instructor live session notes will be made available to participants upon request at the end of the workshop. ## Workshop Day 1 (83) ### First name and Last Name/Organization/Dept./Email | Name (first & last) | Organization | Dept. | Email | | ------------------------- | ------------ | ----- | --------------- | |Joanna Eckhardt| UCSD | Neurosciences|jeckhardt@ucsd.edu | | Kyle Rokes|UCSB | -kyle_rokes@ucsb.edu | | (example) Jane Doe | UCSD | IT | jdoe1@ucsd.edu | | Juan Sanchez Arcila (helper) | UCM | | | | Chelsea Chapman | UCSD |Public Health |crchapma@health.ucsd.edu | Ha Vu | UCSD | Economics | vha@ucsd.edu | | Yulissa Perez Rojas | UCM | ES | yperezrojas@ucmerced.edu | |Shang Su | The University of Toledo | Cell and Cancer Biology | shang.su@utoledo.edu | | Guru Kalyan Jayasingh | UCSD | Physics| gjayasingh@ucsd.edu | | Brittany Johnson | UCM |Cognitive Science |bjohnson44@ucmerced.edu| | Daira Melendez | UCSD | Bioinfo | damelendez@ucsd.edu | | Tom Le | UCM | | tle267@ucmerced.edu | | Govind Sah| UCSD | Pathology|gsah@health.ucsd.edu | |Brett Taylor | UCSD|Biomedical Sciences |b5taylor@ucsd.edu | |Osika Tripathi |UCSD |Public Health |otripathi@health.ucsd.edu | | Nicole | UCLA | Master of Urban Planning |nlmatteson@g.ucla.edu | |Kenan Chan |UCSD |SCRIPPS |kmc001@ucsd.edu | |Felipe Vásquez|UCSD |Bioinformatics |fvasquezcastro@ucsd.edu | |Alexander Bernal |UCSD |Economics |a1bernal@ucsd.edu | | Roberto Silva | UCSD | Scripps | rosilva@ucsd.edu | |Melodi Frey | UCSD | CMM | mtastemel@health.ucsd.edu | | Dexin Zhou | UCSD | Mathematics | dzhou@ucsd.edu | | Dane Whicker | UCSD | SIO | dwhicker@ucsd.edu | | Melisa Rodriguez | UCSD | Scripps | mdr013@ucsd.edu | |Alexander Frey|UCSD | Rady School of Management |alexander.frey@rady.ucsd.edu | |Cemil Can Ali Marandi|UCSD |BISB |calimarandi@ucsd.edu| |Fangke Jiang |UCSD |economics |fjiang@ucsd.edu | | Oishee Misra |UCSD | Economics |omisra@ucsd.edu | | Jessica Murillo | UCSD |Med |jdmurillosaich@health.ucsd.edu| | | | | Steven Krehel|UCSD |Economics | skrehel@ucsd.edu | |Melodi Frey | UCSD |CMM | mtastemel@health.ucsd.edu | |Govind Sah | UCSD | Pathology | gsah@ealth.ucsd.edu | | m Ghebremedhin UCSD |aghebremedhin@health.ucsd.edu | | | | Alex Chaim | UCSD | CMM | iachaim@ucsd.edu | | Alissa Jae Lazo Kim | UCSD | DBMI internship | alazokim@health.ucsd | Mugen Blue | UCM | EECS | mblue3@ucmerced.edu | Glen MacDonald Laura Lopez UCM lgarcialopez2@ucmerced.edu | Danny Lee | UCSD | Economics | dannytylee@ucsd.edu | | Edgar Reyna UCLA MURP eareyna@ucla.edu | Sarina Qin | UCM | sqin@ucmerced.edu |AJ Rice | UCSB | Political Science | riceaj@ucsb.edu Julia Alvarez | UCM | Nat Sci | jhampton3@ucmerced.edu |Nicole Rosenberg|UCSD|Scripps|nrosenberg@ucsd.edu ## Day 1 Questions: Please enter any questions not answered during live session here: 1. ### End Day 1 --- ## Workshop Day 2 ### First name and Last Name/Organization/Dept./Email | Name (first & last) | Organization | Dept. | Email| | ------------------------- | ------------ | ----- | --------------- | Kyle Rokes UCSB kyle_rokes@ucsb.edu | | Agnieszka Pluta | UCLA | | agpluta@ucla.edu | | Alexander Bernal | UCSD | Economics |a1bernal@ucsd.edu | | Eastern Kang (helper)| UCSD |Pediatrics |dkangsim@health.ucsd.edu | | Jessica Murillo | UCSD |Med |jdmurillosaich@health.ucsd.edu | | **Reid Otsuji (Instructor)** | UCSD | Library | rotsuji@ucsd.edu | | Dane Whicker | UCSD | SIO | dwhicker@ucsd.edu | |Fangke |UCSD |Economics |fjiang@ucsd.edu | |Cemil Can Ali Marandi |UCSD |BISB |calimarandi@ucsd.edu | | Oishee Misra | UCSD | Economics | omisra@ucsd.edu | | Brittany Johnson |UCM |Cognitive Science |bjohnson44@ucmerced.edu | | Steven Krehel |UCSD |Economics |skrehel@ucsd.edu |Roberto Silva|UCSD|Scripps |rosilva@ucsd.edu | |kenan Chan |UCSD |SIO | |kmc001@ucsd.edu | Alexander Frey | UCSD | Rady School of Management | alexander.frey@rady.ucsd.edu | | Melisa Rodriguez | UCSD | Scripps | mdr013@ucsd.edu | |Anghesom Ghebremedhin |UCSD | | aghebremedhin@health.ucsd.edu | | Ha Vu | UCSD | Economics | vha@ucsd.edu | | Melodi Frey |UCSD |CMM | mtastemel@health.ucsd.edu | | Shang Su | U Toledo | Cell and Cancer Biology | shang.su@utoledo.edu | | Alex Chaim | UCSD | CMM | iachaim@ucsd.edu | | Osika Tripathi |UCSD |Public Health | otripathi@health.ucsd.edu | | Chelsea Chapman | UCSD |Public Health |crchapma@health.ucsd.edu | | | | | Glen MacDonald | UCLA |Geog |glen@geog.ucla.edu | | Alissa Jae Lazo Kim | UCSD | DBMI internship | alazokim@health.ucsd.edu | | Dexin Zhou | UCSD | Mathematics | dzhou@ucsd.edu | | Brett Taylor |UCSD |Biomedical Sciences | b5taylor@ucsd.edu | |Juan Sanchez Arcila (helper/carpentries instructor) | UCM | jsanchezarcila@ucmerced.edu | | |Susana Tejeda-Garibay | UCM | Quantitative Systems Biology | stejeda-garibay@ucmerced.edu | | Laura Lopez|University of California Merced | lgarcialopez2@ucmerced.edu | | |Joanna Eckhardt | UCSD| Neurosciences | jeckhardt@ucsd.edu| Cristian Gruppi| UCLA |IoES | cristiangruppi@g.ucla.edu | | Mugen Blue | UCM | EECS | mblue3@ucmerced.edu | Julia Alvarez | UCM | Nat Sci | jhampton3@ucmerced.edu | Danny Lee | UCSD | Economics | dannytylee@ucsd.edu | |Felipe Vasquez | UCSD | Bioinformatics | fvasquezcastro@ucsd.edu |Edgar Reyna | UCLA MURP eareyna@ucla.edu Daira Melendez | UCSD | Bioinformatics| damelendez@ucsd.edu Yulissa Perez Rojas | UCM | Environ. | yperezrojas@ucmerced.edu | Sarina Qin | UCM | sqin@ucmerced.edu |AJ Rice | UCSB | Political Science | riceaj@ucsb.edu ## Day 2 Shared Notes ### Subsetting Data We can **subset** using corresponding indices. To demonstrate this, let's create a simple numeric vector: ``` x<- c(5.4, 6.2, 7.1, 4.8, 7.5) ``` #any number would do. You can also assign names to the numeric vectors. ``` names(x) <- c('a', 'b', 'c', 'd', 'e') ``` remember `c()` denots for combine or concatenate. To extract a single value, we can use the square bracket `[]`. For example, if we use `x[1]`, where `x` is the object of we created and `[1]` is the corresponding index. We can use the `c()` and extract multiple elements at once. `x[c(1,3)]` would extract first and third elements from the vector. --- As for the `setwd()`, it is best practice to have the user running the script begin in a **consistent** directory on their machine and then use relative file paths from that directory to access files. --- We will be using the `Dplyr and Tidyr` packages that are a part of the `Tidyverse` package. If. you have not instaleld the `tidyverse` package, you can install it using the following command: ``` install.packages('tidyverse') ``` **NOTE**: installing a package needs to be done only once ``` library(tidyverse) ``` **NOTE**: loading library needs to be done every time you run a new R session. (for more information about the tidyverse, you can visit: https://www.tidyverse.org) Previously we used `x` for our object name; let's use the same approach and load the `.csv` dataset and call the object `gapminder`. ``` gapminder <- read.csv("working_directory/gapminder_data.csv") ``` Alternative way of loading the dataset: ``` gapminder <- read.csv(file.choose()) ```` a new window will pop-up and you can select the file you want to load. --- Using the `dplyr` package, we can subset variable names. ``` year_country_gap <- select (gapminder, year, country, gdpPercap) ``` If you have multiple pacakges loaded, some function share the same name. To avoid any conflict, you may specify the package name. ``` year_country_gap <- dplyr::select(gapminder, year, country, gdpPercap) ``` The `::` tells R console that we want to use a function from a specific package. --- The **pipe operator** `%>%` makes the data wrangling process much easier and smoother. The pipe basically means passing down (streaming down) information from one script to the next. Having multiple functions in one command improves the thinking process and improves readability of the script. ``` year_count <- gapminder %>% select (year, country, gdpPercap) ``` We can rename an existing variable name: ``` tidy_gdp<- year_country_gdp %>% rename(gdp_per_capita = gdpPercap) ``` You can also choose to do all these in one chunk of codes using the %>% ``` tidy_gdp<- gapminder %>% select(year, country, gdpPercap) %>% rename(gdp_per_capita = gdpPercap) ``` --- Now we are going to cover the `filter()` function. Previously we covered the `select()` function, which subsets data by the column (variable) names. The `filter()` function subsets data by rows. ``` year_country_gdp_euro <- gapminder %>% filter (continent == "Europe") %>% select(year, country, gdpPercap) ``` Basically, the script above means: take the `gapminder` dataset, subset it by `Europe`, and select variables `year`, `country`, and `gdpPercap`. By using the **pipe operator**, `%>%`, we can tell the story of how our data were manipulated. Another example: ``` europe_lifeExp_2007 <- gapminder %>% filter (continent =="Europe", year ==2007)%>% select(country, lifeExp) ``` --- #### Challenge 1 Write a single command (which can span multiple lines and includes pipes) that will produce a data frame that has the African [or any other country you pick] values for lifeExp, country and year, but not for other Continents. How many rows does your data frame have and why? ``` lifeExp_africa_asia<- gapminder%>% filter(continent == c("Africa", "Asia"))%>% select(year, country, lifeExp) ``` --- Instead of using the `filter()` function, which will only pass observations that meet your criteria, we can use the `group_by()`, which will use every unique criteria that you could have used in filter. Example: ``` gdp_bycontinents <- gapminder%>% group_by(continent) %>% summarize(mean_gdp_Percap = mean(gdpPercap)) ``` The script above basically states: take the `gapminder` dataset, group values by the variable `continent`, then summarize values (mean, sd, etc.). The end result should be the mean gdpPercap for each continent. --- #### Challenge 2 Calculate the average life expectancy per country. Which has the longest average life expectancy and which has the shortest average life expectancy? ``` lifeExp_bycountry <- gapminder %>% group_by(country) %>% summarize(mean_lifeExp = mean(lifeExp)) lifeExp_bycountry %>% filter(mean_lifeExp == min(mean_lifeExp) | mean_lifeExp == max(mean_lifeExp)) ``` --- Depends on the research questions, we may use the function `group_by()` to group multiple variables. ``` gdp_bycontinents_byyear <- gapminder %>% group_by(continent, year) %>% summarize( mean_gdpPercap = mean(gdpPercap), sd_gdpPercap = sd(gdpPercap)) ``` Sometimes, instead of summarizing informatoin, we want to create a new variable. The `mutate()` function creates a new variable (adds a new column into the dataset). ``` gap_GDP <- gapminder%>% mutate(gdp = gdpPercap * pop) ``` The script above states: take the `gapminder` data, create a new variable called `gdp` which is defined as multiplication between `gdpPercap` and `pop` variables. We can extend the script a bit further: ``` gdp_pop_bycontinents_byyear <- gapminder %>% mutate(gdp_billion = gdpPercap*pop/10^9) %>% group_by(continent,year) %>% summarize(mean_gdpPercap = mean(gdpPercap), sd_gdpPercap = sd(gdpPercap), mean_pop = mean(pop), sd_pop = sd(pop), mean_gdp_billion = mean(gdp_billion), sd_gdp_billion = sd(gdp_billion)) ``` By following the `%>%` operator, we can follow the thinking process of our data manipulation. ***This improves readability and make the script more reproducible.*** ## Day 2 Questions: Please enter any questions not answered during live session here: 1. ### End Day 2 ## Workshop Day 3 ### First name and Last Name/Organization/Dept./Email | Name (first & last) | Organization | Dept. | Email| | ------------------------- | ------------ | ----- | --------------- | |Joanna Eckhardt | UCSD | Neurosciences | jeckhardt@ucsd.edu | |Alexander Frey |UCSD |Rady School of Management | alexander.frey@rady.ucsd.edu| |Melodi Frey|UCSD|CMM|mtastemel@health.ucsd.edu | | Cemil Can Ali Marandi | UCSD | BISB |calimarandi@ucsd.edu| | Chelsea Chapman | UCSD |Public Health |crchapma@health.ucsd.edu | | | | | | | Yulissa Perez Rojas | UCM | Environ. | yperezrojas@ucmerced.edu |Kenan Chan |UCSD |UCSD |SIO | Kmc001@ucsd.edu | | Alissa Jae Lazo-Kim | UCSD | UCSD | DBMI internship | alazokim@health.ucsd.edu | | Brittany Johnson | UCM | Cognitive Science | bjohnson44@ucmerced.edu | |Anghesom Ghebremedhin | UCSD| | aghebremedhin@health.ucsd.edu| |AJ Rice |UCSB |Political Science |riceaj@ucsb.edu | | Melisa Rodriguez | UCSD | Scripps |mdr013@ucsd.edu | |Osika Tripathi | UCSD | Public health | otripathi@health.ucsd | | Dane Whicker | UCSD | SIO | dwhicker@ucsd.edu | |Jessica Murillo| UCSD | Medicine |jdmurillosaich@health.ucsd.edu | |Kat Koziar| UCR| Library |katherine.koziar@ucr.edu| |Danny Lee |UCSD |Economics |dannytylee@ucsd.edu | | Oishee Misra |UCSD |UCSD |Economics |omisra@ucsd.edu | | Ha Vu | UCSD | Economics | vha@ucsd.edu | | Shang Su | U Toledo | Cell and Cancer Biology | shang.su@utoledo.edu | | Marta Sala Climent | UCSD | Medicine | msalacliment@health.ucsd.edu | |Roberto Silva |UCSD|Scripps|rosilva@ucsd.edu| | Mugen Blue | UCM | EECS | mblue3@ucmerced| | Alexander Bernal | UCSD|Economics|a1bernal@ucsd.edu| |Edgar Reyna | UCLA |Urban planning| eareyna@ucla.edu | | Felipe Vasquez Castro | UCSD |Bioinformatics |fvasquezcastro@ucsd.edu | | Dexin Zhou | UCSD | Mathematics | dzhou@ucsd.edu | | ***Daira Melendez*** | UCSD |Bionformatics | damelendez@ucsd.edu| | Julia Alvarez | UCM| Nat sci | jhampton3@ucmerced.edu| |Steven Krehel | UCSD | Economics |skrehel@ucsd.edu| |Govind Sah | UCSD | Pathology | gsah@health.ucsd.edu| | Sarina Qin | UCM | Nat Sci | sqin@ucmerced.edu| Brett Taylor | UCSD | Biomedical Sciences | b5taylor@ucsd.edu ## Day 3 Shared Notes For when your computer doesn't have admin permissions to install things like packages, [RStudio Cloud](https://) is a good option Today we'll be plotting using the `ggplot2` package, which is a popular R plotting package Today's notes and cheat sheet: https://hackmd.io/3M1KawkLRhezD0u1TAkpfQ?view **[ggplot2 cheat sheet](https://kekoziar.github.io/cheatsheets/img/ggplot2_data-visualization-2-1_Page_1.png)** Once `ggplot` is installed, we can call functions from this package by calling the ``` library(ggplot2) ``` ``` ggplot(data = gapminder_csv, mapping = aes(x = gdpPercap, y = lifeExp)) + geom_point() ``` #### _Challenge 1_ **Part A** In the gapminder example we’ve been using, ``` ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) + geom_point() ``` use the column “year” to show how life expectancy has changed over time. Solution: ``` ggplot(data = gapminder, mapping = aes(x = year, y = lifeExp)) + geom_point() ``` But this doesn't look great, does it? So, Part B: **Part B** We’ve been using the `aes` function to tell the scatterplot geom about the x and y locations of each point. Another aesthetic property we can modify is the point color. Modify the code from the Part A to color the points by the “continent” column. Is it easier to detect trends? ``` ggplot(data = gapminder, mapping = aes(x = year, y = lifeExp, color=continent)) + geom_point() ``` We can use `color` argument in `aes()` to specify we want points colored by value of `continent` ### Geom arguments If we want to use lines rather than a scatterplot, we can use `geom_line()` ```ggplot(data = gapminder, mapping = aes(x=year, y=lifeExp, color=continent)) + geom_line() ``` Instead of adding a geom_point layer, we’ve added a geom_line layer. However, the result doesn’t look quite as we might have expected: it seems to be jumping around a lot in each continent. Let’s try to separate the data by country, plotting one line for each country: ``` ggplot(data = gapminder, mapping = aes(x=year, y=lifeExp, by=country, color=continent)) + geom_line() ``` Note the `by =` additional portion We’ve added the by aesthetic, which tells ggplot to draw a line for each country. But what if we want to visualize both lines and points on the plot? We can add another layer to the plot: ``` ggplot(data = gapminder, mapping = aes(x=year, y=lifeExp, by=country, color=continent)) + geom_line() + geom_point() ``` It’s important to note that each layer is drawn on top of the previous layer. In this example, the points have been drawn on top of the lines. Here’s a demonstration: ``` ggplot(data = gapminder, mapping = aes(x=year, y=lifeExp, by=country)) + geom_line(mapping = aes(color=continent)) + geom_point() ``` The items within ggplot() `aes()` apply to the entire plot, but can override with `aes()` command in individual `geom()` ### _Challenge 2_ Switch the order of the point and line layers from the previous example. What happened? ``` ggplot(data = gapminder, mapping = aes(x=year, y=lifeExp, by=country)) + geom_point() + geom_line(mapping = aes(color=continent)) ``` Now the lines are on top of the points ### Transformations and trend lines We can adjust plots in other ways, including transformations and overlaying statistical models Returning to a scatterplot: ``` ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) + geom_point() ``` To change the x axis to a log scale using `scale_x_log10()`: ``` ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) + geom_point() + scale_x_log10() ``` And to change the transparency of the points, since there's lots of overplotting, we can use `alpha` in `geom_point()`: ``` ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) + geom_point(alpha = 0.5) + scale_x_log10() ``` To add a trend line, we can use `geom_smooth()` and specify the type of model (here, a linear model, with `method = 'lm'`): ``` ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) + geom_point(alpha = 0.5) + scale_x_log10() + geom_smooth(method="lm") ``` And can change line thickness using `size` in `geom_smooth()`: ``` ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) + geom_point(alpha = 0.5) + scale_x_log10() + geom_smooth(method="lm", size=1.5) ``` ### _Challenge 3_ **Part A** Modify the color and size of the points on the point layer, but don’t use the aes() function in that layer. ``` ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) + geom_point(size=3, color="orange") + scale_x_log10() + geom_smooth(method="lm", size=1.5) ``` Note the `color = "orange"` and `size = 3` within `geom_point()` **Part B** Modify your solution to Part A so that the points are now a different shape and are colored by continent with new trendlines. Hint: The color argument can be used inside the aes() function. ``` ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp, color = continent)) + geom_point(size=3, shape=17) + scale_x_log10() + geom_smooth(method="lm", size=1.5) ``` want trend line for each continent: ``` ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp, color = continent)) + geom_point(size=3, shape=17) + scale_x_log10() + geom_smooth(method="lm", size=1.5) ``` ### Multi-panel plots Subset to only Americas data ``` americas <- gapminder[gapminder$continent == "Americas",] ``` Then use `ggplot()` with `facet_wrap()` which lets us facet by variable (in this case, by country variable): ``` ggplot(data = americas, mapping = aes(x = year, y = lifeExp)) + geom_line() + facet_wrap( ~ country) + theme(axis.text.x = element_text(angle = 45)) ``` This gives us a panel for each country The `theme()` command lets us specify things like x and y axis text and angles. In this case, we're specifying `axis.text.x` to say we want the x axis text to be at a 45 degree angle. To modify text: To clean this figure up for a publication we need to change some of the text elements. The x-axis is too cluttered, and the y axis should read “Life expectancy”, rather than the column name in the data frame. We can do this by adding a couple of different layers. The theme layer controls the axis text, and overall text size. Labels for the axes, plot title and any legend can be set using the labs function. Legend titles are set using the same names we used in the aes specification. Thus below the color legend title is set using color = "Continent", while the title of a fill legend would be set using fill = "MyTitle". ``` ggplot(data = americas, mapping = aes(x = year, y = lifeExp, color=continent)) + geom_line() + facet_wrap( ~ country) + labs( x = "Year", # x axis title y = "Life expectancy", # y axis title title = "Figure 1", # main title of figure color = "Continent" # title of legend ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) ``` Exporting the plot: We want to assign our plot to an object (here naming it `lifeExp_plot`, then use ggsave to export the plot to png (specifying file format in output name, here "lifeExp.png") ``` lifeExp_plot <- ggplot(data = americas, mapping = aes(x = year, y = lifeExp, color=continent)) + geom_line() + facet_wrap( ~ country) + labs( x = "Year", # x axis title y = "Life expectancy", # y axis title title = "Figure 1", # main title of figure color = "Continent" # title of legend ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) ggsave(filename = "results/lifeExp.png", plot = lifeExp_plot, width = 12, height = 10, dpi = 300, units = "cm") ``` Here we're specifying we want a png as output, width of 12 cm, height of 10 cm, with dpi (basically, resolution) of 300 ### Producing reports with knitr Check the notes for screenshots: https://hackmd.io/3M1KawkLRhezD0u1TAkpfQ?view#Create-Reports-with-knitr (Fun fact, this document uses markdown! You can see how we're making items bold, or putting in code chunks, by selecting the icon in the top banner between the pencil and the eye, to see the raw markdown on theput on the right.) ### _Challenge_ **Question 1** Create a new R Markdown document. Delete all of the R code chunks and write a bit of Markdown (some sections, some italicized text, and an itemized list). Convert the document to a webpage. **Question 2** Load the ggplot2 package Read the gapminder data Create a plot ## Day 3 Questions: Please enter any questions not answered during live session here: 1. How to pick a color for a specific subset of data 2. (see above - we did cover this) ## Other R resources R graph gallery - https://r-graph-gallery.com/ (specific to ggplot 2 - https://r-graph-gallery.com/ggplot2-package.html) R markdown cookbook - https://bookdown.org/yihui/rmarkdown-cookbook/ R for Data Science (data viz chapter) - https://r4ds.had.co.nz/data-visualisation.html ### End Day 3