Merge pull request #681 from jhudsl/minor_fixes_stats_dat_out_W25

small edits to slides

modules/Data_Output/Data_Output.Rmd

@@ -18,7 +18,7 @@ pre { /* Code block - slightly smaller in this lecture */
 </style>
 
 
-## Data Output
+## Data Output {.smaller}
 
 While its nice to be able to read in a variety of data formats, it's equally important to be able to output data somewhere.
 
@@ -102,7 +102,7 @@ save.image(file = "my_environment.RData")
 ```
 
 
-## Using RStudio for importing/exporting data
+## Using RStudio for importing/exporting data {.smaller}
 
 If there is an `.rds` or `.RData` file that you want to work with, you can open it into your environment using the file icon.
 
@@ -131,12 +131,14 @@ ggsave(filename = "saved_plot.png",  # will save in working directory
        width = 6, height = 3.5)               # by default in inches
 ```
 
-## Summary {.small}
+## Summary
 
 - Use `write_csv()` and `write_delim()` from the `readr` package to write your (modified) data
 - `.rds` files can be handy for saving intermediate work
 - Can save environment (or subset) using `save()` and `save.image()`
 
+## Resources & Lab {.small}
+
 🏠 [Class Website](https://jhudatascience.org/intro_to_r/)
 
 💻 [Data Output Lab](https://jhudatascience.org/intro_to_r/modules/Data_Output/lab/Data_Output_Lab.Rmd)
@@ -145,7 +147,7 @@ ggsave(filename = "saved_plot.png",  # will save in working directory
 
 📃 [Day 2 Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-2.pdf)
 
-```{r, fig.alt="The End", out.width = "50%", echo = FALSE, fig.align='center'}
+```{r, fig.alt="The End", out.width = "35%", echo = FALSE, fig.align='center'}
 knitr::include_graphics(here::here("images/the-end-g23b994289_1280.jpg"))
 ```
 

modules/Data_Summarization/Data_Summarization.Rmd

@@ -515,15 +515,17 @@ yts_loc %>% unique() %>% length() # similar to n_distinct()
 * `range()`: minimum and maximum of the data
 * `IQR()`: interquartile range of the data
 
-## Summary & Lab Part 2 {.small}
+## Summary
 
 - `count(x)`: what unique values do you have? 
   - `distinct()`: what are the distinct values?
   - `n_distinct()` with `pull()`: how many distinct values?
 - `group_by()`: changes subsequent functions (remove with `ungroup()`)
   - combine with `summarize()` to get statistics per group
   - combine with `mutate()` to add column
-- `summarize()` with `n()` gives the count (NAs included) 
+- `summarize()` with `n()` gives the count (NAs included)
+
+## Resources & Lab Part 2 {.small}
 
 🏠 [Class Website](https://jhudatascience.org/intro_to_r/)
 
@@ -533,7 +535,7 @@ yts_loc %>% unique() %>% length() # similar to n_distinct()
 
 📃 [Posit's data transformation Cheatsheet](https://rstudio.github.io/cheatsheets/data-transformation.pdf)
 
-```{r, fig.alt="The End", out.width = "20%", echo = FALSE, fig.align='center'}
+```{r, fig.alt="The End", out.width = "35%", echo = FALSE, fig.align='center'}
 knitr::include_graphics(here::here("images/the-end-g23b994289_1280.jpg"))
 ```
 

modules/Reproducibility/Reproducibility.Rmd

@@ -131,7 +131,7 @@ Occasionally we might forget to save a step of our code in our R Markdown file t
 knitr::include_graphics("images/clean.png")
 ```
 
-## Check if your file knits regularly
+## Check if your file knits regularly {.small}
 
 Regularly checking if your file knits will help you spot a missing step or error earlier when you have less code to try to identify where your code might have gone wrong.
 
@@ -143,6 +143,8 @@ knitr::include_graphics("images/knit.png")
 knitr::include_graphics("images/error_monster.png")
 ```
 
+Image by [Allison Horst](https://allisonhorst.com/data-science-art).
+
 ## Tell your future self and others what you did!
 
 Provide sufficient detail so that you can understand what you did and why.
@@ -250,7 +252,7 @@ These are just some quick tips, for more information:
 - [Jenny Bryan's organizational strategies](https://www.stat.ubc.ca/~jenny/STAT545A/block19_codeFormattingOrganization.html).
 - [Write efficient R code for science](https://www.earthdatascience.org/courses/earth-analytics/automate-science-workflows/write-efficient-code-for-science-r/).
 
-## Summary {.smaller}
+## Summary
 
 To help make your work more reproducible:
 
@@ -260,6 +262,8 @@ To help make your work more reproducible:
 - Tell your future self and others what you did!
 - Print session info!
 
+## Resources & Lab {.small}
+
 🏠 [Class Website](https://jhudatascience.org/intro_to_r/)
 
 💻 [Lab](https://jhudatascience.org/intro_to_r/modules/Reproducibility/lab/Reproducibility_Lab.Rmd)
@@ -268,7 +272,7 @@ To help make your work more reproducible:
 
 🗒 [RStudio Cheatsheet](https://d33wubrfki0l68.cloudfront.net/374f4c769f97c4ded7300d521eb59b24168a7261/c72ad/lesson-images/cheatsheets-1-cheatsheet.png)
 
-```{r, fig.alt="The End", out.width = "15%", echo = FALSE, fig.align='center'}
+```{r, fig.alt="The End", out.width = "35%", echo = FALSE, fig.align='center'}
 knitr::include_graphics(here::here("images/the-end-g23b994289_1280.jpg"))
 ```
 

modules/Statistics/Statistics.Rmd

@@ -96,8 +96,8 @@ cor(x, y = NULL, use = c("everything", "complete.obs"),
 Function `cor.test()` also computes correlation and tests for association.
 
 ```
-cor.test(x, y = NULL, alternative(c("two.sided", "less", "greater")),
-    method = c("pearson", "kendall", "spearman"))
+cor.test(x, y = NULL, alternative = c("two.sided", "less", "greater"),
+    method = c("pearson", "kendall", "spearman"), ...)
 ```
 - provide two numeric vectors of the same length (arguments `x`, `y`), or
 - provide a data.frame / tibble with numeric columns only
@@ -164,7 +164,7 @@ glimpse(cor_result)
 
 ## Correlation for two vectors with plot {.smaller}
 
-In plot form... `geom_smooth()` and `annotate()` can help.
+In plot form, `geom_smooth()` and `annotate()` can help.
 
 ```{r}
 corr_value <- pull(cor_result, estimate) %>% round(digits = 4)
@@ -515,7 +515,7 @@ esdcomp %>% count(residency)
 
 ## Linear regression: factors {.smaller}
 
-Yes relative to No -- baseline is no
+Yes relative to No -- baseline is No
 
 ```{r regressbaseline, comment="", fig.height=4,fig.width=8}
 fit_3 <- glm(visits ~ residency, data = esdcomp)
@@ -672,9 +672,9 @@ The odds ratio is 21.4. When the predictor is TRUE (aka the individual ate vanil
 ## Functions you might also see {.smaller}
 
 - the `stat_cor()` function in the `ggpubr` can add correlation coefficients and p-values as a layer to `ggplot` objects
-- the `graphics::pairs()` or `GGally::ggpairs()` functions are also useful for visualizing correlations across variables in a data frame
+- the `pairs()` (`graphics` package) or `ggpairs()` (`GGally` package) functions are also useful for visualizing correlations across variables in a data frame
 - `acf()` in the `stats` package can compute autocorrelation and cross-correlation with lags
-- calculate confidence intervals for intercept and slopes from `glm`/lm` objects using `confint()`
+- calculate confidence intervals for intercept and slopes from `glm`/`lm` objects using `confint()`
 - principal components analysis -- use `prcomp()`
 
 ```{r, fig.alt="There's an R package for everything", out.width = "20%", echo = FALSE, fig.show='hold',fig.align='center'}
@@ -719,15 +719,15 @@ For classes at JHU School of Public Health:
 - [PH.140.778 - Statistical Computing, Algorithm, and Software Development](https://www.jhsph.edu/courses/course/36737/2022/140.778.01/statistical-computing-algorithm-and-software-devel) - A more advanced course for working with data in R.
 Content for similar topics as this course can also be found on Leanpub.
 
-## Lab Part 2
+## Lab Part 2 {.small}
 
 🏠 [Class Website](https://jhudatascience.org/intro_to_r/)  
 
 💻 [Lab](https://jhudatascience.org/intro_to_r/modules/Statistics/lab/Statistics_Lab.Rmd)
 
 📃 [Day 8 Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-8.pdf)
 
-```{r, fig.alt="The End", out.width = "50%", echo = FALSE, fig.align='center'}
+```{r, fig.alt="The End", out.width = "35%", echo = FALSE, fig.align='center'}
 knitr::include_graphics(here::here("images/the-end-g23b994289_1280.jpg"))
 ```