class: center, middle, inverse, title-slide # Responding to analysis and communication: Data science the R way ## R-Ladies ### Jovana, Tijana, Katica & Tatjana ### November 2019 --- background-image: url(https://upload.wikimedia.org/wikipedia/commons/c/c1/Rlogo.png) ??? Image credit: [Wikimedia Commons](https://commons.wikimedia.org/wiki/File:Rlogo.png) --- class: center, middle #How do we do it? &#9728; ###Steps of a typical data science project: <img src="images/Program_HW.png" width="500px" /> --- class: inverse, center, middle #Get Started 🤫😴 <img src="images/George_Desk.gif" width="600px" /> --- ##Before there was R, there was S! 🤠 **R** is a dialect of **S** language that was developed in 1976 by *Rick Becker* and *John Chambers* at the Bell Laboratories. *Rick Becker* gave an excellent keynote talk *"Forty Years of S"* at UseR!2016 conference: [Rick Becker @ UseR!2016](http://blog.revolutionanalytics.com/2016/07/rick-becker-s-talk.html) where he talked about development of **S** language that gives explanations for many characteristics of **R** as we know it, including "<-" assignment operator. 1993 Bell Labs gave StatSci (later Insightful Corp.) an exclusive license to develop and sell the **S** language. Insightful sold its implementation of the **S** language under the product name **S-PLUS**. You can read more about the [history of S, R, and S-PLUS](https://www.whoishostingthis.com/resources/s-plus/) --- ## then, R was born 😇🎶 In early nineties at the University of Aucklandn in the Department of Statistics **R** was created by *Ross Ihaka* and *Robert Gentleman*. They used [GNU General Public License](http://www.gnu.org/licenses/gpl-2.0.html) to make R open source free software. **R**oss Ihaka and **R**obert Gentleman. *R: A language for data analysis and graphics.* Journal of Computational and Graphical Statistics, 5(3):299–314, 1996 Currently **R** is developed by the **R Development Core Team**, of which *John Chambers* is a member. --- ##Write R Code To start using **R** you need to: 1) Install [R](https://cran.r-project.org/) [(and RStudio)](https://www.rstudio.com/products/rstudio/download/#download) 2) Launch it and set your working directory: letting R know where to find all of your files. 3) Start writing **R** code! **Tip**💡: - When start working on a new R code/R Project in [RStudio IDE](https://support.rstudio.com/hc/en-us/sections/200107586-Using-the-RStudio-IDE) use ***File -> New Project*** This way your working directory would be set up when you start a new project and it will save all your files in it. Next time you open your project it would set project's directory as a working directory... It would help you with so much [more](https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects). --- class: center, middle ##[RStudio IDE Cheatsheet](https://www.rstudio.com/wp-content/uploads/2016/01/rstudio-IDE-cheatsheet.pdf) <img src="images/RStudio.png" width="500px" /> ***Top Left:*** Code Editor; -- ***Bottom Left:*** R Console; -- ***Top Right:*** Environment -- ***Bottom Right:*** Plots and Files --- ##Dataset **gapminder** dataset available from **gapminder** package. For each of 142 countries, the package provides values for life expectancy, GDP per capita, and population, every five years, from 1952 to 2007. <!--htd="htmlwidget-61c4ae97191590742e71" style="width:100%;height:auto;" class="datatables html-widget"></div> <script type="application/json" data-for="htmlwidget-61c4ae97191590742e71">{"x":{"filter":"none","data":[["1","2","3","4"],["Afghanistan","Afghanistan","Afghanistan","Afghanistan"],["Asia","Asia","Asia","Asia"],[1952,1957,1962,1967],[28.801,30.332,31.997,34.02],[8425333,9240934,10267083,11537966],[779.4453145,820.8530296,853.10071,836.1971382]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>country<\/th>\n <th>continent<\/th>\n <th>year<\/th>\n <th>lifeExp<\/th>\n <th>pop<\/th>\n <th>gdpPercap<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[3,4,5,6]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script><!--/h thet there are 6 columns, each of which we call a **variable**. --- class: inverse, center, middle #How do we do it in R? 🤓 --- ##Before Tidyverse R, there is Base R! When you download and install **R** for the first time, you are installing **the Base R** software. **Base R** contains most of the functions you’ll use on a daily basis: `mean()`, `subset()`... To learn about **R**'s basic operations, data structures and base functions you could look at one of the R-Ladies Manchester's handouts: [Introduction to base R](https://tanjakec.github.io/blog/introduction-to-r/). If you want to access data and code written by other people, you’ll need to install it as a **package**. An **R package** is a bundle of functions (code), data, documentation, vignettes (examples), stored in one neat place. "In **R**, the fundamental unit of shareable code is the package." [Hadley Wickham](http://r-pkgs.had.co.nz/intro.html) --- ##The verse! 😇🎶 An opinionated collection of **R packages** for data science. [`install.packages("tidyverse")`](https://www.tidyverse.org/) [`library(tidyverse)`](https://www.tidyverse.org/packages/) - Have you tried learning data science by reading books? 📖📘 [**R for Data Science**](http://r4ds.had.co.nz/) by Garrett Grolemund & Hadley Wickham - Have you tried learning data science by posting your questions and discussing it with other people within the R community? 👥💻📊📈🗣 [**RStudio Community**](https://community.rstudio.com/) --- ##The <span style="color:blue">`dplyr`</span> Package: provides a “grammar” (the verbs) for data manipulation and for operating on data frames. The **key opertor and the esential verbs** are : - <span style="color:blue">`%>%`</span>: **the “pipe” operator** used to connect multiple verb actions together into a pipeline. - <span style="color:blue">`select()`</span>: return a subset of the columns of a data frame. - <span style="color:blue">`mutate()`</span>: add new variables/columns or transform existing variables. - <span style="color:blue">`filter()`</span>: extract a subset of rows from a data frame based on logical conditions. - <span style="color:blue">`arrange()`</span>: reorder rows of a data frame according to single or multiple variables. - <span style="color:blue">`summarise()`</span> / <span style="color:blue">`summarize()`</span>: reduces each group to a single row by calculating aggregate measures. --- ##Gapminder Data Excerpt of the Gapminder data on life expectancy, GDP per capita, and population by country. The main data frame gapminder has **1704 rows** and **6 variables**: - **country**: factor with 142 levels - **continent**: factor with 5 levels - **year**: ranges from 1952 to 2007 in increments of 5 years - **lifeExp**: life expectancy at birth, in years - **pop**: population - **gdpPercap**: GDP per capita ```r gapminder::gapminder[1:3,] ``` ``` ## # A tibble: 3 x 6 ## country continent year lifeExp pop gdpPercap ## <fct> <fct> <int> <dbl> <int> <dbl> ## 1 Afghanistan Asia 1952 28.8 8425333 779. ## 2 Afghanistan Asia 1957 30.3 9240934 821. ## 3 Afghanistan Asia 1962 32.0 10267083 853. ``` --- class: center ##Setting up Working Environment 💡 .pill-left[ Install necessary packages you will be working with! ```r install.packages("dplyr", repos = "http://cran.us.r-project.org") install.packages("ggplot2", repos = "http://cran.us.r-project.org") install.packages("gapminder", repos = "http://cran.us.r-project.org") ``` ] -- Now we're ready to start practicing Elain's Dance!!! 😃🎵🎶 <img src="images/ElainDanceI.png" width="300px" /> --- ## 1st look at the data: <span style="color:blue">`dim()`</span> & <span style="color:blue">`head()`</span> ```r library(gapminder) dim(gapminder) ``` ``` ## [1] 1704 6 ``` ```r head(gapminder, n=10) ``` ``` ## # A tibble: 10 x 6 ## country continent year lifeExp pop gdpPercap ## <fct> <fct> <int> <dbl> <int> <dbl> ## 1 Afghanistan Asia 1952 28.8 8425333 779. ## 2 Afghanistan Asia 1957 30.3 9240934 821. ## 3 Afghanistan Asia 1962 32.0 10267083 853. ## 4 Afghanistan Asia 1967 34.0 11537966 836. ## 5 Afghanistan Asia 1972 36.1 13079460 740. ## 6 Afghanistan Asia 1977 38.4 14880372 786. ## 7 Afghanistan Asia 1982 39.9 12881816 978. ## 8 Afghanistan Asia 1987 40.8 13867957 852. ## 9 Afghanistan Asia 1992 41.7 16317921 649. ## 10 Afghanistan Asia 1997 41.8 22227415 635. ``` --- ##Examine the structure of the data: <span style="color:blue">`str()`</span> ```r str(gapminder) ``` ``` ## Classes 'tbl_df', 'tbl' and 'data.frame': 1704 obs. of 6 variables: ## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ... ## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ... ## $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ... ## $ lifeExp : num 28.8 30.3 32 34 36.1 ... ## $ pop : int 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ... ## $ gdpPercap: num 779 821 853 836 740 ... ``` The **output could look messy** and it might not fit the screen when dealing with a big data set that has lots of variables! 🤪 --- ##Do it in a tidy way: <span style="color:blue">`glimpse()`</span> ```r suppressPackageStartupMessages(library(dplyr)) glimpse(gapminder) ``` ``` ## Observations: 1,704 ## Variables: 6 ## $ country <fct> Afghanistan, Afghanistan, Afghanistan, Afghanistan, ... ## $ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia... ## $ year <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992... ## $ lifeExp <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.8... ## $ pop <int> 8425333, 9240934, 10267083, 11537966, 13079460, 1488... ## $ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 78... ``` --- ##The pipeline operater: <span style="color:blue">`%>%`</span> <pre> **Left Hand Side (LHS)** <span style="color:blue">`%>%`</span> **Right Hand Side (RHS)** </pre> <pre> <span style="color:blue">x %>% f(..., y)</span> <span style="color:blue"> f(x,y)</span> </pre> The "pipe" passes the **result** of the **LHS** as the 1st operator argument of the **function** on the **RHS** <pre> <span style="color:blue">3 %>% sum(4)</span> <==> <span style="color:blue"> sum(3, 4)</span> </pre> <span style="color:blue">`%>%`</span> is very practical for chaining together multiple <span style="color:blue">`dplyr`</span> functions in a sequence of operations. --- ##pick variables by their names: <span style="color:blue">`select()`</span>, <img src="images/select().png" width="450px" /> - <span style="color:blue">`starts_with("X")`</span> every name that starts with "X". - <span style="color:blue">`ends_with("X")`</span> every name that ends with "X". - <span style="color:blue">`contains("X")`</span> every name that contains "X". - <span style="color:blue">`matches("X")`</span> every name that matches "X", where "X" can be a regular expression. - <span style="color:blue">`num_range("x", 1:5)`</span> the variables named x01, x02, x03, x04, x05. - <span style="color:blue">`one_of(x)`</span> => every name that appears in x, which should be a character vector. --- ##Select your variables 1) that ends with letter `p` 2) starts with letter `co`. Try to do this selection using base R. --- ##Solutions: ```r gm_pop_gdp <- select(gapminder, ends_with("p")) head(gm_pop_gdp, n = 1) ``` ``` ## # A tibble: 1 x 3 ## lifeExp pop gdpPercap ## <dbl> <int> <dbl> ## 1 28.8 8425333 779. ``` ```r gm_cc <- select(gapminder, starts_with("co")) head(gm_cc, n = 1) ``` ``` ## # A tibble: 1 x 2 ## country continent ## <fct> <fct> ## 1 Afghanistan Asia ``` of course all of this could be done using **base R** like for example: ```r gm_cc <- gapminder[c("country", "continent")] ``` but it's less intuitive and often requires more typing. --- ##Create new variables of existing variables: <span style="color:blue">`mutate()`</span> <img src="images/mutate().png" width="400px" /> It would allow you to add to the data frame `df` a new column, `z`, which is the multiplication of the columns `x` and `y`: `mutate(df, z = x * y)`. If we would like to observe `lifeExp` measured in months we could create a new column `lifeExp_month`: ```r gapminder2 <- mutate(gapminder, LifeExp_month = lifeExp * 12) head(gapminder2, n = 2) ``` ``` ## # A tibble: 2 x 7 ## country continent year lifeExp pop gdpPercap LifeExp_month ## <fct> <fct> <int> <dbl> <int> <dbl> <dbl> ## 1 Afghanistan Asia 1952 28.8 8425333 779. 346. ## 2 Afghanistan Asia 1957 30.3 9240934 821. 364. ``` --- ##Pick observations by their values: <span style="color:blue">`filter()`</span> <img src="images/filter().png" width="450px" /> There is a set of logical operators in **R** that you can use inside `filter()`: - `x < y`: `TRUE` if `x` is less than `y` - `x <= y`: `TRUE` if `x` is less than or equal to `y` - `x == y`: `TRUE` if `x` equals `y` - `x != y`: `TRUE` if `x` does not equal `y` - `x >= y`: `TRUE` if `x` is greater than or equal to `y` - `x > y`: `TRUE` if `x` is greater than `y` - `x %in% c(a, b, c)`: `TRUE` if `x` is in the vector `c(a, b, c)` - `is.na(x)`: Is `NA` - `!is.na(x)`: Is not `NA` --- ##Filter your data: Use `gapminder2` `df` to filter: 1) only Europian countries and save it as `gapmEU` 2) only Europian countries from 2000 onward and save it as `gapmEU21c` 3) rows where the life expectancy is greater than 80 Don't forget to **use `==` instead of `=`**! and Don't forget the quotes ** `""` ** --- ##Solutions: ```r gapmEU <- filter(gapminder2, continent == "Europe") head(gapmEU, 2) ``` ``` ## # A tibble: 2 x 7 ## country continent year lifeExp pop gdpPercap LifeExp_month ## <fct> <fct> <int> <dbl> <int> <dbl> <dbl> ## 1 Albania Europe 1952 55.2 1282697 1601. 663. ## 2 Albania Europe 1957 59.3 1476505 1942. 711. ``` ```r gapmEU21c <- filter(gapminder2, continent == "Europe" & year >= 2000) head(gapmEU21c, 2) ``` ``` ## # A tibble: 2 x 7 ## country continent year lifeExp pop gdpPercap LifeExp_month ## <fct> <fct> <int> <dbl> <int> <dbl> <dbl> ## 1 Albania Europe 2002 75.7 3508512 4604. 908. ## 2 Albania Europe 2007 76.4 3600523 5937. 917. ``` ```r filter(gapminder2, lifeExp > 80) ``` --- ##Reorder the rows: <span style="color:blue">`arrange()`</span> is used to reorder rows of a **d**ata **f**rame (df) according to one of the variables/columns. <img src="images/arrange().png" width="300px" /> - If you pass `arrange()` a character variable, **R** will rearrange the rows in alphabetical order according to values of the variable. - If you pass a factor variable, **R** will rearrange the rows according to the order of the levels in your factor (running `levels()` on the variable reveals this order). --- ##Arranging your data 1) Arrange countries in `gapmEU21c` `df` by life expectancy in ascending and descending order. 2) Using `gapminder df` - Find the records with the smallest population - Find the records with the largest life expectancy. --- ##Solution 1): ```r gapmEU21c_h2l <- arrange(gapmEU21c, lifeExp) head(gapmEU21c_h2l, 2) ``` ``` ## # A tibble: 2 x 7 ## country continent year lifeExp pop gdpPercap LifeExp_month ## <fct> <fct> <int> <dbl> <int> <dbl> <dbl> ## 1 Turkey Europe 2002 70.8 67308928 6508. 850. ## 2 Romania Europe 2002 71.3 22404337 7885. 856. ``` ```r *gapmEU21c_l2h <- arrange(gapmEU21c, desc(lifeExp)) head(gapmEU21c_l2h, 2) ``` ``` ## # A tibble: 2 x 7 ## country continent year lifeExp pop gdpPercap LifeExp_month ## <fct> <fct> <int> <dbl> <int> <dbl> <dbl> ## 1 Iceland Europe 2007 81.8 301931 36181. 981. ## 2 Switzerland Europe 2007 81.7 7554661 37506. 980. ``` --- ##Solution 2): ```r arrange(gapminder, pop) ``` ``` ## # A tibble: 1,704 x 6 ## country continent year lifeExp pop gdpPercap ## <fct> <fct> <int> <dbl> <int> <dbl> ## 1 Sao Tome and Principe Africa 1952 46.5 60011 880. ## 2 Sao Tome and Principe Africa 1957 48.9 61325 861. ## 3 Djibouti Africa 1952 34.8 63149 2670. ## 4 Sao Tome and Principe Africa 1962 51.9 65345 1072. ## 5 Sao Tome and Principe Africa 1967 54.4 70787 1385. ## 6 Djibouti Africa 1957 37.3 71851 2865. ## 7 Sao Tome and Principe Africa 1972 56.5 76595 1533. ## 8 Sao Tome and Principe Africa 1977 58.6 86796 1738. ## 9 Djibouti Africa 1962 39.7 89898 3021. ## 10 Sao Tome and Principe Africa 1982 60.4 98593 1890. ## # ... with 1,694 more rows ``` --- ```r arrange(gapminder, desc(lifeExp)) ``` ``` ## # A tibble: 1,704 x 6 ## country continent year lifeExp pop gdpPercap ## <fct> <fct> <int> <dbl> <int> <dbl> ## 1 Japan Asia 2007 82.6 127467972 31656. ## 2 Hong Kong, China Asia 2007 82.2 6980412 39725. ## 3 Japan Asia 2002 82 127065841 28605. ## 4 Iceland Europe 2007 81.8 301931 36181. ## 5 Switzerland Europe 2007 81.7 7554661 37506. ## 6 Hong Kong, China Asia 2002 81.5 6762476 30209. ## 7 Australia Oceania 2007 81.2 20434176 34435. ## 8 Spain Europe 2007 80.9 40448191 28821. ## 9 Sweden Europe 2007 80.9 9031088 33860. ## 10 Israel Asia 2007 80.7 6426679 25523. ## # ... with 1,694 more rows ``` --- ##Collapse many values down to a single summary: <span style="color:blue">`summarise()`</span> <img src="images/summarise().png" width="450px" /> - uses the same syntax as `mutate()`, but the resulting dataset consists of a single row instead of an entire new column in the case of `mutate()`. - builds a new dataset that contains only the summarising statistics. Use `summarise()`: 1) to print out a summary of gapminder containing two variables: max_lifeExp and max_gdpPercap. 2) to print out a summary of gapminder containing two variables: mean_lifeExp and mean_gdpPercap. --- ##Solution: Summarise your data ```r summarise(gapminder, max_lifeExp = max(lifeExp), max_gdpPercap = max(gdpPercap)) ``` ``` ## # A tibble: 1 x 2 ## max_lifeExp max_gdpPercap ## <dbl> <dbl> ## 1 82.6 113523. ``` ```r summarise(gapminder, mean_lifeExp = mean(lifeExp), mean_gdpPercap = mean(gdpPercap)) ``` ``` ## # A tibble: 1 x 2 ## mean_lifeExp mean_gdpPercap ## <dbl> <dbl> ## 1 59.5 7215. ``` --- class: inverse, center, middle ## Let's `%>%` all up! Confer with your neighbours. What relationship do you expect to see between population size (`pop`) and life expectancy (`lifeExp`)? --- <img src="images/pipe_short_cut.png" width="750px" style="display: block; margin: auto;" /> --- **Do you know what this code does?** ```r gapminder_pipe <- gapminder %>% filter(continent == "Europe" & year == 2007) %>% mutate(pop_e6 = pop / 1000000) plot(gapminder_pipe$pop_e6, gapminder_pipe$lifeExp, cex = 0.5, col = "red") ``` <img src="Cosmo.jpg" width="250px" style="display: block; margin: auto;" /> --- <img src="DSRWay_files/figure-html/unnamed-chunk-30-1.png" style="display: block; margin: auto;" /> --- class: inverse, center, middle ##We have learnt all of Elain's moves!!! 😃🎵🎶 <img src="images/ElainDanceII.png" width="300px" /> --- class: inverse, center, middle ## Can we make it look better? 😁 --- class: inverse, center, middle #"The simple graph has brought more information to the data analyst’s mind than any other device." John Tukey --- ## grammar of graphics Enables you to specify building blocks of a plot and to combine them to create graphical display you want. There are 8 building blocks: - data - aesthetic mapping - geometric object - statistical transformations - scales - coordinate system - position adjustments - faceting --- ##<span style="color:blue">ggplot()</span> 1. "Initialise" a plot with `ggplot()` 2. Add layers with `geom_` functions ```r library(ggplot2) ggplot(gapminder_pipe, aes(x = pop_e6, y = lifeExp)) + geom_point(col ="red") ``` <img src="DSRWay_files/figure-html/unnamed-chunk-32-1.png" style="display: block; margin: auto;" /> **Tip**: You can use the following code template to make graphs with **ggplot2**: ```r ggplot(data = <DATA>, (mapping = aes(<MAPPINGS>)) + <GEOM_FUNCTION>() ``` --- #<span style="color:blue">ggplot()</span> gallery Run the following code to see what graphs it's going to produce. ```r ggplot(data = gapminder, mapping = aes(x = lifeExp), binwidth = 10) + geom_histogram() # ggplot(data = gapminder, mapping = aes(x = lifeExp)) + geom_density() # ggplot(data = gapminder, mapping = aes(x = continent, color = continent)) + geom_bar() # ggplot(data = gapminder, mapping = aes(x = continent, fill = continent)) + geom_bar() ``` --- ##Confer with your neighbours: **Does the life expectancy depend upon the population size?** `$$\hat{y}=\hat{\beta_0} + \hat{\beta_1} x + e$$` Run this code in your console to fit the model `pop` vs `lifeExp`. Pay attention to spelling, capitalization, and parentheses! ```r m1 <- lm(gapminder_pipe$lifeExp ~ gapminder_pipe$pop_e6) summary(m1) ``` --- **Can you answer the question usig the output of the fitted model?** ```r m1 <- lm(gapminder_pipe$lifeExp ~ gapminder_pipe$pop_e6) summary(m1) ``` ``` ## ## Call: ## lm(formula = gapminder_pipe$lifeExp ~ gapminder_pipe$pop_e6) ## ## Residuals: ## Min 1Q Median 3Q Max ## -6.324 -2.562 1.007 2.245 4.277 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 77.477421 0.721723 107.351 <2e-16 *** ## gapminder_pipe$pop_e6 0.008762 0.023779 0.368 0.715 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 3.025 on 28 degrees of freedom ## Multiple R-squared: 0.004826, Adjusted R-squared: -0.03072 ## F-statistic: 0.1358 on 1 and 28 DF, p-value: 0.7153 ``` --- ## Your turn! Use gapminder data. **Does the life expectancy depend upon the GDP per capita?** 1) Have a glance at the data. (tip: `sample_n(df, n)`) 2) Produce a scattep plot: what does it tell you? 3) Fit a regression model: is there a relationship? How strong is it? Is the relationship linear? What conclusion(s) can you draw? 4) What are the other questions you could ask; could you provide the answers to them? --- ## Possible Solution: code Q1; sample ```r sample_n(gapminder, 30) ``` ``` ## # A tibble: 30 x 6 ## country continent year lifeExp pop gdpPercap ## <fct> <fct> <int> <dbl> <int> <dbl> ## 1 Indonesia Asia 1952 37.5 82052000 750. ## 2 Malaysia Asia 1982 68 14441916 4920. ## 3 Hong Kong, China Asia 1967 70 3722800 6198. ## 4 Chile Americas 1977 67.1 10599793 4757. ## 5 Trinidad and Tobago Americas 1997 69.5 1138101 8793. ## 6 Bosnia and Herzegovina Europe 1952 53.8 2791000 974. ## 7 Croatia Europe 1952 61.2 3882229 3119. ## 8 Jordan Asia 2002 71.3 5307470 3845. ## 9 Nigeria Africa 1997 47.5 106207839 1625. ## 10 Kenya Africa 2007 54.1 35610177 1463. ## # ... with 20 more rows ``` --- ## Possible Solution: code Q2; Plot the data; ```r ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) + geom_point(alpha = 0.2, shape = 21, fill = "blue", colour="black", size = 5) + geom_smooth(method = "lm", se = F, col = "maroon3") + geom_smooth(method = "loess", se = F, col = "limegreen") ``` <img src="DSRWay_files/figure-html/unnamed-chunk-38-1.png" style="display: block; margin: auto;" /> --- ## Possible Solution: code Q3; simple regression model ```r my.model <- lm(gapminder_pipe$lifeExp ~ gapminder_pipe$gdpPercap) summary(my.model) ``` ``` ## ## Call: ## lm(formula = gapminder_pipe$lifeExp ~ gapminder_pipe$gdpPercap) ## ## Residuals: ## Min 1Q Median 3Q Max ## -2.79839 -1.30472 0.00807 1.33443 2.87766 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 7.227e+01 6.942e-01 104.113 < 2e-16 *** ## gapminder_pipe$gdpPercap 2.146e-04 2.514e-05 8.537 2.8e-09 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 1.598 on 28 degrees of freedom ## Multiple R-squared: 0.7225, Adjusted R-squared: 0.7125 ## F-statistic: 72.88 on 1 and 28 DF, p-value: 2.795e-09 ``` --- ## Adding layers to your <span style="color:blue">`ggplot()`</span> ```r ggplot(gapminder, aes(x = gdpPercap, y = lifeExp, col = "red")) + geom_point(alpha = 0.2, shape = 21, fill = "blue", colour="black", size = 5) + geom_smooth(method = "lm", se = F, col = "maroon3") + geom_smooth(method = "loess", se = F, col = "limegreen") + labs (title= "Life Exp. vs. Population Size", x = "population", y = "Life Exp.") + theme(legend.position = "none", panel.border = element_rect(fill = NA, colour = "black", size = .75), plot.title=element_text(hjust=0.5)) + geom_text(x = 80000, y = 125, label = "regression line", col = "maroon3") + geom_text(x = 90000, y = 75, label = "smooth line", col = "limegreen") ``` --- ## Voila <img src="DSRWay_files/figure-html/unnamed-chunk-41-1.png" style="display: block; margin: auto;" /> --- ## **There is a challenge:** - `dplyr`'s `group_by()` function enables you to group your data. It allows you to create a separate df that splits the original df by a variable. - `boxplot()` function produces boxplot(s) of the given (grouped) values. Knowing about `group_by()` and `boxplot()` function, coud you compute the median life expectancy for year 2007 by continent and visualise your result? --- ## Possible Solution: Let us look at the median life expectancy for each continent ```r gapminder %>% group_by(continent) %>% summarise(lifeExp = median(lifeExp)) ``` ``` ## # A tibble: 5 x 2 ## continent lifeExp ## <fct> <dbl> ## 1 Africa 47.8 ## 2 Americas 67.0 ## 3 Asia 61.8 ## 4 Europe 72.2 ## 5 Oceania 73.7 ``` **We are lucky that we live in Serbia, ie. Europe!!!** --- ## Possible Solution: ```r # visualise the information code library("ggplot2") ggplot(gapminder, aes(x = continent, y = lifeExp)) + geom_boxplot(outlier.colour = "hotpink") + geom_jitter(position = position_jitter(width = 0.1, height = 0), alpha = .2) + labs (title= "Life Exp. vs. Continent", x = "Continent", y = "Life Exp.") + theme(legend.position = "none", panel.border = element_rect(fill = NA, colour = "black", size = .75), plot.title=element_text(hjust=0.5)) ``` --- ## Possible Solution: graph <img src="DSRWay_files/figure-html/unnamed-chunk-44-1.png" style="display: block; margin: auto;" /> --- class: inverse, center, middle ##Let's do Elain's Dance!!! 😃🎵🎶 <img src="images/Elain_dance.gif" width="500px" /> --- ## useful links: cheatsheets: - [data-wrangling-cheatsheet](https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf) - [ggplot2-cheatsheet](https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf) websites: - [tidyverse, visualization, and manipulation basics](https://www.rstudio.com/resources/webinars/tidyverse-visualization-and-manipulation-basics/) - [ggplot part of tidy verse](http://ggplot2.tidyverse.org/index.html) - [Introduction to R graphics with ggplot2](http://tutorials.iq.harvard.edu/R/Rgraphics/Rgraphics.html#introduction) --- class: center, middle # Thanks! [tanjakec.github.io](tanjakec.github.io) @RLadiesBelgrade @JovanaSavic2 @tikiblagojev @KaticaR1 @Tatjana_Kec Slides created via the R package [**xaringan**](https://github.com/yihui/xaringan). The chakra comes from [remark.js](https://remarkjs.com), [**knitr**](http://yihui.name/knitr), and [R Markdown](https://rmarkdown.rstudio.com).