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 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

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 to make R open source free software.

Ross Ihaka and Robert 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.

To start using R you need to:

1. Install R (and RStudio)

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 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.

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 R-Ladies Manchester's handouts: Introduction to base 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

An opinionated collection of R packages for data science.

install.packages("tidyverse")

library(tidyverse)

• Have you tried learning data science by reading books?

R for Data Science 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

provides a “grammar” (the verbs) for data manipulation and for operating on data frames. The key opertor and the esential verbs are :

• %>%: the “pipe” operator used to connect multiple verb actions together into a pipeline.
• select(): return a subset of the columns of a data frame.
• mutate(): add new variables/columns or transform existing variables.
• filter(): extract a subset of rows from a data frame based on logical conditions.
• arrange(): reorder rows of a data frame according to single or multiple variables.
• summarise() / summarize(): reduces each group to a single row by calculating aggregate measures.

Description: Chicago daily air pollution and death rate data. A data frame with 7 columns and 5114 rows. Each row refers to one day. The columns are:

• death total deaths (per day).

• pm10median median particles in 2.5-10 per cubic m

• pm25median medianparticles < 2.5 mg per cubic m (more dangerous).

• o3median Ozone in parts per billion

• so2median Median Sulpher dioxide measurement

• time time in days

• tmpd temperature in fahrenheit

# install.packages("gamair")
library(gamair)
data(chicago)
dim(chicago)

## [1] 5114    7

head(chicago)

##   death pm10median pm25median  o3median  so2median    time tmpd
## 1   130 -7.4335443         NA -19.59234  1.9280426 -2556.5 31.5
## 2   150         NA         NA -19.03861 -0.9855631 -2555.5 33.0
## 3   101 -0.8265306         NA -20.21734 -1.8914161 -2554.5 33.0
## 4   135  5.5664557         NA -19.67567  6.1393413 -2553.5 29.0
## 5   126         NA         NA -19.21734  2.2784649 -2552.5 32.0
## 6   130  6.5664557         NA -17.63400  9.8585839 -2551.5 40.0

str(chicago) 

## 'data.frame':    5114 obs. of  7 variables:
##  $ death     : int  130 150 101 135 126 130 129 109 125 153 ...
##  $ pm10median: num  -7.434 NA -0.827 5.566 NA ...
##  $ pm25median: num  NA NA NA NA NA NA NA NA NA NA ...
##  $ o3median  : num  -19.6 -19 -20.2 -19.7 -19.2 ...
##  $ so2median : num  1.928 -0.986 -1.891 6.139 2.278 ...
##  $ time      : num  -2556 -2556 -2554 -2554 -2552 ...
##  $ tmpd      : num  31.5 33 33 29 32 40 34.5 29 26.5 32.5 ...

The output could look messy and it might not fit the screen if you're dealing with a big data set that has lots of variables!

suppressPackageStartupMessages(library(dplyr))
glimpse(chicago) 

## Observations: 5,114
## Variables: 7
## $ death      <int> 130, 150, 101, 135, 126, 130, 129, 109, 125, 153, 1...
## $ pm10median <dbl> -7.4335443, NA, -0.8265306, 5.5664557, NA, 6.566455...
## $ pm25median <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ o3median   <dbl> -19.592338, -19.038614, -20.217338, -19.675671, -19...
## $ so2median  <dbl> 1.9280426, -0.9855631, -1.8914161, 6.1393413, 2.278...
## $ time       <dbl> -2556.5, -2555.5, -2554.5, -2553.5, -2552.5, -2551....
## $ tmpd       <dbl> 31.5, 33.0, 33.0, 29.0, 32.0, 40.0, 34.5, 29.0, 26....

Left Hand Side (LHS)    %>%    Right Hand Side (RHS)

x %>% f(..., y)
        f(x,y)

The "pipe" passes the result of the LHS as the 1st operator argument of the function on the RHS

3 %>% sum(4)     <==>     sum(3, 4)

%>%   is very practical for chaining together multiple dplyr functions in a sequence of operations.

• starts_with("X") every name that starts with "X".
• ends_with("X") every name that ends with "X".
• contains("X") every name that contains "X".
• matches("X") every name that matches "X", where "X" can be a regular expression.
• num_range("x", 1:5) the variables named x01, x02, x03, x04, x05.
• one_of(x) => every name that appears in x, which should be a character vector.

##   death pm10median pm25median  o3median  so2median    time tmpd
## 1   130  -7.433544         NA -19.59234  1.9280426 -2556.5 31.5
## 2   150         NA         NA -19.03861 -0.9855631 -2555.5 33.0

chicago_air_measurements <- select(chicago, ends_with("median"))
head(chicago_air_measurements, n = 1)

##   pm10median pm25median  o3median so2median
## 1  -7.433544         NA -19.59234  1.928043

chicago_air_pm <- chicago[c("pm10median", "pm25median")]
head(chicago_air_pm, n = 1)

##   pm10median pm25median
## 1  -7.433544         NA

chicago_air_pm2 <- select(chicago, starts_with("pm"))
head(chicago_air_pm2, n = 1)

##   pm10median pm25median
## 1  -7.433544         NA

For example, 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)

Let us convert °F into °C:     T(°C) = (T(°F) - 32) × 5/9

chicago2 <- mutate(chicago, tmpdc = round((tmpd - 32) / 1.8, digits = 1)) 
head(chicago2, n = 3)

##   death pm10median pm25median  o3median  so2median    time tmpd tmpdc
## 1   130 -7.4335443         NA -19.59234  1.9280426 -2556.5 31.5  -0.3
## 2   150         NA         NA -19.03861 -0.9855631 -2555.5 33.0   0.6
## 3   101 -0.8265306         NA -20.21734 -1.8914161 -2554.5 33.0   0.6

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)

high_death <- filter(chicago2, death > 200) 
high_death

##   death pm10median pm25median  o3median  so2median  time tmpd tmpdc
## 1   226  20.941667         NA 29.703545  2.2685856 559.5 91.5  33.1
## 2   411  14.798103         NA 28.115091  0.6976599 560.5 86.0  30.0
## 3   287  -8.333333         NA 21.115009 -0.9330126 561.5 83.0  28.3
## 4   228  -3.232732         NA  5.649732 -2.3158882 562.5 78.5  25.8

high_temp_death <- filter(chicago2, death > 200 & tmpdc >= 30)
high_temp_death

##   death pm10median pm25median o3median so2median  time tmpd tmpdc
## 1   226   20.94167         NA 29.70355 2.2685856 559.5 91.5  33.1
## 2   411   14.79810         NA 28.11509 0.6976599 560.5 86.0  30.0

is used to reorder rows of a data frame (df) according to one of the variables/columns.

• 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).

low_2_high <- arrange(chicago, death)
head(low_2_high, n = 4)

##   death pm10median pm25median   o3median   so2median    time tmpd
## 1    69  -1.818182         NA  -8.029279  1.12452237  1313.5 64.5
## 2    73 -19.320548         NA  -5.869187  0.07297014  2052.5 66.0
## 3    77  -8.801262         NA -13.170360 -3.48994781 -2363.5 64.5
## 4    77 -19.165746  -10.14961   3.436157  3.60026234  1646.5 70.0

high_2_low <- arrange(chicago, desc(death))
head(high_2_low, n = 4)

##   death pm10median pm25median  o3median  so2median  time tmpd
## 1   411  14.798103         NA 28.115091  0.6976599 560.5 86.0
## 2   287  -8.333333         NA 21.115009 -0.9330126 561.5 83.0
## 3   228  -3.232732         NA  5.649732 -2.3158882 562.5 78.5
## 4   226  20.941667         NA 29.703545  2.2685856 559.5 91.5

• 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.

Let us use summarise() to print out a summary of chicago data containing two variables: max_detht and the max_tmpd

summarise(chicago, max_deth = max(death), max_tmpd = max(tmpd))

##   max_deth max_tmpd
## 1      411       92

chicago_pipe <- chicago %>%
  filter(!is.na(pm10median) & !is.na(so2median)) %>%
  mutate(tmpdC = round((tmpd - 32) / 1.8, digits = 1))
plot(chicago_pipe$tmpdC, chicago_pipe$death, cex = 0.5, col = "red")

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

library(ggplot2)
ggplot(chicago_pipe, aes(x = tmpdC, y = death)) +
  geom_point(col ="red")

ggplot(chicago_pipe, aes(x = tmpdC, y = death, col = "red")) +
  geom_point(alpha = 0.2) +
  geom_smooth(col = "blue") +
  labs (title= " death vs temperature ", 
        x = "°C", y = "death") +
  theme(legend.position = "none", 
        panel.border = element_rect(fill = NA, 
                                    colour = "black",
                                    size = .75),
        plot.title=element_text(hjust=0.5))

cheatsheets:

• data-wrangling-cheatsheet

• ggplot2-cheatsheet

websites:

• tidyverse, visualization, and manipulation basics

• ggplot part of tidy verse

• Introduction to R graphics with ggplot2

1. upload Daily Mortality Weather and Pollution Data for Chicago: chicagoNMMAPS available from dlnm package.
2. have a glance at the data.
3. what are the questions you could ask; could you provide the answers to them?

There is a chalange:

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.

Knowing about group_by() function, coud you compute the average pollutant level by month and visualise your result?

# install and open `dlnm' package and access the data
install.packages("dlnm")
library(dlnm)
data("chicagoNMMAPS")

# group data by month and calculate average monthly polution
my_ch <- chicagoNMMAPS %>%
  group_by(month) %>%
  summarise(pm10 = mean(pm10, na.rm = TRUE))

# visualise the information
ggplot(my_ch, aes(x=month, y = pm10)) +
  geom_line() + geom_point(col = "red") +
  xlab("Month") + ylab("average pm10") +
  scale_x_continuous(breaks = seq(1, 12, 1), labels = seq(1, 12, 1))