This course provides a practical guide using R and the tidyverse, guiding statistical concepts and analysis techniques.

Rp.tar.gz

datascience

Rlang

The world is full of data and it can be quite challenging to infer the knowledge based on statistical reasoning. This course aims to equip you with the skills you need to play with your data, wrangle it, and visualize it.  

Starting with data visualization, the course gets learners building ggplot2 graphs early on and then continues to reinforce important concepts graphically throughout the course. After moving through data wrangling and data importing, you’ll be introduced to modeling, which plays a prominent role, with a focus on building regression models and inference for regression. Lastly, statistical inference is presented through a computational lens with calculations done via the infer package.

By the end of this course, you’ll develop your data science toolbox, equipping yourself with tools such as data visualization, data formatting, data wrangling, and data modeling using regression.

Data-Centric Statistical Inference Using R and Tidyverse

```R
not_BTV_SEA <- flights %>% filter(!dest == "BTV" & !dest == "SEA")
```

```R
not_BTV_SEA <- flights %>%
 filter(dest != "BTV" & dest != "SEA")
```

What’s another way of using the "not" operator `!` to filter only the rows that aren’t going to Burlington, VT or Seattle, WA in the `flights` data frame?

What’s another way of using the “not” operator <code>!</code> to filter only the rows that aren’t going to Burlington, VT or Seattle, WA in the <code>flights</code> data frame?

Yes, you can do that. It’ll give you the delay time for departure.

There will be no difference, and both results will be the same.

No, because you can't do direct arithmetic on times. The difference in time between `12:03` and `11:59` is `4` minutes, but `1203-1159 = 44`.

Can you create the `dep_delay` and `arr_delay` columns by simply subtracting `dep_time` from `sched_dep_time` and similarly for arrivals? 


Can you create the <code>dep_delay</code> and <code>arr_delay</code> columns by simply subtracting <code>dep_time</code> from <code>sched_dep_time</code> and similarly for arrivals?

```R
summary_temp_by_day <- weather %>%
 group_by(year, month, day) %>%
 summarize
(
 mean = mean(temp, na.rm = TRUE),
 std_dev = sd(temp, na.rm = TRUE)
 )
summary_temp_by_day
```

```R
summary_temp_by_day <- weather %>%
 group_by(day) %>%
 summarize
(
 mean = mean(temp, na.rm = TRUE),
 std_dev = sd(temp, na.rm = TRUE)
 )
summary_temp_by_day
```

```R
summary_temp_by_day <- weather %>%
 group_by(month, day) %>%
 summarize
(
 mean = mean(temp, na.rm = TRUE),
 std_dev = sd(temp, na.rm = TRUE)
 )
summary_temp_by_day
```

```R
summary_temp_by_day <- weather %>%
 group_by(year, day) %>%
 summarize
(
 mean = mean(temp, na.rm = FALSE),
 std_dev = sd(temp, na.rm = FALSE)
 )
summary_temp_by_day
```

What code would be required to get the mean and standard deviation temperature for each day in 2013 for NYC?

What code would be required to get the mean and standard deviation temperature for each day in 2013 for NYC?

There's no difference between them as both filter out the data according to the conditional statement.

The `filter` operations picks out rows from the original dataset without modifying them, whereas
`group_by %>% summarize` computes the summaries of numerical variables and
reports new values.

The `filter` operation picks out columns from the original dataset and modifies them while `group_by %>% summarize`  computes the summaries of numerical variables and reports no values.

The `filter` operation picks out rows from the original dataset randomly, whereas
`group_by %>% summarize` computes summaries of continuous variables and reports no values.

How does the `filter` operation differ from `group_by` followed by `summarize`?

How does the <code>filter</code> operation differ from <code>group_by</code> followed by <code>summarize</code>?

```R
flights %>%
  select(
    -year, -month, -day, -dep_time, -sched_dep_time, -dep_delay, -arr_time,
    -sched_arr_time, -arr_delay, -carrier, -flight, -tailnum, -origin,
    -hour, -minute, -time_hour
  )
```

```R
flights %>%
  select(dest, air_time, distance)
```

How do we select all three of the `dest`, `air_time`, and `distance` variables from `flights`?

How do we select all three of the <code>dest</code>, <code>air_time</code>, and <code>distance</code> variables from <code>flights</code>?

Getting Started with Data in R

Data Visualization

Data Wrangling

Data Importing and “Tidy” Data

Basic Regression

Multiple Regression

Statistical Inference with the infer Package

Bootstrapping and Confidence Intervals

Hypothesis Testing

Inference for Regression

Tell a Story with Data

Appendix

Test Your Skills on Data Wrangling