# A Quick and Dirty Introduction to R

# Some Data

I will start with some inline data.

```
library(tidyverse); library(skimr);
Support.Times <- structure(list(Screened = c(26.9, 28.4, 23.9, 21.8, 22.4, 25.9,
26.5, 20, 23.7, 23.7, 22.6, 19.4, 27.3, 25.3, 27.7, 25.3, 28.4,
24.2, 20.4, 29.6, 27, 23.6, 18.3, 28.1, 20.5, 24.1, 27.2, 26.4,
24.5, 25.6, 17.9, 23.5, 25.3, 20.2, 26.3, 27.9), Not.Screened = c(24.7,
19.1, 21, 17.8, 22.8, 24.4, 17.9, 20.5, 20, 26.2, 14.5, 22.4,
21.1, 24.3, 22, 24.3, 23.9, 19.6, 23.8, 29.2, 19.7, 20.9, 25.2,
22.5, 23.1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA)), class = "data.frame", row.names = c(NA, -36L))
```

Now I will use the tidyverse to stack it. This can also be done with `stack(Support.Times)`

.

`stack(Support.Times) %>% drop_na()`

Using the `tidyverse`

, the new data `SSTimes`

will stack the data using pivot longer into two variables that I will name `Self.Screen`

and `Call.Time`

to store the stacked data. The final command drops the missing data. Then I will group them and skim them.

```
SSTimes <- Support.Times %>% pivot_longer(., c(Screened,Not.Screened), names_to = "Self.Screen", values_to = "Call.Time") %>% drop_na()
SSTimes %>% group_by(Self.Screen) %>% skim()
```

Name | Piped data |

Number of rows | 61 |

Number of columns | 2 |

_______________________ | |

Column type frequency: | |

numeric | 1 |

________________________ | |

Group variables | Self.Screen |

**Variable type: numeric**

skim_variable | Self.Screen | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
---|---|---|---|---|---|---|---|---|---|---|---|

Call.Time | Not.Screened | 0 | 1 | 22.04 | 3.11 | 14.5 | 20.00 | 22.4 | 24.30 | 29.2 | ▁▅▇▇▁ |

Call.Time | Screened | 0 | 1 | 24.44 | 3.08 | 17.9 | 22.55 | 24.9 | 26.92 | 29.6 | ▃▃▆▇▅ |

So I have 25 observations that are not screened and 36 that are screened. What does it look like?

`ggplot(SSTimes, aes(x=Self.Screen, y=Call.Time, fill=Self.Screen)) + geom_violin(alpha = 0.2) + scale_fill_discrete(guide=FALSE) + labs(title = "Self Screen and Non-Self Screen Call Times")`

```
## Warning: It is deprecated to specify `guide = FALSE` to remove a guide. Please
## use `guide = "none"` instead.
```

Here is a picture of the distributions of the two means.

`gplots::plotmeans(Call.Time~Self.Screen, data=SSTimes, n.label=FALSE, ci.label=TRUE, ylim=c(20,27))`

What does the t-test look like?

`t.test(Support.Times$Not.Screened, Support.Times$Screened)`

```
##
## Welch Two Sample t-test
##
## data: Support.Times$Not.Screened and Support.Times$Screened
## t = -2.9793, df = 51.512, p-value = 0.004399
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.0216630 -0.7841148
## sample estimates:
## mean of x mean of y
## 22.03600 24.43889
```

`t.test(Call.Time~Self.Screen, data=SSTimes)`

```
##
## Welch Two Sample t-test
##
## data: Call.Time by Self.Screen
## t = -2.9793, df = 51.512, p-value = 0.004399
## alternative hypothesis: true difference in means between group Not.Screened and group Screened is not equal to 0
## 95 percent confidence interval:
## -4.0216630 -0.7841148
## sample estimates:
## mean in group Not.Screened mean in group Screened
## 22.03600 24.43889
```

It is worth noting that R stores a bunch of stuff. For example, it stores the standard error of the difference and that is worth looking at in this case; the standard error that describes the difference in the averages is 0.8065242.

```
Resample.Times <- ResampleProps::ResampleDiffMeans(Support.Times$Screened,Support.Times$Not.Screened)
sd(Resample.Times)
```

`## [1] 0.8073286`