# Exploring Boston Weather Data

Published

June 15, 2020

``weather = readRDS(gzcon(url('https://assets.datacamp.com/production/repositories/34/datasets/b3c1036d9a60a9dfe0f99051d2474a54f76055ea/weather.rds')))``

# Libraries

``````library(readr)
library(dplyr)
library(lubridate)
library(stringr)
library(installr)
library(tidyr)``````
``````Warning message:
"package 'tidyr' was built under R version 3.6.3"``````
``````# Verify that weather is a data.frame
class(weather)

# Check the dimensions
dim(weather)

# View the column names
names(weather)``````
'data.frame'
1. 286
2. 35
1. 'X'
2. 'year'
3. 'month'
4. 'measure'
5. 'X1'
6. 'X2'
7. 'X3'
8. 'X4'
9. 'X5'
10. 'X6'
11. 'X7'
12. 'X8'
13. 'X9'
14. 'X10'
15. 'X11'
16. 'X12'
17. 'X13'
18. 'X14'
19. 'X15'
20. 'X16'
21. 'X17'
22. 'X18'
23. 'X19'
24. 'X20'
25. 'X21'
26. 'X22'
27. 'X23'
28. 'X24'
29. 'X25'
30. 'X26'
31. 'X27'
32. 'X28'
33. 'X29'
34. 'X30'
35. 'X31'

We’ve confirmed that the object is a data frame with 286 rows and 35 columns.

# Summarize the data

Next up is to look at some summaries of the data. This is where functions like `str()`, `glimpse()` from dplyr, and `summary()` come in handy.

``````# View the structure of the data
str(weather)

# Look at the structure using dplyr's glimpse()
glimpse(weather)

# View a summary of the data
summary(weather)``````
``````'data.frame':   286 obs. of  35 variables:
\$ X      : int  1 2 3 4 5 6 7 8 9 10 ...
\$ year   : int  2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 ...
\$ month  : int  12 12 12 12 12 12 12 12 12 12 ...
\$ measure: chr  "Max.TemperatureF" "Mean.TemperatureF" "Min.TemperatureF" "Max.Dew.PointF" ...
\$ X1     : chr  "64" "52" "39" "46" ...
\$ X2     : chr  "42" "38" "33" "40" ...
\$ X3     : chr  "51" "44" "37" "49" ...
\$ X4     : chr  "43" "37" "30" "24" ...
\$ X5     : chr  "42" "34" "26" "37" ...
\$ X6     : chr  "45" "42" "38" "45" ...
\$ X7     : chr  "38" "30" "21" "36" ...
\$ X8     : chr  "29" "24" "18" "28" ...
\$ X9     : chr  "49" "39" "29" "49" ...
\$ X10    : chr  "48" "43" "38" "45" ...
\$ X11    : chr  "39" "36" "32" "37" ...
\$ X12    : chr  "39" "35" "31" "28" ...
\$ X13    : chr  "42" "37" "32" "28" ...
\$ X14    : chr  "45" "39" "33" "29" ...
\$ X15    : chr  "42" "37" "32" "33" ...
\$ X16    : chr  "44" "40" "35" "42" ...
\$ X17    : chr  "49" "45" "41" "46" ...
\$ X18    : chr  "44" "40" "36" "34" ...
\$ X19    : chr  "37" "33" "29" "25" ...
\$ X20    : chr  "36" "32" "27" "30" ...
\$ X21    : chr  "36" "33" "30" "30" ...
\$ X22    : chr  "44" "39" "33" "39" ...
\$ X23    : chr  "47" "45" "42" "45" ...
\$ X24    : chr  "46" "44" "41" "46" ...
\$ X25    : chr  "59" "52" "44" "58" ...
\$ X26    : chr  "50" "44" "37" "31" ...
\$ X27    : chr  "52" "45" "38" "34" ...
\$ X28    : chr  "52" "46" "40" "42" ...
\$ X29    : chr  "41" "36" "30" "26" ...
\$ X30    : chr  "30" "26" "22" "10" ...
\$ X31    : chr  "30" "25" "20" "8" ...
Rows: 286
Columns: 35
\$ X       <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, ...
\$ year    <int> 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014,...
\$ month   <int> 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,...
\$ measure <chr> "Max.TemperatureF", "Mean.TemperatureF", "Min.TemperatureF"...
\$ X1      <chr> "64", "52", "39", "46", "40", "26", "74", "63", "52", "30.4...
\$ X2      <chr> "42", "38", "33", "40", "27", "17", "92", "72", "51", "30.7...
\$ X3      <chr> "51", "44", "37", "49", "42", "24", "100", "79", "57", "30....
\$ X4      <chr> "43", "37", "30", "24", "21", "13", "69", "54", "39", "30.5...
\$ X5      <chr> "42", "34", "26", "37", "25", "12", "85", "66", "47", "30.6...
\$ X6      <chr> "45", "42", "38", "45", "40", "36", "100", "93", "85", "30....
\$ X7      <chr> "38", "30", "21", "36", "20", "-3", "92", "61", "29", "30.6...
\$ X8      <chr> "29", "24", "18", "28", "16", "3", "92", "70", "47", "30.77...
\$ X9      <chr> "49", "39", "29", "49", "41", "28", "100", "93", "86", "30....
\$ X10     <chr> "48", "43", "38", "45", "39", "37", "100", "95", "89", "29....
\$ X11     <chr> "39", "36", "32", "37", "31", "27", "92", "87", "82", "29.8...
\$ X12     <chr> "39", "35", "31", "28", "27", "25", "85", "75", "64", "29.8...
\$ X13     <chr> "42", "37", "32", "28", "26", "24", "75", "65", "55", "29.8...
\$ X14     <chr> "45", "39", "33", "29", "27", "25", "82", "68", "53", "29.9...
\$ X15     <chr> "42", "37", "32", "33", "29", "27", "89", "75", "60", "30.1...
\$ X16     <chr> "44", "40", "35", "42", "36", "30", "96", "85", "73", "30.1...
\$ X17     <chr> "49", "45", "41", "46", "41", "32", "100", "85", "70", "29....
\$ X18     <chr> "44", "40", "36", "34", "30", "26", "89", "73", "57", "29.8...
\$ X19     <chr> "37", "33", "29", "25", "22", "20", "69", "63", "56", "30.1...
\$ X20     <chr> "36", "32", "27", "30", "24", "20", "89", "79", "69", "30.3...
\$ X21     <chr> "36", "33", "30", "30", "27", "25", "85", "77", "69", "30.3...
\$ X22     <chr> "44", "39", "33", "39", "34", "25", "89", "79", "69", "30.4...
\$ X23     <chr> "47", "45", "42", "45", "42", "37", "100", "91", "82", "30....
\$ X24     <chr> "46", "44", "41", "46", "44", "41", "100", "98", "96", "30....
\$ X25     <chr> "59", "52", "44", "58", "43", "29", "100", "75", "49", "29....
\$ X26     <chr> "50", "44", "37", "31", "29", "28", "70", "60", "49", "30.1...
\$ X27     <chr> "52", "45", "38", "34", "31", "29", "70", "60", "50", "30.2...
\$ X28     <chr> "52", "46", "40", "42", "35", "27", "76", "65", "53", "29.9...
\$ X29     <chr> "41", "36", "30", "26", "20", "10", "64", "51", "37", "30.2...
\$ X30     <chr> "30", "26", "22", "10", "4", "-6", "50", "38", "26", "30.36...
\$ X31     <chr> "30", "25", "20", "8", "5", "1", "57", "44", "31", "30.32",...``````
``````       X               year          month          measure
Min.   :  1.00   Min.   :2014   Min.   : 1.000   Length:286
1st Qu.: 72.25   1st Qu.:2015   1st Qu.: 4.000   Class :character
Median :143.50   Median :2015   Median : 7.000   Mode  :character
Mean   :143.50   Mean   :2015   Mean   : 6.923
3rd Qu.:214.75   3rd Qu.:2015   3rd Qu.:10.000
Max.   :286.00   Max.   :2015   Max.   :12.000
X1                 X2                 X3                 X4
Length:286         Length:286         Length:286         Length:286
Class :character   Class :character   Class :character   Class :character
Mode  :character   Mode  :character   Mode  :character   Mode  :character

X5                 X6                 X7                 X8
Length:286         Length:286         Length:286         Length:286
Class :character   Class :character   Class :character   Class :character
Mode  :character   Mode  :character   Mode  :character   Mode  :character

X9                X10                X11                X12
Length:286         Length:286         Length:286         Length:286
Class :character   Class :character   Class :character   Class :character
Mode  :character   Mode  :character   Mode  :character   Mode  :character

X13                X14                X15                X16
Length:286         Length:286         Length:286         Length:286
Class :character   Class :character   Class :character   Class :character
Mode  :character   Mode  :character   Mode  :character   Mode  :character

X17                X18                X19                X20
Length:286         Length:286         Length:286         Length:286
Class :character   Class :character   Class :character   Class :character
Mode  :character   Mode  :character   Mode  :character   Mode  :character

X21                X22                X23                X24
Length:286         Length:286         Length:286         Length:286
Class :character   Class :character   Class :character   Class :character
Mode  :character   Mode  :character   Mode  :character   Mode  :character

X25                X26                X27                X28
Length:286         Length:286         Length:286         Length:286
Class :character   Class :character   Class :character   Class :character
Mode  :character   Mode  :character   Mode  :character   Mode  :character

X29                X30                X31
Length:286         Length:286         Length:286
Class :character   Class :character   Class :character
Mode  :character   Mode  :character   Mode  :character

``````

Now that we have a pretty good feel for how the table is structured, we’ll take a look at some real observations!

# Take a closer look

After understanding the structure of the data and looking at some brief summaries, it often helps to preview the actual data. The functions `head()` and `tail()` allow us to view the top and bottom rows of the data, respectively.

``````# View first 6 rows

# View first 15 rows

# View the last 6 rows
tail(weather)

# View the last 10 rows
tail(weather, n=10)``````
XyearmonthmeasureX1X2X3X4X5X6...X22X23X24X25X26X27X28X29X30X31
1 2014 12 Max.TemperatureF 64 42 51 43 42 45 ... 44 47 46 59 50 52 52 41 30 30
2 2014 12 Mean.TemperatureF52 38 44 37 34 42 ... 39 45 44 52 44 45 46 36 26 25
3 2014 12 Min.TemperatureF 39 33 37 30 26 38 ... 33 42 41 44 37 38 40 30 22 20
4 2014 12 Max.Dew.PointF 46 40 49 24 37 45 ... 39 45 46 58 31 34 42 26 10 8
5 2014 12 MeanDew.PointF 40 27 42 21 25 40 ... 34 42 44 43 29 31 35 20 4 5
6 2014 12 Min.DewpointF 26 17 24 13 12 36 ... 25 37 41 29 28 29 27 10 -6 1
XyearmonthmeasureX1X2X3X4X5X6...X22X23X24X25X26X27X28X29X30X31
1 2014 12 Max.TemperatureF 64 42 51 43 42 45 ... 44 47 46 59 50 52 52 41 30 30
2 2014 12 Mean.TemperatureF 52 38 44 37 34 42 ... 39 45 44 52 44 45 46 36 26 25
3 2014 12 Min.TemperatureF 39 33 37 30 26 38 ... 33 42 41 44 37 38 40 30 22 20
4 2014 12 Max.Dew.PointF 46 40 49 24 37 45 ... 39 45 46 58 31 34 42 26 10 8
5 2014 12 MeanDew.PointF 40 27 42 21 25 40 ... 34 42 44 43 29 31 35 20 4 5
6 2014 12 Min.DewpointF 26 17 24 13 12 36 ... 25 37 41 29 28 29 27 10 -6 1
7 2014 12 Max.Humidity 74 92 100 69 85 100 ... 89 100 100 100 70 70 76 64 50 57
8 2014 12 Mean.Humidity 63 72 79 54 66 93 ... 79 91 98 75 60 60 65 51 38 44
9 2014 12 Min.Humidity 52 51 57 39 47 85 ... 69 82 96 49 49 50 53 37 26 31
10 2014 12 Max.Sea.Level.PressureIn 30.45 30.71 30.4 30.56 30.68 30.42 ... 30.4 30.31 30.13 29.96 30.16 30.22 29.99 30.22 30.36 30.32
11 2014 12 Mean.Sea.Level.PressureIn30.13 30.59 30.07 30.33 30.59 30.24 ... 30.35 30.23 29.9 29.63 30.11 30.14 29.87 30.12 30.32 30.25
12 2014 12 Min.Sea.Level.PressureIn 30.01 30.4 29.87 30.09 30.45 30.16 ... 30.3 30.16 29.55 29.47 29.99 30.03 29.77 30 30.23 30.13
13 2014 12 Max.VisibilityMiles 10 10 10 10 10 10 ... 10 10 2 10 10 10 10 10 10 10
14 2014 12 Mean.VisibilityMiles 10 8 5 10 10 4 ... 10 5 1 8 10 10 10 10 10 10
15 2014 12 Min.VisibilityMiles 10 2 1 10 5 0 ... 4 1 0 1 10 10 10 10 10 10
XyearmonthmeasureX1X2X3X4X5X6...X22X23X24X25X26X27X28X29X30X31
281281 2015 12 Mean.Wind.SpeedMPH6 NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA
282282 2015 12 Max.Gust.SpeedMPH 17 NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA
283283 2015 12 PrecipitationIn 0.14 NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA
284284 2015 12 CloudCover 7 NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA
285285 2015 12 Events Rain NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA
286286 2015 12 WindDirDegrees 109 NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA
XyearmonthmeasureX1X2X3X4X5X6...X22X23X24X25X26X27X28X29X30X31
277277 2015 12 Max.VisibilityMiles 10 NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA
278278 2015 12 Mean.VisibilityMiles8 NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA
279279 2015 12 Min.VisibilityMiles 1 NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA
280280 2015 12 Max.Wind.SpeedMPH 15 NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA
281281 2015 12 Mean.Wind.SpeedMPH 6 NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA
282282 2015 12 Max.Gust.SpeedMPH 17 NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA
283283 2015 12 PrecipitationIn 0.14 NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA
284284 2015 12 CloudCover 7 NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA
285285 2015 12 Events Rain NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA
286286 2015 12 WindDirDegrees 109 NA NA NA NA NA ... NA NA NA NA NA NA NA NA NA NA

# Let’s tidy the data

## Column names are values

The `weather` dataset suffers from one of the five most common symptoms of messy data: column names are values. In particular, the column names `X1-X31` represent days of the month, which should really be values of a new variable called `day`.

The tidyr package provides the `gather()` function for exactly this scenario.

``gather(df, time, val, t1:t3)``

`gather()` allows us to select multiple columns to be gathered by using the `:` operator.

``````# Gather the columns
weather2 <- gather(weather, day, value, X1:X31, na.rm = TRUE)

Xyearmonthmeasuredayvalue
1 2014 12 Max.TemperatureF X1 64
2 2014 12 Mean.TemperatureFX1 52
3 2014 12 Min.TemperatureF X1 39
4 2014 12 Max.Dew.PointF X1 46
5 2014 12 MeanDew.PointF X1 40
6 2014 12 Min.DewpointF X1 26

## Values are variable names

Our data suffer from a second common symptom of messy data: values are variable names. Specifically, values in the `measure` column should be variables (i.e. column names) in our dataset.

The `spread()` function from tidyr is designed to help with this.

``````
``````# First remove column of row names
without_x <- weather2[, -1]

yearmonthdayCloudCoverEventsMax.Dew.PointFMax.Gust.SpeedMPHMax.HumidityMax.Sea.Level.PressureInMax.TemperatureF...Mean.VisibilityMilesMean.Wind.SpeedMPHMeanDew.PointFMin.DewpointFMin.HumidityMin.Sea.Level.PressureInMin.TemperatureFMin.VisibilityMilesPrecipitationInWindDirDegrees
2014 12 X1 6 Rain 46 29 74 30.45 64 ... 10 13 40 26 52 30.01 39 10 0.01 268
2014 12 X10 8 Rain 45 29 100 29.58 48 ... 3 13 39 37 89 29.43 38 1 0.28 357
2014 12 X11 8 Rain-Snow37 28 92 29.81 39 ... 7 13 31 27 82 29.44 32 1 0.02 230
2014 12 X12 7 Snow 28 21 85 29.88 39 ... 10 11 27 25 64 29.81 31 7 T 286
2014 12 X13 5 28 23 75 29.86 42 ... 10 12 26 24 55 29.78 32 10 T 298
2014 12 X14 4 29 20 82 29.91 45 ... 10 10 27 25 53 29.78 33 10 0.00 306

This dataset is looking much better already!

# Prepare the data for analysis

## Clean up dates

Now that the weather dataset adheres to tidy data principles, the next step is to prepare it for analysis. We’ll start by combining the `year`, `month`, and `day` columns and recoding the resulting character column as a `date`. We can use a combination of base R, stringr, and lubridate to accomplish this task.

``````# Remove X's from day column
weather3\$day <- str_replace(weather3\$day, 'X', '')

# Unite the year, month, and day columns
weather4 <- unite(weather3, date, year, month, day, sep = "-")

# Convert date column to proper date format using lubridates's ymd()
weather4\$date <- ymd(weather4\$date)

# Rearrange columns using dplyr's select()
weather5 <- select(weather4, date, Events, CloudCover:WindDirDegrees)

# View the head of weather5
dateEventsCloudCoverMax.Dew.PointFMax.Gust.SpeedMPHMax.HumidityMax.Sea.Level.PressureInMax.TemperatureFMax.VisibilityMilesMax.Wind.SpeedMPH...Mean.VisibilityMilesMean.Wind.SpeedMPHMeanDew.PointFMin.DewpointFMin.HumidityMin.Sea.Level.PressureInMin.TemperatureFMin.VisibilityMilesPrecipitationInWindDirDegrees
2014-12-01Rain 6 46 29 74 30.45 64 10 22 ... 10 13 40 26 52 30.01 39 10 0.01 268
2014-12-10Rain 8 45 29 100 29.58 48 10 23 ... 3 13 39 37 89 29.43 38 1 0.28 357
2014-12-11Rain-Snow 8 37 28 92 29.81 39 10 21 ... 7 13 31 27 82 29.44 32 1 0.02 230
2014-12-12Snow 7 28 21 85 29.88 39 10 16 ... 10 11 27 25 64 29.81 31 7 T 286
2014-12-13 5 28 23 75 29.86 42 10 17 ... 10 12 26 24 55 29.78 32 10 T 298
2014-12-14 4 29 20 82 29.91 45 10 15 ... 10 10 27 25 53 29.78 33 10 0.00 306

## A closer look at column types

It’s important for analysis that variables are coded appropriately. This is not yet the case with our weather data.

``````# View the structure of weather5
str(weather5)

# Examine the first 20 rows of weather5. Are most of the characters numeric?

# See what happens if we try to convert PrecipitationIn to numeric
as.numeric(weather5\$PrecipitationIn)``````
``````'data.frame':   366 obs. of  23 variables:
\$ date                     : Date, format: "2014-12-01" "2014-12-10" ...
\$ Events                   : chr  "Rain" "Rain" "Rain-Snow" "Snow" ...
\$ CloudCover               : chr  "6" "8" "8" "7" ...
\$ Max.Dew.PointF           : chr  "46" "45" "37" "28" ...
\$ Max.Gust.SpeedMPH        : chr  "29" "29" "28" "21" ...
\$ Max.Humidity             : chr  "74" "100" "92" "85" ...
\$ Max.Sea.Level.PressureIn : chr  "30.45" "29.58" "29.81" "29.88" ...
\$ Max.TemperatureF         : chr  "64" "48" "39" "39" ...
\$ Max.VisibilityMiles      : chr  "10" "10" "10" "10" ...
\$ Max.Wind.SpeedMPH        : chr  "22" "23" "21" "16" ...
\$ Mean.Humidity            : chr  "63" "95" "87" "75" ...
\$ Mean.Sea.Level.PressureIn: chr  "30.13" "29.5" "29.61" "29.85" ...
\$ Mean.TemperatureF        : chr  "52" "43" "36" "35" ...
\$ Mean.VisibilityMiles     : chr  "10" "3" "7" "10" ...
\$ Mean.Wind.SpeedMPH       : chr  "13" "13" "13" "11" ...
\$ MeanDew.PointF           : chr  "40" "39" "31" "27" ...
\$ Min.DewpointF            : chr  "26" "37" "27" "25" ...
\$ Min.Humidity             : chr  "52" "89" "82" "64" ...
\$ Min.Sea.Level.PressureIn : chr  "30.01" "29.43" "29.44" "29.81" ...
\$ Min.TemperatureF         : chr  "39" "38" "32" "31" ...
\$ Min.VisibilityMiles      : chr  "10" "1" "1" "7" ...
\$ PrecipitationIn          : chr  "0.01" "0.28" "0.02" "T" ...
\$ WindDirDegrees           : chr  "268" "357" "230" "286" ...``````
dateEventsCloudCoverMax.Dew.PointFMax.Gust.SpeedMPHMax.HumidityMax.Sea.Level.PressureInMax.TemperatureFMax.VisibilityMilesMax.Wind.SpeedMPH...Mean.VisibilityMilesMean.Wind.SpeedMPHMeanDew.PointFMin.DewpointFMin.HumidityMin.Sea.Level.PressureInMin.TemperatureFMin.VisibilityMilesPrecipitationInWindDirDegrees
2014-12-01Rain 6 46 29 74 30.45 64 10 22 ... 10 13 40 26 52 30.01 39 10 0.01 268
2014-12-10Rain 8 45 29 100 29.58 48 10 23 ... 3 13 39 37 89 29.43 38 1 0.28 357
2014-12-11Rain-Snow 8 37 28 92 29.81 39 10 21 ... 7 13 31 27 82 29.44 32 1 0.02 230
2014-12-12Snow 7 28 21 85 29.88 39 10 16 ... 10 11 27 25 64 29.81 31 7 T 286
2014-12-13 5 28 23 75 29.86 42 10 17 ... 10 12 26 24 55 29.78 32 10 T 298
2014-12-14 4 29 20 82 29.91 45 10 15 ... 10 10 27 25 53 29.78 33 10 0.00 306
2014-12-15 2 33 21 89 30.15 42 10 15 ... 10 6 29 27 60 29.91 32 10 0.00 324
2014-12-16Rain 8 42 10 96 30.17 44 10 8 ... 9 4 36 30 73 29.92 35 5 T 79
2014-12-17Rain 8 46 26 100 29.91 49 10 20 ... 6 11 41 32 70 29.69 41 1 0.43 311
2014-12-18Rain 7 34 30 89 29.87 44 10 23 ... 10 14 30 26 57 29.71 36 10 0.01 281
2014-12-19 4 25 23 69 30.15 37 10 17 ... 10 11 22 20 56 29.86 29 10 0.00 305
2014-12-02Rain-Snow 7 40 29 92 30.71 42 10 24 ... 8 15 27 17 51 30.4 33 2 0.10 62
2014-12-20Snow 6 30 26 89 30.31 36 10 21 ... 10 10 24 20 69 30.17 27 7 T 350
2014-12-21Snow 8 30 20 85 30.37 36 10 16 ... 9 9 27 25 69 30.28 30 6 T 2
2014-12-22Rain 7 39 22 89 30.4 44 10 18 ... 10 8 34 25 69 30.3 33 4 0.05 24
2014-12-23Rain 8 45 25 100 30.31 47 10 20 ... 5 13 42 37 82 30.16 42 1 0.25 63
2014-12-24Fog-Rain 8 46 15 100 30.13 46 2 13 ... 1 6 44 41 96 29.55 41 0 0.56 12
2014-12-25Rain 6 58 40 100 29.96 59 10 28 ... 8 14 43 29 49 29.47 44 1 0.14 250
2014-12-26 1 31 25 70 30.16 50 10 18 ... 10 11 29 28 49 29.99 37 10 0.00 255
2014-12-27 3 34 21 70 30.22 52 10 17 ... 10 9 31 29 50 30.03 38 10 0.00 251
``````Warning message in eval(expr, envir, enclos):
"NAs introduced by coercion"``````
1. 0.01
2. 0.28
3. 0.02
4. <NA>
5. <NA>
6. 0
7. 0
8. <NA>
9. 0.43
10. 0.01
11. 0
12. 0.1
13. <NA>
14. <NA>
15. 0.05
16. 0.25
17. 0.56
18. 0.14
19. 0
20. 0
21. 0.01
22. 0
23. 0.44
24. 0
25. 0
26. 0
27. 0.11
28. 1.09
29. 0.13
30. 0.03
31. 2.9
32. 0
33. 0
34. 0
35. 0.2
36. 0
37. <NA>
38. 0.12
39. 0
40. 0
41. 0.15
42. 0
43. 0
44. 0
45. 0
46. <NA>
47. 0
48. 0.71
49. 0
50. 0.1
51. 0.95
52. 0.01
53. <NA>
54. 0.62
55. 0.06
56. 0.05
57. 0.57
58. 0
59. 0.02
60. <NA>
61. 0
62. 0.01
63. 0
64. 0.05
65. 0.01
66. 0.03
67. 0
68. 0.23
69. 0.39
70. 0
71. 0.02
72. 0.01
73. 0.06
74. 0.78
75. 0
76. 0.17
77. 0.11
78. 0
79. <NA>
80. 0.07
81. 0.02
82. 0
83. 0
84. 0
85. 0
86. 0.09
87. <NA>
88. 0.07
89. 0.37
90. 0.88
91. 0.17
92. 0.06
93. 0.01
94. 0
95. 0
96. 0.8
97. 0.27
98. 0
99. 0.14
100. 0
101. 0
102. 0.01
103. 0.05
104. 0.09
105. 0
106. 0
107. 0
108. 0.04
109. 0.8
110. 0.21
111. 0.12
112. 0
113. 0.26
114. <NA>
115. 0
116. 0.02
117. <NA>
118. 0
119. 0
120. <NA>
121. 0
122. 0
123. 0.09
124. 0
125. 0
126. 0
127. 0.01
128. 0
129. 0
130. 0.06
131. 0
132. 0
133. 0
134. 0.61
135. 0.54
136. <NA>
137. 0
138. <NA>
139. 0
140. 0
141. 0.1
142. 0.07
143. 0
144. 0.03
145. 0
146. 0.39
147. 0
148. 0
149. 0.03
150. 0.26
151. 0.09
152. 0
153. 0
154. 0
155. 0.02
156. 0
157. 0
158. 0
159. <NA>
160. 0
161. 0
162. 0.27
163. 0
164. 0
165. 0
166. <NA>
167. 0
168. 0
169. <NA>
170. 0
171. 0
172. <NA>
173. 0
174. 0
175. 0
176. 0.91
177. 0
178. 0.02
179. 0
180. 0
181. 0
182. 0
183. 0.38
184. 0
185. 0
186. 0
187. <NA>
188. 0
189. 0.4
190. <NA>
191. 0
192. 0
193. 0
194. 0.74
195. 0.04
196. 1.72
197. 0
198. 0.01
199. 0
200. 0
201. <NA>
202. 0.2
203. 1.43
204. <NA>
205. 0
206. 0
207. 0
208. <NA>
209. 0.09
210. 0
211. <NA>
212. <NA>
213. 0.5
214. 1.12
215. 0
216. 0
217. 0
218. 0.03
219. <NA>
220. 0
221. <NA>
222. 0.14
223. <NA>
224. 0
225. <NA>
226. <NA>
227. 0
228. 0
229. 0.01
230. 0
231. <NA>
232. 0.06
233. 0
234. 0
235. 0
236. 0.02
237. 0
238. <NA>
239. 0
240. 0
241. 0.02
242. <NA>
243. 0.15
244. <NA>
245. 0
246. 0.83
247. 0
248. 0
249. 0
250. 0.08
251. 0
252. 0
253. 0.14
254. 0
255. 0
256. 0
257. 0.63
258. <NA>
259. 0.02
260. <NA>
261. 0
262. <NA>
263. 0
264. 0
265. 0
266. 0
267. 0
268. 0
269. 0.49
270. 0
271. 0
272. 0
273. 0
274. 0
275. 0
276. 0.17
277. 0.66
278. 0.01
279. 0.38
280. 0
281. 0
282. 0
283. 0
284. 0
285. 0
286. 0
287. <NA>
288. 0
289. 0
290. 0
291. 0
292. 0
293. 0
294. 0
295. 0
296. 0.04
297. 0.01
298. 2.46
299. <NA>
300. 0
301. 0
302. 0
303. 0.2
304. 0
305. <NA>
306. 0
307. 0
308. 0
309. 0.12
310. 0
311. 0
312. <NA>
313. <NA>
314. <NA>
315. 0
316. 0.08
317. <NA>
318. 0.07
319. <NA>
320. 0
321. 0
322. 0.03
323. 0
324. 0
325. 0.36
326. 0.73
327. 0.01
328. 0
329. 0
330. 0
331. 0
332. 0
333. 0
334. 0
335. 0.34
336. <NA>
337. 0.07
338. 0.54
339. 0.04
340. 0.01
341. 0
342. 0
343. 0
344. 0
345. 0
346. <NA>
347. 0
348. 0.86
349. 0
350. 0.3
351. 0.04
352. 0
353. 0
354. 0
355. 0
356. 0.21
357. 0
358. 0
359. 0
360. 0
361. 0
362. 0
363. 0
364. 0
365. 0
366. 0.14

## Column type conversions

`"T"` was used to denote a trace amount (i.e. too small to be accurately measured) of precipitation in the `PrecipitationIn` column. In order to coerce this column to numeric, wwe’ll need to deal with this somehow. To keep things simple, we will just replace `"T"` with zero, as a string (`"0"`).

``````# Replace "T" with "0" (T = trace)
weather5\$PrecipitationIn <- str_replace(weather5\$PrecipitationIn, "T", "0")

# Convert characters to numerics
weather6 <- mutate_at(weather5, vars(CloudCover:WindDirDegrees), funs(as.numeric))

# Look at result
str(weather6)``````
``````Warning message:
"`funs()` is deprecated as of dplyr 0.8.0.
Please use a list of either functions or lambdas:

# Simple named list:
list(mean = mean, median = median)

# Auto named with `tibble::lst()`:
tibble::lst(mean, median)

# Using lambdas
list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated."``````
``````'data.frame':   366 obs. of  23 variables:
\$ date                     : Date, format: "2014-12-01" "2014-12-10" ...
\$ Events                   : chr  "Rain" "Rain" "Rain-Snow" "Snow" ...
\$ CloudCover               : num  6 8 8 7 5 4 2 8 8 7 ...
\$ Max.Dew.PointF           : num  46 45 37 28 28 29 33 42 46 34 ...
\$ Max.Gust.SpeedMPH        : num  29 29 28 21 23 20 21 10 26 30 ...
\$ Max.Humidity             : num  74 100 92 85 75 82 89 96 100 89 ...
\$ Max.Sea.Level.PressureIn : num  30.4 29.6 29.8 29.9 29.9 ...
\$ Max.TemperatureF         : num  64 48 39 39 42 45 42 44 49 44 ...
\$ Max.VisibilityMiles      : num  10 10 10 10 10 10 10 10 10 10 ...
\$ Max.Wind.SpeedMPH        : num  22 23 21 16 17 15 15 8 20 23 ...
\$ Mean.Humidity            : num  63 95 87 75 65 68 75 85 85 73 ...
\$ Mean.Sea.Level.PressureIn: num  30.1 29.5 29.6 29.9 29.8 ...
\$ Mean.TemperatureF        : num  52 43 36 35 37 39 37 40 45 40 ...
\$ Mean.VisibilityMiles     : num  10 3 7 10 10 10 10 9 6 10 ...
\$ Mean.Wind.SpeedMPH       : num  13 13 13 11 12 10 6 4 11 14 ...
\$ MeanDew.PointF           : num  40 39 31 27 26 27 29 36 41 30 ...
\$ Min.DewpointF            : num  26 37 27 25 24 25 27 30 32 26 ...
\$ Min.Humidity             : num  52 89 82 64 55 53 60 73 70 57 ...
\$ Min.Sea.Level.PressureIn : num  30 29.4 29.4 29.8 29.8 ...
\$ Min.TemperatureF         : num  39 38 32 31 32 33 32 35 41 36 ...
\$ Min.VisibilityMiles      : num  10 1 1 7 10 10 10 5 1 10 ...
\$ PrecipitationIn          : num  0.01 0.28 0.02 0 0 0 0 0 0.43 0.01 ...
\$ WindDirDegrees           : num  268 357 230 286 298 306 324 79 311 281 ...``````

It looks like our data are finally in the correct formats and organized in a logical manner! Now that our data are in the right form, we can begin the analysis.

# Missing, extreme, and unexpected values

## Find missing values

Before dealing with missing values in the data, it’s important to find them and figure out why they exist in the first place.

If the dataset is too big to look at all at once, like it is here, we will use `sum()` and `is.na()` to quickly size up the situation by counting the number of NA values.

The `summary()` function also come in handy for identifying which variables contain the missing values. Finally, the `which()` function is useful for locating the missing values within a particular column.

``````# Count missing values
sum(is.na(weather6))

# Find missing values
summary(weather6)

# Find indices of NAs in Max.Gust.SpeedMPH
ind <- which(is.na(weather6\$Max.Gust.SpeedMPH))

# Look at the full rows for records missing Max.Gust.SpeedMPH
weather6[ind, ]``````
6
``````      date               Events            CloudCover    Max.Dew.PointF
Min.   :2014-12-01   Length:366         Min.   :0.000   Min.   :-6.00
1st Qu.:2015-03-02   Class :character   1st Qu.:3.000   1st Qu.:32.00
Median :2015-06-01   Mode  :character   Median :5.000   Median :47.50
Mean   :2015-06-01                      Mean   :4.708   Mean   :45.48
3rd Qu.:2015-08-31                      3rd Qu.:7.000   3rd Qu.:61.00
Max.   :2015-12-01                      Max.   :8.000   Max.   :75.00

Max.Gust.SpeedMPH  Max.Humidity     Max.Sea.Level.PressureIn Max.TemperatureF
Min.   : 0.00     Min.   :  39.00   Min.   :29.58            Min.   :18.00
1st Qu.:21.00     1st Qu.:  73.25   1st Qu.:30.00            1st Qu.:42.00
Median :25.50     Median :  86.00   Median :30.14            Median :60.00
Mean   :26.99     Mean   :  85.69   Mean   :30.16            Mean   :58.93
3rd Qu.:31.25     3rd Qu.:  93.00   3rd Qu.:30.31            3rd Qu.:76.00
Max.   :94.00     Max.   :1000.00   Max.   :30.88            Max.   :96.00
NA's   :6
Max.VisibilityMiles Max.Wind.SpeedMPH Mean.Humidity
Min.   : 2.000      Min.   : 8.00     Min.   :28.00
1st Qu.:10.000      1st Qu.:16.00     1st Qu.:56.00
Median :10.000      Median :20.00     Median :66.00
Mean   : 9.907      Mean   :20.62     Mean   :66.02
3rd Qu.:10.000      3rd Qu.:24.00     3rd Qu.:76.75
Max.   :10.000      Max.   :38.00     Max.   :98.00

Mean.Sea.Level.PressureIn Mean.TemperatureF Mean.VisibilityMiles
Min.   :29.49             Min.   : 8.00     Min.   :-1.000
1st Qu.:29.87             1st Qu.:36.25     1st Qu.: 8.000
Median :30.03             Median :53.50     Median :10.000
Mean   :30.04             Mean   :51.40     Mean   : 8.861
3rd Qu.:30.19             3rd Qu.:68.00     3rd Qu.:10.000
Max.   :30.77             Max.   :84.00     Max.   :10.000

Mean.Wind.SpeedMPH MeanDew.PointF   Min.DewpointF     Min.Humidity
Min.   : 4.00      Min.   :-11.00   Min.   :-18.00   Min.   :16.00
1st Qu.: 8.00      1st Qu.: 24.00   1st Qu.: 16.25   1st Qu.:35.00
Median :10.00      Median : 41.00   Median : 35.00   Median :46.00
Mean   :10.68      Mean   : 38.96   Mean   : 32.25   Mean   :48.31
3rd Qu.:13.00      3rd Qu.: 56.00   3rd Qu.: 51.00   3rd Qu.:60.00
Max.   :22.00      Max.   : 71.00   Max.   : 68.00   Max.   :96.00

Min.Sea.Level.PressureIn Min.TemperatureF Min.VisibilityMiles PrecipitationIn
Min.   :29.16            Min.   :-3.00    Min.   : 0.000      Min.   :0.0000
1st Qu.:29.76            1st Qu.:30.00    1st Qu.: 2.000      1st Qu.:0.0000
Median :29.94            Median :46.00    Median :10.000      Median :0.0000
Mean   :29.93            Mean   :43.33    Mean   : 6.716      Mean   :0.1016
3rd Qu.:30.09            3rd Qu.:60.00    3rd Qu.:10.000      3rd Qu.:0.0400
Max.   :30.64            Max.   :74.00    Max.   :10.000      Max.   :2.9000

WindDirDegrees
Min.   :  1.0
1st Qu.:113.0
Median :222.0
Mean   :200.1
3rd Qu.:275.0
Max.   :360.0
``````
dateEventsCloudCoverMax.Dew.PointFMax.Gust.SpeedMPHMax.HumidityMax.Sea.Level.PressureInMax.TemperatureFMax.VisibilityMilesMax.Wind.SpeedMPH...Mean.VisibilityMilesMean.Wind.SpeedMPHMeanDew.PointFMin.DewpointFMin.HumidityMin.Sea.Level.PressureInMin.TemperatureFMin.VisibilityMilesPrecipitationInWindDirDegrees
1612015-05-18Fog 6 52 NA 100 30.30 58 10 16 ... 8 10 48 43 57 30.12 49 0 0 72
2052015-06-03 7 48 NA 93 30.31 56 10 14 ... 10 7 45 43 71 30.19 47 10 0 90
2732015-08-08 4 61 NA 87 30.02 76 10 14 ... 10 6 57 54 49 29.95 61 10 0 45
2752015-09-01 1 63 NA 78 30.06 79 10 15 ... 10 9 62 59 52 29.96 69 10 0 54
3082015-10-12 0 56 NA 89 29.86 76 10 15 ... 10 8 51 48 41 29.74 51 10 0 199
3582015-11-03 1 44 NA 82 30.25 73 10 16 ... 10 8 42 40 31 30.06 47 10 0 281

In this situation it’s unclear why these values are missing and there doesn’t appear to be any obvious pattern to their missingness, so we’ll leave them alone for now.

## An obvious error

Besides missing values, we want to know if there are values in the data that are too extreme or bizarre to be plausible. A great way to start the search for these values is with `summary()`.

Once implausible values are identified, they must be dealt with in an intelligent and informed way.

Sometimes the best way forward is obvious and other times it may require some research and/or discussions with the original collectors of the data.

``````# Review distributions for all variables
summary(weather6)

# Find row with Max.Humidity of 1000
ind <- which(weather6\$Max.Humidity==1000)

# Look at the data for that day
weather6[ind, ]

# Change 1000 to 100
weather6\$Max.Humidity[ind] <- 100``````
``````      date               Events            CloudCover    Max.Dew.PointF
Min.   :2014-12-01   Length:366         Min.   :0.000   Min.   :-6.00
1st Qu.:2015-03-02   Class :character   1st Qu.:3.000   1st Qu.:32.00
Median :2015-06-01   Mode  :character   Median :5.000   Median :47.50
Mean   :2015-06-01                      Mean   :4.708   Mean   :45.48
3rd Qu.:2015-08-31                      3rd Qu.:7.000   3rd Qu.:61.00
Max.   :2015-12-01                      Max.   :8.000   Max.   :75.00

Max.Gust.SpeedMPH  Max.Humidity     Max.Sea.Level.PressureIn Max.TemperatureF
Min.   : 0.00     Min.   :  39.00   Min.   :29.58            Min.   :18.00
1st Qu.:21.00     1st Qu.:  73.25   1st Qu.:30.00            1st Qu.:42.00
Median :25.50     Median :  86.00   Median :30.14            Median :60.00
Mean   :26.99     Mean   :  85.69   Mean   :30.16            Mean   :58.93
3rd Qu.:31.25     3rd Qu.:  93.00   3rd Qu.:30.31            3rd Qu.:76.00
Max.   :94.00     Max.   :1000.00   Max.   :30.88            Max.   :96.00
NA's   :6
Max.VisibilityMiles Max.Wind.SpeedMPH Mean.Humidity
Min.   : 2.000      Min.   : 8.00     Min.   :28.00
1st Qu.:10.000      1st Qu.:16.00     1st Qu.:56.00
Median :10.000      Median :20.00     Median :66.00
Mean   : 9.907      Mean   :20.62     Mean   :66.02
3rd Qu.:10.000      3rd Qu.:24.00     3rd Qu.:76.75
Max.   :10.000      Max.   :38.00     Max.   :98.00

Mean.Sea.Level.PressureIn Mean.TemperatureF Mean.VisibilityMiles
Min.   :29.49             Min.   : 8.00     Min.   :-1.000
1st Qu.:29.87             1st Qu.:36.25     1st Qu.: 8.000
Median :30.03             Median :53.50     Median :10.000
Mean   :30.04             Mean   :51.40     Mean   : 8.861
3rd Qu.:30.19             3rd Qu.:68.00     3rd Qu.:10.000
Max.   :30.77             Max.   :84.00     Max.   :10.000

Mean.Wind.SpeedMPH MeanDew.PointF   Min.DewpointF     Min.Humidity
Min.   : 4.00      Min.   :-11.00   Min.   :-18.00   Min.   :16.00
1st Qu.: 8.00      1st Qu.: 24.00   1st Qu.: 16.25   1st Qu.:35.00
Median :10.00      Median : 41.00   Median : 35.00   Median :46.00
Mean   :10.68      Mean   : 38.96   Mean   : 32.25   Mean   :48.31
3rd Qu.:13.00      3rd Qu.: 56.00   3rd Qu.: 51.00   3rd Qu.:60.00
Max.   :22.00      Max.   : 71.00   Max.   : 68.00   Max.   :96.00

Min.Sea.Level.PressureIn Min.TemperatureF Min.VisibilityMiles PrecipitationIn
Min.   :29.16            Min.   :-3.00    Min.   : 0.000      Min.   :0.0000
1st Qu.:29.76            1st Qu.:30.00    1st Qu.: 2.000      1st Qu.:0.0000
Median :29.94            Median :46.00    Median :10.000      Median :0.0000
Mean   :29.93            Mean   :43.33    Mean   : 6.716      Mean   :0.1016
3rd Qu.:30.09            3rd Qu.:60.00    3rd Qu.:10.000      3rd Qu.:0.0400
Max.   :30.64            Max.   :74.00    Max.   :10.000      Max.   :2.9000

WindDirDegrees
Min.   :  1.0
1st Qu.:113.0
Median :222.0
Mean   :200.1
3rd Qu.:275.0
Max.   :360.0
``````
dateEventsCloudCoverMax.Dew.PointFMax.Gust.SpeedMPHMax.HumidityMax.Sea.Level.PressureInMax.TemperatureFMax.VisibilityMilesMax.Wind.SpeedMPH...Mean.VisibilityMilesMean.Wind.SpeedMPHMeanDew.PointFMin.DewpointFMin.HumidityMin.Sea.Level.PressureInMin.TemperatureFMin.VisibilityMilesPrecipitationInWindDirDegrees
1352015-04-21 Fog-Rain-Thunderstorm6 57 94 1000 29.75 65 10 20 ... 5 10 49 36 42 29.53 46 0 0.54 184

Once you find obvious errors, it’s not too hard to fix them if you know which values they should take.

## Another obvious error

We’ve discovered and repaired one obvious error in the data, but it appears that there’s another. Sometimes we get lucky and can infer the correct or intended value from the other data. For example, if you know the minimum and maximum values of a particular metric on a given day…

``````# Look at summary of Mean.VisibilityMiles
summary(weather6\$Mean.VisibilityMiles)

# Get index of row with -1 value
ind <- which(weather6\$Mean.VisibilityMiles == -1)

# Look at full row
weather6[ind,]

# Set Mean.VisibilityMiles to the appropriate value
weather6\$Mean.VisibilityMiles[ind] <- 10``````
``````   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
-1.000   8.000  10.000   8.861  10.000  10.000 ``````
dateEventsCloudCoverMax.Dew.PointFMax.Gust.SpeedMPHMax.HumidityMax.Sea.Level.PressureInMax.TemperatureFMax.VisibilityMilesMax.Wind.SpeedMPH...Mean.VisibilityMilesMean.Wind.SpeedMPHMeanDew.PointFMin.DewpointFMin.HumidityMin.Sea.Level.PressureInMin.TemperatureFMin.VisibilityMilesPrecipitationInWindDirDegrees
1922015-06-18 5 54 23 72 30.14 76 10 17 ... -1 10 49 45 46 29.93 57 10 0 189

Our data are looking tidy. Just a quick sanity check left!

## Check other extreme values

In addition to dealing with obvious errors in the data, we want to see if there are other extreme values. In addition to the trusty `summary()` function, `hist()` is useful for quickly getting a feel for how different variables are distributed.

``````# Review summary of full data once more
summary(weather6)

# Look at histogram for MeanDew.PointF
hist(weather6\$MeanDew.PointF)

# Look at histogram for Min.TemperatureF
hist(weather6\$Min.TemperatureF)

# Compare to histogram for Mean.TemperatureF
hist(weather6\$Mean.TemperatureF)``````
``````      date               Events            CloudCover    Max.Dew.PointF
Min.   :2014-12-01   Length:366         Min.   :0.000   Min.   :-6.00
1st Qu.:2015-03-02   Class :character   1st Qu.:3.000   1st Qu.:32.00
Median :2015-06-01   Mode  :character   Median :5.000   Median :47.50
Mean   :2015-06-01                      Mean   :4.708   Mean   :45.48
3rd Qu.:2015-08-31                      3rd Qu.:7.000   3rd Qu.:61.00
Max.   :2015-12-01                      Max.   :8.000   Max.   :75.00

Max.Gust.SpeedMPH  Max.Humidity    Max.Sea.Level.PressureIn Max.TemperatureF
Min.   : 0.00     Min.   : 39.00   Min.   :29.58            Min.   :18.00
1st Qu.:21.00     1st Qu.: 73.25   1st Qu.:30.00            1st Qu.:42.00
Median :25.50     Median : 86.00   Median :30.14            Median :60.00
Mean   :26.99     Mean   : 83.23   Mean   :30.16            Mean   :58.93
3rd Qu.:31.25     3rd Qu.: 93.00   3rd Qu.:30.31            3rd Qu.:76.00
Max.   :94.00     Max.   :100.00   Max.   :30.88            Max.   :96.00
NA's   :6
Max.VisibilityMiles Max.Wind.SpeedMPH Mean.Humidity
Min.   : 2.000      Min.   : 8.00     Min.   :28.00
1st Qu.:10.000      1st Qu.:16.00     1st Qu.:56.00
Median :10.000      Median :20.00     Median :66.00
Mean   : 9.907      Mean   :20.62     Mean   :66.02
3rd Qu.:10.000      3rd Qu.:24.00     3rd Qu.:76.75
Max.   :10.000      Max.   :38.00     Max.   :98.00

Mean.Sea.Level.PressureIn Mean.TemperatureF Mean.VisibilityMiles
Min.   :29.49             Min.   : 8.00     Min.   : 1.000
1st Qu.:29.87             1st Qu.:36.25     1st Qu.: 8.000
Median :30.03             Median :53.50     Median :10.000
Mean   :30.04             Mean   :51.40     Mean   : 8.891
3rd Qu.:30.19             3rd Qu.:68.00     3rd Qu.:10.000
Max.   :30.77             Max.   :84.00     Max.   :10.000

Mean.Wind.SpeedMPH MeanDew.PointF   Min.DewpointF     Min.Humidity
Min.   : 4.00      Min.   :-11.00   Min.   :-18.00   Min.   :16.00
1st Qu.: 8.00      1st Qu.: 24.00   1st Qu.: 16.25   1st Qu.:35.00
Median :10.00      Median : 41.00   Median : 35.00   Median :46.00
Mean   :10.68      Mean   : 38.96   Mean   : 32.25   Mean   :48.31
3rd Qu.:13.00      3rd Qu.: 56.00   3rd Qu.: 51.00   3rd Qu.:60.00
Max.   :22.00      Max.   : 71.00   Max.   : 68.00   Max.   :96.00

Min.Sea.Level.PressureIn Min.TemperatureF Min.VisibilityMiles PrecipitationIn
Min.   :29.16            Min.   :-3.00    Min.   : 0.000      Min.   :0.0000
1st Qu.:29.76            1st Qu.:30.00    1st Qu.: 2.000      1st Qu.:0.0000
Median :29.94            Median :46.00    Median :10.000      Median :0.0000
Mean   :29.93            Mean   :43.33    Mean   : 6.716      Mean   :0.1016
3rd Qu.:30.09            3rd Qu.:60.00    3rd Qu.:10.000      3rd Qu.:0.0400
Max.   :30.64            Max.   :74.00    Max.   :10.000      Max.   :2.9000

WindDirDegrees
Min.   :  1.0
1st Qu.:113.0
Median :222.0
Mean   :200.1
3rd Qu.:275.0
Max.   :360.0
``````

It looks like you have sufficiently tidied your data!

# Finishing touches

Before officially calling our weather data clean, we want to put a couple of finishing touches on the data. These are a bit more subjective and may not be necessary for analysis, but they will make the data easier for others to interpret, which is generally a good thing.

There are a number of stylistic conventions in the R language. Depending on who you ask, these conventions may vary. Because the period (`.`) has special meaning in certain situations, we will be using underscores (`_`) to separate words in variable names. We also prefer all lowercase letters so that no one has to remember which letters are uppercase or lowercase.

Finally, the `events` column (renamed to be all lowercase in the first instruction) contains an empty string (““) for any day on which there was no significant weather event such as rain, fog, a thunderstorm, etc. However, if it’s the first time you’re seeing these data, it may not be obvious that this is the case, so it’s best for us to be explicit and replace the empty strings with something more meaningful.

``````new_colnames = c("date", "events",
"cloud_cover", "max_dew_point_f",
"max_gust_speed_mph", "max_humidity",
"max_sea_level_pressure_in", "max_temperature_f",
"max_visibility_miles", "max_wind_speed_mph",
"mean_humidity", "mean_sea_level_pressure_in",
"mean_temperature_f", "mean_visibility_miles",
"mean_wind_speed_mph", "mean_dew_point_f",
"min_dew_point_f", "min_humidity",
"min_sea_level_pressure_in", "min_temperature_f",
"min_visibility_miles", "precipitation_in","wind_dir_degrees")``````
``````# Clean up column names
names(weather6) <- new_colnames

# Replace empty cells in events column
weather6\$events[weather6\$events == ""] <- "None"

# Print the first 6 rows of weather6
dateeventscloud_covermax_dew_point_fmax_gust_speed_mphmax_humiditymax_sea_level_pressure_inmax_temperature_fmax_visibility_milesmax_wind_speed_mph...mean_visibility_milesmean_wind_speed_mphmean_dew_point_fmin_dew_point_fmin_humiditymin_sea_level_pressure_inmin_temperature_fmin_visibility_milesprecipitation_inwind_dir_degrees
2014-12-01Rain 6 46 29 74 30.45 64 10 22 ... 10 13 40 26 52 30.01 39 10 0.01 268
2014-12-10Rain 8 45 29 100 29.58 48 10 23 ... 3 13 39 37 89 29.43 38 1 0.28 357
2014-12-11Rain-Snow 8 37 28 92 29.81 39 10 21 ... 7 13 31 27 82 29.44 32 1 0.02 230
2014-12-12Snow 7 28 21 85 29.88 39 10 16 ... 10 11 27 25 64 29.81 31 7 0.00 286
2014-12-13None 5 28 23 75 29.86 42 10 17 ... 10 12 26 24 55 29.78 32 10 0.00 298
2014-12-14None 4 29 20 82 29.91 45 10 15 ... 10 10 27 25 53 29.78 33 10 0.00 306
``tail(weather6)``
dateeventscloud_covermax_dew_point_fmax_gust_speed_mphmax_humiditymax_sea_level_pressure_inmax_temperature_fmax_visibility_milesmax_wind_speed_mph...mean_visibility_milesmean_wind_speed_mphmean_dew_point_fmin_dew_point_fmin_humiditymin_sea_level_pressure_inmin_temperature_fmin_visibility_milesprecipitation_inwind_dir_degrees
3612015-11-05None 4 61 31 100 30.30 76 10 22 ... 9 12 55 48 53 30.09 50 5 0.00 224
3622015-11-06None 4 62 32 93 30.07 73 10 26 ... 10 15 61 54 64 29.71 62 10 0.00 222
3632015-11-07None 6 45 33 57 30.02 69 10 25 ... 10 13 38 33 39 29.83 50 10 0.00 280
3642015-11-08None 0 34 25 65 30.38 56 10 18 ... 10 12 30 24 30 30.04 44 10 0.00 283
3652015-11-09None 2 36 20 70 30.43 60 10 16 ... 10 9 32 30 33 30.32 41 10 0.00 237
3662015-12-01Rain 7 43 17 96 30.40 45 10 15 ... 8 6 35 25 69 30.01 32 1 0.14 109
``str(weather6)``
``````'data.frame':   366 obs. of  23 variables:
\$ date                      : Date, format: "2014-12-01" "2014-12-10" ...
\$ events                    : chr  "Rain" "Rain" "Rain-Snow" "Snow" ...
\$ cloud_cover               : num  6 8 8 7 5 4 2 8 8 7 ...
\$ max_dew_point_f           : num  46 45 37 28 28 29 33 42 46 34 ...
\$ max_gust_speed_mph        : num  29 29 28 21 23 20 21 10 26 30 ...
\$ max_humidity              : num  74 100 92 85 75 82 89 96 100 89 ...
\$ max_sea_level_pressure_in : num  30.4 29.6 29.8 29.9 29.9 ...
\$ max_temperature_f         : num  64 48 39 39 42 45 42 44 49 44 ...
\$ max_visibility_miles      : num  10 10 10 10 10 10 10 10 10 10 ...
\$ max_wind_speed_mph        : num  22 23 21 16 17 15 15 8 20 23 ...
\$ mean_humidity             : num  63 95 87 75 65 68 75 85 85 73 ...
\$ mean_sea_level_pressure_in: num  30.1 29.5 29.6 29.9 29.8 ...
\$ mean_temperature_f        : num  52 43 36 35 37 39 37 40 45 40 ...
\$ mean_visibility_miles     : num  10 3 7 10 10 10 10 9 6 10 ...
\$ mean_wind_speed_mph       : num  13 13 13 11 12 10 6 4 11 14 ...
\$ mean_dew_point_f          : num  40 39 31 27 26 27 29 36 41 30 ...
\$ min_dew_point_f           : num  26 37 27 25 24 25 27 30 32 26 ...
\$ min_humidity              : num  52 89 82 64 55 53 60 73 70 57 ...
\$ min_sea_level_pressure_in : num  30 29.4 29.4 29.8 29.8 ...
\$ min_temperature_f         : num  39 38 32 31 32 33 32 35 41 36 ...
\$ min_visibility_miles      : num  10 1 1 7 10 10 10 5 1 10 ...
\$ precipitation_in          : num  0.01 0.28 0.02 0 0 0 0 0 0.43 0.01 ...
\$ wind_dir_degrees          : num  268 357 230 286 298 306 324 79 311 281 ...``````
``glimpse(weather6)``
``````Rows: 366
Columns: 23
\$ date                       <date> 2014-12-01, 2014-12-10, 2014-12-11, 201...
\$ events                     <chr> "Rain", "Rain", "Rain-Snow", "Snow", "No...
\$ cloud_cover                <dbl> 6, 8, 8, 7, 5, 4, 2, 8, 8, 7, 4, 7, 6, 8...
\$ max_dew_point_f            <dbl> 46, 45, 37, 28, 28, 29, 33, 42, 46, 34, ...
\$ max_gust_speed_mph         <dbl> 29, 29, 28, 21, 23, 20, 21, 10, 26, 30, ...
\$ max_humidity               <dbl> 74, 100, 92, 85, 75, 82, 89, 96, 100, 89...
\$ max_sea_level_pressure_in  <dbl> 30.45, 29.58, 29.81, 29.88, 29.86, 29.91...
\$ max_temperature_f          <dbl> 64, 48, 39, 39, 42, 45, 42, 44, 49, 44, ...
\$ max_visibility_miles       <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, ...
\$ max_wind_speed_mph         <dbl> 22, 23, 21, 16, 17, 15, 15, 8, 20, 23, 1...
\$ mean_humidity              <dbl> 63, 95, 87, 75, 65, 68, 75, 85, 85, 73, ...
\$ mean_sea_level_pressure_in <dbl> 30.13, 29.50, 29.61, 29.85, 29.82, 29.83...
\$ mean_temperature_f         <dbl> 52, 43, 36, 35, 37, 39, 37, 40, 45, 40, ...
\$ mean_visibility_miles      <dbl> 10, 3, 7, 10, 10, 10, 10, 9, 6, 10, 10, ...
\$ mean_wind_speed_mph        <dbl> 13, 13, 13, 11, 12, 10, 6, 4, 11, 14, 11...
\$ mean_dew_point_f           <dbl> 40, 39, 31, 27, 26, 27, 29, 36, 41, 30, ...
\$ min_dew_point_f            <dbl> 26, 37, 27, 25, 24, 25, 27, 30, 32, 26, ...
\$ min_humidity               <dbl> 52, 89, 82, 64, 55, 53, 60, 73, 70, 57, ...
\$ min_sea_level_pressure_in  <dbl> 30.01, 29.43, 29.44, 29.81, 29.78, 29.78...
\$ min_temperature_f          <dbl> 39, 38, 32, 31, 32, 33, 32, 35, 41, 36, ...
\$ min_visibility_miles       <dbl> 10, 1, 1, 7, 10, 10, 10, 5, 1, 10, 10, 2...
\$ precipitation_in           <dbl> 0.01, 0.28, 0.02, 0.00, 0.00, 0.00, 0.00...
\$ wind_dir_degrees           <dbl> 268, 357, 230, 286, 298, 306, 324, 79, 3...``````
``summary(weather6)``
``````      date               events           cloud_cover    max_dew_point_f
Min.   :2014-12-01   Length:366         Min.   :0.000   Min.   :-6.00
1st Qu.:2015-03-02   Class :character   1st Qu.:3.000   1st Qu.:32.00
Median :2015-06-01   Mode  :character   Median :5.000   Median :47.50
Mean   :2015-06-01                      Mean   :4.708   Mean   :45.48
3rd Qu.:2015-08-31                      3rd Qu.:7.000   3rd Qu.:61.00
Max.   :2015-12-01                      Max.   :8.000   Max.   :75.00

max_gust_speed_mph  max_humidity    max_sea_level_pressure_in
Min.   : 0.00      Min.   : 39.00   Min.   :29.58
1st Qu.:21.00      1st Qu.: 73.25   1st Qu.:30.00
Median :25.50      Median : 86.00   Median :30.14
Mean   :26.99      Mean   : 83.23   Mean   :30.16
3rd Qu.:31.25      3rd Qu.: 93.00   3rd Qu.:30.31
Max.   :94.00      Max.   :100.00   Max.   :30.88
NA's   :6
max_temperature_f max_visibility_miles max_wind_speed_mph mean_humidity
Min.   :18.00     Min.   : 2.000       Min.   : 8.00      Min.   :28.00
1st Qu.:42.00     1st Qu.:10.000       1st Qu.:16.00      1st Qu.:56.00
Median :60.00     Median :10.000       Median :20.00      Median :66.00
Mean   :58.93     Mean   : 9.907       Mean   :20.62      Mean   :66.02
3rd Qu.:76.00     3rd Qu.:10.000       3rd Qu.:24.00      3rd Qu.:76.75
Max.   :96.00     Max.   :10.000       Max.   :38.00      Max.   :98.00

mean_sea_level_pressure_in mean_temperature_f mean_visibility_miles
Min.   :29.49              Min.   : 8.00      Min.   : 1.000
1st Qu.:29.87              1st Qu.:36.25      1st Qu.: 8.000
Median :30.03              Median :53.50      Median :10.000
Mean   :30.04              Mean   :51.40      Mean   : 8.891
3rd Qu.:30.19              3rd Qu.:68.00      3rd Qu.:10.000
Max.   :30.77              Max.   :84.00      Max.   :10.000

mean_wind_speed_mph mean_dew_point_f min_dew_point_f   min_humidity
Min.   : 4.00       Min.   :-11.00   Min.   :-18.00   Min.   :16.00
1st Qu.: 8.00       1st Qu.: 24.00   1st Qu.: 16.25   1st Qu.:35.00
Median :10.00       Median : 41.00   Median : 35.00   Median :46.00
Mean   :10.68       Mean   : 38.96   Mean   : 32.25   Mean   :48.31
3rd Qu.:13.00       3rd Qu.: 56.00   3rd Qu.: 51.00   3rd Qu.:60.00
Max.   :22.00       Max.   : 71.00   Max.   : 68.00   Max.   :96.00

min_sea_level_pressure_in min_temperature_f min_visibility_miles
Min.   :29.16             Min.   :-3.00     Min.   : 0.000
1st Qu.:29.76             1st Qu.:30.00     1st Qu.: 2.000
Median :29.94             Median :46.00     Median :10.000
Mean   :29.93             Mean   :43.33     Mean   : 6.716
3rd Qu.:30.09             3rd Qu.:60.00     3rd Qu.:10.000
Max.   :30.64             Max.   :74.00     Max.   :10.000

precipitation_in wind_dir_degrees
Min.   :0.0000   Min.   :  1.0
1st Qu.:0.0000   1st Qu.:113.0
Median :0.0000   Median :222.0
Mean   :0.1016   Mean   :200.1
3rd Qu.:0.0400   3rd Qu.:275.0
Max.   :2.9000   Max.   :360.0
``````