mtcars
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
aggregate(mtcars$mpg, list(mtcars$am), mean)
## Group.1 x
## 1 0 17.14737
## 2 1 24.39231
library(readxl)
DataDan <- read_excel("D:/NURSING/2021/Dan/DataDan.xlsx")
## New names:
## * `` -> ...25
## * `` -> ...26
## * `` -> ...27
## * `` -> ...28
DataDan
## # A tibble: 100 x 28
## Timestamp `I have read, understood ~` `Name (Optiona~` Age Gender
## <dttm> <chr> <lgl> <dbl> <chr>
## 1 2021-05-12 17:41:46 Agree NA 18 Female
## 2 2021-05-18 07:57:59 Agree NA 17 Female
## 3 2021-05-18 08:19:13 Agree NA 18 Female
## 4 2021-05-18 08:22:07 Agree NA 16 Female
## 5 2021-05-18 08:37:43 Agree NA 17 Female
## 6 2021-05-18 08:48:36 Agree NA 18 Female
## 7 2021-05-18 08:54:00 Agree NA 19 Female
## 8 2021-05-18 08:56:50 Agree NA 17 Female
## 9 2021-05-18 08:57:40 Agree NA 17 Female
## 10 2021-05-18 08:58:19 Agree NA 18 Female
## # ... with 90 more rows, and 23 more variables: `Social support` <chr>,
## # `Living condition1` <chr>, `Family income per month` <chr>,
## # `1. Feeling nervous, anxious, or on edge` <dbl>,
## # `2. Not being able to stop or control worrying` <dbl>,
## # `3. Worrying too much about different things` <dbl>,
## # `4. Trouble relaxing` <dbl>,
## # `5. Being so restless that it is hard to sit still` <dbl>, ...
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
mtcars%>%
group_by(am)%>%
summarize(count = n(), meanmpg = mean(mpg))
## # A tibble: 2 x 3
## am count meanmpg
## <dbl> <int> <dbl>
## 1 0 19 17.1
## 2 1 13 24.4
library(dplyr)
mtcars%>%
group_by(am, cyl)%>%
summarize(count = n(), meanmpg = mean(mpg))
## `summarise()` has grouped output by 'am'. You can override using the `.groups`
## argument.
## # A tibble: 6 x 4
## # Groups: am [2]
## am cyl count meanmpg
## <dbl> <dbl> <int> <dbl>
## 1 0 4 3 22.9
## 2 0 6 4 19.1
## 3 0 8 12 15.0
## 4 1 4 8 28.1
## 5 1 6 3 20.6
## 6 1 8 2 15.4
library(dplyr)
mtcars%>%
group_by(am, cyl, gear)%>%
summarize(count = n(), meanmpg = mean(mpg), staandardd = sd(mpg))
## `summarise()` has grouped output by 'am', 'cyl'. You can override using the
## `.groups` argument.
## # A tibble: 10 x 6
## # Groups: am, cyl [6]
## am cyl gear count meanmpg staandardd
## <dbl> <dbl> <dbl> <int> <dbl> <dbl>
## 1 0 4 3 1 21.5 NA
## 2 0 4 4 2 23.6 1.13
## 3 0 6 3 2 19.8 2.33
## 4 0 6 4 2 18.5 0.990
## 5 0 8 3 12 15.0 2.77
## 6 1 4 4 6 28.0 5.12
## 7 1 4 5 2 28.2 3.11
## 8 1 6 4 2 21 0
## 9 1 6 5 1 19.7 NA
## 10 1 8 5 2 15.4 0.566