library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
- Read in the
ChickWeight dataset. This data is from an
experiment on the effect of diet on early growth of chicks. Call it
chicks.
chicks <- datasets::ChickWeight
- Reshape to wide format using
pivot_wider. Use the
arguments names_from = Time and
values_from = weight. Include the following in the
pivot_wider function: names_prefix = "Time_"
to update all the new column names. Call the new data
chicks_wide.
chicks_wide <- chicks %>% pivot_wider(names_from = Time, values_from = weight, names_prefix = "Time_")
chicks_wide
## # A tibble: 50 × 14
## Chick Diet Time_0 Time_2 Time_4 Time_6 Time_8 Time_10 Time_12 Time_14
## <ord> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 42 51 59 64 76 93 106 125
## 2 2 1 40 49 58 72 84 103 122 138
## 3 3 1 43 39 55 67 84 99 115 138
## 4 4 1 42 49 56 67 74 87 102 108
## 5 5 1 41 42 48 60 79 106 141 164
## 6 6 1 41 49 59 74 97 124 141 148
## 7 7 1 41 49 57 71 89 112 146 174
## 8 8 1 42 50 61 71 84 93 110 116
## 9 9 1 42 51 59 68 85 96 90 92
## 10 10 1 41 44 52 63 74 81 89 96
## # ℹ 40 more rows
## # ℹ 4 more variables: Time_16 <dbl>, Time_18 <dbl>, Time_20 <dbl>,
## # Time_21 <dbl>
- Filter the data so that
Time_0 is <= 40 and
Time_21 >= 200. Reassign to
chicks_wide.
chicks_wide <- chicks_wide %>% filter(Time_0 <= 40 & Time_21 >= 200)
chicks_wide
## # A tibble: 9 × 14
## Chick Diet Time_0 Time_2 Time_4 Time_6 Time_8 Time_10 Time_12 Time_14 Time_16
## <ord> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2 1 40 49 58 72 84 103 122 138 162
## 2 21 2 40 50 62 86 125 163 217 240 275
## 3 25 2 40 49 62 78 102 124 146 164 197
## 4 28 2 39 46 58 73 92 114 145 156 184
## 5 29 2 39 48 59 74 87 106 134 150 187
## 6 36 3 39 48 61 76 98 116 145 166 198
## 7 46 4 40 52 62 82 101 120 144 156 173
## 8 48 4 39 50 62 80 104 125 154 170 222
## 9 49 4 40 53 64 85 108 128 152 166 184
## # ℹ 3 more variables: Time_18 <dbl>, Time_20 <dbl>, Time_21 <dbl>
- Select columns Chick, Diet,
Time_0, and
Time_21. Reassign to chicks_wide.
chicks_wide <- chicks_wide %>% select(Chick, Diet, Time_0, Time_21)
chicks_wide
## # A tibble: 9 × 4
## Chick Diet Time_0 Time_21
## <ord> <fct> <dbl> <dbl>
## 1 2 1 40 215
## 2 21 2 40 331
## 3 25 2 40 265
## 4 28 2 39 233
## 5 29 2 39 309
## 6 36 3 39 220
## 7 46 4 40 238
## 8 48 4 39 322
## 9 49 4 40 237
- Reshape to long format using
pivot_longer. Pivot the
numeric columns. Use the arguments names_to = and
values_to =. Call the data chicks_long.
chicks_wide %>% pivot_longer(c(Time_0, Time_21), names_to = "Time", values_to = "weight")
## # A tibble: 18 × 4
## Chick Diet Time weight
## <ord> <fct> <chr> <dbl>
## 1 2 1 Time_0 40
## 2 2 1 Time_21 215
## 3 21 2 Time_0 40
## 4 21 2 Time_21 331
## 5 25 2 Time_0 40
## 6 25 2 Time_21 265
## 7 28 2 Time_0 39
## 8 28 2 Time_21 233
## 9 29 2 Time_0 39
## 10 29 2 Time_21 309
## 11 36 3 Time_0 39
## 12 36 3 Time_21 220
## 13 46 4 Time_0 40
## 14 46 4 Time_21 238
## 15 48 4 Time_0 39
## 16 48 4 Time_21 322
## 17 49 4 Time_0 40
## 18 49 4 Time_21 237