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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
library(readr)
library(tidyr)
part1 <- read_csv("https://raw.githubusercontent.com/mbtoomey/Biol_7263/main/Data/assignment6part1.csv")
## Rows: 2 Columns: 21
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): ID
## dbl (20): Sample1_Male_Control, Sample2_Male_Control, Sample3_Male_Control, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
part2 <- read_csv("https://raw.githubusercontent.com/mbtoomey/Biol_7263/main/Data/assignment6part2.csv")
## Rows: 1 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): ID
## dbl (16): Sample16.Treatment, Sample12.Control, Sample3.Control, Sample6.Tre...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
1. Pivot and merge these two data sets into a single tidy tibble.
Export this tibble as a .csv file saved to a folder called “Results”
folder within your R project.
part1NEW <- part1 %>% pivot_longer(cols = "Sample1_Male_Control":"Sample20_Female_Treatment", names_to = c("Sample#", "Sex", "TreatmentType"), names_sep = "_", values_drop_na = TRUE) %>% pivot_wider(names_from = ID, values_from = value)
part2NEW <- part2 %>% pivot_longer(cols = "Sample16.Treatment":"Sample13.Control", names_to = c("Sample#", "TreatmentType"), names_sep = "\\.", values_drop_na = TRUE) %>% pivot_wider(names_from = ID, values_from = value)
Finaldoc <- part1NEW %>% full_join(part2NEW, by = c("Sample#", "TreatmentType"))
Finaldoc
## # A tibble: 20 × 6
## `Sample#` Sex TreatmentType body_length age mass
## <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 Sample1 Male Control 1.68 3 NA
## 2 Sample2 Male Control 4.31 7 NA
## 3 Sample3 Male Control 4.54 11 8.46
## 4 Sample4 Male Control 1.09 8 3.87
## 5 Sample5 Male Control 3.55 10 3.12
## 6 Sample6 Male Treatment 8.19 4 9.38
## 7 Sample7 Male Treatment 8.45 8 7.14
## 8 Sample8 Male Treatment 0.893 11 7.41
## 9 Sample9 Male Treatment 1.10 9 NA
## 10 Sample10 Male Treatment 8.46 3 12.3
## 11 Sample11 Female Control 2.90 10 19.8
## 12 Sample12 Female Control 5.37 5 3.99
## 13 Sample13 Female Control 2.88 3 8.65
## 14 Sample14 Female Control 3.82 3 9.06
## 15 Sample15 Female Control 4.15 1 5.01
## 16 Sample16 Female Treatment 1.73 7 10.1
## 17 Sample17 Female Treatment 2.03 0 8.80
## 18 Sample18 Female Treatment 3.85 4 1.11
## 19 Sample19 Female Treatment 10.7 6 16.1
## 20 Sample20 Female Treatment 6.68 11 NA
2. With this tidy tibble, generate a new tibble of the mean +/-
standard deviation of the residual mass (mass/body length) by treatment
and sex. Export this tibble as a .csv file saved to a folder called
“Results” folder within your R project.
part2DOC <- Finaldoc %>% mutate(residual_mass = mass/body_length) %>% group_by(Sex, TreatmentType) %>% summarise(mean = mean(residual_mass, na.rm = TRUE), StandardDev = sd(residual_mass, na.rm = TRUE))
## `summarise()` has grouped output by 'Sex'. You can override using the `.groups`
## argument.
part2DOC
## # A tibble: 4 × 4
## # Groups: Sex [2]
## Sex TreatmentType mean StandardDev
## <chr> <chr> <dbl> <dbl>
## 1 Female Control 2.83 2.41
## 2 Female Treatment 2.99 2.54
## 3 Male Control 2.10 1.36
## 4 Male Treatment 2.93 3.58