Chapter 11 Assignments part I

Try to make a histogram or a density plot on the basis of your own data! If you have multiple groups in your data, try to compare densities! If you did not send in your own data, you can try any of the dataframes below; the “Van Rooij” data might be good for practice!

11.1 Hagoort

library(tidyverse)
# install.packages("openxlsx")
library(openxlsx)

df_Ha <- read.xlsx("https://stulp.gmw.rug.nl/21-03-2019/ggplotworkshop/data/Hagoort.xlsx",
                   sheet = "Gait parameters ", startRow = 2)
# Note; typically I would use the readxl-package, but this package
# Does not allow you to read in online-excel files

# It is wise not to have your variables be named identically! Let's fix this
df_Ha <- as_tibble(df_Ha, .name_repair = "unique")

11.2 Hogelin

library(tidyverse)
# install.packages("openxlsx")
library(openxlsx)

df_Ho <- read.xlsx("https://stulp.gmw.rug.nl/21-03-2019/ggplotworkshop/data/Hogeling.xlsx")
# Note; typically I would use the readxl-package, but this package
# Does not allow you to read in online-excel files

# It is wise not to have your variable be named identically! 
df_Ho <- as_tibble(df_Ha, .name_repair = "unique")

df_Ho_red <- df_Ho %>% select(1:100) %>% slice(1:100) %>% # select 100 vars & cases
  mutate_at(2:99, as.numeric) # turn to numeric variables

11.3 Maat

library(tidyverse)

# The "bed"-file; has no column names! 
df_Ma1 <- read_delim("https://stulp.gmw.rug.nl/21-03-2019/ggplotworkshop/data/mobPBSC_H3K4me3_D2_peaks.bed",
                     delim = "\t", col_names = FALSE)

# wig is a funky format that I don't understand! 
# rtracklayer seems like a package needed

11.4 Van Rooij

library(tidyverse)
# install.packages("openxlsx")
library(openxlsx)

df_Ro <- read.xlsx("https://stulp.gmw.rug.nl/21-03-2019/ggplotworkshop/data/Van%20Rooij.xlsx",
                   startRow = 3)
# Note; typically I would use the readxl-package, but this package
# Does not allow you to read in online-excel files

11.5 Santhakumar

library(tidyverse)
# install.packages("openxlsx")
library(openxlsx)

# I've turned your xls file into xlsx
df_Sa1 <- read.xlsx("https://stulp.gmw.rug.nl/21-03-2019/ggplotworkshop/data/Santhakumar1.xlsx",
                    sheet = "Data", startRow = 4)
# Note; typically I would use the readxl-package, but this package
# Does not allow you to read in online-excel files

df_Sa2 <- read_csv("https://stulp.gmw.rug.nl/21-03-2019/ggplotworkshop/data/Santhakumar2.csv")

11.6 Seibel

library(tidyverse)
# install.packages("haven")
library(haven)

df_Se <- read_sav("https://stulp.gmw.rug.nl/21-03-2019/ggplotworkshop/data/Seibel.sav")

# SPSS handles labels of categorical variables a bit differently than R. 
# It’s better to convert all labelled variables into factors, 
# that R can more easily deal with (you don’t have to do this though!). 
# You can do this simply by:

df_Se <- as_factor(df_Se)

11.7 Zhang

library(tidyverse)
# install.packages("openxlsx")
library(openxlsx)


df_Za1 <- read.xlsx("https://stulp.gmw.rug.nl/21-03-2019/ggplotworkshop/data/Zhang.xlsx")
# I had to make the below excel file compatible with R by copying data to new sheet
df_Za2 <- read.xlsx("https://stulp.gmw.rug.nl/21-03-2019/ggplotworkshop/data/Zhang2.xlsx",
                    sheet = "Sheet1")

# Note; typically I would use the readxl-package, but this package
# Does not allow you to read in online-excel files