## Subtracting mean of every two columns from two dataframes R

Suppose I have two data frames as follows:

df1 <- data.frame(ceiling(runif(10,1,10)), ceiling(runif(10,1,10)), ceiling(runif(10,1,10))) colnames(df1) <- c("V1","V2","V3") df2 <- data.frame(ceiling(runif(10,1,10)), ceiling(runif(10,1,10)), ceiling(runif(10,1,10))) colnames(df2) <- c("V1","V2","V3")

Using this dummy data, I want to create a new dataframe with 1 column and 3 rows:

V1 1 mean(df1$V1) - mean(df2$V1) 2 mean(df1$V2) - mean(df2$V2) 3 mean(df1$V3) - mean(df2$V3)

I also want to create another dataframe as follows:

V1 1 wilcox.test(df1$V1,df2$V1)$p.value 2 wilcox.test(df1$V2,df2$V2)$p.value 3 wilcox.test(df1$V3,df2$V3)$p.value

My real data has 54 columns, so for my data each dataframe would be of 54 rows.

Means:

data.frame(mean = colMeans(df1) - colMeans(df2)) # mean # V1 1.4 # V2 2.0 # V3 1.4

P-values:

data.frame( p.value = mapply(function(x, y) wilcox.test(x, y)$p.value, df1, df2) ) # p.value # V1 0.32060365 # V2 0.07784363 # V3 0.21779915

**Match columns in two different data frames and subtract ,** Match columns in two different data frames and subtract corresponding values I am a newbie to R but I am slowly discovering its' potential and its' handiness and I The second data set is composed of the average y-values for every y-value Now that you’ve reviewed the rules for creating subsets, you can try it with some data frames in R. You just have to remember that a data frame is a two-dimensional object and contains rows as well as columns. This means that you need to specify the subset for rows and columns independently. To do …

Q1

data.frame(mean=sapply(df1, mean)-sapply(df2,mean))

Q2

out <- NULL for(i in 1:ncol(df1)) out[[i]] <- wilcox.test(df1[,i], df2[,i])$p.value data.frame(p=unlist(out))

**Understanding and Applying Basic Statistical Methods Using R,** For the data in Exercise 9, use R to subtract the average from each value, and then data set called chickwts, which is stored in a data frame with two columns. Hi I am confused how to do this. I have two tables and each table has an "Amount" colum. I need to sum up all the rows for column "Amount" in table A and sum up all the rows for column "Amount" in

You can do it using a vector of ones:

m1 = (t(df1) %*% rep(1, nrow(df1))) / nrow(df1) # Equivalent to a mean m2 = (t(df2) %*% rep(1, nrow(df2))) / nrow(df2) m1-m2

**how to subtract a column to the other colums in a data frame,** R does math in a matrix element-wise. That means if you have subtract two matrices, it subtracts the values in each spot in the two matrices from each other. DataFrame.subtract (self, other, axis = 'columns', level = None, fill_value = None) [source] ¶ Get Subtraction of dataframe and other, element-wise (binary operator sub ). Equivalent to dataframe - other , but with support to substitute a fill_value for missing data in one of the inputs.

Here's a `tidyverse`

approach to create a table with info about the tests you've performed:

# for reproducibility set.seed(215) # example datasets df1 <- data.frame(ceiling(runif(10,1,10)), ceiling(runif(10,1,10)), ceiling(runif(10,1,10))) colnames(df1) <- c("V1","V2","V3") df2 <- data.frame(ceiling(runif(10,1,10)), ceiling(runif(10,1,10)), ceiling(runif(10,1,10))) colnames(df2) <- c("V1","V2","V3") library(tidyverse) list(df1, df2) %>% # put your dataframes in a list map_df(data.frame, .id = "df") %>% # create a dataframe with an id value for each dataset tbl_df() %>% # for visualisation purposes only gather(v, x, -df) %>% # reshape data nest(-v) %>% # nest data mutate(w.t = map(data, ~wilcox.test(.x$x ~ .x$df)), # perfom wilcoxon test pval = map_dbl(w.t, "p.value"), # extract p value mean_diff = map_dbl(data, ~mean(.x$x[.x$df==1])-mean(.x$x[.x$df==2]))) # calculate mean difference # # A tibble: 3 x 5 # v data w.t pval mean_diff # <chr> <list> <list> <dbl> <dbl> # 1 V1 <tibble [20 x 2]> <S3: htest> 0.730 0.600 # 2 V2 <tibble [20 x 2]> <S3: htest> 0.145 -1.8 # 3 V3 <tibble [20 x 2]> <S3: htest> 0.0295 2.8

Column `v`

represents your variables (initial columns).

Column `data`

includes the variables used for the corresponding test.

Column `w.t`

includes the test output.

Column `pval`

is the extracted p value from each test.

Column `mean_diff`

is the mean difference.

If you save the above process as `results`

you'll be able to use `results$w.t`

and see the test outputs

**Manipulating, analyzing and exporting data with tidyverse,** Select certain columns in a data frame with the dplyr function select . Packages in R are basically sets of additional functions that let you do more stuff. for example to do unit conversions, or to find the ratio of values in two columns. Use group_by() and summarize() to find the mean, min, and max hindfoot length for Dear R helpers I have a dataframe as df = data.frame(x = c(1, 14, 3, 21, 11), y = c(102, 500, 40, 101, 189)) > df x y 1 1 102 2 14 500 3 3 40 4 21 101 5 11 189 # Actually I am having dataframe having multiple columns. I am just giving an example.

**Introduction to R Exercise Answers,** I'll use this notebook to provide worked answers to all of the exercises we did in up and we will see the subtraction results from the same positions, ie 2-5, 5-6, Join the two vectors into a data frame called mouse.info containing 2 columns Use this logical vector to define which rows to keep in a standard 2 dimension The main difference between the two is that when you input a data frame into sapply(), it’s treated as a list of columns. Whereas apply() can basically treat the data frame as a list of columns (MARGIN=2) or rows (MARGIN=1). Below is an example of apply() to find the max value in each column and [soucecode lang=”splus”] ~~~~~

**5 Data Wrangling via dplyr,** In other words, the observational unit of the flights tidy data frame is a flight. the rows based on one or more variables; join() : Join/merge two data frames by and mean of the temperature variable temp in the weather data frame of and is how R encodes missing values; if in a data frame for a particular row and column Pandas dataframe.subtract () function is used for finding the subtraction of dataframe and other, element-wise. This function is essentially same as doing dataframe - other but with a support to substitute for missing data in one of the inputs. Syntax: DataFrame.subtract (other, axis=’columns’, level=None, fill_value=None)

**Exploratory Data Analysis with R,** Here we create a pm25detrend variable that subtracts the mean from the pm25 -5.393846 Note that there are only two columns in the transmuted data frame. column subtraction by row. sum or mean. Plus, all the apply/lapply examples I'm looking at seem to depend on a data frame being just the two columns on which to