# expect_output(as.character(string), 'hello')Ģ38) Making a package in R using devtools ( link)ĭevtools::test(filter = 'test.R') # where test.R is belowĢ40) Read in and write Stata (.dta) files Mat_empty_matrix <- matrix(data = 0, nrow = 100, ncol = 21 )ĭf_empty <- ame( mat_empty_matrix, stringsAsFactors = FALSE)Ģ35) Convert a data frame to a matrix using data.matrixĢ36) Show fit of linear model lm() using ggplotĢ37) Testing using testthat ( VERY GOOD link) ( link) ( link)Įxpect_match(string, 'hello', ignore.case = TRUE) (id=id, row > row, date_up > date_down),ĭt_egfr_with_age_merged_lithium <- dt_egfr_with_age_merged_lithiumĢ35) Leave one out cross validation in R using the loocv package ( link)Ģ36) Create an empty data frame initialise an empty data frame of a certain size (from stackoverflow) # For all rows of dt, create found_date by reference :=ĭt[.(.SD), # our subset (or another data.table), joined to. # TODO: do for each patient patid = temp_patient_id Subject_mortality_f20 = lubridate::year(subject_start_date) &Ģ26) Union of two tables (like union in SQL) using rbindlist ( link) # question: for each patient, does the drug occur?Ĭat("Thinking about drug:", drugname, "\n")Ĭount_this_drug_by_patient = 1, 1, 0) # does the drug occur?ĭrugval 300) ~ s(year,df=4) + s(age,df=4),īoxplot(frequency ~ attitude*gender, data=df_politeness, col=c("white","lightgray") )Ģ03) ggplot axis label tilt to help with visibility ( link) # - to take a list of drugs, and map them to a list of patients, Table(lubridate::year(mdat$Diagnosis_Last_date))ġ97) (CONTINUED FROM ABOVE) Convert data to long format using dcast (courtesy Shanquan Chen) ( link)ĭf_meds_summarized 0, 1, 0)}, value.var = 'drug')ġ98) (CONTINUED FROM ABOVE) Alternative using data.table Year in lubridate gets the year from a date Table command displays summary statistics (like frequency) Mdatġ96) (CONTINUED FROM ABOVE) Data summarization using dplyr and lubridate (courtesy Shanquan Chen) Sqldf::sqldf("select * from mdat where Diagnosis_Code like 'F20' ")ġ95) (CONTINUED FROM ABOVE) Pattern matching using stringr (courtesy Shanquan Chen) Hist(apply(data.matrix,1,var), breaks=30) Gp 1 & data$padj 2 fold, padj 2 fold, padj 4,] Y_comb % group_by(source) %>% arrange(source, desc(V1)) %>% as.ame() # use dplyr to sort data frame helps with geom_path Rawdata % group_by(source) %>% arrange(source, desc(V1)) %>% as.ame()
![r multipanel plots eps r multipanel plots eps](https://i.stack.imgur.com/u6kSg.png)
Rownames(all_gene_matched_withprobeid_osm_agg) 10 # only take in rows that have max > 10
R MULTIPANEL PLOTS EPS HOW TO
IMPORTANT: how to remove column (courtesy Stephen Sansom) I_col_to_remove = grep("Comment_IBD_in_family", colnames(df_filename_scseq_infl_cluster))ĭf_filename_scseq_infl_cluster = df_filename_scseq_infl_cluster # use grep to column position and then remove it # remove that column with notationĭf_file_str_filename_RNASeq_withpath_NORM_PCAREMOVE = df_file_str_filename_RNASeq_withpath_NORM IMPORTANT: how to remove column (courtesy Maria Jose Gomez) Grepl(pattern = '*bla*|*caribb*', x = str_temp)Ĥ4) Socio-economic data from Quandl ( link)Ĥ6) Remove elements from vector ( stack exchange) # grepl for grep that returns logical TRUE or FALSE Target_ts_train = window(target_ts, start=train_start, end=train_end)
R MULTIPANEL PLOTS EPS SERIES
RemoveNA(forecast_var_model$res) # remove NA in time series
![r multipanel plots eps r multipanel plots eps](https://statisticsglobe.com/wp-content/uploads/2020/12/figure-1-plot-export-plot-to-eps-file-in-r-programming-language.png)
# can subtract two time series objects
![r multipanel plots eps r multipanel plots eps](https://statisticsglobe.com/wp-content/uploads/2020/12/Export-Plot-to-EPS-File-in-R-Feature-Image.png)
InterpNA(, method=“linear”) # interpolate NA in time series with imputed values Ts.intersect # intersection of two different time series with different frequency and start and end dates Ts.union # union of two different time series with different frequency and start and end dates Window(, start, end) # new time series object with new start and end Sma(formula='log10crime~log10comm', data=df)ģ0) View command in R (view data frame in a window)ģ1) Concatenate two or more vectors to create a matrix (also works for time series objects)ģ4) Return multiple arguments in R function (from stackoverflow) # Note: log10crime and log10commare headersĥ) Reference column of data frame by column nameħ) Formula in R (note: sma is a package in SMATR package load using library(smatr) )
R MULTIPANEL PLOTS EPS INSTALL
If there are errors, sudo apt-get install